bioRxiv Science⌕ Search

Biology subjects

Wang, R.

Publications and source records attributed to Wang, R..

At least 163 records · Page 9Linked to original sources
bioRxiv2025

Excitation Spectral Phasor Microscopy (ExSPM) unveils spatiotemporal heterogeneities in polarity of intracellular lipid droplets

Spatiotemporal heterogeneities in intracellular LD polarity are central to cellular homeostasis but remain largely unexplored. Here, we introduce Excitation Spectral Phasor Microscopy (ExSPM), which integrates high-throughput excitation spectral imaging with phasor analysis to map LD polarity with exceptional sensitivity and spatiotemporal resolution. Using the lipophilic dye Nile Red, ExSPM revealed extensive heterogeneity and dynamics of LD polarity within single cells, reflecting lipid metabolism and energy status. Notably, high-polarity LDs emerged dynamically at lysosome- and mitochondria-contact sites, revealing pathway-specific kinetics of LD catabolism that distinguish lipophagy from neutral lipolysis. ExSPM further uncovered a tightly interconnected regulatory network among lipolytic pathways, in which perturbation of one pathway reshapes others while preserving overall energy supply. ExSPM thus provides a versatile platform to dissect intracellular compositional heterogeneity and the coordinated regulation of lipid metabolism, offering new insights into organelle function and cellular homeostasis.

cell biology↗
bioRxiv2025

Attention Guided Mechanism Interpretable Drug-Gene Interaction (MIDI) Modeling for Cancer Drug Response Prediction and Target Effect Explanation

Cancer drug discovery using genetic information is still poorly developed. Precisely locating drug atoms and explaining the targeting effect is crucial in precision medicine since it helps understand the drugs mechanism of action. Much data has been collected regarding drug response against cancer cell lines, and many models predict the drug response based on genomic information. However, to our knowledge, none of the data-driven techniques propose to detect the targeting mechanism of small drug molecules against genetic targets. In this work, we propose MIDI (Mechanism Interpretable Drug-Gene Interaction) model to delve deep into the targeting relation between drug molecules against genetic patterns. We show that purely based on a data-driven approach, the attention mechanism in our model could capture the important binding effect of small molecules towards gene targets. We provide both theoretical derivation and experiment results to show the information flow regarding the attention mechanism. In the meantime, we demonstrate that our model presents much higher prediction performance with the interpretation mechanism than the other state-of-the-art drug response prediction models.

cancer biology↗
bioRxiv2025

Effect of Azithromycin treatment on the microbial composition, functional dynamics and resistomes of endocervical, vaginal and rectal microbiomes of women in Fiji with Chlamydia trachomatis infection

Antibiotics disrupt mucosal microbial communities, yet the effects on microbiomes with Chlamydia trachomatis (Ct) infection remain poorly understood. Some data exist on vaginal microbiomes pre- and post-treatment, but none are available for the endocervix or rectum that are primary sites of infection. We applied metagenomic shotgun sequencing to vaginal, endocervical and rectal samples from women who, overtime, had Ct persistence, clearance, or no infection to evaluate azithromycin-induced changes in microbial composition, function, and the resistome. Our results show a shift in composition and function that support Ct post-treatment with azithromycin resistance mutations in the Ct rplV gene and significant endocervical enrichment of azithromycin resistance genes in Lactobacillus iners and Gardnerella vaginalis, the strains of which have moderate/high potential for biofilm formation. These findings highlight the unintended ecological consequences of azithromycin, including resistance gene propagation and microbiome shifts that support persistent/recurrent Ct, emphasizing the need for novel treatment and microbiome- preserving strategies.

microbiology↗
bioRxiv2025

Evolutionary responses and genomic consequences of polyploidization in natural populations of Orychophragmus

