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Cellular senescence is an important factor leading to pulmonary fibrosis. Deficiency of 8-oxoguanine DNA glycosylase (OGG1) in mice leads to alleviation of bleomycin (BLM)-induced mouse pulmonary fibrosis, and inhibition of the OGG1 enzyme reduces the epithelial mesenchymal transition (EMT) in lung cells. In the present study, we find decreased expression of OGG1 in aged mice and BLM-induced cell senescence. In addition, a decrease in OGG1 expression results in cell senescence, such as increases in the percentage of SA-β-gal-positive cells, and in the p21 and p-H2AX protein levels in response to BLM in lung cells. Furthermore, OGG1 promotes cell transformation in A549 cells in the presence of BLM. We also find that OGG1 siRNA impedes cell cycle progression and inhibits the levels of telomerase reverse transcriptase (TERT) and LaminB1 in BLM-treated lung cells. The increase in OGG1 expression results in the opposite phenomenon. The mRNA levels of senescence-associated secretory phenotype (SASP) components, including IL-1α, IL-1β, IL-6, IL-8, CXCL1/CXCL2, and MMP-3, in the absence of OGG1 are obviously increased in A549 cells treated with BLM. Interestingly, we demonstrate that OGG1 binds to p53 to inhibit the activation of p53 and that silencing of p53 reverses the inhibition of OGG1 on senescence in lung cells. Additionally, the augmented cell senescence is shown in vivo in OGG1-deficient mice. Overall, we provide direct evidence in vivo and in vitro that OGG1 plays an important role in protecting tissue cells against aging associated with the p53 pathway.

Hypokalaemia is a side-effect of diuretics. We aimed to use machine learning to identify features predicting hypokalaemia risk in hypertensive patients. Participants with hypertension in the United States National Health and Nutrition Examination Survey 1999–2018 were included for analysis. To select the most suitable algorithm, we tested and evaluated five machine learning algorithms commonly employed in epidemiological studies: Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting. These algorithms were accessed using a set of 38 screened features. We then selected the key hypokalaemia-associated features in the hypertension group and their cardiovascular diseases (CVD) subgroup using the SHapley Additive exPlanations (SHAP) values. Using SHAP values, the key features and their impact pattern on hypokalaemia risk were determined. A total of 25,326 hypertensive participants were included for analysis, of whom 4,511 had known CVD. The Random Forest algorithm had the highest AUROC (hypertension dataset: 0.73 [95%CI, 0.71–0.76]; CVD subgroup: 0.72 [95%CI, 0.66–0.78]). Moreover, the nomogram based on the top twelve key features screened by random forest retained good performance: age, sex, race, poverty income ratio, body mass index, systolic and diastolic blood pressure, non-potassium-sparing diuretics use and duration, renin-angiotensin blockers use and duration, and CVD history in hypertension dataset; while in CVD subgroup, the additional key features were comorbid diabetes, education level, smoking status, and use of bronchodilators. Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients. Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms, and hypokalemia-associated key features have been identified in hypertensive patients and the subgroup with cardiovascular disease. The nomogram we developed including twelve key features might be useful and applied in primary clinical consultations to identify the hypertensive patients at risk of hypokalaemia. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients [ABSTRACT FROM AUTHOR]

Background and Purpose: The causal relationship between altered host microbiome composition, especially the respiratory tract microbiome, and the occurrence of pulmonary hypertension (PH) has not yet been studied. An increased abundance of airway streptococci is seen in patients with PH compared with healthy individuals. This study aimed to determine the causal link between elevated airway exposure to Streptococcus and PH. Experimental Approach: The dose‐, time‐ and bacterium‐specific effects of Streptococcus salivarius (S. salivarius), a selective streptococci, on PH pathogenesis were investigated in a rat model established by intratracheal instillation. Key Results: Exposure to S. salivarius successfully induced typical PH characteristics, such as elevated right ventricular systolic pressure (RVSP), right ventricular hypertrophy (Fulton's index) and pulmonary vascular remodelling, in a dose‐ and time‐dependent manner. Moreover, the S. salivarius‐induced characteristics were absent in either the inactivated S. salivarius (inactivated bacteria control) treatment group or the Bacillus subtilis (active bacteria control) treatment group. Notably, S. salivarius‐induced PH is characterized by elevated inflammatory infiltration in the lungs, in a pattern different from the classic hypoxia‐induced PH model. Moreover, in comparison with the SU5416/hypoxia‐induced PH model (SuHx‐PH), S. salivarius‐induced PH causes similar histological changes (pulmonary vascular remodelling) but less severe haemodynamic changes (RVSP, Fulton's index). S. salivarius‐induced PH is also associated with altered gut microbiome composition, suggesting potential communication of the lung‐gut axis. Conclusion and Implications: This study provides the first evidence that the delivery of S. salivarius in the respiratory tract could cause experimental PH in rats. [ABSTRACT FROM AUTHOR]