Biology subjects

Wang, X.-J.

Publications and source records attributed to Wang, X.-J..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

A Practical and Safe Model of Nitrogen Mustard Injury in Cornea

PurposeSulfur mustard (SM) is an alkylating agent used in warfare and terrorism that inflicts devastating ocular injuries. Although the clinical symptoms are well described, the underlying mechanisms are not fully understood, hindering the development of effective treatments. One major roadblock is the lack of a suitable model due to the extremely hazardous nature of SM, which requires strict safety measures. As a safe and practical alternative, we report a novel model that uses mechlorethamine (nitrogen mustard) gel, an FDA-approved topical chemotherapeutic administered by patients at home. Here we demonstrate its suitability to induce mustard corneal injury in any laboratory. MethodsEx vivo porcine corneas were injured with mechlorethamine gel. Hematoxylin-eosin staining, and immunohistochemistry were performed to evaluate histopathology of SM-like corneal injuries: epithelium thickness and stromal separation, keratocyte and inflammatory cell counts, and expression of inflammation and fibrosis markers. ResultsThis model showed the characteristic histopathology and expression of cyclooxygenase-2 (inflammation) and fibronectin-1 (fibrosis), which were consistent with other well-established SM-like corneal injury models. ConclusionGiven its ease of implementation and safety, this mechlorethamine model could be used to study the full course of mustard corneal injuries. This model would greatly facilitate mustard injury research, shedding light on new knowledge that would increase our understanding of mustard ocular injuries while investigating novel therapeutics. Translational relevancethis model will allow safe evaluation of SM-like corneal injuries within 24 hours, facilitating the identification of early/new molecules that might help to develop novel treatments which could be readily translated into the clinic.

pharmacology and toxicology↗

bioRxiv2024

DRED: A Comprehensive Database of Genes Related to Repeat Expansion Diseases

Expansion of tandem repeats in genes often causes severe neuromuscular diseases, such as fragile X syndrome, huntingtons disease, and spinocerebellar ataxia. However, information on genes associated with repeat expansion diseases is scattered throughout the literature, systematic prediction of potential genes that may cause diseases via repeat expansion is also lacking. Here, we develop DRED, a Database of genes related to Repeat Expansion Diseases, as a manually-curated database that covers all known 61 genes related to repeat expansion diseases reported in PubMed and OMIM, along with detailed repeat information for each gene. DRED also includes 516 genes with the potential to cause diseases via repeat expansion, which were predicted basing on their repeat composition, genetic variations, genomic features, and disease associations. Various types of information on repeat expansion diseases and their corresponding genes/repeats are presented in DRED, together with links to external resources, such as NCBI and ClinVar. DRED provides user-friendly interfaces with comprehensive functions, and can serve as a central data resource for basic research and repeat expansion disease-related medical diagnosis. DRED is freely accessible at http://omicslab.genetics.ac.cn/dred, and is frequently updated to include newly reported genes related to repeat expansion diseases.

bioinformatics↗

bioRxiv2024

Telomere-to-telomere genome assembly of a male goat reveals novel variants associated with cashmere traits

A complete goat (Capra hircus) reference genome enhances analyses of genetic variation, thus providing novel insights into domestication and selection in goats and related species. Here, we assembled a telomere-to-telomere (T2T) gap-free genome (2.86 Gb) from a cashmere goat (T2T-goat1.0), which included a Y chromosome of 20.9 Mb. With a base accuracy of >99.999%, T2T-goat1.0 corrected numerous genome-wide structural and base errors in previous assemblies and added 288.5 Mb of previously unresolved regions (PURs) and 446 newly assembled genes to the reference genome. We sequenced the genomes of five representative goat breeds for PacBio reads, and used T2T-goat1.0 as a reference to identify a total of 63,417 structural variations (SVs) with up to 4711 SVs (7.41%) in the PURs and 507 overlapping genes significantly enriched (Padj. < 0.05) in the keratin filament pathway. T2T-goat1.0 was applied in population analyses of global wild and domestic goats, which revealed 32,419 SVs and 25,397,794 SNPs, including 870 SVs and 545,026 SNPs in the PURs. Also, our analyses revealed a set of novel selective variants and genes associated with domestication (e.g., NKG2D and ABCC4) and cashmere traits (e.g., ABCC4 and ASIP).

