Biology subjects

Han, Y.

Publications and source records attributed to Han, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

N-cadherin orientational order decreases with mechanical load at cardiomyocyte adherens junctions

Adherens junctions physically connect neighboring cells and are built around classical cadherins, homophilic transmembrane proteins that link to the actin cytoskeleton. Classical cadherins can organize into ordered arrays in vitro, but whether they do so in cells remains to be established. Here, we use fluorescence polarization microscopy to show that the classical cadherin N-cadherin is orientationally ordered at cardiomyocyte cell-cell junctions. Whereas the desmosomal cadherin desmoglein 2 was similarly ordered across junction types, N-cadherin order was spatially heterogeneous. Order was lowest where organized myofibrils terminate at high-load, vinculin-enriched axial junctions and highest at low-load, vinculin-poor lateral junctions. This inverse relationship between order and mechanical load suggests that robust cadherin-mediated adhesion does not require ectodomain order. Our findings provide evidence that a classical cadherin is orientationally ordered in cells and show that mechanically active adhesions adopt distinct organizational strategies according to local mechanical demands. Summary StatementAt cardiomyocyte junctions, N-cadherin is ordered where mechanical load is low but disordered where load is high, suggesting that cadherin organization adapts to local force conditions.

cell biology↗

bioRxiv2026

Targeting folate-dependent purine synthesis sensitizes melanoma cells to immune attack through suppressing glycolysis

Cytotoxic T lymphocytes (CTLs) play a central role in antitumor immunity; however, metabolic reprogramming within the tumor microenvironment often compromises their effector function, making metabolic targeting crucial for the improvement of T cell function. Folate-dependent purine synthesis, a core pathway sustaining the nucleotide pool, is highly activated in tumors, yet its role in regulating tumor immune sensitivity remains unclear. Here, by establishing a co-culture system of melanoma cells and human T Cell Receptor (TCR)-engineered T cells, we systematically evaluated the effects of folate-dependent purine synthesis inhibitors on tumor cell response to CD8+ T cell cytotoxicity. We found that inhibition of key enzymes such as methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) and glycinamide ribonucleotide transformylase (GART) markedly enhanced tumor cell sensitivity to T cell killing, an effect also observed with exogenous nucleoside supplementation. Mechanistically, inhibition of folate-dependent purine synthesis suppresses glycolysis by downregulating critical glycolytic enzymes, thereby reducing lactate production. Reduction in lactate further weakens lactylation and stability of the immune checkpoint protein PD-L1. In parallel, impaired purine synthesis disrupts uridine metabolism, blocks ribose salvage, and distally influences glycolysis. Collectively, our study identified the folate-dependent purine synthesis - glycolysis axis as key regulator of tumor immune response and highlights metabolic targeting as a promising strategy to improve cancer immunotherapy.

cancer biology↗

bioRxiv2026

Pathogen context reshapes antimicrobial peptide generation

Antimicrobial peptides are a promising source of new anti-infective agents, but their discovery is limited by a practical bottleneck: selecting a small set of candidates to synthesize and test for a specific pathogen. Existing peptide generators can expand antimicrobial-like sequence space, but most leave pathogen specificity to predictors or filters after generation. Here, we use AMPHORA, a pathogen-conditioned sequence-structure generator, to build antimicrobial peptide libraries shaped by target context. AMPHORA couples a short-peptide representation model to latent-flow generation conditioned on target class, genome-derived features and strain-description text. In matched, partial and shuffled controls, pathogen context redirected generated peptide pools beyond broad activity labels, with the strongest effects when genome and text conditions were combined. Counterfactual generation from the same initial noise showed that strain descriptions primarily changed amino-acid choices, whereas genome-derived features contributed more strongly to predicted structural properties. Across species, AMPHORA produced target-dependent enrichment, and matched bacterial context shifted APEX-predicted potency-score distributions compared with class-only generation. The generated peptides remained diverse, novel and peptide-like by sequence and predicted-structure analyses. Collectively, our results establish pathogen context as an active design signal for antimicrobial peptide generation and provide a target-aware route to pre-synthesis library design.

