Biology subjects

Gao, P.

Publications and source records attributed to Gao, P..

At least 19 recordsLinked to original sources

bioRxiv2026

An engineered IdeS variant with enhanced activity and performance for IgG degradation

IgG-degrading enzymes have emerged as innovative therapeutic agents for treating conditions driven by pathogenic antibodies. Here, we used structure-guided rational design to engineer IdeSM33, a double mutant (K167R/D226E) of the IgG-specific bacterial protease IdeS from Streptococcus pyogenes, with improved catalytic efficiency. Biolayer interferometry revealed a fourfold increase in binding affinity relative to wild-type IdeS (IdeSWT). This enhancement is likely attributable to mutations that strengthen hydrogen bonding at the enzyme-IgG Fc interface. In vitro, IdeSM33 has higher performance than IdeSWT in cleaving serum IgG. In vivo studies in rabbits demonstrated that IdeSM33 effectively depleted circulating IgG and showed better performance at a dose of 0.005 mg/kg than the IdeSWT. Although doses greater than 0.2 mg/kg demonstrated higher plasma concentrations of IdeS and a larger AUClast, they did not show a significant enhancement in the pharmacodynamics of IgG degradation. Importantly, a single dose of IdeSM33 (0.2 mg/kg) potently degraded binding and neutralizing antibodies against AAV9 within 1-2 days and restored hepatic AAV9 transduction in pre-immunized animals. Together, these findings highlight IdeSM33 as a potent and safe engineered enzyme with therapeutic potential for autoimmune disorders, transplant rejection, and overcoming pre-existing humoral immunity in gene therapy.

bioengineering↗

bioRxiv2026

The emergent mental operation of the learning brain

How does the learning brain give rise to emergent mental operations that enable skill internalization and generalization? Using five-year longitudinal tracking of children acquiring abacus-based mental calculation, we reveal how sustained practice progressively reconfigures whole-brain states. Learning induces nonlinear transitions from stable baseline configurations through a transient exploratory phase toward restabilized associative brain networks. This hierarchical remodeling cascades from perceptual systems to action and default networks, mediated by adaptive neural variability and selective connectivity reweighting, with the salience-parietal memory network acting as a dynamic relay. These brain-state transitions accompany an ordered cognitive transfer cascade, emerging within arithmetic and extending to visuospatial and executive functions. Our findings are reproducible and provide a systems-level account of how ecological learning sculpts large-scale networks to enable complex skill acquisition.

neuroscience↗

bioRxiv2026

An orthogonal near-infrared optical switch for wireless neuromodulation in freely behaving mice

Modulating neuronal activity with light is a powerful tool for neuroscience research. However, currently available technologies often require invasive fibers for delivery of visible light, causing tissue damage and limiting behavioral studies. Although near-infrared (NIR) neuronal modulation improves tissue penetration depth, sustained NIR illumination during neuromodulation raises significant concerns about photothermal effects. Here, we introduce a dual-wavelength near-infrared switch (Dual-NIR Switch) that uses two transcranial NIR inputs to initiate and terminate neuronal activation, enabling tether-free neuromodulation in freely moving mice. Dual-NIR Switch employs orthogonal dichromatic upconversion nanoparticles that emit blue and green light under 980 nm and 808 nm excitation, respectively, to activate and inactivate the step-function opsin SOUL. Therefore, transient 980 nm NIR illumination initiates neuronal excitation, which will remain excited without further stimulation but will be rapidly terminated on demand by a subsequent 808 nm illumination. Upon transcranial 980 nm and 808 nm NIR illuminations in freely moving mice, we achieve on-demand control of behavioral paradigms across tunable timescales, ranging from seconds to minutes and even extending to sub-hour durations. By eliminating the need for sustained NIR irradiation, Dual-NIR Switch offers an on-demand, duration-tunable neuromodulation tool for both basic neuroscience and potential therapeutic applications in treating brain diseases.

neuroscience↗

bioRxiv2026

Complete genome-derived metabolic interactions reveal impact of gut ecology on human health

