Biology subjects

Qiao, J.

Publications and source records attributed to Qiao, J..

At least 19 recordsLinked to original sources

bioRxiv2026

DNA sequence sets the destination of spontaneous epimutations in plants

Plant genomes gain and lose cytosine methylation spontaneously, far faster than DNA mutates, and these epimutations are inherited. The genome is thought to set a steady state around which methylation fluctuates. Here we show that this steady state, the set point, is written in the DNA of each CG site: REED, a deep-learning model trained on one methylome, reads it. In pedigrees of seven Arabidopsis thaliana accessions, gains concentrated at high set points and losses at low ones, and the equilibrium implied by their rates matched the set point, tracking it within genes. Stress sped epimutation but barely shifted its destination. The set point predicted germline epimutations in rice and changes accumulated in the wild and in a 330-year-old tree. Its rules, cooperation between nearby CG sites and transcription-factor motifs that lower methylation, are shared across flowering plants, so a genome and one methylome reveal where epigenetic variation is heading.

plant biology↗

bioRxiv2026

Reaction-aware hypergraph learning of chemical representations with LARK

Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transformations. A key challenge is to integrate this reaction information into chemical representations. Here we introduce LARK, a unified framework for learning atom-molecule-reaction knowledge from chemical transformations. Using approximately 0.6 million reactions encompassing 0.88 million unique molecular components, LARK combines bidirectional bond-electron reconstruction with structural and geometric supervision through a role-aware hierarchical hypergraph Transformer that links local atom representations to reaction context. The learned representations transfer to both reaction prediction and single-molecule property prediction, with strong performance across 42 tasks covering reaction outcomes, physicochemical properties, toxicity, metabolism and pharmacokinetics. Reactioncentre analyses show that LARK identifies where bonds break and form, while targeted interventions reveal that the learned atom representations contribute to reconstructing these changes. These findings indicate that LARK learns chemically informative representations of substructures involved in molecular transformations. Together, the results establish chemical transformations as a source of structured supervision for representation learning across molecular and reaction scales.

bioinformatics↗

bioRxiv2026

stCNASim: Allele-aware spatial RNA-seq simulator enables systematic benchmarking of copy number inference

Spatial transcriptomics (ST) is revolutionizing the study of tumor evolution by enabling spatially resolved copy-number alteration (CNA) analysis. However, evaluating the accuracy and robustness of current single-cell (SC) and ST-specific CNA inference tools remains challenging due to the absence of ground-truth datasets. Here, we present stCNASim, an allele-aware spatial RNA-seq simulator that generates raw reads within realistic spatial contexts. We synthesized 46 benchmarking datasets across varying technical settings and spatial architectures to evaluate five widely used computational methods. Our analysis reveals that while SC-based methods adapt well to ST data, ST-specific methods successfully benefit from considering spatial autocorrelation but struggle under high spatial intermixing. The allele-aware methods CalicoST, Numbat, and XClone achieved top-tier performance with unique advantages in extreme scenarios, yet showed distinct sensitivities to low purity, mirrored alleles, and low coverage, respectively. By providing a scalable simulator and a rigorous benchmark, this work establishes a much-needed framework to guide and accelerate future tool development in spatial CNA analysis.

bioinformatics↗

bioRxiv2026

Unmasking Early Renal Fibrosis in Polycystic Kidney Disease Using Noninvasive Precision Molecular MRI of Collagen

