ANKRD1 aggravates renal ischaemia‒reperfusion injury via promoting TRIM25-mediated ubiquitination of ACSL3.

BACKGROUND: Renal ischaemia‒reperfusion injury (IRI) is the primary cause of acute kidney injury (AKI). To date, effective therapies for delaying renal IRI and postponing patient survival remain absent. Ankyrin repeat domain 1 (ANKRD1) has been implicated in some pathophysiologic processes, but its role in renal IRI has not been explored. METHODS: The mouse model of IRI-AKI and in vitro model were utilised to investigate the role of ANKRD1. Immunoprecipitation-mass spectrometry was performed to identify potential ANKRD1-interacting proteins. Protein‒protein interactions and protein ubiquitination were examined using immunoprecipitation and proximity ligation assay and immunoblotting, respectively. Cell viability, damage and lipid peroxidation were evaluated using biochemical and cellular techniques. RESULTS: First, we unveiled that ANKRD1 were significantly elevated in renal IRI models. Global knockdown of ANKRD1 in all cell types of mouse kidney by recombinant adeno-associated virus (rAAV9)-mitigated ischaemia/reperfusion-induced renal damage and failure. Silencing ANKRD1 enhanced cell viability and alleviated cell damage in human renal proximal tubule cells exposed to hypoxia reoxygenation or hydrogen peroxide, while ANKRD1 overexpression had the opposite effect. Second, we discovered that ANKRD1's detrimental function during renal IRI involves promoting lipid peroxidation and ferroptosis by directly binding to and decreasing levels of acyl-coenzyme A synthetase long-chain family member 3 (ACSL3), a key protein in lipid metabolism. Furthermore, attenuating ACSL3 in vivo through pharmaceutical approach and in vitro via RNA interference mitigated the anti-ferroptotic effect of ANKRD1 knockdown. Finally, we showed ANKRD1 facilitated post-translational degradation of ACSL3 by modulating E3 ligase tripartite motif containing 25 (TRIM25) to catalyse K63-linked ubiquitination of ACSL3, thereby amplifying lipid peroxidation and ferroptosis, exacerbating renal injury. CONCLUSIONS: Our study revealed a previously unknown function of ANKRD1 in renal IRI. By driving ACSL3 ubiquitination and degradation, ANKRD1 aggravates ferroptosis and ultimately exacerbates IRI-AKI, underlining ANKRD1's potential as a therapeutic target for kidney IRI. KEY POINTS/HIGHLIGHTS: Ankyrin repeat domain 1 (ANKRD1) is rapidly activated in renal ischaemia‒reperfusion injury (IRI) models in vivo and in vitro. ANKRD1 knockdown mitigates kidney damage and preserves renal function. Ferroptosis contributes to the deteriorating function of ANKRD1 in renal IRI. ANKRD1 promotes acyl-coenzyme A synthetase long-chain family member 3 (ACSL3) degradation via the ubiquitin‒proteasome pathway. The E3 ligase tripartite motif containing 25 (TRIM25) is responsible for ANKRD1-mediated ubiquitination of ACSL3.

The Development and Evaluation of a Prediction Model for Kidney Transplant-Based Pneumocystis carinii Pneumonia Patients Based on Hematological Indicators.

BACKGROUND: This study aimed to develop a simple predictive model for early identification of the risk of adverse outcomes in kidney transplant-associated Pneumocystis carinii pneumonia (PCP) patients. METHODS: This study encompassed 103 patients diagnosed with PCP, who received treatment at our hospital between 2018 and 2023. Among these participants, 20 were categorized as suffering from severe PCP, and, regrettably, 13 among them succumbed. Through the application of machine learning techniques and multivariate logistic regression analysis, two pivotal variables were discerned and subsequently integrated into a nomogram. The efficacy of the model was assessed via receiver operating characteristic (ROC) curves and calibration curves. Additionally, decision curve analysis (DCA) and a clinical impact curve (CIC) were employed to evaluate the clinical utility of the model. The Kaplan-Meier (KM) survival curves were utilized to ascertain the model's aptitude for risk stratification. RESULTS: Hematological markers, namely Procalcitonin (PCT) and C-reactive protein (CRP)-to-albumin ratio (CAR), were identified through machine learning and multivariate logistic regression. These variables were subsequently utilized to formulate a predictive model, presented in the form of a nomogram. The ROC curve exhibited commendable predictive accuracy in both internal validation (AUC = 0.861) and external validation (AUC = 0.896). Within a specific threshold probability range, both DCA and CIC demonstrated notable performance. Moreover, the KM survival curve further substantiated the nomogram's efficacy in risk stratification. CONCLUSIONS: Based on hematological parameters, especially CAR and PCT, a simple nomogram was established to stratify prognostic risk in patients with renal transplant-related PCP.

Machine Learning Models for Prediction of Severe Pneumocystis carinii Pneumonia after Kidney Transplantation: A Single-Center Retrospective Study.

