Biology subjects

Fu, Z.

Publications and source records attributed to Fu, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Information Shielding Through Neural Deactivation: A Fundamental Mechanism of Human Information Processing

Information processing is a core issue in cognitive neuroscience. The brain can either accept or shield information. However, information shielding has traditionally been regarded as a byproduct of other cognitive processes that prepare brain areas for activation, with the accompanying brain deactivation seen as merely supportive. Here, we offer an alternative perspective, demonstrating that people can directly perform information shielding without engaging other cognitive processes. Using information shielding as a novel investigative lens, we provide direct evidence that deactivation serves as a primary mechanism in its own right. We found that information shielding was characterized by a deactivation-dominant neural dynamic, with initial executive control region activation giving way to increased sensory region deactivation, especially after a 3-second boundary. Neural decoding results further confirmed this deactivation trend. This deactivation pattern was consistent across both auditory and visual modalities. Furthermore, non-invasive brain stimulation demonstrated a causal relationship between the deactivation pattern and information shielding, and we found that this deactivation mechanism extended to general unnecessary information processing, regardless of whether the necessity was determined objectively or subjectively. This challenges the conventional emphasis on activation as the primary mode of information processing and opens new fundamental avenues for understanding inhibitory cognitive processes.

neuroscience↗

bioRxiv2025

Deciphering Multiway Multiscale Brain Network Connectivity:Insights from Birth to 6 Months

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order relationships. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success. Exploring higher-order functional relationships in the brain can provide insight into brain function and development. To the best of our knowledge, there is no existing research on multiway, multiscale brain network interactions in infants. In this study, we comprehensively investigate the interactions among brain intrinsic connectivity networks (ICNs), including both pairwise (pair-FNC) and triple relationships (tri-FNC). We focused on an infant dataset collected between birth and six months, a critical period for brain maturation. Our results revealed significant hierarchical, multiway, multiscale brain functional network interactions in the infant brain. These findings suggest that tri-FNC provide additional insights beyond what pairwise interactions reveal during early brain development. The tri-FNC predominantly involve the default mode, sensorimotor, visual, limbic, language, salience, and central executive domains. Notably, these triplet networks align with the classical triple network model of the human brain, which includes the default mode network, the salience network, and the central executive network. This suggests that the brain network system might already be initially established during the first six months of infancy. Interestingly, tri-FNC in the default mode and salience domains showed significantly stronger nonlinear interactions with age compared to pair-FNC. We also found that pair-FNC were less effective at detecting these networks. The present study suggests that exploring tri-FNC can offer additional insights beyond pair-FNC by capturing higher-order nonlinear interactions, potentially yielding more reliable biomarkers to characterize developmental trajectories.

neuroscience↗

bioRxiv2025

Structure of an LGR dimer - an evolutionary predecessor of glycoprotein hormone receptors

The glycoprotein hormones of humans, produced in the pituitary and acting through receptors in the gonads to support reproduction and in the thyroid gland for metabolism, have co-evolved from invertebrate counterparts1,2. These hormones are heterodimeric cystine-knot proteins; and their receptors bind the cognate hormone at an extracellular domain and transmit the signal of this binding through a transmembrane domain that interacts with a heterotrimeric G protein. Structures determined for the human receptors as isolated for cryogenic electron microscopy (cryo-EM) are all monomeric3-6 despite compelling evidence for their functioning as dimers7-10. Here we describe the cryo-EM structure of the homologous receptor from a neuroendocrine pathway that promotes growth in a nematode11. This structure is an asymmetric dimer that can be activated by the hormone from that worm12, and it shares features especially like those of the thyroid stimulating hormone receptor (TSHR). When studied in the context of the human homologs, this dimer provides a structural explanation for the transactivation evident from functional complementation of binding-deficient and signaling-deficient receptors7, for the negative cooperativity in hormone action that is manifest in the 1:2 asymmetry of primary TSH:TSHR complexes8,9, and for switches in G-protein usage that occur as 2:2 complexes form9,10.

molecular biology↗

bioRxiv2024

ISCO: Intelligent Framework for Accurate Segmentation and Comparative Analysis of Organoids

