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Real-Time Heteroplasmy Analysis on Microarrays

微阵列的实时异质性分析

基本信息

批准号：
7921575
负责人：
Alexander Michael Chagovetz
金额：
$ 75.45万
依托单位：
SALT LAKE CITY BIOSCIENCE, INC.
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 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7921575
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 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 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 Laboratory Research Licensing Life Link Malignant Neoplasms Maps Market Research Marketing Medical Medicine Metabolic Diseases Methodology Methods Metric Microarray Analysis Microscope Mitochondrial DNA Modeling Molecular Molecular Diagnostic Testing Monitor Mutation National Human Genome Research Institute 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 Polymorphism Analysis Predisposition Prevalence Publications Qualifying Reagent Relative (related person)Reliability of Results Reproducibility Research Research Personnel Resources Risk Sampling Screening procedure Series Services Severities Side Signal Transduction Single Nucleotide Polymorphism Slide Small Business Technology Transfer Research Sodium Chloride Sorting - Cell Movement Spottings Surface System Taxes Techniques Technology Temperature Testing Theoretical Studies Therapeutic Thermodynamics Time TimeLine United States National Institutes of Health Universities Utah Validation Variant Work base blind college commercialization comparative cost data management design drug discovery economic impact evaluation/testing genetic risk factor genetic variant human disease improved innovation instrumentation interest killings meetings mitochondrial genome model development molecular recognition neuromuscular new technology news next generation novel novel diagnostics optic nerve disorder patient home care performance tests point-of-care diagnostics 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基因有关。主要的政府和私人研究努力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，即。比如微阵列，它们阻碍了我们，因为大多数只提供一般的定性结果，限制了它们的用途。一个关键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，因为不完善的分子识别（交叉杂交）和用微阵列进行的假平衡分析，限制了它们的使用（例如，初步筛选，如Affymetrix SNP chip）。微阵列技术试图通过过度冗余来补偿，导致大量不准确/不可复制的数据。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。一项关键任务需要下一代实时、可靠、定量SNP分析工具，包括新的数据管理/分析能力和简单而强大的化学技术，即异质性（半定量线粒体DNA SNP分析）。异质性可以满足对癌症、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/成本效益定量测试的主要未满足需求。我们的目标是开发，原型，并商业化实时和定量异质性研究工具，该工具基于犹他大学授权的技术，并由Sigma创始人开发。使用我们专有的“竞争替代分析”（CDA）的初步数据-关键创新-强烈表明在这个快速通道项目下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对手在未标记靶标存在下的非线性结合动力学，开发我们的新方法来进行实时SNP微阵列分析（通过CDA）。我们第一阶段的目标是1)证明相关的结合动力学；2)证明/验证在模型系统中使用CDA进行基于杂质的SNP检测的可行性；3)基于合成目标、竞争对手和低亲和力物种（背景），开发基于cda的多组分模型系统分析方法。达到关键的I期里程碑将使我们能够实现II期目标：4)表征CDA用于A3243G突变位点的异质性分析；5)扩大CDA以询问多个突变；6)完成CDA与参考方法的对比验证。第二阶段的成功将提供吸引“第三阶段”工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数十亿美元的研究/诊断行业的关键利基市场，从而造福全球人类健康。