Biology subjects

Nan, X.

Publications and source records attributed to Nan, X..

17 recordsLinked to original sources

bioRxiv2026

An oxytocin-gated circuit from the hypothalamus silences olfactory tubercle neurons to drive prosocial grooming

Spontaneous helping behaviors such as allogrooming are vital for survival in social species, yet their underlying neural mechanisms remain largely unknown. Although oxytocin (OXT) is known to modulate allogrooming, the precise neural circuits, molecular pathways, and sensory drivers remain unclear. Here, using a social emergency paradigm in mice, we show that observer mice selectively allogroom distressed, anesthetized conspecifics, a behavior that is guided by main olfactory system cues and mitigates the demonstrators anxiety. We identify an oxytocinergic circuit from the paraventricular hypothalamus to the medial olfactory tubercle (PVNOXT[->]mOT) that is necessary and sufficient for this helping behavior. In vivo fiber photometry reveals that PVNOXT[->]mOT activity and OXT release are temporally locked to allogrooming initiation. This behavioral effect requires oxytocin receptor (OXTR) signaling specifically on mOT dopamine D1 receptor-expressing neurons, where OXTRs suppress neuronal excitability via G protein-gated inwardly rectifying K+ (GIRK) channels. Disruption of this inhibitory mechanism induces neuronal hyperexcitability and impairs allogrooming, a deficit rescued by restoring GIRK function. These findings define a PVNOXT-mOT circuit for prosocial helping, revealing an oxytocinergic pathway with potential therapeutic relevance for neuropsychiatric disorders characterized by social deficits.

neuroscience↗

bioRxiv2025

IgG-Bridging-Seeded Synergistic Aggregation of SARS-CoV-2 Spikes Underlies Potent Neutralization by A Low-Affinity Antibody

Mechanistic studies of viral neutralization typically prioritize high-affinity antibodies, relegating low-affinity binders to the sidelines. We report P5-1C8, a Class 1 SARS-CoV-2 antibody that exemplifies this underexplored "low-affinity yet high-potency" phenotype, retaining strong neutralization of Omicron JN.1 despite markedly weakened trimer binding (KD = 225 nM; IC50 = 0.06 nM). Structural and biophysical analyses reveal that P5-1C8 engages WT and BA.1 spikes through canonical intra-spike bivalency, but with JN.1 it induces aggregation. Using virion-like nanoparticles displaying multiple spikes, we show that IgG remains bound with no detectable dissociation and triggers pronounced aggregation. Coarse-grained molecular dynamics delineate the stepwise pathway in which weak IgG-spike contacts seed aggregation via transient inter-spike bridging. Together, these findings establish the first mechanistic framework demonstrating how weak-binding antibodies can nonetheless achieve potent neutralization through higher-order aggregation, thereby expanding the conceptual landscape of antibody function and opening new directions for antibody evaluation and design. Graphical AbstractLow-affinity antibodies are frequently disregarded in discovery pipelines. This work reports P5-1C8, a Class 1 SARS-CoV-2 antibody with weak trimer binding (KD-to-IC50 > 3,700-fold) yet potent neutralization of Omicron JN.1. Structural, biophysical, functional and coarse-grained simulations collectively demonstrate that transient inter-spike IgG bridging seeds higher-order aggregation, which in turn drives neutralization and provides a mechanistic framework. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/691454v1_ufig1.gif" ALT="Figure 1000"> View larger version (79K): org.highwire.dtl.DTLVardef@1d4f132org.highwire.dtl.DTLVardef@1274400org.highwire.dtl.DTLVardef@e186a6org.highwire.dtl.DTLVardef@4f1870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Distinct Binding Modes of Inter-Spike Cross-Linking Suggest a Supplementary Mechanism for SARS-CoV-2 Antibody Neutralization

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its Omicron subvariants drastically amplifies transmissibility, infectivity, and immune escape, mainly due to their resistance to most neutralizing antibodies. Thus, exploring the mechanisms underlying antibody evasion is crucial. Although the full-length native form of antibody, immunoglobulin G (IgG), offers valuable insights into the neutralization, structural investigations primarily focus on the fragment of antigen-binding (Fab). Here, we employ single-particle cryo-electron microscopy (cryo-EM) to characterize a W328-6H2 antibody, in its native IgG form complexed with severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 wild-type (WT) and Omicron variant BA.1 spike protein (S). Three high-resolution structures reveal that the full-length IgG forms a centered head-to-head dimer of trimer when bound fully stoichiometrically with both SARS and WT S, while adopting a distinct offset configuration with Omicron BA.1 S. Combined with functional assays, our results suggest that, beyond the binding affinity between the RBD epitope and Fab, the higher-order architectures of S trimer and full-length IgG play an additional role in neutralization, elucidate a potential molecular basis of Omicron immune escape, and expand our understanding on antibody evasion mechanisms.

