13(S)-HOTrE
目录号 : GC41897A 15-LO product derived from GLA
Cas No.:87984-82-5
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >98.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
13(S)-HOTrE is the 15-lipoxygenase (15-LO) product of linolenic acid. It has been detected in cell membranes and as the cholesteryl ester associated with the lesions of atherosclerosis, and in the biomembranes of soybeans exposed to 15-LO.
| Cas No. | 87984-82-5 | SDF | |
| Canonical SMILES | CC/C=C\C[C@@H](O)/C=C/C=C\CCCCCCCC(O)=O | ||
| 分子式 | C18H30O3 | 分子量 | 294.4 |
| 溶解度 | 0.1 M Na2CO3: 2 mg/ml,DMF: Miscible,DMSO: Miscible,Ethanol: Miscible,PBS pH 7.2: 0.8 mg/ml | 储存条件 | Store at -20°C |
| General tips | 请根据产品在不同溶剂中的溶解度选择合适的溶剂配制储备液；一旦配成溶液，请分装保存，避免反复冻融造成的产品失效。 储备液的保存方式和期限：-80°C 储存时，请在 6 个月内使用，-20°C 储存时，请在 1 个月内使用。 为了提高溶解度，请将管子加热至37℃，然后在超声波浴中震荡一段时间。 |
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| Shipping Condition | 评估样品解决方案：配备蓝冰进行发货。所有其他可用尺寸：配备RT，或根据请求配备蓝冰。 | ||
| 制备储备液 | |||
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1 mg | 5 mg | 10 mg |
| 1 mM | 3.3967 mL | 16.9837 mL | 33.9674 mL |
| 5 mM | 0.6793 mL | 3.3967 mL | 6.7935 mL |
| 10 mM | 0.3397 mL | 1.6984 mL | 3.3967 mL |
| 第一步：请输入基本实验信息（考虑到实验过程中的损耗，建议多配一只动物的药量） | ||||||||||
| 给药剂量 | mg/kg |  |
动物平均体重 | g | |
每只动物给药体积 | ul | |
动物数量 | 只 |
| 第二步：请输入动物体内配方组成（配方适用于不溶于水的药物；不同批次药物配方比例不同，请联系GLPBIO为您提供正确的澄清溶液配方） | ||||||||||
| % DMSO % % Tween 80 % saline | ||||||||||
| 计算重置 | ||||||||||
计算结果:
工作液浓度： mg/ml；
DMSO母液配制方法： mg 药物溶于 μL DMSO溶液（母液浓度 mg/mL，
体内配方配制方法：取 μL DMSO母液，加入 μL PEG300，混匀澄清后加入μL Tween 80，混匀澄清后加入 μL saline，混匀澄清。
1. 首先保证母液是澄清的；
2.
一定要按照顺序依次将溶剂加入，进行下一步操作之前必须保证上一步操作得到的是澄清的溶液，可采用涡旋、超声或水浴加热等物理方法助溶。
3. 以上所有助溶剂都可在 GlpBio 网站选购。
Oxylipins as Biomarkers for Aromatase Inhibitor-Induced Arthralgia (AIA) in Breast Cancer Patients
Metabolites 2023 Mar 20;13(3):452.PMID:36984892DOI:10.3390/metabo13030452.
Aromatase inhibitor-induced arthralgia (AIA) presents a major problem for patients with breast cancer but is poorly understood. This prospective study explored the inflammatory metabolomic changes in the development of AIA. This single-arm, prospective clinical trial enrolled 28 postmenopausal women with early-stage (0-3) ER+ breast cancer starting adjuvant anastrozole. Patients completed the Breast Cancer Prevention Trial (BCPT) Symptom Checklist and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) at 0, 3, and 6 months. The plasma levels of four polyunsaturated fatty acids (PUFAs) and 48 oxylipins were quantified at each timepoint. The subscores for WOMAC-pain and stiffness as well as BCPT-total, hot flash, and musculoskeletal pain significantly increased from baseline to 6 months (all p < 0.05). PUFA and oxylipin levels were stable over time. The baseline levels of 8-HETE were positively associated with worsening BCPT-total, BCPT-hot flash, BCPT-musculoskeletal pain, WOMAC-pain, and WOMAC- stiffness at 6 months (all p < 0.05). Both 9-HOTrE and 13(S)-HOTrE were related to worsening hot flash, and 5-HETE was related to worsening stiffness (all p < 0.05). This is the first study to prospectively characterize oxylipin and PUFA levels in patients with breast cancer starting adjuvant anastrozole. The oxylipin 8-HETE should be investigated further as a potential biomarker for AIA.
