Biology subjects

Ke, Y.

Publications and source records attributed to Ke, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Endosperm secretion remodels the maternal seed coat

In flowering plants and mammals, maternal tissues must accommodate the growth of the offspring they enclose. The maternal seed coat expands around the developing embryo and endosperm. Endosperm pressure and hormonal signals coordinate seed-coat development1,2, but whether the endosperm directly promotes maternal cell elongation remains unclear. Here we show that the coenocytic Arabidopsis endosperm directly supplies cell-wall-loosening proteins to maternal seed-coat walls. The seed-size-regulating HAIKU-WRKY10 pathway activates an endosperm secretory programme enriched in cell-wall-associated proteins. These proteins spread through a shared extracellular domain across multiple inner seed-coat layers, revealing an extensive functional maternal-filial interface through which the endosperm reaches maternal cells it does not directly contact. Among these proteins, three expansins are necessary and sufficient for maternal cell elongation and seed growth. The endosperm therefore promotes its own growth by enabling expansion of the maternal tissues that enclose it. Paternal DNA methylation and maternal Polycomb activity jointly regulate this programme, with Polycomb also required for timely shutdown of expansin expression. These findings link parental epigenetic states to maternal tissue expansion through proteins supplied by the offspring. This cross-generational control of growth parallels epigenetically regulated signaling from mammalian extraembryonic tissues3, suggesting a shared strategy for adapting the maternal environment to offspring development.

plant biology↗

bioRxiv2026

Memory encoding reprograms neuronal transcriptional responses via durable chromatin remodeling

The mammalian brains long-term memory circuits integrate information from prior and new experiences. The medial prefrontal cortex (mPFC) has a crucial role in this process and can reliably store information for weeks to months in rodents and over years in humans. To maintain information over these extended timescales, the neural encoding of remote memories involves persistent synaptic, transcriptional, and epigenetic changes that outlast the more transient forms of molecular activation that occur in the initial minutes to hours of memory storage. However, whether these persistent effects include long-lasting changes to chromatin structure and whether chromatin states mainly reflect prior episodes of neural activation or retune transcriptional responses to future bouts of activation remain unknown. Here we show that mPFC neurons engaged during the initial formation of memory undergo progressive changes in chromatin accessibility over the first four weeks of memory storage, evincing long-lasting modifications to the genetic programs activated during subsequent memory retrieval. Our experiments involved genetic trapping and single-cell multiomic sequencing analyses of mouse mPFC engram neurons activated during contextual fear conditioning. In the absence of subsequent memory recall, memory storage-related changes to chromatin structure were modestly reflected in gene expression patterns at 7 and 28 days after fear conditioning. However, upon memory recall, the genetically trapped engram neurons executed distinct transcriptional programs from those of other neurons of the same genetic types, suggesting that chromatin rearrangements arising during remote memory storage alter the transcriptional control logic by which engram neurons respond to new experiences. These metaplastic changes to transcriptional programs preferentially affect gene-regulatory and post-transcriptional control mechanisms, show substantial enrichment for transcription factor motifs related to neural development and cell-state regulation, and downregulate the neurons transcriptional responses to future excitation. Thus, rather than merely preserving a molecular record of prior learning, chromatin architectural changes in engram neurons occur over timescales of weeks and appear, in part, to repurpose conserved regulatory machinery to dampen the extent to which these neurons will engage in further information storage. Based on these findings, we propose that chromatin structural changes provide a slow-timescale component of neural computation that reduces interference between the representations of different memories.

neuroscience↗

bioRxiv2026

Adipose GPD2 Couples Glycolytic Signals to Epigenetic Control of Metabolic Homeostasis