immunology↗

bioRxiv2024

De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

Artificial intelligence (AI)-driven discovery of antimicrobial peptides (AMPs) is yet to fully utilise their three-dimensional (3D) structural characteristics, microbial specie-specific antimicrobial activities and mechanisms. Here, we constructed a QLAPD database comprising the sequence, structures and antimicrobial properties of 12,914 AMPs. QLAPD underlies a multimodal, multitask, multilabel, and conditionally controlled AMP discovery (M3-CAD) pipeline, which is proposed for the de novo design of multi-mechanism AMPs to combat multidrug-resistant organisms (MDROs). This pipeline integrates the generation, regression, and classification modules, using a innovative 3D voxel coloring method to capture the nuanced physicochemical context of amino acids, significantly enhancing structural characterizations. QL-AMP-1, discovered by M3-CAD, which possesses four antimicrobial mechanisms, exhibited low toxicity and significant activity against MDROs. The skin wound infection model demonstrates its considerable antimicrobial effects and negligible toxicity. Altogether, integrating 3D features, specie-specific antimicrobial activities and mechanisms enhanced AI-driven AMP discovery, making the M3-CAD pipeline a viable tool for de novo AMP design.

bioengineering↗

bioRxiv2023

Adenine methylation is very scarce in the drosophila genome and not erased by the Ten Eleven Translocation dioxygenase.

N6-methyladenine (6mA) DNA modification has recently been described in metazoans, including in drosophila, for which the erasure of this epigenetic mark has been ascribed to the Ten Eleven Translocation (TET) enzyme. Here, we re-evaluated 6mA presence and TET impact on drosophila genome. Using axenic or conventional breeding conditions, we found traces of 6mA by LC-MS/MS and no significant increase in 6mA levels in the absence of TET, suggesting that this modification is present at very low levels in the drosophila genome but not regulated by TET. Consistent with this latter hypothesis, further molecular and genetic analyses showed that TET does not demethylate 6mA but acts essentially in an enzymatic-independent manner. Our results call for further caution concerning the role and regulation of 6mA DNA modification in metazoans and underline the importance of TET non-enzymatic activity for fly development.

molecular biology↗

bioRxiv2023

MsPBRsP: Multi-scale Protein Binding Residues Prediction Using Language Model

Accurate prediction of protein binding residues (PBRs) from sequence is important for the understanding of cellular activity and helpful for the design of novel drug. However, experimental methods are time-consuming and expensive. In recent years, a lot of computational predictors based on machine learning and deep learning models are proposed to reduce such consumption. But those methods often use MSA tools such as PSI-BLAST or NetSurfP to generate some statistical features and enter them into predictive models as necessary supplementary input. The input generation process normally takes long time, and there is no standard to specify which and how many statistic results should be provided to a prediction model. In addition, prediction of PBRs relies on residue local context, but the most appropriate scale is undetermined. Most works pre-selected certain residue features as input and a scale size based on expertise for certain type of PBRs. In this study, we propose a general tool-free end-to-end framework that can be applied to all types of PBRs, Multi-scale Protein Binding Residues Prediction using language model (MsPBRsP). We adopt a pre-trained language model ProtTrans to save the large consumption caused by MSA tools, and use protein sequence alone as input to our model. To ease scale size uncertainty, we construct multi-size windows in attention layer and multi-size kernels in convolutional layer. We test our framework on various benchmark datasets including PBRs from protein-protein, protein-nucleotide, protein-small ligand, heterodimer, homodimer and antibody-antigen interactions. Compared with existing state-of-the-art methods, MsPBRsP achieves superior performance with less running time and higher prediction rates on every PBRs prediction task. Specifically, we boost F1 score by 27.1% and AUPRC score by 7.6% on NSP448 dataset and decrease running time from over 10 minutes to under 0.1s on average. The source code and datasets are available at https://github.com/biolushuai/MsPBRsP-for-multiple-PBRs-prediction.

bioinformatics↗

bioRxiv2022

Multiplexed and millimeter-scale superresolution imaging of cells and tissue sections via prism-illumination and microfluidics-enhanced DNA-PAINT

