Whole-animal connectomes of both Caenorhabditis elegans sexes

Molecular profiling of invertebrate glia

Caenorhabditis elegans and Drosophila melanogaster are powerful experimental models for uncovering fundamental tenets of nervous system organization and function. Findings over the last two decades show that molecular and cellular features are broadly conserved between invertebrates and vertebrates, indicating that insights derived from invertebrate models can broadly inform our understanding of glial operating principles across diverse species. In recent years, these model systems have led to exciting discoveries in glial biology and mechanisms of glia-neuron interactions. Here, we summarize studies that have applied current state-of-the-art "-omics" techniques to C. elegans and D. melanogaster glia. Coupled with the remarkable acceleration in the pace of mechanistic studies of glia biology in recent years, these indicate that invertebrate glia also exhibit striking molecular complexity, specificity, and heterogeneity. We provide an overview of these studies and discuss their implications as well as emerging questions where C. elegans and D. melanogaster are well-poised to fill critical knowledge gaps in our understanding of glial biology.

Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans

We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.org). With this repository, we train three existing automated cell-identification algorithms, CPD, StatAtlas, and CRF_ID, to enable accuracy that generalizes across labs, recovering all human-labeled neurons in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. This growing resource of data, code, apps, and tutorials enables users to (1) study neuroanatomical organization and neural activity across diverse experimental paradigms, (2) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (3) share data with the community and comply with data-sharing policies.

Perception of Enterococcus faecalis without infection induces fmo-2 in C. elegans

C. elegans pathogenic susceptibility is influenced by the worm's detection of its environment and its capacity to resist and resolve damage following infection. Here, we use a model where worms can sense, but not ingest, the pathogen Enterococcus faecalis (EF) . We identify that perception of EF without infection induces the stress-response gene fmo-2. We further identify that neural and intestinal signaling genes are necessary for fmo-2 induction without active infection. Finally, we show that fmo-2 overexpression is sufficient to extend lifespan with EF exposure, while fmo-2 KO is not detrimental, suggesting that additional fmo-2 expression benefits worms in this condition.

Wavenumber-dependent transmission of subthreshold waves on electrical synapses network model of Caenorhabditis elegans

Recent experimental studies showed that electrically coupled neural networks like in mammalian inferior olive nucleus generate synchronized rhythmic activity by the subthreshold sinusoidal-like oscillations of the membrane voltage. Understanding the basic mechanism and its implication of such phenomena in the nervous system bears fundamental importance and requires preemptively the connectome information of a given nervous system. Inspired by these necessities of developing a theoretical and computational model to this end and, however, in the absence of connectome information for the inferior olive nucleus, here we investigated interference phenomena of the subthreshold oscillations in the reference system Caenorhabditis elegans for which the structural anatomical connectome was completely known recently. We evaluated how strongly the sinusoidal wave was transmitted between arbitrary two cells in the model network. The region of cell-pairs that are good at transmitting waves changed according to the wavenumber of the wave, for which we named a wavenumber-dependent transmission map. Also, we unraveled that (1) the transmission of all cell-pairs disappeared beyond a threshold wavenumber, (2) long distance and regular patterned transmission existed in the body-wall muscles part of the model network, and (3) major hub cell-pairs of the transmission were identified for many wavenumber conditions. A theoretical and computational model presented in this study provided fundamental insight for understanding how the multi-path constructive/destructive interference of the subthreshold oscillations propagating on electrically coupled neural networks could generate wavenumber-dependent synchronized rhythmic activity.

Aberrant neuronal hyperactivation causes an age-dependent behavioral decline in Caenorhabditis elegans

Age-dependent sensory impairment, memory loss, and cognitive decline are generally attributed to neuron loss, synaptic dysfunction, and decreased neuronal activities over time. Concurrently, increased neuronal activity is reported in humans and other organisms during aging. However, it is unclear whether neuronal hyperactivity is the cause of cognitive impairment or a compensatory mechanism of circuit dysfunction. The roundworm Caenorhabditis elegans exhibits age-dependent declines in an associative learning behavior called thermotaxis, in which its temperature preference on a thermal gradient is contingent on food availability during its cultivation. Cell ablation and calcium imaging demonstrate that the major thermosensory circuit consisting of AFD thermosensory neuron and AIY interneuron is relatively intact in aged animals. On the other hand, ablation of either AWC sensory neurons or AIA interneurons ameliorates the age-dependent thermotaxis decline. Both neurons showed spontaneous and stochastic hyperactivity in aged animals, enhanced by reciprocal communication between AWC and AIA via neurotransmitters and neuropeptides. Our findings suggest that AWC and AIA hyperactivity mediates thermotaxis decline in aged animals. Furthermore, dietary modulation could ameliorate age-dependent thermotaxis decline by suppressing neuronal hyperactivity. We propose that aberrantly enhanced, not diminished, neuronal activities can impair the behavior of aged animals.

Backbone extraction through statistical edge filtering: A comparative study

The backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives. Interestingly, ordering the other methods from GloSS to NC, PF, LANS, and MLF, we observe that each method's output encapsulates the backbone of the previous one. Correlation analysis between edge features (weight, degree, betweenness) and the test significance level reveals that the DF and LANS filters favor high-weighted edges while ECM assigns them lower significance to edges with high degrees. Furthermore, the results suggest a limited influence of the edge betweenness on the filtering process. The backbones global properties analysis (edge fraction, node fraction, weight fraction, weight entropy, reachability, number of components, and transitivity) identifies three typical behavior types for each property. Notably, the LANS filter preserves all nodes and weight entropy. In contrast, DF, PF, ECM, and GloSS significantly reduce network size. The MLF, NC, and ECM filters preserve network connectivity and weight entropy. Distribution analysis highlights the PU filter's ability to capture the original weight distribution. NC filter closely exhibits a similar capability. NC and MLF filters excel for degree distribution. These insights offer valuable guidance for selecting appropriate backbone extraction methods based on specific properties.

