Biology subjects

Gao, B.

Publications and source records attributed to Gao, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning

TCR specificity to peptide-HLA antigens is central to immunology, impacting responses in infection, autoimmunity and cancer. Achieving precise recognition while avoiding off-target reactivity is critical for effective immunity and safe therapeutic interventions. Comprehensive, proteome-wide specificity profiling of TCRs is challenging with current methods, which notably lack integrated machine learning for large-scale analysis. Here, we report a synthetic immune cell system coupled with machine learning to enable TCR functional and specificity mapping of peptide-HLA antigens at proteome-scale. Multi-step immunogenomic engineering of synthetic antigen-presenting cells (APCs) was performed to enable stable mono-allelic integration and precise display of peptide antigens and HLA class I from a defined genomic locus, ensuring genomically-encoded antigen presentation. Compatible with a synthetic TCR displaying T cell system, this platform incorporates a fluorescent reporter of cytokine-mediated signaling for real-time activation detection in both synthetic APCs and T cells. We combined this screening with diverse peptide antigen libraries and deep sequencing to train supervised machine learning models. These models were applied to predict TCR specificity to peptide-HLA antigens across the entire human proteome. Experimental validation confirmed novel off-targets for therapeutic TCR candidates, including for a clinically-approved TCR therapeutic. This integrated synthetic immune cell and machine learning approach provides unprecedented proteome-wide peptide-HLA specificity mapping to support the development of safer TCR-based therapies. One sentence summaryWe present a synthetic immune cell platform integrated with machine learning that enables prediction of TCR specificity to peptide-HLA antigens at proteome-scale.

immunology↗

bioRxiv2025

5-azacytosine induces cytotoxicity via 5-methylcytosine depletion on chromatin-associated RNA in leukemia

5-azacytidine (5-azaC) is a DNA hypomethylating agent clinically used to improve outcomes in myeloid malignancies. However, 5-azaC treatment causes gene dysregulation inconsistent with DNA hypomethylation changes, suggesting alternative mechanisms of action by 5-azaC. As a ribonucleoside analogue, 5-azaC is more readily incorporated into nascent RNA. Here, we demonstrate that RNA 5-methylcytosine (m5C) depletion by 5-azaC treatment, particularly at early time points, is sufficient to induce leukemia cell death. In contrast to its DNA demethylation function, the RNA-dependent effect of 5-azaC causes transcriptional repression, disrupting genes involved in cell cycle regulation and DNA repair. Mechanistically, 5-azaC impairs two specific m5C-mediated transcriptional regulatory pathways. First, depletion of m5C in chromatin-associated RNA (caRNA) disrupts the MBD6-mediated H2AK119ub deubiquitination. In parallel, this also impairs SRSF2 recruitment and the downstream H3K27ac deposition by p300. Indeed, loss of the caRNA methyltransferase NSUN2 caused prolonged cell cycle, defective DNA repair, and shifted hematopoietic lineage commitment toward erythropoiesis, mirroring the effects of 5-azaC treatment. Furthermore, we performed a leukemia cell line screen and identified that TET2 and IKZF1 depletion can sensitize 5-azaC treatment, consistent with the observed RNA-dependent cytotoxicity of 5-azaC in leukemic cells. In summary, our findings highlight the transcription repression by 5-azaC through depleting caRNA m5C, providing additional insights into the mechanism of action for 5-azaC, the prediction of its efficacy, and future directions for therapy developments based on 5-azaC. HIGHLIGHTO_LIRNA-dependent effects of 5-azaC are sufficient to drive leukemia cell cytotoxicity through transcriptional repression. C_LIO_LI5-azaC-induced caRNA m5C depletion impairs MBD6 binding and H2AK119ub deubiquitination. C_LIO_LI5-azaC-induced caRNA m5C depletion disrupts SRSF2 chromatin-binding, impeding p300 recruitment and H3K27ac deposition. C_LIO_LITET2 or IKZF1 depletion synergizes leukemia sensitivity to 5-azaC C_LI

molecular biology↗

bioRxiv2025

Peptides alleviate cognitive impairment by inhibiting and disassembling amyloid-beta aggregates in Alzheimer disease

