Biology subjects

Xue, Z.

Publications and source records attributed to Xue, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

RADF: Reference-Anchored Dynamic Flow for Spatial Perturbation Profile Completion

Spatial perturbation profiling is becoming an important tool in functional genomics because it reveals how genetic interventions reshape transcription within intact tissue contexts. However, destructive readout and limited screening capacity leave many perturbation-by-location response profiles unmeasured, motivating the task of spatial perturbation profile completion. The task is to infer the held-out response population at query locations from reported profiles of the same perturbation. Existing methods either generate responses de novo or reuse these profiles without spatial adaptation. These strategies make it difficult to preserve empirical population structure while modeling location-specific variation. Our key insight is that the reported population already defines an empirical response distribution for the target perturbation. To exploit this empirical support, we propose Reference-Anchored Dynamic Flow (RADF), which employs a Sinkhorn-balanced decoder to construct a population-valued anchor in which every reference profile has equal total contribution. Additionally, a bounded dynamic relational flow is used to recompute spatial relations from the evolving expression state and query geometry. Across diverse spatial contexts, RADF reduces macro E-distance by 70.6% compared with an existing state-of-the-art spatial method, highlighting the advantage of combining a reference-supported population anchor with bounded, location-dependent refinement. Code will be made publicly available upon acceptance.

bioinformatics↗

bioRxiv2026

Logic-Gated Fluorogenic RNA Reporters for Multiplexed Live-Cell Imaging

Multiplexed imaging of biomolecular networks in living cells is limited by the small number of spectral separable fluorophores and the need to monitor dynamic processes in real time. Here, we present logicFRIES, a fluorogenic RNA (FR)-based platform that enables eight-plex live-cell imaging through programmable, logic-gated activation coupled with sequential fluorescence imaging. By integrating small-molecule-binding RNA aptamers into dye-activating fluorogenic RNAs, we engineered trigger-responsive FR reporters. This design implements an AND-gated mechanism in which fluorescence activation requires both a cognate trigger molecule and its corresponding fluorogenic dye, thereby expanding multiplexing capacity without adding new fluorophores. Using three membrane-permeable triggers, tetracycline, ASP2905, and guanine, we generated three distinct trigger-defined activation states for each engineered Broccoli and Pepper FR. Combined with orthogonal Corn/DFHO and DNB/TMR-DN reporter pairs, logicFRIES supports eight-plex imaging through sequential trigger/dye addition, imaging, and wash-based stripping cycles. We demonstrate robust, specific, and reversible fluorescence switching of these multiplexed FR reporters in living HEK293T and SKBR3 cells. Overall, logicFRIES extends live-cell imaging beyond conventional spectral limitations and provides a modular foundation for potentially developing multiplexed sensors targeting endogenous RNAs, proteins, and small molecules in complex cellular systems.

molecular biology↗

bioRxiv2026

A multi-agent molecular optimization framework leads to a rapid-recovery intravenous anesthetic candidate with an improved safety margin

Lead optimization, the systematic refinement of therapeutic compounds through iterative structural modification, faces a dual challenge in modern drug discovery: navigating astronomically vast molecular design spaces while balancing conflicting demands on potency, pharmacokinetics, and safety. We present MASCOT (Multi-Agent SearCh for molecular OpTimization), a role-specialized multi-agent framework for molecular optimization. Integrated with a chemically constrained graph-editing search, MASCOT coordinates three specialized agents: a trade-off agent that reprioritizes competing objectives, a strategy agent that adapts how molecular edits are proposed, and a reflection agent that distills lessons from previous decisions. Computational experiments showed that MASCOT achieved the best performance over competing methods on six benchmark settings. On the SARS-CoV-2 main protease task, its mean docking-score improvement was 3.6 times that of the strongest baseline. Applied to the clinically used anesthetic remimazolam (RM), MASCOT prioritized RM-1, which showed a shorter liver microsomal half-life, higher brain exposure, and a larger therapeutic index than RM. Subsequent derivative design yielded RM-7. Extensive animal studies established RM-7 as a rapid-recovery intravenous anesthetic candidate with greater potency, faster functional recovery, a wider safety margin, and preserved flumazenil reversibility. These results demonstrate that multi-agent coordination can link adaptive molecular search to medicinal chemistry and experimental pharmacology.

