Kuwahara, H. | bioRxiv Science

bioRxiv2019

Stable maintenance of a hidden switch as a way to increase the gene expression stability

In response to severe stress, wild-type organisms can release alternative phenotypes that are hidden under normal conditions and are associated with underlying genetic variations. A number of such stress-induced phenotypic switchings have been reported to be based on reactivation of hidden thresholds; under the normal condition, a high barrier separating alternative phenotypes ensures the expression of single discrete phenotype, but a severe perturbation can lower the barrier to a level at which to expose cryptic alternatives. While the importance of such threshold-based switches as the mechanism to generate adaptive novelties under variable environments has been appreciated, it still remains elusive how naturally selected organisms can maintain the phenotypic switching capability when such switching has been disused for a long period of time. Here, through the use of computer simulation, we analyzed adaptive evolution of gene circuits under stabilizing selection. We found that different strategies evolved to acquire reduced levels of gene expression noise around the optimum expression level. To incrementally improve the gene expression stability from a founding population with bistable individuals, the evolution consistently took the direction to raise the height of the potential barrier of bistable systems. Our results demonstrate that hidden phenotypic switches can be stably maintained during environmental stasis, facilitating the release of potentially adaptive phenotypic alternatives in the event of substantial perturbations.

evolutionary biology↗

bioRxiv2019

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

MotivationProper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. ResultsHere, we propose a novel graph convolutional network-based disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. AvailabilityThe main program and the data are available at https://github.com/lykaust15/Disease_gene_prioritization_GCN.

bioinformatics↗