FUNCTIONS OF C. ELEGANS NEURONS FROM SYNAPTIC CONNECTIVITY.

Despite decades of research on the C. elegans nervous system based on an anatomical description of synaptic connectivity, the circuits underlying behavior remain incompletely described and the functions of many neurons are still unknown. Updated and more complete chemical and gap junction connectomes of both adult sexes covering the entire animal including the muscle end organ have become available recently. Here these are analyzed to gain insight into the overall structure of the connectivity network and to suggest functions of individual neuron classes. Modularity analysis divides the connectome graph into ten communities that can be correlated with broad categories of behavior. A significant role of the body wall musculature end organ is emphasized as both a site of significant information convergence and as a source of sensory input in a feedback loop. Convergence of pathways for multisensory integration occurs throughout the network - most interneurons have similar indegrees and outdegrees and hence disperse information as much as they aggregate it. New insights include description of a set of high degree interneurons connected by many gap junctions running through the ventral cord that may represent a previously unrecognized locus of information processing. There is an apparent mechanosensory and proprioceptive field covering the entire body formed by connectivity of the many mechanosensory neurons of multiple types to two interneurons with output connections across the nervous system. Several additional significant, previously unrecognized circuits and pathways are uncovered, some involving unstudied neurons. The insights are valuable for guiding theoretical investigation of network properties as well as experimental studies of the functions of individual neurons, groups of neurons, and circuits.

Prediction of interactions between cell surface proteins by machine learning.

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.

Prediction of Interactions between Cell Surface Proteins by Machine Learning.

Cells detect changes of external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and thus challenging to detect using traditional experimental techniques. Here we tackle this challenge by a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells, or between proteins on the same cell surface. In practice, we collected all structural data of Ig domain interactions and transformed them into an interface fragment pair library. A high dimensional profile can be then constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile, so that the probability of interaction between the query proteins can be predicted. We tested our models to an experimentally derived dataset which contains 564 cell surface proteins in human. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in C elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literatures. In conclusion, our computational platform serves a useful tool to help identifying potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study interactions of proteins in other domain superfamilies.

The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations.

Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.

Function of cell adhesion molecules in differentiation of ray sensory neurons in C. elegans.

For proper functioning of the nervous system, it is crucial that neurons find their appropriate partners and build the correct neural connection patterns. Although cell adhesion molecules (CAMs) have been studied for many years as essential players in neural connections, we have yet to unravel the code by which CAMs encode synaptic specificity. We analyzed the effects of mutations in CAM genes on the morphology and synapses of a set of sensory neurons in the Caenorhabditis elegans male tail. B-type ray sensory neurons express 10 genes encoding CAMs. We examined the effect on axon trajectory and localization of pre-synaptic components in viable mutants of nine of these. We found axon trajectory defects in mutants of UNC-40/DCC, SAX-3/ROBO, and FMI-1/Flamingo/Celsr1. None of the mutations caused loss of pre-synaptic components in axons, and in several the level even appeared to increase, suggesting possible accumulation of pre-synapses. B-type sensory neurons fasciculate with a second type of ray sensory neuron, the A-type, in axon commissures. We found a CAM expressed in A-type functions additively with a CAM expressed in B-type in axon guidance, and lack of a CAM expressed in B-type affected A-type axon guidance. Overall, single and multiple mutants of CAM genes had limited effects on ray neuron trajectories and accumulation of synaptic components.

Methods for analyzing neuronal structure and activity in Caenorhabditis elegans.

The model research animal Caenorhabditis elegans has unique properties making it particularly advantageous for studies of the nervous system. The nervous system is composed of a stereotyped complement of neurons connected in a consistent manner. Here, we describe methods for studying nervous system structure and function. The transparency of the animal makes it possible to visualize and identify neurons in living animals with fluorescent probes. These methods have been recently enhanced for the efficient use of neuron-specific reporter genes. Because of its simple structure, for a number of years, C. elegans has been at the forefront of connectomic studies defining synaptic connectivity by electron microscopy. This field is burgeoning with new, more powerful techniques, and recommended up-to-date methods are here described that encourage the possibility of new work in C. elegans. Fluorescent probes for single synapses and synaptic connections have allowed verification of the EM reconstructions and for experimental approaches to synapse formation. Advances in microscopy and in fluorescent reporters sensitive to Ca2+ levels have opened the way to observing activity within single neurons across the entire nervous system.

A multi-scale brain map derived from whole-brain volumetric reconstructions.

