Down-regulation of N4-acetylcytidine modification in myeloid cells attenuates immunotherapy and exacerbates hepatocellular carcinoma progression
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE244613 | GEO | 2024/01/31
REPOSITORIES: GEO
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Project description:Down-regulation of N4-acetylcytidine modification in myeloid cells attenuates immunotherapy and exacerbates hepatocellular carcinoma progression
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Project description:THUMPD1 is a specific RNA adaptor that assists acetylation of mRNA and production of N4-acetylcytidine (ac4C). However, it remains unclear whether THUMPD1 plays a part in tumorigenesis and therapeutic efficacy. Here, we analyzed the expression profiles and prognostic value of THUMPD1 in pan-cancer and gained insights into the correlation between THUMPD1 expression level and immunotherapy efficacy. Gene expression pattern and its correlation with prognosis, immune cell infiltration in pan-cancer were obtained from Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) databases, with Kaplan-Meier method and Spearman correlation analysis used. Western blotting and immunofluorescence on clinical samples were performed to validate our database-derived results. Correlation between THUMPD1 expression level and immunotherapy responses was also explored, based on clinical cohorts receiving programmed cell death protein 1 ligand (PD-L1) antibody therapy. Finally, gene set enrichment analysis (GSEA) was performed to show the possible tumorigenic mechanism. THUMPD1 was highly expressed in most cancer types, and this elevated expression indicated poor or improved prognosis for different cancers. In kidney renal clear cell carcinoma (KIRC) and rectum adenocarcinoma (READ), patients with higher THUMPD1 expression exhibited a better prognosis, while liver hepatocellular carcinoma (LIHC) patients had worse prognosis. Besides, THUMPD1 was significantly associated with immune cell infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), immune checkpoints and neoantigen in many cancer types. Further, more clinical advantages and therapeutic responses were observed in patients with high THUMPD1 expression. THUMPD1 may serve as a novel predictor to evaluate cancer prognosis and immune therapy efficacy in diverse cancer types.
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Project description:N4-acetylcytidine (ac4C) is a highly conserved RNA modification and is the first acetylation event described in mRNA. ac4C in mRNA has been demonstrated to be involved in the regulation of mRNA stability, processing and translation, but the exact means by which ac4C works remain unclear. In addition, ac4C is widely distributed within the human transcriptome at physiologically relevant levels and so far only a small fraction of modified sequences have been detected by experiments. In this study, we developed a predictor of ac4C sites in human mRNA named PACES to help mining possible modified motifs. PACES combines two random forest classifiers, position-specific dinucleotide sequence profile and K-nucleotide frequencies. With genomic sequences as input, PACES gives possible modified sequences based on the training model. PACES is freely available at http://www.rnanut.net/paces/.
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Project description:N4 acetylcytidine (ac4C) modification mainly occurs on tRNA, rRNA, and mRNA, playing an important role in the expression of genetic information. However, it is still unclear whether microRNAs have undergone ac4C modification and their potential physiological and pathological functions. In this study, we identified that NAT10/THUMPD1 acetylates primary microRNAs (pri-miRNAs) with ac4C modification. Knockdown of NAT10 suppresses and augments the expression levels of mature miRNAs and pri-miRNAs, respectively. Molecular mechanism studies found that pri-miRNA ac4C promotes the processing of pri-miRNA into precursor miRNA (pre-miRNA) by enhancing the interaction of pri-miRNA and DGCR8, thereby increasing the biogenesis of mature miRNA. Knockdown of NAT10 attenuates the oncogenic characters of lung cancer cells by regulating miRNA production in cancers. Moreover, NAT10 is highly expressed in various clinical cancers and negatively correlated with poor prognosis. Thus, our results reveal that NAT10 plays a crucial role in cancer initiation and progression by modulating pri-miRNA ac4C to affect miRNA production, which would provide an attractive therapeutic strategy for cancers.
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Project description:Stress granules are an integral part of the stress response that are formed from non-translating mRNAs aggregated with proteins. While much is known about stress granules, the factors that drive their mRNA localization are incompletely described. Modification of mRNA can alter the properties of the nucleobases and affect processes such as translation, splicing and localization of individual transcripts. Here, we show that the RNA modification N4-acetylcytidine (ac4C) on mRNA associates with transcripts enriched in stress granules and that stress granule localized transcripts with ac4C are specifically translationally regulated. We also show that ac4C on mRNA can mediate localization of the protein NOP58 to stress granules. Our results suggest that acetylation of mRNA regulates localization of both stress-sensitive transcripts and RNA-binding proteins to stress granules and adds to our understanding of the molecular mechanisms responsible for stress granule formation.