Alzheimers disease (AD) is a devasting neurodegenerative disorder characterized by {beta}-amyloid formation, further exacerbated by RIPK1/RIPK3 necrosome-induced programmed necrosis (necroptosis). We previously showed that the RIPK1/RIPK3 necrosome forms a functional amyloid complex using its RIP homotypic interaction motifs (RHIMs). Here, we discovered that the core RIPK1/RIPK3 necrosome shares strikingly structural similarity to the C-terminal region of {beta}-amyloid (A{beta}42), and the RHIM-derived tetrapeptides (IQIG or VQVG) directly inhibit A{beta} aggregation, disassemble preformed A{beta} fibrils (PFFs), and reduce RIPK1 polymerization. Also, the peptides exhibit effective membrane permeability and could reduce A{beta}40 or A{beta}42-induced neural death and TNF-induced necroptosis in SH-SY5Y cells. IQIG and VQVG injected by ICV increase learning and memory abilities by reducing A{beta} plaques and hyperphosphorylated tau in the cortex and hippocampus of APP/PS1 double-transgenic mice. Mechanistically, the peptides directly interact with A{beta} to block A{beta} aggregation and alleviate microglia-mediated neuroinflammation. Strikingly, single-cell RNA sequencing revealed that the peptides-treated AD transgenic mice restored neuronal homeostasis with increased GABAergic neurons and decreased glutamatergic neurons. Furthermore, total cell-cell interaction strength increased while the AD risk gene Apoe expression decreased in the specific oligodendrocyte subtype of peptides-treated AD mice. Thus, our findings revealed that the peptides could improve cognition and memory capabilities and serve as promising structural templates for potential drugs against AD.

neuroscience↗

bioRxiv2025

How Good is AlphaFold3 at Ranking Drug Binding Affinities?

Accurate affinity ranking of small molecules is pivotal for drug discovery. We investigate whether structure prediction models like AlphaFold3, pretrained on protein-ligand interactions, can address this task. Zero-shot evaluation of Protenix (an AlphaFold3-like model) demonstrates superior prioritization of active compounds over conventional scoring functions and state-of-the-art deep learning models. By further fine-tuning Protenix on structure-agnostic protein-ligand bioactivity data from ChEMBL and BindingDB, we develop AlphaRank that predicts pairwise affinity relationships. AlphaRank achieves prediction accuracy comparable to computationally intensive free energy perturbation (FEP+) workflows on standard benchmarks, while requiring substantially less computational resources. Our findings highlight the emergent potential of AlphaFold3-derived models in affinity ranking tasks and emphasize the necessity for targeted methodological exploration to fully harness their capabilities in drug discovery applications.

bioinformatics↗

bioRxiv2025

TypeSINE: Genome-Wide Detection of SINE Retrotransposon Polymorphisms Reveals Functional Variants Linked to Body Size Variation in Pigs

The genotyping of Short Interspersed Element (SINE) retrotransposon insertion polymorphisms (RIPs) from large-scale sequencing data remains technically challenging. To overcome this limitation, we developed TypeSINE to genotype SINE-RIPs from short-read sequencing data. Analyzing 362 porcine genomes, we identified 749,835 SINE-RIPs (>85% accuracy), including 65,917 common variants (5-95% frequency). These showed uniform genomic distribution ([~]22/Mb), with 40% in introns. Population analyses based on SINE-RIPs revealed independent domestication of Asian and European pigs from local wild populations, followed by introgression. GWAS detected body size-associated regions using SINE-RIPs. We created pigRIPdb (82,227 curated SINE-RIPs) with browsing and visualization tools. The methods cross-species applicability is supported by conserved SINE evolutionary patterns, enabling polymorphism discovery of SINEs in livestock and humans. TypeSINE represents an efficient, scalable solution for genome-wide SINE-RIP analysis, advancing population genomics research. TeaserAn integrated SINE-RIP genotyping platform with companion database enables large-scale analysis of SINE evolution and population genetics in swine

bioinformatics↗

bioRxiv2025

Nuclear 2'-O-methylation regulates RNA splicing through its binding protein FUBP1

