SARS-CoV-2 variants evolve convergent strategies to remodel the host response.

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps.

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

The Global Phosphorylation Landscape of SARS-CoV-2 Infection.

The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies.

Tumor-associated macrophage subtypes on cancer immunity along with prognostic analysis and SPP1-mediated interactions between tumor cells and macrophages.

Tumor-associated macrophages (TAM) subtypes have been shown to impact cancer prognosis and resistance to immunotherapy. However, there is still a lack of systematic investigation into their molecular characteristics and clinical relevance in different cancer types. Single-cell RNA sequencing data from three different tumor types were used to cluster and type macrophages. Functional analysis and communication of TAM subpopulations were performed by Gene Ontology-Biological Process and CellChat respectively. Differential expression of characteristic genes in subpopulations was calculated using zscore as well as edgeR and Wilcoxon rank sum tests, and subsequently gene enrichment analysis of characteristic genes and anti-PD-1 resistance was performed by the REACTOME database. We revealed the heterogeneity of TAM, and identified eleven subtypes and their impact on prognosis. These subtypes expressed different molecular functions respectively, such as being involved in T cell activation, apoptosis and differentiation, or regulating viral bioprocesses or responses to viruses. The SPP1 pathway was identified as a critical mediator of communication between TAM subpopulations, as well as between TAM and epithelial cells. Macrophages with high expression of SPP1 resulted in poorer survival. By in vitro study, we showed SPP1 mediated the interactions between TAM clusters and between TAM and tumor cells. SPP1 promoted the tumor-promoting ability of TAM, and increased PDL1 expression and stemness of tumor cells. Inhibition of SPP1 attenuated N-cadherin and ß-catenin expression and the activation of AKT and STAT3 pathway in tumor cells. Additionally, we found that several subpopulations could decrease the sensitivity of anti-PD-1 therapy in melanoma. SPP1 signal was a critical pathway of communication between macrophage subtypes. Some specific macrophage subtypes were associated with immunotherapy resistance and prognosis in some cancer types.

SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding.

Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

Correction: Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

[This corrects the article DOI: 10.1371/journal.ppat.1006851.].

Extracellular Matrix Interactome in Modulating Vascular Homeostasis and Remodeling.

The ECM (extracellular matrix) is a major component of the vascular microenvironment that modulates vascular homeostasis. ECM proteins include collagens, elastin, noncollagen glycoproteins, and proteoglycans/glycosaminoglycans. ECM proteins form complex matrix structures, such as the basal lamina and collagen and elastin fibers, through direct interactions or lysyl oxidase-mediated cross-linking. Moreover, ECM proteins directly interact with cell surface receptors or extracellular secreted molecules, exerting matricellular and matricrine modulation, respectively. In addition, extracellular proteases degrade or cleave matrix proteins, thereby contributing to ECM turnover. These interactions constitute the ECM interactome network, which is essential for maintaining vascular homeostasis and preventing pathological vascular remodeling. The current review mainly focuses on endogenous matrix proteins in blood vessels and discusses the interaction of these matrix proteins with other ECM proteins, cell surface receptors, cytokines, complement and coagulation factors, and their potential roles in maintaining vascular homeostasis and preventing pathological remodeling.

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.

A newly described coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of coronavirus disease 2019 (COVID-19), has infected over 2.3 million people, led to the death of more than 160,000 individuals and caused worldwide social and economic disruption1,2. There are no antiviral drugs with proven clinical efficacy for the treatment of COVID-19, nor are there any vaccines that prevent infection with SARS-CoV-2, and efforts to develop drugs and vaccines are hampered by the limited knowledge of the molecular details of how SARS-CoV-2 infects cells. Here we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins that physically associated with each of the SARS-CoV-2 proteins using affinity-purification mass spectrometry, identifying 332 high-confidence protein-protein interactions between SARS-CoV-2 and human proteins. Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (of which, 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the sigma-1 and sigma-2 receptors. Further studies of these host-factor-targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.