CFI-1 functions unilaterally to restrict gap junction formation in C. elegans

Electrical coupling is vital to neural communication, facilitating synchronized activity among neurons. Despite its significance, the precise mechanisms governing the establishment of gap junction connections between specific neurons remain elusive. Here, we identified that the PVC interneuron in Caenorhabditis elegans forms gap junction connections with the PVR interneuron. The transcriptional regulator CFI-1 (ARID3) is specifically expressed in the PVC but not PVR interneuron. Reducing cfi-1 expression in the PVC interneuron leads to enhanced gap junction formation in the PVR neuron, while ectopic expression of cfi-1 in the PVR neuron restores the proper level of gap junction connections in the PVC neuron, along with the normal touch response. These findings unveil the pivotal role of CFI-1 in bidirectionally regulating the formation of gap junctions within a specific neuronal pair, shedding light on the intricate molecular mechanisms governing neuronal connectivity in vivo.

Diverging network architecture of the C. elegans connectome and signaling network

The connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the C. elegans connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons. Understanding how effective communication pathways relate to or diverge from the underlying structure is a central question in neuroscience. Here, we analyze the modular architecture of the C. elegans signal propagation network, measured via calcium imaging and optogenetics, and compare it to the underlying anatomical wiring measured by electron microscopy. Compared to the connectome, we find that signaling modules are not aligned with the modular boundaries of the anatomical network, highlighting an instance where function deviates from structure. An exception to this is the pharynx which is delineated into a separate community in both anatomy and signaling. We analyze the cellular compositions of the signaling architecture and find that its modules are enriched for specific cell types and functions, suggesting that the network modules are neurobiologically relevant. Lastly, we identify a "rich club" of hub neurons in the signaling network. The membership of the signaling rich club differs from the rich club detected in the anatomical network, challenging the view that structural hubs occupy positions of influence in functional (signaling) networks. Our results provide new insight into the interplay between brain structure, in the form of a complete synaptic-level connectome, and brain function, in the form of a system-wide causal signal propagation atlas.

The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus

Circadian rhythms in mammals arise from the spatiotemporal synchronization of ~20,000 neuronal clocks in the Suprachiasmatic Nucleus (SCN). While anatomical, molecular, and genetic approaches have revealed diverse cell types and signaling mechanisms, the network wiring that enables SCN cells to communicate and synchronize remains unclear. To overcome the challenges of revealing functional connectivity from fixed tissue, we developed MITE (Mutual Information & Transfer Entropy), an information theory approach that infers directed cell-cell connections with high fidelity. By analyzing 3447 hours of continuously recorded clock gene expression from 9011 cells in 17 mice, we found that the functional connectome of SCN was highly conserved bilaterally and across mice, sparse, and organized into a dorsomedial and a ventrolateral module. While most connections were local, we discovered long-range connections from ventral cells to cells in both the ventral and dorsal SCN. Based on their functional connectivity, SCN cells can be characterized as circadian signal generators, broadcasters, sinks, or bridges. For example, a subset of VIP neurons acts as hubs that generate circadian signals critical to synchronize daily rhythms across the SCN neural network. Simulations of the experimentally inferred SCN networks recapitulated the stereotypical dorsal-to-ventral wave of daily PER2 expression and ability to spontaneously synchronize, revealing that SCN emergent dynamics are sculpted by cell-cell connectivity. We conclude that MITE provides a powerful method to infer functional connectomes, and that the conserved architecture of cell-cell connections mediates circadian synchrony across space and time in the mammalian SCN.

An Artificial Neural Network for Image Classification Inspired by the Aversive Olfactory Learning Neural Circuit in Caenorhabditis elegans

This study introduces an artificial neural network (ANN) for image classification task, inspired by the aversive olfactory learning neural circuit in Caenorhabditis elegans (C. elegans). Although artificial neural networks (ANNs) have demonstrated remarkable performance in various tasks, they still encounter challenges including excessive parameterization, high training costs and limited generalization capabilities, etc. C. elegans, boasting a simple nervous system consisting of merely 302 neurons, is capable of exhibiting complex behaviors such as aversive olfactory learning. This research pinpoints key neural circuit related to aversive olfactory learning in C. elegans by means of behavioral experiment and high-throughput RNA sequencing, and then translates it into an architecture of ANN for image classification. Furthermore, other ANNs for image classification with different architectures are constructed for comparative performance analysis to underscore the advantages of the bio-inspired designed architecture. The results show that the ANN inspired by the aversive olfactory learning neural circuit in C. elegans attains higher accuracy, greater consistency and faster convergence rate in the image classification task, particularly when dealing with more complex classification challenges. This study not only demonstrates the potential of bio-inspired design in improving the capabilities of ANNs but also offers a novel perspective and methodology for future ANNs design.

Multiscale organization of neuronal activity unifies scale-dependent theories of brain function

Brain recordings collected at different resolutions support distinct signatures of neural coding, leading to scale-dependent theories of brain function. Here, we show that these disparate signatures emerge from a heavy-tailed, multiscale functional organization of neuronal activity observed across calcium-imaging recordings collected from the whole brains of zebrafish and C. elegans as well as from sensory regions in Drosophila, mice, and macaques. Network simulations demonstrate that this conserved hierarchical structure enhances information processing. Finally, we find that this organization is maintained despite significant cross-scale reconfiguration of cellular coordination during behavior. Our findings suggest that this nonlinear organization of neuronal activity is a universal principle conserved for its ability to adaptively link behavior to neural dynamics across multiple spatiotemporal scales while balancing functional resiliency and information processing efficiency.

