Bi-allelic NRXN1α deletion in microglia derived from iPSC of an autistic patient increases interleukin-6 production and impairs supporting function on neuronal networking.

Autism spectrum disorder (ASD) is a set of heterogeneous neurodevelopmental conditions, with a highly diverse genetic hereditary component, including altered neuronal circuits, that has an impact on communication skills and behaviours of the affected individuals. Beside the recognised role of neuronal alterations, perturbations of microglia and the associated neuroinflammatory processes have emerged as credible contributors to aetiology and physiopathology of ASD. Mutations in NRXN1, a member of the neurexin family of cell-surface receptors that bind neuroligin, have been associated to ASD. NRXN1 is known to be expressed by neurons where it facilitates synaptic contacts, but it has also been identified in glial cells including microglia. Asserting the impact of ASD-related genes on neuronal versus microglia functions has been challenging. Here, we present an ASD subject-derived induced pluripotent stem cells (iPSC)-based in vitro system to characterise the effects of the ASD-associated NRXN1 gene deletion on neurons and microglia, as well as on the ability of microglia to support neuronal circuit formation and function. Using this approach, we demonstrated that NRXN1 deletion, impacting on the expression of the alpha isoform (NRXN1α), in microglia leads to microglial alterations and release of IL6, a pro-inflammatory interleukin associated with ASD. Moreover, microglia bearing the NRXN1α-deletion, lost the ability to support the formation of functional neuronal networks. The use of recombinant IL6 protein on control microglia-neuron co-cultures or neutralizing antibody to IL6 on their NRXN1α-deficient counterparts, supported a direct contribution of IL6 to the observed neuronal phenotype. Altogether, our data suggest that, in addition to neurons, microglia are also negatively affected by NRXN1α-deletion, and this significantly contributes to the observed neuronal circuit aberrations.

Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers.

Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.

Understanding the neurobiology of social behavior through exploring brain-wide dynamics of neural activity.

Social behavior is highly complex and adaptable. It can be divided into multiple temporal stages: detection, approach, and consummatory behavior. Each stage can be further divided into several cognitive and behavioral processes, such as perceiving social cues, evaluating the social and non-social contexts, and recognizing the internal/emotional state of others. Recent studies have identified numerous brain-wide circuits implicated in social behavior and suggested the existence of partially overlapping functional brain networks underlying various types of social and non-social behavior. However, understanding the brain-wide dynamics underlying social behavior remains challenging, and several brain-scale dynamics (macro-, meso-, and micro-scale levels) need to be integrated. Here, we suggest leveraging new tools and concepts to explore social brain networks and integrate those different levels. These include studying the expression of immediate-early genes throughout the entire brain to impartially define the structure of the neuronal networks involved in a given social behavior. Then, network dynamics could be investigated using electrode arrays or multi-channel fiber photometry. Finally, tools like high-density silicon probes and miniscopes can probe neural activity in specific areas and across neuronal populations at the single-cell level.

Huntingtin lowering impairs the maturation and synchronized synaptic activity of human cortical neuronal networks derived from induced pluripotent stem cells.

Despite growing descriptions of wild-type Huntingtin (wt-HTT) roles in both adult brain function and, more recently, development, several clinical trials are exploring HTT-lowering approaches that target both wt-HTT and the mutant isoform (mut-HTT) responsible for Huntington's disease (HD). This non-selective targeting is based on the autosomal dominant inheritance of HD, supporting the idea that mut-HTT exerts its harmful effects through a toxic gain-of-function or a dominant-negative mechanism. However, the precise amount of wt-HTT needed for healthy neurons in adults and during development remains unclear. In this study, we address this question by examining how wt-HTT loss affects human neuronal network formation, synaptic maturation, and homeostasis in vitro. Our findings establish a role of wt-HTT in the maturation of dendritic arborization and the acquisition of network-wide synchronized activity by human cortical neuronal networks modeled in vitro. Interestingly, the network synchronization defects only became apparent when more than two-thirds of the wt-HTT protein was depleted. Our study underscores the critical need to precisely understand wt-HTT role in neuronal health. It also emphasizes the potential risks of excessive wt-HTT loss associated with non-selective therapeutic approaches targeting both wt- and mut-HTT isoforms in HD patients.