2'-O-methylation (Nm) is an abundant RNA modification exists on different mammalian RNA species. However, potential Nm recognition by proteins has not been extensively explored. Here, we employed RNA affinity purification followed by mass spectrometry to identify Nm-binding proteins. The candidates exhibit enriched binding at known Nm sites. Interestingly, some candidates display nuclear localization and functions. We focused on the splicing factor FUBP1. Electrophoretic mobility shift assay (EMSA) validated preference of FUBP1 to Nm-modified RNA. As FUBP1 predominantly binds intronic regions, we profiled Nm sites in chromatin-associated RNA (caRNA) and found Nm enrichment within introns. Depletion of Nm led to increased exon skipping, suggesting Nm-dependent splicing regulation. The caRNA Nm sites overlap with FUBP1 binding sites, and Nm depletion reduced FUBP1 occupancy on modified regions. Furthermore, FUBP1 depletion induced exon skipping in Nm-modified genes, supporting its role in mediating Nm-dependent splicing regulation. Overall, our findings identify FUBP1 as an Nm-binding protein and uncover previously unrecognized nuclear functions for RNA Nm modification.

molecular biology↗

bioRxiv2025

The Promotive Effect of Ocean Literacy on Marine Conservation Behavior: A Qualitative Study Based on Chinese University Students

IntroductionOcean literacy is crucial in shaping marine conservation behaviors among Chinese university students. This study explores the intricate relationship between ocean literacy and pro-environmental behaviors, emphasizing the roles of emotional connections, cultural significance, and educational experiences. By examining the multidimensional aspects of ocean literacy, the research seeks to identify key factors that influence conservation actions and the practical application of marine knowledge. MethodsUtilizing qualitative methodologies, this study conducted semi-structured interviews with 16 Chinese university students to delve into their perceptions and interactions with the marine environment. The interviews explored various dimensions such as oceanic knowledge, environmental beliefs, value orientations, and actions related to marine conservation. NVivo software was used for data analysis, allowing for the identification of key themes through a rigorous coding process. The studys analysis also included a moderated mediation model to examine how educational experiences influence the relationship between ocean literacy and conservation behaviors. ResultsThe findings reveal that ocean literacy among university students comprises key components such as scientific knowledge, environmental ethics, conservation action, policy understanding, and critical thinking. Significant gaps in marine knowledge were observed, alongside strong emotional and cultural connections to the ocean, which significantly mediate the relationship between ocean literacy and conservation behaviors. Educational experiences further moderated these effects, highlighting the need for enhanced marine education. ConclusionThis study underscores the necessity of integrating comprehensive educational strategies with emotional and cultural engagement to effectively promote marine conservation behaviors. The results advocate for educational reforms that incorporate practical and interactive learning approaches, aimed at deepening students understanding and involvement in marine conservation. Strengthening these educational frameworks can significantly enhance ocean literacy and foster proactive environmental stewardship among university students.

scientific communication and education↗

bioRxiv2025

A systematic review of climate-change driven range shifts in mosquito vectors

As global temperatures rise, concerns about shifting mosquito ranges--and accompanying changes in the transmission of malaria, dengue, and other diseases--are mounting. However, systematic evidence for climate-driven changes in mosquito ranges remains limited. We conducted a systematic review of studies documenting expansions or contractions in medically important mosquito species. In total, 178 studies on six continents identified range expansions in 118 mosquito species. While over a third of these studies cited warming as a driver, fewer than 10% performed statistical tests of the role of climate. Instead, most expansions were linked to human-aided dispersal (e.g., trade, travel), land-use changes, and urbanization. Although several studies reported poleward or upward expansions consistent with climate warming, none demonstrated warm-edge contractions driven by rising temperatures, which are theoretically predicted in some settings. Rather than expanding into newly suitable areas, many expansions appear to be filling preexisting thermally suitable habitats. Our findings highlight the need for long-term mosquito monitoring, rigorous climate-attribution methods, and better documentation of confounding factors like land-use change and vector control efforts to disentangle climate-driven changes from other anthropogenic factors.

ecology↗

bioRxiv2025

An alternative framework for transcriptome-wide association studies to detect and decipher gene-trait associations

Transcriptome-wide association studies (TWAS) are widely used to uncover transcriptomic mechanisms underlying disease. Here, we present VINTAGE, an alternative TWAS framework designed to identify and decipher gene-trait associations through two complementary tests: a genetic variance test that generalizes TWAS and unifies it with SKAT, offering a clearer understanding of TWAS false signals when expression is not relevant to SNP-trait associations; and a local genetic correlation test that distinguishes it from TWAS by explicitly quantifying and testing the proportion of genetic effects on trait mediated through gene expression. Applied to eQTLGen and eighteen traits from UK Biobank, VINTAGE improved gene-trait association power by an average of 5% and 87% over SKAT and TWAS, respectively. VINTAGE is also the only method effective in assessing potential gene mediation effects, revealing that most genes lack detectable mediation effects (median = 12%), which explains the power advantage of VINTAGE and SKAT over TWAS. Notably, VINTAGE identified 61 genes with significant mediation, highlighting the role of expression in genetic influences on traits.