Adult single-nucleus neuronal transcriptomes of insulin signaling mutants reveal regulators of behavior and learning

Gene expression in individual neurons can change during development to adulthood and can have large effects on behavior. Additionally, the insulin/insulin-like signaling (IIS) pathway regulates many of the adult functions of Caenorhabditis elegans, including learning and memory, via transcriptional changes. We used the deep resolution of single-nucleus RNA sequencing to define the adult transcriptome of each neuron in wild-type and daf-2 mutants, revealing expression differences between L4 larval and adult neurons in chemoreceptors, synaptic genes, and learning/memory genes. We used these data to identify adult new AWC-specific regulators of chemosensory function that emerge upon adulthood. daf-2 gene expression changes correlate with improved cognitive functions, particularly in the AWC sensory neuron that controls learning and associative memory; behavioral assays of AWC-specific daf-2 genes revealed their roles in cognitive function. Combining technology and functional validation, we identified conserved genes that function in specific adult neurons to control behavior, including learning and memory.

Conflict during learning reconfigures the neural representation of positive valence and approach behavior

Punishing and rewarding experiences can change the valence of sensory stimuli and guide animal behavior in opposite directions, resulting in avoidance or approach. Often, however, a stimulus is encountered with both positive and negative experiences. How is such conflicting information represented in the brain and resolved into a behavioral decision? We address this question by dissecting a circuit for sexual conditioning in C. elegans. In this learning paradigm, an odor is conditioned with both a punishment (starvation) and a reward (mates), resulting in odor approach. We find that negative and positive experiences are both encoded by the neuropeptide pigment dispersing factor 1 (PDF-1) being released from, and acting on, different neurons. Each experience creates a distinct memory in the circuit for odor processing. This results in the sensorimotor representation of the odor being different in naive and sexually conditioned animals, despite both displaying approach. Our results reveal that the positive valence of a stimulus is not represented in the activity of any single neuron class but flexibly represented within the circuit according to the experiences and predictions associated with the stimulus.

Glia Development and Function in the Nematode Caenorhabditis elegans

The nematode Caenorhabditis elegans is a powerful experimental setting for uncovering fundamental tenets of nervous system organization and function. Its nearly invariant and simple anatomy, coupled with a plethora of methodologies for interrogating single-gene functions at single-cell resolution in vivo, have led to exciting discoveries in glial cell biology and mechanisms of glia-neuron interactions. Findings over the last two decades reinforce the idea that insights from C. elegans can inform our understanding of glial operating principles in other species. Here, we summarize the current state-of-the-art, and describe mechanistic insights that have emerged from a concerted effort to understand C. elegans glia. The remarkable acceleration in the pace of discovery in recent years paints a portrait of striking molecular complexity, exquisite specificity, and functional heterogeneity among glia. Glial cells affect nearly every aspect of nervous system development and function, from generating neurons, to promoting neurite formation, to animal behavior, and to whole-animal traits, including longevity. We discuss emerging questions where C. elegans is poised to fill critical knowledge gaps in our understanding of glia biology.

Aberrant Positions of the Chemosensory Neurons in the Neurotransmitter-Release Mutant unc-13

Secretion of neurotransmitter- and neuropeptide-containing vesicles is a regulated process orchestrated by multiple proteins. Of these, mutants, defective in the unc-13 and unc-31 genes, responsible for neurotransmitter and neuropeptide release, respectively, are routinely used to elucidate neural and circuitry functions. While these mutants result in severe functional deficits, their neuroanatomy is assumed to be intact. Here, using C. elegans as the model animal system, we find that the head sensory neurons show aberrant positional layout in neurotransmitter (unc-13), but not in neuropeptide (unc-31), release mutants. This finding suggests that synaptic activity may be important for proper cell migration during neurodevelopment and warrants considering possible anatomical defects when using unc-13 neurotransmitter release mutants.

The analysis of raw and processed Polygonatum kingianum saponins and stimulatory mechanism in Caenorhabditis elegans

Polygonati Rhizoma, a Chinese medicine often used in the clinic, can irritate the tongue and throat, so it must be processed before use. Polygonati Rhizoma contains a variety of chemical components, with saponins being one of the main active ingredients. Saponins can be highly irritating to human mocous membranes and have toxicity. In this study, total saponins were extracted from raw and processed Polygonati Rhizoma and detected by UPLC-Q-TOF-MS to identify their constituents. A total of 46 saponins were detected in TSRPR(total saponins of raw Polygonati Rhizoma), TSSPR(total saponins of steamed Polygonati Rhizoma) and TSWPR(total saponins of Polygonati Rhizoma steamed in wine). Of these, 9 compounds that were present in TSRPR were not detected in TSSPR and TSWPR. C.elegans was used as a model animal to study the neurotoxic effect and its mechanisms. TSRPR was found to have neurotoxic effects on C.elegans, but TSSPR and TSWPR had no adverse effects on the nematodes. The disappearance of the irritant effect of raw Polygonati Rhizoma after processing might be related to the changes in the composition of saponins, and the main reason might be the structural transformation of saponins. In particular, the sugar chains of some highly irritating saponins may have been removed or highly irritating saponins isomerized into weakly irritating saponins. The mechanisms of neurotoxic effects on C.elegans may include upregulation of ced-3 and egl-1 expression to promote apoptosis, damage to GABAergic and cholinergic neurons, downregulation of the GABA transmitter receptor genes ggr-1 and gab-1, and a decrease in glutamate levels that impairs nerve signal transmission.

The Effect of glna Loss on the Physiological and Pathological Phenotype of Parkinson's Disease C. elegans

BACKGROUND

Parkinson's disease (PD) is a common neurodegenerative disease. Glutamate(Glu) excitotoxicity is one of the main pathogenesis of PD. Glutaminase (Gls) is an enzyme primarily responsible for catalyzing the hydrolysis and deamidation of glutamine (Gln) to produce Glu and ammonia. Inhibiting the function of Gls may have a beneficial effect on the treatment of PD by reducing the production of Glu. The homologous gene of Gls in C. elegans is glna.

