Citations

Cellular and molecular heterogeneity in the liver has been increasingly recognized to drive liver fibrosis progression, but the particular events that occur initially in response to liver injury and trigger immune cell recruitment remain unclear. Here, we identify epigenetically aberrant liver sinusoidal endothelial cells (LSECs) as key players in this process. Mechanistically, the epigenetic readers like bromodomain-containing protein 4–dependent (BRD4-dependent) super enhancers (SEs) activate proinflammatory genes, including promyelocytic leukemia (PML). The PML protein, in turn, binds BRD4 and amplifies proinflammatory angiocrine signaling through phase separation–dependent SE activation via PML/BRD4 condensate formation. In mouse models, LSEC-specific depletion of the PML/BRD4 complex mitigates liver inflammation and fibrosis. Single-cell RNA-seq reveals that epigenetically aberrant LSECs exhibit a reprogrammed proinflammatory angiocrine landscape in mouse fibrotic livers. TIMP1 + LSECs promote the recruitment of CD63 + monocyte–derived macrophages (MoMFs) during liver fibrosis progression. Thereby, PML/BRD4 in LSECs governs inflammatory immune cell recruitment in liver fibrosis. Pharmacological BRD4 inhibition or epigenetic PML-SE repression alleviates liver inflammation and fibrosis. In conclusion, PML/BRD4-mediated SE activation via phase separation drives proinflammatory angiocrine signaling in LSECs, initiating the inflammatory cascade and subsequent immune cell recruitment during liver fibrosis.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. In this study, we introduce a novel, exomic foundational model that leverages a combination of the human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short-range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in predicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest sequence transformer for range adaptive nucleotide decoding variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuracy of 0.880 [an 8.2% improvement over the original nucleotide transformer (NT) and a 7% improvement over NT-v2]. Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomics-based diagnosis and personalized medicine, including tailored therapeutic development.

Background & Aims: The clinical course and outcomes of alcohol-associated hepatitis (AH) remain poorly understood. Major adverse liver outcomes do not capture the added risk of return to drinking. We examined the natural history of AH and developed a composite endpoint using a contemporary observational cohort of AH. Approach & Results: A cohort of 1127 participants: 712 AH patients, 256 heavy drinking controls without clinically evident liver disease, and 159 healthy controls, were prospectively followed for 6 months at 8 United States centers as part of the Alcoholic Hepatitis Network (AlcHepNet) consortium. Outcomes included mortality and a composite endpoint (AlcHepNet composite index) that included death, liver transplantation, hepatic decompensation (new onset/worsening ascites, HE, variceal bleeding), liver-related hospital admission, MELD increase ≥5, and return to drinking. Of 712 AH patients (age 45±10.7 y; 59.1% male), 558 (79.0%) had severe and 148 (21.0%) had moderate AH, 232 (32.5%) died, and 86 (12.1%) underwent liver transplantation. Mortality rates in moderate AH and severe AH were 0.7% versus 17.2% (30 d), 3.4% versus 26.5% (90 d), and 8.8% versus 30.5% (180 d), respectively (all p <0.001). Composite liver/alcohol use events were noted in 459 (64.5%) AH patients. Higher MELD score, lower mean arterial pressure, and baseline leukocytosis were associated with higher 90-day mortality in AH (all p <0.05). College education and higher ALP were associated with lower mortality. Heavy drinking controls had low mortality (n=3; 1.2%). Conclusions: This large observational study showed a high incidence of composite liver and alcohol-use events within 6 months, reiterating the need for early interventions.

Background: How parental alcohol use disorder and liver disease-related mortality influence the risk and the outcomes of alcohol-associated hepatitis (AH) in the offspring is unknown. Methods: We analyzed data from 2 prospective observational studies of AH cases and heavy drinking controls (HDCs). Family history of parental alcohol use disorder and liver disease mortality was assessed at the study entry. Logistic regression and Cox proportional hazard models were used to assess the influences of family history on AH development and outcome. Results: Data from 1356 participants in two prospective cohorts (926 AH cases and 430 HDC) were combined and analyzed. Parental alcohol use disorder was found in 56.9% of AH cases and 61.1% of HDC; parental death due to liver disease was reported in 7.5% of AH cases and 5.7% of HDC. Multivariable logistic regression showed that parental liver disease-related mortality was associated with more than a doubled risk of AH development in the offspring after controlling for their demographic characteristics and drinking behavior (OR=2.26, 95% CI: [1.22, 4.20]). Moreover, among the AH cases, having a parent die of liver disease significantly increased the 90-day mortality of study participants after adjusting for the effects of other risk factors (HR=2.26, 95% CI: [1.05, 4.86]). Conclusions: The study highlights the influences of parental death due to liver disease on AH development and mortality. Identifying patients at risk of AH through family history might help facilitate discussions on reducing alcohol consumption.