bioinformatics↗

bioRxiv2026

Chemoproteomic Characterization of GPX4 Covalent Ligands and Targeted Degradation

Despite intensive efforts, the ferroptosis gatekeeper glutathione peroxidase 4 (GPX4) remains difficult to selectively target due to stringent structural constraints surrounding its catalytic selenocysteine, which impose tight requirements on warhead reactivity and geometry. Here, leveraging a chemoproteomic approach, we characterize a potent and selective covalent GPX4 inhibitor featuring a pyrimidinylmethyl isourea warhead and define the chemical features underlying its proteome-wide selectivity. This chemotype enables tunable electrophile reactivity through steric and electronic modulation of leaving group ability, suggesting potential broader utility for targeting other recalcitrant proteins. Building on this scaffold, we further develop two selective GPX4 degraders - one CRBN-dependent and the other CRBN-independent - enabling complementary modulation of GPX4 through both inhibition and degradation. Together, these molecules expand the GPX4 chemical toolbox for more nuanced interrogation of GPX4 biology.

cancer biology↗

bioRxiv2026

Programmable and Switchable RNA Scaffolds for Synthetic Condensate Engineering in Mammalian Cells

The ability to engineer synthetic biomolecular condensates in living cells offers new opportunities to control intracellular organization, yet robust and programmable RNA-based systems have remained limited. Here, we introduce genetically encoded, modular platforms that generate RNA-driven condensates using nanostar-derived scaffolds. Systematic comparison of repeat-based and de novo designs identified nanostar variants that reliably assemble nuclear condensates in mammalian cells. Unexpectedly, condensate formation in cells is governed primarily by double-stranded RNA stems that recruit endogenous RNA-binding proteins, rather than by the kissing-loop interactions that drive assembly in vitro. This mechanistic shift highlights the divergence between cellular and in vitro environments and accounts for the limited orthogonality among scaffolds. Sequence refinement to reduce nonspecific pairing improved homotypic assembly and enhanced orthogonality. We further demonstrated functional compartmentalization by recruiting protein and RNA clients to modulate their stability and activity, and we incorporated an acyclovir-responsive allosteric switch to achieve reversible, small-molecule control of condensation. Together, this work establishes a versatile RNA-based toolkit for constructing programmable cellular compartments, advancing strategies for controlling RNA-protein organization and enabling new biosensing and therapeutic applications.

bioengineering↗

bioRxiv2026

Synaptopodin enables directional mechanoadaptation of integrin-based adhesions

The attachment of cells to their substrate through adhesion complexes is fundamental to tissue architecture and function. These adhesions are inherently optimized to resist shear forces parallel to the substrate, yet certain specialized cells must also withstand substantial perpendicular forces. How cells adapt their adhesion machinery to resist forces in different directions has remained unclear. In the kidney, podocytes experience perpendicular forces from pressurized filtrate flow while maintaining attachment to the glomerular basement membrane through integrin-based adhesions. Here we show that synaptopodin converts adhesions from shear-resistant to perpendicular force-resistant structures through coordinated reorganization of the actin cytoskeleton and adhesion complexes. Using an inertial force application system, we demonstrate that synaptopodin triggers force-dependent redistribution of {beta}1-integrin to the cell periphery specifically in response to perpendicular loading, while synaptopodin-deficient cells lack this directional adaptation and detach. This mechanism operates in multiple cell types and is physiologically essential: synaptopodin-null mice subjected to elevated glomerular pressure develop significant proteinuria and podocyte foot process effacement. These findings reveal a molecular basis for directional mechanoadaptation, whereby a single protein enables cells to reconfigure their adhesion architecture in response to the direction of applied force.

biophysics↗

bioRxiv2025

The histone methyltransferase SDG26 shapes cold stress responses in Arabidopsis through chromatin-based regulation of ABA-dependent and ABA-independent pathways

