Biology subjects

Zhong, S.

Publications and source records attributed to Zhong, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Hidden molecular states of bacterial replicons beyond the chromosome-plasmid dichotomy

Bacterial genomes are organized into autonomous replicons, traditionally classified as either chromosomes or plasmids-a binary framework that underpins genome annotation and evolution models. Yet whether this binary framework captures the full diversity of replicon organization remains unclear. Here we show that bacterial replicons occupy three recurrent organizational states rather than two canonical categories. By integrating quantitative measures of chromosome-plasmid sequence affinity (plasmidness) across more than 72,000 replicons from 21 bacterial genera, we identify a distinct class-intermediate replicons-that occupies a positional and functional middle ground. These replicons are plasmid-sized, harbor substantial chromosomal sequence ancestry, and lack canonical replication signatures typically associated with either class. Multiple complementary molecular properties converge on this same state. Comparative genomic analyses reveal their enrichment near recurrent chromosome remodeling regions and reveal close evolutionary ties to conjugative and antimicrobial resistance plasmids. Metagenomic data further corroborate their presence across natural ecosystems. Together, these findings reveal a previously unrecognized replicon state and redefine bacterial genome organization beyond the chromosome-plasmid dichotomy.

microbiology↗

bioRxiv2026

Aggrecan hypomorphism accelerates the progression of post-traumatic osteoarthritis in mice

ObjectiveThe Agc1CreERT2 mouse line is a powerful tamoxifen-inducible genetic tool used for conditional gene manipulation specifically in cartilage. The aim of this study was to investigate the effects of aggrecan hypomorphism on the progression of post-traumatic osteoarthritis (PT-OA) in Agc1CreERT2 mice. MethodsProteoglycan content in cartilage samples from the knees of E18.5 embryos were quantified by sulfated glycosaminoglycan (sGAG) assay. Destabilization of the medial meniscus (DMM) surgery was performed to induce PT-OA in 12-week-old wild-type, heterozygous Agc1CreERT2/+ and homozygous Agc1CreERT2/CreERT2 mice. Progression of OA was assessed at 4-, 8-, and 12-weeks post-DMM by OARSI, synovitis and osteophyte maturation histopathology scores and micro-computed tomography ({micro}CT). Aggrecan deposition, cartilage matrix-degrading proteases, aggrecan and collagen II degradation neoepitopes were investigated by immunohistochemical staining, and serum C-terminal cross-linked telopeptide of type II collagen (CTX-II) levels by an enzyme-linked immunosorbent assay (ELISA). Chondrocyte apoptosis was analyzed with the terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) assay. The biomechanical properties of articular cartilage (AC) were investigated with indentation-type atomic force microscopy (IT-AFM). ResultsBefore DMM, homozygous Agc1CreERT2/CreERT2 mice had reduced sGAG and aggrecan levels and increased cartilage stiffness. After DMM, they exhibited increased cartilage degradation, synovitis, osteophyte formation and meniscus mineralization, chondrocyte apoptosis and cartilage stiffness compared with wild-type mice. Immunohistochemistry demonstrated increased expression of the aggrecanase ADAMTS-5, the metalloproteinase MMP-13, the aggrecan degradation neoepitope NITEGE and the collagen degradation neoepitope C1,2C in AC. ELISA also revealed elevated serum CTX-II levels. Heterozygous Agc1CreERT2/+ mice also exhibited accelerated PT-OA compared with wild-type mice, characterized by elevated CTX-II levels at 4-weeks, increased synovitis, osteophyte and soft tissue mineralization at 8-weeks, and more severe cartilage degeneration at 12-weeks post-DMM. ConclusionBoth homozygous and heterozygous Agc1CreERT2mice exhibit increased susceptibility to PT-OA, underscoring the importance of physiological aggrecan expression in maintaining joint homeostasis and regulating joint pathophysiology. These findings indicate that Agc1CreERT2/+mice are not phenotypically neutral in the DMM model and that this intrinsic susceptibility should be considered when interpreting studies employing inducible, cartilage-specific gene deletion.

molecular biology↗

bioRxiv2026

Intranuclear polyglycine aggregation drives neurodegeneration through epigenetic repression of chromatin accessibility and transcription

