RNA-binding proteins in metabolic-associated fatty liver disease (MAFLD): From mechanism to therapy

Rbm39 ameliorates metabolic dysfunction-associated steatotic liver disease by regulating Apob and Fabp4

Excessive hepatic lipid accumulation is the hallmark of metabolic dysfunction-associated steatotic liver disease (MASLD), yet its underlying mechanisms still not fully understood. In this study, we identified RNA binding motif protein 39 (Rbm39) as a key modulator of hepatic lipid homeostasis during MASLD progression. To establish in vivo MASLD model, mice were fed either a high-fat diet (HFD) or a Gubra-Amylin NASH (GAN) diet. We employed adeno-associated virus to manipulate Rbm39 expression levels to assess its role in MASLD. Transcriptome analysis was conducted to pinpoint the genes targeted by Rbm39. Western blot, RT-PCR, dual-luciferase reporter gene assays, and alternative splicing analysis were utilized to delve into the molecular mechanisms. Our results showed that Rbm39 expression was notably decreased in the livers of MASLD mice. Knockdown of hepatic Rbm39 aggravated HFD-induced hepatic steatosis and GAN diet-induced MASH, along with a notable decrease in serum lipid levels. Conversely, overexpression of Rbm39 attenuated MASLD development and progression. RNA sequencing data analysis indicated that Rbm39 regulated the expression of apolipoprotein B (Apob) and fatty acid-binding protein 4 (Fabp4), both of which are crucial for lipid transport. Mechanistically, Rbm39 enhanced the transcription of Apob by upregulating hepatocyte nuclear factor 4α (Hnf4α), while it suppressed Fabp4 transcription by regulating alternative splicing of hypoxia inducible factor-1α (Hif-1α). These findings highlight the pivotal role of Rbm39 in maintaining hepatic lipid homeostasis and suggest its potential as a therapeutic target for MASLD.

Alternative splicing: hallmark and therapeutic opportunity in metabolic liver disease

Metabolic dysfunction-associated fatty liver disease (MAFLD) has become the leading cause of chronic liver disease worldwide, with fibrosis recognized as the main prognostic factor and therapeutic target. While early-stage fibrosis is reversible, advanced fibrosis poses a significant clinical challenge due to limited treatment options, highlighting the need for innovative management strategies. Recent studies have shown that alternative pre-mRNA splicing, a critical mechanism regulating gene expression and protein diversity, plays a fundamental role in the pathogenesis of MAFLD and associated fibrosis. Understanding the complex relationship between alternative splicing and fibrosis progression in MAFLD could pave the way for novel therapeutic approaches and improve clinical outcomes. In this review, we describe the intricate mechanisms of alternative splicing in fibrosis associated with MAFLD. Specifically, we explored the pivotal of splicing factors, and RNA-binding proteins, highlighting their critical interactions with metabolic and epigenetic regulators. Furthermore, we provide an overview of the latest advancements in splicing-based therapeutic strategies and biomarker development. Particular emphasis is placed on the potential application of antisense oligonucleotides for rectifying splicing anomalies, thereby laying the foundation for precision medicine approaches in the treatment of MAFLD-associated fibrosis.

Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023

BACKGROUND

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use machine learning in the identification of significant features and the development of a predictive model to determine its usefulness in discrimination of MAFLD's risk stratification (low, moderate, and high) in adults.

METHODS

The data of the 2021-2023 NHANES database were analyzed. Vibration-controlled transient elastography measurements, including controlled attenuation parameter for the evaluation of steatosis and liver stiffness for the evaluation of fibrosis, were used for risk stratification. The participants were grouped into low-risk, moderate-risk, and high-risk groups based on specific criteria. Feature selection was conducted through Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest classification.

RESULTS

A total of 4,227 participants were included in the study. There were 16 significant predictors identified by LASSO regression, among which the top 10 predictors were demographic (age, gender, race, hypertension history), clinical (body mass index, waist circumference, hemoglobin, glycohemoglobin, lymphocyte count), and education level. The area under the receiver operating characteristic curve (AUC) of the random forest model in the validation set was 0.80, and the individual AUC was 0.83, 0.66 and 0.79 for the low-, moderate-, and high-risk groups, respectively.

CONCLUSION

Our machine learning model has excellent performance in stratification of risk for MAFLD with readily available clinical and demographic parameters. This model could be employed as a valuable screening tool to refer high-risk patients for further hepatological evaluation.

Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk

BACKGROUND

The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China.

METHODS

We collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model.

RESULTS

The average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening.

CONCLUSION

This study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD.

Emerging roles of RNA-binding proteins in fatty liver disease

A rampant and urgent global health issue of the 21st century is the emergence and progression of fatty liver disease (FLD), including alcoholic fatty liver disease and the more heterogenous metabolism-associated (or non-alcoholic) fatty liver disease (MAFLD/NAFLD) phenotypes. These conditions manifest as disease spectra, progressing from benign hepatic steatosis to symptomatic steatohepatitis, cirrhosis, and, ultimately, hepatocellular carcinoma. With numerous intricately regulated molecular pathways implicated in its pathophysiology, recent data have emphasized the critical roles of RNA-binding proteins (RBPs) in the onset and development of FLD. They regulate gene transcription and post-transcriptional processes, including pre-mRNA splicing, capping, and polyadenylation, as well as mature mRNA transport, stability, and translation. RBP dysfunction at every point along the mRNA life cycle has been associated with altered lipid metabolism and cellular stress response, resulting in hepatic inflammation and fibrosis. Here, we discuss the current understanding of the role of RBPs in the post-transcriptional processes associated with FLD and highlight the possible and emerging therapeutic strategies leveraging RBP function for FLD treatment. This article is categorized under: RNA in Disease and Development > RNA in Disease.