Identification of core cuprotosis-correlated biomarkers in abdominal aortic aneurysm immune microenvironment based on bioinformatics

BackgroundThe occurrence of abdominal aortic aneurysms (AAAs) is related to the disorder of immune microenvironment. Cuprotosis was reported to influence the immune microenvironment. The objective of this study is to identify cuprotosis-related genes involved in the pathogenesis and progression of AAA.MethodsDifferentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in mouse were identified following AAA through high-throughput RNA sequencing. The enrichment analyses of pathway were selected through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG). The validation of cuprotosis-related genes was conducted through immunofluorescence and western blot analyses.ResultsTotally, 27616 lncRNAs and 2189 mRNAs were observed to be differentially expressed (|Fold Change| ≥ 2 and q< 0.05) after AAA, including 10424 up-regulated and 17192 down-regulated lncRNAs, 1904 up-regulated and 285 down-regulated mRNAs. Gene ontology and KEGG pathway analysis showed that the DElncRNAs and DEmRNAs were implicated in many different biological processes and pathways. Furthermore, Cuprotosis-related genes (NLRP3, FDX1) were upregulated in the AAA samples compared with the normal one.ConclusionCuprotosis-related genes (NLRP3,FDX1) involved in AAA immune environment might be critical for providing new insight into identification of potential targets for AAA therapy

The small molecule luteolin inhibits N-acetyl-α-galactosaminyltransferases and reduces mucin-type O-glycosylation of amyloid precursor protein

Mucin-type O-glycosylation is the most abundant type of O-glycosylation. It is initiated by the members of the polypeptide N-acetyl-α-galactosaminyltransferase (ppGalNAc-T) family and closely associated with both physiological and pathological conditions, such as coronary artery disease or Alzheimer's disease. The lack of direct and selective inhibitors of ppGalNAc-Ts has largely impeded research progress in understanding the molecular events in mucin-type O-glycosylation. Here, we report that a small molecule, the plant flavonoid luteolin, selectively inhibits ppGalNAc-Ts in vitro and in cells. We found that luteolin inhibits ppGalNAc-T2 in a peptide/protein-competitive manner but not promiscuously (e.g. via aggregation-based activity). X-ray structural analysis revealed that luteolin binds to the PXP motif-binding site found in most protein substrates, which was further validated by comparing the interactions of luteolin with wild-type enzyme and with mutants using 1H NMR-based binding experiments. Functional studies disclosed that luteolin at least partially reduced production of β-amyloid protein by selectively inhibiting the activity of ppGalNAc-T isoforms. In conclusion, our study provides key structural and functional details on luteolin inhibiting ppGalNAc-T activity, opening up the way for further optimization of more potent and specific ppGalNAc-T inhibitors. Moreover, our findings may inform future investigations into site-specific O-GalNAc glycosylation and into the molecular mechanism of luteolin-mediated ppGalNAc-T inhibition.This work was supported by the National Basic Research Program of China Grants 2012CB822103 and 2011CB910603 (to Y. Z.); National High Technology Research and Development Program of China Grant 2012AA020203 (to Y. Z.); National Natural Science Foundation Grants 31170771 (to Y. Z.), 31370806 (to Y. Z.), and 31570796 (to Y. Z.); National Basic Research Program of China Grant 2012CB822103 (to F. W.); National Natural Science Foundation Grants 31270853 and 81102377 (to F. W.); Agencia Aragonesa para la Investigación y Desarrollo (ARAID), Ministerio de Economía y Competitividad, Grants CTQ2013-44367-C2-2-P and BFU2016-75633-P (to R. H.-G.); Diputación General de Aragón (DGA) Grant B89 (to R. H.-G.); and the EU Seventh Framework Programme (2007–2013) under BioStruct-X (Grant Agreement 283570 and BIOSTRUCTX 5186) (to R. H.-G.).Peer Reviewe

Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study

Objectives To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.Design Retrospective cohort study.Setting Data were collected from the electronic records and the databases of Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.Participants 380 inpatients diagnosed with acute AD were included in the study.Primary outcome Preoperative in-hospital mortality rate.Results A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.Conclusion In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.Trial registration number ChiCTR1900025818