Fibrotic remodeling of extracellular matrix is a central driver of autosomal dominant polycystic kidney disease (ADPKD) progression since early stages of the disease. Unfortunately, it cannot be assessed with currently available methodologies before irreversible structural and functional decline, creating a diagnostic blind spot that hinders accurate early risk stratification and management. Here, we report the development of Gd-hProCA32.Collagen, a collagen -targeted protein MRI contrast agent that enables precision molecular MRI (pMRI) of early fibrosis by directly imaging collagen type I deposition in vivo before conventional laboratory and imaging methods detect changes in kidneys and liver of Pkhd1PCK/PCK (PCK) rats and Pkd2 mutant mice. Gd-hProCA32.Collagen, used at 10-fold lower dose, outperformed the widely clinically used agent gadobutrol (Gadovist), detecting approximately 2.8-fold greater total renal cyst volume ([~]8,500 vs [~]3,000 mm3, p<0.0001) and 1.5-fold higher total cyst count ([~]245 vs [~]160, p<0.0001), with superior T1W and T2W kidney AUC (p<0.01 and p<0.001) and preferential sensitivity to small and medium cysts. Signal enhancement in kidneys and liver correlated strongly with histological collagen burden quantified by Sirius red staining, whereas Gadovist showed no meaningful correlation. Gd-hProCA32.Collagen also enabled in vivo visualization of previously undetectable changes resembling radial striations at sites of microcyst cluster-adjacent microfibrosis and sustained delayed MRI enhancement due to specific collagen binding. These results reveal a previously inaccessible subclinical fibrotic phase of cystic kidney and liver disease and establish collagen-targeted pMRI as a strategy for early noninvasive detection and spatial mapping of multi-organ extracellular matrix remodeling when conventional biomarkers remain non-discriminating. One-sentence summaryWe report a first-in-class collagen-targeted MRI contrast agent with Precision molecular MRI that noninvasively reveals a previously inaccessible subclinical fibrotic phase of polycystic kidney disease across kidney and liver, overcoming limitations of current diagnostic methodology.

molecular biology↗

bioRxiv2026

Many-body Interaction Competition Drives Reentrant Phase Transitions

Reentrant phase transitions, in which multicomponent systems phase separate at intermediate concentrations but dissolve at experimentally accessible higher concentrations, are ubiquitous in mixtures such as biomolecular condensates. We show that introducing reversible dimerization into a multicomponent Flory-Huggins model and integrating out the dimer state generate an effective three-body repulsion that reshapes phase diagram geometry. This emergent higher-order interaction arises naturally from an interaction competition and mass-action equilibrium, providing a microscopic explanation to the previously phenomenological three-body interaction. We derive a closed-form phase boundary equation capturing phase separation and reentrant dissolution in this minimal model, and predict explicit interdependence between competition strength, emergent many-body interactions, and dissociation constants. We recover and extend the reentrant phase boundary scaling relations through interaction renormalization, with regime of validity. We apply our model to G3BP1-RNA-suramin and explain the underlying mechanisms from the physical parameters inferred from reentrant phase boundaries.

biophysics↗

bioRxiv2026

A Critical Sites-Driven and Light-weighted Protein Engineering Platform

Protein large language models (PLMs) provide a novel computational paradigm for the deep mining of sequence co-evolutionary information, significantly accelerating the generation of functional proteins for biotechnological and medical applications. However, the misalignment between zero-shot predicted evolutionary fitness and industrial application requirements leads to a limited success rate in acquiring beneficial mutations, while the high training cost presents another drawback of large models. Here, we developed CASPE (Critical Amino acids Streamline Protein Evolution), a lightweight protein engineering platform for the precise localization and adaptation of critical residues, consisting of the CAS (Critical amino acid sites) and APCNet (Amino acid Point Cloud Classification Network). CAS utilizes gradient activation mapping and multi-layer attention matrices to directly extract key information determining target properties from PLMs and transform it into explicit site importance indicators, without relying on additional structural information or prior knowledge. Working in tandem with APCNet, CASPE establishes a workflow encompassing the entire trajectory from site localization to residue prediction. CASPE achieved remarkable hit rates in identifying beneficial variants for thermostability (31.3-60%) and pH tolerance (40-80%), further uncovering the potential mechanisms of action at these key target sites. Directed evolution of phytase further validated the generalizability of CASPE. CASPET-Phytase achieved a 33.3% success rate in obtaining beneficial mutants, which was significantly better than FoldX (6.7%) and ESM2-t33 (13.3%). CASPE guides enzyme evolution towards precise, site-targeted optimization, providing an efficient computational framework for developing industrial enzymes.