Polyglycine (polyG) proteins translated from expanded GGC trinucleotide repeats are implicated in a growing group of neuromuscular degenerative disorders characterized by intranuclear inclusions, yet the pathogenic importance of aggregate localization and the mechanisms underlying polyG-induced neurodegeneration remain unclear. Here we show that intranuclear polyG aggregates are markedly more pathogenic than cytoplasmic aggregates in cellular and mouse models. Intranuclear aggregation causes greater cell death, more severe behavioral deficits and neuropathology, and earlier mortality. Mechanistically, intranuclear polyG aggregates impair nascent RNA synthesis and are associated with a transcriptionally repressive chromatin state marked by globally reduced chromatin accessibility, decreased H3K27 acetylation, and increased HDAC3 expression across cellular, mouse, and human disease tissue. Using a light-inducible system, we further show that this transcriptional impairment depends on insoluble intranuclear aggregate formation rather than diffuse polyG alone. Pharmacological HDAC inhibition partially restores histone acetylation and transcriptional output and ameliorates behavioral and pathological abnormalities. Together, these findings identify intranuclear polyG aggregates as the more pathologically relevant species and uncover epigenetic repression of chromatin accessibility and transcription as a potentially common mechanism underlying polyG diseases.

neuroscience↗

bioRxiv2026

Evaluation of Diffusion Tensor Imaging in the Corpus Callosum on a Portable 100 mT MRI System

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

bioengineering↗

bioRxiv2026

METTL14-dependent m6A modification restrains interferon signaling to prevent myocarditis and dilated Cardiomyopathy

The impact of inflammation on heart failure is increasingly recognized; but how cardiomyocyte restrains innate immune activation remains poorly defined, and nor does the role of N-methyladenosine (mA) modification in maintaining cardiac immune homeostasis. Here, we demonstrate that cardiomyocyte-specific deletion of the mA methyltransferase METTL14 triggers myocarditis, dilated cardiomyopathy, and premature lethality. Meanwhile, widespread hypomethylation and upregulation of innate immune and necroptosis-related transcripts in Mettl14-deficient hearts exemplified by IFN-1 and STAT1. Mechanistically, METTL14 deficiency promotes RIPK1 accumulation thereby priming cardiomyocytes for necroptosis and inflammatory cell death. Genetic ablation of IFN-I receptor Ifnar1 can largely rescue the processes and improve cardiac function and survival. Furthermore, METTL14 loss disrupts mitochondrial integrity and autophagy/mitophagy flux, suggesting mitochondrial dysfunction-driven innate immune activation upstream of IFN-I signaling. Collectively, these findings identify METTL14-mediated mA modification as a critical safeguard against cardiomyocyte-intrinsic IFN-I signaling and necroptosis and establish an epitranscriptomic-innate immune axis that drives inflammatory heart failure.

pathology↗

bioRxiv2026

CsFDL1-CsFTL3 complex represses CsFTL3 via negative feedback to fine-tune flowering in Chrysanthemum seticuspe

In many flowering plants, the transition from vegetative growth to reproductive development is regulated by seasonal changes in photoperiod. Under inductive photoperiods, leaves produce the florigen FT (FLOWERING LOCUS T), which is transported to the shoot apex to promote flowering. The photoperiod is known to have a major effect on the flowering of chrysanthemum. In the perennial short-day (SD) plant Chrysanthemum seticuspe, the expression of CsFTL3 (FT-like gene) does not increase immediately after shifting from long-day (LD) to SD conditions but gradually accumulates under continuous SD conditions, peaking during inflorescence development. However, the underlying mechanism remains elusive. We show that CsFDL1 (an ortholog of FD) and CsFTL3 exhibit a significant inverse expression pattern in leaves during the initial stage of short-day inductions. Furthermore, the expression of CsFTL3 is upregulated in the leaves of CsFDL1-knockdown transgenic lines. CsFDL1 is expressed in leaves and forms a complex with CsFTL3 to recognize several TCGA- and ACGT-containing motifs in the CsFTL3 promoter. The CsFTL3-CsFDL1 complex downregulates CsFTL3 expression, thereby preventing its excessive induction by SD signals and inhibiting precocious floral transition. This study reveals that CsFDL1 acts as a key early repressor in the photoperiodic flowering pathway of chrysanthemum leaf, mediating negative feedback regulation by forming a complex with CsFTL3 to achieve precise temporal control of short-day-dependent flowering responses.

plant biology↗

bioRxiv2026

Disentangled multimodal evolutionary representations for cross-virus predictive modeling of antigenic change