bioinformatics↗

bioRxiv2025

MSMCE: A Novel Representation Module for Classification of Raw Mass Spectrometry Data

Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approach in data analysis, and while some deep learning methods have achieved progress in MS classification, their feature representation capabilities remain limited. Most existing methods rely on single-channel representations, which struggle to effectively capture the structural information within MS data. To address these limitations, we propose a Multi-Channel Embedding Representation Module (MSMCE), which focuses on modeling inter-channel dependencies to generate multi-channel representations of raw MS data. Additionally, we introduce a "residual" connection along the channel dimension, significantly enhancing the classification performance of subsequent models. Experimental results on four public datasets demonstrate that the proposed MSMCE module not only achieves substantial improvements in classification performance but also reduces computational resource consumption and enhances training efficiency, highlighting its effectiveness and generalizability in raw MS data classification.

bioengineering↗

bioRxiv2025

The architecture, assembly, and evolution of a complex flagellar motor

Bacterial flagella drive motility in many species, likely including the last bacterial common ancestor 1,2. Knowledge of flagellar assembly and function has mainly come from studies of Escherichia coli and Salmonella enterica, which have simple flagellar motors 3-7. However, most flagellated bacteria possess complex motors with unique, species-specific adaptations whose mechanisms and evolution remain largely unexplored 8-10. Here, we deploy a multidisciplinary approach to build a near-complete model of the flagellar motor in Campylobacter jejuni, revealing its remarkable complexity in architecture and composition. We identify an E-ring around the MS-ring, a periplasmic cage with two distinctive conformations, and an intricate interaction network between the E-ring and cage. These scaffolds play critical roles in stabilizing and regulating 17 torque-generating stator complexes for optimal motility. In-depth evolutionary analyses uncover the ancient origin and prevalence of the E-ring in flagellated species of the domain Bacteria as well as a unique exaptation of type IV pili components PilMNOPQF in the ancestral motor of the phylum Campylobacterota. Collectively, our studies reveal novel mechanisms of assembly and function in complex flagellar motors and shed light on the evolution of flagella and modern bacterial species.

microbiology↗

bioRxiv2025

xOmicsShiny: an R shiny application for cross-omics data analysis and pathway mapping

SummaryWe developed xOmicsShiny, a feature-rich R Shiny-powered application that enables biologists to fully explore omics datasets across experiments and data types, with an emphasis on uncovering biological insights at the pathway level. The data merging feature ensures flexible exploration of cross-omics data, such as transcriptomics, proteomics, metabolomics, and lipidomics. The pathway mapping function covers a broad range of databases, including WikiPathways, Reactome, and KEGG pathways. In addition, xOmicsShiny offers several visualization options and analytical tasks for everyday omics data analysis, namely, PCA, Volcano plot, Venn Diagram, Heatmap, WGCNA, and advanced clustering analyses. The application employs customizable modules to perform various tasks, generating both interactive plots and publication-ready figures. This dynamic, modular design overcomes the issue of slow loading in R Shiny tools and allows it to be readily expanded by the research and developer community. Availability and implementationThe R Shiny application is publicly available at: http://xOmicsShiny.bxgenomics.com. Researchers can upload their own data to the server or use the pre-loaded demo dataset. The source code, under MIT license is provided at https://github.com/interactivereport/xOmicsShiny for local installation. A full tutorial of the application is available at https://interactivereport.github.io/xOmicsShiny/tutorial/docs/index.html. Contactyuhenry.sun@biogen.com or baohong.zhang@biogen.com Supplementary dataSupplementary data are available at bioRxiv online.

bioinformatics↗

bioRxiv2025

Single-molecule mitochondrial DNA imaging reveals heteroplasmy dynamics shaped by developmental bottlenecks and selection in different organs in vivo