bioengineering↗

bioRxiv2026

Spatial-neighbour encoding enables fast RNA 3D structure search

Experimental and predicted RNA three-dimensional structures are expanding rapidly, but RNA structure search still lacks a compact residue-level representation that supports database-scale comparison. Using family-held-out ablations across the currently available experimental RNA structure collection, we found that spatial-neighbour features are markedly more informative for family-level discrimination than conventional backbone and base descriptors. Building on this result, we developed RiboSeek, a search framework based on a 20-letter geometric alphabet (RS-20), an 80-letter structure-and-base composite alphabet (RS-80). Across family-level classification and retrieval benchmarks, RS-80 delivered the strongest overall performance, whereas RS-20 most closely tracked US-align TM-score, indicating better preservation of geometric similarity. RiboSeek searches the full experimental RNA structure database in 204 ms per query and can be applied to predicted RNA structure libraries to prioritize candidate structural relationships for downstream analysis.

bioinformatics↗

bioRxiv2026

Phenotypic reversion and target prioritization for cellular inflammation via representation learning with foundation models

The identification of genetic perturbations that can reverse disease-associated cellular phenotypes toward a healthy state is a central challenge in early drug discovery. We present a proof-of-concept framework leveraging single-cell foundation models (scFMs) and a large-scale Perturb-seq dataset to prioritize targets for phenotypic reversion of cellular inflammation. We incorporated both basal and proinflammatory signaling conditions, specifically stimulation with interleukin-1 beta (IL-1{beta}) and tumor necrosis factor alpha (TNF-), to assess whether atherosclerotic disease-relevant stimulation improved identification of genes and pathways critical to disease progression. Our dataset comprised 864,115 endothelial cells subjected to 1,740 unique genetic perturbations. Through having both conditions, we identified targets that exhibited differential effects on gene expression dependent on cellular state. Using scFMs, we embedded single-cell transcriptomes into high-dimensional latent spaces and ranked perturbations by their ability to shift the inflammatory transcriptomic profile toward that of untreated controls. Benchmarking against both annotated gene sets and expert-curated targets, we found robust enrichment for known regulators of inflammation and biologically relevant targets, despite these models having no prior information about these targets. Importantly, including both basal and proinflammatory conditions improved identification of inflammation-associated targets compared to using just the basal condition. This underscored the value of incorporating disease-relevant stimulations in perturbation experiments. Our results highlight the utility of scFMs for data-driven target nomination, emphasize the role of cellular state in regulatory responses, and provide a scalable, model-agnostic approach for ML-guided target discovery. This work offers a valuable community resource for advancing biological understanding of inflammatory-associated disease.

bioinformatics↗

bioRxiv2026

Compartment-specific core microbiomes in potato tubers are associated with plant health across genotypes, soils, and years

Harnessing plant microbiomes for sustainable agriculture requires understanding not only whether they can boost crop performance, but also how they assemble, persist, and support plant growth and health across environments. While we previously showed that seed tuber microbiomes can predict potato vigour using machine learning, it remained unclear how ecological processes shape tuber microbiome stability and functionality across host genotypes, tuber compartments, soil types, and years. Here, we analyzed the national-scale dataset of 240 field-collected potato seedlots, spanning six genotypes, two soil types, and two growing years, with a focus on the spatially distinct heel and eye compartments of the potato tuber. By profiling over 1,200 bacterial and fungal communities and linking microbiome composition to plant performance using leaf area as a health proxy, we show that plant genotype and tuber compartment are the strongest determinants of microbial diversity and composition. Compartment-specific enrichment of functional traits was observed, with organic compound conversion and nitrogen cycling dominant in the heel, and energy metabolism enriched in the eye. Using a macroecological abundance-occupancy framework, we identified a stable core microbiome of bacterial and fungal taxa that persisted across environments and years. Core members were more strongly associated with plant performance than non-core taxa, and pathogen-suppressive functions were spatially structured, with distinct protective taxa dominating in the heel versus the eye. Together, our findings demonstrate that tuber compartments act as selective microbial filters, shaping persistent microbiomes with specialized functions. By providing an ecological and functional framework for compartment-resolved, stable core microbiomes, this study complements our predictive modelling work and highlights persistent microbial partners as promising targets for improving potato resilience and productivity.

ecology↗

bioRxiv2025

Melody: Decoding the Sequence Determinants of Locus-Specific DNA Methylation Across Human Tissues

DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals. Across 39 human tissues, Melody accurately predicts methylation profiles and consistently outperforms existing state-of- the-art methods in whole-chromosome, hypomethylated-region, and cell-type-specific benchmarks. Melody also generalizes to methylation quantitative trait locus (meQTL) effect prediction and identifies regulatory sequence motifs associated with methylation variability. To extend prediction beyond profiled tissues, we further develop Melody-G, which incorporates single-cell RNA-seq foundation model embeddings to infer methylation states in previously unseen cell types directly from transcriptomic data. Together, Melody provides a unified framework for linking genomic sequence and cellular state to DNA methylation and offers new insights into the regulatory logic governing the human methylome.

bioinformatics↗

bioRxiv2025

From Single Molecular Detail to Subcellular Dynamics: Real-Time Kinetics Study of dPA Uptake with Raman-based Spectroscopy

Real-time observation of cellular transformations at the subcellular level provides insight into the dynamic biochemical processes underlying cell function and disease. However, many challenges remain in developing methods to analyze metabolic transformations in cells quantitatively. Our study applies Raman spectroscopy (RS) and stimulated Raman scattering (SRS) to link single-molecule resolution with cellular-scale lipid turnover tracking. Using HL-60 cells as a model, we monitor the uptake kinetics of deuterated palmitic acid (dPA), a marker of de novo lipogenesis, from the first minutes to hours. SRS enables microscopic, high-temporal-resolution visualization of dPA incorporation across entire cell volumes, capturing both its distribution and uptake rate. Complementary RS measurements identify distinct metabolic phases: rapid incorporation into pre-existing lipid droplets (LDs) followed by the emergence of new, partially unsaturated LDs associated with cytochrome-linked metabolism. Statistical and spectral analyses reveal cell-to-cell heterogeneity, emphasizing the complexity of lipid metabolism at both the cellular and subcellular levels. By integrating SRS and RS data into time-dependent kinetic profiles, we establish a unified submicron-to-cellular model of fatty acid metabolism. This multi-resolution spectroscopic approach demonstrates how real-time, label-based tracking can uncover dynamic metabolic heterogeneity, advancing understanding of the fatty acid uptake and metabolism in the microscale.

cell biology↗

bioRxiv2025

Bidirectional translocation of actomyosin drives epithelial invagination in ascidian siphon tube morphogenesis

How epithelia perform a spatiotemporal heterogeneous force generating program to drive a sequential tissue morphogenesis remains unclear, particularly the underlying precise mechanical mechanisms. This study investigated dynamic actomyosin reorganization between apical and lateral membrane cortex regions during two sequentially invaginated stages during ascidian atrial siphon tube morphogenesis. At the initial invagination stage, the originally lateral-located actomyosin redistributed to the apical domains, while those actomyosin redistributed back to lateral domains at the accelerated invagination stage. Using genetic mutants to modulate myosin activities, the initial invagination was strengthened or abolished, indicating invagination is apical constriction dependent. Optogenetic inhibition of myosin activities in lateral domains after initial invagination stage blocked the further processes, suggesting lateral constriction of actomyosin is required for the accelerated invagination. Vertex model simulations uncovered a coupled mechanism underlying epithelial invagination driven by apicobasal tension imbalance and lateral contraction. We thus propose an actomyosin redistribution mechanical model: lateral actomyosin first redistributes apically to drive apical constriction and shape the initial invagination, then apical actomyosin redistributes laterally to promote lateral contractility and accelerate invagination. Our findings reveal a bidirectional reorganization of actomyosin network as a central mechanism driving epithelial invagination, providing insights on epithelial invagination and the organ morphogenesis during development.

