Bone marrow stromal cell antigen-1 deficiency protects from acute kidney injury

Urine Measurements of the Renin-Angiotensin System-Regulated Proteins Predict Death and Graft Loss in Kidney Transplant Recipients Enrolled in a Ramipril versus Placebo Randomized Controlled Trial

The renin-angiotensin system (RAS) is involved in kidney fibrosis. We previously identified six RAS-regulated proteins (RHOB, BST1, LYPA1, GLNA, TSP1, and LAMB2) that were increased in the urine of patients with kidney allograft fibrosis, compared to patients without fibrosis. We hypothesized that these urinary RAS-regulated proteins predicted primary outcomes in kidney transplant recipients enrolled in the largest RAS inhibitor randomized controlled trial. Urine excretion of 10 peptides corresponding to the six RAS-regulated proteins was quantified using parallel reaction monitoring mass spectrometry assays (normalized by urine creatinine) in a subset of patients in the trial. Machine learning models predicting outcomes based on urine peptide excretion rates were developed and evaluated. Urine samples (n = 111) from 56 patients were collected at 0, 6, 12, and 24 months. Twenty-four primary outcomes (doubling of serum creatinine, graft loss, or death) occurred in 17 patients. Logistic regression utilizing eight peptides of TSP1, BST1, LAMB2, LYPA1, and RHOB, from the last urine sample prior to outcomes, predicted a graft loss with an AUC of 0.78 (p = 0.00001). A random forest classifier utilizing BST1 and LYPA1 peptides predicted death with an AUC of 0.80 (p = 0.0016). Urine measurements of RAS-regulated proteins may predict outcomes in kidney transplant recipients, although further prospective studies are required.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.