Biology subjects

Li, S.

Publications and source records attributed to Li, S..

At least 433 records · Page 24Linked to original sources

bioRxiv2025

Defined cellular reprogramming of androgen receptor-active prostate cancer to neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) arises primarily through neuroendocrine transdifferentiation (NEtD) as an adaptive mechanism of therapeutic resistance. Models to define the functional effects of putative drivers of this process on androgen receptor (AR) signaling and NE cancer lineage programs are lacking. We adapted a genetically defined strategy from the field of cellular reprogramming to directly convert AR-active prostate cancer (ARPC) to AR-independent NEPC using candidate factors. We delineated critical roles of the pioneer factors ASCL1 and NeuroD1 in NEtD and uncovered their abilities to silence AR expression and signaling by remodeling chromatin at the somatically acquired AR enhancer and global AR binding sites with enhancer activity. We also elucidated the dynamic temporal changes in the transcriptomic and epigenomic landscapes of cells undergoing acute lineage conversion from ARPC to NEPC which should inform future therapeutic development. Further, we distinguished the activities of ASCL1 and NeuroD1 from the inactivation of RE-1 silencing transcription factor (REST), a master suppressor of a major neuronal gene program, in establishing a NEPC lineage state and in modulating the expression of genes associated with major histocompatibility complex class I (MHC I) antigen processing and presentation. These findings provide important, clinically relevant insights into the biological processes driving NEtD of prostate cancer.

cancer biology↗

bioRxiv2025

A two-step clockwork mechanism opens a proteo-lipidic pore in PIEZO2 channels

Mechanosensitive PIEZO channels are thought to open their pore through tension-induced flattening of large transmembrane arm domains. Yet, the structural basis of this activation remains unclear. Here, we uncover the conformational coupling between arm flattening and pore opening in PIEZO2 by capturing protein motions across length scales using hybrid-resolution molecular dynamics simulations. Sampling multiple microsecond-long trajectories under physiological activation tension show that arm flattening correlates with anticlockwise rotation of the pore domain and with clockwise twisting of inner pore helices, enabling dilation and hydration of a transmembrane pore gate. These clockwork motions enable PIEZO2 to populate two open states with distinct conductance depending on applied membrane tension, in agreement with single-channel electrophysiology. Pore opening is accompanied by the separation of pore helices, creating interhelical gaps which become filled with lipids, resulting in the fully conducting pore being walled by both lipids and protein. The fully open PIEZO2 state recapitulates minimal pore size, conductance, ion selectivity, and outward rectification of chloride currents observed electrophysiologically. This work reveals how tension-induced large-scale rearrangements of the PIEZO2 arms funnel into subtle and dynamic gating motions, providing invaluable structural insights for future structure-function and drug discovery studies.

biophysics↗

bioRxiv2025

Multi-Omics Analysis of Wound Microbiome and Staphylococcus aureus in Pressure Ulcer

This study employs a multi-omics approach to investigate the wound microbiome and Staphylococcus aureus in pressure ulcers. Metagenomic sequencing and associated technologies were utilized to examine bacterial populations, identify dominant species, and study biofilm formation in S. aureus through metabolomic and proteomic characteristics. The results revealed a significant reduction in microbial diversity in pressure ulcer samples compared to controls, with Staphylococcus, Corynebacterium, and Klebsiella being the most prevalent genera. Functional prediction analysis indicated differences in pathogenic invasiveness and biofilm formation factors among the groups. The presence of antimicrobial resistance genes was higher in the pressure ulcer group, particularly in Staphylococcus spp. strains. Whole-genome sequencing of 29 S. aureus isolates identified various clonal complexes and spa types, with the majority possessing genes conferring resistance to {beta}-lactam antimicrobials and virulence factors. Histopathological examination and fluorescent in situ hybridization confirmed the presence of S. aureus in biofilm structures. In vitro biofilm formation tests and metabolomic and proteomic analyses provided insights into the interactions between S. aureus strains and their biofilm formation, revealing enriched pathways related to metabolic processes and membrane composition. This research offers a scientific foundation for understanding the colonization patterns of S. aureus biofilms in pressure ulcer wounds.

