Biology subjects

Yuan, B.

Publications and source records attributed to Yuan, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Neural hierarchy for coding articulatory dynamics in speech imagery and production

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery--the internal simulation of speech without overt articulation--has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiotemporal dynamics of this neural architecture remain controversial, constraining the understanding of mental states and the development of reliable speech imagery decoders. Here, we leveraged high-resolution electrocorticography recordings to investigate the shared and modality-specific cortical coding of articulatory kinematic trajectories (AKTs) during speech imagery and articulation. Applying a linear model, we identified robust neural dynamics in frontoparietal cortex that encoded AKTs across both modalities. Shared neural populations across the middle premotor cortex, subcentral gyrus, and postcentral-supramarginal junction exhibited consistent spatiotemporal stability during the integrative articulatory planning. In contrast, modality-specific populations for speech imagery and articulation were somatotopically interleaved along the primary sensorimotor cortex, revealing a hierarchical spatiotemporal organization distinct from shared encoding regions. We further developed a generalized neural network to decode multi-population neural dynamics. The model achieved high syllable prediction accuracy for speech imagery (79% median accuracy), closely matching the performance of speech articulation (81%). This model robustly extrapolated AKT decoding to untrained syllables within each modality while demonstrating cross-modal generalization across shared populations. These findings uncover a somato-cognitive hierarchy linking high-level supramodal planning with modality-specific neural manifestation, revolutionizing an imagery-based brain-computer interface that directly decodes thoughts for synthetic telepathy.

neuroscience↗

bioRxiv2025

Mapping the functional differentiation and interactions among the inferior, medial frontal and posterior temporal cortex in semantic control

Semantic control refers to the ability to flexibly retrieve and manipulate stored knowledge to support context-appropriate behavior. A left-lateralized network comprising the left inferior frontal gyrus (IFG), posterior middle temporal gyrus (pMTG), and dorsal medial prefrontal cortex (dmPFC) has been consistently implicated in this process. While previous studies have established the necessity of the IFG and pMTG in semantic control, the causal role of the left dmPFC remains unclear. Additionally, it is unknown whether each of these three regions exhibits internal functional differentiation and how they interact to support semantic control. To address these questions, we combined task-based functional magnetic resonance imaging (fMRI) with fMRI-guided transcranial magnetic stimulation (TMS). We found that dmPFC, like IFG and pMTG, is causally involved in semantic control. All three regions exhibited a consistent anterior-posterior functional gradient: anterior subregions were selectively engaged during high-demand semantic processing, whereas posterior subregions responded to both easy and hard tasks. Furthermore, combined activation patterns of these regions better predicted the behavioral differences between hard and easy semantic tasks compared to the activation patterns of any single region. Semantic control modulated both the autoinhibition within individual regions and the functional connectivity among them, suggesting these regions operate in a coordinated network rather than in isolation. These findings advance our understanding of the neural architecture supporting flexible semantic behavior. Significance StatementUnderstanding how the brain supports flexible semantic behavior is critical for both cognitive neuroscience and clinical neuropsychology. While prior studies have consistently implicated the left IFG, pMTG, and dmPFC in semantic control, the causal contribution of the dmPFC and the functional dynamics among these regions have remained unclear. This study provides the first causal evidence for the dmPFCs role in semantic control, reveals functional differentiation within the IFG and pMTG, and shows that these regions interact as an integrated network. These findings challenge the notion of functionally homogeneous control nodes and highlight a topographically organized, interactive system underlying controlled semantic retrieval. This work refines our mechanistic understanding of semantic control and may inform clinical models of language and conceptual deficits.

neuroscience↗

bioRxiv2025

Discovery of a new evolutionarily conserved short linear F-actin binding motif

Regulation of the actin cytoskeleton by actin binding proteins (ABPs) is essential for cellular homeostasis, and the mode of actin binding determines the activity of ABPs. Here, we discovered a novel "Short linear F-actin binding motif (SFM)" on the basis of the cryo-EM structure of the ITPKA-F-actin complex. We developed the computational pipeline SLiMFold, which identified 103 human SFM containing-proteins exhibiting diverse cellular functions. The SFM probably developed ex nihilo and remained conserved in eukaryotes, with a binding affinity to F-actin ranging from 13 to 89 {micro}M. Furthermore, we uncovered the essential amino acids of this SFM for F-actin binding and affinity modulation. Together, the SFM seems to serve as a low affinity anchor to target proteins to F-actin, in order to connect the regulation of actin dynamics with broad cellular functions. These findings will shed new light on the role of a wide variety of proteins.