Organoids are self-organizing 3D cell clusters that closely mimic the structure and function of in vivo tissues and organs. Quantifying organoid morphology is critical for advancing our understanding of organ development, drug discovery, and toxicity assessment. Recent advances in microscopy have provided powerful tools to capture detailed morphological features of organoids, yet manual image analysis remains labor-intensive and time-consuming. In response, we present a comprehensive microscopy-based analysis pipeline that utilizes SegmentAnything 2.1 to accurately segment individual organoids. Additionally, we introduce a suite of morphological features--including perimeter, area, radius, non-smoothness, and non-circularity--that enable researchers to quantitatively and automatically analyze organoid structures. To further standardize organoid analysis across the field, Intelligent segmentation and comparison of organoids(ISCO), an intelligent AI-driven open-source algorithm, is developed with the aim of establishing a comprehensive toolset for organoid characterization.

bioengineering↗

bioRxiv2024

Evidence for white matter intrinsic connectivity networks at rest and during a task: a large-scale study and templates

Understanding white matter (WM) functional connectivity is crucial for unraveling brain function and dysfunction. In this study, we present a novel WM intrinsic connectivity network (ICN) template derived from over 100,000 fMRI scans, identifying 97 robust WM ICNs using spatially constrained independent component analysis (scICA). This WM template, combined with a previously identified gray matter (GM) ICN template from the same dataset, was applied to analyze a resting-state fMRI (rs-fMRI) dataset from the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (BSNIP2; 590 subjects) and a task-based fMRI dataset from the MIND Clinical Imaging Consortium (MCIC; 75 subjects). Our analysis highlights distinct spatial maps for WM and GM ICNs, with WM ICNs showing higher frequency profiles. Modular structure within WM ICNs and interactions between WM and GM modules were identified. Task-based fMRI revealed event-related BOLD signals in WM ICNs, particularly within the corticospinal tract, lateralized to finger movement. Notable differences in static functional network connectivity (sFNC) matrices were observed between controls (HC) and schizophrenia (SZ) subjects in both WM and GM networks. This open-source WM NeuroMark template and automated pipeline offer a powerful tool for advancing WM connectivity research across diverse datasets.

neuroscience↗

bioRxiv2024

Biophysical modeling of thalamic reticular nucleus subpopulations and their differential contribution to network dynamics

The burst firing mode of thalamic reticular neurons plays a pivotal role in the generation and maintenance of sleep rhythms and is implicated in sleep-related deficits characteristic of neurodevelopmental disorders. Although several models of reticular neurons have been developed to date, we currently lack a biophysically detailed model able to accurately reproduce the heterogeneity of burst firing observed experimentally. Using electrophysiology recordings of patch-clamped fluorescently tagged Spp1+ and Ecel1+ reticular neurons, we leverage a previously established statistical framework to introduce differentiation of cell types in model thalamic reticular neurons. We developed a population of biophysically detailed models of thalamic reticular neurons that capture the diversity of their firing properties, particularly their ability to generate rebound bursts. These models incorporate key ion channels, such as T-type Ca2+ and small conductance potassium channels (SK), and enable systematic investigations into the impact of these channels on single-cell dynamics. By integrating these models into a thalamic microcircuit, we demonstrate that T-type Ca2+ and SK channel conductances have opposing effects on spindle oscillations. We identify a simple relationship between these conductances and the peak firing frequency of spindles, maintained across circuits with mixed reticular neuron populations, providing a framework for understanding how ion channel expression influences thalamic network dynamics. Collectively, these models establish a foundation for relating intrinsic cellular properties of reticular cell populations to network-level activity in both healthy and pathological conditions.

neuroscience↗

bioRxiv2024

Effective and Reliable Metrics to Select Crystal Diffraction Data

X-ray crystal diffraction has provided atomic-level structural information on biological macromolecules. Data quality determines the reliability of structural models. In most cases, multiple data sets are available from different crystals and/or collected with different experimental settings. Reliable metrics are critical to rank and select the data set with the highest quality. Many measures have been created or modified for data quality evaluation. However, some are duplicate in functionality, and some are likely misused due to misunderstanding, which causes confusion or problems, especially at synchrotron beamlines where experiments proceed quickly. In this work, these measures are studied through both theoretical analysis and experimental data with various characteristics, which demonstrated that: 1). {Rmerg, Rmeas, Rpim, CC1/2} all measure the equivalence of reflections, and the low-shell values of these metrics can be used as reliable indicators for correctness (or trueness) of Laue symmetry; 2). High-shell I/{sigma}I is a reliable and better indicator to select resolution cutoff while the overall value measures the overall strength of the data.

biophysics↗

bioRxiv2024

Born Connected: Do Infants Already Have Adult-Like Multi-Scale Connectivity Networks?

