Biology subjects

Feng, J.

Publications and source records attributed to Feng, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Discovering Novel Circuit Mechanisms in Higher Cognition through Factor-Centric Recurrent Neural Network Modeling

Recurrent neural networks (RNNs) have transformed how systems neuroscientists generate hypotheses about circuit mechanisms in higher cognition. Yet their promise has been constrained by a fundamental limitation: conventional RNNs are neuron-centric and therefore often difficult to interpret mechanistically. Here we introduce Restricted-RNN, a factor-centric RNN modeling framework developed to uncover interpretable circuit mechanisms. By formalizing factor communication through subpopulations, the dual of neuron communication through subspaces, Restricted-RNN departs fundamentally from standard neuron-centric RNN models and provides a distinct framework for describing circuit mechanisms. Using this approach, we identify novel circuit mechanisms underlying sequence working memory control and the counterintuitive firing-rate reversal observed in perceptual decision-making, with key predictions supported by neurophysiological recordings from monkey frontal and parietal cortex. More importantly, factor-centric RNN modeling reveals a unified low-dimensional neural control state space that links seemingly disparate phenomena across tasks, providing a geometric framework for understanding the pervasive role of control in higher cognition.

neuroscience↗

bioRxiv2026

Inferring norepinephrine dynamics from partial observations reveals the temporal structure of elevations during arousal

Hemodynamic artifacts present a significant challenge for two-photon fluorescence imaging of genetically encoded reporters, particularly when the timescale of relevant measurements matches those of vascular dynamics. This is an acute challenge for sensors in which the hemodynamic artifact is of comparable magnitude to the biological signal of interest. However, standard correction methods, such as isobestic recording or repeated experiments, are often impractical. Here we introduce a tiered framework for inferring norepinephrine (NE) dynamics across varying levels of recording information. First, we verify that dual-channel recording using an inert fluorescent reporter alongside the neuromodulator indicator enables direct hemodynamic correction within the same recording session. For contexts in which a dedicated reference channel is unavailable, we trained an LSTM-based model that predicts and removes hemodynamic contributions post-hoc from the recorded NE signal and behavioral variables. Finally, we show that key features of NE dynamics can be recovered from behavioral variables alone, providing an estimate of neuromodulatory state even when fluorescence recordings are unavailable. These methods enabled simultaneous multi-spectral measurements of axonal activity and neuromodulator release via simultaneous two-photon imaging of LC noradrenergic axons and extracellular NE in the same field of view. Cortical NE signals are graded with respect to behavioral intensity, scaling with both locomotion duration and pupil dilation amplitude. As expected, axonal activity precedes increases in ambient NE levels, but NE peaks later within a run and remains elevated after axonal activity has subsided, suggesting that extracellular NE integrates LC output over time rather than tracking instantaneous LC firing. Together, these findings demonstrate that accurate hemodynamic correction is essential for interpreting NE dynamics, and reveal a clearer view of the temporal structure of cortical norepinephrine signaling.

neuroscience↗

bioRxiv2026

Beyond Acoustic Cues: Olfactory-Mediated Avoidance of Bats by Crickets

The evolutionary arms race between insectivorous bats and their insect prey is a classic paradigm of acoustic predation and evasion, with insects having evolved sophisticated auditory countermeasures. Both bats and insects also rely heavily on olfaction for key behaviors, such as social communication. Moreover, predator-derived odors are well-established as risk cues in many other predator-prey systems. However, whether olfaction plays a role in the bat-insect arms race remains unknown. Here, we unveil a previously unknown olfactory dimension to this interaction. We demonstrated that the body odor of the insectivorous bat Scotophilus kuhlii triggered robust avoidance and electrophysiological antennal responses in a common cricket prey, Loxoblemmus equestris. We identified limonene as a behaviorally active volatile in bat odor that elicited electrophysiological responses in cricket antennae and was sufficient to elicit avoidance in crickets. Field experiments confirmed that limonene exposure reduced cricket calling activity, demonstrating the ecological relevance of this cue. Our findings establish that insects can detect and initiate avoidance of phylogenetically distant vertebrate predators via olfaction, a process that could be mediated by the elemental perception of individual odor compounds. This work broadens the sensory framework of a classic predator-prey system, and highlights olfactory eavesdropping as a functional strategy in phylogenetically distant predator-prey systems.