AIMS

To explore the effects of glna loss on physiological and pathological phenotype of PD C. elegans, and to provide new ideas and references for the research and treatment of PD.

MATERIALS & METHODS

We used PD C. elegans UA44 and QIN27 to detect development and lifespan, behavior, degeneration of dopaminergic neurons, lipid levels, ROS levels, expression levels of common amino acids.

RESULTS

Glna loss had no significant impact on the development and lifespan of PD C. elegans. Glna loss saved part of the decline of motor function, including the head thrash frequency and the body bend frequency, and the difference was significant. There was a trend of improvement in some motor behaviors, such as the ethanol avoidance experiment, while no improvement was observed in other experiments. Glna loss slowed down the degeneration of dopaminergic neurons. Glna loss increased the lipid levels and ROS levels in C. elegans. Glna loss decreased Glu content and increased Gln content in C. elegans.

DISCUSSION

The effect of glna loss on PD C. elegans may be the result of multiple factors, such as the tissue types of α-syn expression in C. elegans, the PD C. elegans model used, the adverse effects of glna loss on other systems, and the changes in ROS levels in C. elegans. The specific mechanisms causing these phenomena are still unclear and need to be further explored.

CONCLUSION

Glna loss has a certain protective effect on dopaminergic neurons in PD C. elegans, while the improvement effect on movement and behavior is limited.

Animal models in neuroscience with alternative approaches: Evolutionary, biomedical, and ethical perspectives

Animal models have been a crucial tool in neuroscience research for decades, providing insights into the biomedical and evolutionary mechanisms of the nervous system, disease, and behavior. However, their use has raised concerns on several ethical, clinical, and scientific considerations. The welfare of animals and the 3R principles (replacement, reduction, refinement) are the focus of the ethical concerns, targeting the importance of reducing the stress and suffering of these models. Several laws and guidelines are applied and developed to protect animal rights during experimenting. Concurrently, in the clinic and biomedical fields, discussions on the relevance of animal model findings on human organisms have increased. Latest data suggest that in a considerable amount of time the animal model results are not translatable in humans, costing time and money. Alternative methods, such as in vitro (cell culture, microscopy, organoids, and micro physiological systems) techniques and in silico (computational) modeling, have emerged as potential replacements for animal models, providing more accurate data in a minimized cost. By adopting alternative methods and promoting ethical considerations in research practices, we can achieve the 3R goals while upholding our responsibility to both humans and other animals. Our goal is to present a thorough review of animal models used in neuroscience from the biomedical, evolutionary, and ethical perspectives. The novelty of this research lies in integrating diverse points of views to provide an understanding of the advantages and disadvantages of animal models in neuroscience and in discussing potential alternative methods.

Comprehensive analysis of the C. elegans connectome reveals novel circuits and functions of previously unstudied neurons

Despite decades of research on the Caenorhabditis 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 10 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 2 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.

An integrative data-driven model simulating C. elegans brain, body and environment interactions

The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body-environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body-environment model. The brain model was built by multicompartment models with realistic morphology, connectome and neural population dynamics based on experimental data. Simultaneously, the body-environment model used a lifelike body and a three-dimensional physical environment. Through the closed-loop interaction between the two submodels, BAAIWorm reproduced the realistic zigzag movement toward attractors observed in C. elegans. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, BAAIWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment.

Electrical synapse molecular diversity revealed by proximity-based proteomic discovery

Neuronal circuits are composed of synapses that are either chemical, where signals are transmitted via neurotransmitter release and reception, or electrical, where signals pass directly through interneuronal gap junction channels. While the molecular complexity that controls chemical synapse structure and function is well appreciated, the proteins of electrical synapses beyond the gap-junction-forming Connexins are not well defined. Yet, electrical synapses are expected to be molecularly complex beyond the gap junctions. Connexins are integral membrane proteins requiring vesicular transport and membrane insertion/retrieval to achieve function, homeostasis, and plasticity. Additionally, electron microscopy of neuronal gap junctions reveals neighboring electron dense regions termed the electrical synapse density (ESD). To reveal the molecular complexity of the electrical synapse proteome, we used proximity-dependent biotinylation (TurboID) linked to neural Connexins in zebrafish. Proteomic analysis of developing and mature nervous systems identifies hundreds of Connexin-associated proteins, with overlapping and distinct representation during development and adulthood. The identified protein classes span cell adhesion molecules, cytoplasmic scaffolds, vesicular trafficking, and proteins usually associated with the post synaptic density (PSD) of chemical synapses. Using circuits with stereotyped electrical and chemical synapses, we define molecular sub-synaptic compartments of ESD localizing proteins, we find molecular heterogeneity amongst electrical synapse populations, and we examine the synaptic intermingling of electrical and chemical synapse proteins. Taken together, these results reveal a new complexity of electrical synapse molecular diversity and highlight a novel overlap between chemical and electrical synapse proteomes. Moreover, human homologs of the electrical synapse proteins are associated with autism, epilepsy, and other neurological disorders, providing a novel framework towards understanding neuro-atypical states.