Protein Interaction Map of APOBEC3 Enzyme Family Reveals Deamination-Independent Role in Cellular Function.

Human APOBEC3 enzymes are a family of single-stranded (ss)DNA and RNA cytidine deaminases that act as part of the intrinsic immunity against viruses and retroelements. These enzymes deaminate cytosine to form uracil which can functionally inactivate or cause degradation of viral or retroelement genomes. In addition, APOBEC3s have deamination-independent antiviral activity through protein and nucleic acid interactions. If expression levels are misregulated, some APOBEC3 enzymes can access the human genome leading to deamination and mutagenesis, contributing to cancer initiation and evolution. While APOBEC3 enzymes are known to interact with large ribonucleoprotein complexes, the function and RNA dependence are not entirely understood. To further understand their cellular roles, we determined by affinity purification mass spectrometry (AP-MS) the protein interaction network for the human APOBEC3 enzymes and mapped a diverse set of protein-protein and protein-RNA mediated interactions. Our analysis identified novel RNA-mediated interactions between APOBEC3C, APOBEC3H Haplotype I and II, and APOBEC3G with spliceosome proteins, and APOBEC3G and APOBEC3H Haplotype I with proteins involved in tRNA methylation and ncRNA export from the nucleus. In addition, we identified RNA-independent protein-protein interactions with APOBEC3B, APOBEC3D, and APOBEC3F and the prefoldin family of protein-folding chaperones. Interaction between prefoldin 5 (PFD5) and APOBEC3B disrupted the ability of PFD5 to induce degradation of the oncogene cMyc, implicating the APOBEC3B protein interaction network in cancer. Altogether, the results uncover novel functions and interactions of the APOBEC3 family and suggest they may have fundamental roles in cellular RNA biology, their protein-protein interactions are not redundant, and there are protein-protein interactions with tumor suppressors, suggesting a role in cancer biology. Data are available via ProteomeXchange with the identifier PXD044275.

TTD: Therapeutic Target Database describing target druggability information.

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

TheMarker: a comprehensive database of therapeutic biomarkers.

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

A peptidomimetic modulator of the CaV2.2 N-type calcium channel for chronic pain.

Transmembrane Cav2.2 (N-type) voltage-gated calcium channels are genetically and pharmacologically validated, clinically relevant pain targets. Clinical block of Cav2.2 (e.g., with Prialt/Ziconotide) or indirect modulation [e.g., with gabapentinoids such as Gabapentin (GBP)] mitigates chronic pain but is encumbered by side effects and abuse liability. The cytosolic auxiliary subunit collapsin response mediator protein 2 (CRMP2) targets Cav2.2 to the sensory neuron membrane and regulates their function via an intrinsically disordered motif. A CRMP2-derived peptide (CBD3) uncouples the Cav2.2-CRMP2 interaction to inhibit calcium influx, transmitter release, and pain. We developed and applied a molecular dynamics approach to identify the A1R2 dipeptide in CBD3 as the anchoring Cav2.2 motif and designed pharmacophore models to screen 27 million compounds on the open-access server ZincPharmer. Of 200 curated hits, 77 compounds were assessed using depolarization-evoked calcium influx in rat dorsal root ganglion neurons. Nine small molecules were tested electrophysiologically, while one (CBD3063) was also evaluated biochemically and behaviorally. CBD3063 uncoupled Cav2.2 from CRMP2, reduced membrane Cav2.2 expression and Ca2+ currents, decreased neurotransmission, reduced fiber photometry-based calcium responses in response to mechanical stimulation, and reversed neuropathic and inflammatory pain across sexes in two different species without changes in sensory, sedative, depressive, and cognitive behaviors. CBD3063 is a selective, first-in-class, CRMP2-based peptidomimetic small molecule, which allosterically regulates Cav2.2 to achieve analgesia and pain relief without negative side effect profiles. In summary, CBD3063 could potentially be a more effective alternative to GBP for pain relief.