Polyploidization has occurred throughout the tree of life and is particularly common in plants. Despite its ubiquity, our understanding of the short- and long-term effects and consequences of genome doubling in natural populations remains incomplete. In this study, we identified a novel ploidy-variable species system within the ornamental and industrial oilseed genus Orychophragmus (Brassicaceae), which comprises six species, including diploid and tetraploid cytotypes of O. taibaiensis. By integrating population-scale genomic and transcriptomic datasets across the species in this genus, we constructed a robust phylogenetic framework and investigated the divergence and demographic history of O. taibaiensis in comparison to its relatives. Specifically, we characterized the geographical distribution patterns of diploids and tetraploids in natural populations of O. taibaiensis, confirmed the autopolyploid origin of tetraploids, and inferred their origin time relative to diploid counterparts. Our findings further revealed that, following genome doubling, tetraploids accumulated a higher genetic load of deleterious mutations, likely due to relaxed purifying selection facilitated by allelic redundancy. Additionally, genome doubling was associated with pronounced changes in gene expression patterns, with differentially expressed genes evolving under relaxed selective constraints. These results highlight that the initial masking of deleterious mutations, changes in expression regulation, and divergent efficacy of selection likely all contribute to shaping the establishment and evolutionary potential of polyploids.

molecular biology↗
bioRxiv2025

SynBioGPT: A Retrieval-Augmented Large Language Model Platform for AI-Guided Microbial Strain Development

Synthetic biology seeks to engineer microbial cell factories for sustainable bioproduction, yet the optimization of these systems is impeded by the complexity of metabolic engineering and the protracted timelines of iterative design-build-test-learn (DBTL) cycles. Traditional computational approaches, such as constraint-based modeling, provide valuable insights but demand extensive manual curation. Large language models (LLMs) hold promises for automating knowledge extraction and strain design, yet conventional models like GPT-4 suffer from outdated corpora and hallucination errors in domain-specific tasks. SynBioGPT v1.0, a Retrieval-Augmented Generation (RAG)-enhanced LLM, enhanced knowledge retrieval using vector search but often retrieved semantically similar yet contextually irrelevant documents. Here, we introduce SynBioGPT v2.0 (https://synbiogpt.biodesign.ac.cn), which mitigates these limitations by decomposing queries into sub-questions and employing keyword-based searches. Tested on a 100-question synthetic biology benchmark, SynBioGPT v2.0 achieved 98% accuracy with the Claude-3.7-sonnet backend, a 10% improvement over v1.0s 88% with Llama3-8B-Instruct. This advance highlight the efficacy of query decomposition and precise retrieval in enhancing LLM utility for synthetic biology.

synthetic biology↗
bioRxiv2025

Social stress, cortisol awakening response and sex: Impact on hippocampus and amygdala volume

ObjectiveVolumes of the hippocampus and amygdala, both major hubs for neural stress regulation amongst others, are influenced by social stressors, cortisol awakening response (CAR) and sex, i.e. psychological and physiological stress factors. Importantly, the interplay of these different factors in affecting the morphology of both brain regions remains unclear. This study aimed to elucidate the intricate influence of these factors on grey matter volumes (GMV) of the hippocampus and amygdala. MethodsWe analyzed associations between structural brain data, self-reported chronic social stress (including subscales on social tension, social overload, lack of social recognition and social isolation) and CAR of 83 healthy participants (40 females) with multiple regression analyses. ResultsIn males, but not females, higher social tension predicted lower bilateral hippocampus GMV. In individuals not showing the typical CAR, higher social tension predicted reduced bilateral hippocampus GMV. Amygdala GMV was also related to social stress and CAR, with social overload associated with reduced amygdala GMV in individuals not showing the typical CAR, while the opposite pattern emerged in those with an atypical CAR. ConclusionsChronic social stress is associated with hippocampus and amygdala volumes, interacting with CAR and sex. The brain morphology in males and in individuals with atypical CAR seems to be more vulnerable to chronic social stress. Our findings enhance our understanding of how brain architecture, social stress, CAR patterns and sex shape individual stress responses.