genomics↗

bioRxiv2023

Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies

Training recurrent neural networks (RNNs) has become a go-to approach for generating and evaluating mechanistic neural hypotheses for cognition. The ease and efficiency of training RNNs with backpropagation through time and the availability of robustly supported deep learning libraries has made RNN modeling more approachable and accessible to neuroscience. Yet, a major technical hindrance remains. Cognitive processes such as working memory and decision making involve neural population dynamics over a long period of time within a behavioral trial and across trials. It is difficult to train RNNs to accomplish tasks where neural representations and dynamics have long temporal dependencies without gating mechanisms such as LSTMs or GRUs which currently lack experimental support and prohibit direct comparison between RNNs and biological neural circuits. We tackled this problem based on the idea of specialized skip-connections through time to support the emergence of task-relevant dynamics, and subsequently reinstitute biological plausibility by reverting to the original architecture. We show that this approach enables RNNs to successfully learn cognitive tasks that prove impractical if not impossible to learn using conventional methods. Over numerous tasks considered here, we achieve less training steps and shorter wall-clock times, particularly in tasks that require learning long-term dependencies via temporal integration over long timescales or maintaining a memory of past events in hidden-states. Our methods expand the range of experimental tasks that biologically plausible RNN models can learn, thereby supporting the development of theory for the emergent neural mechanisms of computations involving long-term dependencies.

neuroscience↗

bioRxiv2023

Ellagic acid ameliorates atherosclerosis by targeting the epidermal growth factor receptor

BACKGROUNDHigh levels of plasma low-density lipoprotein (LDL) are a key risk factor for atherosclerosis. Low-density lipoprotein receptor (LDLR) mediates the degradation of plasma LDL. Therefore, it may be possible to prevent and treat atherosclerosis by increasing the levels of LDLR. The natural polyphenolic compound ellagic acid (EA) has various biological activities. In mice, EA alleviated the progression of atherosclerosis; however, the underlying mechanism remains unclear. METHODSMolecular interaction, cell, and animal experiments were used to explore the role and mechanism of EA in improving atherosclerosis. RESULTSEA binds to the extracellular domain of the epidermal growth factor receptor (EGFR), thus activating the EGFR-extracellular signal-regulated kinase (EGFR-ERK) signaling pathway, stabilizing LDLR mRNA, and promoting the expression of LDLR protein. The development of EA-loaded human serum albumin nanoparticles enabled intravenous administration in animal experiments. CONCLUSIONSThe research verified the in vivo effects of EA on the EGFR-ERK signaling pathway, LDLR levels, and atherosclerosis. EA may assist in the prevention and treatment of atherosclerosis.

biochemistry↗

bioRxiv2023

An emergent mechanism for internally gated task switching in modular recurrent neural networks with multiple cell types

Behavioral flexibility relies on the brains ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phe-nomenological description of representational subspaces is explained by a specific circuit mechanism.

neuroscience↗

bioRxiv2023

Bifurcation in space: Emergence of function modularity in the neocortex

Recent reports of widespread neural representations challenge the notion that brain areas are specialized in distinct aspects of cognition. Here we tackle this challenge, using connectome-based neocortex models endowed with macro-scopic gradients of neurobiological properties. We show that a bifurcation (an abrupt change of behavior) occurring locally in the cortical space gives rise to specialization of a subset of areas, that defines a multi-regional functional module, for subjective decision making or working memory coding. We found that mnemonic activity exhibits an inverted-V shaped pattern of timescales across the cortical hierarchy, which represents an experimentally testable prediction. A plethora of bifurcations in space coexist, corresponding to a variety of functional modules not well predicted by network analysis of structural modules; their associated timescale profiles could be observed in animals performing tasks that require different mental processes. These findings suggest bifurcation in space as a fundamental principle for understanding brains modular organization. TeaserThe theory of bifurcation in space mechanistically accounts for the emergence of functional specialization of cortical areas dedicated to distinct cognitive functions that is compatible with distributed neural representations in a multiregional cortex.

neuroscience↗

bioRxiv2023

Comparing rapid rule-learning strategies in humans and monkeys

Inter-species comparisons are key to deriving an understanding of the behavioral and neural correlates of human cognition from animal models. We perform a detailed comparison of macaque monkey and human strategies on an analogue of the Wisconsin Card Sort Test, a widely studied and applied multi-attribute measure of cognitive function, wherein performance requires the inference of a changing rule given ambiguous feedback. We found that well-trained monkeys rapidly infer rules but are three times slower than humans. Model fits to their choices revealed hidden states akin to feature-based attention in both species, and decision processes that resembled a Win-stay lose-shift strategy with key differences. Monkeys and humans test multiple rule hypotheses over a series of rule-search trials and perform inference-like computations to exclude candidates. An attention-set based learning stage categorization revealed that perseveration, random exploration and poor sensitivity to negative feedback explain the under-performance in monkeys.

animal behavior and cognition↗

bioRxiv2022

Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome

Recent advances in connectome and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the mouse multiregional brain for a cardinal cognitive function called working memory, the brains ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for inter-areal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

neuroscience↗

bioRxiv2022

Brain mechanism of foraging: reward-dependentsynaptic plasticity or neural integration of values?

