Citations

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Background: Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. Methods: Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. Results: In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). Conclusions: Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.

Abstract Lung fibrosis, including idiopathic pulmonary fibrosis (IPF), represents a spectrum of progressive interstitial lung diseases characterized by disrupted epithelial repair, fibroblast activation, and excess extracellular matrix accumulation. A central feature of fibrotic progression is the loss of alveolar type 2 epithelial (AT2) cell identity and the emergence of aberrant transitional states that fail to support normal regeneration. Based on our re-analysis of multiple publicly available scRNA-seq datasets from the IPF Cell Atlas, S100A2 expression is tightly associated with basal-like reprogramming of AT2 cells and is a top upregulated gene at both mRNA and protein levels in IPF lungs. Furthermore, scRNA-seq data from human lung organoid models reveal that AT2 cells co-cultured with fibroblasts acquire a basal-like phenotype and express high levels of S100A2. Functionally, overexpression of S100A2 in human iPSC-derived lung alveolar epithelial type 2 (iAT2) cells leads to loss of AT2 cellular identity, increased generation of reactive oxygen species, and activation of RAGE signaling pathway. We demonstrate that pharmacologic inhibition of RAGE using Azeliragon preserves AT2 cell populations, associated with reduced oxidative stress in iAT2 cells, and significantly attenuates collagen deposition in bleomycin-induced lung fibrosis models. Collectively, our results indicate that S100A2 is a key driver of epithelial dysfunction in lung fibrosis, promoting loss of AT2 identity, aberrant basal fate acquisition, and persistent epithelial injury. Targeting the unrecognized S100A2–RAGE pathway may offer a new therapeutic strategy to restore epithelial homeostasis in lung fibrosis.

Abstract Objective This study was undertaken to examine acute and chronic electroencephalographic (EEG) and seizure characteristics in new onset refractory status epilepticus (NORSE). Methods Multicenter inpatient and follow‐up clinical and EEG data were analyzed using International League Against Epilepsy definitions and American Clinical Neurophysiology Society (ACNS) EEG terminology. Results Forty‐four patients were included from the Yale NORSE/FIRES Biorepository (median age = 29 years, 33 female, 38 cryptogenic). Nine hundred twenty inpatient EEG days were reviewed (median = 13.5 days/patient). Presenting status epilepticus (SE) types included 24 convulsive SE (CSE), five focal–motor (FMSE), and 15 nonconvulsive (NCSE). Of 39 patients with post discharge follow‐up (median = 14.7 months), 61.5% ( n = 24) had seizures and required more antiseizure medications than seizure‐free patients (median = 4 vs. 1, p < .001). Inpatient EEG captured epileptiform discharges in all and periodic discharges in 38 patients. Seizures were captured in 38 patients: 33 electrographic (11 only electrographic) and 27 electroclinical. Total seizure burden ranged 6.5–29 615.0 min (median = 50.7). Seizures fulfilling ACNS EEG criteria for SE were captured in 20 patients; two had CSE and the remainder NCSE with coma, nine of whom also had FMSE. Inpatient seizure days (median = 50.0 vs. 15.5%, p = .004) and seizure burden (median = 42.9 vs. 0 min, p = .009) were higher in the first half of monitored days. Interictal findings were equally present and did not predict postdischarge seizures, whereas electroclinical seizures (90.9% vs. 45.5%, p = .037), seizure burden (median = 116.8 vs. 32.5 min, p = .045), and proportion of seizure days (36.3% vs. 20.0%, p = .037) did. Among 12 patients with follow‐up EEGs (median = 19.6 months post onset), posterior‐dominant rhythm (PDR) returned in nine; five had periodic discharges, and three had seizure captured. Significance Seizures are most commonly convulsive upon presentation and nonconvulsive or clinically subtle throughout the inpatient course in NORSE. Inpatient seizures, but not interictal abnormalities, occur earlier in the inpatient course and presence of electroclinical seizures, seizure burden, and proportion of seizure days but not interictal findings are associated with postdischarge seizures. Although return of PDR reflects neurological recovery, seizure persistence and EEG abnormalities post‐NORSE are common.