Experience-dependent, sexually dimorphic synaptic connectivity defined by sex-specific cadherin expression

Early-life experience influences subsequent maturation and function of the adult brain, sometimes even in a sex-specific manner, but underlying molecular mechanisms are poorly understood. We describe here how juvenile experience defines sexually dimorphic synaptic connectivity in the adult Caenorhabditis elegans nervous system. Starvation of juvenile males disrupts serotonin-dependent activation of the CREB transcription factor in a nociceptive sensory neuron, PHB. CREB acts through a cascade of transcription factors to control expression of an atypical cadherin protein, FMI-1/Flamingo/CELSR. During postembryonic development, FMI-1 promotes and maintains synaptic connectivity of PHB to a command interneuron, AVA, in both sexes, but a serotonin-dependent transcriptional regulatory cassette antagonizes FMI-1 expression in males, thereby establishing sexually dimorphic connectivity between PHB and AVA. A critical regulatory node is the CREB-target LIN-29, a Zn finger transcription factor that integrates four layers of information: sexual specificity, past experience, time and cell-type specificity. Our findings provide the mechanistic details of how an early juvenile experience defines sexually dimorphic synaptic connectivity.

Transcriptomic analysis reveals decreased expression of detoxification genes in Caenorhabditis elegans exposed to mepanipyrim and cyprodinil: Implications for multigenerational exposure effects

The broad-spectrum fungicides mepanipyrim (Mep) and cyprodinil (Cyp) have been reported to be used worldwide to control gray mold of fruit crops. Consequently, they are often detected in the water and food items. However, the impacts and potential mechanisms of these two pesticides on environmental organisms remain unclear. Utilizing Caenorhabditis elegans (C. elegans) as the model, the toxic effects and mechanisms were analyzed after Mep and Cyp exposure over four generations (P0-F3). The results showed that Mep and Cyp at concentrations over 0.1 µg/L affected the motility behavior, while 1.0 µg/L dosage and above inhibited the growth of C. elegans. In addition, multigenerational exposure could significantly impair both locomotion and development of C. elegans even at 0.01 µg/L dosage. Notably, constant exposure resulted in the function abnormalities of cholinergic, dopaminergic and GABAergic neurons, as well as the accumulation of ROS. Further transcriptome analysis showed reduced expression of 14 genes and 26 genes in xenobiotic metabolic pathway following Mep and Cyp exposure, respectively. The buildup of Mep/Cyp and ROS might have led to nerve impairment and behavioral abnormality. These findings enhanced understanding of the toxic effects of Mep and Cyp and provided insights into their eco-toxicological evaluation.

Molecular, neural, and tissue circuits underlying physiological temperature responses in Caenorhabditis elegans

Temperature is a constant environmental factor on Earth, acting as a continuous stimulus that organisms must constantly perceive to survive. Organisms possess neural systems that receive various types of environmental information, including temperature, and mechanisms for adapting to their surroundings. This paper provides insights into the neural circuits and intertissue networks involved in physiological temperature responses, specifically the mechanisms of "cold tolerance" and "temperature acclimation," based on an analysis of the nematode Caenorhabditis elegans as an experimental system for neural and intertissue information processing.

Neuropeptide signaling network of Caenorhabditis elegans: from structure to behavior

Neuropeptides are abundant signaling molecules that control neuronal activity and behavior in all animals. Owing in part to its well-defined and compact nervous system, Caenorhabditis elegans has been one of the primary model organisms used to investigate how neuropeptide signaling networks are organized and how these neurochemicals regulate behavior. We here review recent work that has expanded our understanding of the neuropeptidergic signaling network in C. elegans by mapping the evolutionary conservation, the molecular expression, the receptor-ligand interactions, and the system-wide organization of neuropeptide pathways in the C. elegans nervous system. We also describe general insights into neuropeptidergic circuit motifs and the spatiotemporal range of peptidergic transmission that have emerged from in vivo studies on neuropeptide signaling. With efforts ongoing to chart peptide signaling networks in other organisms, the C. elegans neuropeptidergic connectome can serve as a prototype to further understand the organization and the signaling dynamics of these networks at organismal level.

Hierarchical regulation of functionally antagonistic neuropeptides expressed in a single neuron pair

Neuronal communication involves small-molecule transmitters, gap junctions, and neuropeptides. While neurons often express multiple neuropeptides, our understanding of the coordination of their actions and their mutual interactions remains limited. Here, we demonstrate that two neuropeptides, NLP-10 and FLP-1, released from the same interneuron pair, AVKL/R, exert antagonistic effects on locomotion speed in Caenorhabditis elegans. NLP-10 accelerates locomotion by activating the G protein-coupled receptor NPR-35 on premotor interneurons that promote forward movement. Notably, we establish that NLP-10 is crucial for the aversive response to mechanical and noxious light stimuli. Conversely, AVK-derived FLP-1 slows down locomotion by suppressing the secretion of NLP-10 from AVK, through autocrine feedback via activation of its receptor DMSR-7 in AVK neurons. Our findings suggest that peptidergic autocrine motifs, exemplified by the interaction between NLP-10 and FLP-1, might represent a widespread mechanism in nervous systems across species. These mutual functional interactions among peptidergic co-transmitters could fine-tune brain activity.

Ageing, proteostasis, and the gut: Insights into neurological health and disease

Recent research has illuminated the profound bidirectional communication between the gastrointestinal tract and the brain, furthering our understanding of neurological ailments facilitating possible therapeutic strategies. Technological advancements in high-throughput sequencing and multi-omics have unveiled significant alterations in gut microbiota and their metabolites in various neurological disorders. This review provides a thorough analysis of the role of microbiome-gut-brain axis in neurodegenerative disease pathology, linking it to reduced age-associated proteostasis. We discuss evidences that substantiate the existence of a gut-brain cross talk ranging from early clinical accounts of James Parkinson to Braak's hypothesis. In addition to understanding of microbes, the review particularly entails specific metabolites which are altered in neurodegenerative diseases. The regulatory effects of microbial metabolites on protein clearance mechanisms, proposing their potential therapeutic implications, are also discussed. By integrating this information, we advocate for a combinatory therapeutic strategy that targets early intervention, aiming to restore proteostasis and ameliorate disease progression. This approach not only provides a new perspective on the pathogenesis of neurodegenerative diseases but also highlights innovative strategies to combat the increasing burden of these age-related disorders.