Background Severe alcohol-associated hepatitis (sAH) is a life-threatening condition with high mortality, where corticosteroid use is the only treatment that has shown short-term benefits. Pentoxifylline, an anti-tumour necrosis factor-alpha agent, has been proposed for its potential to improve outcomes, especially in patients with acute kidney injury (AKI). We aimed to evaluate the impact of pentoxifylline on mortality in patients with sAH and AKI in a well-characterised global cohort. Methods We conducted a retrospective, registry-based study including patients meeting the National Institute on Alcohol Abuse and Alcoholism clinical criteria for sAH and AKI. Mortality was the primary endpoint, with liver transplantation as a competing risk. Statistical analysis included Cox regression and Kaplan-Meier survival estimates. Results We included 525 patients from 20 centres across eight countries. The median age was 48 years, with 26.1% females, and 76.9% had a history of cirrhosis. Multivariable Cox regression models showed that pentoxifylline use was not associated with survival (HR 1.20, 95% CI 0.85 to 1.69, p=0.291). Factors associated with mortality included age (HR 1.23, 95% CI 1.10 to 1.36, p<0.001), Model for End-Stage Liver Disease score at admission (HR 1.06, 95% CI 1.04 to 1.08, p<0.001) and renal replacement therapy use (HR 1.39, 95% CI 1.05 to 1.84, p=0.019). The main causes of death were multiple organ failure (42%), infections (10%), oesophageal varices bleeding (7%) and renal failure (6%). Conclusion Pentoxifylline showed no significant benefit on mortality in patients with sAH and AKI. Further studies are needed to refine treatment strategies for this high-risk group.

Spatial heterogeneity of liver viscoelasticity, measured at three-dimensional vector MR elastography, positively correlated with metabolic dysfunction–associated steatotic liver disease progression.

Background: Severe alcohol-associated hepatitis (sAH) is a well-characterized disease with high short-term mortality. However, there is limited research on those with a “less severe condition” (moderate AH). This study aims to characterize in-depth patients with moderate AH (mAH), including the performance of mortality scoring systems, key prognostic factors, and survival over time. Methods: A multicenter retrospective cohort study (2009–2019) included patients with mAH (MELD score ≤20 at admission). Cox regression and receiver operating characteristic curves with AUC were used for analysis. Results: We included 1845 patients with AH (20 centers, 8 countries) between 2009 and 2019. mAH was defined as a MELD score ≤20 at admission. Twenty-four percent met the criteria for an mAH episode. Patients with mAH tend to be older and have a higher proportion of females, with a median MELD of 17 (15–19), Maddrey discriminant function (mDF) of 33 (22–40), the trajectory of serum bilirubin of 0.83 (0.60–1.21), and neutrophil-to-lymphocyte ratio (NLR) of 5 (2.96–8.60). The primary causes of death in mAH included multiple organ failure (34.1%) and infections (16.6%). The cumulative survival rates at 30, 90, and 180 days were 94.3%, 90.4%, and 88.2%, respectively. In multivariable analysis, age was the only significant predictor of 30-day mortality (HR 1.49, 95% CI: 1.27–1.76, p <0.001). Mortality prediction models showed poor performance, with AUC for MELD (0.671), mDF (0.726), trajectory of serum bilirubin (0.733), and NLR (0.697). Conclusions: Patients with moderate AH exhibited a mortality of 11.8% at 6 months, primarily driven by multiple organ failure and infections. These patients also exhibit a different clinical profile compared to those with sAH. Tailored models and therapeutic strategies are needed to improve long-term outcomes in mAH.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.