The human brain undergoes remarkable development during the first six postnatal months, a period of dramatic structural and functional change critical for understanding neurodevelopmental trajectories. Previous infant resting-state functional MRI (rsfMRI) studies identified intrinsic connectivity networks (ICNs) but reported widely varying network numbers and organization, limiting cross-study comparisons. A recent analysis of over 100,000 subjects spanning from adolescents to adults generated a 105-network multi-scale template that greatly enhanced replicability, but the presence of these canonical ICNs in infants has not been investigated. We analysed resting-state scans from infants aged 0-6 months using two complementary approaches: burst independent component analysis (burstICA), a fully data-driven, model-order-agnostic method, and the reference-informed NeuroMark framework. burstICA successfully recovered the full set of canonical ICNs directly from infant data, and simulation-based validation confirmed that the recovered networks were biologically specific rather than methodological biases. The NeuroMark framework enhanced spatial correspondence with the reference template and maintained distinct, reproducible network profiles across scans, demonstrating high reliability for cross-study and cross-age comparisons. Networks critical for higher-order cognition, such as the default mode and salience networks, displayed adult-like topography comparable to that of primary sensory networks even within the first 6 months after birth. Subcortical networks also exhibited precise spatial organization, underscoring early maturation of deep brain structures. Functional network connectivity analyses revealed close similarity to adult profiles and clear antagonistic patterns between sensory systems and higher-order networks, indicating that foundational aspects of mature brain organization are already emerging in early infancy. Together, these findings establish a methodological foundation for early network mapping in infancy and lay critical groundwork for longitudinal and translational studies of brain development.

neuroscience↗

bioRxiv2024

Aberrant expression of histone H2B variants reshape chromatin and alter oncogenic gene expression programs

Chromatin architecture governs DNA accessibility and gene expression. Thus, any perturbations to chromatin can significantly alter gene expression programs and promote disease. Prior studies demonstrate that every amino acid in a histone is functionally significant, and that even a single amino acid substitution can drive specific cancers. We previously observed that naturally occurring H2B variants are dysregulated during the epithelial to mesenchymal transition (EMT) in bronchial epithelial cells. Naturally occurring H2B variants differ from canonical H2B by only a few amino acids, yet single amino acid changes in other histone variants (e.g., H3.3) can drive cancer. We therefore hypothesized that H2B variants might function like oncohistones, and investigated how they modify chromatin architecture, dynamics, and function. We find that H2B variants are frequently dysregulated in many cancers, and correlate with patient prognosis. Despite high sequence similarity, mutations in each H2B variant tend to occur at specific "hotspots" in cancer. Some H2B variants cause tighter DNA wrapping around nucleosomes, leading to more compact chromatin structures and reduced transcription factor accessibility to nucleosomal DNA. They also altered genome-wide accessibility to oncogenic regulatory elements and genes, with concomitant changes in oncogenic gene expression programs. Although we did not observe changes in cell proliferation or migration in vitro, our Gene Ontology (GO) analyses of ATAC-seq peaks and RNA-seq data indicated significant changes in oncogenic pathways. These findings suggest that H2B variants may influence early-stage, cancer-associated regulatory mechanisms, potentially setting the stage for oncogenesis later on. Thus, H2B variant expression could serve as an early cancer biomarker, and H2B variants might be novel therapeutic targets.

cancer biology↗

bioRxiv2024

Targeting SLC7A11-mediated cysteine metabolism for the treatment of trastuzumab resistant HER2 positive breast cancer