Plants constantly face adverse environmental conditions, including temperature drops that can severely impair growth and productivity. To cope with such stresses, they have evolved complex mechanisms of transcriptional reprogramming. While various cold-responsive pathways have been described, the contribution of chromatin-level regulation, and in particular histone modifications, remains largely obscure. Here, we identify the histone methyltransferase SET DOMAIN GROUP 26 (SDG26) as a positive regulator of cold stress responses in Arabidopsis thaliana. We show that SDG26 is transcriptionally induced and post-transcriptionally stabilized by cold, and that its loss of function leads to increased freezing tolerance but reduced drought tolerance. At the molecular level, SDG26 promotes expression of cold-responsive genes, including members of the CBF-COR regulon, through direct binding and histone H3 lysine 36 trimethylation (H3K36me3) at their chromatin. Concomitantly, SDG26 modulates abscisic acid (ABA) biosynthesis, catabolism, and transport, thereby promoting ABA accumulation, stomatal closure, and drought tolerance. Collectively, our results reveal that SDG26 integrates ABA-dependent and ABA-independent pathways to fine-tune Arabidopsis responses to abiotic stresses. We further establish SDG26 as a chromatin modifier contributing to stress-responsive H3K36me3 enrichment at specific loci. Together, our work identifies SDG26 as a chromatin-based hub balancing cold acclimation with water conservation, thereby enhancing plant resilience.

plant biology↗

bioRxiv2025

Climate Shaped the Global Population Structure of Leopards and their Extinction in Europe

Environmental change is often invoked as a key force shaping species evolution and demography, but quantifying its role is challenging. Leopards (Panthera pardus), a widely distributed generalist species, provide an ideal case for studying the role of the environment. The population dynamics of African and Asian leopards differ dramatically, with near panmixia in Africa versus a strong structure and eight subspecies in Asia. Fossil records show that a population in Europe disappeared after the Last Glacial Maximum (LGM), further pointing to complex range dynamics in Eurasia. In this study, we explicitly test the role of climate in shaping leopards population dynamics across the continents, by quantitatively combining paleoclimatic, demographic, and genetic simulations over the last 450 thousand years. Using an Approximate Bayesian Computational framework, we show that the genetic structure differences between Africa and Asia can be explained by distinct historical climatic conditions in these two continents: most of sub-Saharan Africa and parts of Southeast Asia exhibited a stable range without geographical barriers, while other areas such as Morocco, Afghanistan and Northeast Asia showed expansion and extinction cycles during glacial and interglacials. We further model population dynamics in Europe and validate it against fossil and radiocarbon dates records. We find that European populations were likely fragile, with extinctions repeatedly predicted over multiple glacial cycles, indicating that climate change possibly led to the extinction of the European subspecies following the LGM. Notably, we found that climate during the Holocene should have allowed a more recent recolonisation that did not happen, indicating other factors such as human presence might have blocked it.

zoology↗

bioRxiv2025

MetaFold-RNA: Accurate prediction of RNA secondary structure using a meta-learning-guided deep network

Accurately predicting the secondary structure of ribonucleic acid (RNA) is a critical step toward deciphering its biological roles and engineering novel RNA-based technologies. However, achieving high accuracy and generalization, especially for unseen RNA families, has long remained a central challenge in computational biology. Here we present MetaFold-RNA, a novel meta-learning framework that sets a new state of the art in RNA secondary structure prediction. Instead of relying on a single predictive paradigm, MetaFold-RNA synergizes insights from disparate models through a meta-learning approach and refines predictions via a co-evolutionary optimization process. On the challenging bpRNA-new benchmark, which specifically tests performance on unseen RNA families, MetaFold-RNA surpasses all SOTA methods, outperforming the best previous approach by 7.3%. This breakthrough performance is consistently maintained across other stringent assessments, including the PDB-new dataset and blind CASP predictions. Furthermore, our method demonstrates high robustness by accurately resolving the complex topologies of artificial RNA nanostructures. This work establishes a new performance benchmark, particularly in cross-family generalization, and provides a powerful computational tool for accelerating the study and design of RNA.

bioinformatics↗

bioRxiv2025

ESMDynamic: A Fast and Accurate Prediction of Protein Dynamic Contact Maps from Single Sequences

