Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 325 records · Page 18Linked to original sources

bioRxiv2026

Auxin promotes GPI-anchored protein-mediated trafficking of ABP1 to enable cell-surface auxin signaling

Rapid auxin responses are triggered by Auxin-Binding Protein 1 (ABP1) signaling; however, the coupling of hormone perception to signaling activation is unclear. ABP1, a cell surface auxin receptor, is localized to endoplasmic reticulum (ER), raising a fundamental question of where auxin is perceived and how signaling-competent receptors are mobilized to cell surface. Herein, we established a dark-induced auxin synthesis system in Arabidopsis hypocotyl; abp1 mutants showed pronounced elongation. Auxin dose-dependently stimulates LORELEI-like glycosyl-phosphatidylinositol-anchored protein 1 (GPI-AP, LLG1) interaction with ABP1, driving their outward trafficking from ER. However, this complex dissociated in acidic apoplast, facilitating H+-ATPase-mediated auxin signaling. Our findings reveal how intracellular auxin perception is converted into rapid cell-surface signaling, establishing a mechanistic framework for fast auxin responses and uncovering a GPI-AP-mediated trafficking mechanism for ER-retained receptors.

plant biology↗

bioRxiv2026

Detergent-Free Nuclear-Cytoplasmic Fractionation Enables Spatially Resolved PELSA for Enhanced Nuclear Drug Target Identification

Accurate identification of drug target proteins remain major challenges in proteomics-based target discovery, particularly for low-abundance nuclear proteins that are difficult to detect because of the complexity of whole-cell lysates. Here, we developed a detergent-free nuclear-cytoplasmic fractionation strategy compatible with peptide-centric local stability analysis (PELSA), which markedly improves detection of nuclear drug targets. Using K562 cells, we demonstrated that mild detergent-free fractionation enables high-fidelity nuclear-cytoplasmic separation with minimal cross-contamination. When coupled with PELSA, this workflow significantly increases the number of detected nuclear targets relative to whole-cell analysis. Benchmarking with well-characterized nuclear drugs, including the histone deacetylase inhibitor panobinostat and the RNA polymerase II inhibitor -amanitin, our results showed improved identification of canonical nuclear targets. Broad profiling of staurosporine target further revealed expanded kinase target coverage by combining the results of nuclear and cytoplasmic fraction, with the CLK family kinases detected exclusively in the nuclear fractions. Additionally, we showed that PELSA can also be performed on intact nucleus level. Collectively, these findings establish detergent-free nuclear-cytoplasmic fractionation-PELSA as a robust and scalable strategy for spatially resolved drug target identification, improving sensitivity for nuclear and low-abundance proteins.

biochemistry↗

bioRxiv2026

A DERIVED RELAXATION CONTRAST FROM SYNTHETIC MRI FOR DETECTING NETWORK MICROSTRUCTURAL VULNERABILITY

BackgroundOdor identification impairment is an early marker of Alzheimers disease (AD) that predicts memory decline, yet its underlying microstructural basis remains unclear. We hypothesized that mild cognitive impairment (MCI) involves early myelin and lipid disruption within olfactory-limbic circuits, detectable using a synthetic MRI-derived contrast that provides complementary sensitivity to myelin volume fraction (MVF). MethodsThirty-three older adults (healthy controls [HC], n = 16; mild cognitive impairment [MCI], n = 17) completed olfactory and cognitive testing and underwent 3T brain MRI using a QALAS sequence. An MVF map and synthetic FLAIR and DIR images were generated, and a FLAIR-DIR-derived metric (FD) was computed as FD = (FLAIR - DIR) / FLAIR. We investigated ROI-based group differences in olfactory-limbic gray-matter regions and associated white-matter tracts, voxel-wise regressions investigating FD-odor identification associations, and ROI-based MCI vs HC classification using cross-validated logistic regression models. ResultsCompared with HC, MCI showed significantly lower FD across olfactory-limbic gray-matter regions and white-matter pathways--including hippocampus, amygdala, orbitofrontal cortex, thalamus, and corpus callosum--whereas MVF differences were more limited. FD achieved moderate discrimination, with baseline performance comparable to MVF. Voxel-wise analyses revealed that better odor identification was associated with higher FD in the hippocampus/parahippocampal and insula; the association persisted after adjusting for voxel-wise MVF. MVF also showed significant positive voxel-wise associations with odor identification in the insula and genu of the corpus callosum. ConclusionFD is a practical, myelin- and lipid-sensitive contrast derived from routinely acquired synthetic FLAIR & DIR images that complement quantitative MVF. It captures behaviorally relevant variance beyond local myelin content and may improve detection of early olfactory-limbic microstructural changes in MCI. These findings support FD as a scalable candidate marker linking early network disruption to olfactory symptoms across the AD continuum.