Metabolic interactions govern gut microbiome assembly, yet functional rules remain obscured by genomic incompleteness and fragmentation. Here, we leverage 1,150 complete genomes to construct genome-scale metabolic models, demonstrating that draft genomes introduce systemic artifacts missing critical transport functions. We observe that metabolic interactions are intrinsically driven by genomic traits and niche specialization rather than random association. We stratifies strains into four ecological groups, including active players, resource predators, utilizers, and contributors, each of which exhibits distinct metabolite exchange/competition signatures. This functional architecture is strongly coupled to secondary metabolism. Applying this framework to inflammatory bowel disease revealed distinct temporal dynamics of ecological groups across disease subtypes, where group-specific dysbiosis predicted clinical phenotypes better than whole community profiles. Furthermore, integrating metabolic and co-occurrence networks to identify keystone for classifiers as features significantly improved disease diagnosis. Our study provides novel insights into gut metabolic interaction landscape, bridging the gap between genomic potential and clinical utility for precision microbiome therapeutics.

microbiology↗

bioRxiv2026

Complete Genomes of Cultivated Gut Bacteria Reveal Mobile Genetic Element-Driven Functional Diversity with Therapeutic Implications

Cultivated bacterial isolates are essential for elucidating gut microbiota functions, yet reliance on fragmented draft genomes and limited mobile genetic element (MGE) annotation has constrained high-resolution analyses. Here, we present the Cultivated Complete Genome Reference (CCGR), a comprehensive compendium of 1,150 fully circularized human gut bacterial genomes. Overcoming draft limitations, CCGR achieves chromosome-scale resolution and reveals distinct evolutionary strategies shaping gene topological organization in response to bacterial growth. Furthermore, we decode the dynamic landscape of MGEs, demonstrating that broad-host-range phages follow strict positional patterns by integrating at conserved chromosomal hotspots flanked by nutrient transporter loci. Importantly, we provide mechanistic in vivo evidence that accessory genomic elements drive strain-level functional heterogeneity. Specifically, the plasmid-encoded scrK gene in Levilactobacillus brevis involved in fructose catabolism is required to mitigate fructose-diet-exacerbated colitis. Collectively, CCGR establishes a framework linking chromosomal architecture and MGEs to bacterial evolution and host health, enabling precise, strain-resolved functional investigations.

microbiology↗

bioRxiv2026

DNA-triggered AIM2 condensation orchestrates immune activation and regulation

The innate immune sensor AIM2 detects cytosolic DNA and initiates inflammatory responses, yet its activation mechanism remains incompletely understood. Here, we show that AIM2 undergoes liquid-liquid phase separation upon DNA binding, forming dynamic condensates both in vitro and in cells. These condensates serve as platforms for inflammasome and PANoptosome assembly, promoting immune activation across multiple pathways. Direct structural determination from condensates reveals the assembly of active-form ASC filaments. Mechanistically, liquid-phase condensation is governed by multivalent interactions involving different AIM2 domains, including previously uncharacterized regions and species-specific elements. In vitro and in vivo assays show that mutants specifically disrupting condensation impair immune complex assembly, cell death initiation, antimicrobial defense, and intestinal homeostasis. Moreover, AIM2-DNA condensates function as regulatory hubs targeted by host- and pathogen-derived factors to balance immune homeostasis or facilitate immune evasion. These findings establish liquid-phase condensation as a fundamental mechanism of AIM2 activation and a potential therapeutic target.

immunology↗

bioRxiv2026

ProMeta: A meta-learning framework for robust disease diagnosis and prediction from plasma proteomics

The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of [~] 0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts.

bioinformatics↗

bioRxiv2026

A curvilinear coordinate flatmap for visualizing hippocampal structure and development