During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is the neural mechanism for action value maintenance and updating? Here we explore two contrasting network models: synaptic learning of action value versus neural integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about the underlying biological neural circuits. In particular, the neural integrator model but not the synaptic model requires that reward signals are mediated by neural pools selective for action alternatives and their projections are aligned with linear attractor axes in the valuation system. We demonstrate experimentally observable neural dynamical signatures and feasible perturbations to differentiate the two contrasting scenarios, suggesting that the synaptic model is a more robust candidate mechanism. Overall, this work provides a modeling framework to guide future experimental research on probabilistic foraging.

neuroscience↗

bioRxiv2022

Mechanisms of Dominant Electrophysiological Features of Four Subtypes of Layer 1 Interneurons

Neocortical Layer 1 (L1) consists of the distal dendrites of pyramidal cells and GABAergic interneurons (INs) and receives extensive long-range "top-down" projections, but L1 INs remain poorly understood. In this work, we systematically examined the distinct dominant electrophysiological features for four unique IN subtypes in L1 that were previously identified from mice of either gender: Canopy cells show an irregular firing pattern near rheobase; Neurogliaform cells (NGFCs) are late-spiking, and their firing rate accelerates during current injections; cells with strong expression of the 7 nicotinic receptor (7 cells), display onset (rebound) bursting; vasoactive intestinal peptide (VIP) expressing cells exhibit high input resistance, strong adaptation, and irregular firing. Computational modeling revealed that these diverse neurophysiological features could be explained by an extended exponential-integrate-and-fire neuron model with varying contributions of a slowly inactivating K+ channel (SIK), a T-type Ca2+ channel, and a spike-triggered Ca2+-dependent K+ channel. In particular, we show that irregular firing results from square-wave bursting through a fast-slow analysis. Furthermore, we demonstrate that irregular firing is frequently observed in VIP cells due to the interaction between strong adaptation and a SIK channel. At last, we reveal that the VIP and 7 cell models resonant with Alpha/Theta band input through a dynamic gain analysis. Significance StatementIn the neocortex, about 25% of neurons are interneurons. Interestingly, only somas of interneurons reside within Layer 1 (L1) of the neocortex, but not of excitatory pyramidal cells. L1 interneurons are diverse and believed to be important in the cortical-cortex interactions, especially top-down signaling in the cortical hierarchy. However, the electrophysiological features of L1 interneurons are poorly understood. Here, we systematically studied the electrophysiological features within each L1 interneuron subtype. Furthermore, we build computational models for each subtype and study the mechanisms behind these features. These electrophysiological features within each subtype should be incorporated to elucidate how different L1 interneuron subtypes contribute to communication between cortexes.

neuroscience↗

bioRxiv2022

A connectome-based model of conscious access in monkey cortex

A growing body of evidence suggests that conscious perception of a sensory stimulus coincides with all-or-none activity across multiple cortical areas, a phenomenon called ignition. In contrast, the same stimulus, when undetected, induces only transient activity. In this work, we report a large-scale model of the macaque cortex based on recently quantified structural mesoscopic connectome data. We use this model to simulate a detection task, and demonstrate how a dynamical bifurcation mechanism produces ignition-like events in the model network. The model predicts that feedforward excitatory transmission is primarily mediated by the fast AMPA receptors to ensure rapid signal propagation from sensory to associative areas. In contrast, a greater proportion of the inter-areal feedback projections and local recurrent excitation depend on the slow NMDA receptors, to ensure ignition of distributed frontoparietal activity. Our model predicts, counterintuitively, that fast-responding sensory areas contain a higher ratio of NMDA to AMPA receptors compared to association cortical areas that show slow, sustained activity. We validate this prediction using cortex-wide in-vitro receptor autoradiography data. Finally, we show how this model can account for various behavioral and physiological effects linked to consciousness. Together, these findings clarify the neurophysiological mechanisms of conscious access in the primate cortex and support the concept that gradients of receptor densities along the cortical hierarchy contribute to distributed cognitive functions.

neuroscience↗

bioRxiv2021

Hierarchical Timescales in the Neocortex: Mathematical Mechanism and Biological Insights

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short-time scales whereas higher association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense inter-areal cortical connectivity known in modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically-based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: (1) a macroscopic gradient of synaptic excitation, (2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and (3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks. Significance StatementIn the neocortex, while early sensory areas encode and process external inputs rapidly, higher association areas are endowed with slow dynamics suitable for accumulating information over time. Such a hierarchy of temporal response windows along the cortical hierarchy naturally emerges in a model of multi-areal primate cortex. This finding raises the question of why diverse temporal modes are not mixed in roughly the same way across the whole cortex, despite high connection density and an abundance of feedback loops. We investigate this question by mathematically analyzing the anatomically-based network model of macaque cortex, and show that three general principles of synaptic excitation and inhibition are crucial for timescale segregation in a hierarchy, a functionally important characteristic of the cortex.