neuroscience↗

bioRxiv2026

Engineering age-adaptive mRNA lipid nanoparticle cancer vaccines via reprogramming systemic gene expression

Lipid nanoparticle (LNP)-based mRNA vaccines have transformed cancer immunotherapy, yet their efficacy in older individuals, who represent the majority of cancer patients, remains poorly understood. Here, we uncover a critical and previously underappreciated barrier to mRNA LNP cancer vaccine performance in aged hosts: impaired systemic transgene expression. Using the SM-102 mRNA LNPs as a benchmark formulation, we show that while local immune activation and antigen presentation at the injection site and draining lymph nodes remain largely intact with age, transgene expression in peripheral organs, including the liver, lungs, and spleen, is markedly reduced. This deficit limits the magnitude and durability of CD8 and CD4 T cell responses and substantially compromises tumour control. Transcriptomic profiling further reveals that attenuated transgene expression parallels broad attenuation of antigen processing, presentation, and activation pathways in immune cells from aged animals, implicating impaired systemic mRNA translation as a central driver of the downstream immune defects and antitumour efficacy loss. Building on these insights, we identify a rationally selected LNP formulation that reestablishes distal antigen expression across age groups as an engineering strategy to revive T cell immunity, achieving full rescue of therapeutic efficacy in aged mice without additional intervention. Together, these findings establish systemic mRNA translation as a tuneable lever of vaccine performance and highlight that optimizing LNP formulations to sustain systemic transgene expression across age groups may enable next-generation, age-adaptive mRNA vaccines for cancer and other diseases of aging.

bioengineering↗

bioRxiv2026

Extending the MARTINI 3 Coarse-Grained Forcefield to Polypeptoids

Polypeptoids (poly-N-substituted glycines) are synthetic peptidomimetic polymers with their sidechains attached to the backbone amide nitrogen rather than the -carbon in natural peptides. Peptoids display pronounced sequence-dependent conformational flexibility arising from the absence of backbone hydrogen bonding and slow cis/trans {omega}-dihedral isomerization. Despite growing interest in peptoid-based biomaterials, a coarse-grained (CG) model compatible with the modern MARTINI 3 framework is not yet available, limiting mesoscale simulation of peptoid structure and self-assembly. In this work, we develop the first MARTINI 3 compatible peptoid CG forcefield, covering 19 commonly used residue types. Extensive all-atom reference simulations employing parallel bias metadynamics (PBMetaD) were performed to ensure converged sampling of {omega}-dihedral transitions. Bonded parameters were derived from atomistic distribution functions via direct Boltzmann inversion (DBI), while nonbonded interactions were primarily adopted from the standard MARTINI 3 parameter library. The resulting CG model reproduces structural and thermodynamic properties in close agreement with all-atom simulations, while providing up to 57-fold enhanced computational efficiency. To facilitate its adoption by the research community, we have integrated all parameters and workflows to the MARTINI-based martinize2 tool, enabling automated generation of MARTINI 3 peptoid structures and topologies. This work establishes a transferable and computational efficient framework for simulating large-scale peptoid confirmations, assemblies, membrane interactions, and nanostructure formation, and supports the rational design of next-generation sequence-specific functional peptoid-based materials.

