Biology subjects

Zhuang, Y.

Publications and source records attributed to Zhuang, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Fine-tuning an intracellular allosteric pocket programs GPCR signaling bias

Biased signaling of G protein-coupled receptors (GPCRs) offers a route for precise therapeutics with fewer adverse effects. Intracellular allosteric ligands can impose such bias, yet how subtle chemical changes within these pockets reshape receptor-transducer coupling and downstream signaling remains unclear. Here, we systematically fine-tuned the intracellular allosteric pocket of neurotensin receptor 1 (NTSR1) through chemical modification of the {beta}-arrestin-biased ligand SBI-553. Minor changes to the ligand scaffold altered transducer-coupling preferences, generating distinct profiles of G-protein and {beta}-arrestin signaling. Cryo-electron microscopy structures of NTSR1-{beta}-arrestin2 complexes bound to designed allosteric probes revealed ligand-dependent remodeling of the intracellular cavity that reshapes the receptor-effector interface. Alanine scanning identified transducer-sensitive residues within and beyond the immediate ligand-contact region. In mice, SBI-553 and N065 produced distinct physiological profiles, with N065 improving survival in a lethal endotoxemia model. Finally, phosphoproteomic profiling revealed partially overlapping but distinct downstream signaling profiles among the three modulators. Together, these findings establish the intracellular allosteric pocket as a tunable determinant of transducer coupling and show that closely related modulators can generate distinct signaling and physiological profiles.

pharmacology and toxicology↗

bioRxiv2026

Intracellular complement Factor B couples inflammatory sensing to metabolism, chromatin regulation and maladaptive tissue repair

Inflammatory signals are broadly conserved, yet how tissue context converts them into programs of recovery or maladaptive repair remains unclear. Using kidney injury as a model of inflammation-to-repair transitions, we identify an injury-induced proximal-tubule complement program in which cell-intrinsic complement proteins are repurposed across subcellular compartments to couple inflammation to metabolic and chromatin regulation and promote maladaptive fibrotic remodeling. Mechanistically, myeloid-derived IL-1b; signals through IL-1R1 on injured tubular cells to recruit NF-kB and AP-1 to C3 and Cfb, coordinately inducing complement component 3 (C3) and Factor B (FB). Cytoplasmic FB drives cleavage-activation of cell-intrinsic C3, which supports tubular cell mitochondrial metabolic adaptation to stress and accumulation of C3aR-expressing macrophages around injured tubules. Nuclear FB, on the other hand, binds chromatin through a previously unrecognized DNA-binding interface and mediates histone H3 proteolysis and the preferential remodeling of chromatin promotors enriched in transcriptional programs relevant to maladaptive repair. Key features of this injury-associated C3-FB response, including intranuclear FB, are conserved in human kidney disease, associate with kidney dysfunction, and distinguish diseased from healthy kidneys. Together, these findings suggest that extracellular complement-targeting therapies may miss disease-relevant intracellular functions and suggest nuclear FB as a distinct target whose chromatin interaction and processing capacity could selectively limit maladaptive tissue responses beyond conventional inhibition.

immunology↗

bioRxiv2026

VINTER: a generative vision-language model for annotating and interrogating spatial tissues

Spatial transcriptomics links gene expression to tissue structure, yet current models learn embeddings whose meaning is assigned afterwards or read histology without measured expression. Tissue decisions are therefore rarely made from morphology, genes and neighbourhood together, or examined through them. Here we introduce VINTER, a generative vision-language model aligned on 1.45 million histology-caption pairs that reads histology, measured gene expression and spatial neighbourhood as one sequence. It returns the probability of every tissue class, and because genes enter as text, its molecular evidence can be edited with the image held fixed. Reading all three sources together, VINTER annotated tissue more accurately than spatial-domain methods and a spatial foundation model, and transferred across cohorts without target labels. Editing only the neighbourhood resolved the follicular B-cell and plasma-cell programmes behind its reading of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. Its class probabilities exposed what single labels conceal in triple-negative breast cancer: immune heterogeneity within stroma and cancer hidden beneath benign labels. Without retraining, the same model transferred to Visium HD and single-cell Xenium data and resolved follicular architecture within colorectal cancer TLS. VINTER thus turns spatial annotation into an interrogable reading of tissue across platforms and resolutions.