Selective expansion of motor cortical projections in the evolution of vocal novelty

Deciphering how cortical architecture evolves to drive behavioral innovations is a long-standing challenge in neuroscience and evolutionary biology. Here, we leverage a striking behavioral novelty in the Alston's singing mouse (Scotinomys teguina), compared to the laboratory mouse (Mus musculus), to quantitatively test models of motor cortical evolution. We used bulk tracing, serial two-photon tomography, and high-throughput DNA sequencing of over 76,000 barcoded neurons to discover a specific and substantial expansion (200%) of orofacial motor cortical (OMC) projections to the auditory cortical region (AudR) and the midbrain periaqueductal gray (PAG), both implicated in vocal behaviors. Moreover, analysis of individual OMC neurons' projection motifs revealed preferential expansion of exclusive projections to AudR. Our results imply that selective expansion of ancestral motor cortical projections can underlie behavioral divergence over short evolutionary timescales, suggesting potential mechanisms for the evolution of enhanced cortical control over vocalizations-a crucial preadaptation for human language.

NeuroSCAN: Exploring Neurodevelopment via Spatiotemporal Collation of Anatomical Networks

Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifications of cell biological relationships in the connectome result in rich multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSCAN, an online open-source platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published C. elegans brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights on the structure-function relationships of the circuits and their changes during development. NeuroSCAN is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions.

A neurotransmitter atlas of C. elegans males and hermaphrodites

Mapping neurotransmitter identities to neurons is key to understanding information flow in a nervous system. It also provides valuable entry points for studying the development and plasticity of neuronal identity features. In the Caenorhabditis elegans nervous system, neurotransmitter identities have been largely assigned by expression pattern analysis of neurotransmitter pathway genes that encode neurotransmitter biosynthetic enzymes or transporters. However, many of these assignments have relied on multicopy reporter transgenes that may lack relevant cis-regulatory information and therefore may not provide an accurate picture of neurotransmitter usage. We analyzed the expression patterns of 16 CRISPR/Cas9-engineered knock-in reporter strains for all main types of neurotransmitters in C. elegans (glutamate, acetylcholine, GABA, serotonin, dopamine, tyramine, and octopamine) in both the hermaphrodite and the male. Our analysis reveals novel sites of expression of these neurotransmitter systems within both neurons and glia, as well as non-neural cells, most notably in gonadal cells. The resulting expression atlas defines neurons that may be exclusively neuropeptidergic, substantially expands the repertoire of neurons capable of co-transmitting multiple neurotransmitters, and identifies novel sites of monoaminergic neurotransmitter uptake. Furthermore, we also observed unusual co-expression patterns of monoaminergic synthesis pathway genes, suggesting the existence of novel monoaminergic transmitters. Our analysis results in what constitutes the most extensive whole-animal-wide map of neurotransmitter usage to date, paving the way for a better understanding of neuronal communication and neuronal identity specification in C. elegans.

Neurogenesis in Caenorhabditis elegans

Animals rely on their nervous systems to process sensory inputs, integrate these with internal signals, and produce behavioral outputs. This is enabled by the highly specialized morphologies and functions of neurons. Neuronal cells share multiple structural and physiological features, but they also come in a large diversity of types or classes that give the nervous system its broad range of functions and plasticity. This diversity, first recognized over a century ago, spurred classification efforts based on morphology, function, and molecular criteria. Caenorhabditis elegans, with its precisely mapped nervous system at the anatomical level, an extensive molecular description of most of its neurons, and its genetic amenability, has been a prime model for understanding how neurons develop and diversify at a mechanistic level. Here, we review the gene regulatory mechanisms driving neurogenesis and the diversification of neuron classes and subclasses in C. elegans. We discuss our current understanding of the specification of neuronal progenitors and their differentiation in terms of the transcription factors involved and ensuing changes in gene expression and chromatin landscape. The central theme that has emerged is that the identity of a neuron is defined by modules of gene batteries that are under control of parallel yet interconnected regulatory mechanisms. We focus on how, to achieve these terminal identities, cells integrate information along their developmental lineages. Moreover, we discuss how neurons are diversified postembryonically in a time-, genetic sex-, and activity-dependent manner. Finally, we discuss how the understanding of neuronal development can provide insights into the evolution of neuronal diversity.

Brain bilateral asymmetry - insights from nematodes, zebrafish, and Drosophila

Chirality is a fundamental trait of living organisms, encompassing the homochirality of biological molecules and the left-right (LR) asymmetry of visceral organs and the brain. The nervous system in bilaterian organisms displays a lateralized organization characterized by the presence of asymmetrical neuronal circuits and brain functions that are predominantly localized within one hemisphere. Although body asymmetry is relatively well understood, and exhibits robust phenotypic expression and regulation via conserved molecular mechanisms across phyla, current findings indicate that the asymmetry of the nervous system displays greater phenotypic, genetic, and evolutionary variability. In this review we explore the use of nematode, zebrafish, and Drosophila genetic models to investigate neuronal circuit asymmetry. We discuss recent discoveries in the context of body-brain concordance and highlight the distinct characteristics of nervous system asymmetry and its cognitive correlates.