The glycerol-3-phosphate shuttle (G3PS) is a classic reducing equivalent transferring system, yet its role in adipose metabolism remains poorly understood. Here, we identify mitochondrial glycerol-3-phosphate dehydrogenase (GPD2), the core enzyme of G3PS, as a critical orchestrator of adipose thermogenesis and systemic metabolic homeostasis. Adipocyte-specific GPD2 knockout mice exhibited impaired thermogenic capacity and energy expenditure, rendering them susceptible to obesity and metabolic dysfunction. Mechanistically, GPD2 deficiency elevated cytosolic NADH, which suppressed glycolysis and decreased the levels of key metabolites, such as acetyl-CoA, thereby diminishing H3K27ac at thermogenic gene loci and silencing these genes. NADH depletion with an NADH oxidase rescued both glycolysis and thermogenic gene expression. In support of this mechanism, we defined a previously undescribed enhancer of Ucp1 whose H3K27ac was tightly controlled by GPD2. Remarkably, replenishing the acetyl-CoA pool or directly restoring H3K27ac at the Ucp1 enhancer reversed UCP1 expression in GPD2-deficient adipocytes. Importantly, we find that human adipose GPD2 expression is inversely correlated with BMI, WHR, and HOMA-IR, underscoring its clinical relevance. Collectively, our findings establish GPD2 as an essential node in the metabolic-epigenetic axis that governs thermogenic activation and energy balance.

biochemistry↗

bioRxiv2026

De novo design of ligand binding and sensing with a physics based generative approach

The de novo design of ligand-binding proteins has tremendous potential to revolutionize biosensor technology, yet converting these designs into functional sensors remains a major challenge due to the need for ligand-induced conformational changes or modulation of protein-protein interactions. Here, we introduce a physics-based generative approach for the de novo creation of proteins that bind small molecules and metal ions. Our method achieves customizable ligand-binding pocket formation in parallel with simulated protein folding, allowing for precise architectural control of the protein-ligand complex and facilitating the development of biosensors based on either ligand-triggered protein reassociation via split-protein reassembly or ligand-induced protein folding. We demonstrate the versatility of our computational method through successful designs targeting five small molecules, including the very small neurotransmitters serotonin and dopamine, and two metal ions. Biophysical characterization confirmed correct ligand binding, and crystal structures closely matched computational models. We demonstrated the biosensor engineering potential of these designs by constructing serotonin and dopamine sensors using a split protein strategy and explored several approaches to enhance sensor activity. Additionally, we developed a zinc sensor through a zinc-induced protein folding mechanism. Overall, our physics-based generative approach provides a robust framework for the de novo design of ligand-binding proteins, opening new avenues for the development of ligand-responsive biosensors.

bioinformatics↗

bioRxiv2026

Integrating Machine-learning and Ultra-high-throughput Screening for Enzyme spaces exploration

AbstractThe systematic navigation of biocatalyst space is constrained by elusive structure-activity rules and a lack of evolutionary history. Here, we present IMUSE, a strategy integrating machine learning with ultra-high-throughput screening. By screening millions of droplet-encapsulated de novo enzymes, we generated massive synthetic sequence-structure datasets to train models that capture their complex fitness landscapes and biophysical principles. These models effectively guide functional exploration across both sequence and novel structure spaces. IMUSE identified synergistic triple mutations yielding [~]5-fold activity improvements and discovered active second-generation designs with novel catalytic pockets, boosting the experimental success rate >4.9-fold ([~]30%). This work demonstrates how synthetic fitness landscapes bridge the data gap in de novo enzyme space, transforming stochastic search into deterministic navigation to unlock highly proficient biocatalysts beyond natural boundaries.

biochemistry↗

bioRxiv2026

ANCHOR: haplotype-aware allelic and isoform inference from single-cell long-read RNA sequencing with de novo variant calling