bioinformatics↗

bioRxiv2026

Structural basis for GluA1 AMPA receptor regulation by PRRT1/SynDIG4 in LTP

AMPA receptor (AMPAR) signalling underlies long-term potentiation (LTP), a cellular mechanism for learning that selectively involves GluA1-type AMPARs through incompletely understood mechanisms. Here, we report a key role for the auxiliary subunit PRRT1/SynDIG4 in LTP. Cryo-EM structures of hippocampal AMPARs reveal preferential association of PRRT1 with GluA1 via its membrane-embedded CD225 domain, which sequesters the GluA1 C-terminus through a previously unexplored lipid modification on Cys825. Consequently, GluA1 regulatory motifs implicated in LTP, including the CaMKII binding site, are inaccessible. PRRT1 overexpression impairs LTP, which is rescued by the Cys825Ser mutation, presumably by restoring access of the GluA1 tail to Ser831 phosphorylation by CaMKII. Our data uncover a mechanism wherein PRRT1/SynDIG4 controls the availability of the GluA1 pool that is released upon the induction of LTP.

neuroscience↗

bioRxiv2026

SenoQuant: One-stop AI software for senescence marker analysis and prediction

Senescent cells accumulate with age and contribute to tissue dysfunction, yet their identification in tissues is challenging due to low abundance, heterogeneous phenotypes, and the lack of specific markers. Senescence-associated features span multiple subcellular compartments, including nuclear DNA damage foci, cytosolic protein changes, and perinuclear alterations, each requiring tailored detection strategies. To overcome these challenges, we developed SenoQuant (https://github.com/HaamsRee/senoquant), a versatile software designed for comprehensive, accurate, and unbiased spatial quantification and prediction of senescence markers across diverse tissue contexts. Utilizing AI models, SenoQuant enables precise nuclear and cytoplasmic segmentation and detection of senescence markers across low- and high-plex imaging modalities, applicable to cultured cells and tissue sections from mice and humans. The platform also supports custom AI models; for example, we built SenCeption, a proof-of-concept predictor of single-cell p21 status from DAPI-stained nuclei in human skin. Available as a free napari plugin, SenoQuant is widely accessible to researchers. By providing a unified approach to senescence analysis and prediction, SenoQuant opens new opportunities for exploring the complex biology of senescence and its impacts on aging and disease.

Cell Biology↗

bioRxiv2026

BiXformer: A Bidirectional Cross Attention Transformer for Disentangling Inter-Regional Neural Dynamics

Advances in high-throughput neural recording technologies enable simultaneous measurement of activity across multiple brain regions in behaving animals, producing datasets of unprecedented scale and richness. Interpreting these data remains challenging due to the bidirectional and temporally offset nature of inter-regional communication, where feedforward and feedback signals are superimposed within neural populations. We introduce BiXformer, a bidirectional cross-attention transformer that disentangles these interactions by decomposing inter-regional communication into causal and acausal streams using directionally masked attention. By enforcing temporal constraints within attention heads, BiXformer recovers low-dimensional, directed latent dynamics and estimates communication delays without relying on linearity or stationarity assumptions. We validate the model on synthetic datasets with known ground-truth delays, demonstrating accurate recovery of both latent structure and inter-regional timing. Applied to simultaneous neural-behavioral recordings and multi-region neural recordings during a movement task, BiXformer reveals interpretable, temporally structured components consistent with the coexistence of sensory feedback and motor-related signals. These results establish BiXformer as a flexible framework for uncovering dynamic, directed communication in complex neural circuits.

neuroscience↗

bioRxiv2026

Self-supervised Internal Learning Enhances Isotropic Resolution for Three-dimensional Fluorescence Microscopy

Three-dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many approaches require demanding system calibration or rely on accurate PSF models and assumptions that are difficult to satisfy across all samples and modalities. Here we present DeepIso, a self-supervised isotropy restoration framework that couples supervised pretraining with an internal-learning inference stage to estimate degradation directly from the measured volume. Without explicit PSF specification or enforced lateral-axial structural equivalence, DeepIso recovers axial frequency content and improves the continuity of elongated structures while retaining fine features, with superior performance over existing computational approaches in terms of both visual inspection and quantitative metrics. The method is validated on synthetic benchmarks and experimental datasets, demonstrating isotropy enhancement across confocal, light-sheet, and 3D structured illumination microscopy, thereby supporting downstream volumetric analysis including segmentation and tracking.