The hippocampal formation is a highly curved and topographically complex forebrain structure. This complex geometry presents persistent challenges for analyzing subregional, laminar, and connectivity patterns. Here, we present a computational workflow that generates curvilinear-coordinate flatmaps from Common Coordinate Framework (CCF) registered hippocampal and retrohippocampal regions by solving the Laplacian equation to derive geodesic streamlines. This transformation unfolds the hippocampus into a planar slab, bounded by the meningeal and ventricular surfaces, with the depth defined along the radial axis. We apply this transform to image volumes, single neuron reconstructions, and point data, including spatial transcriptomic and rabies tracing datasets, revealing topographic variations in the dorsoventral and radial axes that are obscured in the CCF coordinate space. As proof of principle, we use flatmaps to show connectivity loss in a mouse model of Alzheimers disease and track postnatal development of microglial distribution in the hippocampus. This work provides an efficient and accessible resource for visualizing hippocampal organization across development and disease, offering new opportunities to interrogate the structure and function of this important brain region.

neuroscience↗

bioRxiv2025

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networks

The emergence of large-scale biobanks has opened unprecedented opportunities for the development of data-driven approaches, especially deep learning-based methods, for genotype-to-phenotype (G2P) prediction. However, designing an end-to-end framework capable of directly leveraging extremely high-dimensional genotypic data while simultaneously mitigating overfitting and ensuring robust prediction remains a significant challenge. In this study, we introduce PRSNet-2, an interpretable end-to-end deep learning framework designed to predict complex phenotypes directly from large-scale genotypic data. PRSNet-2 proposes a novel hierarchical graph neural network (GNN) architecture, which first employs a multi-kernel aggregator to map high-dimensional genotypic features to gene-level representations. Next, it models gene-gene interactions through message-passing operations and uses an attention-based readout module to generate interpretable phenotypic predictions. We further introduce significance-guided regularization strategies to boost models generalizability based on prior genetic associations. Extensive empirical evaluations across multiple complex traits and diseases demonstrate that PRSNet-2 consistently outperforms a variety of baseline methods and exhibits superior capabilities in overcoming overfitting, even when modeling around half a million single nucleotide polymorphisms (SNPs). Moreover, PRSNet-2 is shown to be easily extendable to integrate multiple genome-wide association study (GWAS) datasets into a single model, thereby enhancing predictive performance for both single-phenotype and multi-phenotype prediction tasks. The inherent interpretability of PRSNet-2 further facilitates the identification of disease-relevant genes, functional gene modules, and potential therapeutic targets. In summary, PRSNet-2 offers a powerful, versatile tool for both genetic risk stratification and the discovery of biological insights.

genetics↗

bioRxiv2025

Elevated Temperatures Representing Heatwave Conditions Shift Active Nitrifying Communities and Their Viruses in Tidal Flats and Agricultural Soils

Global heatwave intensification associated with climate change will impact the nitrogen cycle, yet its effect on specific nitrifier groups or their interactions with viruses remains unclear. Using 13CO2-DNA-based stable isotope probing (SIP) coupled with metagenomics, we show that elevated temperatures associated with heatwave conditions restructure active nitrifying communities and their viruses in Yangtze River estuary tidal flats and adjacent agricultural soils. In tidal flats, high temperatures shifted active ammonia-oxidizing archaea and bacteria (AOA and AOB), and nitrite-oxidizing bacteria (NOB) from marine to terrestrial ecotypes. In contrast, heatwave conditions stimulated terrestrial ecotypes of AOA but suppressed AOB in agricultural soils. 13C-labeled nitrifier-infecting viruses also showed temperature-driven shifts in activity, lifestyle, and auxiliary metabolic genes in concert with hosts. Notably, AOA viruses carried the plastocyanin gene, potentially augmenting host metabolism. These findings demonstrate heatwaves drive complex shifts in nitrifier communities and their interactions with viruses, impacting global nutrient cycling under climate extremes.

microbiology↗

bioRxiv2025

Prolactin links psychological stress to psoriasis via a fibroblast-chemokine pathway