Long-read RNA sequencing enables haplotype- and isoform-resolved allelic analysis of transcriptomes, yet extending this capability to single cells and distinct cell types remains computationally challenging due to sparse coverage, sequencing errors, incomplete variant information, and reference-biased transcript assignment. Here we present ANCHOR, a haplotype-aware framework for single-cell long-read RNA sequencing that performs de novo expressed-variant discovery, molecule-level haplotype assignment and isoform-resolved allelic quantification. ANCHOR combines a signed-graph variant caller, pair hidden Markov modelling and beta-binomial UMI aggregation to infer parental allele counts for genes and splice-resolved isoforms, without requiring a pre-existing phased genotype or deep learning. In human single-cell long-read RNA benchmarks, ANCHOR improved variant-calling performance over tested long-read RNA callers at single-cell and low-to-moderate coverage, and its beta-binomial model reduced depth-driven false positives in allele-specific expression testing. Applied to newly generated single-cell long-read RNA-seq data from reciprocal mouse crosses during gastrulation, ANCHOR resolved cell-type- and isoform-specific parent-of-origin imprinting and identified an antagonistic maternally biased Sgce isoform. ANCHOR provides a general framework for allele-and isoform-resolved analysis of diploid single-cell long-read transcriptomes.

bioinformatics↗

bioRxiv2026

ProteinFlux: accurate, rapid and scalable generative prediction of protein dynamics driven by post-translational modifications

The function of proteins, the building blocks of life, in health and disease depends not only on their 3D-conformational states but most importantly on the dynamic transition between states controlled by a wide array of post-translational modifications (PTMs). Recent major advances have been made in our ability to predict static 3D structures; however, understanding and predicting the impact of PTMs on protein conformational dynamics remains a major question and challenge in the field. Molecular dynamics (MD) simulation remains the major computational approach for studying protein dynamics. However, the high computational cost, lack of integration of PTMs as conditioning inputs and inefficient generation of continuous protein dynamics largely precludes PTM-regulated conformational dynamics and the study of slow conformational processes. To address this critical bottleneck, we developed ProteinFlux, a flow-matching generative framework that links PTM-conditioned conformational dynamics to evolutionary constraints encoded by PTM sites. Evolutionary information plays a critical role in capturing conformational dynamics beyond sequence identity, and PTM sites inherently encode evolutionary constraints critical to protein functional regulation. We therefore built FluxSite, a dual-modal PTM site predictor that integrates sequence evolutionary information and 3D structural features to generate a continuous conditional signal encoding conservation and functional importance for each predicted site. FluxSite achieves robust generalization across 18 PTM types and 30 disease-associated proteomes. ProteinFlux generates phosphorylation-conditioned, all-atom conformational trajectories across diverse protein fold classes, faithfully reproducing both thermodynamic properties such as free energy landscapes and kinetic features such as conformational transition pathways. It outperforms state-of-the-art predictors while achieving inference speeds several orders of magnitude faster than traditional MD. In addition, we introduce DynaMo-phos, a benchmark dataset of phosphorylated protein MD simulations. Together, ProteinFlux, FluxSite and DynaMo-phos provide a scalable, high-throughput platform for elucidating PTM-driven conformational mechanisms, with potential applications across allosteric drug design, functional annotation of disease-associated modifications and mechanism-guided therapeutic development.

bioinformatics↗

bioRxiv2026

LNGCN: A Distance-Aware Dynamics Network for Protein-Protein Interaction Prediction

High-throughput PPI screening requires not only interaction discrimination but also stable, calibrated scores for candidate prioritization and efficient experimental allocation. Conventional graph neural networks may over-smooth deep residue representations, reducing score separation among high-confidence candidates. We therefore developed LNGCN, a distance-aware continuous-time graph framework for experimental PPI prioritization. LNGCN integrates residue-level structural graphs with liquid neural dynamics, uses radial distance to drive continuous graph evolution, and applies hierarchical calibration to convert raw outputs into prioritization scores. It performed robustly on balanced human, 1:10 imbalanced, and cross-species yeast bench-marks. On the imbalanced dataset, the top 5% of candidates achieved 85.1% precision. LNGCN also preserved residue-level representation diversity better than conventional GCN variants. In FGF23-FGFR1c--Klotho, SHP2 signaling, Tdk1 oligomer-dependent binding, and a TPR study, it enriched known partners near the ranking top, and experimentally supported novel interactions. Overall, LNGCN enables sustained enrichment of experimentally relevant PPI candidates.