neuroscience↗
bioRxiv2025

FMR1 KH0-KH1 Domains Coordinate m6A Binding and Phase Separation in Fragile X Syndrome

The Fragile X Mental Retardation Protein (FMR1) regulates neurodevelopment via m6A RNA interactions, but domain-specific roles of KH0 and KH1 in RNA binding and pathology remain unclear. Through mutagenesis and AlphaFold3 modeling, we demonstrate that KH1 serves as the primary m6A-binding interface, whereas the KH0 domain (e.g., Arg138) modulates liquid-liquid phase separation (LLPS). Pathogenic mutations in KH0 disrupt RNA binding and enhance LLPS aggregation, while m6A RNA suppresses LLPS at KH0. Structural simulations reveal synergistic cooperation between KH0 and KH1 through hydrophobic and electrostatic networks. These dual-domain interactions establish a mechanistic link between m6A dysregulation, aberrant phase separation, and Fragile X syndrome pathogenesis. Our findings propose KH0 as a therapeutic target for RNA-driven neurodevelopmental disorders.

cell biology↗
bioRxiv2025

Yeast HMGB protein Hmo1 is a multifaceted regulator of DNA damage tolerance

The chromosome architecture protein Hmo1 in Saccharomyces cerevisiae is categorized as an HMGB protein as it consists of two structure-specific DNA binding HMGB motifs. However, it deviates from canonical HMGB proteins that have acidic C-terminal domains (CTDs) by bearing a basic one. Hmo1 plays diverse functions in gene regulation and genome integrity. There is evidence implicating Hmo1 in DNA damage tolerance (DDT) that enables DNA replication to bypass lesions on the template. Hmo1 is believed to direct DNA lesions to the error-free template switching (TS) pathway of DDT and aids in the formation of the key TS intermediate sister chromatid junction (SCJ), but the underlying mechanism(s) has yet to be resolved. In this work, we took genetics and molecular biology approaches to further investigate the role of Hmo1 in DDT. We found extensive functional interactions of Hmo1 with components of the genome integrity network in cellular response to the genotoxin methyl methanesulfonate (MMS), implicating Hmo1 in multiple processes in the execution or regulation of homology-directed DNA repair, replication-coupled chromatin assembly, and cell cycle checkpoint. Notably, our data pointed to a role of Hmo1 in directing SJC to the nuclease-mediated resolution pathway instead of the helicase/topoisomerase mediated dissolution pathway for processing/removal. They also suggested that Hmo1 modulates both the recycling of parental histones and the deposition of newly synthesized histones at the replication fork to ensure proper chromatin formation on nascent DNA that is important for DDT. We found evidence that Hmo1 antagonizes the function of histone H2A variant H2A.Z (also known as Htz1 in yeast) in DDT. We also found that Hmo1 is required for DNA damage checkpoint signaling induced by MMS as wells as for DNA negative supercoiling as a proxy of chromatin structure. Moreover, we obtained evidence indicating that the CTD of Hmo1 may or may not be required for its function in DDT depending on the genetic background of the host. Taken together, our findings demonstrate that Hmo1 can contribute to, or regulate, multiple processes of DDT via different mechanisms.

genetics↗
bioRxiv2025

Neural-physics adaptive reconstruction reveals 3D subcellular nanostructures over a large depth of field

Single-molecule localization microscopy (SMLM) enables volumetric nanoscopy by retrieving 3D molecular positions from engineered 2D fluorescence patterns. However, achieving nanoscale resolution over large axial ranges in complex biological samples remains challenging due to optical aberrations and overlapped single molecules. Here, we introduce LUNAR, a blind SMLM framework that can precisely localize high-density molecules even with inaccurate and highly distorted PSFs. LUNAR employs a self-supervised neural-physics learning strategy that jointly optimizes a physical PSF model and a neural network to infer key molecular parameters, including 3D positions, photon counts, and aberrations. Through simulations and experiments, we show that LUNAR achieves superior robustness to aberrations, high-density performance across diverse PSFs over multiple frames. We showcase its capabilities through whole-cell nanoscopy of mitochondria, nuclear pores, and neuronal cytoskeletons at large imaging depths. By uniting deep learning with physical modeling, LUNAR provides a calibration-free solution for aberration-robust volumetric nanoscopy and establishes a general framework for adaptive, data-driven 3D imaging.

