Search Results (4)

Severe dental pulp inflammation can lead to tissue lysis and destruction, underscoring the necessity for effective treatment of pulpitis. N-acetyltransferase 10 (NAT10)-mediated N4-acetylcytidine (ac⁴C) modification has recently emerged as a key regulator in inflammatory processes. However, whether NAT10 affects the inflammatory response in human dental pulp stem cells (hDPSCs) remains unelucidated. In this study, elevated NAT10 expression was observed in pulpitis tissues and LPS-stimulated hDPSCs. Knockdown of NAT10 led to reduced inflammatory gene expression and lower reactive oxygen species (ROS) production in LPS-stimulated hDPSCs, while the chemotactic migration of macrophages was also suppressed. Similar results were observed when hDPSCs were treated with Remodelin, an inhibitor of NAT10. Differentially expressed genes identified through RNA sequencing were significantly enriched in inflammatory signaling pathways after NAT10 depletion. Among the differential genes, pentraxins 3 (PTX3) was identified as the potential target gene due to the presence of the ac⁴C modification site and its known ability to regulate dental pulp inflammation. The mRNA and protein levels of PTX3 were reduced in NAT10-deficient cells, along with a decrease in its mRNA stability. Exogenous PTX3 supplementation partially reversed the inflammatory inhibition induced by NAT10 knockdown. Further evidence in vivo revealed that Remodelin treatment attenuated the severity of dental pulp inflammation in rats with pulpitis. In summary, these data indicated that NAT10 deficiency inhibited the stability of PTX3 mRNA and further inhibited hDPSC inflammation, while Remodelin might be a potential therapeutic agent for pulp capping. Full article

Understanding the causes of tumorigenesis and progression in triple-receptor negative breast cancer (TNBC) can help the design of novel and personalized therapies and prognostic assessments. Abnormal RNA modification is a recently discovered process in TNBC development. TNBC samples from The Cancer Genome Atlas database were categorized according to the expression level of NAT10, which drives acetylation of cytidine in RNA to N(4)-acetylcytidine (ac4C) and affects mRNA stability. A total of 703 differentially expressed long non-coding RNAs (lncRNAs) were found between high- and low-expressed NAT10 groups in TNBC. Twenty of these lncRNAs were significantly associated with prognosis. Two breast cancer tissues and their paired normal tissues were sequenced at the whole genome level using acetylated RNA immunoprecipitation sequencing (acRIP-seq) technology to identify acetylation features in TNBC, and 180 genes were significantly differentially ac4c acetylated in patients. We also analyzed the genome-wide lncRNA expression profile and constructed a co-expression network, containing 116 ac4C genes and 1080 lncRNAs. Three of these lncRNAs were prognostic risk lncRNAs affected by NAT10 and contained in the network. The corresponding reciprocal pairs were “LINC01614-COL3A1”, “OIP5-AS1-USP8”, and “RP5-908M14.9-TRIR”. These results indicate that RNA ac4c acetylation involves lncRNAs and affects the tumor process and prognosis of TNBC. This will aid the prediction of drug targets and drug sensitivity. Full article

Translation is a fundamental process in all living organisms that involves the decoding of genetic information in mRNA by ribosomes and translation factors. The dysregulation of mRNA translation is a common feature of tumorigenesis. Protein expression reflects the total outcome of multiple regulatory mechanisms that change the metabolism of mRNA pathways from synthesis to degradation. Accumulated evidence has clarified the role of an increasing amount of mRNA modifications at each phase of the pathway, resulting in translational output. Translation machinery is directly affected by mRNA modifications, influencing translation initiation, elongation, and termination or altering mRNA abundance and subcellular localization. In this review, we focus on the translation initiation factors associated with cancer as well as several important RNA modifications, for which we describe their association with cancer. Full article

Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs. Full article