bioRxiv Subject Collection: Neuroscience's Journal
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Wednesday, May 1st, 2024
| Time |
Event |
| 3:47a |
Activation of the proton-sensing GPCR, GPR65 on fibroblast-like synoviocytes contributes to inflammatory joint pain.
Inflammation is associated with localised acidosis, however, attributing physiological and pathological roles to proton-sensitive receptors is challenging due to their diversity and widespread expression. Here, agonists of the proton-sensing GPCR, GPR65, were systematically characterised. The synthetic agonist BTB09089 (BTB) recapitulated many proton-induced signalling events and demonstrated selectivity for GPR65. BTB was used to show that GPR65 activation on fibroblast-like synoviocytes (FLS), cells that line synovial joints, results in the secretion of pro-inflammatory mediators capable of recruiting immune cells and sensitising sensory neurons. Intra-articular injection of BTB resulted in GPR65-dependent sensitisation of knee-innervating neurons and nocifensive behaviours in mice. Stimulation of GPR65 on human FLS also triggered the release of inflammatory mediators and synovial fluid samples from human osteoarthritis patients were shown to activate GPR65. These results suggest a role of GPR65 in mediating cell-cell interactions that drive inflammatory joint pain in both mice and humans. | | 3:47a |
Modeling mTORopathy-related epilepsy in cultured murine hippocampal neurons using the multi-electrode array
The mechanistic target of rapamycin complex 1 (mTORC1) signaling pathway is a ubiquitous cellular pathway. mTORopathies, a group of disorders characterized by hyperactivity of the mTORC1 pathway, illustrate the prominent role of the mTOR pathway in disease pathology, often profoundly affecting the central nervous system. One of the most debilitating symptoms of mTORopathies is drug-resistant epilepsy, emphasizing the urgent need for a deeper understanding of disease mechanisms to develop novel anti-epileptic drugs. In this study, we explored the multiwell Multi-electrode array (MEA) system as a tool to identify robust network activity parameters in an approach to model mTORopathy-related epilepsy in vitro. To this extent, we cultured mouse primary hippocampal neurons on the multiwell MEA to identify robust network activity phenotypes in mTORC1-hyperactive neuronal networks. mTOR-hyperactivity was induced either through deletion of Tsc1 or overexpression of a constitutively active RHEB variant identified in patients, RHEBp.P37L. mTORC1 dependency of the phenotypes was assessed using rapamycin, and vigabatrin was applied to treat epilepsy-like phenotypes. We show that hyperactivity of the mTORC1 pathway leads to aberrant network activity. In both the Tsc1-KO and RHEB-p.P37L models, we identified changes in network synchronicity, rhythmicity, and burst characteristics. The presence of these phenotypes is prevented upon early treatment with the mTORC1-inhibitor rapamycin. Application of rapamycin in mature neuronal cultures could only partially rescue the network activity phenotypes. Additionally, treatment with the anti-epileptic drug vigabatrin reduced network activity and restored burst characteristics. Taken together, we showed that mTORC1-hyperactive neuronal cultures on the multiwell MEA system present reliable network activity phenotypes that can be used as an assay to explore the potency of new drug treatments targeting epilepsy in mTORopathy patients and may give more insights into the pathophysiological mechanisms underlying epilepsy in these patients. | | 3:47a |
An electrodiffusive network model with multicompartmental neurons and synaptic connections
Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, shortcuts are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of units, each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+ , K+ , Cl- , and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks. | | 3:47a |
Sacrificing Adaptability for Functionality: The Ivory Tower of Macular Muller Cells
The predilection of many conditions, such as macular telangiectasia type 2, for the human macula suggests it may be more susceptible to stress than the peripheral retina. In this study, we have comprehensively investigated the transcriptomic profiling of the macula and peripheral retina in response to stress. We conducted single-cell RNA sequencing analysis on the macula and peripheral retina of four donors cultured ex vivo with or without exposure to light stress. We found that the peripheral retina generally exhibited more transcriptional changes than the macula in response to stress. Interestingly, one of the most pronounced changes was observed in a subgroup of Muller cells that are dominant in the peripheral retina. Genes more abundantly expressed in peripheral retinal Muller cells were mainly associated with stress responses and were more influenced by light stress. In contrast, genes that were highly expressed in Muller cells that predominate in the macula played roles in cellular function and were less influenced by light stress. We identified that Metallothionein 1 (MT1), A Kinase Anchor Protein 12 (AKAP12) and MAF BZIP Transcription Factor F (MAFF) were more abundantly expressed in peripheral Muller cells than in macular Muller cells. We found that these genes were also activated in the mouse retina in the early stages of development of subretinal neovascularisation. Knockdown of the MT1, AKAP12 and MAFF genes in human primary Muller cells reduced cell viability in response to light stress and disrupted several stress response pathways. Taken together, our findings indicate that macular Muller cells are more directed toward maintaining retinal cell function rather than mounting a stress response when they are exposed to acute stress, which may contribute to the vulnerability of macula to degenerative disease. | | 3:47a |