biochemistry↗

bioRxiv2025

Effective in vivo RNA base editing via engineered cytidine deaminase APOBECs fused with PUF proteins

Base editing stands at the forefront of genetic engineering, heralding precise genetic modifications with broad implications. While CRISPR-based DNA and RNA base editing systems capitalize on sgRNA-guided specificity and diverse deaminase functionalities, the pursuit of efficient C-to-U RNA editing has been hampered by the inherent constraints of cytidine deaminases. Here, we report an RNA base editing platform by refining cytidine deaminases, termed professional APOBECs (ProAPOBECs), through systematic enhancements and AI-driven protein engineering. ProAPOBECs demonstrate unprecedented catalytic versatility, particularly fused with RNA-recognizing Pumilio and FBF (PUF) proteins. We present the first effective use of RNA base editing in the brain with ProAPOBECs in Mef2c mutant mice, a model for autism. The AAV-mediated RNA base editing via ProAPOBEC not only corrects genetic mutation in mRNAs but significantly alleviates autistic-like behaviors in the mice. This work introduces a pioneering collection of RNA base editing instruments, emphasizing their therapeutic potential in combatting genetic disorders.

molecular biology↗

bioRxiv2024

NaDyNet: A Toolbox for Dynamic Network Analysis of Naturalistic Stimuli

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brains responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their enhanced versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) enhanced versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods, and presented numerous examples. We also summarized research progress in comparing methodological efficacy, offered our recommendations for method selection in dynamic network analysis, and discussed the limitations of current approaches and directions for future research. We hope that this open source toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

neuroscience↗

bioRxiv2024

The aberrant language network dynamics in autism ages 5-40 years

Language impairments across both structural components and pragmatic use are frequently observed in individuals with autism spectrum disorder (ASD). These difficulties are thought to stem from atypical brain development and abnormal network interactions, yet an integrative network-level model accounting for such impairments remains lacking. To bridge this gap, we applied the dynamic meta-networking framework of language, a theoretical model capturing domain-segregation dynamics during rest, to examine age-related changes (5-60 years) in cortical language networks in individuals with ASD. To further probe the biological underpinnings of these dynamics, we quantified spatial correspondences between network state hubs and gene co-expression modules as well as neurotransmitter receptor distributions. We identified distinct language meta-states characterized by domain-segregation connectivity patterns, which exhibited spatial alignment with gene co-expression modules and neurotransmitter systems. Individuals with ASD showed state-dependent developmental trajectories marked by age-related hypo- and hyper-connectivity. Critically, these network alterations strongly predicted verbal IQ and communicative difficulties, but were unrelated to social functioning or stereotyped behaviors. Our findings provide novel evidence that language-related network dynamics in ASD are developmentally altered, biologically grounded, and selectively linked to verbal and communicative impairments. These results advance a network-level model of language dysfunction in ASD and highlight potential mechanistic pathways for targeted interventions. Lay SummaryLanguage challenges are common in autism and can affect both learning language and using it in everyday conversations. In people aged 5-60, we found distinct, age-changing patterns in how the brains language areas connect at rest; these patterns align with basic biology (genes and brain chemicals) and strongly relate to verbal ability and daily communication, but not to social difficulties or repetitive behaviors. These results suggest clear, brain-based targets for earlier and more tailored support for language problems in autism.

neuroscience↗

bioRxiv2024

Microbiome Data Integration via Shared Dictionary Learning

Data integration is a powerful tool for facilitating a comprehensive and generalizable understanding of microbial communities and their association with outcomes of interest. However, integrating data sets from different studies remains a challenging problem because of severe batch effects, unobserved confounding variables, and high heterogeneity across data sets. We propose a new data integration method called MetaDICT, which initially estimates the batch effects by weighting methods in causal inference literature and then refines the estimation via novel shared dictionary learning. Compared with existing methods, MetaDICT can better avoid the overcorrection of batch effects and preserve biological variation when there exist unobserved confounding variables, data sets are highly heterogeneous across studies, or the batch is completely confounded with some covariates. Furthermore, MetaDICT can generate comparable embedding at both taxa and sample levels that can be used to unravel the hidden structure of the integrated data and improve the integrative analysis. Applications to synthetic and real microbiome data sets demonstrate the robustness and effectiveness of MetaDICT in integrative analysis. Using MetaDICT, we characterize microbial interaction, identify generalizable microbial signatures, and enhance the accuracy of outcome prediction in two real integrative studies, including an integrative analysis of colorectal cancer metagenomics studies and a meta-analysis of immunotherapy microbiome studies.

bioinformatics↗

bioRxiv2024

The connectional diaschisis and normalization of cortical language network dynamics after basal ganglia and thalamus stroke