Antigenic evolution drives immune escape and complicates early assessment of emerging pathogens, especially when genomic surveillance is sparse and functional measurements lag behind real-world spread. Early prediction and generalization to unknown viruses require faithful and information-rich representations of viral evolution, a need that is not fully addressed by existing protein language model-based approaches. Here we introduce DERIVE, a flow-based generative framework that learns a disentangled latent representation of multimodal evolutionary history by integrating sequence homology with physicochemical and structural features. Using only pre-pandemic SARS-CoV-2 sequences, DERIVE prioritizes high-risk mutations, reconstructs strain-level evolutionary trajectories, forecasts immune escape and prevalence trends, and produces mutation-level effect maps. The learned representation transfers robustly across viral families, with strong concordance to functional data for influenza virus, HIV, rabies lyssavirus and chikungunya virus. Together, these results position DERIVE as a generalizable and interpretable framework for anticipating antigenic evolution and supporting early, actionable assessment of future "Disease X" threats.

bioinformatics↗

bioRxiv2025

Genome assembly and population genomic analysis of Aegilops caudata uncover a rich source for disease and insect pest resistance for wheat improvement

Aegilops caudata L. is a rich source of resistance genes to major wheat pathogens and pests, yet its complex genome structure and lack of a reference sequence have hindered genetic analysis, gene discovery, and introgression. Here, we report a high-quality genome sequence assembly of Ae. caudata accession S740-69, integrated with optical mapping to precisely delineate introgressed segments from structurally altered genomic regions in wheat-Ae. caudata translocation lines carrying a novel stem rust resistance gene. Resequencing and phenotyping of 95 diverse accessions, coupled with k-mer-based association mapping, enabled population-level identification of several novel loci for resistance to wheat pests and diseases, such as greenbug and stem rust. Virus-induced gene silencing confirmed Aecau6C01G127270 (SrAect1) as the first functionally characterized stem rust resistance gene from Ae. caudata. This new genomic framework provides a foundation for systematic mining of resistance genes in this species and their efficient introgression into wheat.

genomics↗

bioRxiv2025

Dissecting sequence determinants of DNA methylation and in silico perturbation

DNA methylation shapes cellular identity and genome function, yet the sequence logic that governs its establishment remains largely unresolved. Here we present MethylAI, a cross-species-pretrained and human-specialized deep learning framework that predicts single-CpG methylation states directly from genomic sequence with high fidelity. Through quantitative attribution, MethylAI uncovers intrinsic cis-regulatory principles embedded in DNA, revealing conserved transcription factor (TF) motifs whose influence extends beyond CpG composition. Activated motifs demarcate gene-body CpG islands, encode lineage-defining methylation states, and align with TF occupancy and diverse histone modifications, thereby linking sequence syntax to local chromatin regulation. CTCF perturbation experiments validated MethylAI-predicted methylation shifts at activated motifs, showing strong concordance with in silico perturbations. Mapping genetic variants onto methylation-linked active motifs further uncovered widespread connections to zinc-finger TF families, providing mechanistic insight into how noncoding variation contributes to human traits and diseases. By transforming DNA sequence into interpretable methylation grammar, MethylAI establishes a generalizable framework for decoding the regulatory architecture of the human epigenome.

genomics↗

bioRxiv2025

An integrative computational approach for obstacle avoidance during action selection

Action selection in cluttered environments, where individuals must simultaneously pursue goals and avoid obstacles, presents a significant challenge for the brain. To understand the underlying mechanisms of action selection in such contexts, we propose a computational model that extends stochastic optimal control theory through a novel framework for obstacle avoidance. The model decomposes action selection as a weighted combination of individual control policies, each generated for either target approach or obstacle avoidance. By integrating value information from goals, obstacles, and actions into a unified measure of "relative desirability", the model dynamically determines the contribution of each policy to the overall action selection process. We evaluated the framework using simulated target-reaching tasks in cluttered environments based on previous human studies. The results showed that the model captures key features of human motor behavior, including the influence of obstacle properties on movement trajectories and the transient tendency to initiate movements toward obstacles before avoidance. This work offers new insights into the dynamic interaction between approach and avoidance behaviors, providing a comprehensive framework for understanding action selection in complex and naturalistic settings. Author SummaryEvery day, we make countless movements that require us to reach for something while avoiding obstacles--for example, grabbing a cup without knocking over nearby objects. How the brain selects such actions in cluttered environments remains poorly understood. In this study, we developed a computational model that explains how people plan and control movements when both goals and obstacles are present. Our model assumes that the brain prepares multiple potential actions at once--some for reaching the goal and others for avoiding obstacles--and then combines them based on how desirable each option is at any moment. Using computer simulations of reaching movements, we found that the model reproduced key patterns observed in human behavior, including the brief tendency to move toward an obstacle before steering away from it. By showing how the brain might continuously balance approach and avoidance drives, our work offers a new way to understand how people make rapid and flexible movement decisions in complex, everyday settings.

