Real-Time Heteroplasmy Analysis on Microarrays

微阵列的实时异质性分析

基本信息

批准号：
7482573
负责人：
Alexander Michael Chagovetz
金额：
$ 20.04万
依托单位：
PHILOTEK, LLC
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-07-18 至 2009-08-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7482573
关键词：
Accounting Address Adult Affect Affinity Algorithms Alzheimer&apos s Disease American Applications Grants Area Arts Assisted Living Facilities Back Behavior Binding Biological Assay Biological Models Buffers Businesses Bypass Capital Chemistry Cities Clinical Collaborations Comparative Study Complex Computer Simulation Condition DNA DNA amplification DNA analysis Data Day Care Development Diabetes Mellitus Diagnosis Diagnostic Diagnostics Research Discrimination Disease Disease Progression Disease Resistance Economics Environment Environmental Risk Factor Equilibrium Equipment Evaluation Film Fluorescence Frequencies Future Genetic Genetic Variation Genomics Genotype Goals Government Health Healthcare Heart Diseases Heredity Hospitalization Hospitals Human Human Genetics Human Genome Human Genome Project Hypertension Indirect Expenditures Industry Intervention Investments Journals Kinetics Knowledge Label Laboratories Licensing Life Link Malignant Neoplasms Maps Market Research Marketing Medical Medicine Metabolic Diseases Methodology Methods Metric Microarray Analysis Microscope Mitochondria Mitochondrial DNA Modeling Molecular Molecular Diagnostic Testing Monitor Mutation National Institute of Diabetes and Digestive and Kidney Diseases Nature Nerve Degeneration Non-Insulin-Dependent Diabetes Mellitus Nucleic Acids Nucleotides Oligonucleotides Outcome Pathology Patients Performance Pharmaceutical Preparations Pharmacologic Substance Phase Point Mutation Polymerase Chain Reaction Polymorphism Analysis Predisposition Prevalence Publications Purpose Qualifying Range Reagent Relative (related person)Reliability of Results Reproducibility Research Resources Risk Sampling Score Screening procedure Series Services Severities Side Signal Transduction Single Nucleotide Polymorphism Slide Small Business Technology Transfer Research Sodium Chloride Sorting - Cell Movement Spottings Standards of Weights and Measures Statistically Significant Surface System Taxes Techniques Technology Temperature Testing Theoretical Studies Thermodynamics Time TimeLine Today United States National Institutes of Health Universities Utah Validation Work base blind college commercialization comparative cost data management design desire evaluation/testing genetic risk factor genetic variant human disease improved innovation instrumentation interest killings mitochondrial genome model development molecular recognition new technology news next generation novel novel diagnostics optic nerve disorder patient home care performance tests point of care prevent prototype research and development research study scale up social success synthetic construct time use tool

