Homogeneity and heterogeneity of the cytopathological mechanisms in different etiology-induced acute kidney injury (AKI) are poorly understood. Here, we performed single-cell sequencing (scRNA) on mouse kidneys with five common AKI etiologies (CP-Cisplatin, IRI-Ischemia-reperfusion injury, UUO-Unilateral ureteral obstruction, FA-Folic acid, and SO-Sodium oxalate). We constructed a potent multi-model AKI scRNA atlas containing 20 celltypes with 80,689 high-quality cells. The data suggest that compared to IRI and CP-AKI, FA- and SO-AKI exhibit injury characteristics more similar to UUO-AKI, which may due to tiny crystal-induced intrarenal obstruction. Through scRNA atlas, 7 different functional proximal tubular cell (PTC) subtypes were identified, we found that Maladaptive PTCs and classical Havcr1 PTCs but not novel Krt20 PTCs affect the pro-inflammatory and pro-fibrotic levels in different AKI models. And cell death and cytoskeletal remodeling events are widespread patterns of injury in PTCs. Moreover, we found that programmed cell death predominated in PTCs, whereas apoptosis and autophagy prevailed in the remaining renal tubules. We also identified S100a6 as a novel AKI-endothelial injury biomarker. Furthermore, we revealed that the dynamic and active immune (especially Arg1 Macro_2 cells) -parenchymal cell interactions are important features of AKI. Taken together, our study provides a potent resource for understanding the pathogenesis of AKI and early intervention in AKI progression at single-cell resolution.
Human parainfluenza virus type 3 (HPIV3) is one of the primary pathogens that causing severe respiratory tract diseases in newborns and infants. It could induce inclusion bodies (IBs) in infected cells. Comprised of viral nucleoprotein (N) and phosphoprotein (P), as well as some cellular factors, HPIV3 IBs are unique platform for efficient viral synthesis. Although several studies have demonstrated the formation of IBs, little is known about cellular proteins involved in HPIV3 IBs formation. By quantitative real-time PCR assays after cytochalasin D treatment, we found actin microfilaments of the cytoskeleton were indispensible for HPIV3 RNA synthesis. Using co-immunoprecipitation and immunofluorescence assays, an actin-modulating protein, cofilin was found to involve in the IBs formation through interaction with the N protein in N–P induced IBs complex. Viral IBs formation reduced upon RNA interference knockdown of cellular cofilin, thus viral RNA synthesis and protein expression level were also suppressed. What’s more, the inactive form of cofilin, p-cofilin was increased after HPIV3 infection, and phosphorylation of cofilin was required for interacting with N–P complex and IBs formation. We further identified that the regions in cofilin interacting with N protein lies in the C-terminus. Our findings for the first time to state that cellular cofilin involves in HPIV3 IBs and interaction with N is critical for cofilin to aid IBs formation and enhancing viral RNA synthesis.
Background. Sepsis is a major cause of morbidity and mortality worldwide. Sepsis with acute kidney injury (AKI) is associated with higher mortality risk when compared with those with sepsis and without AKI. Therefore, it is necessary to detect the predictors of sepsis-associated acute kidney injury (SA-AKI) in order to timely prevent, diagnose and treat this complication. Methods. From July 2016 to December 2019, 419 patients with sepsis admitted to the intensive care unit (ICU) were randomly divided into two groups: training group (n= 302) and validation group (n = 117). A least absolute shrinkage and selection operator (LASSO) regression was constructed to select variables within 24 h of admission, and then were included in a logistic regression model to find the independent risk factors of AKI. Hence, a nomogram for predicting SA-AKI with statistically significant covariates was constructed. Discrimination, calibration, and clinical utility of the nomogram performance were assessed and then validated.Results. The risk factors yielded by logistic regression were hypertension(HT), diabetes mellitus(DM), C-reactive protein(CRP), procalcitonin(PCT), activated partial thromboplastin time(APTT), platelet(PLT), and then were incorporated into the nomogram. The areas under the ROC curve of the nomogram in the training and validation groups were 0.856 and 0.885, respectively. The calibration curves demonstrated favorable consistency between the predictions of nomogram and the actual observations in both training as well as validation groups. Decision curve analysis (DCA) showed clinical usefulness of the proposed nomogram model.Conclusions. A risk prediction model by integrating variables can assist in identifying patients who are at high risk of developing SA-AKI. The nomogram had excellent predictive ability and might have significant clinical implications for early detection of AKI in patients undergoing sepsis.
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