Fluorescence nanoscopy has become increasingly powerful for biomedical research, but it has historically afforded a small field-of-view (FOV) around 50 {micro}m x 50 {micro}m at once and more recently up to ~200 {micro}m x 200 {micro}m. Efforts to further increase the FOV in fluorescence nanoscopy have thus far relied on the use of fabricated waveguide substrates, adding cost and sample constraints on the applications. Here we report PRism-Illumination and Microfluidics-Enhanced DNA-PAINT (PRIME-PAINT) for multiplexed fluorescence nanoscopy across millimeter-scale FOVs. Built upon the well-established prism-type total internal reflection microscopy, PRIME-PAINT achieves robust single-molecule localization with up to ~520 {micro}m x 520 {micro}m single FOVs and 25-40 nm lateral resolutions. Through stitching, nanoscopic imaging over mm2 sample areas can be completed in as little as 40 minutes per target. An on-stage microfluidics chamber facilitates probe exchange for multiplexing and enhances image quality particularly for formalin-fixed paraffin-embedded (FFPE) tissue sections. We demonstrate the utility of PRIME-PAINT by analyzing ~106 caveolae structures in ~1,000 cells and imaging entire pancreatic cancer lesions from patient tissue biopsies. By imaging from nanometers to millimeters with multiplexity and broad sample compatibility, PRIME-PAINT will be useful for building multiscale, Google-Earth-like views of biological systems.

biophysics↗

bioRxiv2021

A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binding to an antigen, known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing local and global information of antibody sequence and partner features from partner antigen sequence. Convolution Neural Networks(CNNs) and a sliding window approach on antibody sequence are used to extract local information. Attention-based Bidirectional Long Short-Term Memory (Att-BLSTM) on antibody sequence are used to extract global information. Also, the partner antigen is vital for paratope prediction, and we employ Att-BLSTM on the partner antigen sequence as well. The outputs of CNNs and Att-BLSTM networks are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.

bioinformatics↗

bioRxiv2021

A Structure-based B-cell Epitope Prediction Model Through Combing Local and Global Features

B-cell epitopes (BCEs) are a set of specific sites on the surface of an antigen that binds to an antibody produced by B-cell. The recognition of BCEs is a major challenge for drug design and vaccines development. Compared with experimental methods, computational approaches have strong potential for BCEs prediction at much lower cost. Moreover, most of the currently methods focus on using local information around target residue without taking the global information of the whole antigen sequence into consideration. We propose a novel deep leaning method through combing local features and global features for BCEs prediction. In our model, two parallel modules are built to extract local and global features from the antigen separately. For local features, we use Graph Convolutional Networks(GCNs) to capture information of spatial neighbors of a target residue. For global features, Attention-Based Bidirectional Long Short-Term Memory(Att-BLSTM) networks are applied to extract information from the whole antigen sequence. Then the local and global features are combined to predict BCEs. The experiments show that the proposed method achieves superior performance over the state-of-the-art BCEs prediction methods on benchmark datasets. Also, we compare the performance differences between data with or without global features. The experimental results show that global features play an important role in BCEs prediction. Our detailed case study on the BCEs prediction for SARS-Cov-2 receptor binding domain confirms that our method is effective for predicting and clustering true BCEs.

bioinformatics↗

bioRxiv2021

Attention-based convolutional neural networks for protein-protein interaction site prediction

Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach in which local neighbors are concatenated to present a target residue. However, those neighbors are not been distinguished by pairwise information between a neighbor and the target. In this study, we propose a novel PPIs prediction model AttCNNPPISP, which combines attention mechanism and convolutional neural networks (CNNs). The attention mechanism dynamically captures the pairwise correlation of each neighbor-target pair within a sliding window, and therefore makes a better understanding of the local environment of target residue. And then, CNNs take the local representation as input to make prediction. Experiments are employed on several public benchmark datasets. Compared with the state-of-the-art models, AttCNNPPISP significantly improves the prediction performance. Also, the experimental results demonstrate that the attention mechanism is effective in terms of constructing comprehensive context information of target residue.

bioinformatics↗

bioRxiv2020

An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer

Mechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancers genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

Leveraging Sequential and Spatial Neighbors Information by Using CNNs Linked With GCNs for Paratope Prediction

Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.

bioinformatics↗

bioRxiv2020

Crosstalk between invadopodia and the extracellular matrix

The scaffold protein Tks5 is required for invadopodia-mediated cancer invasion both in vitro and in vivo. We have previously also revealed a role for Tks5 in tumor cell growth using three-dimensional (3D) culture model systems and mouse transplantation experiments. Here we use both 3D and high-density fibrillar collagen (HDFC) culture to demonstrate that native type I collagen, but not a form lacking the telopeptides, stimulated Tks5-dependent growth, which was dependent on the DDR collagen receptors. We used microenvironmental microarray (MEMA) technology to determine that laminin, collagen I, fibronectin and tropoelastin also stimulated invadopodia formation. A Tks5-specific monoclonal antibody revealed its expression both on microtubules and at invadopodia. High- and super-resolution microscopy of cells in and on collagen was then used to place Tks5 at the base of invadopodia, separated from much of the actin and cortactin, but coincident with both matrix metalloprotease and cathepsin proteolytic activity. Inhibition of the Src family kinases, cathepsins or metalloproteases all reduced invadopodia length but each had distinct effects on Tks5 localization. These studies highlight the crosstalk between invadopodia and extracellular matrix components, and reveal the invadopodium to be a spatially complex structure.