bioinformatics↗

bioRxiv2026

Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolution

Parallel revolutions in intravital microscopy and spatial biology techniques have respectively enabled large-scale recordings of cellular dynamics in live animals and multi-dimensional molecular profiling at single-cell resolution. However, due to the challenges of aligning data from different modalities at cellular resolution, these two transformational approaches have generally been applied on separate biological samples, stymying the ability to link activity patterns and molecular attributes in the same exact cells. To enable routine, multimodal investigations of cells in vivo dynamics and molecular content, we created TRU-FACT (Total Registration Under Functional Activity, Connectivity, and Transcriptomics), a broadly applicable experimental and computational pipeline for registering large populations of individual cells across intravital imaging and spatial biology datasets. The pipeline combines three key innovations: an optomechanical tissue handling and alignment method to parallelize specimen planes, a graph-theoretic method to register individual cells based on their geometric relationships to neighboring cells, and a statistical framework that provides for each cell an a posteriori probability of correct registration. We validated TRU-FACT with several preparations for imaging neural Ca2+ activity in cortical and deep brain areas in head-fixed and freely behaving mice, RNA-barcode-expressing viruses for labeling neural projections, and low- and high-plex spatial transcriptomic methods. In mice performing a skilled reaching task, TRU-FACT alignments revealed the movement-related signaling patterns of intratelencephalic, extratelencephalic, and striatum-, superior colliculus-, and thalamus-projecting motor cortical neurons. Overall, TRU-FACT constitutes a scalable, multimodal discovery platform that is applicable to diverse tissue-types and spatial biology techniques, thereby enabling multiscale analyses of many complex biological systems.

neuroscience↗

bioRxiv2026

Coding Agents as a Mechanism for Formalizing and Transferring Domain Knowledge in DNA Origami Design

This work investigates the use of a coding agent to design and manipulate DNA origami structures through the caDNAno2 design interface. The design methodology produced by the agent is saved as transferable, repeatable text-file instructions interpretable by another coding agent, converting informal domain expertise into formalized, distributable protocols. We demonstrate this through a parametric pipeline for simple single and multi-layer designs, identifying several failure modes that each required explicit domain knowledge from the human designer. Once encoded, this knowledge enabled the agent to autonomously extend designs to arbitrary lattice dimensions, generate structures to physical specifications, execute targeted edits from natural language prompts, and run the full pipeline from design through molecular dynamics simulation.

bioengineering↗

bioRxiv2026

Transformation and recombination of neural information in a brain network

Mammalian brain function relies on integrated interactions among interconnected neural structures. The principles by which regional activity is transformed into projection-specific signals and then recombined at targets elsewhere in the brain are fundamental to brain processing, but remain poorly understood. Here we study these phenomena using a genetically encoded probe that provides neurophysiological readouts from virally labeled projections on a brain-wide scale via functional magnetic resonance imaging (fMRI). By analyzing outputs from thalamic and cortical somatosensory processing regions in rats, we find that projection-specific neural population activity undergoes shifts in tuning and temporal characteristics as it emanates from source regions. Patterns of neural information flow to targeted brain structures reconfigure under different conditions of stimulation and rest, contrasting with intrinsic fMRI functional connectivity profiles, which remain constant. Excitatory and inhibitory projections are coactivated during stimulation, but their relative response amplitudes change dynamically between stimulus conditions and across repeated stimuli, suggesting mechanistic roles for network-wide shifts in excitation/inhibition balance. Our results thus reveal how information flow throughout a neural system reshapes to promote stimulus selectivity and provide underpinnings of large-scale brain phenomena.