Effects of chronic insecticide exposure on neuronal network development in vitro in rat cortical cultures.

Developmental exposure to carbamates, organophosphates, and pyrethroids has been associated with impaired neurodevelopmental outcomes. Sex-specific differences following chronic insecticide exposure are rather common in vivo. Therefore, we assessed the chronic effects of in vitro exposure to different carbamates (carbaryl, methomyl and aldicarb), organophosphates [chlorpyrifos (CPF), chlorpyrifos-oxon (CPO), and 3,5,6,trichloropyridinol (TCP)], and pyrethroids [permethrin, alpha-cypermethrin and 3-phenoxy benzoic acid (3-PBA)] on neuronal network development in sex-separated rat primary cortical cultures using micro-electrode array (MEA) recordings. Our results indicate that exposure for 1 week to carbaryl inhibited neurodevelopment in male cultures, while a hyperexcitation was observed in female cultures. Methomyl and aldicarb evoked a hyperexcitation after 2 weeks of exposure, which was more pronounced in female cultures. In contrast to acute MEA results, exposure to ≥ 10 µM CPF caused hyperexcitation in both sexes after 10 days. Interestingly, exposure to 10 µM CPO induced a clear hyperexcitation after 10 days of exposure in male but not female cultures. Exposure to 100 µM CPO strongly inhibited neuronal development. Exposure to the type I pyrethroid permethrin resulted in a hyperexcitation at 10 µM and a decrease in neuronal development at 100 µM. In comparison, exposure to ≥ 10 µM of the type II pyrethroid alpha-cypermethrin decreased neuronal development. In female but not in male cultures, exposure to 1 and 10 µM permethrin changed (network) burst patterns, with female cultures having shorter (network) bursts with fewer spikes per (network) burst. Together, these results show that MEA recordings are suitable for measuring sex-specific developmental neurotoxicity in vitro. Additionally, pyrethroid exposure induced effects on neuronal network development at human-relevant concentrations. Finally, chronic exposure has different effects on neuronal functioning compared to acute exposure, highlighting the value of both exposure paradigms.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation.

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin-Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

Microglial Modulation of Synaptic Maturation, Activity, and Plasticity.

Microglia, which are the resident immune cells of the CNS, also have important functions in physiological conditions. In this chapter, we review the experimental evidence that microglia modulate neuronal and synaptic activity during normal development and in adults. We show that microglia can regulate the maturation and function of both inhibitory and excitatory synapses that can be stimulated or repressed. We further review the fact that these regulations occur in various brain regions, through soluble and membrane molecules, directly or through other cell partners. This review emphasizes the fact that microglia are genuine and highly context-dependent and thus adaptable regulators of neuronal activity.

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

Investigating Communication Dynamics in Neuronal Network using 3D Gold Microelectrode Arrays.

Although in vitro neuronal network models hold great potential for advancing neuroscience research, with the capacity to provide fundamental insights into mechanisms underlying neuronal functions, the dynamics of cell communication within such networks remain poorly understood. Here, we develop a customizable, polymer modified three-dimensional gold microelectrode array with sufficient stability for high signal-to-noise, long-term, neuronal recording of cultured networks. By using directed spatial and temporal patterns of electrical stimulation of cells to explore synaptic-based communication, we monitored cell network dynamics over 3 weeks, quantifying communication capability using correlation heatmaps and mutual information networks. Analysis of synaptic delay and signal speed between cells enabled us to establish a communication connectivity model. We anticipate that our discoveries of the dynamic changes in communication across the neuronal network will provide a valuable tool for future studies in understanding health and disease as well as in developing effective platforms for evaluating therapies.

The angiotensin II receptors type 1 and 2 modulate astrocytes and their crosstalk with microglia and neurons in an in vitro model of ischemic stroke.