cancer biology↗

bioRxiv2020

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

bioinformatics↗

bioRxiv2019

HER2 Cancer Protrusion Growth Signaling Regulated by Unhindered, Localized Filopodial Dynamics

Protrusions are plasma membrane extensions that are found in almost every cell in the human body. Cancer cell filopodial and lamellipodial protrusions play key roles in the integral processes of cell motility and signaling underlying tumor invasion and metastasis. HER2 (ErbB-2) is overexpressed in diverse types of tumors and regulates PI3K-pathway-mediated protrusion growth. It is known that HER2 resides at breast cancer cell protrusions, but how protrusion-based HER2 spatiotemporal dynamics shape cancer signaling is unclear. Here, we study how HER2 location and motion regulate protrusion signaling and growth using quantitative spatio-temporal molecular imaging approaches. Our data highlight morphologically-segregated features of filopodial and lamellipodial protrusions, in in vitro 2D breast cancer cells and in vivo intact breast tumor. Functional-segregation parallels morphological-segregation, as HER2 and its activated downstream pAKT-PI3K signaling remain spatially-localized at protrusions, provoking new protrusion growth proximal to sites of HER2 activation. HER2 in SKBR3 breast cancer cell filopodia exhibits fast, linearly-directed motion that is distinct from lamellipodia and non-protrusion subcellular regions ([~]3-4 times greater diffusion constant, rapid speeds of 2-3 um2/s). Surprisingly, filopodial HER2 motion is passive, requiring no active energy sources. Moreover, while HER2 motion in lamellipodia and non-protrusion regions show hindered diffusion typical of membrane proteins, HER2 diffuses freely within filopodia. We conclude that HER2 activation, propagation, and functional protrusion growth is a local process in which filopodia have evolved to exploit Brownian thermal fluctuations within a barrier-free nanostructure to transduce rapid signaling. These results support the importance of developing filopodia and other protrusion-targeted strategies for cancer.

cancer biology↗

bioRxiv2019

Improved single-cell ATAC-seq reveals chromatin dynamics of in vitro corticogenesis

Development is a complex process that requires the precise modulation of regulatory gene networks controlled through dynamic changes in the epigenome. Single-cell-omic technologies provide an avenue for understanding the mechanisms of these processes by capturing the progression of epigenetic cell states during the course of cellular differentiation using in vitro or in vivo models1. However, current single-cell epigenomic methods are limited in the information garnered per individual cell, which in turn limits their ability to measure chromatin dynamics and state shifts. Single-cell combinatorial indexing (sci-) has been applied as a strategy for identifying single-cell-omic originating libraries and removes the necessity of single-cell, single-compartment chemistry2. Here, we report an improved sci-assay for transposase accessible chromatin by sequencing (ATAC-seq), which utilizes the small molecule inhibitor Pitstop 2 (scip-ATAC-seq)3. We demonstrate that these improvements, which theoretically could be applied to any in situ transposition method for single-cell library preparation, significantly increase the ability of transposase to enter the nucleus and generate highly complex single-cell libraries, without altering biological signal. We applied sci-ATAC-seq and scip-ATAC-seq to characterize the chromatin dynamics of developing forebrain-like organoids, an in vitro model of human corticogenesis4. Using these data, we characterized novel putative regulatory elements, compared the epigenome of the organoid model to human cortex data, generated a high-resolution pseudotemporal map of chromatin accessibility through differentiation, and measured epigenomic changes coinciding with a neurogenic fate decision point. Finally, we combined transcription factor motif accessibility with gene activity (GA) scores to directly observe the dynamics of complex regulatory programs that regulate neurogenesis through developmental pseudotime. Overall, scip-ATAC-seq increases information content per cell and bolsters the potential for future single-cell studies into complex developmental processes.

genomics↗

bioRxiv2019

High-throughput single-particle tracking reveals nested membrane nanodomain organization that dictates Ras diffusion and trafficking

Membrane nanodomains have been implicated in Ras signaling, but what these domains are and how they interact with Ras remain obscure. Using high throughput single particle tracking with photoactivated localization microscopy and detailed trajectory analysis, here we show that distinct membrane domains dictate KRas diffusion and trafficking in U2OS cells. KRas exhibits an immobile state in domains [~]70 nm in size, each embedded in a larger domain ([~]200 nm) that confers intermediate mobility, while the rest of the membrane supports fast diffusion. Moreover, KRas is continuously removed from the membrane via the immobile state and replenished to the fast state, likely coupled to internalization and recycling. Importantly, both the diffusion and trafficking properties of KRas remain invariant over a broad range of protein expression levels. Our results reveal how membrane organization dictates KRas diffusion and trafficking and offer insight into how Ras signaling may be regulated through spatial mechanisms.

biophysics↗