microbiology↗

bioRxiv2025

SpecImmune accurately genotypes diverse immune-related gene families using long-read data

Polymorphic immune-related genes (HLA, KIR, IG, TCR, and CYP) exhibit significant complexity due to their extensive heterozygosity and inter-loci homology, necessitating specific methods for accurate characterization. We present SpecImmune, the first comprehensive tool leveraging long-read sequencing data to resolve the full spectrum of these immune-related genes. The method adopts an iterative graph-based algorithm for haplotype reconstruction. We validated SpecImmune across 1,019 samples from the 1kGP ONT cohort, 42 PacBio CLR and 9 PacBio HiFi samples from the HGSVC project, and 47 PacBio HiFi and 37 ONT samples from the HPRC project. SpecImmune achieved an accuracy of 98% in HLA typing, which represents a 12% improvement over both SpecHLA and HLA*LA. SpecImmune is the initial method to type multiple CYP loci, as well as the foremost approach to allow precise KIR and germline IG/TCR typing using long reads. Comprehensive genotyping of these loci by SpecImmune unveils a new observation of substantial linkage disequilibrium among HLA, KIR, and CYP loci. The proteins derived from these loci exhibit strong binding affinities, which suggest the origin of the marked linkage disequilibrium. Further, SpecImmune unveils a novel finding of elevated IG/TCR heterozygosity in African populations. Additionally, SpecImmune facilitates the detection of de novo mutations and enables allele-specific drug recommendations.

bioinformatics↗

bioRxiv2025

Monitoring Whole-Body Inflammation with Gallium-68 Labeled Polydopamine Iron Oxide Particles via Hybrid Immuno-PET-MRI

Monitoring immune reactions via inflammation imaging provides valuable disease prognosis but remains limited by currently available probes and imaging capabilities. In this study, a bimodal imaging probe was developed from microsized polydopamine matrix-based magnetic particles (M3P) functionalized with antibodies that target vascular cell adhesion molecule-1 (VCAM-1) and that are radiolabeled with the radioisotope Gallium-68. This probe combines sensitive inflammation detection via Positron Emission Tomography (immuno-PET) and high-resolution mapping via Magnetic Resonance Imaging (immuno-MRI). The probe represents a powerful disease diagnosis tool to assess the status of systemic inflammation in sepsis as whole-body immuno-PET enables rapid evaluation of the situation, and immuno-MRI provides a more detailed characterization of the identified affected tissues. The elimination profile of the M3P probe is restricted to the mononuclear phagocyte system and does not undergo renal clearance, which is suitable for application in kidney disorders. The efficacy of the hybrid immuno-PET-MRI diagnosis protocol was tested in a murine model of rhabdomyolysis. Kidney inflammation was detected by whole-body immuno-PET, and vascular inflammation patterns could be revealed by high-resolution immuno-MRI. This bimodal PET-MRI probe could significantly improve inflammation monitoring in pathologies characterized by systemic inflammation or lung and kidney dysfunction.

bioengineering↗

bioRxiv2025

A systematic analysis of in-source fragments in LC-MS metabolomics

There is no consensus on how to interpret the large number of unknown features in untargeted metabolomics, which are sometimes referred as the "dark matter". Are these features real compounds or artifacts? Understanding this problem is critical to the annotation and interpretation of metabolomics data and future development of the field. We propose a "detectable khipu" model here, to show that compounds exhibit ion group patterns that depend on their abundance. We apply this model to a systematic analysis of 61 representative public datasets from blood LC-MS metabolomics, the most common data type in biomedical studies. The results indicate that majority of abundant features have identifiable ion patterns, and in-source fragments contribute to less than 10% of features. Each dataset detects 1[~]2,000 high confidence compounds, over half of which are unknown. The major knowledge gap in LC-MS metabolomics is therefore not the methods of grouping ions or counting fragments, but the identification of unknown compounds.

