THE HUMAN BRAINOME:genome, transcriptome and proteome interaction in human cortex

描述（由申请人提供）：我们的人类脑组项目旨在定义正常衰老的人类大脑皮层和受神经退行性疾病影响的大脑中基因组-转录组-蛋白质组-现象组的相互作用。我们假设，目前通过观察单层信息（基因型）来发现新的遗传风险位点的公认方法缺乏力量，采取更系统范围的方法可能会增加发现新靶点的成功。我们打算将我们的基因型信息（约180万个单核苷酸多态性）和我们的表达信息（约46,000个转录本）与一个新的蛋白质组学数据集进行比较，该数据集由液相色谱在线耦合高质量精度质谱（LC- MS，提供约2000-3000个蛋白质的定量）生成。我们将研究单个相关的顺式和反式关系（即DNA变化影响一个转录物或蛋白质的下游调控），并进行分析以了解转录组和蛋白质组水平上发生的关系网络。我们很适合做这项工作。首先，我们有大量的冷冻人脑样本（约1500例，约60%的晚发性阿尔茨海默病样本），其中有基因型和表达数据。为了获得足够的能力来准确评估人类基因组，以及克服转录组学和蛋白质组学样本分析所固有的一些噪声和其他问题，需要大量的样本。这些现有的基因型和基因表达数据集对这项资助的成功至关重要。其次，为了完成蛋白质组分析，我们将利用太平洋西北国家实验室开发的精确质量和时间（AMT）标签方法来避免传统方法的灵敏度限制，并提高测量的吞吐量，提供广泛的蛋白质组覆盖。虽然具有相同的蛋白质组覆盖范围，但AMT标签方法通常将每个样品分析的仪器时间减少1-2个数量级（例如1小时与正常情况下的24小时）。因此，AMT标签方法是唯一合理的选择，以提供灵敏度和测量吞吐量必不可少的这个项目。最后，我们期望使用我们的新型去噪算法实现灵敏度的10倍增加，这将允许对人类大脑皮层的完整蛋白质组进行更准确的评估。通过对人类大脑表达过程的更全面的了解，我们将能够将新的遗传发现与其下游神经病理生物学相关性联系起来。这将有助于开发新的神经退行性疾病的遗传和分子生物标志物。识别能够进一步划分临床前亚组和快速转化亚类的生物标志物将有助于显著降低药物试验的成本。这些生物标记物将有额外的好处，它们不仅是分子的，而且还具有绘制的基因型谱，这应该比分子标记物更容易测定。

Description (provided by applicant): Our Human Brainome project seeks to define the genome-transriptome-proteome- phenome interactions in the cortexes of normally aged human brains and brains affected by neurodegenerative disease. We hypothesize that the current accepted approach for discovering novel genetic risk loci by looking at a single layer of information (genotypes) is lacking power and taking a more systems-wide approach might increase the success of finding novel targets. We intend upon comparing our genotype information (~ 1.8 million single nucleotide polymorphisms) and our expression information (~ 46,000 transcripts) with a novel proteomics dataset generated by running Liquid Chromatography coupled online with high mass accuracy Mass Spectrometry (LC- MS, providing quantification of ~2000-3000 proteins). We will look at both single correlative cis and trans relationships (i.e. DNA change affects downstream regulation of one transcript or protein), as well as perform analyses to understand the networks of relationships occurring both at the transcriptome and proteome level. We are well situated to perform this work. First, we have an extensive collection of frozen human brain samples (n~1500, ~60% late onset Alzheimer's disease samples) for which there is genotype and expression data available. Large sample sizes are needed to obtain sufficient power to accurately assess the human genome as well as overcome some of the noise and other issues inherent to transcriptomics and proteomics sample analysis. These existing genotype and gene expression datasets are essential to success in this grant. Second, to accomplish the proteome analyses we will utilize the accurate mass and time (AMT) tag approach developed at the Pacific Northwest National Laboratories to avoid the sensitivity constraints of conventional approaches and improve the throughput of measurements providing broad proteome coverage. While having the same coverage of the proteome, the AMT tag approach typically reduces by 1-2 orders (e.g. 1 hour vs normally 24 hours) of magnitude the instrument time per sample analysis. Thus, the AMT tag approach is the only reasonable option to provide the sensitivity and measurement throughput essential to this project. Finally, we expect to achieve an additional 10-fold increase in sensitivity using our novel de-noising algorithm that will allow for a more accurate assessment of the complete proteome of the human brain cortex. By developing a more global view of the processes involved in human brain expression we will be able to relate new genetic findings to their downstream neuro-pathobiological relevance. This should aid in the development of novel genetic and molecular biomarkers of neurodegenerative disease. Identifying biomarkers that could further classify pre-clinical subgroups and identify sub-classes of rapid converters would help to significantly reduce the cost of drug trials. These biomarkers will have the added benefit that they are not only molecular, but in addition have mapped genotype profiles, which should be easier to assay than a molecular marker.