ecology↗

bioRxiv2026

Molecular Dosimetry of DNA Adducts in Mice Exposed to Ethylene Oxide

Ethylene oxide (EtO) is a highly reactive industrial chemical and classified as a known human carcinogen with a putative mutagenic mode of action (MOA). Its genotoxic potential is primarily mediated through alkylation of DNA, resulting in the formation of the mutagenic adduct O6-(2-hydroxyethyl)-2-deoxyguanosine (O6-HE-dG). The N7-(2-hydroxyethyl)guanine (N7-HE-G) adduct is formed in greater abundance and is generally considered to be non-mutagenic. However, dose-response relationships of these DNA adducts, particularly at low inhalation exposure levels (i. e., below 3 ppm), remain unknown. These data are necessary to inform the biological plausibility of different statistical dose-response models that have been applied to human or animal data used for cancer risk assessment. In the present study, male and female B6C3F1 mice were exposed to EtO (0, 0.05, 0.1, 0.5, 1, 50, 100, and 200 ppm) 6 hours/day for 28 consecutive days. Immediately following the last exposure, DNA was extracted from lung, liver, bone marrow, and mammary gland, and further utilized to measure DNA adduct levels using highly sensitive mass spectrometry platforms. N7-HE-G was detected in all tissues and exposure groups, showing linear dose-response relationships in the low-dose range ([≤]1 ppm) and increased sharply and exposure-disproportionately in the high-dose range ([≥]50 ppm). Despite a very low limit of detection, O6-HE-dG, in contrast, was not detected at exposures <50 ppm in any tissue consistent with at most a shallow linear exposure response. At higher exposures ([≥]50 ppm), O6-HE-dG exhibited a dose-response pattern of N7-HE-G. Notably the mammary gland, despite being anatomically distant from the site of inhalation, exhibited the second-highest levels of both adducts at higher doses. This study provides the first reliable quantitative dose-response evidence of DNA adducts in tumor target and non-target (liver) tissues across a wide range of EtO exposures. The two DNA adducts differ markedly in their abundance, repairability and mutagenic potential and together provide a molecular MOA dose-response framework to inform both quantitative cancer risk assessment and genotoxic hazard characterization.

pharmacology and toxicology↗

bioRxiv2026

PopGenAgent: Tool-Aware, Reproducible, Report-Oriented Workflows for Population Genomics

Population genetics analyses typically require orchestrating multiple tools across heterogeneous tasks, from data curation to inference and visualization. In practice, a central bottleneck lies not only in reproducibility, but in maintaining consistency between intermediate diagnostics, analytical decisions, and downstream results as analyses evolve. Here we present PopGenAgent, an agentic workflow that formalizes population-genetic analyses as explicit, multi-step plans with declared inputs, outputs, and dependencies. Each step is instantiated from curated tool and visualization templates, while all intermediate artefacts are preserved within a unified, inspectable context. An interactive agent interface supports iterative plan construction and revision, and leverages accumulated outputs to assist interpretation and report drafting. In a case study of 26 populations from the 1000 Genomes Project, starting from a filtered PLINK dataset, PopGenAgent coordinated analyses including ROH, LD decay, PCA, ADMIXTURE, TreeMix, and f3. By structuring analyses as explicit, revisable plans with preserved intermediate states, PopGenAgent provides a transparent and extensible environment for conducting population-genetic analyses.

bioinformatics↗

bioRxiv2026

MAP: A Knowledge-driven Framework for Predicting Single-cell Responses for Unprofiled Drugs

Predicting how cells respond to chemical perturbations is one of the goals for building virtual cells, yet experimentally profiled compounds cover only a small fraction of this space. Existing models struggle to generalize to unprofiled compounds, as they typically treat drugs as isolated identifiers without encoding their mechanistic relationships. We present MAP, a framework that integrates structured biological knowledge into cellular perturbation modeling and supports zero-shot prediction for small molecules with scarce or absent perturbation profiles. Specifically: (i) we construct MAP-KG, a large-scale knowledge graph tailored for cellular perturbation modeling that unifies 14 public resources, spanning 187k drugs, 23k genes, and 694k mechanistic relationships; (ii) we propose a knowledge-driven pre-training strategy that aligns molecular structures, protein sequence features, and textual mechanistic descriptions into a unified embedding space via contrastive learning, producing mechanism-aware and transferable gene and compound embeddings. The resulting knowledge-informed gene and drug representations are then coupled with a pretrained single-cell foundation model to condition perturbation response prediction; (iii) we evaluate MAP under two zero-shot generalization regimes: unseen cell type-drug combinations and the stricter setting of unprofiled drugs, where it improves top-50 DEG Pearson delta correlation by up to +13.3% and +12.2%, respectively, over the strongest baselines across three benchmarks. We further perform pathway-level functional analysis via GSEA for in-silico screening, where MAP predicts coherent, mechanism-consistent programs on unprofiled candidate drugs, and prioritizes 4 of 5 approved anti-cancer drugs in A-549 (non-small cell lung cancer).