bioengineering↗

bioRxiv2026

Genomic Diagnostics for Drug-Resistant Mycobacterium tuberculosis: Computational Prediction of Antimicrobial Resistance

Tuberculosis (TB) remains a leading cause of infectious disease mortality, and the continued emergence of drug-resistant Mycobacterium tuberculosis (MTB) strains threatens the effectiveness of standard treatment regimens. Culture-based antibiotic susceptibility testing (AST) remains the clinical reference standard for resistance determination but typically requires six to eight weeks, delaying initiation of optimized therapy for patients with drug-resistant disease. Whole-genome sequencing (WGS)-based approaches provide a rapid alternative for predicting antimicrobial resistance directly from genomic data and are increasingly being incorporated into diagnostic workflows. This survey reviews computational approaches for genomic resistance prediction in MTB, focusing on two major classes of methods: catalog-based tools that identify established resistance-conferring variants, and de novo machine learning approaches that infer resistance from genome-wide sequence features. We examine the strengths and limitations of these approaches with respect to interpretability, scalability, computational requirements, and concordance with phenotypic testing. We further discuss emerging directions in quantitative minimum inhibitory concentration (MIC) prediction, challenges in pyrazinamide susceptibility testing, and the limited availability of resistant isolates for newer and repurposed drugs used in multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) treatment regimens. Continued expansion of paired phenotypic and genomic datasets, standardized MIC testing protocols, and rigorous lineage-aware evaluation frameworks will be essential for improving the clinical reliability and global deployment of genomic resistance prediction for tuberculosis diagnostics.

microbiology↗

bioRxiv2026

Astrocytic D1 Dopamine-Signaling Regulates Synaptic Remodeling and Cocaine Seeking

D1-type receptor (D1R)-mediated dopaminergic signaling within the nucleus accumbens shell (NAcSh) is essential for forming adaptive circuit changes that embed persistent memories associated with drug seeking and craving. While D1R is expressed in both NAcSh neurons and astrocytes, the specific contribution of astrocytic D1R to drug-related circuit plasticity remains poorly understood. Here, we demonstrate in mouse NAcSh slices that D1R agonists increase astrocytic Ca2+ activity, an effect that is attenuated by astrocyte-specific D1R knockdown. Furthermore, selective knockdown of astrocytic D1R in the NAcSh prior to cocaine self-administration inhibits cocaine-induced generation of silent synapses, therefore hampering the associated remodeling of NAcSh circuits. Behaviorally, knockdown of NAcSh astrocytic D1R expedites the extinction of cocaine seeking and reduces cue-induced reinstatement. These results identify astrocytic D1R as a fundamental component of NAcSh dopamine signaling during cocaine experience that remodels synaptic connections and neural networks underlying drug seeking and relapse.

neuroscience↗

bioRxiv2026

From Spectra to Digital Phenotypes: Wearable Multispectral Sensing for Precision Light and Green Space Exposure

Light is a modifiable determinant of health, yet real-world exposure assessment is often reduced to illuminance alone, lacks environmental context, or relies on privacy-sensitive sensing. We present SpectraVita, a low-cost, compact multispectral wearable that continuously samples 11 ultraviolet-to-near-infrared bands and, through a privacy-preserving pipeline without cameras or location tracking, produces interpretable digital phenotypes of lighting environment (natural vs. artificial and source type) and vegetation context alongside standard visual and non-visual light metrics. In extensive in-the-wild recordings spanning diverse scenes, times of day, weather conditions, and light sources, we observe distinctive spectral signatures that enable supervised models to achieve a macro-averaged F1 score of 0.988{+/-}0.004 for light-source classification and green-space detection in boundary-free environments. A sensor-derived normalized difference vegetation index (NDVI) emerges as an explainable, physically grounded marker linking natural light exposure and greenness. Robustness is supported by scenario-shift testing, image-segmentation validation, and mixed-environment experiments that demonstrate sensitivity to partial and transient exposures, as well as by longitudinal stationary monitoring and deployment in a cohort of thousands of participants capturing seasonal and behavioral variability. SpectraVita enables individualized, privacy-preserving, longitudinal monitoring of light and greenness exposure at scale, addressing a key measurement gap for precision and population health studies of daily photic environments.

