Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families.

The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C. elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes of C. elegans neurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.

See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons.

In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG.

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Collective behavior and self-organization in neural rosette morphogenesis.

Neural rosettes develop from the self-organization of differentiating human pluripotent stem cells. This process mimics the emergence of the embryonic central nervous system primordium, i.e., the neural tube, whose formation is under close investigation as errors during such process result in severe diseases like spina bifida and anencephaly. While neural tube formation is recognized as an example of self-organization, we still do not understand the fundamental mechanisms guiding the process. Here, we discuss the different theoretical frameworks that have been proposed to explain self-organization in morphogenesis. We show that an explanation based exclusively on stem cell differentiation cannot describe the emergence of spatial organization, and an explanation based on patterning models cannot explain how different groups of cells can collectively migrate and produce the mechanical transformations required to generate the neural tube. We conclude that neural rosette development is a relevant experimental 2D in-vitro model of morphogenesis because it is a multi-scale self-organization process that involves both cell differentiation and tissue development. Ultimately, to understand rosette formation, we first need to fully understand the complex interplay between growth, migration, cytoarchitecture organization, and cell type evolution.

Modeling of olfactory transduction in AWCON neuron via coupled electrical-calcium dynamics.

Amphid wing "C" (AWC) neurons are among the most important and studied neurons of the nematode Caenorhabditis elegans. In this work, we unify the existing electrical and intracellular calcium dynamics descriptions to obtain a biophysically accurate model of olfactory transduction in AWCON neurons. We study the membrane voltage and the intracellular calcium dynamics at different exposure times and odorant concentrations to grasp a complete picture of AWCON functioning. Moreover, we investigate the complex cascade of biochemical processes that allow AWC activation upon odor removal. We analyze the behavior of the different components of the models and, by suppressing them selectively, we extrapolate their contribution to the overall neuron response and study the resilience of the dynamical system. Our results are all in agreement with the available experimental data. Therefore, we provide an accurate mathematical and biophysical model for studying olfactory signal processing in C. elegans.

The recurrent pathogenic Pro890Leu substitution in CLTC causes a generalized defect in synaptic transmission in Caenorhabditis elegans.

De novo CLTC mutations underlie a spectrum of early-onset neurodevelopmental phenotypes having developmental delay/intellectual disability (ID), epilepsy, and movement disorders (MD) as major clinical features. CLTC encodes the widely expressed heavy polypeptide of clathrin, a major component of the coated vesicles mediating endocytosis, intracellular trafficking, and synaptic vesicle recycling. The underlying pathogenic mechanism is largely unknown. Here, we assessed the functional impact of the recurrent c.2669C > T (p.P890L) substitution, which is associated with a relatively mild ID/MD phenotype. Primary fibroblasts endogenously expressing the mutated protein show reduced transferrin uptake compared to fibroblast lines obtained from three unrelated healthy donors, suggesting defective clathrin-mediated endocytosis. In vitro studies also reveal a block in cell cycle transition from G0/G1 to the S phase in patient's cells compared to control cells. To demonstrate the causative role of the p.P890L substitution, the pathogenic missense change was introduced at the orthologous position of the Caenorhabditis elegans gene, chc-1 (p.P892L), via CRISPR/Cas9. The resulting homozygous gene-edited strain displays resistance to aldicarb and hypersensitivity to PTZ, indicating defective release of acetylcholine and GABA by ventral cord motor neurons. Consistently, mutant animals show synaptic vesicle depletion at the sublateral nerve cords, and slightly defective dopamine signaling, highlighting a generalized deficit in synaptic transmission. This defective release of neurotransmitters is associated with their secondary accumulation at the presynaptic membrane. Automated analysis of C. elegans locomotion indicates that chc-1 mutants move slower than their isogenic controls and display defective synaptic plasticity. Phenotypic profiling of chc-1 (+/P892L) heterozygous animals and transgenic overexpression experiments document a mild dominant-negative behavior for the mutant allele. Finally, a more severe phenotype resembling that of chc-1 null mutants is observed in animals harboring the c.3146 T > C substitution (p.L1049P), homologs of the pathogenic c.3140 T > C (p.L1047P) change associated with a severe epileptic phenotype. Overall, our findings provide novel insights into disease mechanisms and genotype-phenotype correlations of CLTC-related disorders.

Phenotypic Assessment of Pathogenic Variants in GNAO1 and Response to Caffeine in C. elegans Models of the Disease.