neuroscience↗

bioRxiv2025

MF-ProtDisMap: protein real-valued distance prediction with fusion of sequence and coevolutionary features

The precise estimation of protein inter-residue distances is essential for high-accuracy protein structure modeling. Currently, the prediction methods are predominantly based on MSA-derived coevolutionary features or language model-based sequence features. To effectively leverage the strengths of both methods, this study developed MF-ProtDisMap (Multi-Feature Protein Distance Map), an integrative framework that effectively combines both feature types to achieve superior real-valued distance prediction. Briefly, MSA Transformer is employed to extract the coevolutionary features from protein multiple sequence alignments, whereas ESM2 is used to capture long-range interactions and sequence-level features. To reduce the computational cost while maximumly represent fused feature information, we implement group pooling for feature dimensionality reduction and introduce Diff-former--a novel module combining a diffusion model with a triangular attention mechanism to enhance representation learning. MF-ProtDisMap achieved a MAE of 2.20 [A] and an RMSE of 3.40 [A] in the protein real-valued distance prediction task. The predicted distances can be converted into contact results, achieving ROC and PR values of 84.56% and 81.01%, respectively. These results demonstrate that MF-ProtDisMap outperforms state-of-the-art real-valued protein distance methods.

bioinformatics↗

bioRxiv2025

The spliceosome component U1-70k loads nascent transcripts onto FVE/SGS3 via co-condensation to embark on RNA silencing

Despite biochemical framework of RNA silencing has been studied well, how nascent transcripts enter RNA silencing remains poorly understood. Here we recovered U1-70k, a canonical spliceosome factor, as a novel component in posttranscriptional gene silencing (PTGS). We show that U1-70k interacts with SGS3 and FVE, two key components that supply RNA substrate for RNA-dependent RNA polymerase 6 (RDR6) to generate dsRNA. U1-70k promotes the loading of nascent RNA into the SGS3/FVE complex in the nucleus, which is subsequently translocated onto RDR6 in the cytosol. Importantly, U1-70k contains C-terminal low complexity (LC) domain and displays liquid-liquid phase separation (LLPS) pattern. U1-70k forms co-condensate with SGS3, another intrinsically disordered protein (IDP). The co-condensation is critical for PTGS as U1-70k LC variants display reduced SGS3 co-condensation, loss of RNA loading onto SGS3, and impaired RNAi function. Taken together, U1-70k channels nascent RNA to SGS3/FVE via its LLPS action to enter PTGS pathway.

molecular biology↗

bioRxiv2025

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

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

bioengineering↗

bioRxiv2024

Functional fingerprinting for the developing brain using deep metric learning: an ABCD study

Brain fingerprinting is a promising approach for characterizing the uniqueness of individual brain functioning using functional magnetic resonance imaging (fMRI) data. Here, we propose a novel deep learning framework, the metric-BoIT, for brain fingerprinting and demonstrate its effectiveness in capturing individual variability among early adolescents undergoing dramatic brain changes. Utilizing resting-state fMRI data from the Adolescent Brain Cognitive Development (ABCD) dataset, we identified brain functional fingerprints that achieved remarkable individual identification accuracy, reaching 97.6% within a single session and maintaining 86.6% accuracy over a four-year developmental period. Annotation analysis revealed that higher-order association regions, particularly those within the default-mode network, contributed most significantly to these distinctive brain fingerprints (t = 5.618, p < 0.001). Moreover, these brain fingerprints were relevant to cognitive functions, as evidenced by significant correlations with fluid intelligence (F = 1.282, p = 0.027) and crystallized intelligence (F = 1.405, p < 0.001). The extracted brain fingerprints were additionally associated with genetics, showing that individuals with strong genomic relationships exhibited more similar brain fingerprint patterns (t = -12.330, p < 0.001). Together, our study not only presents an innovative approach to brain functional fingerprinting but also provides valuable insights into the individual variability underlying adolescent neurodevelopment.

