Abstract
BackgroundIt 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.ResultsTo 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.ConclusionsOur 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.
Highlights
It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms on complex phenotypes
We have developed an integrated multiscale modeling framework to decipher the impact of non-synonymous Single Nucleotide Polymorphisms on the information flow from the activity of a single molecular component, to the function of the complete molecular machinery, and to the emergent properties of the biological network
Knowledge-driven network inference of driver mutations responsible for hypoxia tolerance Complex phenotypic changes typically arise from re-regulated cellular signaling and regulatory pathways
Summary
It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Recent advances in generation sequencing have generated abundant genetic variants and “omics” data Together, these extremely large, multidimensional datasets present an exciting opportunity to identify genes, and to predict pathways likely to be involved in diseases and traits. These extremely large, multidimensional datasets present an exciting opportunity to identify genes, and to predict pathways likely to be involved in diseases and traits These complex data sources plus the broad spectrum of phenotypes, challenge the quest to uncover the genetic, molecular, and cellular mechanisms that underlie phenotypes [1,2,3]. The “causal” relationships inferred from these methods are mathematical correlations They may not provide biological insight into the underlying molecular and cellular mechanisms that associate genotypes with phenotypes
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