bioinformatics↗

bioRxiv2026

Multi-vector retrieval enables residue-resolved prediction of protein partners by ColBERT-PPI

Protein-protein interactions (PPIs) are central to biological processes, making the identification of both interacting partners and their binding sites important for understanding molecular function and guiding therapeutic discovery. However, connecting large-scale partner prediction to residue-level interaction evidence remains challenging. Here we present ColBERT-PPI, a structure-aware multi-vector framework that links protein partner retrieval to interaction-site localisation. The model retains reusable residue-level representations and compares them in a partner-dependent manner, producing raw residue compatibility maps for localisation and calibrated smooth MaxSim scores for retrieval. On PINDER, ColBERT-PPI achieved an area under the precision-recall curve of 0.482, compared with 0.151 for the strongest evaluated external comparator. Both complete and sequence-only multi-vector models outperformed the single-vector control on an independent human yeast two-hybrid screen. Residue-level comparisons highlighted experimental interfaces, captured changes in local evidence across partners and achieved a mean contact AUROC of 0.806 versus 0.526 for the single-vector control. After task-specific fine-tuning, PPI-trained representations also improved protein-RNA interaction retrieval, indicating transfer beyond the molecular context in which they were learned. By connecting candidate partners with their supporting residue-level evidence, ColBERT-PPI provides a computational basis for investigating physiological and disease-associated interactions and prioritizing proteins and interaction regions for therapeutic exploration. Code is available at https://github.com/UR-Free/ColBERT-PPI.

bioinformatics↗

bioRxiv2026

Concurrent AXL inhibition enhances RAS and ERK inhibitor efficacy in KRAS-mutant pancreatic and lung cancer

Resistance limits the clinical efficacy of RAS inhibitors. We applied chemical and genetic screens and identified the AXL receptor tyrosine kinase as a driver of resistance to RAS-ERK inhibition. We determined that combination treatment with the AXL inhibitor bemcentinib (AXLi) together with the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 (RASi) or the ERK-selective inhibitor SCH772984 (ERKi) significantly enhanced growth suppression in human KRAS-mutant pancreatic and lung cancer models. Combined AXLi and RASi treatment of human KRAS-mutant pancreatic cell line-derived xenograft tumors synergistically suppressed ERK activation and MYC expression, and caused tumor regression. Analyses of immunocompetent mouse allograft pancreatic tumor models revealed a largely tumor cell-intrinsic response to inhibitor treatment. We identified an unexpected mechanism whereby KRAS inhibition upregulated the AXL ligand GAS6, activating AXL but inducing an AXL-dependent adaptive resistance mechanism wherein AXL antagonizes RASi efficacy. Our observations support concurrent AXL inhibition as a strategy to enhance RAS inhibitor clinical efficacy. STATEMENT OF SIGNIFICANCEOur findings identify AXL as a driver of resistance to RAS inhibitors, establishing a combination strategy to overcome resistance and enhance RAS inhibitor therapeutic efficacy in KRAS-mutant cancer by maximally inhibiting oncogenic RAS signaling.

cancer biology↗

bioRxiv2026

Molecular principles for graded activation of dopamine D1 receptor

G protein-coupled receptors (GPCRs) signal across a continuum of activation states, yet how ligands encode distinct signaling efficacies remains poorly understood. Here, we define the molecular mechanism underlying graded activation of the dopamine D1 receptor (D1R), a major therapeutic target for neuropsychiatric disorders. Functional analyses reveal that two widely used pharmacological tools, LE300 and SCH23390, possess intrinsic efficacy as an inverse agonist and a weak partial agonist, respectively, rather than the efficacy-silent neutral antagonists. Structural, molecular dynamics and mutagenesis analyses capture previously unrecognized inactive and intermediate receptor activation states that bridge known active conformations, and reveal that ligand efficacy is encoded through the progressive engagement of a conserved activation pathway centered on the W6.48 toggle switch. Guided by this mechanism, a single chemical modification markedly increases the agonist efficacy of SCH23390. Comparison with dopamine D2 receptor structures further reveals a conserved mechanism of inverse agonism despite distinct subtype-specific recognition. Together, these findings establish a structural framework for graded agonism at D1R and provide general principles for the rational design of efficacy-tuned therapeutics at dopamine receptors and related GPCRs.