bioinformatics↗

bioRxiv2026

A novel proliferative candidate genes panel for idiopathic pulmonary fibrosis: insights from integrated bulk and single-cell RNA sequencing

BackgroundIdiopathic pulmonary fibrosis (IPF) remains a fatal interstitial lung disease with limited diagnostic specificity and therapeutic options. This study integrates bulk and single-cell RNA sequencing (RNA-seq) to identify novel biomarkers and elucidate molecular mechanisms underlying IPF pathogenesis. MethodsWe prospectively enrolled 14 treatment-naive IPF patients and 6 controls. Bulk RNA-seq was performed on bronchoalveolar lavage fluid (BALF), while single-cell RNA-seq analyzed lung tissues from 4 IPF patients and 3 controls. Differentially expressed genes (DEGs) were identified (|log2FC| >1, FDR <0.05), followed by functional enrichment, protein-protein interaction (PPI) network analysis, and cell-type-specific expression profiling. Results1. DEG Identification: Bulk RNA-seq revealed 108 DEGs (24 upregulated, 84 downregulated). KEGG enrichment analysis of DEGs revealed that upregulated genes were mainly enriched in inflammation and immune pathways (such as NF-{kappa}B signaling pathway, Fc epsilon RI signaling pathway, B cell receptor signaling pathway, phagosome, Fc gamma R-mediated phagocytosis), pyrimidine metabolism, cell cycle, and PI3K-Akt signaling pathway. 2. PPI Network: Module analysis identified a proliferative gene module 1 (NUF2, CEP55, ANLN, TTK, TK1, MYBL2, CCNA2, RRM2, CDT1) linked to cell division and cycle regulation. 3. Single-Cell Insights: scRNA-seq of 30,477 cells delineated 11 populations. Module 1 genes exhibited predominant expression in proliferating cells, Module 1 signature score of proliferating cells was significantly higher in IPF than in control group. 4. Pathogenic Links: Key genes (e.g., CEP55, TTK) were associated with PI3K/AKT signaling, epithelial-mesenchymal transition (EMT), and anti-apoptotic pathways, mirroring oncogenic mechanisms. ConclusionThis multi-omics approach uncovers a proliferation-centric gene module in IPF, revealing shared molecular pathways with tumorigenesis. Our findings highlight novel diagnostic biomarkers and suggest repurposing cell cycle inhibitors as potential therapies. Future studies should validate these targets in preclinical models to advance precision medicine for IPF.

genetics↗

bioRxiv2026

Artificial Intelligence of Things-Enhanced Automated Surveillance System for Global Antimicrobial Resistance in Food Supply Chain

Antimicrobial resistance (AMR) threatens food safety across the farm-to-fork continuum. Real-time surveillance is crucial to mitigate its global escalation, yet conventional antimicrobial susceptibility testing (AST) remains slow, labor-intensive, and impractical for large-scale monitoring. We developed an Artificial Intelligence of Things (AIoT)-integrated multiplex microfluidic platform enabling automated AMR surveillance of pathogens in food supply chain. Each node combines a single-board AIoT controller (Orange Pi 5B), portable incubator, colorimetric microfluidic chips, and environmental sensors, reducing costs by 98% compared with standard AST. A lightweight YOLOv5 model embedded in the controller achieved >99% accuracy in identifying bacterial growth and inhibition under antibiotic pressure, showing 96% and 95% agreement with standard results for Salmonella and Campylobacter, respectively. Data are synchronized to a cloud server for real-time aggregation and early resistance warning. This fully automated and low-cost system minimizes human error and workload, providing a scalable sample-to-answer solution for AMR surveillance in global agri-food system.