项目摘要

DESCRIPTION (provided by applicant): Many human diseases-if not all human diseases-and human health appear to be linked to our genetics. Major government and private research efforts are focused on investigating this linkage. Unfortunately, one of our best tools for this critical work-i.e., microarrays-holds us back, as most provide only generally qualitative results, limiting their usefulness. One key example is single nucleotide polymorphism (SNP) detection. Current SNP analysis is not quantitative because of imperfect molecular recognition (cross-hybridization) and pseudoequilibrium analyses performed with microarrays, limiting their use (e.g., to preliminary screening, as with the Affymetrix SNPChip). The microarray techniques attempt to compensate via excessive redundancy, leading to massive quantities of inaccurate/irreproducible data. The need to sort through these copious amounts of data extends analysis time, leads to improper conclusions, initiates scientific controversy, and may misdirect diagnostic and pharmaceutical research. Fortunately, these outcomes can be improved upon-which is the primary goal of the multi-phase STTR Fast-Track project proposed here. One key task that requires next-generation tools for real-time, reliable, quantitative SNPs analysis-including new data-management/analysis capabilities and simple yet powerful chemistries-is heteroplasmy (semiquantitative mitochondrial DNA SNP assays). Heteroplasmy could fulfill a major unmet need for reliable/costefficient quantitative tests involving cancer, diabetes, Alzheimer's disease, hypertension and a variety of neuromuscular, neurodegenerative, and metabolic diseases. Our goal is to develop, prototype, and commercialize real-time and quantitative heteroplasmy research tools based on technology licensed from the U of Utah and developed by Sigma founders. Preliminary data using our proprietary "competitive displacement analysis" (CDA)-the key innovation-strongly indicates the potential for successful development and rapid commercialization under this Fast-Track project. Specifically, we propose to develop our new method of performing real-time SNP microarray analysis (via CDA) by monitoring non-linear binding kinetics of known competitors in the presence of unlabeled targets. Our Phase I Aims are to 1) Demonstrate relevant binding kinetics; 2) Prove/validate feasibility of using CDA for heteroplasmy-based SNP detection in a model system; and 3) Develop CDA-based analytical approaches for multi-component model systems, based on synthetic targets, competitors, and lower-affinity species (background). Meeting the key Phase I milestones will allow us to pursue Phase II Aims: 4) Characterize CDA for heteroplasmy analysis on A3243G mutation locus; 5) Scale up CDA to interrogate multiple mutations; and 6) Complete comparative validation of CDA versus reference methods. Phase II success will provide the data needed to attract "Phase III" industry and financial partners. This will facilitate rapid product introduction into a key niche in a multi-billion-dollar research/diagnostics industry that benefits human health worldwide.

描述（由申请人提供）：许多人类疾病（如果不是所有人类疾病）和人类健康似乎与我们的遗传学有关。政府和私人的主要研究工作都集中在调查这种联系上。不幸的是，我们用于这项关键工作的最佳工具之一——微阵列——阻碍了我们，因为大多数工具只能提供一般性的定性结果，限制了它们的实用性。一个重要的例子是单核苷酸多态性 (SNP) 检测。目前的 SNP 分析不是定量的，因为分子识别（交叉杂交）和微阵列进行的伪平衡分析不完善，限制了它们的使用（例如，初步筛选，如 Affymetrix SNPChip）。微阵列技术试图通过过度冗余进行补偿，从而导致大量不准确/不可重复的数据。对这些大量数据进行分类的需要延长了分析时间，导致不正确的结论，引发科学争议，并可能误导诊断和药物研究。幸运的是，这些结果可以得到改善——这是这里提出的多阶段 STTR 快速通道项目的主要目标。需要下一代实时、可靠、定量 SNP 分析工具（包括新的数据管理/分析功能和简单而强大的化学物质）的一项关键任务是异质性（半定量线粒体 DNA SNP 测定）。异质性可以满足对涉及癌症、糖尿病、阿尔茨海默病、高血压和各种神经肌肉、神经退行性和代谢疾病的可靠/具有成本效益的定量测试的主要未满足的需求。我们的目标是基于犹他大学许可并由 Sigma 创始人开发的技术来开发、原型化和商业化实时定量异质性研究工具。使用我们专有的“竞争位移分析”（CDA）（关键创新）得出的初步数据强烈表明了该快速通道项目成功开发和快速商业化的潜力。具体来说，我们建议通过监测已知竞争者在未标记靶标存在的情况下的非线性结合动力学来开发执行实时 SNP 微阵列分析（通过 CDA）的新方法。我们第一阶段的目标是 1) 展示相关的结合动力学； 2) 证明/验证在模型系统中使用CDA进行基于异质性的SNP检测的可行性； 3) 基于合成目标、竞争者和低亲和力物种（背景），为多组分模型系统开发基于 CDA 的分析方法。达到第一阶段的关键里程碑将使我们能够追求第二阶段的目标：4) 表征 CDA，以对 A3243G 突变位点进行异质性分析； 5）扩大CDA规模以询问多种突变； 6) 完成 CDA 与参考方法的比较验证。第二阶段的成功将提供吸引“第三阶段”行业和金融合作伙伴所需的数据。这将有助于将产品快速引入价值数十亿美元的研究/诊断行业的关键利基市场，造福全球人类健康。