biochemistry↗

bioRxiv2026

A five-dimensional functional state space for fingerprinting disease transcriptomes

High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (5-D) functional state space that represents disease transcriptomes as coordinated activity patterns across major biological systems. The framework maps transcriptomic signals onto five functional systems, 14 subcategories, and a distinct infrastructure layer, and was implemented as a reproducible pipeline for functional scoring, cross-condition profiling, benchmarking, and large language model (LLM)-assisted interpretation. Applied to wound healing, sepsis, colorectal cancer-related datasets, an extended GEO atlas of 38 complete case-control disease fingerprints spanning diverse disease contexts, and a TCGA-COAD/READ stage benchmark, the approach recovered interpretable disease-state patterns and retained progression-related information under strong compression. It also improved the quantitative grounding of LLM-generated summaries. This framework provides a compact and auditable representation for comparing disease transcriptomes and supporting human-AI biological interpretation. TeaserA five-dimensional functional framework turns complex disease transcriptomes into compact, interpretable fingerprints for human-AI analysis.

bioinformatics↗

bioRxiv2026

Machine Learning Gap-Fills Missing Transporter Kinetics in Biosystems Across Scales

Understanding transporter kinetics is essential for deciphering metabolite exchanges in biosystems, particularly for cells subject to substrate gradients. Nevertheless, the prediction of transporter kinetic parameters, maximum rate per gram protein (Vmax) and Michaelis-Menten constant (Km), has not yet been tackled. Here, we developed the first compound-protein interaction machine learning model of transporter Vmax and Km, MMTKPred, which achieved R2=0.553, RMSE=1.155 mmol/hr/g Protein and R2=0.330, RMSE=0.935 mM for log10-scaled Vmax and Km prediction, respectively. Moreover, we demonstrated MMTKPreds predictive power across biosystem scales, from capturing transporter kinetics modulated by point mutations and substrate changes at the molecular level, to enabling substrate-sensitive metabolic modelling of non-model yeasts at the cellular level, and rationalizing inter-species substrate competition in co-cultures. Collectively, MMTKPred effectively models metabolite transport spanning from molecular to multi- species scales, thereby offering a computational tool for rational microbial cell factory optimization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/735998v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@18e7bb6org.highwire.dtl.DTLVardef@15bec93org.highwire.dtl.DTLVardef@8ce92org.highwire.dtl.DTLVardef@31fb44_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMMTKPred, first transporter kinetics CPI model, reaches [~]1 log10 RMSE for Vmax and Km. C_LIO_LIMMTKPred captures the effects of point mutations and substrate changes on transporters. C_LIO_LIPredicted kinetics enables substrate sensitivity in metabolic flux modelling. C_LIO_LIPredicted kinetics explains inter-species substrate competition outcomes. C_LI

systems biology↗

bioRxiv2026

Accurate de novo design of peptides from programming biophysical landscape

Peptides regulate virtually every cellular process and emerge as transformative modalities in bioengineering and therapeutics. However, the de novo design of functional peptides remains challenging, as peptide-protein interactions are governed by delicate and dynamic biophysics that are difficult to model, and experimental datasets are scarce. Here we present NeoPep, a generative deep-learning framework that encodes biophysical principles to accurately design functional peptides de novo. By integrating over 5 million peptide-protein complexes spanning experimentally determined, sequence mimics, and structure ensembles, NeoPep learns the complex biophysical landscape governing peptide function and enables its programmable control. In prospective application across 10 diverse and challenging targets, NeoPep generated potent peptide binders, agonists, and antagonists with hit rates of 12.5-66.7%, even in the absence of defined binding sites or structure information. Beyond de novo co-design, NeoPep supports standalone structure or sequence redesign, readily discriminating subtle context differences. In the structure redesign mode, it generates highly selective peptides with atomic-level conformational accuracy (C RMSD < 2.0 [A] to our solved cryo-EM structure). This structural precision circumvents the need for experimental structure determination, further accelerating iterative sequence redesign to yield a 43.3-fold improvement in potency. These findings establish a general framework for translating biophysical principles into actionable peptide functions, with broad implications for basic research, bioengineering, and medicine.