Stroke affecting the basal ganglia and thalamus can lead to language deficits. In addition to the lesions direct impact on language processing, connectional diaschisis involving cortical-subcortical interactions also plays a critical role. This study investigated connectional diaschisis using the "dynamic meta-networking framework of language" in patients with basal ganglia and thalamus stroke, analyzing longitudinal resting-state fMRI data collected at 2 weeks (n = 32), 3 months (n = 19), and one year post-stroke (n = 23). As expected, we observed dynamic cortico-subcortical interactions between cortical language regions and subcortical regions in healthy controls (HC, n = 25). The cortical language network exhibited dynamic domain-segregation patterns in HCs, severely disrupted in the acute phase following stroke. The connectional diaschisis manifested as dual effects characterized by both hypo- and hyper-connectivity, which positively and negatively correlated with language deficits, respectively. State-specific changes in nodal and topological properties were also identified. Throughout language recovery, cortical language network dynamics gradually normalized toward sub-optimal domain-segregation patterns, accompanied by the normalization of nodal and topological properties. These findings underscore the crucial role of cortico-subcortical interactions in language processing.

neuroscience↗

bioRxiv2024

How do we imagine a speech? A triple network model for situationally simulated inner speech

Inner speech is a silent verbal experience and plays central roles in human consciousness and cognition. Despite impressive studies over the past decades, the neural mechanisms of inner speech remain largely unknown. In this study, we adopted an ecological paradigm called situationally simulated inner speech. Unlike mere imaging speech of words, situationally simulated inner speech involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data, where participants were instructed to engage in inner speech prompted by cue words across 10 different contextual backgrounds. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for a truncated form of overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and sense-making processing. HighlightsO_LIIn ten contextual backgrounds, subjects were instructed to perform situationally simulated inner speech based on cue words. C_LIO_LIThe ventral parts of the bilateral somatosensory areas and middle superior temporal gyrus were as centers for seed-based co-activation pattern analyses. C_LIO_LIA triple network model of language network, sensorimotor network, and default mode network was proposed for deliberate inner speech. C_LI

neuroscience↗

bioRxiv2024

A systematic evaluation of dynamic functional connectivity methods using simulation data

Numerous dynamic functional connectivity (dFC) methods have been proposed to study time-resolved network reorganization in rest and task fMRI. However, a comprehensive comparison of their performance is lacking. In this study, we compared the efficacy of seven dFC methods (and their enhanced versions) to track transient network reconfiguration using simulation data. The seven methods include flexible least squares (FLS), dynamic conditional correlation (DCC), general linear Kalman filter (GLKF), multiplication of temporal derivatives (MTD), sliding-window functional connectivity with L1-regularization (SWFC), hidden Markov models (HMM), and hidden semi-Markov models (HSMM). Multiple datasets of non-fMRI-BOLD and fMRI-BOLD signals with predefined covariance structures, signal-to-noise ratio levels, and sojourn time distributions were simulated. We adopted inter-subject analysis to eliminate the effects of signals of non-interest, resulting in enhanced methods: ISSWFC, ISMTD, ISDCC, ISFLS, ISKF, ISHMM, and ISHSMM. Efficacy was defined as the spatiotemporal association between simulated and estimated data. We found that all enhanced dFC methods outperformed their original versions. Efficacies depend on several factors, such as considering the neurovascular effect in simulated data, the covariance structure between two time series, state sojourn distribution, and signal-to-noise ratio levels. These results highlight the importance of selecting appropriate dFC methods in fMRI study.

neuroscience↗

bioRxiv2024

Stromal DUF760-1 and DUF760-2 proteins are degraded by the chloroplast Clp protease system but have very different half-lives

The chloroplast AAA+ chaperone CLPC1 aids to select, unfold and deliver hundreds of proteins to the stromal CLP protease core complex for degradation. Through in vivo CLPC1 trapping we previously identified dozens of trapped proteins that are substrate adaptors (e.g. CLPS1 and CLPF), other chaperones (CLPC2 and CLPD) or (potential) substrates (e.g. RH3) for the CLP chaperone-protease system. Here we show that two of these highly trapped proteins, DUF760-1 and DUF760-2, are substrates for the chloroplast CLP protease system. In planta BiFC and yeast-2-hybrid analyses show that both DUF760 proteins can directly interact with the N-domain of CLPC1. Immunoblotting and confocal microscopy analysis demonstrates that both DUF760 proteins are highly enriched in clpc1-1 and clpr2-1 loss-of function mutants. In vivo cycloheximide chase assays in different genetic backgrounds show that DUF760-1 and 2 are both degraded by the chloroplast CLP protease. The half-life of DUF760-1 is 4-6 hours, whereas DUF760-2 is so unstable that it is very hard to detect unless degradation is inhibited in CLP loss-of-function alleles. Null mutants for DUF760-1 and DUF760-2 show weak but differential pigment phenotypes and differential sensitivity of protein translation inhibitors. The functions of these DUF760 proteins are unknown; the lack of shared mRNA co-expressors and the large difference in half-life and protein abundance, suggest that they play different roles within the chloroplast. Taken together, our results demonstrate that DUF760-1 and 2 are newly discovered substrates of CLP chaperone-protease system.