Compression-based inference of network motif sets

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1-3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex ( https://codex.flywire.ai ) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Behavioral screening reveals a conserved residue in Y-Box RNA-binding protein required for associative learning and memory in C. elegans

RNA-binding proteins (RBPs) regulate translation and plasticity which are required for memory. RBP dysfunction has been linked to a range of neurological disorders where cognitive impairments are a key symptom. However, of the 2,000 RBPs in the human genome, many are uncharacterized with regards to neurological phenotypes. To address this, we used the model organism C. elegans to assess the role of 20 conserved RBPs in memory. We identified eight previously uncharacterized memory regulators, three of which are in the C. elegans Y-Box (CEY) RBP family. Of these, we determined that cey-1 is the closest ortholog to the mammalian Y-Box (YBX) RBPs. We found that CEY-1 is both necessary in the nervous system for memory ability and sufficient to promote memory. Leveraging human datasets, we found both copy number variation losses and single nucleotide variants in YBX1 and YBX3 in individuals with neurological symptoms. We identified one predicted deleterious YBX3 variant of unknown significance, p.Asn127Tyr, in two individuals with neurological symptoms. Introducing this variant into endogenous cey-1 locus caused memory deficits in the worm. We further generated two humanized worm lines expressing human YBX3 or YBX1 at the cey-1 locus to test evolutionary conservation of YBXs in memory and the potential functional significance of the p.Asn127Tyr variant. Both YBX1/3 can functionally replace cey-1, and introduction of p.Asn127Tyr into the humanized YBX3 locus caused memory deficits. Our study highlights the worm as a model to reveal memory regulators and identifies YBX dysfunction as a potential new source of rare neurological disease.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Asymmetry in synaptic connectivity balances redundancy and reachability in the Caenorhabditis elegans connectome

The brain is overall bilaterally symmetrical, but also exhibits considerable asymmetry. While symmetry may endow neural networks with robustness and resilience, asymmetry may enable parallel information processing and functional specialization. How is this tradeoff between symmetrical and asymmetrical brain architecture balanced? To address this, we focused on the Caenorhabditis elegans connectome, comprising 99 classes of bilaterally symmetrical neuron pairs. We found symmetry in the number of synaptic partners between neuron class members, but pronounced asymmetry in the identity of these synapses. We applied graph theoretical metrics for evaluating Redundancy, the selective reinforcement of specific neural paths by multiple alternative synaptic connections, and Reachability, the extent and diversity of synaptic connectivity of each neuron class. We found Redundancy and Reachability to be stochastically tunable by the level of network asymmetry, driving the C. elegans connectome to favor Redundancy over Reachability. These results elucidate fundamental relations between lateralized neural connectivity and function.

Hierarchical communities in the larval Drosophila connectome: Links to cellular annotations and network topology

One of the longstanding aims of network neuroscience is to link a connectome's topological properties-i.e., features defined from connectivity alone-with an organism's neurobiology. One approach for doing so is to compare connectome properties with annotational maps. This type of analysis is popular at the meso-/macroscale, but is less common at the nano-scale, owing to a paucity of neuron-level connectome data. However, recent methodological advances have made possible the reconstruction of whole-brain connectomes at single-neuron resolution for a select set of organisms. These include the fruit fly, Drosophila melanogaster, and its developing larvae. In addition to fine-scale descriptions of connectivity, these datasets are accompanied by rich annotations. Here, we use a variant of the stochastic blockmodel to detect multilevel communities in the larval Drosophila connectome. We find that communities partition neurons based on function and cell type and that most interact assortatively, reflecting the principle of functional segregation. However, a small number of communities interact nonassortatively, forming form a "rich-club" of interneurons that receive sensory/ascending inputs and deliver outputs along descending pathways. Next, we investigate the role of community structure in shaping communication patterns. We find that polysynaptic signaling follows specific trajectories across modular hierarchies, with interneurons playing a key role in mediating communication routes between modules and hierarchical scales. Our work suggests a relationship between system-level architecture and the biological function and classification of individual neurons. We envision our study as an important step toward bridging the gap between complex systems and neurobiological lines of investigation in brain sciences.

A zinc metal complex as an NIR emissive probe for real-time dynamics and in vivo embryogenic evolution of lysosomes using super-resolution microscopy

Zinc (Zn) based fluorescent metal complexes have gained increasing attention due to their non-toxicity and high brightness with marked fluorescence quantum yield (QY). However, they have rarely been employed in super-resolution microscopy (SRM) to study live cells and in vivo dynamics of lysosomes. Here, we present an NIR emissive highly photostable Zn-complex as a multifaceted fluorescent probe for the long-term dynamical distribution of lysosomes in various cancerous and non-cancerous cells in live condition and in vivo embryogenic evolution in Caenorhabditis elegans (C. elegans). Apart from the normal fission, fusion, and kiss & run, the motility and the exact location of lysosomes at each point were mapped precisely. A notable difference in the lysosomal motility in the peripheral region between cancerous and non-cancerous cells was distinctly observed. This is attributed to the difference in viscosity of the cytoplasmic environment. On the other hand, along with the super-resolved structure of the smallest size lysosome (∼77 nm) in live C. elegans, the complete in vivo embryogenic evolution of lysosomes and lysosome-related organelles (LROs) was captured. We were able to capture the images of lysosomes and LROs at different stages of C. elegans, starting from a single cell and extending to a fully matured adult animal.

Analysis of Shannon's entropy to contrast between the Embodied and Neurocentrist hypothesis of conscious experience

We usually accept that consciousness is in the brain. This statement corresponds to a Neurocentrist view. However, with all the physical and physiological data currently available, a convincing explanation of how consciousness emerges has not been given this topic is aborded by Anil Seth (2021). Because of this, a natural question arises: Is consciousness really in the brain or not? If the answer is no, this corresponds to the Embodied perspective. We cannot discriminate between these two points of view because we cannot identify how the organism processes the information. If we try to measure information processing in the brain, then the Neurocentrist view is unavoidable. For example, the information integration theory of Tononi's research group and the global work area theory developed by Dehaene and Baars, focus solely on the brain without considering aspects of Embodied vision (See Tononi, 2021; Dehaene, 2021). In this article, we propose an index based on Shannon's entropy, capable of identifying the leading processing elements acting: Are they mainly inner or external? In order to validate it, we performed simulations with networks accounting for different amounts of internal and outer layers. Since Shannon's entropy is an abstract measure of the information content, this index is not dependent on the physical network nor the proportion of different layers. Therefore, we validate the index as free of bias. This index is a way to discriminate between Embodied from Neurocentrist hypotheses.

