Mathematical model relating gene expression to glycoform structure

将基因表达与糖型结构联系起来的数学模型

• 批准号: 7476337
• 负责人: Frederick J Krambeck
• 金额: $ 14.41万
• 依托单位: REACTECH, INC.
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7476337
• 关键词: Affect; Appearance; Cell physiology; Cell surface; Cells; Chemical Structure; Clinical; Complex; Computer Simulation; Computer Systems; Computer software; Computers; Condition; DNA Microarray Chip; DNA Microarray format; Data; Data Sources; Databases; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Enzymes; Gene Expression; Genes; Genetic; Genome; Glycobiology; Glycolipids; Goals; Immune System Diseases; Link; Lipids; Literature; Malignant - descriptor; Malignant Neoplasms; Mammalian Cell; Maps; Medical; Membrane Proteins; Microarray Analysis; Modeling; Modification; Normal Cell; Numbers; Oligosaccharides; Online Systems; Pathogenicity; Phase; Play; Polysaccharides; Prion Diseases; Procedures; Property; Proteins; Publishing; Research; Research Personnel; Resources; Role; Science; Specialist; Structure; System; Technology; Testing; Therapeutic Intervention; base; cancer cell; carbohydrate structure; glycosylation; human disease; link protein; mathematical model; novel diagnostics; programs; protein structure; prototype; research study; software systems; sugar; tool

DESCRIPTION (provided by applicant): Glycans are highly variable and structurally diverse sugar chains that, when attached to membrane proteins and lipids, are the dominant feature of the mammalian cell surface. In many diseases, including cancer, the distribution of glycan structures often differs from normal cells leading to the enticing possibility that disease- specific glycan structures and their functions can be used diagnostically and therapeutically. Development of glycan-based clinical procedures, however, has been hindered by the difficulty of analyzing the thousands of oligosaccharide structures found on cells, which are usually present in minute quantities that make structural characterization a formidable challenge. These difficulties are compounded by the fact that, in contrast to protein structures, glycan structures are not directly encoded in the genome and no template is therefore available that would allow PCR-type amplification. Instead, the structure of glycans is determined by a complex interaction of many glycosylation enzymes with proteins and other metabolites. The objective of this project is to develop mathematical modeling technology to predict how changes in the expression of glycosylation genes - now readily obtained through DNA microarray experiments - affect the highly diverse repertoire of glycan structures found on cells. Linking glycan structure distributions to genetic modifications or easily obtained gene expression data in this manner will greatly facilitate development of glycan-based diagnostic markers or therapeutic interventions; the goal of this project is to develop a computer model to predict disease-specific glycans and to make this technology commercially available to medical researchers. The project will primarily utilize publicly available experimental data sources on enzyme properties, glycogene expression levels and glycan profiles to develop and refine the mathematical model. Full advantage will be taken of web based compilations of these data in addition to literature resources. The project will also collaborate with ongoing experimental glycobiology programs at Johns Hopkins in order to evaluate the use of a prototype implementation interactively for research guidance. The technology includes both a qualitative and quantitative aspect. The qualitative aspect involves predicting the possible glycan structures that can result from a given starting structure and a given list of glycosylation enzymes. For example, the de novo appearance of one (or a small number) of structures is a powerful indicator of the malignant status of certain cancers. The quantitative aspect relates the abundances of the various glycan structures produced to the set of expression levels of the glycosylation enzymes; this level of sophistication will be valuable for assessing conditions such as prion disease where subtle differences in the abundances of dozens, or hundreds, of structures may be related to pathogenicity. The final product of the project would be a software system, made available to the public as a commercial software package to map the relationship between gene expression and glycan structure in either direction. The results of this project will provide a system of computer tools to help researchers determine new diagnostic methods for serious diseases, such as cancer, and develop new therapies for treating these diseases. The tools will make a link between fundamental cellular processes and the chemical structures displayed on cell surfaces, which control, for example, interactions between normal and malignant cells. The final product of the project would be a software system that contains both qualitative and quantitative tools to map the relationship between gene expression and glycan structure in either direction. The system would be made available to the public as a commercial software package.

描述（由申请人提供）：聚糖是高度可变且结构多样化的糖链，当连接到膜蛋白和脂质时，是哺乳动物细胞表面的主要特征。在许多疾病（包括癌症）中，聚糖结构的分布通常不同于正常细胞，导致疾病特异性聚糖结构及其功能可用于诊断和治疗的诱人可能性。然而，基于聚糖的临床程序的开发一直受到分析细胞上发现的数千种寡糖结构的困难的阻碍，这些寡糖结构通常以微量存在，使得结构表征成为一个艰巨的挑战。与蛋白质结构相反，聚糖结构不直接编码在基因组中，因此没有允许PCR型扩增的模板，这一事实加剧了这些困难。相反，聚糖的结构是由许多糖基化酶与蛋白质和其他代谢物的复杂相互作用决定的。该项目的目标是开发数学建模技术，以预测糖基化基因表达的变化-现在很容易通过DNA微阵列实验获得-如何影响细胞上发现的高度多样化的聚糖结构。以这种方式将聚糖结构分布与遗传修饰或容易获得的基因表达数据联系起来，将极大地促进基于聚糖的诊断标志物或治疗干预措施的开发;该项目的目标是开发一种计算机模型来预测疾病特异性聚糖，并使这种技术在商业上可供医学研究人员使用。该项目将主要利用公开的酶特性、糖原表达水平和聚糖谱的实验数据源来开发和完善数学模型。除了文献资源外，还将充分利用这些数据的网络汇编。该项目还将与约翰霍普金斯正在进行的实验糖生物学计划合作，以评估交互式研究指导原型实现的使用。该技术包括定性和定量两个方面。定性方面涉及预测给定起始结构和给定糖基化酶列表可能产生的聚糖结构。例如，一个（或少量）结构的从头出现是某些癌症恶性状态的有力指标。定量方面将所产生的各种聚糖结构的丰度与糖基化酶的表达水平的集合相关联;这种复杂性水平对于评估诸如朊病毒疾病的病症将是有价值的，在朊病毒疾病中，数十或数百种结构的丰度的细微差异可能与致病性相关。该项目的最终产品将是一个软件系统，作为一个商业软件包向公众提供，以绘制基因表达与聚糖结构之间的关系。该项目的成果将提供一个计算机工具系统，帮助研究人员确定癌症等严重疾病的新诊断方法，并开发治疗这些疾病的新疗法。这些工具将在基本细胞过程和细胞表面显示的化学结构之间建立联系，这些化学结构控制着正常细胞和恶性细胞之间的相互作用。该项目的最终产品将是一个软件系统，其中包含定性和定量工具，以绘制基因表达和聚糖结构之间的关系。该系统将作为商业软件包向公众提供。