biophysics↗
bioRxiv2025

High-throughput, unbiased single-molecule displacement mapping with deep learning reveals spatiotemporal heterogeneities in intracellular diffusivity

Single-molecule displacement/diffusivity mapping (SMdM) has revolutionized the study of molecular motion in live cells by providing high spatial resolution insights. However, its utility is restricted by measurement biases and low throughput, making it challenging to capture temporal dynamics of intracellular diffusivity. Here we report a high-throughput single-molecule diffusivity microscopy approach (Hi-SMdM) that rapidly and unbiasedly maps nanoscale heterogeneities in molecular motion inside mammalian cells, enabling time-resolved imaging of local diffusivity dynamics. Hi-SMdM employs a self-supervised deep-learning denoising framework with a general noise model to effectively restore the scarce signals of fast-moving single molecules, while eliminating artifact motions without relying on spatial redundancies or temporal correlations. It then provides unbiased estimations of nanoscale diffusion coefficient and improve both throughput and temporal resolution by up to an order of magnitude. We demonstrate the versatility of Hi-SMdM through time-resolved mapping of the spatially heterogeneous diffusivity of free proteins in the cytoplasm. Hi-SMdM also unveils the temporal dynamics of intracellular diffusion under hypotonic conditions in live cells. Additionally, Hi-SMdM tracks the spatiotemporal dynamics of intraorganellar diffusivity during rapid rearrangements of the ER network and functional activities of mitochondria. Overall, Hi-SMdM provides exceptional opportunities for high-throughput and unbiased single-molecule diffusivity mapping with excellent spatiotemporal resolution.

biophysics↗
bioRxiv2025

Few-shot in-context learning with large language models for antibody characterization

Large language models (LLMs) can learn new tasks by in-context learning (ICL), but it is unknown whether this ability reliably transfers to biological sequence classification. Here, we systematically evaluate how demonstration selection, shot count, and prompting strategies affect performance across 20 general-purpose LLMs. Using antibody characterization as a representative test case, we compare zero-shot, few-shot, and chain-of-thought (CoT) ICL on three classification tasks: humanness, antigen specificity, and isotype. Our results reveal a clear performance hierarchy: while zero-shot prompting performs near chance, few-shot prompting with randomly selected demonstrations improves performance, showing that LLMs can perform ICL using biological sequences from minimal supervision. However, matching protein-language model (pLM)-based classifier accuracy is only achieved when using label-diverse demonstrations drawn from antibodies similar to the query sequence. To leverage this insight, we introduce Sim-ICL, a framework that automatically retrieves such demonstrations. Using only 32-shot prompting, Sim-ICL achieves performance competitive with pLM-based classifiers, matching or outperforming them in two of the three tasks. Furthermore, reasoning-oriented prompts yield marginal gains and often produce fluent but biologically incorrect rationales, suggesting that current CoT explanations function as after-the-fact rationalizations rather than capturing mechanistic determinants of antibody properties. From these experiments, we derive practical design principles for ICL on biological sequences: use similarity-based, label-diverse demonstrations and modest shot counts, and treat reasoning prompts primarily as post hoc narratives rather than drivers of performance. Sim-ICL implements these principles in a streamlined, prompt-based framework for antibody sequence classification and, in principle, could be adapted to other biological sequence tasks.

bioinformatics↗
bioRxiv2025

Clonal relationships between Tph and Tfh cells in patients with SLE and in murine lupus

