The LEPIS-HuR-TMOD4 axis regulates hepatic cholesterol homeostasis and accelerates atherosclerosis.

BACKGROUND AND AIMS: Long noncoding RNAs (lncRNAs) play important roles in the progression of atherosclerosis. In this study, we identified an uncharacterized lncRNA, Liver Expressions by PSRC1 Induced Specifically (LEPIS). This study aimed to clarify the mechanism though which LEPIS affects atherosclerosis (AS). METHODS: The expression of LEPIS and its potential target, tropomodulin 4 (TMOD4), was increased in the livers of ApoE-/- mice fed a high-fat diet (HFD). An ApoE-/- mouse model in which LEPIS or TMOD4 was overexpressed in the liver was established. The plaque load in the aorta was assessed, plasma was collected to measure blood lipid levels, and the liver was collected to study cholesterol metabolism. RESULTS: We found that both LEPIS and TMOD4 increased the AS burden and reduced hepatic cholesterol levels. A further study revealed that LEPIS and TMOD4 affected the expression of genes related to hepatic cholesterol homeostasis, including proprotein convertase subtilisin/kexin type 9 (PCSK9) and low-density lipoprotein receptor (LDLR), which are closely related to hypercholesterolemia. Mechanistically, human antigen R (HuR), an RNA-binding protein (RBP), was shown to be critical for the regulation of TMOD4 by LEPIS. Furthermore, we found that verexpression of LEPIS promoted the shuttling of HuR from the nucleus to the cytoplasm, enhanced the stability of TMOD4 mRNA, and in turn promoted the expression of TMOD4. In addition, TMOD4 was found to affect intracellular cholesterol levels through PCSK9. CONCLUSIONS: These results suggest that the LEPIS-HuR-TMOD4 axis is a potential intervention target for dysregulated hepatic cholesterol homeostasis and AS and may provide the basis for further reductions in the circulating LDL-C concentration and arterial plaque burden.

Reducing SULT2B1 promotes the interaction of LncRNAgga3-204 with SMAD4 to inhibit the macrophage inflammatory response and delay atherosclerosis progression.

Inflammation is a crucial pathophysiological mechanism in atherosclerosis (AS). This study aims to investigate the impact of sulfotransferase family 2b member 1 (SULT2B1) on the inflammatory response of macrophages and the progression of AS. Here, we reported that SULT2B1 expression increased with the progression of AS. In AS model mice, knockdown of Sult2b1 led to remission of AS and reduced inflammation levels. Further exploration of the downstream molecular mechanisms of SULT2B1 revealed that suppressing Sult2b1 in macrophages resulted in decreased levels of 25HC3S in the nucleus, elevated expression of Lxr, and increased the transcription of Lncgga3-204. In vivo, knockdown of Lncgga3-204 aggravated the inflammatory response and AS progression, while the simultaneous knockdown of both Sult2b1 and Lncgga3-204 exacerbated AS and the inflammatory response compared with knockdown of Sult2b1 alone. Increased binding of Lncgga3-204 to SMAD4 in response to oxidized-low density lipoprotein (ox-LDL) stimulation facilitated SMAD4 entry into the nucleus and regulated Smad7 transcription, which elevated SMAD7 expression, suppressed NF-κB entry into the nucleus, and ultimately attenuated the macrophage inflammatory response. Finally, we identified the presence of a single nucleotide polymorphism (SNP), rs2665580, in the SULT2B1 promoter region in monocytes from coronary artery disease (CAD) patients. The predominant GG/AG/AA genotypes were observed in the Asian population. Elevated SULT2B1 expression in monocytes with GG corresponded to elevated inflammatory factor levels and more unstable coronary plaques. To summarize, our study demonstrated that the critical role of SULT2B1/Lncgga3-204/SMAD4/NF-κB in AS progression. SULT2B1 serves as a novel biomarker indicating inflammatory status, thereby offering insights into potential therapeutic strategies for AS.

Dataset construction method of cross-lingual summarization based on filtering and text augmentation.