Animal nervous system organization is crucial for all body functions and its disruption can lead to severe cognitive and behavioural impairment1. This organization relies on features across scales-from the localization of synapses at the nanoscale, through neurons, which possess intricate neuronal morphologies that underpin circuit organization, to stereotyped connections between different regions of the brain2. The sheer complexity of this organ means that the feat of reconstructing and modelling the structure of a complete nervous system that is integrated across all of these scales has yet to be achieved. Here we present a complete structure-function model of the main neuropil in the nematode Caenorhabditis elegans-the nerve ring-which we derive by integrating the volumetric reconstructions from two animals with corresponding3 synaptic and gap-junctional connectomes. Whereas previously the nerve ring was considered to be a densely packed tract of neural processes, we uncover internal organization and show how local neighbourhoods spatially constrain and support the synaptic connectome. We find that the C. elegans connectome is not invariant, but that a precisely wired core circuit is embedded in a background of variable connectivity, and identify a candidate reference connectome for the core circuit. Using this reference, we propose a modular network architecture of the C. elegans brain that supports sensory computation and integration, sensorimotor convergence and brain-wide coordination. These findings reveal scalable and robust features of brain organization that may be universal across phyla.

Direct glia-to-neuron transdifferentiation gives rise to a pair of male-specific neurons that ensure nimble male mating.

Sexually dimorphic behaviours require underlying differences in the nervous system between males and females. The extent to which nervous systems are sexually dimorphic and the cellular and molecular mechanisms that regulate these differences are only beginning to be understood. We reveal here a novel mechanism by which male-specific neurons are generated in Caenorhabditis elegans through the direct transdifferentiation of sex-shared glial cells. This glia-to-neuron cell fate switch occurs during male sexual maturation under the cell-autonomous control of the sex-determination pathway. We show that the neurons generated are cholinergic, peptidergic, and ciliated putative proprioceptors which integrate into male-specific circuits for copulation. These neurons ensure coordinated backward movement along the mate's body during mating. One step of the mating sequence regulated by these neurons is an alternative readjustment movement performed when intromission becomes difficult to achieve. Our findings reveal programmed transdifferentiation as a developmental mechanism underlying flexibility in innate behaviour.

The connectome of the Caenorhabditis elegans pharynx.

Detailed anatomical maps of individual organs and entire animals have served as invaluable entry points for ensuing dissection of their evolution, development, and function. The pharynx of the nematode Caenorhabditis elegans is a simple neuromuscular organ with a self-contained, autonomously acting nervous system, composed of 20 neurons that fall into 14 anatomically distinct types. Using serial electron micrograph (EM) reconstruction, we re-evaluate here the connectome of the pharyngeal nervous system, providing a novel and more detailed view of its structure and predicted function. Contrasting the previous classification of pharyngeal neurons into distinct inter- and motor neuron classes, we provide evidence that most pharyngeal neurons are also likely sensory neurons and most, if not all, pharyngeal neurons also classify as motor neurons. Together with the extensive cross-connectivity among pharyngeal neurons, which is more widespread than previously realized, the sensory-motor characteristics of most neurons define a shallow network architecture of the pharyngeal connectome. Network analysis reveals that the patterns of neuronal connections are organized into putative computational modules that reflect the known functional domains of the pharynx. Compared with the somatic nervous system, pharyngeal neurons both physically associate with a larger fraction of their neighbors and create synapses with a greater proportion of their neighbors. We speculate that the overall architecture of the pharyngeal nervous system may be reminiscent of the architecture of ancestral, primitive nervous systems.

Expressional artifact caused by a co-injection marker rol-6 in C. elegans.

In transgenic research, selection markers have greatly facilitated the generation of transgenic animals. A prerequisite for a suitable selection marker to be used along with a test gene of interest is that the marker should not affect the phenotype of interest in transformed animals. One of the most common selection markers used in C. elegans transgenic approaches is the rol-6 co-injection marker, which induces a behavioral roller phenotype due to a cuticle defect but is not known to have other side effects. However, we found that the rol-6 co-injection marker can cause expression of GFP in the test sequence in a male-specific interneuron called CP09. We found that the rol-6 gene sequence included in the marker plasmid is responsible for this unwanted expression. Accordingly, the use of the rol-6 co-injection marker is not recommended when researchers intend to examine precise expression or perform functional studies especially targeting male C. elegans neurons. The rol-6 sequence region we identified can be used to drive a specific expression in CP09 neuron for future research.

Map equation with metadata: Varying the role of attributes in community detection.

Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata are well aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.

Whole-animal connectomes of both Caenorhabditis elegans sexes.

Knowledge of connectivity in the nervous system is essential to understanding its function. Here we describe connectomes for both adult sexes of the nematode Caenorhabditis elegans, an important model organism for neuroscience research. We present quantitative connectivity matrices that encompass all connections from sensory input to end-organ output across the entire animal, information that is necessary to model behaviour. Serial electron microscopy reconstructions that are based on the analysis of both new and previously published electron micrographs update previous results and include data on the male head. The nervous system differs between sexes at multiple levels. Several sex-shared neurons that function in circuits for sexual behaviour are sexually dimorphic in structure and connectivity. Inputs from sex-specific circuitry to central circuitry reveal points at which sexual and non-sexual pathways converge. In sex-shared central pathways, a substantial number of connections differ in strength between the sexes. Quantitative connectomes that include all connections serve as the basis for understanding how complex, adaptive behavior is generated.