plant biology↗

bioRxiv2024

An Organism-Level Quantitative Flux Model of Mammalian Energy Metabolism

Mammalian tissues feed on nutrients in the blood circulation. At the organism-level, mammalian energy metabolism comprises of oxidation, storage, interconverting, and releasing of circulating nutrients. Though much is known about the individual processes and nutrients, a holistic and quantitative model describing these processes for all major circulating nutrients is lacking. Here, by integrating isotope tracer infusion, mass spectrometry, and isotope gas analyzer measurement, we developed a framework to systematically quantify fluxes through these metabolic processes for 10 major circulating energy nutrients in mice, resulting in an organism-level quantitative flux model of energy metabolism. This model revealed in wildtype mice that circulating nutrients have more dominant metabolic cycling fluxes than their oxidation fluxes, with distinct partition between cycling and oxidation flux for individual circulating nutrients. Applications of this framework in obese mouse models showed on a per animal basis extensive elevation of metabolic cycling fluxes in ob/ob mice, but not in diet-induced obese mice. Thus, our framework describes quantitatively the functioning of energy metabolism at the organism-level, valuable for revealing new features of energy metabolism in physiological and disease conditions. HighlightsO_LIA flux model of energy metabolism integrating 13C labeling of metabolites and CO2 C_LIO_LICirculating nutrients have characteristic partition between oxidation and storage C_LIO_LICirculating nutrients total cycling flux outweighs their total oxidation flux C_LIO_LICycling fluxes are extensively elevated in ob/ob but not in diet-induced obese mice C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/579776v2_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@2175c4org.highwire.dtl.DTLVardef@ba96edorg.highwire.dtl.DTLVardef@aac3corg.highwire.dtl.DTLVardef@b1cb7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

DMSO derives Trophectoderm and Clonal Blastoid from Single Human Pluripotent Stem Cell

Human naive pluripotent stem cells (nPSCs) can differentiate into extra-embryonic trophectoderm (TE), a critical step in the generation of the integrated embryo model termed blastoid. The current paradigm of blastoid generation necessitates the aggregation of dozens of nPSCs treated with multiple small molecule inhibitors, growth factors, or genetic modifications to initiate TE differentiation. The presence of complex crosstalk among pathways and cellular heterogeneity in these models complicates mechanistic study and genetic screens. Here, we show that a single small molecule, dimethyl sulfoxide (DMSO), potently induces TE differentiation in basal medium without pharmacological and genetic perturbations. DMSO enhances blastoid generation and, more importantly, is sufficient for blastoid generation by itself. DMSO blastoids resemble blastocysts in morphology and lineage composition. DMSO induces blastoid formation through PKC signaling and cell cycle regulation. Lastly, DMSO enables single nPSC-derived clonal blastoids, which could facilitate genetic screens for mechanistic understanding of human embryogenesis.

developmental biology↗

bioRxiv2023

Chronic activation of a negative engram induces behavioral and cellular abnormalities

Negative memories engage a brain and body-wide stress response in humans that can alter cognition and behavior. Prolonged stress responses induce maladaptive cellular, circuit, and systems-level changes that can lead to pathological brain states and corresponding disorders in which mood and memory are affected. However, its unclear if repeated activation of cells processing negative memories induces similar phenotypes in mice. In this study, we used an activity-dependent tagging method to access neuronal ensembles and assess their molecular characteristics. Sequencing memory engrams in mice revealed that positive (male-to-female exposure) and negative (foot shock) cells upregulated genes linked to anti- and pro-inflammatory responses, respectively. To investigate the impact of persistent activation of negative engrams, we chemogenetically activated them in the ventral hippocampus over three months and conducted anxiety and memory-related tests. Negative engram activation increased anxiety behaviors in both 6- and 14-month-old mice, reduced spatial working memory in older mice, impaired fear extinction in younger mice, and heightened fear generalization in both age groups. Immunohistochemistry revealed changes in microglial and astrocytic structure and number in the hippocampus. In summary, repeated activation of negative memories induces lasting cellular and behavioral abnormalities in mice, offering insights into the negative effects of chronic negative thinking-like behaviors on human health.