neuroscience↗

bioRxiv2021

Elucidating the neural mechanisms of Learning-to-Learn

Learning-to-learn, a progressive speedup of learning while solving a series of similar problems, represents a core process of knowledge acquisition that draws attention in both neuroscience and artificial intelligence. To investigate its underlying brain mechanism, we trained a recurrent neural network model on arbitrary sensorimotor mappings known to depend on the prefrontal cortex. The network displayed an exponential time course of accelerated learning. The neural substrate of a schema emerges within a low-dimensional subspace of population activity; its reuse in new problems facilitates learning by limiting connection weight changes. Our work highlights the weight-driven modifications of the vector field, which determines the population trajectory of a recurrent network and behavior. Such plasticity is especially important for preserving and reusing the learnt schema in spite of undesirable changes of the vector field due to the transition to learning a new problem; the accumulated changes across problems account for the learning-to-learn dynamics.

neuroscience↗

bioRxiv2021

Accurate and fast cell marker gene identification with COSG

Accurate cell classification is the groundwork for downstream analysis of single-cell sequencing data, yet how to identify marker genes to distinguish different cell types still remains as a big challenge. We developed COSG as a cosine similarity-based method for more accurate and scalable marker gene identification. COSG is applicable to single-cell RNA sequencing data, single-cell ATAC sequencing data and spatially resolved transcriptome data. COSG is fast and scalable for ultra-large datasets of million-scale cells. Application on both simulated and real experimental datasets demonstrates the superior performance of COSG in terms of both accuracy and efficiency as compared with other available methods. Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.

bioinformatics↗

bioRxiv2021

Neural Basis of the Delayed Gratification

Balancing instant gratification versus delayed, but better gratification is important for optimizing survival and reproductive success. Although psychologists and neuroscientists have long attempted to study delayed gratification through human psychological and brain activity monitoring, and animal research, little is known about its neural basis. We successfully trained mice to perform a waiting-and-water-reward delayed gratification task and used these animals in physiological recording and optical manipulation of neuronal activity during the task to explore its neural basis. Our results showed that the activity of DA neurons in ventral tegmental area (VTA) increases steadily during the waiting period. Optical activation vs. silencing of these neurons, respectively, extends or reduces the duration of waiting. To interpret this data, we developed a reinforcement learning (RL) model that reproduces our experimental observations. In this model, steady increases in DAergic activity signal the value of waiting and support the hypothesis that delayed gratification involves real-time deliberation. TEASERSustained ramping dopaminergic activation helps individuals to resist impulsivity and wait for laerger but later return.

neuroscience↗

bioRxiv2021

Gradients of receptor expression in the macaque cortex

Dynamics and functions of neural circuits depend on synaptic interactions mediated by receptors. Therefore, a comprehensive map of receptor organization is needed to understand how different functions may emerge across distinct cortical regions. Here we use in-vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrate the receptor data with other anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of increasing receptor expression per neuron aligned with cortical hierarchy from early sensory cortex to higher cognitive areas. A second gradient, primarily driven by 5-HT1A receptors, peaks in the anterior and subcallosal cingulate, suggesting that the macaque may be a promising animal model for major depressive disorder. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration of information in higher cognitive areas.

neuroscience↗

bioRxiv2020

Novel enzyme for dimethyl sulfide-releasing in bacteria reveals a missing route in the marine sulfur cycle

Dimethylsulfoniopropionate (DMSP) is an abundant and ubiquitous organosulfur molecule and plays important roles in the global sulfur cycle. Cleavage of DMSP produces volatile dimethyl sulfide (DMS), which has impacts on the global climate. Multiple pathways for DMSP catabolism have been identified. Here we identified yet another novel pathway, the ATP DMSP lysis pathway. The key enzyme, AcoD, is an ATP-dependent DMSP lyase. AcoD belongs to the acyl-CoA synthetase superfamily, which is totally different from other DMSP lyases, showing a new evolution route. AcoD catalyses the conversion of DMSP to DMS by a two-step reaction: the ligation of DMSP with CoA to form the intermediate DMSP-CoA, which is then cleaved to DMS and acryloyl-CoA. The novel catalytic mechanism was elucidated by structural and biochemical analyses. AcoD is widely distributed in many bacterial lineages including Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Firmicutes, revealing this new pathway plays important roles in global DMSP/DMS cycles.

ecology↗