The maltose-related starch degradation pathway promotes the formation of large and spherical transitory starch granules.

Previously, in Arabidopsis thaliana, we found atypical spherical starch granules in dpe2ss4 and dpe2phs1ss4. However, the mechanism of such abnormal morphogenesis is still obscure. By tracking starch granule length and thickness with leaf ageing, we reported that the starch granules in dpe2phs1ss4 gradually change to a spherical shape over time. In comparison, Col-0 and the parental line ss4 did not exhibit macroscopic morphological alteration. In this study, firstly, we specify that the additional lack of DPE2 resulted in the gradual alteration of starch granule morphology over time. Similar gradual morphological alterations were also found in dpe2, mex1, and sex4 but not in the other starch degradation-related mutants, such as sex1-8, pwd, and bam3. The gradual alteration of starch morphology can be eliminated by omitting the dark phase, suggesting that the particular impaired starch degradation in dpe2- and mex1-related mutants influences starch morphology. Secondly, we observed that spherical starch morphology generation was accompanied by prominent elevated short glucan chains of amylopectin and an increased amylose proportion. Thirdly, the interplay between soluble starch synthase 2 and branching enzymes was affected and resulted in the formation of spherical starch granules. The resulting spherical starch granules allow for elevated starch synthesis efficiency. Fourthly, the starch phosphate content at the granule surface correlated with the morphology alteration of the starch granules. Herewith, we propose a model that spherical starch granules, accumulated in mutants with a misbalance of the starch degradation pathway, are result of elevated starch synthesis to cope with overloaded carbohydrates.

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

Palmatine ameliorates N-methyl-N'-nitrosoguanidine-induced chronic atrophic gastritis through the STAT1/CXCL10 axis.

Chronic atrophic gastritis (CAG) is a prevalent preneoplastic condition of the stomach. Palmatine (PAL), an isoquinoline alkaloid isolated from Rhizoma Coptidis (RC), has significant anti-inflammatory properties and is often used to treat gastrointestinal disorders. However, the mechanism of PAL on CAG remains unclear. In this study, N-methyl-N'-nitrosoguanidine (MNNG) was used to induce CAG inflammatory disease models in vivo and in vitro. The efficacy of five alkaloids in RC and the dose-dependent effects of the most effective PAL in CAG mice were evaluated in two animal experiments. RNA-seq and western blot revealed that PAL significantly improved IL-17, TNF, and NF-kappa B inflammation-related signaling pathways. Further hub gene prediction and experimental validation revealed that PAL modulated the STAT1/CXCL10 axis, thereby exerting attenuation of CAG through the regulation of IL-17, TNF-α, and p-p65 expression. In conclusion, PAL was proposed to mitigate MNNG-induced CAG, potentially through the inhibition of oxidative stress and inflammatory responses via the STAT1/CXCL10 axis. This approach is an effective complement to the use of PAL in the treatment of CAG.

Liquid-Liquid Phase Separation of DDR1 Counteracts the Hippo Pathway to Orchestrate Arterial Stiffening.