bioengineering↗

bioRxiv2026

C9orf72-associated G4C2 hexanucleotide repeat expression in Drosophila mushroom bodies causes age dependent TDP-43 pathology and dementia relevant phenotypes mediated in part by the glypican Dlp/GPC6

Hexanucleotide repeat expansions (HREs) in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), yet the age-, sex-, repeat-length-, and circuit-specific influence on the pathology of neurons remains incompletely understood. Here, we established a Drosophila model of C9orf72-associated dementia by expressing G4C2 repeats in mushroom body neurons (MBNs), a brain region critical for memory, locomotion, and sleep. Expression of 44X G4C2 repeats ((G4C2)44X) led to progressive axonal thinning, age-dependent accumulation of Repeat Associated Non-AUG (RAN) translated GR-GFP dipeptide repeat (DPR) puncta, premature nuclear-to-cytoplasmic mislocalization of endogenous TDP-43, increased caspase, reduced lifespan and a loss of presynaptic active zones. Behaviorally, (G4C2)44X expression caused locomotor hyperactivity, altered spatial working memory, and fragmentation of sleep architecture in an age- and sex-dependent manner, recapitulating core features of FTD. Surprisingly, the shorter (G4C2)12X repeat, traditionally considered a control, also produced detectable RAN translation and intermediate phenotypes in aging MBNs, suggesting that length- and tissue-associated factors modulate repeat toxicity. We further identified a repeat-length- and age-dependent reduction of the glypican Dally-like protein (Dlp) in (G4C2)44X consistent with disrupted Wnt-related signaling linked to TDP-43 proteinopathies. Restoring Dlp expression in MBNs mitigated locomotor and working-memory alterations, and loss of presynaptic active zones. In contrast, axonal degeneration, TDP-43 mislocalization, and lifespan were not significantly improved by restoring Dlp, suggesting that multiple mechanisms contribute to G4C2-induced toxicity. Supporting our findings in Drosophila MBNs, a CRISPRi screen in TDP-43 knock-down iNeurons identified GPC6, a human ortholog of Dlp, as a significant contributor to TDP-43 dependent synaptic loss. Together, our findings reveal an aging-sensitive, circuit-specific model of C9orf72-associated neurodegeneration and highlight roles for DPR accumulation and Dlp/GPC6 dependent synaptic loss in FTD pathomechanisms.

neuroscience↗

bioRxiv2026

Distinct HPO axis responses and ovarian aging trajectories to chronic unpredictable mild stress in reproductively young versus middle-aged female mice

Psychosocial stressors are key contributors to ovarian functional decline. Chronic unpredictable mild stress (CUMS) is widely used to model stress-induced premature ovarian insufficiency (POI) in mice; however, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward POI. Here, female C57BL/6 mice aged 2 or 6 months were subjected to CUMS for 8 or 12 weeks. Estrous cyclicity, endocrine profiles, ovarian histology, and transcriptomic changes in HPO axis-related tissues were systematically analyzed. After 8 weeks of exposure, 2-month-old mice exhibited impaired pituitary responsiveness to estradiol negative feedback, as evidenced by dysregulated FSH secretion, indicating reduced stress tolerance compared with 6-month-old mice. Following 12 weeks of CUMS exposure, both age groups showed significant reductions in ovarian size and follicle numbers across all developmental stages. These findings demonstrate that CUMS induces an age-dependent progression toward POI, with short-term exposure eliciting compensatory phases preceding overt ovarian insufficiency, accompanied by distinct endocrine and reproductive alterations and differential responsiveness of the HPO axis. Transcriptomic analyses revealed age-dependent stress responses: ovaries of 2-month-old mice displayed marked activation of inflammatory and immune-related pathways, whereas 6-month-old mice showed sustained upregulation of protein kinase-related signaling networks. Notably, the 6-month-old CUMS model more closely recapitulates stress-associated reproductive aging in adult women. In briefCUMS has been widely used to establish mouse models of psychosocial stress-induced POI. However, current animal models do not adequately reflect middle-aged women, who represent a key population exposed to chronic psychosocial stress, nor do they capture the dynamic progression toward premature ovarian insufficiency (POI). In this study, we demonstrate that different durations of CUMS exposure induce distinct stages of ovarian dysfunction in both young and middle-aged mice, with short-term exposure driving age-dependent compensatory phases and prolonged exposure leading to overt POI, both accompanied by divergent endocrine and reproductive alterations, alongside age-dependent changes in HPO axis responsiveness to CUMS. Notably, the 6-month-old CUMS model shows greater clinical relevance in recapitulating chronic psychosocial stress and stress-related reproductive aging in adult women.