Psoriasis is a chronic inflammatory skin disease that forms a vicious cycle with psychological stress. Whether and how the hypothalamic-pituitary (HP) axis-mediated neuroendocrine system regulates psoriasis remains obscure. Here, we report elevated levels of the pituitary hormone prolactin (PRL) in both psoriasis patients and imiquimod (IMQ)-induced psoriasis mice. Mechanistically, PRL acts on dermal PRLR-expressing fibroblasts to promote the production of the chemokines CCL2 and CCL7, which then recruit monocytes/macrophages into psoriatic lesional skin, thereby activating local IL-17A-producing T cells. Accordingly, pharmacological targeting of PRL signaling inhibits the recruitment of monocytes/macrophages, decreases the frequency of IL-17A-producing T cells, and alleviates IMQ-induced psoriasis in mice. In summary, our results delineate a mechanism by which the neuroendocrine hormone PRL aggravates psoriasis and highlight a potential therapeutic strategy of inhibiting PRL-PRLR signaling, particularly in psoriasis patients experiencing psychological stress.

immunology↗

bioRxiv2025

Leveraging Large Language Models for Redundancy-Aware Pathway Analysis and Deep Biological Interpretation

Extracting coherent, biologically meaningful insights from vast, complex multi-omics data remains challenging. Currently, pathway enrichment analysis serves as a cornerstone for the functional interpretation of such data. However, conventional approaches often suffer from extensive functional redundancy caused by shared molecular components and overlapping pathway definitions across databases. This redundancy can obscure key biological signals and compromise the interpretability of pathway enrichment results. Here, we present MAPA (Functional Module Identification and Annotation for Pathway Analysis Results Using Large Language Models [LLM]), an open-source computational framework that resolves redundancy and enhances pathway analysis result interpretation. MAPA computes functional similarity between pathways using LLM-based text embeddings, enabling comparison across different databases. It constructs pathway similarity networks and identifies functional modules via community detection algorithms. Crucially, MAPA employs LLMs for automated functional annotation, integrating Retrieval-Augmented Generation (RAG) to generate comprehensive and real-time biological summaries and reduce hallucinations. Benchmarking demonstrated MAPAs superior performance: the biotext embedding similarity showed a large effect size (Cliffs {delta} = 0.96) compared with the Jaccard index ({delta} = 0.73), and module identification achieved high accuracy (Adjusted Rand Index [ARI] = 0.95) versus existing methods (ARI = 0.23-0.33). Human expert evaluation confirmed that MAPAs annotations match expert-quality interpretations. Finally, a multi-omics aging case study illustrates that MAPA uncovers coherent functional modules and generates insights extending beyond conventional pathway analyses. Collectively, MAPA represents a significant advance in redundancy-aware pathway analysis, transforming pathway enrichment results from fragmented lists into biologically coherent narratives. By leveraging the capabilities of LLMs, MAPA offers researchers a robust, scalable tool for deriving deep mechanistic insights from complex and vast multi-omics datasets, marking a new direction for AI-driven bioinformatics.

bioinformatics↗

bioRxiv2025

A new subgenome of the Camelina genus reveals genome dominance is controlled by chromosomal proximity