microbiology↗

bioRxiv2026

Reaction-enabled, highly sensitive super-resolution imaging

Super-resolution microscopy breaking the optical diffraction limit has led to success in illuminating the complexity of biology with fluorescence--light-excited luminescence1,2. Here, we propose another approach for super-resolution imaging with electrochemiluminescence (ECL)--reaction-excited luminescence3,4. While featuring high sensitivity in molecular analytics owing to its laser-free excitation, the current reaction-driven imaging suffers from limited resolution due to low photon budget4. Efficient ECL imaging of intracellular organelles remains elusive. To meet this challenge, we develop RIED (Reaction-based super-resolution Imaging via Entropy-weighted correlation combined with Deconvolution), which builds upon two pivotal advancements: (i) unprecedented ECL intracellular organelles imaging through a 1,000-fold enhancement in ECL emission, and (ii) the super-resolution ECL imaging by the spatiotemporal acquisition and efficient utilization of highly dynamic ECL profiles. RIED allows for the first three-dimensional super-resolution ECL imaging with a spatial resolution of {bsim}97 nm in the lateral dimension and {bsim}235 nm in the axial dimension. This allows to reveal outer mitochondrial membrane topology and distinct microtubule organizations in different mitosis phases, offering a chemistry-based imaging capability for high-throughput imaging and highly sensitive interfacial imaging, which provides a new dimension into biology.

biochemistry↗

bioRxiv2026

On the sensitivity of qPCR diagnostics for the canola clubroot pathogen Plasmodiophora brassicae

Clubroot, caused by Plasmodiophora brassicae, is an important disease of canola and other Brassica crops. Polymerase chain reaction (PCR), particularly probe-based quantitative PCR (qPCR), is widely used for the detection of P. brassicae in soil samples. To improve consistency in clubroot detection while maintaining efficiency, diagnostic laboratories would benefit from adopting a single, highly efficient qPCR system for routine testing. In this study, we analyzed the primer and probe sequences of all published PCR and qPCR systems for P. brassicae detection. Based on these analyses, three independently developed probe-based qPCR systems were selected and their performance was evaluated using synthesized target DNA (gBlock). One probe-based qPCR system exhibiting superior sensitivity on gBlock was subsequently evaluated on P. brassicae genomic DNA. This system consistently detected DNA equivalent to four resting spores per reaction, corresponding to a soil sample containing 1,000 spores per g soil when the DNA extraction protocol was considered as a component of the qPCR system. The sensitivity of the system was further validated using DNA extracted from soil samples collected from multiple locations across Alberta, where P. brassicae was detected at levels below those associated with visible clubroot symptoms. Based on these results, we recommend this qPCR system for routine clubroot diagnostics in laboratories across Canada.

microbiology↗

bioRxiv2026

Semantic-Guided Spatial Representation Learning for Spatial Domain Identification

MotivationSpatial domain identification is a fundamental task in spatial transcriptomics analysis, aiming to partition tissue sections into coherent regions that reflect underlying biological organization. Most existing methods rely on gene expression similarity and spatial proximity, which can be insufficient when expression measurements are sparse, noisy, or weakly discriminative. In such settings, representation learning faces ambiguity in determining how spatial neighborhood information and expression-based similarity should be jointly utilized. ResultsWe present GreS, a spatial domain identification framework that incorporates gene-level semantic priors into spatial representation learning. GreS models spatial adjacency and expression-driven similarity using two complementary neighborhood graphs and leverages aggregated gene semantic information to guide how these views are weighted at the spot level. Rather than redefining neighborhood relationships, semantic priors act as contextual signals that modulate the relative contribution of spatial and expression-based cues during domain inference. We evaluate GreS on diverse spatial transcriptomics datasets, including layered brain tissue, embryonic development, and heterogeneous tumor microenvironments. Across these settings, GreS consistently identifies spatial domains that are structurally coherent and biologically interpretable, outperforming existing methods in quantitative accuracy and qualitative spatial organization. Our ablation analyses further demonstrate that performance gains arise from biologically meaningful and properly aligned semantic information, rather than from increased model complexity or auxiliary features. Availability and Implementationhttps://github.com/ai4nucleome/GreS Contactyanlinzhang@hkust-gz.edu.cn Supplementary InformationA Supplementary file is submitted together with this manuscript.

bioinformatics↗

bioRxiv2026

Wetness modulates the effects of grazing on net ecosystem productivity in global grasslands