Mitochondrial DNA (mtDNA) occurs in many copies per cell, with cell-to-cell variability in mutation load, known as heteroplasmy. Developmental and age-related expansion of pathogenic mtDNA mutations contributes to mitochondrial and neurodegenerative disease pathogenesis. Here, we describe an approach for in situ sequence-specific detection of single mtDNA molecules (mtDNA-smFISH). We apply this method to visualize and measure in situ mtDNA and heteroplasmy levels at single-cell resolution in whole-mount Drosophila tissue and cultured human cells. In Drosophila, we identify a somatic mtDNA bottleneck during neurogenesis. This amplifies heteroplasmy variability between neurons, as predicted from a mathematical bottleneck model, predisposing individual neurons to a high mutation load and degeneration. However, both during neurogenesis and oogenesis, mtDNA segregation is accompanied by purifying selection, promoting wild-type over mutant mtDNA. mtDNA-smFISH thus elucidates novel mechanisms whereby developmental cell-fate transitions, accompanied by changes in cell morphology, behaviour and metabolism, will shape disease-relevant and tissue-specific transmission and selection of mtDNA mutations.

developmental biology↗

bioRxiv2025

Peripheral alcohol metabolism dictates ethanol consumption and drinking microstructure in mice

BackgroundEthanol metabolism is intimately linked with the physiological and behavioral aspects of ethanol consumption. Ethanol is mainly oxidized by alcohol dehydrogenase (ADH) to acetaldehyde and further to acetate via aldehyde dehydrogenases (ALDHs). Understanding how ethanol and its metabolites work together to initiate and drive continued ethanol consumption is crucial for identifying interventions for alcohol use disorder (AUD). Therefore, the goal of our study was to determine how ADH1, which is mainly peripherally-expressed and metabolizes >90% of ingested ethanol, modulates ethanol metabolite distribution and downstream behaviors. MethodsEthanol consumption in drinking-in-the-dark (DID) and two-bottle choice (2BC) drinking paradigms, ethanol metabolite concentrations, and lickometry were assessed after ADH1 inhibition and/or in Adh1-knockout (Adh1 KO) mice. ResultsWe found that Adh1 KO mice of both sexes exhibited decreased ethanol consumption and preference compared to wild-type (WT) mice in DID and 2BC. ADH1 inhibitor fomepizole (4-MP) also significantly decreased normal and sweetened ethanol consumption in DID studies. Measurement of ethanol and its metabolites revealed that ethanol was increased at 1h but not 15 min, peripheral acetaldehyde was slightly decreased at both time points, and ethanol-induced increases in acetate were abolished after ethanol administration in Adh1 KO mice compared to controls. Similarly, ethanol accumulation as a function of consumption was 2-fold higher in Adh1 KO or 4-MP treated mice compared to controls. We then used lickometry to determine how this perturbation in ethanol metabolism affects drinking microstructure. Adh1 KO mice consume most of their ethanol in the first 30 min like WT mice but display altered temporal shifts in drinking behaviors and do not form normal bout structures, resulting in lower ethanol consumption. ConclusionsOur study demonstrates that ADH1-mediated ethanol metabolism is a key determinant of ethanol consumption, highlighting a fundamental knowledge gap around how ethanol and its metabolites drive ethanol consumption.

neuroscience↗

bioRxiv2024

Evolutionary conserved reciprocal senescence and tumor suppressor signals limit lifetime cancer

Cellular senescence features a durable exit from the cell cycle triggered by stress or carcinogens. The INK4 locus is inactivated in various cancers, yet in senescence, p16Ink4a is activated. Whether senescence is tumor-suppressing or -promoting remains a conundrum. We discovered an evolutionally-conserved Vertebrata INK4-homolog. This ink4ab triggers senescence upon oxidative- and/or carcinogenic-stress. Adult Ink4ab-deficient animals failed to activate senescence and developed spontaneous cancers. Combined Ink4ab and Tp53 deficiency revealed a reciprocal senescence and apoptosis regulation, controlling tumorigenesis, including retinoblastoma. INK4-hematopoietic-deficient mice exhibited p19Arf-dependent enhanced senescence-like phenotypes, uncontrolled cell proliferation, defective stem cell differentiation, and splenomegaly, with single-splenocytes spatially-enriched in senescence-associated secretory profiles. Our studies reveal the evolutionary origin of paradigms co-regulating senescence and tumor suppression and offer strategies to exploit these reciprocal pathways for cancer prevention and therapy.