BACKGROUND: Astrocytes are the most abundant cell type of the central nervous system and are fundamentally involved in homeostasis, neuroprotection, and synaptic plasticity. This regulatory function of astrocytes on their neighboring cells in the healthy brain is subject of current research. In the ischemic brain we assume disease specific differences in astrocytic acting. The renin-angiotensin-aldosterone system regulates arterial blood pressure through endothelial cells and perivascular musculature. Moreover, astrocytes express angiotensin II type 1 and 2 receptors. However, their role in astrocytic function has not yet been fully elucidated. We hypothesized that the angiotensin II receptors impact astrocyte function as revealed in an in vitro system mimicking cerebral ischemia. Astrocytes derived from neonatal wistar rats were exposed to telmisartan (angiotensin II type 1 receptor-blocker) or PD123319 (angiotensin II type 2 receptor-blocker) under normal conditions (control) or deprivation from oxygen and glucose. Conditioned medium (CM) of astrocytes was harvested to elucidate astrocyte-mediated indirect effects on microglia and cortical neurons. RESULT: The blockade of angiotensin II type 1 receptor by telmisartan increased the survival of astrocytes during ischemic conditions in vitro without affecting their proliferation rate or disturbing their expression of S100A10, a marker of activation. The inhibition of the angiotensin II type 2 receptor pathway by PD123319 resulted in both increased expression of S100A10 and proliferation rate. The CM of telmisartan-treated astrocytes reduced the expression of pro-inflammatory mediators with simultaneous increase of anti-inflammatory markers in microglia. Increased neuronal activity was observed after treatment of neurons with CM of telmisartan- as well as PD123319-stimulated astrocytes. CONCLUSION: Data show that angiotensin II receptors have functional relevance for astrocytes that differs in healthy and ischemic conditions and effects surrounding microglia and neuronal activity via secretory signals. Above that, this work emphasizes the strong interference of the different cells in the CNS and that targeting astrocytes might serve as a therapeutic strategy to influence the acting of glia-neuronal network in de- and regenerative context.

Impact of quantum and neuromorphic computing on biomolecular simulations: Current status and perspectives.

New high-performance computing architectures are becoming operative, in addition to exascale computers. Quantum computers (QC) solve optimization problems with unprecedented efficiency and speed, while neuromorphic hardware (NMH) simulates neural network dynamics. Albeit, at the moment, both find no practical use in all atom biomolecular simulations, QC might be exploited in the not-too-far future to simulate systems for which electronic degrees of freedom play a key and intricate role for biological function, whereas NMH might accelerate molecular dynamics simulations with low energy consumption. Machine learning and artificial intelligence algorithms running on NMH and QC could assist in the analysis of data and speed up research. If these implementations are successful, modular supercomputing could further dramatically enhance the overall computing capacity by combining highly optimized software tools into workflows, linking these architectures to exascale computers.

Threat-dependent scaling of prelimbic dynamics to enhance fear representation.

Promptly identifying threatening stimuli is crucial for survival. Freezing is a natural behavior displayed by rodents toward potential or actual threats. Although it is known that the prelimbic cortex (PL) is involved in both risk evaluation and in fear and anxiety-like behavior expression, here we explored whether PL neuronal activity can dynamically represent different internal states of the same behavioral output (i.e., freezing). We found that freezing can always be decoded from PL activity at a population level. However, the sudden presentation of a fearful stimulus quickly reshaped the PL to a new neuronal activity state, an effect not observed in other cortical or subcortical regions examined. This shift changed PL freezing representation and is necessary for fear memory expression. Our data reveal the unique role of the PL in detecting threats and internally adjusting to distinguish between different freezing-related states in both unconditioned and conditioned fear representations.

Walnut-Derived Peptides Ameliorate Scopolamine-Induced Memory Impairments in a Mouse Model via Activation of Peroxisome Proliferator-Activated Receptor γ-Mediated Excitotoxicity.

We investigated the protective effect of walnut peptides and YVPFPLP (YP-7) on scopolamine-induced memory impairment in mice and ß-amyloid (Aß)-induced excitotoxic injury in primary hippocampal neurons, respectively. Additionally, the protective mechanism of YP-7 on neuronal excitotoxicity was explored. Mouse behavioral and hippocampal slice morphology experiments indicate that YP-7 improves the learning and memory abilities of cognitively impaired mice and protects synaptic integrity. Immunofluorescence, western blotting, and electrophysiological experiments on primary hippocampal neurons indicate that YP-7 inhibits neuronal damage caused by excessive excitation of neurons induced by Aß. HT-22 cell treatment with peroxisome proliferator-activated receptor γ (PPARγ) activators and inhibitors showed that YP-7 activates PPARγ expression and maintains normal neuronal function by forming stable complexes with PPARγ to inhibit the extracellular regulated protein kinase pathway. Therefore, YP-7 can ameliorate glutamate-induced excitotoxicity and maintain neuronal signaling. This provides a theoretical basis for active peptides to ameliorate excitotoxicity and the development of functional foods.

Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase shifts.

Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000 Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.

We have built a mathematical framework to examine how a reduced neuronal coupling within an epileptic focus could lead to very high-frequency (VHFOs) and ultra-fast oscillations (UFOs) in depth EEG signals. By analyzing weakly coupled neurons, we found a bistability synchronization region where in-phase and anti-phase synchrony persist. These dynamics can be detected as very high-frequency EEG signals. The principle of weak coupling aligns with the disturbances in neuronal connections often observed in epilepsy; moreover, VHFOs are important markers of epileptogenicity. Our findings point to the potential significance of weakened neuronal connections in producing VHFOs and UFOs related to focal epilepsy. This could enhance our understanding of brain disorders. We emphasize the need for further investigations of weakly coupled neurons.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models.

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

Effects of acute insecticide exposure on neuronal activity in vitro in rat cortical cultures.

Exposure to pesticides, such as carbamates, organophosphates, organochlorines and pyrethroids, has been linked to various health problems, including neurotoxicity. Although most in vivo studies use only male rodents, some studies have shown in vivo sex-specific effects after acute exposure. Since in vivo studies are costly and require a large number of animals, in vitro assays that take sex-specific effects into account are urgently needed. We therefore assessed the acute effects of exposure to different carbamates (methomyl, aldicarb and carbaryl), organophosphates (chlorpyrifos (CPF), chlorpyrifos-oxon (CPO) and 3,5,6-trichloropyridinol), organochlorines (endosulfan, dieldrin and lindane) and pyrethroids (permethrin, alpha-cypermethrin and 3-phenoxy-benzoic acid (3-PBA)) on neuronal network function in sex-separated rat primary cortical cultures using micro-electrode array (MEA) recordings. Our results indicate that exposure to the carbamate carbaryl and the organophosphates CPF and CPO decreased neuronal activity, with CPO being the most potent. Notably, (network) burst patterns differed between CPF and CPO, with CPO inducing fewer, but more intense (network) bursts. Exposure to low micromolar levels of endosulfan induced a hyperexcitation, most likely due to the antagonistic effects on GABA receptors. Interestingly, females were more sensitive to endosulfan than males. Exposure to dieldrin and lindane also increased neuronal activity, albeit less than endosulfan and without sex-specific effects. Exposure to type I pyrethroid permethrin increased neuronal activity, while exposure to type II pyrethroid alpha-cypermethrin strongly decreased neuronal activity. The increase seen after permethrin exposure was more pronounced in males than in females. Together, these results show that acute exposure to different classes of pesticides exerts differential effects on neuronal activity. Moreover, it shows that MEA recordings are suited to detect sex-specific neurotoxic effects in vitro.

Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images.

Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.

Stretch-Induced Injury Affects Cortical Neuronal Networks in a Time- and Severity-Dependent Manner.

Traumatic brain injury (TBI) is the leading cause of accident-related death and disability in the world and can lead to long-term neuropsychiatric symptoms, such as a decline in cognitive function and neurodegeneration. TBI includes primary and secondary injury, with head trauma and deformation of the brain caused by the physical force of the impact as primary injury, and cellular and molecular cascades that lead to cell death as secondary injury. Currently, there is no treatment for TBI-induced cell damage and neural circuit dysfunction in the brain, and thus, it is important to understand the underlying cellular mechanisms that lead to cell damage. In the current study, we use stretchable microelectrode arrays (sMEAs) to model the primary injury of TBI to study the electrophysiological effects of physically injuring cortical cells. We recorded electrophysiological activity before injury and then stretched the flexible membrane of the sMEAs to injure the cells to varying degrees. At 1, 24, and 72 h post-stretch, we recorded activity to analyze differences in spike rate, Fano factor, burstlet rate, burstlet width, synchrony of firing, local network efficiency, and Q statistic. Our results demonstrate that mechanical injury changes the firing properties of cortical neuron networks in culture in a time- and severity-dependent manner. Our results suggest that changes to electrophysiological properties after stretch are dependent on the strength of synchronization between neurons prior to injury.