ABSTRACT
Macrophage lipid accumulation is a critical contributor to foam cell formation and atherosclerosis. Tumor necrosis factor-α-induced protein 1 (TNFAIP1) is closely associated with cardiovascular disease. However, its role and molecular mechanisms in atherogenesis remain unclear. TNFAIP1 was knocked down in THP-1 macrophage-derived foam cells and apolipoprotein-deficient (apoE-/-) mice using lentiviral vector. The expression of lncRNA enhancing endothelial nitric oxide synthase expression (LEENE), Forkhead box O1 (FoxO1) and ATP binding cassette transporter A1 (ABCA1) was evaluated by qRT-PCR and/or western blot. Lipid accumulation in macrophage was assessed by high-performance liquid chromatography and Oil red O staining. RNA immunoprecipitation and RNA pull-down assay were performed to verify the interaction between LEENE and FoxO1 protein. Atherosclerotic lesions were analyzed using HE, Oil red O and Masson staining. Our results showed that TNFAIP1 was significantly increased in THP-1 macrophages loaded with oxidized low-density lipoprotein. Knockdown of TNFAIP1 enhanced LEENE expression, promoted the direct interaction of LEENE with FoxO1 protein, stimulated FoxO1 protein degradation through the proteasome pathway, induced ABCA1 transcription, and finally suppressed lipid accumulation in THP-1 macrophage-derived foam cells. TNFAIP1 knockdown also up-regulated ABCA1 expression, improved plasma lipid profiles, enhanced the efficiency of reverse cholesterol transport and attenuated lesion area in apoE-/- mice. Taken together, these results provide the first direct evidence that TNFAIP1 aggravates atherosclerosis by promoting macrophage lipid accumulation via the LEENE/FoxO1/ABCA1 signaling pathway. TNFAIP1 may represent a promising therapeutic target for atherosclerotic cardiovascular disease.
Subject(s)
Cardiovascular System , Hyperemia , Humans , Hyperemia/diagnosis , Risk Factors , Heart , Hemorrhage , Endothelium, VascularABSTRACT
Both inflammatory response and oxidative stress are regarded as two critical contributors to atherosclerosis. Kcnq1 overlapping transcript 1 (KCNQ1OT1) is an imprinted antisense long non-coding RNA in the kcnq1 locus. Our previous study has demonstrated that KCNQ1OT1 aggravates atherosclerosis by promoting macrophage lipid accumulation. However, its role in atherogenesis remains to be elucidated. This study aimed to observe the impact of KCNQ1OT1 on oxidized low-density lipoprotein (ox-LDL)-induced inflammatory response and oxidative stress and to explore the underlying mechanism. We found that ox-LDL up-regulated KCNQ1OT1 expression in THP-1 macrophages. Knockdown of KCNQ1OT1 increased miR-137 levels, decreased tumor necrosis factor-α-induced protein 1 (TNFAIP1) expression, and inhibited inflammatory response and alleviated oxidative stress in ox-LDL-treated THP-1 macrophages. A ceRNA regulatory network was identified among KCNQ1OT1, miR-137 and TNFAIP1. The inhibitory effect of KCNQ1OT1 knockdown on inflammatory response and oxidative stress was significantly reversed by miR-137 prevention or TNFAIP1 overexpression. In summary, these findings suggest that silencing of KCNQ1OT1 suppresses inflammatory response and oxidative stress induced by ox-LDL through the miR-137/TNFAIP1 pathway in THP-1 macrophages, thereby providing novel mechanistical insights into its pro-atherosclerotic action.
Subject(s)
Atherosclerosis , MicroRNAs , RNA, Long Noncoding , Adaptor Proteins, Signal Transducing/metabolism , Apoptosis , Atherosclerosis/metabolism , Humans , KCNQ1 Potassium Channel/metabolism , Lipoproteins, LDL/metabolism , Macrophages/metabolism , MicroRNAs/genetics , MicroRNAs/metabolism , Oxidative Stress/genetics , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , THP-1 Cells , Tumor Necrosis Factor-alpha/metabolismABSTRACT
Poria cocos (Schw.) Wolf (PC) is a well-known saprophytic fungus, and its sclerotium without the epidermis (PCS) is widely used in traditional Chinese medicine and as a functional food in many countries. PCS is normally collected from multiple geographical regions, but whether and how the quality of PCS correlates with where it grows have not been determined. This correlation could be significant both for quality control and optimum utilization of PCS as a natural resource. In this study, a qualitative fingerprint profiling method performed by ultra-performance liquid chromatography (UHPLC) with diode array detection (DAD) combining quadrupole time-of-flight-mass spectrometry (QTOF-MS/MS) and a quantitative UHPLC coupled with triple quadrupole mass spectrometry (QqQ-MS/MS) approach were established to investigate whether and how the quality of PCS correlates with its collection location. A standard fingerprint of PCS was generated by median simulation of 25 tested samples collected from four main producing areas of China, and similarity analysis was applied to evaluate the similarities between the fingerprints of samples and the standard fingerprint. Twenty three common peaks occurring in the fingerprint were unequivocally or tentatively identified by UHPLC-QTOF-MS/MS. Meanwhile, principal component analysis (PCA), supervised orthogonal partial least squares-discriminate analysis (OPLS-DA) and hierarchical cluster analysis (HCA) were employed to classify 25 batches of PCS samples into four groups, which were highly consistent with the four geographical regions. Ten compounds were screened out as potential markers to distinguish the quality of PCS. Nine triterpene acids, including five compounds that played important roles in the clusters between different samples collected from the four collection locations, were simultaneously quantified by using the multiple reaction monitoring (MRM) mode of UHPLC-QqQ-MS/MS. The current strategy not only clearly expounded the correlation between quality and geographical origins of PCS, but also provided a fast, accurate and comprehensive qualitative and quantitative method for assessing the quality of PCS.