Language models outperform cloze predictability in a cognitive model of reading
Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are yet lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye- movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than Cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements. | | 3:47a |
Distinct neural computations scale the violation of expected reward and emotion in social transgressions
Traditional decision-making models conceptualize humans as optimal learners aiming to maximize outcomes by leveraging reward prediction errors (PE). While violated emotional expectations(emotional PEs) have recently been formalized, the underlying neurofunctional basis and whether it differs from reward PEs remain unclear. Using a modified fMRI Ultimatum Game on n=43 participants we modelled reward and emotional PEs in response to unfair offers and subsequent punishment decisions. Computational modelling revealed distinct contributions of reward and emotional PEs to punishment decisions, with reward PE exerting a stronger impact. This process was neurofunctionally dissociable such that (1) reward engaged the dorsomedial prefrontal cortex while emotional experience recruited the anterior insula, (2) multivariate decoding accurately separated reward and emotional PEs. Predictive neural expressions of reward but not emotional PEs in fronto-insular systems predicted neurofunctional and behavioral punishment decisions. Overall, these findings suggest distinct neurocomputational processes underlie reward and emotional PEs which uniquely impact social decisions. | | 4:38a |
A claudin5-binding peptide enhances the permeability of the blood-brain-barrier
The blood-brain barrier (BBB) is essential to maintain brain homeostasis and healthy conditions but it also prevents drugs from reaching brain cells. In the BBB, tight junctions (TJs) are multi-protein complexes located at the interface between adjacent brain endothelial cells that regulate paracellular diffusion and claudin-5 (CLDN5) is the major component of the TJ portfolio, playing a pivotal role in restricting the paracellular traffic. In view of obtaining fine control over the transport across the BBB, the use of competing peptides able to bind CLDN5 to induce transient and regulated permeabilization of the paracellular passage is emerging as a potentially translatable strategy for clinical applications. In this work, we designed and tested short peptides with improved solubility and biocompatibility using a combined approach that involved structural modeling techniques and in vitro validation, generating a robust workflow for the design, screening, and optimization of peptides for the modulation of the BBB paracellular permeability. We designed a selection of 11- to 16-mer compounds derived from the first CLDN5 extracellular domain and from the CLDN5-binding domain of Clostridium perfringens enterotoxin and determined their efficiency in enhancing BBB permeability. The computational analysis classified all tested peptides based on solubility and affinity to CLDN5, and provided atom-level details of the binding process. From our screening, we identified a novel CLDN5-derived peptide, here called f1-C5C2, which demonstrated good solubility in biological media, efficient binding to CLDN5 subunits, and capability to increase permeability at low concentrations. The peptidomimetic in silico/in vitro strategy described here can achieve a transient and reversible permeabilization of the BBB with potential applications in the pharmacological treatment of brain diseases. | | 10:31a |
Microstructural pruning in human prefrontal cortex scaffolds its functional reorganization across development
Neural representations in occipitotemporal cortex emerge during development in response to visual experience with ecological stimulus categories, such as faces or words. While similar category-selective representations have also been observed in the frontal lobe, how they emerge across development, whether current models of brain development extend to prefrontal cortex, and the extent to which such high-level representations are anatomically consistent across the lifespan is unknown. Through a combination of functional and quantitative MRI scans, we observe previously undescribed cortical folding patterns in human specific inferior frontal cortex whose consistency reveal that childhood representations for visual object categories rearrange into stable adulthood patterns. This functional restructuring was distinct from occipitotemporal cortex where adult-like response patterns only scale in magnitude across development. The unique form of functional development in prefrontal cortex was accompanied by restructuring of cortical tissue properties: macromolecules are pruned across adolescence in prefrontal cortex while they proliferate in temporal cortex. These results suggest visual representations in distinct cortical lobes undergo distinct developmental trajectories, and that humanspecific prefrontal cortex shows an especially protracted maturational process that necessitates late-stage tissue restructuring detectable in the living brain. | | 10:31a |