molecular biology↗

bioRxiv2026

Candida albicans reprograms host inosine metabolism to drive immunosuppressive macrophage polarization and gastric cancer carcinogenesis

Gastric cancer (GC) is strongly associated with changes in the gastric microbiome. However, the direct contribution of individual pathogenic species to tumor initiation and progression remains poorly defined. Here, we identified Candida albicans (C. albicans), a fungus that ranks as the most significantly enriched taxon in GC tissues, can directly promote gastric carcinogenesis. We found that when C. albicans alone was colonized into the gastric mucosa of mice, it induced the complete sequence of GC-associated preneoplastic lesions, including chronic gastritis, mucinous metaplasia, and atypical hyperplasia, with 16% of colonized animals progressing to low-grade intraepithelial dysplasia. In K19-C2mE transgenic mice, a model of spontaneous GC formation, infection with C. albicans significantly accelerated GC formation, with synergistic effects observed upon co-infection with the well-known gastric carcinogen, Helicobacter pylori. Mechanistically, C. albicans upregulated inosine levels in both gastric tissues and circulation. This inosine promoted polarization of macrophages toward an M2-like phenotype by activating the adenosine A2a receptor (A2aR), creats an immunosuppressive microenvironment that protects GC cells from the tumoricidal effects of anti-tumor immunity. Depletion of M2 macrophages or pharmacological inhibition of A2aR attenuated C. albicans-mediated tumorigenesis. Clinically, elevated inosine levels correlated with prior C. albicans exposure and poorer outcomes of GC patients. Our findings establish C. albicans as a direct pro-tumorigenic pathogen in GC and highlight the inosine-A2aR-M2 macrophage axis as a promising target for therapeutic intervention.

cancer biology↗

bioRxiv2026

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

bioinformatics↗

bioRxiv2026

Transcranial direct current stimulation combined cognitive training modulates risk-taking behavior in older adults

Risk-taking behavior is a complex cognitive function that often declines with aging, contributing to impaired decision-making and reduced quality of life. While transcranial direct current stimulation (tDCS) has shown promising effects in modulating cognitive function, its influence on ecologically valid, complex behaviors like risky decision-making in older adults remains poorly understood. We investigated whether a single-session stimulation over the medial orbitofrontal cortex (MOFC) combined with cognitive training could enhance decision-making in healthy older adults. In a randomized, sham-controlled study, bilateral MOFC stimulation (left/anode, right/cathode) was applied during a training task based on the Iowa Gambling Task. Pre- and post-intervention assessments utilized conventional behavioral measures and the Values-Plus-Perseveration computational model, alongside task-related fMRI to examine MOFC network changes. Compared to sham, tDCS significantly enhanced the ability to distinguish advantageous from disadvantageous options. Modeling analysis revealed stimulation-induced changes in multiple latent components, such as learning rate, loss aversion, and perseveration decay. Generalized Psychophysiological Interaction analysis showed that tDCS reconfigured the MOFC network by reducing fronto-frontal hyper-connectivity and enhancing fronto-striatal connectivity. These behavioral improvements were specifically associated with the left MOFC network targeted by anodal stimulation. Our results provide causal evidence that tDCS can mitigate age-related impairments in risk-taking by reconfiguring MOFC-related networks. These findings advance the mechanistic understanding of tDCS in aging and highlight its potential as an intervention to prevent age-related decline in decision making.

