Understanding the molecular diversity of synapses
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GluN2B-mediated regulation of silent synapses for receptor specification and addiction memory
Psychostimulants, including cocaine, elicit stereotyped, addictive behaviors. The reemergence of silent synapses containing only NMDA-type glutamate receptors is a critical mediator of addiction memory and seeking behaviors. Despite the predominant abundance of GluN2B-containing NMDA-type glutamate receptors in silent synapses, their operational mechanisms are not fully understood. Here, using conditional depletion/deletion of GluN2B in D1-expressing accumbal medium spiny neurons, we examined the synaptic and behavioral actions that silent synapses incur after repeated exposure to cocaine. GluN2B ablation reduces the proportion of silent synapses, but some of them can persist by substitution with GluN2C, which drives the aberrantly facilitated synaptic incorporation of calcium-impermeable AMPA-type glutamate receptors (AMPARs). The resulting precocious maturation of silent synapses impairs addiction memory but increases locomotor activity, both of which can be normalized by the blockade of calcium-impermeable AMPAR trafficking. Collectively, GluN2B supports the competence of cocaine-induced silent synapses to specify the subunit composition of AMPARs and thereby the expression of addiction memory and related behaviors.
Coding principles and mechanisms of serotonergic transmission modes
Serotonin-mediated intercellular communication has been implicated in myriad human behaviors and diseases, yet how serotonin communicates and how the communication is regulated remain unclear due to limitations of available monitoring tools. Here, we report a method multiplexing genetically encoded sensor-based imaging and fast-scan cyclic voltammetry, enabling simultaneous recordings of synaptic, perisynaptic, proximate and distal extrasynaptic serotonergic transmission. Employing this method alongside a genetically encoded sensor-based image analysis program (GESIAP), we discovered that heterogeneous firing patterns of serotonergic neurons create various transmission modes in the mouse raphe nucleus and amygdala, encoding information of firing pulse frequency, number, and synchrony using neurotransmitter quantity, releasing synapse count, and synaptic and/or volume transmission. During tonic and low-frequency phasic activities, serotonin is confined within synaptic clefts due to efficient retrieval by perisynaptic transporters, mediating synaptic transmission modes. Conversely, during high-frequency, especially synchronized phasic activities, or when transporter inhibition, serotonin may surpass transporter capacity, and escape synaptic clefts through 1‒3 outlet channels, leading to volume transmission modes. Our results elucidate a mechanism of how channeled synaptic enclosures, synaptic properties, and transporters collaborate to define the coding principles of activity pattern-dependent serotonergic transmission modes.
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.
Benthic microbial biogeographic trends in the North Sea are shaped by an interplay of environmental drivers and bottom trawling effort
Microbial composition and diversity in marine sediments are shaped by environmental, biological, and anthropogenic processes operating at different scales. However, our understanding of benthic microbial biogeography remains limited. Here, we used 16S rDNA amplicon sequencing to characterize benthic microbiota in the North Sea from the top centimeter of 339 sediment samples. We utilized spatially explicit statistical models, to disentangle the effects of the different predictors, including bottom trawling intensity, a prevalent industrial fishing practice which heavily impacts benthic ecosystems. Fitted models demonstrate how the geographic interplay of different environmental and anthropogenic drivers shapes the diversity, structure and potential metabolism of benthic microbial communities. Sediment properties were the primary determinants, with diversity increasing with sediment permeability but also with mud content, highlighting different underlying processes. Additionally, diversity and structure varied with total organic matter content, temperature, bottom shear stress and bottom trawling. Changes in diversity associated with bottom trawling intensity were accompanied by shifts in predicted energy metabolism. Specifically, with increasing trawling intensity, we observed a transition toward more aerobic heterotrophic and less denitrifying predicted metabolism. Our findings provide first insights into benthic microbial biogeographic patterns on a large spatial scale and illustrate how anthropogenic activity such as bottom trawling may influence the distribution and abundances of microbes and potential metabolism at macroecological scales.
Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.
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