Chen, Yongbin

The interplay between intracellular and intravascular lipolysis is crucial for maintaining circulating lipid levels and systemic energy homeostasis. Adipose triglyceride lipase (ATGL) and lipoprotein lipase (LPL), the primary triglyceride (TG) lipases responsible for these two spatially separate processes, are highly expressed in adipose tissue. Yet the mechanisms underlying their coordinated regulation remain undetermined. Here, we demonstrate that genetic ablation of G0S2, a specific inhibitory protein of ATGL, completely abolished diet-induced hypertriglyceridemia and significantly attenuated atherogenesis in mice. These effects were attributable to enhanced whole-body TG clearance, not altered hepatic TG secretion. Specifically, G0S2 deletion increased circulating LPL concentration and activity, predominantly through LPL production from white adipose tissue (WAT). Strikingly, transplantation of G0S2-deficient WAT normalized plasma TG levels in mice with hypertriglyceridemia. In conjunction with improved insulin sensitivity and decreased ANGPTL4 expression, the absence of G0S2 enhanced the stability of LPL protein in adipocytes, a phenomenon that could be reversed upon ATGL inhibition. Collectively, these findings highlight the pivotal role of adipocyte G0S2 in regulating both intracellular and intravascular lipolysis, and the possibility of targeting G0S2 as a viable pharmacological approach to reducing levels of circulating TGs.

Abstract Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

AbstractIschemia‒reperfusion (I/R) is a common complication in the clinical treatment of acute myocardial infarction (MI), in which cardiomyocytes play a pivotal role in the recovery of cardiac function after reperfusion injury. The expression of numerous circular ribonucleic acids (circRNAs) is disrupted in I/R‐induced cardiac damage, but the potential role of circRNAs in I/R damage has not been fully elucidated. The purpose of the present study was to clarify the biological action and molecular mechanism of circRNA 002166 (also termed circCL2L13) in postmyocardial I/R. Oxygen–glucose deprivation/reoxygenation (OGD/R) in an in vivo model was performed to simulate I/R damage. real‐time polymerase chain reaction analysis was also conducted to evaluate the relationships of the SOD1, SOD2, NRF2, HO1 and GPX4 indicators with oxidative stress injury. TUNEL immunofluorescence was used to evaluate the degree of cardiomyocyte apoptosis in the different treatment groups. The circBCL2L13 level was markedly upregulated in myocardial tissues from a mouse I/R model. Overexpression of circBCL2L13 markedly attenuated the expression of oxidative stress‐related genes and apoptosis in OGD/R‐induced cardiomyocytes. A mechanistic study revealed that circBCL2L13 functions as a ceRNA for miR‐1246 and modulates paternally expressed gene 3 (PEG3). Eventually, circBCL2L13 was proven to regulate PEG3 by targeting miR‐1246, thereby protecting against OGD/R‐induced cardiomyocyte oxidative damage and apoptosis. In conclusion, our study confirmed that the circBCL2L13/miR‐1246/PEG3 axis suppressed the progression of OGD/R injury in cardiomyocytes, which might lead to new therapeutic strategies for cardiac I/R injury.

Adipose tissue plays a central role in the regulation of plasma triglyceride (TG) and fatty acid (FA) levels, whose elevation is associated with atherosclerotic cardiovascular disease (ASCVD). Circulating TGs are hydrolyzed by lipoprotein lipase (LPL), releasing FAs that are taken up by adipocytes. Conversely, lipolytic action of adipose tissue triglyceride lipase (ATGL) mobilizes intracellular TG storage, supplying FAs via circulation for utilization by other tissues. However, how intracellular lipolysis influences intravascular lipolysis and atherosclerotic development remains largely unknown. Herein, the current study aimed to explore the role of G0S2, a selective protein inhibitor of ATGL, in this particular context. When treated with a Western diet and antisense oligonucleotides (ASOs) targeting LDLR, mice with a global ablation of G0S2 exhibited a significantly reduced lesion formation in aorta and brachiocephalic artery than their wild type (WT) littermates. Despite of little to no impact on hypercholesterolemia, total plasma and lipoprotein-specific TG content were normalized in G0S2 -/- mice. While it did not elicit any effects on intestinal lipid adsorption and hepatic TG secretion, the G0S2 ablation resulted in considerable elevation of LPL activity and protein levels in both plasma and adipose tissue. Accordingly, these G0S2 -/- mice showed a greatly improved TG clearance upon an oral lipid challenge. Interestingly, transplantation of G0S2 -/- adipose tissue increased circulating LPL in the WD-treated WT mice, which subsequently displayed a profound reduction of plasma TG levels as well as alleviation of hepatic steatosis. By studying adipose tissue explants and primary adipocytes, we observed that the G0S2 ablation led to markedly improved insulin sensitivity, resulting in increased FOXO1 phosphorylation and decreased expression of the LPL inhibitor ANGPTL4. Together, our data indicate that in the absence of G0S2, upregulation of adipose tissue LPL plays an important role in the clearance of circulating TGs and alleviation of diet-induced hypertriglyceridemia and atherosclerosis. We thus conclude that adipocyte G0S2 acts as a critical switch of both intracellular and vascular lipolysis.

Abstract Background Alzheimer’s disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. Objective Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians’ decisions for AD patients based on the longitude data from electronic health records. Methods In this study, we selected 1736 patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases—depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. Results Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician’s treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician’s policy (mean −3.03 and −2.93 vs. −2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean −4.68 and −2.82 vs. −4.57, respectively). Conclusions Our results highlight the potential of using RL to generate the optimal treatment based on the patients’ longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities.

ABSTRACT G0S2 and HIG2 are two selective inhibitors of ATGL (also known as PNPLA2), the key enzyme for intracellular lipolysis. Whereas G0S2 regulates triglyceride (TG) mobilization in adipocytes and hepatocytes, HIG2 functions to enhance intracellular TG accumulation under hypoxic conditions. A homologous hydrophobic domain (HD) is shared by G0S2 and HIG2 (also known as HILPDA) for binding to ATGL. However, the determinants of their lipid droplet (LD) localization are unknown. Here, we study how G0S2 and HIG2 are targeted to LDs, and identify both ATGL-independent and -dependent mechanisms. Structural prediction and studies in cells reveal that ATGL-independent localization of G0S2 to both the endoplasmic reticulum (ER) and LDs is mediated by a hairpin structure consisting of two hydrophobic sequences. Positively charged residues in the hinge region play a crucial role in sorting G0S2, which initially localizes to ER, to LDs. Interestingly, the role of these positive charges becomes dispensable when ATGL is co-expressed. In comparison, HIG2, which lacks a similar hairpin structure, is dependent on ATGL for its full LD targeting. Thus, our studies identify specific structural features and mechanisms for mediating accumulation of these two ATGL inhibitors on LDs.