Understanding conformational dynamics is essential for elucidating protein function, yet most deep learning models in structural biology predict only static structures. Here, we present ESMDynamic, a deep learning model that predicts residue-residue contact dynamics directly from protein sequence. Built on the ESMFold architecture and trained on conformational variability from experimental structure ensembles and molecular dynamics (MD) simulations, ESMDynamic predicts dynamic contact probabilities, contact frequencies reflecting equilibrium populations, and coarse-grained kinetics of contact formation and dissociation across multiple temperature conditions. On large-scale MD benchmarks (mdCATH and ATLAS), ESMDynamic matches or outperforms state-of-the-art ensemble prediction methods (AlphaFlow, ESMFlow, BioEmu) while requiring orders-of-magnitude less computation. We demonstrate generalization to diverse systems, including membrane transporters, a de novo designed protein, and a homodimer complex. We show that predicted dynamic contacts enable automated selection of collective variables for Markov state model construction. Applied to the human proteome, ESMDynamic generates predictions for over 18,000 proteins, enabling large-scale analysis of conformational variability. Overall, ESMDynamic provides a scalable, sequence-based representation of protein dynamics to inform simulation, analysis, and design workflows.

biophysics↗

bioRxiv2025

SSAlign: Ultrafast and Sensitive Protein Structure Search at Scale

The advent of highly accurate structure prediction techniques such as AlphaFold3 is driving an unprecedented expansion of protein structure databases. This rapid growth creates an urgent demand for novel search tools, as even the current fastest available methods like Foldseek face significant limitations in sensitivity and scalability when confronted with these massive repositories. To meet this challenge, we have developed SSAlign, a protein structure retrieval tool that leverages protein language models to jointly encode sequence and structural information and adopts a two-stage alignment strategy. On large-scale datasets such as AFDB50, SSAlign achieves a two-orders-of-magnitude speedup over Foldseek in search, substantially improving scalability for high-throughput structural analysis. Compared to Foldseek, SSAlign retrieves substantially more high-quality matches on Swiss-Prot and achieves marked performance improvements on SCOPe40, with relative AUC increases of +20.2% at the family level and +33.3% at the superfamily level, demonstrating significantly enhanced sensitivity and recall. In sum, SSAlign achieves TM-align-comparable accuracy with Foldseek-surpassing speed and coverage, offering an efficient, sensitive, and scalable solution for large-scale structural biology and structure-based drug discovery.

bioinformatics↗

bioRxiv2025

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing

Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome-wide coverage of only 0.1-5x, sWGS data exhibit a pronounced zero-inflation phenomenon, whereby a large fraction of loci contains zero sequencing reads. Zero-inflation causes read counts to fluctuate by several-fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy-number gains (false positives), and true deletions often become indistinguishable from pervasive zero-coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a large sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

bioinformatics↗

bioRxiv2025

Pan-African metapopulation model explains Homo sapiens genetic and morphological evolution

A growing body of evidence has challenged the traditional assumption of a single-region origin for Homo sapiens, suggesting instead that our species originated from multiple geographically distinct populations in Africa, which intermittently exchanged genes and culture. However, our understanding of how this Pan-African metapopulation would have evolved through time is still limited. Furthermore, the drivers of such changes are uncertain, and quantitative models of the respective contributions of different African regions are lacking. Here we provide a complete reconstruction of the meta-population dynamics over the last 200,000 years by quantitatively integrating an ecological niche model, informed by archaeological sites, within a spatially explicit population genetic framework. The inferred metapopulation dynamics account for the divergence among all available contemporary and ancient genomes of African hunter-gatherers used to calibrate the model. In addition, it also accurately predicts the patterns of craniometric diversification across the continent from the Late Middle Pleistocene to the present. Finally, we show how the climate-driven changes in population sizes and connectivity are congruent with major patterns of archaeological and phenotypic diversification over the last 200,000 years across the African continent.

