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 - 如果不是所有的人类疾病和人类健康似乎都与我们的遗传学有关。主要的政府和私人研究工作重点是调查这种联系。不幸的是，我们为这项关键工作的最佳工具之一就是，微阵列将我们归还给我们，因为大多数人仅提供一般定性的结果，从而限制了它们的实用性。一个关键例子是单核苷酸多态性（SNP）检测。当前的SNP分析不是由于分子识别不完美（杂交）和使用微阵列进行的伪平衡分析而限制了它们的使用（例如，与Affymetrix Snpchip一样限制了初步筛选）。微阵列技术试图通过过度的冗余来补偿，从而导致大量不准确/不可再现的数据。需要对这些大量数据进行分类的需求延长了分析时间，导致结论不当，引发科学争议，并可能误导诊断和药物研究。幸运的是，这些结果可以改善，这是此处提出的多相STTR快速轨道项目的主要目标。一项需要下一代工具进行实时，可靠，定量SNP分析的关键任务，包括新的数据管理/分析功能以及简单而功能强大的化学作用 - IS异质质（半词性线粒体DNA SNP分析）。杂质可以满足涉及癌症，糖尿病，阿尔茨海默氏病，高血压以及各种神经肌肉，神经退行性和代谢性疾病的可靠/成本效益定量测试的主要未满足需求。我们的目标是基于从犹他州U of of Utah并由Sigma创始人开发的技术，开发，原型和商业化实时和定量异质研究工具。使用我们专有的“竞争流离失所分析”（CDA）的初步数据 - 关键创新表明在这个快速轨道项目下，成功开发和快速商业化的潜力。具体而言，我们建议通过在没有标记的目标的情况下监测已知竞争者的非线性结合动力学来开发我们进行实时SNP微阵列分析（通过CDA）的新方法。我们的I阶段目标是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用于异质的SNP检测的可行性； 3）基于合成目标，竞争者和较低亲和力物种（背景）开发了基于CDA的分析方法。达到关键阶段的里程碑将使我们能够追求II期目标：4）在A3243G突变基因座上进行异质分析的CDA表征； 5）扩展CDA以询问多个突变； 6）CDA与参考方法的完全比较验证。第二阶段的成功将提供吸引“ III期”行业和金融合作伙伴所需的数据。这将促进快速产品在全球范围内受益于人类健康的数十亿美元的研究/诊断行业中的主要利基市场。