cancer biology↗

bioRxiv2024

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data

T cell receptors (TCRs) serve pivotal roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods exist for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics regarding accuracy, sensitivity, adaptability, usability, time, and memory consumption. Yet, a comprehensive understanding of their relative strengths and weaknesses for different applications remains elusive. In our research, we implemented a benchmark analysis utilizing experimental single-cell immune profiling datasets encompassing paired scRNA-seq as input and scTCR-seq datasets as ground truth reference from human and mouse. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another Simulator for single-cell TCR), capable of generating scTCR-seq reads containing a diverse array of TCR- derived sequences under different sequencing depths and read lengths. Our results consistently showed that TRUST4 outperformed others across multiple datasets, while MiXCR and DeRR also demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the most appropriate methods for reconstructing TCR from scRNA-seq data.

bioinformatics↗

bioRxiv2024

Deep contrastive learning enables genome-wide virtual screening

Numerous protein-coding genes are associated with human diseases, yet approximately 90% of them lack targeted therapeutic intervention. While conventional computational methods, such as molecular docking, have facilitated the discovery of potential hit compounds, the development of genome-wide virtual screening against the expansive chemical space remains a formidable challenge. Here we introduce DrugCLIP, a novel framework that combines contrastive learning and dense retrieval to achieve rapid and accurate virtual screening. Compared to traditional docking methods, DrugCLIP improves the speed of virtual screening by up to seven orders of magnitude. In terms of performance, DrugCLIP not only surpasses docking and other deep learning-based methods across two standard benchmark datasets, but also demonstrates high efficacy in wet-lab experiments. Specifically, DrugCLIP successfully identified agonists with < 100 nM affinities for 5HT2AR, a key target in psychiatric diseases. For another target NET, whose structure is newly solved and not included in the training set, our method achieved a hit rate of 15%, with 12 diverse molecules exhibiting affinities better than bupropion. Additionally, two chemically novel inhibitors were validated by structure determination with Cryo-EM. Finally, a novel potential drug target TRIP12, with no experimental structures and inhibitors for reference, was used to challenge DrugCLIP. DrugCLIP achieved a hit rate of 17.5% by screening a pocket identified on an AlphaFold2-predicted structure, verified with multi-cycle SPR assays. Molecules with the highest affinities also showed a dose-dependent inhibition to the enzymatic function of TRIP12. Building on this foundation, we present the results of a pioneering trillion-scale genome-wide virtual screening, encompassing approximately 10,000 AlphaFold2 predicted proteins within the human genome and 500 million molecules from the ZINC and Enamine REAL database. This work provides an innovative perspective on drug discovery in the post-AlphaFold era, where comprehensive targeting of all disease-related proteins is within reach.

bioinformatics↗

bioRxiv2024

Mechanistic basis of lineage restriction

Lineage restriction is the biological phenomenon where cells lose developmental potency during differentiation for all except their adopted lineages. Lineage restriction is foundational to multicellularity as it secures the functional identities of the myriad cell types in the body. As yet, the mechanisms of lineage restriction remain enigmatic. By developing Potency-Seq to systematically map transcriptional potency of genes across the genome, we uncovered a link between the restriction of developmental potency of cells and the occlusion of transcriptional potency of their genomes. We further showed that, strikingly, genes can undergo irreversible occlusion of their transcriptional potency simply by chromatinization into the nucleosomal form with unmodified histones. These findings led to a comprehensive mechanistic account of lineage restriction as driven by gene occlusion. Specifically, naive pluripotent stem cells at the onset of development possess the unique capacity to erase occlusion globally to reset full transcriptional potency of the genome, which in turn establishes full developmental potency of the cells. Such deocclusion capacity is abolished when cells progress from naive to primed pluripotency. From this stage onward, lineage-inappropriate genes can undergo nucleosome-mediated occlusion in an irreversible manner when their cognate transcription factors (TFs) and/or placeholder factors (PFs) no longer exist in cells to protect them. Consequently, the transcriptionally potent portion of the genome shrinks progressively and irreversibly during differentiation, thus permanently restricting the developmental potency of differentiating cells. ONE-SENTENCE SUMMARYDevelopmental potency of cells is linked to transcriptional potency of their genomes

developmental biology↗