Trastuzumab resistance remains a challenge for HER2 positive breast cancer treatment. Targeting metabolic reprogramming would provide novel insights for therapeutic strategies. Here, we integrated metabolomics, transcriptomics and epigenomics data of trastuzumab sensitive and primary resistant HER2 positive breast cancer to identify metabolic alterations. Aberrant cysteine metabolism was discovered in trastuzumab primary resistant breast cancer at both circulating and intracellular levels. The inhibition of SLC7A11 and cysteine starvation could synergize with trastuzumab to induce ferroptosis. Mechanistically, increased H3K4me3 and decreased DNA methylation enhanced SLC7A11 transcription and cystine uptake in trastuzumab resistant breast cancer. The regulation of epigenetic modifications modulated cysteine metabolism and ferroptosis sensitivity. These results revealed an innovative approach for overcoming trastuzumab resistance by targeting specific amino acid metabolism.

cancer biology↗

bioRxiv2024

Brain functional network connectivity interpolation characterizes neuropsychiatric continuum and heterogeneity

Psychiatric and neurodevelopmental disorders such as schizophrenia (SZ) and autism spectrum disorder (ASD) are challenging to characterize in part due to their heterogeneous presentation in individuals, with symptoms now believed to exist on a continuum. Conventional diagnostic and neuroimaging analytical approaches rely on subjective assessment or group differences but typically ignore progression between groups or heterogeneity within a group. To estimate the neuropsychiatric continuum and heterogeneity, we propose a functional network connectivity (FNC) interpolation framework based on a variational autoencoder (VAE) using static FNC (sFNC) and dynamic FNC (dFNC) data from controls and patients with SZ or ASD. We demonstrate that VAEs significantly outperform a linear baseline and a semi-supervised counterpart. For both sFNC and dFNC interpolation, the generated results effectively capture representative and generalizable properties in the original data. The interpolated continua from controls to patients in both disorders reveal group-wise gradients characterized by reduced positive correlations within the auditory, sensorimotor, and visual networks, as well as between the subcortical and cerebellar domains. In contrast, anti-correlations weaken between the subcortical domain and sensory domains, and between the cerebellar domain and sensory regions. Finally, we show examples of how to generate continuous FNC data following pathological or state-based trajectories in the VAE latent space. The proposed framework offers added advantages over traditional methods, including data-driven discovery of hidden relationships, visualization of individual differences, imputation of missing values along a continuous spectrum, and estimation of the stage where an individual falls within the continuum.

neuroscience↗

bioRxiv2024

Spontaneous Brain Dynamics Associated With Acceleration Of Longterm Functional Connectome In Postnatal Development

The first six postnatal months are a critical period for brain development, marked by rapid changes in functional neural circuits. However, long-term changes in neonatal functional connectome lacks an interpretive imaging indicator for the future development due to the non-linearity characteristics. In this study, we introduce an approach to extract intrinsic brain states from short-term brain dynamics to study the long-term (longitudinal) development. We found a high association (r=0.460) between the co-activated pattern of specific brain state and the acceleration pattern of non-linear development of static functional connectome. The fractional occupancy, self-sustaining probability of this short-term state share the similar age tendency with the long-term change rate within the majority of the function connectome. These findings suggest that short-term brain dynamics could serve as potential biomarkers for predicting the long-term development of functional connectome.

neuroscience↗

bioRxiv2024

Microglia modulate cerebral blood flow and neurovascular coupling through ectonucleotidase CD39

Microglia and the border-associated macrophages (BAMs) contribute to the modulation of cerebral blood flow (CBF), but the mechanisms have remained ill-defined. Here, we show that microglia regulate the CBF baseline and upsurges after whisker stimulation or intracisternal magna injection of adenosine triphosphate (ATP). Genetic or pharmacological depletion of microglia reduces the activity-dependent hyperemia but not the cerebrovascular responses to adenosine stimulation. Notably, microglia repopulation corrects these CBF reactivity deficits. The microglial-dependent regulation of CBF requires the ATP-sensing P2ry12 receptor and the ectonucleotidase CD39 that initiates the breakdown of extracellular ATP. Pharmacological inhibition or microglia-specific deletion of CD39 simulates the CBF anomalies detected in microglia-deficient mice and reduces the rise of extracellular adenosine after whisker stimulation. Together, these results suggest that the microglial CD39-initiated conversion of extracellular ATP to adenosine is an important step in neurovascular coupling and the regulation of cerebrovascular reactivity.