biochemistry↗

bioRxiv2025

A novel deep optimal transport framework reveals prostate cancer risk heterogeneity, Alzheimers disease risk heterogeneity, and myeloma cells associated with a short-term sham bortezomib response and progressive disease

Leveraging single-cell gene expression profiles can significantly enhance our understanding of diseases by associating single cells with traits such as disease subtypes, prognosis, and drug response. Although previous efforts have linked single cell clusters and groups with these attributes, they have primarily focused on changes in cell proportions while overlooking transcriptional changes at the single cell level. To further unravel cell heterogeneity with clusters and reveal the nuanced behaviors of cellular subtypes, it is essential to assess the disease associations of individual cells. Previous methods often fail to capture complex patterns that are only discernible through summarizing non-linear relationships across multiple genes. The Diagnostic Evidence GAuge of Single-cells/Spatial-transcriptomics (DEGAS) framework advances these efforts by aligning single cells and/or spatial transcriptomics regions with patients through a unified latent space using non-linear transformations learned from deep neural networks (DNNs). DEGAS achieves superior performance in analyzing single cell and spatial transcriptomics datasets, including Alzheimers disease (AD), multiple myeloma (MM), and prostate cancer (PDAC). Here we present DEGAS version 2, which has been updated with optimial transport based transfer learning and improved time-to-event loss functions, more advanced model architecture, and improved model baseline evaluations. DEGASv2 outperformed other methods in both single cell and spatial transcriptomic baseline comparisons. On the multiple myeloma discovery dataset, DEGASv2 enabled us to discover cell types that exhibited distinct drug response patterns over various time frames and were validated with time series single cell multiomic data that we generated, demonstrating a dangerous subtype of cell and novel therapeutic target.

bioinformatics↗

bioRxiv2025

Telomerase mRNA Reduces Radiation-induced DNA Damage of human skin

Over four million people undergo radiation therapy annually in the United States. Among these, more than 90% experience varying degrees of radiation-induced skin injury. Despite the enormity of the problem, there is currently no FDA-approved agent to prevent or treat skin damage caused by ionizing radiation. In the current study, ionizing radiation induced dose-dependent genomic and mitochondrial DNA damage, leading to apoptosis in primary cutaneous cells. Prior treatment with mRNA encoding telomerase reverse transcriptase (TERT) substantially reduced radiation-induced DNA damage in human primary skin cells and tissues. Mechanistically, TERT mRNA pretreatment enhances DNA repair, reduces mitochondrial ROS, and decreases apoptosis without extending telomere length during the experimental period, suggesting a non-canonical function of TERT to accelerate the cellular recovery from radiation. These findings highlight a potential therapeutic approach for preventing radiation-induced skin injury. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/636031v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@5bcee4org.highwire.dtl.DTLVardef@16c0c28org.highwire.dtl.DTLVardef@922c5dorg.highwire.dtl.DTLVardef@9eafd0_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

mRNA lipid nanoparticle-incorporated nanofiber-hydrogel composite generates a local immunostimulatory niche for cancer immunotherapy

Hydrogel materials have emerged as versatile platforms for various biomedical applications. Notably, the engineered nanofiber-hydrogel composite (NHC) has proven effective in mimicking the soft tissue extracellular matrix, facilitating substantial recruitment of host immune cells and the formation of a local immunostimulatory microenvironment. Leveraging this feature, here we report an mRNA lipid nanoparticle (LNP)-incorporated NHC microgel matrix, termed LiNx, by incorporating LNPs loaded with mRNA encoding tumour antigens. Harnessing the potent transfection efficiency of LNPs in antigen-presenting cells (APCs), LiNx demonstrates remarkable immune cell recruitment, antigen expression and presentation, and cellular interaction. These attributes collectively create an immunostimulating milieu and yield a potent immune response achievable with a single dose, comparable to the conventional three-dose LNP immunization regimen. Further investigations reveal that the LiNx not only generates heightened Th1 and Th2 responses but also elicits a distinctive Type 17 T helper cell-mediated response pivotal for bolstering antitumour efficacy. Our findings elucidate the mechanism underlying LiNxs role in potentiating antigen-specific immune responses, presenting a new strategy for cancer immunotherapy.

