Biology subjects

King, J. G.

Publications and source records attributed to King, J. G..

15 recordsLinked to original sources

bioRxiv2024

A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers

The high energetic demands of the brain arise primarily from neuronal activity. Neurons consume substantial energy to transmit information as electrical signals and maintain their resting membrane potential. These energetic requirements are met by the neuro-glial-vascular (NGV) ensemble, which generates energy in a coupled metabolic process. In ageing, metabolic function becomes impaired, producing less energy and, consequently, the system is unable to sustain the neuronal energetic needs. We propose a multiscale model of electro-metabolic coupling in a reconstructed rat neocortex. This combines an electro-morphologically reconstructed electrophysiological model with a detailed NGV metabolic model. Our results demonstrate that the large-scale model effectively captures electro-metabolic processes at the circuit level, highlighting the importance of heterogeneity within the circuit, where energetic demands vary according to neuronal characteristics. Finally, in metabolic ageing, our model indicates that the middle cortical layers are particularly vulnerable to energy impairment.

neuroscience↗

bioRxiv2024

Under the radar: differential responses of bed bugs to an entomopathogen, environmental bacteria, and a human pathogen

BackgroundBed bugs (Hemiptera: Cimicidae) are a widely distributed, obligately blood-feeding insect, but they have never been linked to pathogen transmission in humans. Most other hematophagous insects that frequently bite humans transmit pathogens, and it is unclear why bed bugs do not. One hypothesis is that bed bugs have evolved a highly robust immune system because their mating system, traumatic insemination, exposes females to consistent wounding and bacterial infections. Although this has been proposed, very little is known about the bed bug immune system and how bed bugs respond to microbial challenges. Understanding the bed bug immune system could give insight to why bed bugs are not known to transmit disease and under what circumstances they could, while also facilitating biological control efforts involving microbes. MethodsTo investigate the immune response of bed bugs to bacterial challenges, we exposed female bed bugs to three bacterial challenges. 1.) Pseudomonas fluorescens, an entomopathogen known to have harmful effects to bed bugs, 2.) bacteria cultured from a bed bug enclosure likely encountered during traumatic insemination, and 3.) Borrelia duttoni, a human vector-borne pathogen that causes relapsing fever. We compared the transcriptomes of infected bed bugs with uninfected bed bugs, focusing on immune-related genes. We also conducted phylogenetic analyses to understand patterns of gene duplication and function of potentially immune-related genes. ResultsWe found many known immune effector genes upregulated in response to P. fluorescens and traumatic insemination-associated bacteria, but interestingly, not in response to B. duttoni. Furthermore, we found significant overlap in the genes differentially expressed in response to P. fluorescens and the traumatic insemination associated bacteria, and between P. fluorescens and B. duttoni, but no significant overlap between traumatic insemination bacteria and B. duttoni. We also show that bed bug diptericin-like antimicrobial peptides underwent a lineage-specific gene duplication, and that they may have further functional specialization. Finally, we identify previously overlooked candidates for future study of immune function in bed bugs, including some putative cuticle-associated genes, a laccase-like gene, and a mucin-like gene. ConclusionsBy taking comprehensive transcriptomic approach, our study is an important step in understanding how bed bugs respond to diverse immune challenges.

molecular biology↗

bioRxiv2023

Comparative genomics and the salivary transcriptome of the redbanded stink bug shed light on its high damage potential to soybean

The redbanded stink bug, Piezodorus guildinii (Westwood) (Hemiptera: Pentatomidae), is a significant soybean pest in the Americas, inflicting more physical damage on soybean than other native stink bugs. Studies suggest that its heightened impact is attributed to the aggressive digestive properties of its saliva. Despite its agricultural importance, the factors driving its greater ability to degrade plant tissues have remained unexplored in a genomic evolutionary context. In this study, we hypothesized that lineage-specific gene family expansions have increased the dosage of digestive genes expressed in the salivary glands. To investigate this, we annotated a previously published genome assembly of the redbanded stink bug and performed a comparative genomic analysis on 11 hemipteran species and reconstructed patterns of gene duplication, gain, and loss in the redbanded stink bug. We also performed RNA-seq on the redbanded stink bugs salivary tissues, along with the rest of the body without salivary glands. We identified hundreds of differentially expressed salivary genes, including a subset lost in other stink bug lineages but retained and expressed in the redbanded stink bugs salivary glands. These genes were significantly enriched with protein families involved in proteolysis, potentially explaining the redbanded stink bugs heightened damage to soybeans. Contrary to our hypothesis, we found no support for an increased dosage of digestive genes in the salivary glands of the redbanded stink bug. Nonetheless, these results provide insight into the evolution of this important crop pest, establishing a link between its genomic history and its agriculturally important physiology. SIGNIFICANCE STATEMENTThe redbanded stink bug, an important soybean pest in the Americas, inflicts greater damage to soybean due to higher salivary digestion. We employed comparative genomics and analyses of the salivary transcriptome to explore this and found that the differential retention of ancestral salivary genes may explain this phenomenon better than gene gains or duplications in the redbanded stink bug genome. We identify a distinct set of genes in the salivary gland that were differentially retained and expressed in the redbanded stink bug lineage, demonstrating enrichment in proteolytic function. This discovery offers a potential explanation for the redbanded stink bugs elevated damage to soybean crops.