neuroscience↗

bioRxiv2024

Spatial Development of Brain Networks During The First Six Postnatal Months

The initial months of life constitute a crucial period for human development. A comprehensive understanding of this early phase is essential for unraveling the origins of neurodevelopmental disorders and promoting infant brain health. This study uniquely focuses on the spatial development of intrinsic brain connectivity networks during infancy, which has been less explored compared to functional connectivity. We utilized independent component analysis on resting-state fMRI data from 74 infants to assess how the spatial organization of infant brain networks evolves between birth and six months. Our findings reveal significant changes in spatial characteristics, including an a notable rise in the network-averaged spatial similarity across age, reflecting how closely each participant-specific spatial map aligns with the group-level map for each network. We also observed a marked reduction in the network engagement range by age, representing the extent of voxel intensity range fluctuation within each network. This suggests a continuing process of consolidation, where voxel contributions to the network become more uniform, as indicated by the narrowing of intensity values. The network strength, calculated as the average of all the voxel intensities in the network, indicating the degree of involvement to the specific functional network, increased across age in several networks, such as frontal-mPFC, primary, and secondary visual networks. The network size, along with the network center of mass, illustrating spatial distribution alterations of brain networks by age, varied across different networks. For instance, both metrics increased across age in the secondary visual network but decreased in the temporal network. Additionally, we examined the networks in relation to their linear versus non-linear developmental trajectories across all spatial characteristics, providing a deeper understanding of how these patterns evolve during early infancy. These findings contribute to early brain development understanding and offer insights into potential markers of consolidation and spatial reorganization in large-scale brain networks during infancy.

neuroscience↗

bioRxiv2024

Reinforcement learning guides single-cell sequencing in decoding lineage and cell fate decisions

The rapid development of single-cell sequencing offers an unparalleled opportunity to delineate the heterogeneity of individual cells. However, current methods struggle to pinpoint the states of cell fate decisions. In this study, we introduce a novel approach called Single-cell Reinforcement Learning (scRL), which integrates reinforcement learning into single-cell data analysis using an actor-critic architecture. Among existing dimensionality reduction methods, we identified one with the best interpretability. Based on this latent space and combined with our scRL algorithm, we assessed the intensity of fate decisions at the single-cell level. Extensive evaluations demonstrate that scRL outperforms existing techniques, as well as their variants and alternative approaches, in assessing cell fate decisions. Moreover, scRL offers an alternative method for evaluating the intensity of cell lineage differentiation which shows competitive interpretability as well. The superiority of scRL in assessing fate decisions is confirmed across several types of single-cell datasets.

bioinformatics↗

bioRxiv2024

NRF2-dependent regulation of the prostacyclin receptor PTGIR drives CD8 T cell exhaustion

The progressive decline of CD8 T cell effector function--also known as terminal exhaustion--is a major contributor to immune evasion in cancer. Yet, the molecular mechanisms that drive CD8 T cell dysfunction remain poorly understood. Here, we report that the Kelch-like ECH-associated protein 1 (KEAP1)-Nuclear factor erythroid 2-related factor 2 (NRF2) signaling axis, which mediates cellular adaptations to oxidative stress, directly regulates CD8 T cell exhaustion. Transcriptional profiling of dysfunctional CD8 T cells from chronic infection and cancer reveals enrichment of NRF2 activity in terminally exhausted (Texterm) CD8 T cells. Increasing NRF2 activity in CD8 T cells (via conditional deletion of KEAP1) promotes increased glutathione production and antioxidant defense yet accelerates the development of terminally exhausted (PD-1+TIM-3+) CD8 T cells in response to chronic infection or tumor challenge. Mechanistically, we identify PTGIR, a receptor for the circulating eicosanoid prostacyclin, as an NRF2-regulated protein that promotes CD8 T cell dysfunction. Silencing PTGIR expression restores the anti-tumor function of KEAP1-deficient T cells. Moreover, lowering PTGIR expression in CD8 T cells both reduces terminal exhaustion and enhances T cell effector responses (i.e. IFN-{gamma} and granzyme production) to chronic infection and cancer. Together, these results establish the KEAP1-NRF2 axis as a metabolic sensor linking oxidative stress to CD8 T cell dysfunction and identify the prostacyclin receptor PTGIR as an NRF2-regulated immune checkpoint that regulates CD8 T cell fate decisions between effector and exhausted states. One Sentence SummaryThe KEAP1-NRF2 pathway is hyperactivated in terminally exhausted CD8 T cells and drives T cell dysfunction via transcriptional regulation of the prostacyclin receptor, Ptgir.