Pathologic T cell-B cell interactions drive disease in systemic lupus erythematosus (SLE). The T cells that activate B cell responses include T peripheral helper (Tph) and T follicular helper (Tfh) cells, yet the developmental and clonal relationships between these B cell-helper T cell populations are unclear. Here we use T cell receptor (TCR) profiling to demonstrate substantial clonal overlap between Tph and Tfh cells in the circulation of patients with SLE. Expanded Tph and Tfh cell clones persist over the course of 1 year in patients with a new diagnosis of SLE, and clones are observed to shift both from Tfh to Tph cells and from Tph to Tfh cells over time. High resolution analysis of cells sorted as Tph cells (CXCR5- PD-1hi) from SLE patients revealed considerable heterogeneity among these cells and highlighted a subpopulation of cells with transcriptomic features of activated B cell-helper T cells. This cell population, marked by expression of TOX and CXCL13, was found in both sorted Tph and Tfh cells, and was clonally linked in these two populations. Analysis of B cell-helper T cells in murine pristane-induced lupus demonstrated similar populations of Tph and Tfh cells in both lung and spleen with strong clonal overlap. T cell-specific loss of Bcl6 prevented accumulation of Tfh cells and reduced accumulation of Tph cells in pristane-treated mice, indicating a role for Bcl6 in the survival and expansion of both populations. Together, these observations demonstrate a shared developmental path among pathologically expanded Tph and Tfh cells in SLE. The persistence of expanded Tph and Tfh cells clones over time may explain the lack of stable tolerance induction by immunosuppressive medications or by B cell depletion.

immunology↗
bioRxiv2025

VPS13D mutations disrupt mitochondrial homeostasis and locomotion in Caenorhabditis elegans

Mitochondria control cellular metabolism, serve as hubs for signaling and organelle communication, and are important for the health and survival of cells. VPS13D encodes a cytoplasmic lipid transfer protein that regulates mitochondrial morphology, mitochondria and endoplasmic reticulum (ER) contact, quality control of mitochondria. VPS13D mutations have been reported in patients displaying ataxic and spastic gait disorders with variable age of onset. Here we used CRISPR/Cas9 gene editing to create VPS13D related-spinocerebellar ataxia-4 (SCAR4) missense mutations and C-terminal deletion in VPS13Ds orthologue vps-13D in C. elegans. Consistent with SCAR4 patient movement disorders and mitochondrial dysfunction, vps-13D mutant worms exhibit locomotion defects and abnormal mitochondrial morphology. Importantly, animals with a vps-13D deletion or a N3017I missense mutation exhibited an increase in mitochondrial unfolded protein response (UPRmt). The cellular and behavioral changes caused by VPS13D mutations in C. elegans advance the development of animal models that are needed to study SCAR4 pathogenesis.

genetics↗
bioRxiv2025

Multi-scale simulations of membrane adhesion mediated by CD47-SIRPα complexes

Adhesion of biological cells is essential for various processes, including tissue formation, immune responses, and signaling. It involves multiple length scales, ranging from nanometers to micrometers, which are characteristic of (a) the intercellular receptor-ligand binding that mediates the cell adhesion, (b) the spatial distribution of the receptor and lignad proteins in the membranes of adhering cells, (c) adhesion-induced deformations and thermal undulations of the membranes, (d) the overall size of the interface between adhering cells. Therefore, computer simulations of cell membrane adhesion require multi-scale modeling and suitable approximations that capture the essential physics of the system under study. Here, we introduce such a multi-scale approach to study membrane adhesion mediated by the CD47-SIRP binding, which is an immunologically relevant process. The synergetic use of coarse-grained molecular dynamics simulations and mesoscale kinetic Monte Carlo simulations allows us to explore both equilibrium properties and dynamical behavior of adhering membranes on the relevant length scales between 1 nm and 1 {micro}m on time scales ranging from 0.1 ns all the way up to about 20 s. The multi-scale simulations not only reproduce available experimental data but also give quantitative predictions on binding-induced conformational changes of SIRP and membrane-mediated cooperativity of the CD47-SIRP binding as well as fluctuation-induced interactions between the CD47-SIRP complexes. Our approach is applicable to various membrane proteins and provides invaluable data for comparison with experimental findings.