bioengineering↗

bioRxiv2025

From Local Interactions to Global Patterns: An Analysis of Silk Density in Spider Webs

This study explores the structural complexity of spider webs through information-theoretic and harmonicity-based frameworks to quantify spatial patterns in silk density across different web regions and reveal the underlying resource allocation strategies. Currently, there is no normalized approach for describing web structure and complexity, particularly for sheet-webs, and this methodology allows for non-destructive scanning and quantification of web characteristics. By analyzing the entropy of silk density distributions, a single scalar that captures the heterogeneity of material investment across the entire web, we observed that the entropy values follow a normal distribution with a mean of 1.24 {+/-} 0.22 bits when using 10 quantization levels. In the second part of the paper, by measuring the harmonicity of the silk density, we reveal that the silk density at a given point can be inferred from its neighbors, with an average harmonicity value of 0.0039 {+/-} 0.0017 (fraction of total points in point cloud data). The harmonic behavior is notable for its maximum principle, suggesting that the strongest parts of the web appear at the boundaries, aligning with existing knowledge of spider web construction. These findings provide a new technique for quantifying web-building strategies and offer new insights into spider behavior and evolution.

animal behavior and cognition↗

bioRxiv2025

TransHLA: A Hybrid Transformer Model for HLA-Presented Epitope Detection

BackgroundPrecise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments. ResultsWe introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an AUC of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes. ConclusionsTransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA Key PointsO_LIWe developed TransHLA, a deep learning tool for predicting epitopes across all HLA alleles using Transformer and Residue CNN architectures. C_LIO_LIThe model uses ESM2 embeddings to improve predictive accuracy and efficiency. C_LIO_LITransHLA shows superior specificity and sensitivity in identifying immunogenic epitopes and neoepitopes compared to existing models. C_LIO_LIOur approach offers potential advancements in vaccine design and immunotherapy through enhanced peptide analysis. C_LI

bioinformatics↗

bioRxiv2025

Complex Water Networks Visualized through 2.2-2.3 Angstrom Cryogenic Electron Microscopy of RNA

The stability and function of biomolecules are directly influenced by their myriad interactions with water. In this study, we investigated water through cryogenic electron microscopy (cryo-EM) on a highly solvated molecule, the Tetrahymena ribozyme, determined at 2.2 and 2.3 [A] resolutions. By employing segmentation-guided water and ion modeling (SWIM), an approach combining resolvability and chemical parameters, we automatically modeled and cross-validated water molecules and Mg2+ ions in the ribozyme core, revealing the extensive involvement of water in mediating RNA non-canonical interactions. Unexpectedly, in regions where SWIM does not model ordered water, we observed highly similar densities in both cryo-EM maps. In many of these regions, the cryo-EM densities superimpose with complex water networks predicted by molecular dynamics (MD), supporting their assignment as water and suggesting a biophysical explanation for their elusiveness to conventional atomic coordinate modeling. Our study demonstrates an approach to unveil both rigid and flexible waters that surround biomolecules through cryo-EM map densities, statistical and chemical metrics, and MD simulations.

biophysics↗

bioRxiv2025

HPV integration in head and neck cancer: downstream splicing events and expression ratios linked with poor outcomes

HPV integration (HPVint) is associated with carcinogenesis and tumor progression in HPV-associated cancers, including head and neck squamous cell carcinomas (HNSCC). While its impact on human DNA has been well characterized, its relationship with clinical outcomes remains unconfirmed. Here we investigate the consequences of HPVint both with respect to human and HPV characteristics by analyzing 261 HPV-associated HNSCC bulk and single-cell RNA-seq samples from five cohorts, and DNA HPVint events from 102 HPV+ participants in two of the cohorts. By leveraging this large meta-cohort, we first reveal an oncogenic network based on the recurrent HPV integration locations in HNSCC. We then classify HPVint-positive (HPVint(+)) participants by HPV RNA features, specifically based on spliced HPV-human fusion transcripts and ratios of HPV gene transcripts, showing that subsets of participants have worse clinical outcomes. Our analyses, focused mainly on RNA instead of DNA, expand our understanding of the carcinogenic mechanisms of HPVint, partially addressing the conflicting findings of whether HPVint is associated with aggressive phenotypes and worse clinical consequences, and provide potential biomarkers to advance precision oncology in HPV-associated HNSCC.