biophysics↗

bioRxiv2026

MTB-KB: A Curated Knowledgebase of Mycobacterium tuberculosis Related Studies

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), has regained its position as the worlds leading killer among infectious diseases. Despite extensive research progress across epidemiology, diagnosis, drug development, treatment regimens, vaccines, drug resistance, virulence factors, and immune mechanisms, MTB-related knowledge remains fragmented across thousands of publications, limiting its effective use. To address this gap, we present MTB-KB, a literature-curated knowledgebase that systematically integrates high-impact findings from eight major sections of TB research. The current release contains 75,170 associations from 1,246 publications, covering 18,439 entities standardized using authoritative databases and WHO-endorsed classifications. A central feature is the interactive knowledge graph, which links cross-section associations to reveal and infer MTB-host interactions, treatment strategies, and vaccine development opportunities. MTB-KB also provides a user-friendly interface with browsing, advanced search, and statistical visualization. Overall, by consolidating dispersed MTB knowledge into a structured and accessible platform, MTB-KB provides a valuable resource for researchers, clinicians, and policymakers, supporting both basic and clinical TB research, enabling evidence-based TB prevention, diagnosis, and treatment, and contributing to global elimination efforts. MTB-KB is accessible at https://ngdc.cncb.ac.cn/mtbkb/.

bioinformatics↗

bioRxiv2026

Lipoengineering of Biomolecular Condensates Controls Material Properties and Multiphase Hierarchy to Guide Organoid Morphogenesis

Cells use post-translational modifications (PTMs) to reconfigure biomolecular condensates across length scales, space, and time.1,2 While charged PTMs are well-known electrostatic switches,3,4 how ubiquitous neutral PTMs shape condensate plasticity and hierarchy remains unclear. Here, we establish a set of design principles for using site-specific lipidation, a class of neutral hydrophobic PTMs, to rationally control properties and interactions of engineered biomolecular condensates. Through systematic analysis of over 80 lipidated synthetic intrinsically disordered proteins (IDPs), we uncovered two distinct axes of control. First, the interplay between the lipid and the local three-residue sequence of its attachment site acts as a programmable switch for cohesion--the homotypic interactions that define the material state of the condensed phase-- directing assemblies toward dynamic liquids, arrested gels, or ordered fibrillar solids. Second, the lipid, together with the global properties of the IDP scaffold, tunes adhesion--the heterotypic interactions that govern condensate miscibility and hierarchical organization. We harnessed these principles to rationally engineer complex, multi-phase architectures and create hybrid hydrogels with programmed microstructure and material properties that guide the morphogenesis of functional intestinal organoids. These findings establish a new framework for lipoengineering advanced biomaterials and provide a blueprint for dissecting structure-property relationships across diverse classes of PTMs.

bioengineering↗

bioRxiv2026

Deep-Plant: a supervised foundation model for plant regulatory genomics

Large-scale sequence-to-function deep learning models have demonstrated unparalleled ability to model biological sequences and have revolutionized the field of regulatory genomics. However, the majority of such efforts have centered on human and mammalian systems, leaving plant regulatory genomics comparatively underexplored. To address this gap, we introduce DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW, a supervised foundation model trained to predict chromatin state directly from genomic sequence. In contrast to large language models, which are trained in a selfsupervised manner using sequence alone, our model is trained to predict chromatin state across tissues and conditions. Training the model on a large collection of genome-wide experiments including DNA accessibility, transcription factor binding, and histone modifications, provides it with added biological context beyond the sequence itself. We demonstrate that the resulting model is an effective platform for developing accurate models of regulatory activity relevant to gene expression and active enhancers, exhibiting large improvements in speed, accuracy, and interpretability over the complementary approach of fine-tuning DNA language models. DO_SCPLOWEEPC_SCPLOW-PO_SCPLOWLANTC_SCPLOW models are available in Arabidopsis and rice, and work well as a building block for sequence modeling in related species such as corn. Together, these results establish supervised, chromatin-informed foundation models as a practical and effective paradigm for regulatory sequence modeling in plants.

genomics↗

bioRxiv2026

STAnalyzer: Transparent Spatial Transcriptomics Analysis via an Agentic Architecture

