Gpihbp1 deficiency accelerates atherosclerosis and plaque instability in diabetic Ldlr-/- mice

Liu, Xuejing; Li, Jingyi; Liao, Jiawei; Wang, Huan; Huang, Xiaomin; Zhao, Jingbo; Shen, Qiang; Zhang, Ling; Wang, Yuhui; Kong, Wei; Liu, George; Huang, Wei

doi:10.1016/j.atherosclerosis.2019.01.025

Cited by 15 publications

(12 citation statements)

References 43 publications

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“…These lesions are much smaller than those that develop in Apoe −/− mice, which have an accumulation of RLPs. A recent study reported that combined GPIHBP1-and LDLR-deficiency results in more extensive and advanced atherosclerosis as well as rare lesions in coronary arteries in diabetic mice, as compared to Ldlr −/− controls (107). Therefore, the combination of severe hypertriglyceridemia, LDLR-deficiency and diabetes appears to worsen the effects of hypertriglyceridemia on atherogenesis.…”

Section: Glycosylphosphatidylinositol-anchored Hdl-binding Protein 1 mentioning

confidence: 99%

Hypertriglyceridemia and Atherosclerosis: Using Human Research to Guide Mechanistic Studies in Animal Models

Basu

Bornfeldt

2020

Front. Endocrinol.

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Human studies support a strong association between hypertriglyceridemia and atherosclerotic cardiovascular disease (CVD). However, whether a causal relationship exists between hypertriglyceridemia and increased CVD risk is still unclear. One plausible explanation for the difficulty establishing a clear causal role for hypertriglyceridemia in CVD risk is that lipolysis products of triglyceride-rich lipoproteins (TRLs), rather than the TRLs themselves, are the likely mediators of increased CVD risk. This hypothesis is supported by studies of rare mutations in humans resulting in impaired clearance of such lipolysis products (remnant lipoprotein particles; RLPs). Several animal models of hypertriglyceridemia support this hypothesis and have provided additional mechanistic understanding. Mice deficient in lipoprotein lipase (LPL), the major vascular enzyme responsible for TRL lipolysis and generation of RLPs, or its endothelial anchor GPIHBP1, are severely hypertriglyceridemic but develop only minimal atherosclerosis as compared with animal models deficient in apolipoprotein (APO) E, which is required to clear TRLs and RLPs. Likewise, animal models convincingly show that increased clearance of TRLs and RLPs by LPL activation (achieved by inhibition of APOC3, ANGPTL3, or ANGPTL4 action, or increased APOA5) results in protection from atherosclerosis. Mechanistic studies suggest that RLPs are more atherogenic than large TRLs because they more readily enter the artery wall, and because they are enriched in cholesterol relative to triglycerides, which promotes pro-atherogenic effects in lesional cells. Other mechanistic studies show that hepatic receptors (LDLR and LRP1) and APOE are critical for RLP clearance. Thus, studies in animal models have provided additional mechanistic insight and generally agree with the hypothesis that RLPs derived from TRLs are highly atherogenic whereas hypertriglyceridemia due to accumulation of very large TRLs in plasma is not markedly atherogenic in the absence of TRL lipolysis products.

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Section: Glycosylphosphatidylinositol-anchored Hdl-binding Protein 1 mentioning

confidence: 99%

Hypertriglyceridemia and Atherosclerosis: Using Human Research to Guide Mechanistic Studies in Animal Models

Basu

Bornfeldt

2020

Front. Endocrinol.

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“…As described previously [32], anesthetized hamsters were perfused with 0.01 M PBS through the left ventricle. In brief, hearts and whole aortas were placed in 4% paraformaldehyde solution overnight, transferred to 20% sucrose solution for 1 week.…”

Section: Oil Red O Stainingmentioning

confidence: 99%

DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

et al. 2020

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Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

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“…[27]. To determine the components of atherosclerosis lesions in the aortic root, 2 immunohistochemical staining for macrophages and SMC was performed [28]. Furthermore, in the validation study of the DLDTI model, we used TMPZ (a drug with known structure) to explore its effects on atherosclerosis in vivo.…”

Section: Tmpz Ameliorated Atherosclerosis Lesion Progressionmentioning

confidence: 99%

DLDTI: A learning-based framework for identification of drug-target interaction using neural networks and network representation

Zhao

Zheng

Guan

et al. 2020

Preprint

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To elucidate novel molecular mechanism of known drugs, efficient and feasible computational methods for predicting potential drug-target interactions (DTI) would be of great importance. A novel calculation model called DLDTI was generated, for predicting DTI based on network representation learning and convolutional neural networks. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning low-dimensional and rich depth features of drugs and proteins. Low-dimensional feature vectors were used to train DLDTI to obtain optimal mapping space and infer new DTIs by ranking DTI candidates based on their proximity to optimal mapping space. DLDTI achieves promising performance under 5-fold cross-validation with AUC values of 0.9172, which was higher than that of the method based on different classifiers or different feature combination technique. Moreover, biomedical experiments were also completed to validate the performance of DLDTI. Consistent with the predicted result, tetramethylpyrazine, an amide analogous monomer, ameliorated atherosclerosis progression and inhibited signal transduction in platelets, via PI3K/Akt, cAMP and calcium signaling pathways. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI

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Gpihbp1 deficiency accelerates atherosclerosis and plaque instability in diabetic Ldlr-/- mice

Cited by 15 publications

References 43 publications

Hypertriglyceridemia and Atherosclerosis: Using Human Research to Guide Mechanistic Studies in Animal Models

Hypertriglyceridemia and Atherosclerosis: Using Human Research to Guide Mechanistic Studies in Animal Models

DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

DLDTI: A learning-based framework for identification of drug-target interaction using neural networks and network representation

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