Existing cross-lingual summarization (CLS) datasets consist of inconsistent sample quality and low scale. To address these problems, we propose a method that jointly supervises quality and scale to build CLS datasets. In terms of quality supervision, the method adopts a multi-strategy filtering algorithm to remove low-quality samples of monolingual summarization (MS) from the perspectives of character and semantics, thereby improving the quality of the MS dataset. In terms of scale supervision, the method adopts a text augmentation algorithm based on the pretrained model to increase the size of CLS datasets with quality assurance. This method was used to build an English-Chinese CLS dataset and evaluate it with a reasonable data quality evaluation framework. The evaluation results show that the dataset is of good quality and large size. These outcomes show that the proposed method may comprehensively improve quality and scale, thereby resulting in a high-quality and large-scale CLS dataset at a lower cost.

Proline/serine-rich coiled-coil protein 1 inhibits macrophage inflammation and delays atherosclerotic progression by binding to Annexin A2.

BACKGROUND: Atherosclerosis (AS), the main pathological basis of life-threatening cardiovascular disease, is essentially caused by chronic macrophage inflammation. Overexpression of proline/serine-rich coiled-coil protein 1 (PSRC1) reduces macrophage inflammatory responses and delays AS development. However, the exact mechanism of PSRC1 is unclear. METHODS: Proteins interacting with PSRC1 were screened by proteomics in RAW264.7 cells, followed by RT-qPCR, immunoprecipitation and immunofluorescence to explore the specific mechanistic pathways affecting inflammation. CRISPR-Cas9 constructs for PSRC1-/- ApoE-/- (DKO) mice and high-fat diet-fed ApoE-/- and DKO mice were used for AS models for in vivo experiments. Upstream transcription factors of PSRC1 were predicted by ATAC-seq, ChIP-seq and UCSC, and the regulatory mechanism was verified by ChIP-qPCR and dual luciferase assays. Peripheral blood serum and monocytes were collected from coronary artery disease (CAD) patients and non-CAD patients. RESULTS: Increased binding of ANXA2 to PSRC1 in macrophages under oxidized low-density lipoprotein stimulation and decreased release of ANXA2 to the extracellular compartment were observed. Knockdown of ANXA2 in AS model mice delayed AS progression. Knockdown of ANXA2 in DKO mice reversed the AS-promoting effect of PSRC1 knockdown. Mechanistically, ANXA2 promotes STAT3 phosphorylation, which in turn promotes inflammatory responses. In addition, SP1 is a PSRC1 upstream repressive transcription factor, and the SP1 inhibitor mithramycin (Mith) elevated PSRC1 expression and exerted anti-AS effects in AS model mice. Patients with CAD had considerably greater serum levels of ANXA2 than those without CAD, and Mith reduced the secretion of ANXA2 in peripheral blood monocytes of CAD patients. CONCLUSION: In macrophages, PSRC1 can interact with ANXA2 to inhibit its extracellular release and delay AS development. SP1 is an upstream transcription factor of PSRC1 and inhibits the transcription of PSRC1. The SP1 inhibitor Mith can elevate PSRC1 levels and slow AS progression while reducing ANXA2 release from monocytes in CAD patients. Mith is expected to be a new agent for AS treatment.

Representation Learning Method with Semantic Propagation on Text-Augmented Knowledge Graphs.

Knowledge graph representation learning aims to provide accurate entity and relation representations for tasks such as intelligent question answering and recommendation systems. Existing representation learning methods, which only consider triples, are not sufficiently accurate, so some methods use external auxiliary information such as text, type, and time to improve performance. However, they often encode this information independently, which makes it challenging to fully integrate this information with the knowledge graph at a semantic level. In this study, we propose a method called SP-TAG, which realizes the semantic propagation on text-augmented knowledge graphs. Specifically, SP-TAG constructs a text-augmented knowledge graph by extracting named entities from text descriptions and connecting them with the corresponding entities. Then, SP-TAG uses a graph convolutional network to propagate semantic information between the entities and new named entities so that the text and triple structure are fully integrated. The results of experiments on multiple benchmark datasets show that SP-TAG attains competitive performance. When the number of training samples is limited, SP-TAG maintains its high performance, verifying the importance of text augmentation and semantic propagation.