Overgrazing has induced widespread grassland degradation globally. However, how grazing intensity affects ecosystem carbon dioxide (CO2) fluxes with wetness fluctuations in grasslands at a global scale remains poorly understood. Here we measured ecosystem CO2 fluxes covering the 7th to 11th years of a continuous grazing experiment in a typical steppe, and conducted a meta-analysis of 585 observations by collecting relevant data in global grasslands. Our experimental results showed that mean gross primary productivity (GPP) and ecosystem respiration (ER) were decreased under heavy grazing (HG) compared with control without grazing (CK), whereas net ecosystem productivity (NEP) was not different between HG and CK in the typical steppe. The NEP was higher under light grazing in the wetter years, but it was not different between LG and CK under drier years in the typical steppe. The structural equation model showed that grazing altered NEP by increasing soil temperature and relative growth rate, while decreasing AGB. In contrast, grazing decreased GPP, ER and NEP, respectively, in global grasslands. Both our field experiment and the meta-analysis revealed that the response of NEP to light grazing, rather than heavy grazing, was lineally correlated with the wetness index. Higher wetness and aboveground biomass (AGB) increased the response of NEP to grazing in global grasslands. However, heavy grazing reduced NEP and AGB even under higher wetness indices in global grasslands, resulting from a loss of their resilience in long-term heavy grazing. These findings indicate that light grazing appears to be a promising management to promote plant relative growth rate and carbon sequestration. Overall, the meta-analysis and field experiment jointly provide global perspectives on the response of ecosystem CO2 fluxes to grazing intensity and improve our understanding of the factors influencing the response of ecosystem CO2 fluxes to grazing intensity.

ecology↗

bioRxiv2026

Single-Cell Raman Profiling Enables Rapid Precision Phage Therapy Against Multidrug-Resistant Hypervirulent Klebsiella pneumoniae

Multidrug-resistant hypervirulent Klebsiella pneumoniae (MDR-hvKP) poses a severe global health threat. Phage therapy is a promising alternative, but requires precise matching of phage to the bacterial strain. Here, we present a proof-of-concept method that integrates single-cell Raman spectroscopy with deep learning to enable rapid and precise selection of lytic phages against MDR-hvKP. By profiling Raman signatures of strains across multiple KL-types (capsule locus types), we trained three deep learning architectures for phage-host matching. Among them, the CNN_MLP-Transformer achieved the best performance (99.7%), slightly outperforming CNN_MLP (99.2%) and CNN_MLP-Attention (99.5%). Validation using 10 hvKP clinical isolates yielded an average phage selection accuracy of 78.3%. These findings demonstrate the feasibility and clinical potential of AI- augmented Raman spectroscopy as a rapid, label-free, precise strategy for guiding phage therapy against MDR-hvKP infections. TeaserAI-guided single-cell Raman profiling enables rapid precision phage selection against multidrug-resistant K. pneumoniae.

microbiology↗

bioRxiv2026

Iron Deficiency Drives Sarcopenia in the Elderly: HIF-1α-Mediated Fibro-Adipogenic Progenitor Differentiation Induces Fat Infiltration and Impairs Muscle Function

Abnormal iron metabolism is closely linked to sarcopenia; however, the specific iron metabolism features of fat-infiltrating sarcopenia remain poorly understood. Proteomic sequencing revealed that in skeletal muscle with severe fat infiltration, the expression of iron utilization-related proteins was significantly downregulated, whereas that of iron uptake and storage proteins was markedly upregulated, thereby presenting an abnormal iron deficiency (ID) phenotype. Nevertheless, the mechanism by which ID drives intramuscular fat infiltration has not been fully elucidated. Using clinical samples, aged murine ID models, and in vitro cell assays, this study is the first to demonstrate that ID stabilizes hypoxia-inducible factor-1 (HIF-1), promoting aberrant adipogenic differentiation of fibro-adipogenic progenitors (FAPs), disrupting the homeostatic balance between satellite cells (SCs) and FAPs, exacerbating skeletal muscle fat infiltration, and impairing muscle repair capacity. Notably, treatment with the HIF-1 inhibitor PX478 reversed these pathological alterations and improved muscle function. Collectively, our findings identify the ID-HIF-1-FAPs axis as a key driver of intramuscular fat infiltration, offering a novel therapeutic target for the clinical management of fat-infiltrating sarcopenia.