neuroscience↗

bioRxiv2023

De novo generation of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language model

Generating T-cell receptors (TCRs) with desired epitope-binding properties is a fundamental step in the development of immunotherapies, yet heavily relies on laborious and expensive wet experiments. Recent advancements in generative artificial intelligence have demonstrated promising power in protein design and engineering. In this regard, we propose a large language model, termed Epitope-Receptor-Transformer (ERTransformer), for the de novo generation of TCRs with the desired epitope-binding property. ERTransformer is built on EpitopeBERT and ReceptorBERT, which are trained using 1.9 million epitope sequences and 33.1 million TCR sequences, respectively. To demonstrate the model capability, we generate 1000 TCRs for each of the five epitopes with known natural TCRs. The artificial TCRs exhibit low sequence identity (average Bit-score 27.64 with a standard deviation of 1.50) but high biological function similarity (average BLOSUM62 score 32.32 with a standard deviation of 12.01) to natural TCRs. Furthermore, the artificial TCRs are not very structurally identical to natural ones (average RMSD 2.84 [A] with a standard deviation of 1.21 [A]) but exhibit a comparable binding affinity towards the corresponding epitopes. Our work highlights the tremendous potential of applying ERTransformer to generate novel TCRs with desired epitope-binding ability.

bioinformatics↗

bioRxiv2023

The A53T mutation in α-synuclein enhances pro-inflammatory activation in human microglia

Parkinsons disease (PD) is characterized by the aggregation of -synuclein into Lewy bodies and Lewy neurites in the brain. Microglia-driven neuroinflammation may contribute to neuronal death in PD, however the exact role of microglia remains unclear and has been understudied. The A53T mutation in the gene coding for -synuclein has been linked to early-onset PD, and exposure to A53T-mutant human -synuclein increases the potential for inflammation of murine microglia. To date, its effect has not been studied in human microglia. Here, we used 2-dimensional cultures of human iPSC-derived microglia and transplantation of these cells into the mouse brain to assess the effects of the A53T mutation on human microglia. We found that A53T-mutant human microglia had an intrinsically increased propensity towards pro-inflammatory activation upon inflammatory stimulus. Additionally, A53T mutant microglia showed a strong decrease in catalase expression in non-inflammatory conditions, and increased oxidative stress. Our results indicate that A53T mutant human microglia display cell-autonomous phenotypes that may worsen neuronal damage in early-onset PD.

neuroscience↗

bioRxiv2023

Wiskott-Aldrich Syndrome Protein Regulates Nucleolar Organization and Function in Innate Immune Response

Wiskott-Aldrich syndrome (WAS) is a primary immunodeficiency disorder caused by the dysfunction of the WAS protein (WASP). Using an isogenic macrophage model derived from genome edited induced pluripotent stem cells we demonstrated that WASP functions in the nucleolus, which plays important roles in immune regulation. The absence of WASP resulted in smaller and misshapen nucleoli, decreased fibrillar center territory, and impaired ribosomal RNA (rRNA) transcription. The nucleolar and rRNA phenotypes were confirmed in WAS patient samples. Furthermore, WASP interacts with nucleolar proteins, including nucleophosmin 1 (NPM1) and fibrillarin (FBL). NPM1 deficiency is known to cause elevated cytokine expression following lipopolysaccharide (LPS) stimulation. Consistently, WASP deficient cells displayed lower levels of NPM1 and a heightened inflammatory cytokine response to LPS, which was rescued by overexpressing NPM1. Together, our research provides novel insights into the critical role of WASP in nucleolar function and the modulation of inflammatory cytokine production.

cell biology↗

bioRxiv2023

RSim: A Reference-Based Normalization Method via Rank Similarity

Microbiome sequencing data normalization is crucial for eliminating technical bias and ensuring accurate downstream analysis. However, this process can be challenging due to the high frequency of zero counts in microbiome data. We propose a novel reference-based normalization method called normalization via rank similarity (RSim) that corrects sample-specific biases, even in the presence of many zero counts. Unlike other normalization methods, RSim does not require additional assumptions or treatments for the high prevalence of zero counts. This makes it robust and minimizes potential bias resulting from procedures that address zero counts, such as pseudo-counts. Our numerical experiments demonstrate that RSim reduces false discoveries, improves detection power, and reveals true biological signals in downstream tasks such as PCoA plotting, association analysis, and differential abundance analysis.

bioinformatics↗