Extensive epigenetic reprogramming involves in memory CD8+ T-cell differentiation. The elaborate epigenetic rewiring underlying the heterogeneous functional states of CD8+ T cells remains hidden. Here, we profile single-cell chromatin accessibility and map enhancer-promoter interactomes to characterize the differentiation trajectory of memory CD8+ T cells. We reveal that under distinct epigenetic regulations, the early activated CD8+ T cells divergently originated for short-lived effector and memory precursor effector cells. We also uncover a defined epigenetic rewiring leading to the conversion from effector memory to central memory cells during memory formation. Additionally, we illustrate chromatin regulatory mechanisms underlying long-lasting versus transient transcription regulation during memory differentiation. Finally, we confirm the essential roles of Sox4 and Nrf2 in developing memory precursor effector and effector memory cells, respectively, and validate cell state-specific enhancers in regulating Il7r using CRISPR-Cas9. Our data pave the way for understanding the mechanism underlying epigenetic memory formation in CD8+ T-cell differentiation.
CD8-Positive T-Lymphocytes/metabolism* ; Cell Differentiation ; Chromatin/immunology* ; Animals ; Mice ; Immunologic Memory ; Epigenesis, Genetic ; SOXC Transcription Factors/immunology* ; NF-E2-Related Factor 2/immunology* ; Mice, Inbred C57BL ; Gene Regulatory Networks ; Enhancer Elements, Genetic

Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.

OBJECTIVETo investigate the plasma levels of coagulation factor VII (FVII) and polymorphisms of FVII gene in patients with coronary heart disease (CHD), and evaluate the effect of plasma FVII levels and FVII gene polymorphisms on CHD.

METHODSPlasma FVIIa, FVII: Ag and FVIIc were measured and polymorphisms of FVII gene were analyzed in 149 control cases and 60 CHD cases, including 33 acute myocardial infarction (AMI) cases by a combination of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and agarose gel electrophoresis.

RESULTSFVIIa, FVIIc in AMI group were significantly higher than that in control group, but FVII: Ag wasn't. There were no significant difference in plasma FVIIa, FVII: Ag and FVIIc between CHD and control group. The IVS7 genotypic frequency in AMI group was significantly different from that in control group. There was no significant difference in genotypic frequencies and allelic frequencies in other polymphism sites. FVII: Ag was significantly higher in -402A homozygote than that in -402G homozygote.

CONCLUSIONSIncreased FVII levels, especially FVIIa and FVIIc in plasma, may contribute to coronary artery thrombosis. There was significant difference in IVS7 genotype frequency between control and AMI groups, but the rest weren't significantly different. FVII: Ag was significantly higher in -402A homozygote than that in -402G homozygote. Polymorphism of -402 G/A may play an indirect role in AMI by regulating plasma FVII levels.

Coronary Disease ; blood ; genetics ; Factor VII ; analysis ; genetics ; Female ; Genotype ; Humans ; Male ; Middle Aged ; Polymerase Chain Reaction ; Polymorphism, Genetic ; Polymorphism, Restriction Fragment Length