developmental biology↗

bioRxiv2025

A hierarchy of causes of death in senescent C. elegans

In experimental gerontology, lifespan is often interpreted as a metric of the rate of the overall aging process. However, interventions that increase lifespan can result from suppression of one or more individual late-life pathologies. Here we show how, in the nematode Caenorhabditis elegans, such pathologies can compete in a hierarchical fashion to cause death, such that removal of one cause of death can unmask another. Under standard culture conditions, a major cause of death in elderly C. elegans is infection by their bacterial food source. We report that only when such infection is prevented is lifespan extended by suppression of a second senescent pathology, teratoma-like uterine tumors. Thus, as in mammals, lifespan in wild-type C. elegans can be limited by naturally-occurring neoplasia. By contrast, blocking bacterial infection attenuated the life-shortening effects of vitellogenesis, and did not unmask a life-shortening effect of distal gonad degeneration. Thus, depending on the masking or unmasking of competing causes of mortality in the hierarchy of causes of death, nematode lifespan limitation in different contexts can reflect action of distinct life-limiting senescent pathologies. This underscores how increases in lifespan do not necessarily reflect a reduction in overall aging rate.

pathology↗

bioRxiv2025

Accurate detection and quantification of single-base m6A RNA modification using nanopore signals with multi-view deep learning

N6-methyladenosine (m6A) is a crucial epitranscriptomic mark. While Nanopore Direct RNA Sequencing (DRS) enables transcriptome-wide detection, most existing methods neglect or lack the capacity to effectively process the intrinsically variable-length raw signals generated by DRS reads.Here, we present MultiNano, a multi-view deep learning framework that converts variable-length raw signals into image-like feature representations, effectively resolving the length inconsistency problem. By integrating raw signal and basecalling features, MultiNano enables accurate and comprehensive transcriptome-wide detection of m6A modifications. Our model achieved state-of-the-art (SOTA) performance in various tasks, including site-level prediction, read-level prediction, cross-species transfer learning, and modification rate estimation. Furthermore, the false positive control strategy implemented in MultiNano significantly enhances the models robustness and predictive accuracy, offering a powerful alternative to traditional thresholding-based filtering algorithms. Collectively, our approach provides novel insights for the absolute quantification and single-base resolution of RNA modifications.

bioinformatics↗

bioRxiv2025

Single oocyte full-length isoform sequencing unveils the impact of transposable elements on RNA diversity and stability during oocyte maturation

The oocyte-specific isoforms play crucial roles in oocyte maturation, while current understanding of the oocyte transcriptome is mainly focused on gene level. Here, we utilized single cell full-length isoform sequencing based on third generation sequencing to detect entire transcripts in human and mouse oocytes. Isoform diversity during oocyte maturation was systematically profiled, including 6,736 and 4,902 putative novel human and mouse transcripts, respectively. More than half of novel isoforms were categorized as the novel-not-in-catalog (NNC) and may serve specific functions in oocytes, including novel isoforms of ARHGAP18, colocalized with microtubules and targeted knockdown disrupting oocyte maturation. Moreover, over 25% of NNC isoforms were derived from transposable elements (TEs), and their incorporation within transcripts could enhance isoform stability during oocyte maturation. Altogether, our findings represent a valuable resource showcasing the complexity and diversity of RNA isoforms in oocytes, as well as TE co-option for novel isoform generation and isoform stability enhancement.

developmental biology↗

bioRxiv2025

RIP2 inhibits DOCK8-mediated platelet dense granule release and alleviates experimental myocardial infarction

ObjectiveReceptor-interacting protein 2 (RIP2) is an essential mediator of inflammation and innate immunity downstream of pattern recognition receptors (PRRs). Platelets express RIP2, while its role in platelet activation, thrombosis, and myocardial infarction (MI) is unknown. Approach and ResultsHere we show that RIP2 deficiency enhances platelet dense granule secretion in response to GPIb and GPVI activation; platelet aggregation in whole blood and adhesion under arterial shear are also increased. Consistently, RIP2 inhibitor WEHI-345 potentiates human platelet dense granule secretion and inhibits RIP2 phosphorylation induced by thrombin and collagen. These phenotypes are translated into shorter bleeding time, accelerated FeCl3-induced arterial thrombosis. Importantly, platelets from patients with coronary artery disease and mice with atherosclerosis express lower RIP2, and RIP2 deficiency deteriorates MI and cardiac function in a mouse ischemia/reperfusion model. Mechanistically, we found that platelets express dedicator of cytogenesis protein 8 (DOCK8), which is sequestered by phosphorylated RIP2 (p-RIP2), causing inhibition of Cdc42 activation and subsequent dense granule release. ConclusionsIn conclusion, RIP2 inhibits platelet activation, thrombosis, and ameliorates MI. Our results also suggest that a novel PRR-independent pathway, p-RIP2-DOCK8-Cdc42, negatively regulates platelet activation downstream of GPIb and GPVI. Platelet RIP2 pathway may be promising for therapeutic intervention of atherothrombotic diseases from early atherosclerosis stage to arterial thrombosis and MI. HighlightsO_LIRIP2 inhibits platelet dense granule release NOD2-independenly via pRIP2-DOCK8-Cdc42. C_LIO_LIThe negative regulation of RIP2 on platelet activation is translated into enhanced thrombosis, exacerbated myocardial infarction (MI). C_LIO_LIRIP2 pathway booster may be an approach for therapeutic intervention of atherothrombotic diseases. C_LI

