Research projects
Genome-wide coevolution networks reveal the global interactome
Genetic interactions are critical to biological function of genes. Understanding the position of proteins within interaction networks has vast potential to reveal the functions of uncharacterized genes, yet our understanding of the full scope of the protein interactome in plants is limited. Correlated changes in rates of sequence evolution across a phylogeny (evolutionary rate covariation [ERC]) can provide a reliable signal for identifying genetic interactions. This method, which relies on the integration of evolution, biological diversity, and cell/molecular biology, has the potential to be scaled to the full genome for large-scale identification of genetic interactions. We previously applied ERC in flowering plants to probe plastid-nuclear interactions, revealing frequent perturbations events that impact large networks of proteins critical to plastid function. This result exemplifies how comparative analyses, spanning deep scales of diversity, can reveal the interplay between organismal-level changes and amino acid-scale molecular interactions within the cell. Extending ERC to the entire interactome will likely reveal an even more sweeping understanding of the nested interactions that define plant protein networks; however, genome-wide ERC has not been applied in plants due to challenges arising from frequent gene and genome duplication. To overcome these challenges, we were recently awarded an NSF Plant Genomes grant (IOS-2114641) to develop the computational resources to perform genome-wide analyses of ERC in plants. This ongoing project aims to develop computational tools to perform genome-wide analyses of ERC in plants in order to create a searchable web-based plant interactome database that can be queried for specific genetic interactions and provide a systems-level view of the global landscape of genetic coevolution in plants and beyond.
Our team at Colorado State University and Oregon State University-Cascades is currently beta testing the tools we've developed for this project, which are available at https://github.com/EvanForsythe/ERCnet. If you're interested in utilizing these resources in your research, please reach out!
Genomic signatures of introgressive hybridization
Hybridization is a driving force in shaping biological diversity, resulting in the union of genetic material from divergent species at localized regions of the genome, creating selective barriers that influence which genes are transferred between species during hybridization and backcrossing (i.e. introgression). Probing genomic landscapes of introgression can reveal the mechanisms by which successful hybrids have overcome genetic incompatibilities in nature. We previously used genomic sequences to identify introgression impacting the genomic makeup of important crop and genetic model species, demonstrating the power of large-scale evolutionary analyses to reveal molecular mechanisms. However, our analyses pushed the limits of existing analytical techniques, highlighting two major shortcomings that impede our ability to gain the full possible scope of functional insight from genome sequences.
First, the most widely applied methods used to identify introgression in genomic sequences are susceptible to false inferences of introgression between two focal species when there has been cryptic introgression from an un-sampled/extinct so-called ‘ghost lineage’. Recognizing this limitation, we are working to develop a novel method to identify ghost lineage introgression. Our preliminary tests on a dataset of north American bear genomes indicate that our method accurately distinguishes ghost lineage introgression from true introgression, meriting further statistical development.
A second shortcoming of existing tools is that they do not have the ability to infer the direction that individual genes are transferred during introgression, impeding our ability to understand the potentially vast genomic impact of bidirectional introgression. This limitation led us to develop a novel statistical appraoch that is sensitive enough to probe individual loci. We've applied this approach to a simulated dataset and accurately inferred the directionality of blocks of introgressed genes. However, further work is required to characterize performance across different simulation parameters and empirical datasets.