BACKGROUND: The Hippo-YAP (yes-associated protein) signaling pathway is modulated in response to various environmental cues. Activation of YAP in vascular smooth muscle cells conveys the extracellular matrix stiffness-induced changes in vascular smooth muscle cells phenotype and behavior. Recent studies have established a mechanoreceptive role of receptor tyrosine kinase DDR1 (discoidin domain receptor 1) in vascular smooth muscle cells. METHODS: We conduced 5/6 nephrectomy in vascular smooth muscle cells-specific Ddr1-knockout mice, accompanied by pharmacological inhibition of the Hippo pathway kinase LATS1 (large tumor suppressor 1), to investigate DDR1 in YAP activation. We utilized polyacrylamide gels of varying stiffness or the DDR1 ligand, type I collagen, to stimulate the cells. We employed multiple molecular biological techniques to explore the role of DDR1 in controlling the Hippo pathway and to determine the mechanistic basis by which DDR1 exerts this effect. RESULTS: We identified the requirement for DDR1 in stiffness/collagen-induced YAP activation. We uncovered that DDR1 underwent stiffness/collagen binding-stimulated liquid-liquid phase separation and co-condensed with LATS1 to inactivate LATS1. Mutagenesis experiments revealed that the transmembrane domain is responsible for DDR1 droplet formation. Purified DDR1 N-terminal and transmembrane domain was sufficient to drive its reversible condensation. Depletion of the DDR1 C-terminus led to failure in co-condensation with LATS1. Interaction between the DDR1 C-terminus and LATS1 competitively inhibited binding of MOB1 (Mps one binder 1) to LATS1 and thus the subsequent phosphorylation of LATS1. Introduction of the single-point mutants, histidine-745-proline and histidine-902-proline, to DDR1 on the C-terminus abolished the co-condensation. In mouse models, YAP activity was positively correlated with collagen I expression and arterial stiffness. LATS1 inhibition reactivated the YAP signaling in Ddr1-deficient vessels and abrogated the arterial softening effect of Ddr1 deficiency. CONCLUSIONS: These findings identify DDR1 as a mediator of YAP activation by mechanical and chemical stimuli and demonstrate that DDR1 regulates LATS1 phosphorylation in an liquid-liquid phase separation-dependent manner.

Quantitative Proteomic Analysis Reveals apoE4-Dependent Phosphorylation of the Actin-Regulating Protein VASP.

Apolipoprotein (apo) E4 is the major genetic risk factor for Alzheimer's disease. While neurons generally produce a minority of the apoE in the central nervous system, neuronal expression of apoE increases dramatically in response to stress and is sufficient to drive pathology. Currently, the molecular mechanisms of how apoE4 expression may regulate pathology are not fully understood. Here, we expand upon our previous studies measuring the impact of apoE4 on protein abundance to include the analysis of protein phosphorylation and ubiquitylation signaling in isogenic Neuro-2a cells expressing apoE3 or apoE4. ApoE4 expression resulted in a dramatic increase in vasodilator-stimulated phosphoprotein (VASP) S235 phosphorylation in a protein kinase A (PKA)-dependent manner. This phosphorylation disrupted VASP interactions with numerous actin cytoskeletal and microtubular proteins. Reduction of VASP S235 phosphorylation via PKA inhibition resulted in a significant increase in filopodia formation and neurite outgrowth in apoE4-expressing cells, exceeding levels observed in apoE3-expressing cells. Our results highlight the pronounced and diverse impact of apoE4 on multiple modes of protein regulation and identify protein targets to restore apoE4-related cytoskeletal defects.

NAT10-mediated N4-acetylcytidine mRNA modification regulates self-renewal in human embryonic stem cells.

NAT10-catalyzed N4-acetylcytidine (ac4C) has emerged as a vital post-transcriptional modulator on the coding transcriptome by promoting mRNA stability. However, its role in mammalian development remains unclear. Here, we found that NAT10 expression positively correlates with pluripotency in vivo and in vitro. High throughput ac4C-targeted RNA immunoprecipitation sequencing (ac4C-RIP-seq), NaCNBH3-based chemical ac4C sequencing (ac4C-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays revealed noticeable ac4C modifications in transcriptome of hESCs, among which transcripts encoding core pluripotency transcription factors are favorable targets of ac4C modification. Further validation assays demonstrate that genetic inactivation of NAT10, the ac4C writer enzyme, led to ac4C level decrease on target genes, promoted the core pluripotency regulator OCT4 (POU5F1) transcript decay, and finally impaired self-renewal and promoted early differentiation in hESCs. Together, our work presented here elucidates a previously unrecognized interconnectivity between the core pluripotent transcriptional network for the maintenance of human ESC self-renewal and NAT10-catalyzed ac4C RNA epigenetic modification.