| S-SCDT-10_1038-S44319-024-00098-6 | biostudies-other
Project description:The wobble base of Escherichia coli elongator tRNA(Met) is modified to N(4)-acetylcytidine (ac(4)C), which is thought to ensure the precise recognition of the AUG codon by preventing misreading of near-cognate AUA codon. By employing genome-wide screen of uncharacterized genes in Escherichia coli ('ribonucleome analysis'), we found the ypfI gene, which we named tmcA (tRNA(Met) cytidine acetyltransferase), to be responsible for ac(4)C formation. TmcA is an enzyme that contains a Walker-type ATPase domain in its N-terminal region and an N-acetyltransferase domain in its C-terminal region. Recombinant TmcA specifically acetylated the wobble base of E. coli elongator tRNA(Met) by utilizing acetyl-coenzyme A (CoA) and ATP (or GTP). ATP/GTP hydrolysis by TmcA is stimulated in the presence of acetyl-CoA and tRNA(Met). A mutation study revealed that E. coli TmcA strictly discriminates elongator tRNA(Met) from the structurally similar tRNA(Ile) by mainly recognizing the C27-G43 pair in the anticodon stem. Our findings reveal an elaborate mechanism embedded in tRNA(Met) and tRNA(Ile) for the accurate decoding of AUA/AUG codons on the basis of the recognition of wobble bases by the respective RNA-modifying enzymes.
| S-EPMC2500205 | biostudies-literature
Project description:The emerging epitranscriptome plays an essential role in autoimmune disease. As a novel mRNA modification, N4-acetylcytidine (ac4C) could promote mRNA stability and translational efficiency. However, whether epigenetic mechanisms of RNA ac4C modification are involved in systemic lupus erythematosus (SLE) remains unclear. Herein, we detected eleven modifications in CD4+ T cells of SLE patients using mass spectrometry (LC-MS/MS). Furthermore, using samples from four CD4+ T cell pools, we identified lower modification of ac4C mRNA in SLE patients as compared to that in healthy controls (HCs). Meanwhile, significantly lower mRNA acetyltransferase NAT10 expression was detected in lupus CD4+ T cells by RT-qPCR. We then illustrated the transcriptome-wide ac4C profile in CD4+ T cells of SLE patients by ac4C-RIP-Seq and found ac4C distribution in mRNA transcripts to be highly conserved and enriched in mRNA coding sequence regions. Using bioinformatics analysis, the 3879 and 4073 ac4C hyper-acetylated and hypoacetylated peaks found in SLE samples, respectively, were found to be significantly involved in SLE-related function enrichments, including multiple metabolic and transcription-related processes, ROS-induced cellular signaling, apoptosis signaling, and NF-κB signaling. Moreover, we demonstrated the ac4C-modified regulatory network of gene biological functions in lupus CD4+ T cells. Notably, we determined that the 26 upregulated genes with hyperacetylation played essential roles in autoimmune diseases and disease-related processes. Additionally, the unique ac4C-related transcripts, including USP18, GPX1, and RGL1, regulate mRNA catabolic processes and translational initiation. Our study identified novel dysregulated ac4C mRNAs associated with critical immune and inflammatory responses, that have translational potential in lupus CD4+ T cells. Hence, our findings reveal transcriptional significance and potential therapeutic targets of mRNA ac4C modifications in SLE pathogenesis.
| S-EPMC7483482 | biostudies-literature
Project description:N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model's adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations.
| S-EPMC10716444 | biostudies-literature
Project description: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.
| S-EPMC10484679 | biostudies-literature
Project description:Introduction: N4-acetylcytidine (ac4C) is a critical acetylation modification that has an essential function in protein translation and is associated with a number of human diseases. Methods: The process of identifying ac4C sites by biological experiments is too cumbersome and costly. And the performance of several existing computational models needs to be improved. Therefore, we propose a new deep learning tool EMDL-ac4C to predict ac4C sites, which uses a simple one-hot encoding for a unbalanced dataset using a downsampled ensemble deep learning network to extract important features to identify ac4C sites. The base learner of this ensemble model consists of a modified DenseNet and Squeeze-and-Excitation Networks. In addition, we innovatively add a convolutional residual structure in parallel with the dense block to achieve the effect of two-layer feature extraction. Results: The average accuracy (Acc), mathews correlation coefficient (MCC), and area under the curve Area under curve of EMDL-ac4C on ten independent testing sets are 80.84%, 61.77%, and 87.94%, respectively. Discussion: Multiple experimental comparisons indicate that EMDL-ac4C outperforms existing predictors and it greatly improved the predictive performance of the ac4C sites. At the same time, EMDL-ac4C could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDLac4C.
| S-EPMC10372626 | biostudies-literature