Camelina sativa is an oilseed of the Brassicaceae, whose close relatives vary in ploidy number, providing a novel platform for studying plant genome evolution. The availability of diploid, tetraploid and hexaploid species of Camelina allow the evolutionary trajectory and fate of duplicated genes in the neopolyploid Camelina species to be elucidated. Here we report an improved assembly of the widely used C. sativa reference DH55 and three new genome assemblies of Camelina microcarpa; one tetraploid CN119243 (2n = 26), and two hexaploids with divergent chromosome numbers, Type 1 - CN119205 (2n=40) and Type 2 - CN120025 (2n=38). The tetraploid represents the first step in the evolutionary path to form C. sativa, while the hexaploids suggest three divergent lineages in the formation of higher ploidy Camelina species. The previously uncharacterized fourth subgenome found in C. microcarpa Type 2, although showing some homology to the C. sativa diploid progenitor genome, C. neglecta, showed numerous unique chromosomal rearrangements differentiating it from other subgenomes present in known Camelina species. Although this species was recently formed, the second subgenome showed gene expression dominance, which was in contrast to both 2n=40 Camelina species where the third subgenome was dominant. The expression dominance in Type 2 C. microcarpa contradicted the accepted two-step evolutionary process which led to the generation of related Brassicaceae species. However, the observed genome dominance in all Camelina species was negatively correlated with inter-subgenome chromatin interaction frequencies, suggesting that chromosome confirmation and proximity in the nucleus contributes to this mechanism of genome evolution. Despite the differences in genome structure, successful inter-specific hybridization provided evidence of chromosomal exchange between the divergent third sub-genomes of C. sativa and C. microcarpa Type 2, opening up a novel avenue to new diversity in the established oilseed. Key pointsO_LIAn improved genomic understanding of Camelina species and identification of distinct subgenome structures and relationships, which will facilitate strategies to increase the genetic diversity in C. sativa. C_LIO_LISubgenome evolution and subgenome dominance in polyploids is associated with chromosomal architecture and proximity in the nucleus. C_LIO_LIGenome assemblies representing all ploidy levels in the Camelina genus provide a unique and valuable platform for polyploid research. C_LI

genomics↗

bioRxiv2025

Impact of Inactivation Methods on Biosafety and Antigen Reactivity of Brucella melitensis from the Perspective of Astral-DIA Proteomics Based on Antibody Immunoprecipitation Mass Spectrometry

Effective Brucella inactivation is imperative for safe vaccine development, diagnostics, and sample handling, particularly in resource-limited regions lacking high-level containment facilities. This study investigated inactivation methods using the Rev.1 vaccine strain and three Brucella melitensis field isolates from Gansu (GS-XG, GS-SN, GS-MQ). Heat (80{degrees}C/95{degrees}C, 10-20 min) and formaldehyde (0.4%/0.6%, 48-72 h) inactivation were evaluated for biosafety and antigenicity. Rev.1 was completely inactivated by 80{degrees}C/95{degrees}C for 20 min or 0.6% formaldehyde for 48-72 h. Superior antigenicity, compared to phenol inactivation, was confirmed by ELISA and Western Blot. However, field isolates demonstrated greater resistance, surviving 80{degrees}C for 20 min and 0.4% formaldehyde for 72 h, necessitating stricter conditions (95{degrees}C, 20 min; 0.6% formaldehyde, 72 h) for their complete inactivation. Astral-DIA proteomics analyzed approximately 60% of the proteome ([~]2000/3300 proteins), revealing 256, 311, and 318 differentially expressed proteins between 80{degrees}C/95{degrees}C heat, 48-h/72-h formaldehyde, and heat/formaldehyde methods, respectively. Gene Ontology and KEGG analyses indicated heat inactivation upregulated cellular structure proteins but downregulated metabolic pathways, with 95{degrees}C potentially damaging conformational epitopes. Formaldehyde treatment (48 h) stabilized soluble antigens, preserving ribosomal and regulatory protein epitopes, while 72-h treatment induced organelle disintegration. Protein-Protein Interaction networks suggested heat inactivation enhanced immunogenicity of membrane and stress proteins, suitable for targeted studies, whereas formaldehyde preserved broader epitopes, beneficial for whole-cell vaccines and multi-epitope screening. Inactivation methods must be tailored to specific strain characteristics and applications. Astral-DIA provides molecular insights into antigenicity loss, guiding future research on protein functions and epitope dynamics for precise brucellosis control.

microbiology↗

bioRxiv2025

Cytosolic DNA structures produced by mismatch-repair deficiency coordinate anti-tumor immunity in colorectal cancer