evolutionary biology↗

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

Active Learning for Protein Structure Prediction

Accurate protein structure prediction is challenging especially for families and types that are not well-represented in current training data. Active learning selects candidates for labeling with the aim of most rapidly improving model performance. In a general, many labeling strategies have been proposed. However, with protein structure prediction, most of these strategies dont apply or are difficult due to the high-dimensional, variable-dimensional regression target and the inherent complexity of the models involved. We applied a novel active learning strategy, DEW-DROP, to protein structure prediction on two different protein datasets: VHH-only antibodies (Nanobodies), and Mycobacterial proteins. We introduce a domain-specific fine-tuned Equifold model for VHH structures and apply DEWDROP to generate ensembles of predictions using Monte Carlo dropout. Using the statistics of these we select batches with high information content for labeling. We show that DEWDROP (1) improves model training efficiency through batch optimization outper-forming baselines, and (2) selects data with relevant high information content.

molecular biology↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

bioRxiv2024

RegVelo: gene-regulatory-informed dynamics of single cells

RNA velocity has emerged as a popular approach for modeling cellular change along the phenotypic landscape but routinely omits regulatory interactions between genes. Conversely, methods that infer gene regulatory networks (GRNs) do not consider the dynamically changing nature of biological systems. To integrate these two currently disconnected fields, we present RegVelo, an end-to-end dynamic, interpretable, and actionable deep learning model that learns a joint model of splicing kinetics and gene regulatory relationships and allows us to perform in silico perturbation predictions. When applied to datasets of the cell cycle, human hematopoiesis, and murine pancreatic endocrinogenesis, RegVelo demonstrates superior predictive power for interactions and perturbation simulations, for example, compared to methods that focus solely on dynamics or GRN inference. To leverage RegVelos full potential, we studied the dynamics of zebrafish neural crest development and underlying regulatory mechanisms using our Smart-seq3 dataset and shared gene expression and chromatin accessibility measurements. Using RegVelos in silico perturbation predictions, validated by CRISPR/Cas9-mediated knockout and single-cell Perturb-seq, we establish transcription factor tfec as an early driver and elf1 as a novel regulator of pigment cell fate and propose a gene-regulatory circuit involving tfec and elf1 interactions via the toggle-switch model.

developmental biology↗

bioRxiv2024

Marine medaka responds differently to dioxin compared with its close freshwater relative, the Japanese medaka: the AHR molecular mechanism

The global water pollution now calls for precise risk assessment of chemicals, e.g., dioxins and the emerging dioxin-like compounds (DLCs). The freshwater and marine medaka have been widely implemented in the toxicity testing, and perhaps give mechanistic information for comparative biology. The question that will they report equal results due to their close phylogenetic relation has been raised, therefore, we explored their physiological and molecular responses to dioxin. As the mediator of the dioxin toxicity, the aryl hydrocarbon receptor (AHR) of marine medaka (Oryzias melastigma) has not been functionally characterized and might be species-specific. In terms of sensitivity to dioxin--2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the EC50 values of omeAHR1a (0.16{+/-}0.12 nM), omeAHR1b (2.96{+/-}2.96 nM), omeAHR2a (0.44{+/-}0.30 nM), and omeAHR2b (9.00{+/-}6.88 nM) exhibit marked variations. The omeAHR2a and omeAHR1a display heightened sensitivity compared to the freshwater Japanese medaka (Oryzias latipes) counterparts olaAHR2a and olaAHR1a, respectively. The results indicate the in vitro sensitivity of AHR among species can vary by one or two orders of magnitude. Physiologically, the marine medaka (LC50: 1.64 ng/L (95% CI: 1.05-2.55 ng/L)) also exhibits a pronounced sensitivity to TCDD than Japanese medaka (LC50: 3.42 ng/L (95% CI: 1.37-6.48 ng/L), aligning with the in vitro AHR sensitivity. Further mechanistic investigations using additional ligands and computational modeling reveal that: 1) most of omeAHR2a, olaAHR2a, dreAHR2, and hsaAHR interact with ligands in the affinity order of TCDD > PCB126 > BNF > indole, mirroring their AHR transactivation potency, but the docking poses and dynamics can vary; 2) one AHR subforms high sensitivity to dioxin--TCDD may extend to DLCs but not to other types of ligands. These insights underscore the difference of AHR biology among species even the close relative species, and finger out the necessity for meticulous consideration when evaluating the toxicity of compounds and when extending predictive toxicity assessments to more species.

ecology↗