ABSTRACT Background & Aims Metabolic dysfunction‐associated steatotic liver disease (MASLD) is highly prevalent and increasingly recognized as a multisystem cardiometabolic disorder. The global burden, incidence and prognostic implications of coexisting chronic kidney disease (CKD) in MASLD remain uncertain. Methods In this meta‐analysis, we searched MEDLINE and EMBASE from inception through September 25, 2025, for observational studies reporting CKD prevalence and/or incidence among adults (≥ 18 years) with MASLD. The primary outcomes were pooled CKD prevalence, estimated using random‐effects models on the logit scale with the Hartung–Knapp adjustment, and pooled CKD incidence (per 1000 person‐years), estimated using random‐effects models with exact Poisson confidence intervals. Secondary outcomes included all‐cause mortality and cardiovascular, renal and cancer outcomes. Results From 2811 records, 36 observational studies met the inclusion criteria. Twenty‐three studies contributed prevalence estimates (575 615 participants; 56 248 CKD cases) and thirteen studies contributed incidence estimates (27 996 new events). The pooled global CKD prevalence among individuals with MASLD was 15.22% (95% CI 9.69–23.12). The pooled CKD incidence was 22.17 per 1000 person‐years (95% CI 11.44–32.89) in the MASLD population. Hypertension (OR 1.49, 95% CI 1.29–1.72), dyslipidaemia (OR 1.22, 95% CI 1.20–1.24) and diabetes (OR 1.94, 95% CI 1.69–2.22) were associated with higher odds of CKD in the MASLD population. All‐cause mortality rates were 18.28 per 1000 person‐years (95% CI 3.45–33.11) in coexistent MASLD and CKD, more than double those in the MASLD‐only population (7.26 per 1000 person‐years (95% CI 2.97–11.56)). Conclusions Coexistent CKD affects one in seven individuals with MASLD worldwide, conferring a more than two‐fold increased risk of all‐cause mortality compared with individuals with MASLD alone.

Key Points Alanyl-transfer RNA synthetase 1 (AARS1) was upregulated and promoted cyst growth in autosomal dominant polycystic kidney disease. AARS1 promoted lysine lactylation of signal transducer and activator of transcription 3 (K140) and NF-κB p65 (K310), enhancing their phosphorylation, interaction, and transcriptional activity. AARS1 transcriptionally repressed its novel targets, Atg5 and Dusp4, to impair autophagy but activate polycystic kidney disease signaling pathways. Background Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disorder, characterized by metabolic reprogramming with enhanced glycolysis and lactate accumulation. However, the enzyme that senses lactate and uses it as a substrate for protein lactylation in ADPKD remains unknown. Alanyl-transfer RNA synthetase 1 (AARS1) is a lactate-responsive enzyme with lactyltransferase activity, while its role and mechanisms in ADPKD have not been defined. Methods To investigate the role of AARS1, we generated Pkd1 and Aars1 double conditional knockout Pkd1 fl/fl :Aars1 fl/fl :Ksp-Cre mice and evaluated the effect of AARS1 inhibitor β -alanine in two ADPKD mouse models. Coimmunoprecipitation and Cleavage Under Targets and Tagmentation analyses were performed to identify novel AARS1 substrates and downstream target genes involved in cystogenesis. Results AARS1 was elevated in Pkd1 mutant renal epithelial cells and kidneys, and genetic deletion of Aars1 significantly delayed cyst growth, preserved kidney function, and reduced renal lactylation in Pkd1 mutant mice. Mechanistically, AARS1 promoted lactylation-dependent activation of signal transducer and activator of transcription 3 (STAT3) and NF-κB (p65), specifically at STAT3 K140 and p65 K310, thereby amplifying pro-proliferative and proinflammatory transcriptional programs. AARS1 also associated with promoter regions and mediated histone H3 lysine 14 lactylation, thereby repressing the transcription of Atg5 to impair autophagy and suppressing the transcription of Dusp4 to sustain phosphorylation of cAMP response element–binding protein and retinoblastoma protein. Cytokine signaling through IL-6 and TNF- α further reinforced AARS1 expression through STAT3- and NF-κB–dependent feed-forward loops. Importantly, inhibition of AARS1 with β -alanine markedly slowed cyst progression in Pkd1 mutant mouse models. Conclusions Our study identified AARS1 as a central metabolic sensor linking lactate-driven lysine lactylation to transcriptional, epigenetic, and signaling pathways that drive ADPKD progression.

Abstract Objective . Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery. Approach . Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use. Main Results . The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels. Significance . DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.