Patients with the microsatellite instable (MSI) subtype of colorectal cancer (CRC) have better prognosis and immunotherapy response than patients with the chromosomally instable (CIN) subtype due to improved cytotoxic T cell responses from high neoantigen levels and production of the chemokines CXCL10 and CCL5 that recruit cytotoxic T cells. This high chemokine production in MSI CRCs is due to constitutive activation of the cytosolic DNA (cyDNA) sensor STING by specific features of MSI cyDNA that lead to more effective STING pathway activation. Here, we investigate the features of MSI and CIN cyDNA to identify structures that more effectively activate STING. We find that MSI cyDNA is enriched in G-quadruplexes which improve STING and CD8+ T cell activation. Additionally, MSI micronuclei are also more efficient at inducing chemokine expression than CIN micronuclei. However, micronuclei are less effective than free cyDNA at inducing anti-tumor immunity and instead lead to increased Treg activation and IL10 production. Overall, these data highlight the role of specific cyDNA structures in anti-tumor immunity and provide essential knowledge for improved design of therapeutic DNA-based STING agonists that could be combined with immune checkpoint therapies to improve the prognosis of poorly immunogenic tumors like CIN CRCs.

cancer biology↗

bioRxiv2025

MetaNet: a scalable and integrated tool for reproducible omics network analysis

Network analysis is a powerful strategy for uncovering complex relationships in high-throughput omics datasets. However, current tools often lack scalability, flexibility, and native support for multi-omics integration, posing significant barriers for exploring complex biological networks. To address these limitations, we developed MetaNet, a high-performance R package designed to construct, visualize, and analyze biological networks from multi-omics datasets. MetaNet supports highly efficient correlation-based network construction, scalable to datasets with over 10,000 features, and includes extensive layout algorithms and visualization options compatible with both static and interactive platforms. It also provides a comprehensive suite of topological and stability metrics for in-depth network characterization. Benchmarking results show that MetaNet outperforms existing R packages by up to 100-fold in computation time and reduces memory usage by up to 50-fold. We demonstrate its utility through two case studies: (1) a longitudinal analysis of microbial co-occurrence networks showing the dynamics of the airborne microbiome, and (2) an integrative exposome-transcriptome network of more than 40,000 features, uncovering distinct regulatory impacts of biological and chemical exposures. MetaNet bridges the gap between network theory and omics application by offering a robust, reproducible, and biologically informed framework for large-scale, interpretable, and integrative network analyses across diverse omics platforms, advancing systems-level understanding in modern life sciences. MetaNet is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/MetaNet).

bioinformatics↗

bioRxiv2025

TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis

Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) an intuitive graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8% to 58.8%, revealing coordinated changes in steroid hormone biosynthesis, carbohydrate metabolism, and amino acid processing. By expanding biological interpretation beyond conventionally MS2 spectra-based annotated metabolites, TidyMass2 enables more comprehensive metabolic phenotyping while upholding strict open-source principles of reproducibility, traceability, and transparency.

bioinformatics↗

bioRxiv2025

H3K79 methylation and H3K36 tri-methylation synergistically regulate gene expression in pluripotent stem cells

In metazoans, nucleosomes harboring H3K79 methylation (H3K79me) deposited by the histone methyltransferase DOT1L decorate actively transcribed genes. Although DOT1L is implicated in transcription regulation and pathogenesis of human diseases such as leukemia and neurological disorders, the role of H3K79me in these biological processes remains elusive. Here, we reveal a novel functional synergism between H3K79me and H3K36 tri-methylation (H3K36me3), another histone modification enriched at active genes, in regulating gene expression and neural cell fate transition. Simultaneous catalytic inactivation of DOT1L and the H3K36 methyltransferase SETD2 via gene editing leads to the global loss of H3K79me and H3K36me3, hyperactive transcription, and failures in neural differentiation. Interestingly, the loss of H3K79me and H3K36me3 causes increased transcription elongation, gained chromatin accessibility at a group of enhancers, and increased binding of TEAD4 transcription factor and its co-activator YAP1 at these enhancers. Furthermore, YAP-TEAD inhibition partially restores the expression levels of hyperactivated genes upon H3K79me/H3K36me3 loss. Taken together, our study demonstrates a synergistic role of H3K79me and H3K36me3 in regulating transcription and cell fate transition, unveils novel mechanisms underlying such synergism, and provides insight into designing therapies that target diseases driven by misregulation or mutations of DOT1L and/or SETD2.

genetics↗