Subject(s)
Geography , Triterpenes/analysis , Triterpenes/chemistry , Wolfiporia/chemistry , Chromatography, High Pressure Liquid , Cluster Analysis , Discriminant Analysis , Least-Squares Analysis , Multivariate Analysis , Principal Component Analysis , Reproducibility of Results , Software , Tandem Mass Spectrometry , Triterpenes/isolation & purificationABSTRACT
Five iridoids, named as chlorovaltrate P-T, together with six known analogues, (4ß,8ß)-8-methoxy-3-methoxy-10-methylene-2,9-dioxatricyclo[4.3.1.03,7]decan-4-ol, chlorovaltrate A, (1R,3R,5R,7S,8R,9S)-3,8-epoxy-1-O-ethyl-5-hydroxyvalechlorine, 8-methoxy-4-acetoxy-3-chlormethyl-10-methylen-2,9-dioxa-tricyclo[4.3.1.03,7]decan, (1S,3R,5R,7S,8R,9S)-3,8-epoxy-1-O-ethyl-5-hydroxyvalechlorine, (1R,3R,5R,7S,8R,9S)-3,8-epoxy-1-O-methyl-5-hydroxyvalechlorine were isolated from the roots of Valeriana jatamansi (syn. Valeriana wallichii). Their structures were elucidated by extensive analysis of 1D, 2D NMR and HRESIMS spectroscopic. The absolute configuration of chlorovaltrate P-T were established by comparing their experimental and calculated electronic circular dichroism (ECD) spectra. 3,8-epoxy iridoids exhibited weak cytotoxicity against the lung adenocarcinoma (A 549) and gastric carcinoma cells (SGC 7901). Some also showed moderate neuroprotective effects against CoCl2-induced neuronal cell death in PC12 cells.
Subject(s)
Iridoids/chemistry , Neuroprotective Agents/chemistry , Plant Roots/chemistry , Valerian/chemistry , A549 Cells , Animals , Cell Death/drug effects , Humans , Iridoids/isolation & purification , Molecular Structure , Neuroprotective Agents/isolation & purification , PC12 Cells , RatsABSTRACT
The recent progresses on chemical components and pharmacological activities of the genus Valerianawere summarized.Besides-essential oil, the chemical composition of Valerianais mainly focused on monoterpenoids,sesquiterpenoids,lignans, flavonoids, alkaloids, etc. Iridoids are the main chemical components ofmonoterpenoids. There are two types ofiridoidson the basis of the cyclopentane open or not. The Valerianahas been drawmuch attention for their significant sedation,spasmolysis,antidepression,antitumor, against adenosine A1 receptors and cytotoxicityactivity,and had certain function for cardiovascular disease treatment. Given to the fact of the lack of systematic review and summary of studies on the Valeriana, we summarized and analyze the study literatures on the pharmacological activity of Valerianain recent years, and providedsome basisfor further study.
Subject(s)
Drugs, Chinese Herbal/chemistry , Drugs, Chinese Herbal/pharmacology , Valerian/chemistry , Humans , Iridoids/analysisABSTRACT
Ontology is widely used in semantic computing and reasoning, and various biomedicine ontologies have become institutionalized to make the heterogeneous knowledge computationally amenable. Relation words, especially verbs, play an important role when describing the interaction between biological entities in molecular function, biological process, and cellular component; however, comprehensive research and analysis are still lacking. In this article, we propose an automatic method to build interaction relation ontology by investigating relation verbs, analyzing the syntactic relation of PubMed abstracts to perform relation vocabulary expansion, and integrating WordNet into our method to construct the hierarchy of relation vocabulary. Five attributes are populated automatically for each word in interaction relation ontology. As a result, the interaction relation ontology is constructed; it contains a total of 963 words and covers the most relation words used in existing methods of proteins interaction relation.
Subject(s)
Biological Ontologies/statistics & numerical data , Computational Biology , Data Mining , Learning , PubMed , SemanticsABSTRACT
Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identification of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifies and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At first, IRO is applied in a binary classifier to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the significant performance of IRO on relation sentences classification and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and BioInfer, respectively, which are superior to most existing extraction methods.