Bridging Auditory Perception and Natural Language Processing with Semantically informed Deep Neural Networks
Sound recognition is effortless for humans but poses a significant challenge for artificial hearing systems. Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have recently surpassed traditional machine learning in sound classification. However, current DNNs map sounds to labels using binary categorical variables, neglecting the semantic relations between labels. Cognitive neuroscience research suggests that human listeners exploit such semantic information besides acoustic cues. Hence, our hypothesis is that incorporating semantic information improves DNN s sound recognition performance, emulating human behavior. In our approach, sound recognition is framed as a regression problem, with CNNs trained to map spectrograms to continuous semantic representations from NLP models (Word2Vec, BERT, and CLAP text encoder). Two DNN types were trained: semDNN with continuous embeddings and catDNN with categorical labels, both with a dataset extracted from a collection of 388,211 sounds enriched with semantic descriptions. Evaluations across four external datasets, confirmed the superiority of semantic labeling from semDNN compared to catDNN, preserving higher-level relations. Importantly, an analysis of human similarity ratings for natural sounds, showed that semDNN approximated human listener behavior better than catDNN, other DNNs, and NLP models. Our work contributes to understanding the role of semantics in sound recognition, bridging the gap between artificial systems and human auditory perception. | | 10:31a |
Neural bases of proactive and predictive processing of meaningful sub-word units in speech comprehension
To comprehend speech, human brains identify meaningful units in the speech stream. But whereas the English 'She believed him.' has 3 words, the Arabic equivalent 'saddaqathu.' is a single word with 3 meaningful sub-word units, called morphemes: a verb stem ('saddaqa'), a subject suffix ('-t-'), and a direct object pronoun ('-hu'). It remains unclear whether and how the brain processes morphemes, above and beyond other language units, during speech comprehension. Here, we propose and test hierarchically-nested encoding models of speech comprehension: a NAIVE model with word-, syllable-, and sound-level information; a BOTTOM-UP model with additional morpheme boundary information; and PREDICTIVE models that process morphemes before these boundaries. We recorded magnetoencephalography (MEG) data as participants listened to Arabic sentences like 'saddaqathu.'. A temporal response function (TRF) analysis revealed that in temporal and left inferior frontal regions PREDICTIVE models outperform the BOTTOM-UP model, which outperforms the NAIVE model. Moreover, verb stems were either length-AMBIGUOUS (e.g., 'saddaqa' could initially be mistaken for the shorter stem 'sadda'='blocked') or length-UNAMBIGUOUS (e.g., 'qayyama'='evaluated' cannot be mistaken for a shorter stem), but shared a uniqueness point, at which stem identity is fully disambiguated. Evoked analyses revealed differences between conditions before the uniqueness point, suggesting that, rather than await disambiguation, the brain employs PROACTIVE PREDICTIVE strategies, processing the accumulated input as soon as any possible stem is identifiable, even if not unique. These findings highlight the role of morpheme processing in speech comprehension, and the importance of including morpheme-level information in neural and computational models of speech comprehension. | | 10:31a |
A metabotropic glutamate receptor agonist enhances visual signal fidelity in a mouse model of retinitis pigmentosa
Many inherited retinal diseases target photoreceptors, which transduce light into a neural signal that is processed by the downstream visual system. As photoreceptors degenerate, physiological and morphological changes to retinal synapses and circuitry reduce sensitivity and increase noise, degrading visual signal fidelity. Here, we pharmacologically targeted the first synapse in the retina in an effort to reduce circuit noise without sacrificing visual sensitivity. We tested a strategy to partially replace the neurotransmitter lost when photoreceptors die with an agonist of receptors that ON bipolars cells use to detect glutamate released from photoreceptors. In rd10 mice, which express a photoreceptor mutation that causes retinitis pigmentosa (RP), we found that a low dose of the mGluR6 agonist L-2-amino-4-phosphonobutyric acid (L-AP4) reduced pathological noise induced by photoreceptor degeneration. After making in vivo electroretinogram recordings in rd10 mice to characterize the developmental time course of visual signal degeneration, we examined effects of L-AP4 on sensitivity and circuit noise by recording in vitro light-evoked responses from individual retinal ganglion cells (RGCs). L-AP4 decreased circuit noise evident in RGC recordings without significantly reducing response amplitudes, an effect that persisted over the entire time course of rod photoreceptor degeneration. Subsequent in vitro recordings from rod bipolar cells (RBCs) showed that RBCs are more depolarized in rd10 retinas, likely contributing to downstream circuit noise and reduced synaptic gain, both of which appear to be ameliorated by hyperpolarizing RBCs with L-AP4. These beneficial effects may reduce pathological circuit remodeling and preserve the efficacy of therapies designed to restore vision. |
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