neuroscience↗

bioRxiv2026

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia

ObjectiveThe immune system plays a role in the occurrence and progression of numerous pregnancy complications, particularly preeclampsia (PE). This study aims to identify critical immune biomarkers via machine learning and assess their predictive ability. MethodsGene expression data were retrieved from the GEO database, while immune-related genes were obtained from the ImmPort repository. Differential expression analysis was then conducted to identify immune genes associated with PE. Different immune-related genes (DIRGs) were subjected to functional and pathway enrichment analysis. We adopted protein-protein interaction (PPI) networks for exploring the connections among various DIRGs and integrated two machine-learning to pinpoint candidate biomarkers in PE. Diagnostic performance was assessed via ROC curve analysis, with predictive accuracy further quantified using nomogram calibration. Findings were validated through integrated computational and experimental analyses. In silico validation utilized additional GEO datasets, while experimental confirmation involved qRT-PCR and IHC assessment of placental tissues. We developed a nomogram to predict PE utilizing two immune-related genes. Cellular composition was inferred from transcriptomic data using CIBERSORT deconvolution.. ResultsWe identified 66 differentially expressed genes (DEGs) and 10 DIRGs between PE pregnancies and normotensive pregnancies. The GO analyses revealed that the DIRGs were enriched in gonadotropin secretion, the regulation of gonadotropin secretion, and the regulation of endocrine processes. Functional annotation revealed enrichment in cytokine and neuroactive ligand-receptor pathways. SERPINA3 and NDRG1 emerged as top-performing biomarkers (training AUCs: 0.812 and 0.866; external validation: 0.795 and 0.781), with overexpression validated in clinical specimens. Both genes inversely regulated M2 macrophage abundance (P < 0.05). ConclusionPE is fundamentally an immune-mediated disorder. SERPINA3 and NDRG1 can be identified as key immune genes associated with M2 macrophages, and these findings provide novel perspectives for the diagnosis and pathogenesis of PE.

immunology↗

bioRxiv2026

Molecular basis of enfumafungin inhibition and resistance in Candida glabrata β-1,3-Glucan synthase FKS1

{beta}-1,3-Glucan synthase FKS1 is essential for fungal cell wall biogenesis and serves as a validated target of widely prescribed antifungal drugs. However, the molecular mechanism of pathogenic FKS1, its modes of inhibition, and the associated resistance mechanisms remain elusive, hindering the antifungal development. Here, we focus on Candida glabrata FKS1, a clinically relevant target frequently linked to antifungal resistance. We present cryo-EM structures of C. glabrata FKS1 in multiple detergent environments and in complex with the triterpenoid antifungal enfumafungin. Integrated functional studies revealed a key catalytic residue within the conserved ED motif and uncovered structural adaptations to different membrane-mimetic environments, rationalizing FKS1s sensitivity to its surrounding membrane context. Enfumafungin binds at the extracellular membrane leaflet of cgFKS1 transmembrane domain, engaging a convex surface separated from the cytosolic active site, consistent with its role as a non-competitive inhibitor. Further analyses identified key enfumafungin-binding residues from TM5-TM6 and elucidated the molecular basis of drug resistance in C. glabrata. Collectively, these findings establish a mechanistic framework for pathogenic FKS1 function and inhibition and provide a molecular basis for the rational design of next-generation antifungal therapeutics.

microbiology↗

bioRxiv2026

Smad7-based biologic targeting epidermis and stroma promotes healing of diabetic wounds in mice and pigs

This study aimed to identify novel mechanisms of diabetic wound healing defects and test a therapeutic intervention using diabetic mouse and pig models. We found Smad7 transgene expression in mouse epidermis promoted wound healing in diabetic mice. To isolate effects of Smad7 on wounds, we created a Smad7-based biologic (Tat-PYC-Smad7) that penetrated cells of the wound. Topical Tat-PYC-Smad7 treatment to diabetic pig and mouse wounds accelerated healing compared to vehicle controls. Tat-PYC-Smad7-treated wounds showed reduced TGF{beta}/NF{kappa}B signaling, faster re-epithelialization and better extracellular matrix remodeling. Tat-PYC-Smad7 also attenuated neutrophil NETosis, potentially acting through reductions in MPO enzymatic activity and MPO nuclear entry, consequently reducing chromatin decondensation and the release of NET components. Our study revealed that Tat-PYC-Smad7 promoted diabetic wound healing by targeting keratinocytes and neutrophils, providing insight into mechanisms of diabetic wound healing defects targetable by Smad7-based therapy.