genomics↗

bioRxiv2023

Do bed bugs transmit human viruses, or do humans transmit bed bug viruses? A worldwide survey of the bed bug RNA virosphere

Bed bugs (Hemiptera: Cimicidae) are a globally distributed hematophagous pest that routinely feed on humans. Unlike many blood-sucking arthropods, they have never been linked to disease transmission in a natural setting, and despite interest in their role as disease vectors, little is known about the viruses that bed bugs naturally harbor. Here, we present a global-scale survey of the bed bug RNA virosphere. We sequenced the metatranscriptomes of 22 individual bed bugs (Cimex lectularius and Cimex hemipterus) from 8 locations around the world. We detected sequences from two known bed bug viruses (Shuangao bedbug virus 1 and Shuangao bedbug virus 2) which extends their geographical range and the host range of Shuangao bedbug virus 1 to Cimex lectularius. We identified three novel bed bug virus sequences from a tenui-like virus (Bunyavirales), a toti-like virus (Ghabrivirales), and a luteo-like virus (Tolivirales). Interestingly, some of the bed bug viruses branch near to insect-transmitted plant-infecting viruses, opening questions regarding the evolution of plant virus infection. When we analyzed the putative viral sequences by their hosts collection location, we found unexpected patterns of geographical diversity that may reflect humans role in bed bug dispersal. Additionally, we investigated the effect that Wolbachia, the primary bed bug endosymbiont, may have on viral abundance and found that Wolbachia infection neither promotes nor inhibits viral infection. Finally, our results provide no evidence that bed bugs transmit any known human pathogenic viruses.

bioinformatics↗

bioRxiv2023

Sparse and specific long-term plasticity emerge without homeostasis in a biophysically detailed cortical model

Synaptic plasticity underlies the brains ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

neuroscience↗

bioRxiv2023

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

neuroscience↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in computational neuroscience have demonstrated the usefulness and importance of stochastic, spatial reaction-diffusion simulations. However, ever increasing model complexity renders traditional serial solvers, as well as naive parallel implementations, inadequate. This paper introduces a new generation of the STochastic Engine for Pathway Simulation (STEPS) project, denominated STEPS 4.0, and its core components which have been designed for improved scalability, performance, and memory efficiency. STEPS 4.0 aims to enable novel scientific studies of macroscopic systems such as whole cells while capturing their nanoscale details. This class of models is out of reach for serial solvers due to the vast quantity of computation in such detailed models, and also out of reach for naive parallel solvers due to the large memory footprint. Based on a distributed mesh solution, we introduce a new parallel stochastic reaction-diffusion solver and a deterministic membrane potential solver in STEPS 4.0. The distributed mesh, together with improved data layout and algorithm designs, significantly reduces the memory footprint of parallel simulations in STEPS 4.0. This enables massively parallel simulations on modern HPC clusters and overcomes the limitations of the previous parallel STEPS implementation. Current and future improvements to the solver are not sustainable without proper software engineering practices. For this reason, we also give an overview of how the STEPS codebase and the development environment have been updated to follow modern software development practices. We benchmark performance improvement and memory footprint on three published models with different complexities, from a simple spatial stochastic reaction-diffusion model, to a more complex one that is coupled to a deterministic membrane potential solver to simulate the calcium burst activity of a Purkinje neuron. Simulation results of these models suggest that the new solution dramatically reduces the per-core memory consumption by more than a factor of 30, while maintaining similar or better performance and scalability.

neuroscience↗

bioRxiv2022

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

AO_SCPLOWBSTRACTC_SCPLOWThe need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURONs ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURONs reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

neuroscience↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2020

Many ways to make darker flies: Intra- and inter-specific variation in Drosophila body pigmentation components