High production of jasmonic acid by Lasiodiplodia iranensis using solid-state fermentation: Optimization and understanding
Biotechnol J 2022 May;17(5):e2100550.PMID:35088946DOI:10.1002/biot.202100550.
Background: Jasmonic acid (JA) is a plant hormone involved in regulating developmental and growth controls as well as photosynthesis. In addition, this hormone protects the plant against insects and has good applications in agriculture, the flavored industry and other fields. Filamentous fungus generally produces JA using liquid static culture. In the present study, a solid-state fermentation (SSF) method is developed for high production of JA using Lasiodiplodia iranensis. Main methods and major results: By selecting the solid substrate and optimizing the initial water content, inoculum volume, loading volume and other culture conditions, the maximum JA yield reached 5306.38 mg kg-1 when fermented for 12 days in a petri dish containing a medium with crushed wheat as the solid substrate and 75% initial water content. The logistic and Luedeking-Piret models were used to characterize the relationship between microbial growth and product synthesis in the SSF process, and the maximum JA production is predicted to be 5263.23 mg kg-1 , which is close to the experimental value. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) is used to examine the metabolic changes that develop during fermentation. The results indicate that JA biosynthesis occurs in the α-linolenic acid metabolic pathway, of which 13(S)-HpOTrE is a key intermediate metabolite and both 13(S)-HOTrE and traumatic acid are byproducts of the branches of its synthesis. Conclusions and implications: The results of this study provide a method for obtaining high JA yields by SSF, and offer new insights for understanding the production of JA by fungal fermentation.
Effects of Tylophora yunnanensis Schltr on regulating the gut microbiota and its metabolites in non-alcoholic steatohepatitis rats by inhibiting the activation of NOD-like receptor protein 3
J Ethnopharmacol 2023 Apr 6;305:116145.PMID:36623753DOI:10.1016/j.jep.2023.116145.
Ethnopharmacological relevance: Tylophora yunnanensis Schltr (TYS) is widely distributed in Yunnan, Guizhou, and other places in China. It is commonly used by folks to treat hepatitis and other liver-related diseases; however, its mechanism of action is still unclear. Aim of the study: This study aimed to determine the effects of TYS on regulating gut microbiota and its metabolites in non-alcoholic steatohepatitis (NASH) rats by inhibiting the activation of NOD-like receptor protein3 (NLRP3). Material and methods: An HFD-induced rat model was established to investigate if the intragastric administration of TYS could mediate gut microbiota and their metabolites to ultimately improve the symptoms of NASH. The improving effects of TYS on NASH rats were assessed by measuring their body weight, lipid levels, histopathology, and inflammatory factor levels in the rat models. The regulatory effects of TYS on NLRP3 in the NASH rats were analyzed using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA), which determined the levels of NLRP3-related factors. The changes in the composition of the gut microbiota of NASH rats were analyzed using 16S rRNA gene sequencing technology. Meanwhile, the Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used for the non-targeted analysis of metabolites in the cecum contents. Results: The results showed that TYS could improve NASH by decreasing the body weight and levels of lipid, AST, ALT, LPS, FFA, VLDL, IL-1β, IL-6, TNF-α, TGF-β, NLRP3, ASC, and Caspase-1 in the NASH rats. The analysis of gut microbiota showed that TYS could improve the diversity and abundance of gut microbiota and alter their composition by decreasing the Firmicutes/Bacteroidetes (F/B) ratio and relative abundances of Lachnospiraceae, Christensenellaceae, Blautia, etc. while increasing those of Muribaculaceae, Rumiaococcus, Ruminococcaceae, etc. The analysis of metabolites in the cecum contents suggested that the arachidonic acid metabolism, bile secretion, serotonergic synapse, Fc epsilon RI signaling pathway, etc. were regulated by TYS. The metabolites enriched in these pathways mainly included chenodeoxycholic acid, prostaglandin D2, TXB2, 9-OxoODE, and 13(S)-HOTrE. Conclusions: These findings suggested that TYS could alleviate the NASH symptoms by decreasing the body weight, regulating the lipid levels, reducing the inflammatory response, and inhibiting the expression levels of NLRP3, ASC, and Caspase-1 in the NASH rats. The changes in the composition of gut microbiota and their metabolic disorder were closely related to the activation of NLRP3. TYS could significantly inhibit the activation of NLRP3 and regulate the composition of gut microbiota and the disorder of metabolites during NASH modeling.