Advancing Lipidomic Bioinformatic Technologies

45年前，因其开创性的生物化学教科书而为我们许多人所熟知的脂质生物化学家阿尔伯特·莱宁格预测，识别我们细胞膜中的个别“脂肪”或脂质的能力将使我们能够对脑细胞如何沟通有一个新的理解。45年后，质谱学的技术进步使我们能够解决实现莱宁格的愿景所需的两个看似简单的问题：有多少种脂类？脂质多样性对细胞功能有什么影响？这是激动人心的日子。成百上千种脂类现在可以在不同的亚细胞膜隔间中形成轮廓。基本的单一概念正受到挑战。甘油二酯，通常被细胞生物学家认为是一种单一的脂质，现在被认为是一个由50多个结构不同的物种组成的家族，每个物种控制着不同的细胞过程。然而，随着这种成功，新的挑战也随之而来。脂组学新兴领域面临着四个艰巨的障碍：(1)数据处理和脂质鉴定--几乎没有能够直接从质谱仪确定分子身份的生物信息学工具；(2)统计分析--几乎没有手段确定膜的组成变化是否具有统计学意义；(3)途径分析--我们迫切需要确定膜之间检测到的组成差异背后的代谢变化的工具；(4)建模工具--我们几乎没有能力快速模拟改变脂组成对功能膜性质的影响。我们建议通过开发新的生物信息学工具来解决其中的一些问题，这些工具用于神经细胞膜和膜微域的脂组学分析。为了解决新出现的脂质组学领域面临的一些障碍，我们的短期目标(1-3年)是开发立即适用的生物信息学工具，用于数据处理和脂质鉴定。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(5年)将是开发新的建模方法，以评估膜系统和生物物理环境中组成脂变化的空间和动态方面。我们建议的一个重要方面是培养一批新的生物信息学家和计算生物学家；他们拥有将加拿大研究人员置于这一令人兴奋的新领域的前沿的知识和技能。我们的行业合作还将把我们的学员直接联系到行业合作伙伴，以促进他们随后的职业成功。

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.