Symmetries and synchronization from whole-neural activity in C. elegans connectome: Integration of functional and structural networks

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the C.elegans backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals.

Quantifying network behavior in the rat prefrontal cortex

The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, specifically the rodent spike train and local field potential recordings during the T-maze alternation task, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. To approach these theoretical limits, we applied dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials. The goal was to quantify neural network synchronicity and the correlation of neuroelectrophysiology with rat behavior. The results show the statistical limitations of existing data, and the fact that making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis is impossible until larger and cleaner datasets are available.

Intricate response dynamics enhances stimulus discrimination in the resource-limited C. elegans chemosensory system

BACKGROUND

Sensory systems evolved intricate designs to accurately encode perplexing environments. However, this encoding task may become particularly challenging for animals harboring a small number of sensory neurons. Here, we studied how the compact resource-limited chemosensory system of Caenorhabditis elegans uniquely encodes a range of chemical stimuli.

RESULTS

We find that each stimulus is encoded using a small and unique subset of neurons, where only a portion of the encoding neurons sense the stimulus directly, and the rest are recruited via inter-neuronal communication. Furthermore, while most neurons show stereotypical response dynamics, some neurons exhibit versatile dynamics that are either stimulus specific or network-activity dependent. Notably, it is the collective dynamics of all responding neurons which provides valuable information that ultimately enhances stimulus identification, particularly when required to discriminate between closely related stimuli.

CONCLUSIONS

Together, these findings demonstrate how a compact and resource-limited chemosensory system can efficiently encode and discriminate a diverse range of chemical stimuli.

Advanced Neural Functional Imaging in C. elegans Using Lab-on-a-Chip Technology

The ability to perceive and adapt to environmental changes is crucial for the survival of all organisms. Neural functional imaging, particularly in model organisms, such as Caenorhabditis elegans, provides valuable insights into how animals sense and process external cues through their nervous systems. Because of its fully mapped neural anatomy, transparent body, and genetic tractability, C. elegans serves as an ideal model for these studies. This review focuses on advanced methods for neural functional imaging in C. elegans, highlighting calcium imaging techniques, lab-on-a-chip technologies, and their applications in the study of various sensory modalities, including chemosensation, mechanosensation, thermosensation, photosensation, and magnetosensation. We discuss the benefits of these methods in terms of precision, reproducibility, and ability to study dynamic neural processes in real time, ultimately advancing our understanding of the fundamental principles of neural activity and connectivity.

Neural Sequences Underlying Directed Turning in C. elegans

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy

Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.

Neuro-intestinal acetylcholine signalling regulates the mitochondrial stress response in Caenorhabditis elegans

Neurons coordinate inter-tissue protein homeostasis to systemically manage cytotoxic stress. In response to neuronal mitochondrial stress, specific neuronal signals coordinate the systemic mitochondrial unfolded protein response (UPRmt) to promote organismal survival. Yet, whether chemical neurotransmitters are sufficient to control the UPRmt in physiological conditions is not well understood. Here, we show that gamma-aminobutyric acid (GABA) inhibits, and acetylcholine (ACh) promotes the UPRmt in the Caenorhabditis elegans intestine. GABA controls the UPRmt by regulating extra-synaptic ACh release through metabotropic GABAB receptors GBB-1/2. We find that elevated ACh levels in animals that are GABA-deficient or lack ACh-degradative enzymes induce the UPRmt through ACR-11, an intestinal nicotinic α7 receptor. This neuro-intestinal circuit is critical for non-autonomously regulating organismal survival of oxidative stress. These findings establish chemical neurotransmission as a crucial regulatory layer for nervous system control of systemic protein homeostasis and stress responses.

Detection of minimal extended driver nodes in energetic costs reduction

Structures of complex networks are fundamental to system dynamics, where node state and connectivity patterns determine the cost of a control system, a key aspect in unraveling complexity. However, minimizing the energy required to control a system with the fewest input nodes remains an open problem. This study investigates the relationship between the structure of closed-connected function modules and control energy. We discovered that small structural adjustments, such as adding a few extended driver nodes, can significantly reduce control energy. Thus, we propose MInimal extended driver nodes in Energetic costs Reduction (MIER). Next, we transform the detection of MIER into a multi-objective optimization problem and choose an NSGA-II algorithm to solve it. Compared with the baseline methods, NSGA-II can approximate the optimal solution to the greatest extent. Through experiments using synthetic and real data, we validate that MIER can exponentially decrease control energy. Furthermore, random perturbation tests confirm the stability of MIER. Subsequently, we applied MIER to three representative scenarios: regulation of differential expression genes affected by cancer mutations in the human protein-protein interaction network, trade relations among developed countries in the world trade network, and regulation of body-wall muscle cells by motor neurons in Caenorhabditis elegans nervous network. The results reveal that the involvement of MIER significantly reduces control energy required for these original modules from a topological perspective. Additionally, MIER nodes enhance functionality, supplement key nodes, and uncover potential mechanisms. Overall, our work provides practical computational tools for understanding and presenting control strategies in biological, social, and neural systems.

Microinstrumentation for Brain Organoids

Brain organoids are three-dimensional aggregates of self-organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs); and optical, magnetic, and acoustic techniques, has limitations when applied to three-dimensional (3D) organoids, primarily due to their limits with inherently two-dimensional geometry and interfacing. Hence, there is an urgent need to develop new instrumentation compatible with live cell culture techniques and with scalable 3D formats relevant to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid-machine interfaces. Specifically, this article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast-scan optical techniques, buckling and self-folding MEAs, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two-way information transfer with brain organoids. This article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.