De novo mutations affecting the G protein α o subunit (Gαo)-encoding gene (GNAO1) cause childhood-onset developmental delay, hyperkinetic movement disorders, and epilepsy. Recently, we established Caenorhabditis elegans as an informative experimental model for deciphering pathogenic mechanisms associated with GNAO1 defects and identifying new therapies. In this study, we generated two additional gene-edited strains that harbor pathogenic variants which affect residues Glu246 and Arg209-two mutational hotspots in Gαo. In line with previous findings, biallelic changes displayed a variable hypomorphic effect on Gαo-mediated signaling that led to the excessive release of neurotransmitters by different classes of neurons, which, in turn, caused hyperactive egg laying and locomotion. Of note, heterozygous variants showed a cell-specific dominant-negative behavior, which was strictly dependent on the affected residue. As with previously generated mutants (S47G and A221D), caffeine was effective in attenuating the hyperkinetic behavior of R209H and E246K animals, indicating that its efficacy is mutation-independent. Conversely, istradefylline, a selective adenosine A2A receptor antagonist, was effective in R209H animals but not in E246K worms, suggesting that caffeine acts through both adenosine receptor-dependent and receptor-independent mechanisms. Overall, our findings provide new insights into disease mechanisms and further support the potential efficacy of caffeine in controlling dyskinesia associated with pathogenic GNAO1 mutations.

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors.

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

Caenorhabditis elegans provides an efficient drug screening platform for GNAO1-related disorders and highlights the potential role of caffeine in controlling dyskinesia.

Dominant GNAO1 mutations cause an emerging group of childhood-onset neurological disorders characterized by developmental delay, intellectual disability, movement disorders, drug-resistant seizures and neurological deterioration. GNAO1 encodes the α-subunit of an inhibitory GTP/GDP-binding protein regulating ion channel activity and neurotransmitter release. The pathogenic mechanisms underlying GNAO1-related disorders remain largely elusive and there are no effective therapies. Here, we assessed the functional impact of two disease-causing variants associated with distinct clinical features, c.139A > G (p.S47G) and c.662C > A (p.A221D), using Caenorhabditis elegans as a model organism. The c.139A > G change was introduced into the orthologous position of the C. elegans gene via CRISPR/Cas9, whereas a knock-in strain carrying the p.A221D variant was already available. Like null mutants, homozygous knock-in animals showed increased egg laying and were hypersensitive to aldicarb, an inhibitor of acetylcholinesterase, suggesting excessive neurotransmitter release by different classes of motor neurons. Automated analysis of C. elegans locomotion indicated that goa-1 mutants move faster than control animals, with more frequent body bends and a higher reversal rate and display uncoordinated locomotion. Phenotypic profiling of heterozygous animals revealed a strong hypomorphic effect of both variants, with a partial dominant-negative activity for the p.A221D allele. Finally, caffeine was shown to rescue aberrant motor function in C. elegans harboring the goa-1 variants; this effect is mainly exerted through adenosine receptor antagonism. Overall, our findings establish a suitable platform for drug discovery, which may assist in accelerating the development of new therapies for this devastating condition, and highlight the potential role of caffeine in controlling GNAO1-related dyskinesia.

A Shearless Microfluidic Device Detects a Role in Mechanosensitivity for AWCON Neuron in Caenorhabditis elegans.

AWC olfactory neurons are fundamental for chemotaxis toward volatile attractants in Caenorhabditis elegans. Here, it is shown that AWCON responds not only to chemicals but also to mechanical stimuli caused by fluid flow changes in a microfluidic device. The dynamics of calcium events are correlated with the stimulus amplitude. It is further shown that the mechanosensitivity of AWCON neurons has an intrinsic nature rather than a synaptic origin, and the calcium transient response is mediated by TAX-4 cGMP-gated cation channel, suggesting the involvement of one or more "odorant" receptors in AWCON mechano-transduction. In many cases, the responses show plateau properties resembling bistable calcium dynamics where neurons can switch from one stable state to the other. To investigate the unprecedentedly observed mechanosensitivity of AWCON neurons, a novel microfluidic device is designed to minimize the fluid shear flow in the arena hosting the nematodes. Animals in this device show reduced neuronal activation of AWCON neurons. The results observed indicate that the tangential component of the mechanical stress is the main contributor to the mechanosensitivity of AWCON . Furthermore, the microfluidic platform, integrating shearless perfusion and calcium imaging, provides a novel and more controlled solution for in vivo analysis both in micro-organisms and cultured cells.

Correction: Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD.

[This corrects the article DOI: 10.1371/journal.pone.0218738.].