Spatial transcriptomics enables high-resolution profiling of gene expression within spatial contexts, yet its potential is often hindered by fragmented toolchains, intricate parameters, and cognitive bottlenecks of interpreting high-dimensional data. While recent Large Language Model agents have attempted to automate this process, they remain constrained by rigid execution logic, lack multimodal feedback for self-correction, and operate in epistemic isolation from established biological knowledge. Here, we present STAnalyzer, an intelligent multi-agent framework designed to automate the end-to-end analytical lifecycle--from raw data processing to biological hypothesis generation. Transcending traditional pipelines, STAnalyzer employs a collaborative intelligence architecture to achieve three core capabilities: (1) Intent-Driven Orchestration, which dynamically translates natural language queries into rigorous bioinformatics workflows; (2) Multi-Modal Self-Refinement, which autonomously ensures analytical robustness through closed-loop synthesis of evidence from visual patterns and statistical metrics; and (3) Evidence-based Cross-Validation, which bridges the gap between data-driven correlations and biological causation by anchoring findings in ground-truth literature and structured databases. By eliminating manual analytical bottlenecks and ensuring rigorous evidentiary traceability and transparency, STAnalyzer makes high-resolution spatial omics more accessible to a broader research community. It provides a robust and scalable framework for cross-platform automated analysis and accelerated biological discovery, translating massive spatial datasets into verifiable biological insights.

bioinformatics↗

bioRxiv2026

Global genomic diversity of the selfing nematode Caenorhabditis tropicalis correlates with geography

Self-fertilization reduces genetic diversity compared to outcrossing and hypothetically decreases the ability to adapt to diverse environments. Among Caenorhabditis nematodes, self-fertilization evolved three times independently in Caenorhabditis elegans, Caenorhabditis briggsae, and the more recently discovered Caenorhabditis tropicalis. To survey C. tropicalis genetic relatedness, the influence of geography and niche on species-wide variation, and the signatures of selection, we collected 785 wild strains, sequenced their genomes, and identified 622 distinct genotypes (isotypes). In contrast to C. elegans and C. briggsae, C. tropicalis relatedness shows substantial association with geography and no transcontinental selective sweeps or broadly sampled isotypes. Populations from the Hawaiian Islands or Taiwan harbor more genetic variation than populations from the Caribbean or Americas, suggesting a Pacific species origin similar to other members of the Elegans subclade. Punctuated genomic regions of extreme genetic variation pervade the genome. These hyper-divergent regions (HDRs) comprise less than 6% of the reference genome in any given strain despite harboring 73% of all variant sites and are enriched for genes likely involved in environmental adaptation. HDRs represent a shared genomic feature of self-fertilizing Caenorhabditis nematodes despite their independent evolutionary origins and suggest a mechanism to explain worldwide distributions despite low species-wide levels of genetic variation.

genomics↗

bioRxiv2026

Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes

The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https://ycclab.cuhk.edu.cn/GeoARG/.

bioinformatics↗

bioRxiv2026

Exploring Chromosomal Position Effects for Predictable Tuning of Metabolic Pathways in Yeast

Precise control of gene expression is essential for optimizing metabolic pathways, yet current tuning strategies based on promoter strength or gene copy number remain largely empirical. Chromosomal position represents an additional regulatory axis, as identical gene expression cassettes can exhibit markedly different expression levels depending on their integration sites. Here, we systematically quantified the expression output of 98 intergenic regions (IGRs) in Saccharomyces cerevisiae using a fluorescent reporter and developed a predictive framework, Yeast IGR Prophet (YeIP), that infers expression potential directly from genomic context. By integrating multi-scale genomic features including transcriptional neighborhood, chromatin state, and chromosome topology, YeIP accurately predicted expression ranks and enabled the construction of a genome-wide atlas of expression hotspot and coldspot. Using this atlas, we rationally optimized a three-gene lycopene pathway solely through genomic integration site selection, achieving optimal transcriptional stoichiometry without modifying promoters or gene copy numbers. These results transform chromosomal integration sites from static genomic coordinates into programmable regulatory elements, establishing a predictive, data-driven framework for rational and scalable design of metabolic pathways in yeast.

synthetic biology↗

bioRxiv2026

Chemotherapy reshapes cellular clonal landscape via persister programs in breast cancer xenografts