neuroscience↗

bioRxiv2024

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation. Foundation models pretrained on large-scale scRNA-seq datasets have shown great promise in analyzing such data, but existing approaches are often limited to modeling a small subset of highly expressed genes and lack the integration of external genespecific knowledge. To address these limitations, we present sc-Long, a billion-parameter foundation model pretrained on 48 million cells. sc-Long performs self-attention across the entire set of 28,000 genes in the human genome. This enables the model to capture long-range dependencies between all genes, including lowly expressed ones, which often play critical roles in cellular processes but are typically excluded by existing foundation models. Additionally, sc-Long integrates gene knowledge from the Gene Ontology using a graph convolutional network, enriching its contextual understanding of gene functions and relationships. In extensive evaluations, sc-Long surpasses both stateof-the-art scRNA-seq foundation models and task-specific models across diverse tasks, including predicting transcriptional responses to genetic and chemical perturbations, forecasting cancer drug responses, and inferring gene regulatory networks.

bioinformatics↗

bioRxiv2024

A regulator controls both DNA damage response and anti-phage defense networks in Moraxellaceae

DNA damage chemicals, including many antibiotics, often induce prophage induction and phage outbreaks in microbial communities, a major threat to bacterial survival. Moraxellaceae contains clinically highly relevant strains with outstanding antibiotic and radio resistance, yet their cellular-level regulation in DNA damage response and anti-phage defense is largely unknown. Here, we identified a WYL family protein that had replaced the ubiquitous SOS system in evolution and directly regulated a transcriptional network of both DNA repair genes and anti-phage defense genes under DNA damage stress. This study depicts a mechanism that how bacteria maintain immunity to phages without compromising on antibiotic resistance, and sheds light on controlling the resistance of Moraxellaceae strains in clinical practice.

microbiology↗

bioRxiv2024

Synaptic and Somatic Targeting of ArcLight, a Genetically Encoded Voltage Indicator

Voltage signals in neurons are highly compartmentalized, which can influence their specific functions within neuronal circuits. Targeting of a genetically encoded voltage indicator (GEVI) to specific subcellular compartments can enhance the signal-to-noise ratio and provide more precise information about the location and timing of synaptic firing across different neuronal regions, reducing spatiotemporal signal convolution. To achieve subcellular targeting of the GEVI, ArcLight, we utilized five different postsynaptic targeting sequences (Shaker K+ channel C-terminus, stargazin C-terminus, rat Neuroligin-1 C-terminus, and anti-homer1 nanobodies HC20 & HC87) to direct ArcLight expression to the excitatory postsynaptic density. Additionally, we assessed a presynaptic-targeting tag (rat Neurexin-1{beta} C-terminus) and a somatodendritic targeting tag (Kv2.1-Lk-Tlcn C-terminus). Patch clamp experiments in HEK293 cells showed that the targeting tags used in this study did not significantly alter ArcLights voltage sensitivity compared to controls. AAV infection in the mouse olfactory bulb demonstrated that the subcellular targeting sequences effectively localized GEVI expression to specific compartments of mitral/tufted cells, including postsynaptic densities, presynaptic terminals, and somatodendritic regions. Furthermore, in vivo voltage imaging in mice expressing targeting-enhanced ArcLight variants revealed odorant-evoked responses similar to those observed with the original ArcLight. This indicates that subcellular targeting did not significantly impact the voltage sensing capability of ArcLight in mitral/tufted cells.

neuroscience↗

bioRxiv2024

Pan-cistrome analysis of the leaf accessible chromatin regions of 214 maize inbred lines

Characterizing the noncoding regions of the genome, particularly the cis-regulatory elements (CREs) located in accessible chromatin regions (ACRs) in gene promoters, is crucial for understanding how gene expression is regulated and how genotype contributes to phenotypic diversity in plants.In this study, we used ATAC-seq to resequence and map the ACRs of 214 maize inbred lines. We identified 82,174 ACRs and reported that 39.55% of them exhibited significant variation across the population. Next, we used the accessibility of ACRs as a quantitative feature and performed a chromatin associability GWAS (caGWAS), resulting in 27,004 caQTLs. Among them, 2,463 were predicted to disrupt the cis-regulatory elements that TF binds in these ACRs, suggesting that TF binding affects chromatin accessibility. Specifically, we identified two caACRs that regulate the expression of fad7, which encodes a fatty acid desaturase and affects the linolenic acid content.Our findings highlight the dynamic nature of maize ACRs and demonstrate that chromatin accessibility information could be used for GWASs, offering key insights into the genetic and regulatory mechanisms of chromatin accessibility and its impact on complex traits in plants.

genomics↗