Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.
BMC Genomics. 2013;14 Suppl 3(Suppl 3):S9. doi: 10.1186/1471-2164-14-S3-S9. Epub 2013 May 28.
It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions.
To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs--from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies.
Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.
确定复杂表型中非同义单核苷酸多态性（nsSNP）的功能作用是现代生物学的一大挑战。统计和机器学习技术建立了基因型与表型之间的相关性，但可能无法推断出生物学上相关的机制。基于网络的关联研究的新兴范式旨在解决统计分析中的这个问题。然而，要了解单个分子成分如何在系统中协同工作，需要了解分子结构及其相互作用。
为了解决理解复杂表型的遗传、分子和细胞基础的挑战，我们首次开发了一种结构系统生物学方法，用于对 nsSNP 进行全基因组多尺度建模——从分子相互作用的原子细节到生物网络的涌现特性。我们应用我们的方法来确定与黑腹果蝇耐缺氧性相关的 nsSNP 的功能作用。在分子和网络水平上，nsSNP 功能作用的综合观点使我们能够识别 H、Rad51D、Ulp1、Wnt5、HDAC4、Sol、Dys、GalNAc-T2 和 CG33714 基因中的驱动突变及其相互作用（上位性），所有这些基因都参与了 Notch 和 Gurken/EGFR 信号通路的上调。此外，我们发现很大一部分驱动突变既不在保守的功能位点，也不负责结构稳定性，而是通过别构转变、蛋白质-蛋白质相互作用或蛋白质-核酸相互作用来调节蛋白质活性。这一发现应该会影响未来的全基因组关联研究。
我们的研究表明，将生物分子及其相互作用的统计、结构和网络观点整合起来，可以为全基因组关联研究中 nsSNP 的功能作用提供新的见解，而仅凭分子结构或生物网络的知识是无法实现的。因此，nsSNP 的多尺度建模可能被证明是在广泛的应用中建立序列变异功能作用的有力工具。
