射血分数保留的高脂血症诱导心力衰竭小鼠模型
March 29th, 2024
该协议提出了一种详细的方法，用于复制高脂血症诱导的射血分数保留（HFpEF）的小鼠模型。该设计结合了腺相关病毒 9-心肌肌钙蛋白 T 低密度脂蛋白受体 （AAV9-cTnT-LDLR） 和泊洛沙姆-407 （P-407） 的给药。
我们的研究旨在检查射血分数保留的心力衰竭，称为 HFpEF，专门研究脂毒性的影响。我们试图阐明代谢综合征和脂毒性在驱动 HFpEF 进展中的错综复杂的关系，并使用这种实验小鼠模型揭示驱动 HFpEF 病理生理学的潜在机制。尽管 HFpEF 的患病率不断上升且治疗选择有限，但其主要与代谢综合征相关的病理生理机制仍然知之甚少。
该模型提供了一个独特的机会来弥合这一差距，并有可能开发出针对HFpEF的新疗法。我们的模型最重要的优势是它能够模拟在HFpEF患者中观察到的严重脂质处理不当。该小鼠模型为HFpEF的病理生理学，尤其是心脏代谢提供了有价值的见解。
我们的研究结果强调了脂肪酸和胆固醇处理不当在心脏代谢HFpEF中的重要性。该模型完美地展示了脂肪酸剥夺如何在不同层面上破坏新陈代谢。它为研究人员提供了发现 HFpEF 患者新型陷阱的潜在途径。
在Shahada实验室，我们将专注于将该模型转化为大型动物。我们将调查与发病机制、猝死以及针对脂毒性、炎症和心脏重塑的潜在治疗干预措施相关的问题。
概览
本协议提出了一种复制高脂血症诱导的射血分数保留型心力衰竭（HFpEF）小鼠模型的详细方法。该研究探讨了脂毒性的影响以及代谢综合征与HFpEF进展之间的关系。
关键研究组成部分
科学领域
- 心血管研究
- 代谢综合征
- 心力衰竭病理生理学
背景
- HFpEF的发病率越来越高，但治疗选择有限。
- 与代谢综合征相关的病理生理机制尚不清楚。
- 该模型模仿了HFpEF患者中观察到的脂质处理不当。
- 对心脏代谢的洞察对开发新疗法至关重要。
研究目的
- 阐明推动HFpEF病理生理学的机制。
- 探索脂肪酸和胆固醇处理不当的影响。
- 为针对脂毒性的潜在治疗干预提供模型。
所用方法
- AAV9-cTnT-LDLR和poloxamer-407的给药。
- 在小鼠模型中诱导高脂血症。
- 使用超声心动图评估心脏功能。
- 病毒载体和poloxamer的注射技术。
主要结果
- 观察到室内松弛时间延长。
- 二尖瓣流速比率改变，表明心力衰竭。
- 模型有效地展示了HFpEF中的脂质处理不当。
- 识别了新型治疗策略的潜在途径。
结论
- 该模型为HFpEF机制提供了宝贵的见解。
- 脂肪酸缺乏破坏了心脏代谢。
- 研究可能导致HFpEF患者治疗的改进。
常见问题
Executive Industry Relevance
Hyperlipidemia-induced murine models of HFpEF provide a translational platform for dissecting the metabolic and lipotoxic drivers of diastolic dysfunction. This model enables mechanistic de-risking and target validation for cardiometabolic pathways implicated in heart failure with preserved ejection fraction. Its predictive value supports early-stage portfolio decisions and informs preclinical strategy for metabolic syndrome-related cardiac disease.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of lipotoxicity and metabolic syndrome as drivers of HFpEF pathogenesis.
- Supports functional target validation for lipid handling and cardiac metabolism pathways.
- Facilitates mechanistic de-risking by modeling human-relevant cardiac phenotypes.
- Provides a platform for hypothesis testing on metabolic and inflammatory contributors to HFpEF.
Screening & Assay Development
- Establishes a validated in vivo system for quantitative assessment of cardiac function and metabolic biomarkers.
- Supports reproducible measurement of diastolic dysfunction and preserved ejection fraction via echocardiography.
- Enables standardized evaluation of candidate interventions targeting lipid metabolism and cardiac remodeling.
- Prepares the groundwork for scalable compound screening in disease-relevant models.
Translational & Preclinical Research
- Aligns with disease-relevant endpoints such as fibrosis, cardiac ischemia, and sudden death observed in HFpEF patients.
- Provides continuity from discovery to preclinical validation for metabolic and inflammatory targets.
- Supports risk-adjusted advancement of therapeutic candidates addressing lipotoxicity-driven cardiac dysfunction.
- Facilitates biomarker alignment for translational studies in larger animal models.
Pipeline & Workflow Integration
This murine HFpEF model integrates into the discovery-to-preclinical continuum, enabling target validation, mechanistic studies, and translational biomarker development for cardiometabolic heart failure.
- Discovery Biology: Supports hypothesis testing on lipid mishandling and metabolic syndrome in cardiac dysfunction.
- Screening: Provides reproducible, quantitative cardiac and metabolic readouts for candidate evaluation.
- Analytics: Enables statistical comparison of diastolic function, fibrosis, and metabolic parameters across cohorts.
- Translational Research: Bridges murine findings to larger animal models and human disease phenotypes.
- Enterprise Reuse: Offers a reusable, standardized platform for cardiometabolic target and pathway interrogation.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in metabolic and lipotoxicity targets for HFpEF.
- Operational Value: Delivers standardized, reproducible in vivo assessments of cardiac and metabolic endpoints.
- Strategic Value: Informs go/no-go decisions for cardiometabolic programs and reduces late-stage biological risk.
- Portfolio Impact: Enables risk-adjusted prioritization of therapeutic candidates targeting HFpEF mechanisms.
Implementation Considerations
- Requires expertise in murine cardiovascular physiology and metabolic disease modeling.
- Demands access to echocardiography, telemetry, and biochemical analysis infrastructure.
- Necessitates cross-team standardization of phenotyping and data interpretation protocols.
- Adaptation to other strains or species may require protocol optimization.
- Model limitations include species-specific responses and translation to human HFpEF heterogeneity.