cancer biology↗

bioRxiv2025

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning

Cell Painting (CP), as a high-throughput imaging technology, generates extensive cell-stained imaging data, providing unique morphological insights for biological research. However, CP data contains three types of technical effects, referred to as triple effects, including batch effects, gradient-influenced row and column effects (well position effects). The interaction of various technical effects can obscure true biological signals and complicate the characterization of CP data, making correction essential for reliable analysis. Here, we propose cpDistiller, a triple-effect correction method specially designed for CP data, which leverages a pre-trained segmentation model coupled with a semi-supervised Gaussian mixture variational autoencoder utilizing contrastive and domain-adversarial learning. Through extensive qualitative and quantitative experiments across various CP profiles, we demonstrate that cpDistiller effectively corrects triple effects, especially well position effects, a challenge that no current methods address, while preserving cellular heterogeneity. Moreover, cpDistiller effectively captures system-level phenotypic responses to genetic perturbations and reliably infers gene functions and interactions both when combined with scRNA-seq data and independently. cpDistiller also excels at identifying gene and compound targets, which is a critical step in drug discovery and broader biological research.

bioinformatics↗

bioRxiv2025

Interpretable adenylation domain specificity prediction using protein language models

Natural products have long been a rich source of diverse and clinically effective drug candidates. Non-ribosomal peptides (NRPs), polyketides (PKs), and NRP-PK hybrids are three classes of natural products that display a broad range of bioactivities, including antibiotic, antifungal, anticancer, and immunosuppressant activities. However, discovering these compounds through traditional bioactivity-guided techniques is costly and time-consuming, often resulting in the rediscovery of known molecules. Consequently, genome mining has emerged as a high-throughput strategy to screen hundreds of thousands of microbial genomes to identify their potential to produce novel natural products. Adenylation domains play a key role in the biosynthesis of NRPs and NRP-PKs by recruiting substrates to incrementally build the final structure. We propose MASPR, a machine learning method that leverages protein language models for accurate and interpretable predictions of A-domain substrate specificities. MASPR demonstrates superior accuracy and generalization over existing methods and is capable of predicting substrates not present in its training data, or zero-shot classification. We use MASPR to develop Seq2Hybrid, an efficient algorithm to predict the structure of hybrid NRP-PK natural products from microbial genomes. Using Seq2Hybrid, we propose putative biosynthetic gene clusters for the orphan natural products Octaminomycin A, Dityromycin, SW-163B, and JBIR-39.

bioinformatics↗

bioRxiv2025

Negative frequency-dependent selection contributes to modular structure of effector repertoires in Pseudomonas syringae

The evolutionary fate of multi-strain pathogens is shaped by host-pathogen ecological interactions. In bacterial pathogens of plants, enhanced strain characterization and advances in our understanding of molecular mechanisms underlying defense pathways open the door for revisiting the role of negative frequency-dependent selection (NFDS) in strain structure, including its interplay with genetic exchange. NFDS arising from specific defense is one potential mechanism for generating, maintaining, and structuring pathogen diversity. In plants, specific protection against microbial pathogens involves Resistance proteins (R-proteins) that recognize virulence factors (effectors) secreted by pathogens, typically to subvert the initial line of host defense. Here we formulate a stochastic computational co-evolution model that explicitly incorporates variable length R-gene and effector repertoires, and migration from their regional pools. We use this model to understand potential mechanisms shaping effector repertoire structure and associated strain coexistence in the generalist plant pathogen P. syringae. The demonstration of a modular structure in our numerical simulations motivates the analysis of genome sequences from 76 strains collected in the Midwestern US and 1104 strains from global sources. We find that effector repertories both locally and globally exhibit a modular structure, with higher similarity within than between clusters. The observed modules are consistent with the core genome phylogeny and are unexplained by plant host species, location of isolation, and genetic linkage between effectors. An extension of the model is needed to take into account the evidence for genetic exchange and the phylogenetic congruence of effector modules. We initialize the system with a phylogenetically congruent modular structure and include recombination rates decreasing as a function of phylogenetic distance. We show that NFDS can counter-balance the effects of mixing due to recombination and in so doing, contributes to the maintenance of strain structure. These findings indicate that the observed similarity clusters may constitute, in part, emergent niches arising from eco-evolutionary dynamics that contribute to strain coexistence.