neuroscience↗

bioRxiv2025

Landscape Expansion Microscopy Reveals Interactions between Membrane and Phase-Separated Organelles

Landscape Expansion Microscopy (land-ExM) is a light microscopy technique that visualizes both lipid and protein ultrastructural context of cells. Achieving this level of detail requires both superresolution and a high signal-to-noise ratio. Although expansion microscopy (ExM) provides superresolution, obtaining high signal-to-noise images of both proteins and lipids remains challenging. Land-ExM overcomes this limitation by using self-retention trifunctional anchors to significantly enhance protein and lipid signals in expanded samples. This improvement enables the accurate visualization of diverse membrane organelles and phase separations, as well as the three-dimensional visualization of their contact sites. As a demonstration, we revealed triple-organellar contact sites among the stress granule, the nuclear tunnel, and the nucleolus. Overall, land-ExM offers a high-contrast superresolution platform that advances our understanding of how cells spatially coordinate interactions between membrane organelles and phase separations. eTOC SummaryZhuang et al. introduce land-ExM, a super-resolution approach that simultaneously maps protein and lipid ultrastructure in cells with high contrast. This method visualizes 3D interactions between membrane-bound organelles and phase-separated condensates, uncovering organelle contact sites such as stress granules at nuclear tunnels adjacent to nucleoli.

cell biology↗

bioRxiv2025

Study on Dianthus superbus L. against Acute Liver Injury via the Gut-Liver Axis -- Regulation of Gut Microbiota and Tryptophan Metabolism to Activate AhR Signaling

BackgroundDianthus superbus L. (DS) treats liver diseases, but its ALI mechanism via gut microbiota/metabolites is unclear. PurposeInvestigate DS effects on CCl-induced ALI and mechanisms. MethodsDS components (HPLC-MS/MS) were identified. ALI rats received DS. Liver/colon injury, fibrosis (biochemistry, histology, IHC, IF, WB for -SMA, Collagen I, Occludin, ZO-1), serum metabolomics, fecal 16S rRNA, and tryptophan-targeted metabolomics were assessed. Antibiotics and FMT from DS-treated donors explored microbiota roles. Results37 components, including Kaempferol and Aurantiamide, were identified. DS attenuated ALI, reducing hepatic -SMA/Collagen I while increasing AhR/Cyp1a1, and upregulating colonic Occludin/ZO-1. FMT replicated DS benefits. Untargeted metabolomics linked effects to Linoleic/Tryptophan metabolism. 16S rRNA showed increased beneficial microbiota (HT002, Lactobacillus, Romboutsia); FMT confirmed Lactobacillus enrichment. DS normalized tryptophan metabolites (4-Hydroxybenzoic acid, L-Kynurenine, Indole-3-carboxaldehyde). ConclusionDS ameliorates ALI gut-microbiota-dependently by modulating tryptophan metabolism, enhancing intestinal barrier function, activating AHR signaling, and suppressing fibrosis biomarkers, indicating its potential as a microbiota modulator for ALI. ImportanceAcute liver injury is a serious health threat with limited treatment options. This study explores how a traditional medicinal herb, Dianthus superbus L. (DS), protects the liver through an unexpected route: the gut. We discovered that DS works by rebalancing the community of gut bacteria, which in turn improves the intestinal barrier and fine-tunes the bodys tryptophan metabolism. This cascade of effects ultimately activates a key cellular pathway (AhR) that reduces liver inflammation and fibrosis. Our findings not only clarify how this traditional remedy works but also highlight the critical role of the gut-liver axis, positioning DS as a promising, microbiota-targeting therapeutic candidate for preventing and treating liver disease.