BACKGROUND: The objective of this study was to formulate and validate a prognostic model for postoperative severe Pneumocystis carinii pneumonia (SPCP) in kidney transplant recipients utilizing machine learning algorithms, and to compare the performance of various models. METHODS: Clinical manifestations and laboratory test results upon admission were gathered as variables for 88 patients who experienced PCP following kidney transplantation. The most discriminative variables were identified, and subsequently, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Light Gradient Boosting Machine (LGBM), and eXtreme Gradient Boosting (XGB) models were constructed. Finally, the models' predictive capabilities were assessed through ROC curves, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1-scores. The Shapley additive explanations (SHAP) algorithm was employed to elucidate the contributions of the most effective model's variables. RESULTS: Through lasso regression, five features-hemoglobin (Hb), Procalcitonin (PCT), C-reactive protein (CRP), progressive dyspnea, and Albumin (ALB)-were identified, and six machine learning models were developed using these variables after evaluating their correlation and multicollinearity. In the validation cohort, the RF model demonstrated the highest AUC (0.920 (0.810-1.000), F1-Score (0.8), accuracy (0.885), sensitivity (0.818), PPV (0.667), and NPV (0.913) among the six models, while the XGB and KNN models exhibited the highest specificity (0.909) among the six models. Notably, CRP exerted a significant influence on the models, as revealed by SHAP and feature importance rankings. CONCLUSIONS: Machine learning algorithms offer a viable approach for constructing prognostic models to predict the development of severe disease following PCP in kidney transplant recipients, with potential practical applications.

Risk factors for BK virus infection in DCD donor kidney transplant recipients.

Background: BK virus infection after kidney transplantation can negatively impact the prognosis of patients. However, current risk factor analyses primarily focus on BK virus nephropathy, while BK viruria and BK viruria progressing to BK viremia receive less attention. This study aims to analyze the risk factors associated with BK viruria and BK viruria progressing to BK viremia in recipients of donation after cardiac death (DCD), with the goal of facilitating early intervention. Methods: Donor characteristics and clinical data of recipients before and after transplantation were evaluated, and logistic univariate and multivariate analyses were performed to determine the risk factors associated with BK viruria and the progression of BK viruria to BK viremia. Additionally, machine learning techniques were employed to identify the top five features associated with BK viruria evolving into BK viremia. Results: During a median follow-up time of 1,072 days (range 739-1,418), 69 transplant recipients (15.6% incidence rate) developed BK viruria after transplantation, with 49.3% of cases occurring within 6 months post-transplantation. Moreover, 19 patients progressed to BK viremia. Donor age [OR: 1.022 (1.000, 1.045), p = 0.047] and donor procalcitonin (PCT) levels [0.5-10 ng/ml; OR: 0.482 (0.280, 0.828), p = 0.008] were identified as independent risk factors for BK viruria. High BK viruria [OR: 11.641 (1.745, 77.678), p = 0.011], recipient age [OR: 1.106 (1.017, 1.202), p = 0.018], and immunoinduction regimen [ATG; OR: 0.063 (0.006, 0.683), p = 0.023] were independent risk factors for BK viruria progressing to BK viremia. Machine learning analysis confirmed the importance of high BK viruria, recipient age, and immunoinduction regimen (ATG) in predicting the progression of BK viruria to BK viremia. Conclusion: The development and progression of BK virus in DCD kidney transplant recipients is influenced by multiple factors. Early intervention and treatment could potentially extend the lifespan of the transplanted organ.

Ubiquitin-specific protease 29 attenuates hepatic ischemia-reperfusion injury by mediating TGF-ß-activated kinase 1 deubiquitination.

Background and aims: In the course of clinical practice, hepatic ischemia/reperfusion (I/R) injury is a prevalent pathophysiological event and is caused by a combination of complex factors that involve multiple signaling pathways such as MAPK and NF-κB. USP29 is a deubiquitinating enzyme important during the development of tumors, neurological diseases, and viral immunity. However, it is unknown how USP29 contributes to hepatic I/R injury. Methods and results: We systematically investigated the role of the USP29/TAK1-JNK/p38 signaling pathway in hepatic I/R injury. We first found reduced USP29 expression in both mouse hepatic I/R injury and the primary hepatocyte hypoxia-reoxygenation (H/R) models. We established USP29 full knockout mice (USP29-KO) and hepatocyte-specific USP29 transgenic mice (USP29-HTG), and we found that USP29 knockout significantly exacerbates the inflammatory infiltration and injury processes during hepatic I/R injury, whereas USP29 overexpression alleviates liver injury by decreasing the inflammatory response and inhibiting apoptosis. Mechanistically, RNA sequencing results showed the effects of USP29 on the MAPK pathway, and further studies revealed that USP29 interacts with TAK1 and inhibits its k63-linked polyubiquitination, thereby preventing the activation of TAK1 and its downstream signaling pathways. Consistently, 5z-7-Oxozeaneol, an inhibitor of TAK1, blocked the detrimental effects of USP29 knockout on H/R-induced hepatocyte injury, further confirming that USP29 plays a regulatory role in hepatic I/R injury by targeting TAK1. Conclusion: Our findings imply that USP29 is a therapeutic target with promise for the management of hepatic I/R injury via TAK1-JNK/p38 pathway-dependent processes.