physiology↗

bioRxiv2026

A correlational study of ABCA3 and SCN4B as exercise-related biomarkers of patients with Stanford type A aortic dissection

BackgroundAccumulating evidence indicates that moderate exercise may reduce the incidence of Stanford type A aortic dissection (TAAD), but the specific mechanisms remain unclear. This study aims to identify exercise-related biomarkers in TAAD patients and to investigate their underlying mechanisms. MethodsTranscriptome data related to TAAD and exercise-related genes were obtained from publicly available databases. Candidate biomarkers for TAAD were identified through an integrative approach incorporating differential expression analysis, machine learning, and expression level assessment, leading to the construction of a diagnostic model. Subsequently, functional enrichment, immune infiltration, regulatory network analysis, and computational drug prediction were conducted to systematically investigate the pathological mechanisms and translational potential of the indentified biomarkers. ResultsABCA3 and SCN4B were identified as exercise-related biomarkers in TAAD progression. A nomogram incorporating these two biomarkers exhibited strong diagnostic performance for identifying the disease. Functional enrichment analysis revealed potential involvement of these biomarkers in disease progression through pathways including circadian rhythm regulation and ribosome biosynthesis. Additionally, immune cells like M1 macrophages and naive B cells, as well as regulatory factors including hsa-miR-1343-3p and XIST, were found to be involved in this process. Finally, zonisamide and MRS1097 were identified through computation prediction as potential therapeutic drugs. ConclusionABCA3 and SCN4B were identified as exercise-related biomarkers associatied with TAAD and represent potential valuable targets for both diagnosis and treatment strategies.

bioinformatics↗

bioRxiv2026

Continental-scale multi-omics reveals a distinct microbial-viral biome in sandy beach ecosystems structured by tidal zonation

Sandy beaches are dynamic coastal interfaces shaped by strong physical forcing and intense exchange between marine and terrestrial environments, yet their microbiomes remain poorly resolved at the genomic scale. Here we present a genome-resolved survey of microbial and viral communities across sandy beaches spanning a continental-scale latitudinal gradient along the Chinese coastline. By integrating cross-shore sampling, coastal geochemistry and large-scale multi-omics, we generated 978 metagenomes, 63 viromes and 72 metatranscriptomes, reconstructing 13,337 metagenome-assembled genomes and 38,255 viral populations. Sandy beach microbiomes exhibit exceptionally high genomic novelty, with more than 90% of species-level genomes representing previously undescribed taxa, suggesting that permeable coastal sediments constitute a distinct microbial and viral reservoir. Tidal zonation emerged as a dominant ecological driver structuring microbial diversity, metabolic strategies and virus-host interactions across cross-shore gradients. Genome-resolved analyses revealed systematic metabolic shifts from oxic heterotrophy in supratidal sediments toward increasingly chemolithotrophic and autotrophic pathways toward the low-intertidal and subtidal zone. Sandy beach microbiomes further encode broad potential for hydrocarbon and plastic transformation, together with diverse biosynthetic and antibiotic resistance repertoires that may mediate microbial chemical interactions. Together, these findings identify sandy beaches as a previously under-recognized microbial-viral biome shaped by tidal forcing, providing insight into microbiome evolution and coastal ecosystem resilience under increasing anthropogenic pressure.