Tumor ecosystems evolve in response to chemotherapy, but how treatment reshapes clonal architecture and the transcriptional programs of distinct cell states remains unclear. To investigate the response to chemotherapy by individual single cell-derived clones, we used a high-complexity lentiviral barcoding strategy by genetically labelling with unique, heritable and expressible barcode sequences individual cells from four human breast cancer patient-derived tumor xenograft models. We tracked 3,248 single cell-derived clones across 39 xenografts and profiled 676,292 cells with single cell RNA sequencing. The most striking changes following chemotherapy occurred in non-responsive xenografts, attributed to the emergence of previously minor cell clones that are chemotherapy resistant. In a triple negative model, epithelial and mesenchymal cell states showed distinct sensitivities to chemotherapy. ER+/HER2-models activated stress adaptive programs in response to carboplatin, highlighting DUSP1 and KLF4 as markers of platinum tolerance and a slow cycling, persister-like state. Together, these findings reveal that chemotherapy triggers an immediate and substantial reorganization of the cellular clonal landscape, driven by the selective engagement of stress response programs in cell clones that survive treatment.

cancer biology↗

bioRxiv2026

From nucleotides to semantics: genomic representation learning via joint-embedding predictive architecture

Decoding the regulatory syntax encoded in genomic sequences is a central objective in computational biology. Most existing genomic foundation models treat DNA as a language and adopt pretraining objectives from natural language processing. DNA sequences, however, lack explicit semantic boundaries and contain substantial evolutionary noise. Nucleotide-level reconstruction in a low-dimensional input space can therefore increase computational overhead and may yield representations with limited discriminative capacity. Downstream tasks often depend on expensive finetuning, which restricts practical use in many biology laboratories. Here we present GenoJEPA, a genomic representation learning framework based on joint-embedding predictive architecture. GenoJEPA combines continuous patching with semantic alignment, shifting the optimization from local base reconstruction to semantic alignment in latent space. Across 55 downstream tasks, GenoJEPA shows strong representational capacity and robust generalization while reducing parameter count and computational cost. The resulting semantic vectors from frozen GenoJEPA support lightweight GPU-free classifiers to achieve competitive accuracy. These results suggest a practical route towards efficient training and broad application of larger-scale genomic foundation models.

bioinformatics↗

bioRxiv2026

Nucleoid-associated proteins sense phage-induced genome damage to elicit abortive infection

Bacteria have evolved diverse immune strategies to detect and neutralize bacteriophage infection. Here, we describe an unprecedented paradigm in which a chromosome-architecting nucleoid-associated protein (NAP) is repurposed as a viral infection sensor. When phage attack leads to genome degradation, the NAP sensor is released from the nucleoid to the cytoplasm, where it binds and activates diverse immune effectors. One such effector is a nucleotide-modifying toxin normally existing as an inactive homotetramer. NAP binding converts it into a catalytically active heterotrimer that halts both transcription and translation. Phylogenetic analyses unveiled the high modularity, polyphyletic origin, and wide distribution of NAP-mediated defenses. Collectively, we define a distinct class of defense systems in which bacteria sense phage-induced genome damage through NAP relocation, highlighting an unexpected but essential role for these proteins as sentinels of genome integrity.

microbiology↗

bioRxiv2026

GPLD1 Regulates the Shedding of IZUMO1R to Block Polyspermy in Porcine Oocyte

Mammalian fertilization commences with the essential interaction between sperm IZUMO1 and its oocyte-surface receptor, JUNO. Following gamete fusion, JUNO is rapidly shed from the oocyte to establish a definitive membrane block to polyspermy, a pathological condition that remains a major hurdle in porcine in vitro fertilization (IVF). Despite its biological importance, the molecular networks driving JUNO cleavage has remained elusive. Here, by integrating proteomics, dual-color live-cell imaging, and functional perturbations, we identify the extended existence of JUNO and the GPI-specific phospholipase D1 (GPLD1) as the requisite enzyme mediating JUNO shedding in porcine oocytes. Targeted knockdown or pharmacological inhibition of GPLD1 stabilizes oocyte JUNO, prolongs the window of oocyte receptivity, and significantly exacerbates polyspermy, ultimately compromising embryonic developmental competence. Conversely, GPLD1 overexpression restricts redundant sperm adherence and enhances the efficiency of monospermic zygote formation and blastocyst development. Live-cell imaging reveals that fertilization triggers a transient, pulsed recruitment of GPLD1 in the oocyte, which precisely coincides with the biphasic kinetics of JUNO depletion. Our findings establish that the enzymatic cleavage of the GPI-anchor by GPLD1 is critical for JUNO release, defining a fundamental mechanism for the membrane-level block to polyspermy. This work provides a molecular framework for ensuring sperm-oocyte recognition and improving in vitro fertilization outcomes in mammals.

