Advancing Lipidomic Bioinformatic Technologies

推进脂质组学生物信息学技术

• 批准号: RGPIN-2014-05377
• 负责人: Bennett, Steffany
• 金额: $ 3.06万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567130
• 关键词: Advancing; Lipidomic; Bioinformatic; Technologies

Forty five years ago, the lipid biochemist Albert Lehninger, known to many of us for his seminal Biochemistry textbook, predicted that the ability to identify the individual “fats” or lipids in our membranes would enable a new understanding of how brain cells communicate. Forty five years later, technological advances in mass spectrometry are allowing us to address the two seemingly simple questions required to realize Lehninger’s vision: How many lipid species are there? What effect does lipid diversity have on cellular function? These are exciting days. Hundreds to thousands of lipid species can now be profiled in different subcellular membrane compartments. Basic unitary conceptions are being challenged. Diacylglycerol, commonly conceived by cell biologists as a single lipid, is now recognized to be a family of over 50 structurally distinct species each controlling different cellular processes. Yet new challenges come with this success. The emerging field of lipidomics faces four formidable roadblocks: (1) Data Processing and Lipid Identification – There are few bioinformatic tools capable of assigning molecular identities directly from the mass spectrometer; (2) Statistical Analysis – There are few means of determining whether compositional changes in membranes are statistically “significant”; (3) Pathway Analysis – We are in desperate need of tools that identify metabolic changes underlying the compositional differences detected between membranes; (4) Modeling Tools – We have little to no capacity to model rapidly the impact that altering lipid composition has on functional membrane properties. We propose here to address some of these issues by developing new bioinformatic tools for the lipidomic analysis of neuronal membrane and membrane microdomains. To address some of the obstacles facing the emerging field of lipidomics, our short term objectives (1-3 years) are to develop immediately applicable bioinformatic tools for data processing and lipid identification. Our medium term objectives (3-5 years) are to add new Bayesian statistical methods and pathway analysis capacities into these toolkits. Our long term objectives (>5 years) will be to develop new modeling approaches to assess the spatial and dynamic aspects of compositional lipid changes in membrane systems and biophysical context. An important aspect of our proposal is the training of a new breed of bioinformatician and computational biologist; one with the knowledge and skillsets to place Canadian researchers at the forefront of this exciting new field. Our industry collaborations will also link our trainees directly to industrial partners to facilitate their subsequent career success.

45年前，脂质生化学家阿尔伯特·莱宁格（Albert Lehninger）预测，识别细胞膜中单个“脂肪”或脂质的能力，将使人们对脑细胞的通讯方式有新的认识。他因其开创性的生物化学教科书而广为人知。45年后，质谱技术的进步使我们能够解决两个看似简单的问题，以实现莱宁格的愿景：有多少种脂质？脂质多样性对细胞功能有什么影响？这是令人兴奋的日子。现在可以在不同的亚细胞膜区室中描绘成百上千种脂质。基本的单一性观念正在受到挑战。二酰基甘油，通常被细胞生物学家认为是一种单一的脂质，现在被认为是一个超过50个结构上不同的物种，每个物种控制不同的细胞过程。然而，这种成功也带来了新的挑战。新兴的脂质组学领域面临着四个巨大的障碍：(1)数据处理和脂质鉴定-很少有生物信息学工具能够直接从质谱仪中分配分子身份；(2)统计分析——很少有方法可以确定膜的成分变化是否具有统计学上的“显著性”；(3)途径分析-我们迫切需要工具来识别膜之间检测到的成分差异背后的代谢变化；(4)建模工具-我们几乎没有能力快速模拟改变脂质组成对功能性膜特性的影响。我们建议通过开发新的生物信息学工具来解决这些问题，用于神经元膜和膜微域的脂质组学分析。为了解决脂质组学新兴领域面临的一些障碍，我们的短期目标（1-3年）是开发可立即应用于数据处理和脂质鉴定的生物信息学工具。我们的中期目标（3-5年）是将新的贝叶斯统计方法和路径分析能力添加到这些工具包中。我们的长期目标（大约5年）将是开发新的建模方法来评估膜系统和生物物理环境中组成脂质的空间和动态变化。我们建议的一个重要方面是培养新一代的生物信息学家和计算生物学家；一个有知识和技能的人，把加拿大的研究人员放在这个令人兴奋的新领域的最前沿。我们的行业合作也将我们的学员直接与行业合作伙伴联系起来，以促进他们随后的职业成功。