C. elegans-based chemosensation strategy for the early detection of cancer metabolites in urine samples.

Chemosensory receptors play a crucial role in distinguishing the wide range of volatile/soluble molecules by binding them with high accuracy. Chemosensation is the main sensory modality in organisms lacking long-range sensory mechanisms like vision/hearing. Despite its low number of sensory neurons, the nematode Caenorhabditis elegans possesses several chemosensory receptors, allowing it to detect about as many odorants as mammals. Here, we show that C. elegans displays attraction towards urine samples of women with breast cancer, avoiding control ones. Behavioral assays on animals lacking AWC sensory neurons demonstrate the relevance of these neurons in sensing cancer odorants: calcium imaging on AWC increases the accuracy of the discrimination (97.22%). Also, chemotaxis assays on animals lacking GPCRs expressed in AWC allow to identify receptors involved in binding cancer metabolites, suggesting that an alteration of a few metabolites is sufficient for the cancer discriminating behavior of C. elegans, which may help identify a fundamental fingerprint of breast cancer.

Investigation of the binding between olfactory receptors and odorant molecules in C. elegans organism.

The molecular mechanisms regulating the complex sensory system that underlies olfaction are still not completely understood. The compounds formed from the interaction of Olfactory Receptors (ORs) with volatile molecules play a crucial role in producing the sense of olfaction. Therefore, it is necessary to investigate the binding mechanisms between these receptors and small ligands. In this work, we focus our attention on C.elegans, this is a particularly suitable model organism because it is characterized by a nervous system composed of only 302 neurons. To study olfaction in C.elegans, we select 21 ORs from its olfactory neurons, and present a pipeline, consisting of several computational methods, with the aim of proposing a set of possible candidates for binding the selected C.elegans ORs. This pipeline introduces an approach based on the selection of templates, and threading, that takes advantage of the structural redundancy among membrane receptors. This procedure is widely replicable because it is based on algorithms that are publicly available and are freely hosted on institutional servers.

Biophysical modeling of C. elegans neurons: Single ion currents and whole-cell dynamics of AWCon and RMD.

C. elegans neuronal system constitutes the ideal framework for studying simple, yet realistic, neuronal activity, since the whole nervous system is fully characterized with respect to the exact number of neurons and the neuronal connections. Most recent efforts are devoted to investigate and clarify the signal processing and functional connectivity, which are at the basis of sensing mechanisms, signal transmission, and motor control. In this framework, a refined modelof whole neuron dynamics constitutes a key ingredient to describe the electrophysiological processes, both at thecellular and at the network scale. In this work, we present Hodgkin-Huxley-based models of ion channels dynamics black, built on data available both from C. elegans and from other organisms, expressing homologous channels. We combine these channel models to simulate the electrical activity oftwo among the most studied neurons in C. elegans, which display prototypical dynamics of neuronal activation, the chemosensory AWCON and the motor neuron RMD. Our model properly describes the regenerative responses of the two cells. We analyze in detail the role of ion currents, both in wild type and in in silico knockout neurons. Moreover, we specifically investigate the behavior of RMD, identifying a heterogeneous dynamical response which includes bistable regimes and sustained oscillations. We are able to assess the critical role of T-type calcium currents, carried by CCA-1 channels, and leakage currents in the regulation of RMD response. Overall, our results provide new insights in the activity of key C. elegans neurons. The developed mathematical framework constitute a basis for single-cell and neuronal networks analyses, opening new scenarios in the in silico modeling of C. elegans neuronal system.

Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks.

In a neural network, an autapse is a particular kind of synapse that links a neuron onto itself. Autapses are almost always not allowed neither in artificial nor in biological neural networks. Moreover, redundant or similar stored states tend to interact destructively. This paper shows how autapses together with stable state redundancy can improve the storage capacity of a recurrent neural network. Recent research shows how, in an N-node Hopfield neural network with autapses, the number of stored patterns (P) is not limited to the well known bound 0.14 N , as it is for networks without autapses. More precisely, it describes how, as the number of stored patterns increases well over the 0.14 N threshold, for P much greater than N, the retrieval error asymptotically approaches a value below the unit. Consequently, the reduction of retrieval errors allows a number of stored memories, which largely exceeds what was previously considered possible. Unfortunately, soon after, new results showed that, in the thermodynamic limit, given a network with autapses in this high-storage regime, the basin of attraction of the stored memories shrinks to a single state. This means that, for each stable state associated with a stored memory, even a single bit error in the initial pattern would lead the system to a stationary state associated with a different memory state. This thus limits the potential use of this kind of Hopfield network as an associative memory. This paper presents a strategy to overcome this limitation by improving the error correcting characteristics of the Hopfield neural network. The proposed strategy allows us to form what we call an absorbing-neighborhood of state surrounding each stored memory. An absorbing-neighborhood is a set defined by a Hamming distance surrounding a network state, which is an absorbing because, in the long-time limit, states inside it are absorbed by stable states in the set. We show that this strategy allows the network to store an exponential number of memory patterns, each surrounded with an absorbing-neighborhood with an exponentially growing size.