developmental biology↗

bioRxiv2026

Coupled beta and high-frequency oscillations emerge from synchronized bursting in a minimal model of the parkinsonian subthalamic nucleus

Local field potentials recorded from the subthalamic nucleus (STN) in Parkinson's disease (PD) exhibit a distinctive multiscale spectral signature: exaggerated beta-band oscillations (13-30 Hz) coupled to high-frequency oscillations (HFOs, 200-400 Hz), with HFO amplitude being phase-locked to the beta cycle. This phase-amplitude coupling (PAC) has been identified as a promising biomarker of the parkinsonian state, yet no biophysical model has explained how it emerges, what determines the HFO frequency, or how HFOs can exist without beta modulation in the medicated STN. Here we show that a heterogeneous population of excitatory Izhikevich neurons with recurrent coupling produces three dynamical regimes: (i) asynchronous tonic firing, (ii) asynchronous bursting, in which neurons burst individually producing broadband HFO power but without coherent population-level PAC, and (iii) synchronous bursting, which gives rise to beta-HFO PAC. The regimes are governed by two biophysically interpretable parameters that capture complementary effects of dopamine depletion: one reflecting changes in intrinsic neuronal excitability, the other reflecting changes in synaptic coupling strength. The transition from asynchronous to synchronous bursting in this model captures the emergence of pathological STN neuronal activity in the parkinsonian state. HFO peak frequency varies continuously across the two-parameter landscape, suggesting a possible mechanism of the clinically observed shift from slow (200-300 Hz) to fast (300-400 Hz) HFOs between medication states. The character of the synchronization transition depends on baseline excitability, ranging from a sharp co-emergence of bursting and synchrony at low excitability to a decoupled transition at intermediate excitability, where the bursting fraction saturates while the population synchronization continues to increase with coupling. We also extend the model to include reciprocal coupling to an inhibitory population of globus pallidus externa (GPe)-like neurons and report a similar asynchronous-to-synchronous bursting transition and beta-HFO PAC emerging in the STN population, with the beta rhythm being set by the delay in the synaptic coupling between the two populations. The model generates testable predictions for future clinical and experimental studies, provides a numerical dissection of how mesoscopic LFP features map onto microscopic neuronal dynamics, and serves as a computational building block for future circuit-level models that may inform brain stimulation strategies tailored to the patient-specific dynamical state of the STN.

neuroscience↗

bioRxiv2026

A lateral linker histone binding mode scaffolds dinucleosome stacking in chromatin fibers

Linker histones are essential for chromatin compaction, yet how they contribute to higher-order fiber assembly remains poorly understood. Here, we determined cryo-electron microscopy structures of Arabidopsis dodeca-nucleosome fibers containing distinct H2A/H3 variants and linker histone H1.3, revealing a noncanonical binding mode that a laterally positioned H1.3 connects the acidic patch of one nucleosome and the DNA of the neighboring nucleosome, thereby scaffolding dinucleosomes into two-start chromatin fibers. This noncanonical binding mode is structurally conserved when H1.3 is replaced by Gallus gallus H5. Furthermore, incorporation of H2A.W and H3.3 further induces back-to-back fiber dimerization. Cryo-electron tomography and in vivo cross-linking mass spectrometry analyses support the physiological relevance of H1 lateral engagement. Our findings establish that linker histones act as active architectural scaffolds in higher-order chromatin organization.

molecular biology↗