microbiology↗

bioRxiv2025

Multimodal AI Decodes Extreme Environment Functional Dark Matter Beyond Homology

Functional annotation of proteins from extreme environments represents a major bottleneck for bioresource discovery, as a vast reservoir of functional dark matter defies existing homology-based methods. We demonstrate that environmental pressures impart conserved physicochemical energy signatures that co-determine protein function with sequence and structure. Here we developed ACCESS, a multimodal graph neural network employing hierarchical contrastive learning with a tailored label-sample co-embedding to fuse energy, sequence, and structural information and overcome homology scarcity. ACCESS surpasses state-of-the-art methods including BLASTp and CLEAN in annotating low-identity enzymes. Applied to extreme environmental metagenomics, we constructed a function map of extremophile enzymes to expand the biocatalyst library, pinpointed functionally critical residues to guide rational design, and enabled large-scale, function-based macro-evolutionary analyses. This paradigm transcends the limitations of homology, illuminating protein dark matter and accelerating the exploration of the biospheres functional diversity for applications in biotechnology and therapeutic development.

bioinformatics↗

bioRxiv2025

Privacy-Preserving Genomic Data Sharing via Hybrid AES/ECC and Blockchain-Based Dynamic Consent

Genomic data are growing exponentially, creating opportunities for discovery but also raising significant concerns regarding privacy, security, and governance. We present a framework that encrypts genome-scale files using AES-256-GCM and protects keys per recipient via Curve25519, anchors only content identifiers and encrypted keys on a blockchain smart contract, and stores ciphertext off-chain in IPFS. The design enables dynamic, auditable consent through on-chain access-control lists (grant/revoke with immutable events) while keeping raw genomes off-chain. A working prototype on the Ethereum Sepolia testnet demonstrates end-to-end registration, retrieval, and revocation. Experiments on the human reference genome (hg38) and synthetic sequences demonstrate near-AES performance for multi-gigabyte files and strong diffusion, as per the avalanche criterion. By separating the data plane (symmetric encryption) from the control plane (public-key keying + smart contracts), the system delivers practical, privacy-preserving genomic data sharing suitable for IRB-approved cohort and clinical workflows.

bioinformatics↗

bioRxiv2025

Multiple Allosteric Sites Allow for Synergistic Enhancement of GPCR Signaling

Allosteric modulation of G protein-coupled receptors (GPCRs) is an emerging therapeutic paradigm that has proven effective, yet the cooperative action of multiple modulators remains unexplored. Here, we reveal how positive allosteric modulators (PAMs) synergistically enable extraordinary signal amplification through the D1 dopamine receptor (D1R). We developed UNC9815 and UNC10062 as enhanced D1R PAMs from the parent compound MLS6585, and then employed cryo-electron microscopy to reveal concurrent occupancy of three distinct allosteric sites by PAMs of different scaffolds, including LY3154207, BMS-A1, and our UNC compounds. Remarkably, we discovered two adjacent allosteric pockets at the transmembrane helix (TM) 1-7 interface: BMS-A1 occupies an intracellular site promoting activation through TM7 conformational shifts, while the UNC compounds complementarily stabilize the extracellular side of the interface. When combined with LY3154207, this cooperative architecture enhances dopamine potency by more than 1,000-fold. These findings elucidate the first structural blueprint for multi-site GPCR cooperativity, unlocking transformative therapeutic strategies inaccessible to orthosteric and single- site allosteric drugs.

molecular biology↗

bioRxiv2025

Fragile nucleosomes are essential for RNA Polymerase II to transcribe in eukaryotes

Nucleosomes are barriers to RNA Polymerase II (Pol II) transcribing along gene bodies in eukaryotes. We found that a fragile "tailless nucleosome" could be generated to resolve the Pol II pausing caused by the +1 nucleosome in metazoans. We hypothesized that "pan-acetylated nucleosomes" along gene bodies are fragile and are generated to resolve the resistance of nucleosomes to the elongation of Pol II. To confirm this hypothesis, we systematically analyzed pan-acetylation and pan-phosphorylation of the entire human genome in HEK293T cells. Herein, we show that both pan-acetylation and pan-phosphorylation of histones in nucleosomes along gene bodies are coupled with the activities of Pol II. Our results show that there are four major types of fragile nucleosomes required for Pol II to transcribe along gene bodies: nucleosomes without tails, nucleosomes with dominant acetylation, nucleosomes with dominant phosphorylation, and nucleosomes with both acetylation and phosphorylation.

genetics↗