Effect of dilution in asymmetric recurrent neural networks.

We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measures the fraction of neuron couples that are connected, and the network asymmetry measures to what extent the underlying connectivity matrix is asymmetric. For each given neural network, we study the dynamical evolution of all the different initial conditions, thus characterizing the full dynamical landscape without imposing any learning rule. Because of the deterministic dynamics, each trajectory converges to an attractor, that can be either a fixed point or a limit cycle. These attractors form the set of all the possible limit behaviors of the neural network. For each network we then determine the convergence times, the limit cycles' length, the number of attractors, and the sizes of the attractors' basin. We show that there are two network structures that maximize the number of possible limit behaviors. The first optimal network structure is fully-connected and symmetric. On the contrary, the second optimal network structure is highly sparse and asymmetric. The latter optimal is similar to what observed in different biological neuronal circuits. These observations lead us to hypothesize that independently from any given learning model, an efficient and effective biologic network that stores a number of limit behaviors close to its maximum capacity tends to develop a connectivity structure similar to one of the optimal networks we found.

On the Maximum Storage Capacity of the Hopfield Model.

Recurrent neural networks (RNN) have traditionally been of great interest for their capacity to store memories. In past years, several works have been devoted to determine the maximum storage capacity of RNN, especially for the case of the Hopfield network, the most popular kind of RNN. Analyzing the thermodynamic limit of the statistical properties of the Hamiltonian corresponding to the Hopfield neural network, it has been shown in the literature that the retrieval errors diverge when the number of stored memory patterns (P) exceeds a fraction (≈ 14%) of the network size N. In this paper, we study the storage performance of a generalized Hopfield model, where the diagonal elements of the connection matrix are allowed to be different from zero. We investigate this model at finite N. We give an analytical expression for the number of retrieval errors and show that, by increasing the number of stored patterns over a certain threshold, the errors start to decrease and reach values below unit for P ≫ N. We demonstrate that the strongest trade-off between efficiency and effectiveness relies on the number of patterns (P) that are stored in the network by appropriately fixing the connection weights. When P≫N and the diagonal elements of the adjacency matrix are not forced to be zero, the optimal storage capacity is obtained with a number of stored memories much larger than previously reported. This theory paves the way to the design of RNN with high storage capacity and able to retrieve the desired pattern without distortions.

Laser propulsion of nanobullets by adiabatic compression of surface plasmon polaritons.

Laser propulsion and guide of nanosized objects is fundamental for a wide number of applications. These applications are often limited by the fact that the optical forces acting on nanoparticles are almost negligible even in the favorable case of metallic particles and hence large laser powers are needed to accelerate and guide nanosize devices in practical applications. Furthermore, metallic nanoparticles exhibit strong absorption bands and scattering and this makes more difficult controlling nanopropulsion. Thus, finding some mechanism enhancing the optomechanical interaction at the nanoscale controlled by laser is specifically challenging and pivotal. Here, we demonstrate a novel physical effect where the well-known adiabatic localization of the enhanced plasmonic surface field on the apex of metallic nanocones produces a significant optical pressure employable as a propulsive mechanism. The proposed method gives the possibility to develop new photonics devices to accelerate metallic nanobullets over long distances for a variety of applications.

Purely nonlinear disorder-induced localizations and their parametric amplification.

We investigate spatial localization in a quadratic nonlinear medium in the presence of randomness. By means of numerical simulations and theoretical analyses we show that, in the down conversion regime, the transverse random modulation of the nonlinear susceptibility generates localizations of the fundamental wave that grow exponentially in propagation. The localization length is optically controlled by the pump intensity that determines the amplification rate. The results also apply to cubic nonlinearities.

Time-resolved dynamics of granular matter by random laser emission.

Because of the huge commercial importance of granular systems, the second-most used material in industry after water, intersecting the industry in multiple trades, like pharmacy and agriculture, fundamental research on grain-like materials has received an increasing amount of attention in the last decades. In photonics, the applications of granular materials have been only marginally investigated. We report the first phase-diagram of a granular as obtained by laser emission. The dynamics of vertically-oscillated granular in a liquid solution in a three-dimensional container is investigated by employing its random laser emission. The granular motion is function of the frequency and amplitude of the mechanical solicitation, we show how the laser emission allows to distinguish two phases in the granular and analyze its spectral distribution. This constitutes a fundamental step in the field of granulars and gives a clear evidence of the possible control on light-matter interaction achievable in grain-like system.