biophysics↗
bioRxiv2025

Modular DNA Barcoding of Nanobodies Enables Multiplexed in situ Protein Imaging and High-throughput Biomolecule Detection

Current immunodetection methods using antibody-DNA conjugates enable multiplexed target detection through orthogonal DNA barcodes, but existing conjugation approaches are labor-intensive and often compromise antibody function. Here we present a modular, site-specific, and cost-efficient DNA tagging strategy -- multiplexed and modular barcoding of antibodies (MaMBA). Utilizing nanobodies as modular adaptors, MaMBA enables direct site-specific labeling of off-the-shelf IgG antibodies with a one-component design. We first applied MaMBA to develop the misHCR method for highly multiplexed in situ protein imaging via orthogonal hybridization chain reaction (HCR). Its cleavable variant, misHCRn, achieves simultaneous visualization of 12 different targets within the same mouse brain sections through iterative probe use. We further extended the cleavable MaMBA to develop the barcode-linked immunosorbent assay (BLISA) for multiplexed and high-throughput biomolecule detections. By combining BLISA with next-generation sequencing, we successfully measured SARS-CoV-2 IgG and HBV-associated antigens in a large number of human serum samples. Additionally, we demonstrated a small-scale drug screen by using BLISA to simultaneously detect eight protein targets. In conclusion, MaMBA offers a highly modular and easily adaptable approach for antibody DNA-barcoding, which can be broadly applied in basic research and clinical diagnostics.

bioengineering↗
bioRxiv2025

Omics-based insights into human liver reveal GTPase-driven mechanisms of MASLD progression in obesity

Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic early on but can progress to irreversible conditions like cirrhosis. Due to limited access to human liver biopsies, systematic and integrative molecular resources remain scarce. In this study, we performed transcriptomic analyses on liver and metabolomic analyses on liver and plasma samples from morbidly obese individuals without liver pathology or at early-stage MASLD. While, the plasma metabolomic profile did not fully mirror liver histological features, dual-omics integration of liver samples revealed significantly remodeled lipid and amino acid metabolism pathways. Integrative network analysis uncoupled metabolic remodeling and gene expression as independent features of hepatic steatosis and fibrosis progression, respectively. Notably, GTPases and their regulators emerged as a novel class of genes linked to early liver fibrosis. This study offers a detailed molecular landscape of early MASLD in obesity and highlights potential targets of obesity-linked liver fibrosis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/632747v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1188eborg.highwire.dtl.DTLVardef@1340070org.highwire.dtl.DTLVardef@108d560org.highwire.dtl.DTLVardef@f0adda_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗
bioRxiv2025

Prairie management practices influence biodiversity, productivity and surface-atmosphere feedbacks

Restoration efforts aim to reestablish grassland cover and maintain ecosystem services. However, there is a lack of systematic evaluation of the effects of grassland restoration options on integrated biodiversity, carbon, ecosystem function, and climate feedbacks. Through a multi-year grassland restoration experiment in a tallgrass prairie site in Nebraska, USA, we investigated how different management practices affected biodiversity, productivity and the surface-atmosphere feedbacks using a combination of in situ measurements and airborne hyperspectral and thermal remote sensing. Our findings suggested that certain management treatments altered species diversity, productivity and energy balance. Higher diversity plots had higher vegetation cover, albedo, canopy water content and lower surface temperature, indicating clear effects of management treatments on grassland ecosystem processes influencing surface-atmosphere feedbacks of mass and energy. The coherent responses of multiple airborne remote sensing indices illustrate clear co-benefits of grassland restoration practices that enhance ecosystem productivity and biodiversity and mitigate climate change through surface atmosphere feedbacks, offering a new strategy to address the global challenges of biodiversity loss and climate change in prairie ecosystems.

ecology↗