microbiology↗

bioRxiv2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

bioinformatics↗

bioRxiv2026

AlphaFold Database expands to proteome-scale quaternary structures

Protein function is governed by molecular interactions, yet structural coverage of these interactions remains sparse. The AlphaFold Protein Structure Database (AFDB) transformed access to accurate monomeric protein structures at scale. Here, we expand the AFDB to quaternary structures by predicting 31M candidate homo- and heterodimeric protein complexes, compiled from 4,777 proteomes, including model- and global health organisms. We established confidence criteria via analysis of experimentally determined structures, resulting in 1.81M high-confidence predictions. These models enabled the discovery of emergent structures and topologies not present in monomeric predictions. Additionally, the top 1% of structural clusters accounted for [~]44% of all complexes, and [~]8.3% of clusters were conserved across multiple domains of life, pointing to a substantial fraction of ancient, universally retained assemblies. Structural search highlighted that high-confidence predictions are anchored in experimental multimer space, yet 31.3% extend beyond detectable PDB coverage. These freely accessible proteome-scale predictions facilitate functional and mechanistic hypothesis generation across biology.

synthetic biology↗

bioRxiv2026

Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology

Single-cell expression quantitative trait loci (sc-eQTL) analyses are powerful in identifying context-specific susceptibility genes from genome-wide association studies (GWAS) loci. However, few studies have comprehensively investigated cells of lung cancer origin in non-European populations. Here, we built a lung sc-eQTL dataset from 129 Korean women never-smokers with epithelial cell enrichment. eQTL mapping identified 2,229 genes with an eQTL in 33 cell types, including East Asian-specific findings when compared to predominantly European datasets. Integration with single-cell chromatin accessibility data demonstrated an enrichment of cell-type specific eQTLs in cell-type matched candidate enhancers, while shared eQTLs were more frequently found near promoters. Colocalization and transcriptome-wide association study unveiled 36 susceptibility genes from 22 cell types in 22 lung cancer loci, including 10 loci not achieving genome-wide significance in prior GWAS. Around 47% of these genes were from cells of the alveoli, underscoring their importance, especially in lung adenocarcinoma (LUAD) susceptibility. Focusing on the trajectory of alveolar epithelial cell regeneration, we detected 785 cell-state-interacting QTLs, which overlapped with 28% (10) of the identified susceptibility genes. Finally, we experimentally validated East Asian-and alveolar type 2 cell-specific eQTL of TCF7L2 underlying East Asian LUAD locus, 10q25.2. Consistent with its role as a Wnt/{beta}-catenin effector, TCF7L2 displayed significant effect on lung adenocarcinoma cell growth. Our data highlighted context-specific susceptibility genes, especially from alveolar cells of lung, contributing to lung cancer etiology.

genetics↗

bioRxiv2026

Metabolic adaptation to maternal hyperglycemia via ACLY-dependent acetyl-CoA production drives epigenetic remodeling and dysregulated placental development

AbstractsGestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy that is paradoxically associated with both fetal overgrowth and fetal growth restriction (FGR). While maternal hyperglycemia is widely presumed to drive macrosomia through excessive nutrient supply, the mechanisms underlying FGR remain poorly understood. Here, using a mouse model that recapitulates the small-for-gestational-age (SGA) phenotype observed in human GDM pregnancies, we identify placental underdevelopment as a principal driver of FGR. Despite systemic nutrient abundance, hyperglycemic placentas exhibit reduced mass and an increased fetal-to-placental weight ratio, indicative of placental insufficiency. Mechanistically, maternal hyperglycemia induces anabolic metabolic rewiring while suppressing oxidative phosphorylation (OXPHOS), accompanied by upregulation and nuclear redistribution of ATP-citrate lyase (ACLY). ACLY converts glucose-derived carbon into acetyl-CoA in the cytosol and nucleus, thereby coupling glycolytic flux to lipid and hexosamine biosynthesis as well as to global histone hyperacetylation. This hyperacetylation-associated epigenetic reprogramming activates metabolic, innate immune, and inflammatory gene programs while repressing pro-proliferative and anti-apoptotic pathways. Consequently, placental growth is compromised despite nutrient excess. Importantly, activation of the ACLY-acetyl-CoA axis and global histone hyperacetylation is consistently observed in human GDM placentas across diverse birth outcomes, suggesting a conserved metabolic-epigenetic adaptation to maternal hyperglycemia. Together, these findings identify ACLY-dependent acetyl-CoA production as a central metabolic node linking maternal hyperglycemia to chromatin remodeling and placental development control, thereby reshaping fetal growth trajectories.

developmental biology↗