immunology↗

bioRxiv2024

scCancerExplorer: a comprehensive database for interactively exploring single-cell multi-omics data of human pan-cancer

Genomic, epigenomic, and transcriptomic alterations are hallmarks of cancer cells, and are closely connected. Especially, epigenetic regulation plays a critical role in tumorigenesis and progression. The growing single-cell epigenome data in cancer research provide new opportunities for data mining from a more comprehensive perspective. However, there is still a lack of databases designed for interactively exploring the single-cell multi-omics data of human pan-cancer, especially for the single-cell epigenome data. To fill in the gap, we developed scCancerExplorer, a comprehensive and user-friendly database to facilitate the exploration of the single-cell genome, epigenome (chromatin accessibility and DNA methylation), and transcriptome data of 50 cancer types. Five major modules were provided to explore those data interactively, including "integrated multi-omics analysis", "single-cell transcriptome", "single-cell epigenome", "single-cell genome", and "TCGA analysis". By simple clicking, users can easily investigate gene expression features, chromatin accessibility patterns, transcription factor activities, DNA methylation states, copy number variations, and TCGA survival analysis results. Taken together, scCancerExplorer is distinguished from previous databases with rich and interactive functions for exploring the single-cell multi-omics data of human pan-cancer. It bridges the gap between single-cell multi-omics data and the end-users, and will facilitate progress in the field of cancer research. scCancerExplorer is freely accessible via https://bianlab.cn/scCancerExplorer.

cancer biology↗

bioRxiv2024

Human tau promotes Warburg effect-like glycolytic metabolism under acute hyperglycemia conditions through modulating the homeostasis of protein-membrane association

The neurofilaments formed by hyperphosphorylated tau is a hallmark of tau-related neurodegenerative disease, including Alzheimers disease, tau related FTDP-17, Picks disease, et al. However, the biological functions of tau and the physiological significance of its phosphorylation are still not fully understood. By using human tau (441 a.a.) transgenic (hTau) mice in which murine tau has been deleted simultaneously, murine tau knockout (Tau KO) mice and C57BL/6J (C57) mice, unexpectedly, we found that under acute hyperglycemia conditions, JNK but not previously reported GSK-3{beta} mediated tau phosphorylation. Moreover, Akt, the upstream GSK-3{beta} inhibitory kinase, was activated in a tau dependent manner. By comparing the membrane-associated proteome, we found that human tau influenced the homeostasis of protein-membrane association under acute hyperglycemia conditions. Of note, with respect to WT and Tau KO mice, the membrane-association of Krts, TFAM, TRAP1, mTOR et al, were strengthened by human tau. Whereas, the membrane-association of ribosomal proteins Rpls, proteasome proteins Psmds, and mitochondrial proteins, such as COXs, Ndufa1, Mtnt4, et al, were impeded by human tau. In vitro study showed that aerobic glycolysis was promoted in the presence of human tau, which maintained NAD+/NADH ratio. On the other hand, it restricted oxidative phosphorylation level, modulated the activity of SDH, and reduced ROS production upon challenging by high glucose. Furthermore, under acute high glucose conditions, the presence of human tau significantly augmented Akt activation, but inhibited 4EBP phosphorylation simultaneously, indicating that human tau is also involved in regulating the alternative activation of mTORC1/2. In summary, the current study revealed that human tau played an important role in regulating glycolytic metabolism under acute high hyperglycemia conditions, which is similar with the Warburg-effect, through influencing the homeostasis of protein-membrane association.

molecular biology↗