cell biology↗

bioRxiv2026

Plasma proteomics reveal heterogeneous subtypes of depression linked to inflammation and aging

The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype ( inflammation/ageing) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdala atrophy; this subtype had highest anhedonia burden. A distinct inflammation/energy dysregulation group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the inflammation-resilient group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions.

neuroscience↗

bioRxiv2026

Therapeutic interleukin-2 rewires skeletal-immune circuits to reverse postmenopausal bone loss

Postmenopausal osteoporosis results from excessive osteoclast activity, but the immune circuits that limit osteoclastogenesis in the bone marrow remain poorly understood. Here, we tested a clinically accessible immunotherapy low-dose interleukin-2 (IL-2) as a therapeutic intervention in mice with established ovariectomy-induced bone loss, integrating microcomputed tomography imaging, single-cell transcriptomics, genetic perturbation, and human monocyte-osteoclast assays. Therapeutic administration of IL-2 reversed trabecular bone loss, expanded CXCR3 regulatory T cells in bone marrow, and promoted IL-10- and CGRP-producing ILC2s that were required for protection. IL-2 also acted directly on the osteoclast lineage, suppressing mouse and human osteoclastogenesis through activation of a STAT1-IRF1-dependent IFN program in RANKL-stimulated monocytes. Together, these data define an IL-2-responsive skeletal-immune network that can be therapeutically engaged to suppress osteoclastogenesis, which opens new avenues for cytokine-based immunotherapies in postmenopausal osteoporosis and related bone disorders.

immunology↗

bioRxiv2026

Disrupted dynamics of brain structure function coupling link genetic risk of Alzheimer's Disease and aging to cognitive decline in 34,067 adults

AbstractUnderstanding how a stable structural connectome supports flexible cognition, especially in aging, is a fundamental question in neuroscience. While static structure-function coupling (SFC) is well-studied, the dynamic decoupling of functional activity from structural constraints--dynamic SFC (DSFC)--remains poorly understood. Leveraging MRI data from 34,067 UK Biobank participants (ages 45-82), we characterized the distinct roles of SFC and DSFC in aging, cognition, and health. We found that SFC and DSFC followed spatially divergent aging trajectories: SFC declined primarily in sensorimotor systems, whereas DSFC decreased most prominently in higher-order networks. Both SFC and DSFC in higher-order networks were positively correlated with cognitive performance (e.g., fluid intelligence). However, the associations with mental and physical health diverged between the two measures: reduced DSFC was predominantly linked to health burdens in high-order default/limbic networks, whereas weakened SFC was primarily associated with health burdens in low-order sensory-motor networks. Genetic analyses revealed that Alzheimers risk, specifically APOE {varepsilon}4 dosage, significantly reduced DSFC in higher-order cognitive networks and SFC in visual cortex. Mediation analyses further demonstrated that aging and APOE {varepsilon}4-linked cognitive decline were mediated via visual SFC and ventral attention DSFC. These findings position static and dynamic coupling as complementary mechanisms: static SFC preserves network robustness, while dynamic SFC enables transient reconfiguration for complex integration. Together, our results highlight these dual mechanisms as crucial for maintaining cognitive flexibility, providing potential biomarkers for age-related neurodegeneration.

neuroscience↗

bioRxiv2025

Plasma Ficolins Enable Liver Macrophages to Capture Blood-Borne Bacteria by Recognizing Capsular Polysaccharides

Ficolins are evolutionarily conserved glycan-binding lectins in plasma or on cell membranes of immune cells, yet their physiological functions remain to be defined. Here we demonstrate that plasma ficolins drives the capture of diverse encapsulated bacteria in the blood circulation by the liver resident macrophages (Kupffer cells). Ficolin-activated immunity in the liver sinusoids provides swift and sterilizing immunity in mice. Mice lacking ficolin-A (the only plasma ficolin) became hypersusceptible to the blood infections of the ficolin-A-recognizable bacterial serotypes. Mechanistically, plasma ficolins activate complement C3 on bacterial surfaces by pattern recognition of acetylated monosaccharides on polysaccharide capsules, and thereby deliver the opsonized bacteria to C3 receptors on Kupffer cells for phagocytic elimination. These findings reveal that plasma ficolins serve as pattern-recognition receptors that are essential for maintaining the bloodstream sterile.

microbiology↗