ABSTRACT Cyclin‐dependent kinase 9 (CDK9) is considered as an important target in the research of antitumor drugs. Taking the CDK2/9 inhibitor CYC065 as the positive control and an in‐house library compound ( 64 ) as the lead compound, four classes of 22 target compounds with 9H purine as the core structure were designed to establish structure–activity relationships (SAR). In general, SAR of 9H purine CDK9 inhibitors is systematically described in this paper, resulting in the discovery of two compounds ( B2 and B5 ) with further research value. After conducting selectivity testing against CDK2/9 kinase, compound B5 demonstrated approximately five‐fold greater selectivity towards CDK9‐cyclinT1 over CDK2‐cyclinE2. This work also provides a reference basis for the subsequent research on CDK9 inhibitors.
Introduction. The occurrence of aortic dissection is related to the proliferation and metastasis of vascular smooth muscle cells. In our present study, we found that the expression of miR-140-5p was inhibited in the wall of abdominal aorta of aortic dissection patients. However, the mechanism of miR-140-5p in the development of aortic dissection is unclear. Material and methods. We detected the expression of miR-140-5p and NCK Associated Protein 1 (NCKAP1) in blood vessel of aortic dissection patients and normal people by PCR. Next, we established the miR-140-5p overexpression and miR-140-5p inhibition vascular smooth muscle cells (CRL-1999 cells). The BrdU assays, wound healing assays and transwell assays were performed to detect the proliferation and invasion ability of these cells. Finally, luciferase reporter assay was performed to detect the relationship between miR-140-5p and NCKAP1. Results. The expression of miR-140-5p was suppressed in blood vessel of aortic dissection patients, and the levels of NCKAP1 in those tissues were upregulated. Overexpression of miR-140-5p inhibited the proliferation, migration and invasion of vascular smooth muscle cells. miR-140-5p targeted and suppressed the expression of NCKAP1. Conclusions. miR-140-5p repressed the proliferation, migration and invasion of vascular smooth muscle cells by targeting and inhibiting the expression of NCKAP1. Furthermore, the results of our study suggest new strategies and targets for the clinical treatment of arterial dissection.
Introduction: Pregnanediol-3-glucuronide (PdG), as the main metabolite of progesterone in urine, plays a significant role in the prediction of ovulation, threatened abortion, and menstrual cycle maintenance. Methods: To achieve a rapid and sensitive assay, we have designed a competitive model-based time-resolved fluorescence microsphere-lateral flow immunochromatography (TRFM-LFIA) strip. Results: The optimized TRFM-LFIA strip exhibited a wonderful response to PdG over the range of 30–2,000 ng/mL, the corresponding limit of detection (LOD) was calculated as low as 8.39 ng/mL. More importantly, the TRFM-LFIA strip was innovatively used for the quantitative detection of PdG in urine sample, and excellent recovery results were also obtained, ranging from 97.39% to 112.64%. Discussion: The TRFMLFIA strip possessed robust sensitivity and selectivity in the determination of PdG, indicating the great potential of being powerful tools in the biomedical and diagnosis region.
Abstract Background Epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented. Results This study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m 6 A, m 1 A, m 5 C, m 5 U, m 6 Am, m 7 G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m 6 A, m 1 A, m 5 C, m 5 U, m 6 Am, m 7 G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php . Conclusions A predictor framework has been developed through binary classification to predict RNA methylation sites.
We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wide-ranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is further evaluated by simulation and an empirical study on a genetic dataset on recombinant inbred mice.
Phosphatidylinositol-4-kinase alpha (PI4KIIIα), encoded by the PI4KA gene, can synthesize phosphatidylinositol-4-phosphate (PI-4-P), which serves as a specific membrane marker and is instrumental in signal transduction. PI4KA mutations can cause autosomal recessive diseases involving neurological, intestinal, and immunological conditions (OMIM:619621, 616531, 619708). We detected sepsis, severe diarrhea, and decreased immunoglobulin levels in one neonate. Two novel compound heterozygous mutations, c.5846T>C (p.Leu1949Pro) and c.3453C>T (p.Gly1151=), were identified in the neonate from the father and the mother, respectively. Sanger sequencing and reverse transcription polymerase chain reaction (RT-PCR) for peripheral blood and minigene splicing assays showed a deletion of five bases (GTGAG) with the c.3453C>T variant at the mRNA level, which could result in a truncated protein (p.Gly1151GlyfsTer17). The missense mutation c.5846T>C (p.Leu1949Pro) kinase activity was measured, and little or no catalytic activity was detected. According to the clinical characteristics and gene mutations with functional verification, our pediatricians diagnosed the child with a combined immunodeficiency and intestinal disorder close to gastrointestinal defects and immunodeficiency syndrome 2 (GIDID2; OMIM: 619708). Medicines such as immunomodulators are prescribed to balance immune dysregulation. This study is the first report of a synonymous mutation in the PI4KA gene that influences alternative splicing. Our findings expand the mutation spectrum leading to PI4KIIIa deficiency-related diseases and provide exact information for genetic counseling.
Abstract Background The purpose of this study was to explore the correlation between N6-methyladenosine (m6A)-regulated lncRNAs and tumor prognosis, immune infiltration, immune checkpoints (ICPs) expression in pancreatic ductal adenocarcinoma (PDAC). Methods We downloaded the raw RNA-sequence data and clinical data of PDAC from https://xenabrowser.net/ (cohort: TCGA Pancreatic Cancer) and Genotype-Tissue Expression project (GTEx). The m6A-regulated lncRNA was obtained by co-expression analysis. After that, lncRNA profiles and PDAC survival information were merged, and m6A-regulated multi-lncRNA prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) analysis. Through consensus clustering algorithm analysis, PDAC samples were divided into C1 and C2 groups. The downstream pathway signals of the two groups were constructed by Gene set enrichment analysis (GSEA) analysis. Finally, we detect the links between m6A regulated lncRNAs, immune infiltration, immune checkpoint gene expression. Results A total of 28 differential expressed m6A-regulated lncRNAs were identified, and based on this, a total of two subtypes of PDAC were obtained. A risk score nomogram consist of 11 m6A-regulated lncRNAs was constructed based on LASSO regression analysis. PDAC patients were divided into low-risk and high-risk groups based on risk scores. In addition to that, we identified IDO1 as a potential novel ICPs in PDAC. Conclusion This study demonstrates an indispensable role for m6A-regulated lncRNAs in the tumor microenvironment and immune infiltration. We could screen patients suitable for immunotherapy. Long term survival of PDAC patients can be predicted by 11 m6A regulated lncRNAs superiorly. The immune infiltration and ICPs expression were further explored in both groups.
Abstract Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA–RNA and RNA–protein interactions but also include RNA–DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.
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