molecular biology↗

bioRxiv2025

The fetal hydrops-associated single-residue mutation L322P abolishes mechanical but not chemical activation of the PIEZO1 ion channel

The mechanically activated PIEZO1 ion channel is genetically linked to numerous physiological and pathophysiological processes. For example, deleting PIEZO1 in mice leads to defective lymphatic vessel development, while nonsense mutations in humans are associated with autosomal recessive generalized lymphatic dysplasia (GLD) and non-immune hydrops fetalis. However, it remains unclear whether PIEZO1-dependent biological processes are directly mediated by its intrinsic mechanosensitivity. Here we identified a human fetal hydrops-associated single-residue mutation, L322P (corresponding to L329P in mouse PIEZO1), which completely abolishes PIEZO1s response to mechanical stimuli while maintaining normal plasma membrane expression and responsiveness to its chemical activators, Yoda1 and Jedi1. Remarkably, the mechanical response of the mutant can be restored by Yoda1. These findings demonstrate a direct link between the loss of PIEZO1s mechanosensitivity and the pathophysiological phenotype of fetal hydrops, and raise the therapeutic potential of using PIEZO1 chemical activators to restore the mechanosensitivity of PIEZO1 missense mutants that are associated with genetic diseases such as GLD and hydrops fetalis. Significance StatementGenetic deletion studies have revealed the diverse cellular, physiological, and pathophysiological roles of the mechanically activated PIEZO1 ion channel. However, whether these functions are directly mediated by its mechanosensitivity has remained experimentally unaddressed. In this study, we identified a novel fetal hydrops-associated single-residue mutation, hPIEZO1-L322P, which specifically abolishes PIEZO1s mechanical response while retaining its chemical activation. Remarkably, the mechanical response of the mutant can be fully restored by the PIEZO1 chemical activator Yoda1. These findings address the long-standing question of whether PIEZO1 relies on its mechanosensitivity to mediate in vivo functions, provide critical insights into the mechanosensing mechanism of PIEZO1, and highlight the therapeutic potential of using PIEZO1 chemical activators to rescue the loss of mechanosensitivity caused by missense mutations.

physiology↗

bioRxiv2025

HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification

The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm of cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. The identification and characterization of cancer-associated ncPEPs assume a crucial role in propelling cancer research forward and augmenting our comprehension of immune-related processes. However, prevailing methods for cancer-associated ncPEPs identification primarily rely on sequence order, neglecting the latent relationships between peptides. As widely acknowledged in the central dogma, peptides are translated from RNA via a many-to-many mapping. In this study, we capitalize on the translation relationship and introduce a hypergraph-based model called HGCPep to enhance the representation of peptide features. Specifically, RNA is regarded as a hyperedge connecting the peptides that are translated from it. Our experimental results validate that the HGCPep approach, leveraging the strengths of hypergraph and convolutional neural networks, outperforms alternative methodologies in the recognition of ncPEPs. Additionally, employing a reduction tool, we visualize the results of multi-label classification within a 2D latent space, shedding light on how multi-label classification task influence the representation of peptides. The dataset and source code of our proposed method can be found via https://github.com/Longwt123/HGCPep_Github.

bioinformatics↗