ecology↗

bioRxiv2025

Reusing wasted bioresources for crop protection: Calcinated oyster shell powder enhances rhizospheric microbial-mediated suppression of root-knot nematodes

The root-knot nematode (RKN) Meloidogyne incognita is one of the most destructive plant-parasitic nematodes (PPNs) affecting crop production worldwide. Our earlier study revealed that calcinated oyster shell powder (OSP) effectively suppressed tobacco RKN disease. However, the suppression mechanism of OSP against RKNs remain unknown. Our study revealed that calcinated OSP reduced the tobacco root-knot index by more than 38% by inhibiting the migration of second-stage juveniles of Meloidogyne incognita (J2) in the soil. Furthermore, OSP reduced the J2 density in the tobacco rhizosphere by 43.69% and significantly increased the soil pH by 0.68; moreover, OSP increased the contents of soil exchangeable calcium (ExchCa) and exchangeable magnesium (ExchMg) by more than 50%. Moreover, soil properties, including ExchMg, ExchCa and pH, enhanced the microbial-mediated suppression of J2. Specifically, Proteobacteria and Gemmatimonadota dominated the microbial community suppressing RKN, and fungal richness contributed to the suppression of RKNs; in addition, Chloroflexi and Acidobacteria dominated the microbial community promoting RKN prosperity. Our study revealed that the reuse of wasted oyster shell powder as an innovative antagonist is a promising avenue for ecofriendly RKN management strategies. IMPORTANCEThere is increased demand for ecofriendly RKN management strategies, and this study is the first to propose that the reuse of wasted oyster shell powder as an innovative antagonist against RKNs is effective, and calcinated oyster shell powder can reduce tobacco root-knot index and Meloidogyne incognita (J2) density by inhibiting the migration of J2s and enhancing the microbially mediated suppression of J2s in the tobacco rhizosphere. Moreover, several soil microbial and physicochemical indicators, including the contents of soil exchangeable calcium, exchangeable magnesium, pH and the relative abundance of Proteobacteria, Gemmatimonadota, Chloroflexi and Acidobacteria in plant rhizosphere were suggested to guide the development of ecofriendly PPN management strategies.

microbiology↗

bioRxiv2025

Non-mutated human tau stimulates Alzheimer's disease-relevant neurodegeneration in a microglia-dependent manner

The accumulation of abnormal, non-mutated tau protein is a key pathological hallmark of Alzheimers disease (AD). Despite its strong association with disease progression, the mechanisms by which tau drives neurodegeneration in the brain remain poorly understood. Here, we selectively expressed non-mutated or mutated human microtubule-associated protein tau (hMAPT) in neurons across the brain and observed neurodegeneration in the hippocampus, especially associated with non-mutated human tau. Single-nuclei RNA sequencing confirmed a selective loss of hippocampal excitatory neurons by the wild-type tau and revealed the upregulation of neurodegeneration-related pathways in the affected populations. The accumulation of phosphorylated tau was accompanied by cellular stress in neurons and reactive gliosis in multiple brain regions. Notably, the lifelong absence of microglia significantly and differentially influenced the extent of neurodegeneration in the hippocampus and thalamus. Therefore, our study established an AD-relevant tauopathy mouse model, elucidated both neuron-intrinsic and neuron-extrinsic responses, and highlighted critical and complex roles of microglia in modulating tau-driven neurodegeneration.

neuroscience↗