immunology↗

bioRxiv2025

HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence design

Deep learning has revolutionized biomolecular modeling, enabling the prediction of diverse structures with atomic accuracy. However, leveraging the atomic-level precision of the structure prediction model for de novo design remains challenging. Here, we present HalluDesign, a general all-atom framework for protein optimization and de novo design, which iteratively update protein structure and sequence. HalluDesign harnesses the inherent hallucination capabilities of AlphaFold3-style structure prediction models and enables fine-tune free, forward-pass only sequence-structure co-optimization. Structure conditioning at different noise level in the structure prediction stage allows precise control over the sampling space, facilitating tasks from local and global protein optimization to de novo design. We demonstrate the versatility of this framework by optimizing suboptimal structures, rescuing previously unsuccessful designs, designing new biomolecular interactions and generating new protein structures from scratch. Experimental characterization of a diverse set of proteins--including protein binders for small molecules, a metal ion and proteins; antibody design of phosphorylation-specific peptide; and monomeric proteins--revealed high design success rates and excellent structural accuracy. Together, our comprehensive computational and experimental results highlight the broad utility of this framework. We anticipate that HalluDesign will further unlock the modeling and design potential of AlphaFold3-like models, enabling the robust creation of complex biomolecules for a wide range of biotechnological applications.

bioinformatics↗

bioRxiv2025

Knowledge-Driven Interpretable Neural Networks for Mechanistic Insight

Analyzing omics data based on pathway knowledge is critical for understanding the molecular mechanism behind pathological changes, but current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and sub-networks where functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics datasets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomics data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation, offering a foundation for integrative pathway analysis.

bioinformatics↗

bioRxiv2025

Macrophage-derived reactive oxygen species promote Salmonella aggresome formation contributing to bacterial antibiotic persistence

In this study, we reveal that macrophage-derived reactive oxygen species (ROS) can trigger the rapid formation of Salmonella aggresomes, which substantially contribute to the increased frequency of persisters induced by phagocytosis. Salmonella containing aggresomes exhibited a dormant phenotype characterized by reduced adenosine triphosphate (ATP) levels and decreased metabolic activity. Furthermore, these dormant bacteria showed upregulated expression of Salmonella pathogenicity island 1 (SPI-1)-encoded type III secretion system (T3SS)-related genes, followed by later expression of SPI-2 T3SS-related genes when macrophages ROS production declined. Our results demonstrate that Salmonella containing aggresomes can enter a dormant state to escape antibiotic attack, while crucially maintaining the ability to resuscitate when the stress environment is improved. Research on bacterial aggresomes could potentially provide therapeutic strategies to combat bacterial antibiotic persistence. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/657582v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@7e5f1forg.highwire.dtl.DTLVardef@970a39org.highwire.dtl.DTLVardef@204449org.highwire.dtl.DTLVardef@632c47_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Intense oocyte competition builds the female reproductive reserve in mice

During the process of ovariogenesis, more than two-thirds of germ cells are sacrificed to enhance the quality of the remaining oocytes. But the detail process of selection is not completely understood in mammals. Here, by creating a high resolution 4- dimensional ovariogenesis imaging system, we recorded the progress of oocyte fate determination in live mouse ovaries, and discovered an oocyte competition mechanism that determines the survival of oocytes. We found that dominant oocytes catch and absorb the cell debris from sacrificed oocytes to enrich their cytoplasm for survival. Single-cell sequencing demonstrated that this competition follows a classic autophagy- driven model. Blocking competition by suppressing autophagy resulted in an enlarged pool of survived oocytes. However, these oocytes lacking competition were unable to fully develop and contribute to fertility. Our study suggests that mammals have evolved a unique intensive competition system to enhance oocyte quality, necessary to support their long reproductive lifespan.

developmental biology↗