Concurrency and reachability in treelike temporal networks.

Network properties govern the rate and extent of various spreading processes, from simple contagions to complex cascades. Recently, the analysis of spreading processes has been extended from static networks to temporal networks, where nodes and links appear and disappear. We focus on the effects of accessibility, whether there is a temporally consistent path from one node to another, and reachability, the density of the corresponding accessibility graph representation of the temporal network. The level of reachability thus inherently limits the possible extent of any spreading process on the temporal network. We study reachability in terms of the overall levels of temporal concurrency between edges and the structural cohesion of the network agglomerating over all edges. We use simulation results and develop heterogeneous mean-field model predictions for random networks to better quantify how the properties of the underlying temporal network regulate reachability.

Neural Circuits of Sexual Behavior in Caenorhabditis elegans.

The recently determined connectome of the Caenorhabditis elegans adult male, together with the known connectome of the hermaphrodite, opens up the possibility for a comprehensive description of sexual dimorphism in this species and the identification and study of the neural circuits underlying sexual behaviors. The C. elegans nervous system consists of 294 neurons shared by both sexes plus neurons unique to each sex, 8 in the hermaphrodite and 91 in the male. The sex-specific neurons are well integrated within the remainder of the nervous system; in the male, 16% of the input to the shared component comes from male-specific neurons. Although sex-specific neurons are involved primarily, but not exclusively, in controlling sex-unique behavior-egg-laying in the hermaphrodite and copulation in the male-these neurons act together with shared neurons to make navigational choices that optimize reproductive success. Sex differences in general behaviors are underlain by considerable dimorphism within the shared component of the nervous system itself, including dimorphism in synaptic connectivity.

Synaptogenesis Is Modulated by Heparan Sulfate in Caenorhabditis elegans.

The nervous system regulates complex behaviors through a network of neurons interconnected by synapses. How specific synaptic connections are genetically determined is still unclear. Male mating is the most complex behavior in Caenorhabditis elegans It is composed of sequential steps that are governed by > 3000 chemical connections. Here, we show that heparan sulfates (HS) play a role in the formation and function of the male neural network. HS, sulfated in position 3 by the HS modification enzyme HST-3.1/HS 3-O-sulfotransferase and attached to the HS proteoglycan glypicans LON-2/glypican and GPN-1/glypican, functions cell-autonomously and nonautonomously for response to hermaphrodite contact during mating. Loss of 3-O sulfation resulted in the presynaptic accumulation of RAB-3, a molecule that localizes to synaptic vesicles, and disrupted the formation of synapses in a component of the mating circuits. We also show that the neural cell adhesion protein NRX-1/neurexin promotes and the neural cell adhesion protein NLG-1/neuroligin inhibits the formation of the same set of synapses in a parallel pathway. Thus, neural cell adhesion proteins and extracellular matrix components act together in the formation of synaptic connections.

Post-Processing Partitions to Identify Domains of Modularity Optimization.

We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition-i.e., the parameter-space domain where it has the largest modularity relative to the input set-discarding partitions with empty domains to obtain the subset of partitions that are "admissible" candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.

Neural Circuitry That Mediates Behavior Governing the Tradeoffs Between Survival and Reproduction in Caenorhabditis elegans.

In all outcrossing sexual species there is a mechanism that brings two parents together. For animals, this reproductive requirement may at times conflict with other needs, such as foraging for food. This tension has been studied using the tiny (1 mm) nematode worm, Caenorhabditis elegans. In a trade off between certainty of survival and possibility of reproduction, the C. elegans male will abandon a food patch lacking mates and explore its environment to find one where mates are present. A quantitative behavioral assay has been used to study the behavioral mechanism of mate searching and nutritional, sexual, and neurohormonal pathways that influence the underlying drive state. Taking advantage of the known connectivity of the C. elegans nervous system, neural pathways have been identified that influence the male's behavior in the presence of food with and without mates.

Multiple conserved cell adhesion protein interactions mediate neural wiring of a sensory circuit in C. elegans.

Nervous system function relies on precise synaptic connections. A number of widely-conserved cell adhesion proteins are implicated in cell recognition between synaptic partners, but how these proteins act as a group to specify a complex neural network is poorly understood. Taking advantage of known connectivity in C. elegans, we identified and studied cell adhesion genes expressed in three interacting neurons in the mating circuits of the adult male. Two interacting pairs of cell surface proteins independently promote fasciculation between sensory neuron HOA and its postsynaptic target interneuron AVG: BAM-2/neurexin-related in HOA binds to CASY-1/calsyntenin in AVG; SAX-7/L1CAM in sensory neuron PHC binds to RIG-6/contactin in AVG. A third, basal pathway results in considerable HOA-AVG fasciculation and synapse formation in the absence of the other two. The features of this multiplexed mechanism help to explain how complex connectivity is encoded and robustly established during nervous system development.