Body pigmentation is an evolutionarily diversified and ecologically relevant trait that shows variation within and between species, and important roles in animal survival and reproduction. Insect pigmentation, in particular, provides some of the most compelling examples of adaptive evolution and its ecological and genetic bases. Yet, while pigmentation includes multiple aspects of color and color pattern that may vary more or less independently, its study frequently focuses on one single aspect. Here, we develop a method to quantify color and color pattern in Drosophila body pigmentation, decomposing thorax and abdominal pigmentation into distinct measurable traits, and we quantify different sources of variation in those traits. For each body part, we measured overall darkness, as well as four other pigmentation properties distinguishing between background color and color of the darker pattern elements that decorate the two body parts. By focusing on two standard D. melanogaster laboratory populations, we show that pigmentation components vary and co-vary in different manners depending on sex, genetic background, and developmental temperature. By studying three natural populations of D. melanogaster along a latitudinal cline and five other Drosophila species, we then show that evolution of lighter or darker bodies can be achieved by changing distinct component traits. Our study underscores the value of detailed phenotyping for a better understanding of phenotypic variation and diversification, and the ecological pressures and genetic mechanisms underlying them.

evolutionary biology↗

bioRxiv2020

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Long-term potentiation (LTP) and long-term depression (LTD) of pyramidal cell connections are among the key mechanisms underlying learning and memory in the brain. Despite their important role, only a few of these connections have been characterized in terms of LTP/LTD dynamics, such as the one between layer 5 thick-tufted pyramidal cells (L5-TTPCs). Comparing the available evidence on different pyramidal connection types reveals a large variability of experimental outcomes, possibly indicating the presence of connection-type-specific mechanisms. Here, we show that a calcium-based plasticity rule regulating L5-TTPC synapses holds also for several other pyramidal-to-pyramidal connections in a digital model of neocortical tissue. In particular, we show that synaptic physiology, cell morphology and innervation patterns jointly determine LTP/LTD dynamics without requiring a different model or parameter set for each connection type. We therefore propose that a similar set of plasticity mechanisms is shared by seemingly very different neocortical connections and that only a small number of targeted experiments is required for generating a complete map of synaptic plasticity dynamics in the neocortex.

neuroscience↗

bioRxiv2019

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

neuroscience↗

bioRxiv2018

The Evolution of Phenotypic Plasticity when Environments Fluctuate in Time and Space

Most studies have explored the evolution of plasticity when the environment, and there-fore the optimal trait, varies in time or space. When the environment varies in time and space we show that genetic adaptation to temporal fluctuations depends on the between-generation autocorrelation in the environment in exactly the same way that genetic adaptation to spatial fluctuations depends on the probability of philopatry. This is because both measure the correlation in parent-offspring environments and therefore the effectiveness of a genetic response to selection. If the capacity to genetically respond to selection is stronger in one dimension (e.g. space) then plasticity mainly evolves in response to fluctuations in the other dimension (e.g. time). If the relationship between the environments of development and selection are the same in time and space then the evolved plastic response to temporal fluctuations is useful in a spatial context and genetic differentiation in space is reduced. However, if the relationship between the environments of development and selection are different then the optimal level of plasticity is different in the two dimensions. In this case the plastic response that evolves to cope with temporal fluctuations may actually be maladaptive in space. This can result in the evolution of hyperplasticity or negative plasticity, the effects of which are mitigated by spatial genetic differentiation. However, genetic differentiation acts in opposition to plasticity resulting in counter-gradient variation. These results highlight the difficulty of making space-for-time substitutions in empirical work but identify the key parameters which need to be measured in order to test whether space-for-time substitutions are likely to be valid.

evolutionary biology↗

bioRxiv2018

Cortical Reliability Amid Noise and Chaos

Typical responses of cortical neurons to identical sensory stimuli are highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is constrained by the intrinsic variability of cortical circuits, which remains largely unexplored. Here, we quantified this intrinsic variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We found that stochastic neurotransmitter release is a critical component of this variability, which, amplified by recurrent connectivity, causes rapid chaotic divergence with a time constant on the order of 10-20 milliseconds. Surprisingly, weak thalamocortical stimuli can transiently overcome the chaos, and induce reliable spike times with millisecond precision. We show that this effect relies on recurrent cortical connectivity, and is not a simple effect of feed-forward thalamocortical input. We conclude that recurrent cortical architecture supports millisecond spike-time reliability amid noise and chaotic network dynamics, resolving a long-standing debate.

neuroscience↗