Search bioRxiv⌕ Search

Biology subjects

Gatts, T. C.

Publications and source records attributed to Gatts, T. C..

2 recordsLinked to original sources

Correlated evolutionary rates reveal novel components and cross-compartment connectivity in plant proteostasis systems

Plant cells rely on an interconnected network of proteins interacting at many levels (e.g. physical enzyme complexes, gene regulatory modules, and biosynthetic pathways). Pairs of proteins that interact at any of these levels have been shown to exhibit phylogenetic signatures of evolutionary rate covariation (ERC), providing a basis for detecting functional interactions among proteins. Here, we apply ERCnet, a bioinformatic tool for performing genome-scale ERC analyses, to predict a plant protein-protein interactome network. We find a clustered set of proteins that exhibit strong signatures of ERC with the plastid caseinolytic protease (Clp) and other plastid proteostasis components, thus forming a functional module within the network. In addition to including proteins with known or predicted functions in protein import, transcription, translation, and degradation in plastids, the module also includes proteins with previously unknown molecular function, thus providing evidence that these proteins may contribute to plastid proteostasis in novel ways. Perhaps the most surprising members of this module are a set of proteins that are not thought to localize to the plastid at all. These proteins include a mitochondrial-localized pentatricopeptide repeat (PPR) protein with genetic evidence of interaction with the mitochondrial Clp system and two nuclear-localized actin-related proteins involved in chromatin remodeling and epigenetic regulation of nuclear genes. We speculate that these non-plastid-localized proteins act as mediators of organellar crosstalk and retrograde signaling of cellular proteostasis status in plants. In summary, our results highlight the connected nature of plant proteostasis systems and point to a promising set of novel proteostasis protein candidates.

molecular biology↗

Phylogenomic prediction of interaction networks in the presence of gene duplication

Assigning gene function from genome sequences is a rate-limiting step in molecular biology research. A proteins position within an interaction network can potentially provide insights into its molecular mechanisms. Phylogenetic analysis of evolutionary rate covariation (ERC) in protein sequence has been shown to be effective for large-scale prediction of functional relationships and interactions. However, gene duplication, gene loss, and other sources of phylogenetic incongruence are barriers for analyzing ERC on a genome-wide basis. Here, we developed ERCnet, a bioinformatic program designed to overcome these challenges, facilitating efficient all- vs-all ERC analyses for large protein sequence datasets. We simulated proteome datasets and found that ERCnet achieves combined false positive and negative error rates well below 10% and that our novel branch-by-branch length measurements outperforms root-to-tip approaches in most cases, offering a valuable new strategy for performing ERC. We also compiled a sample set of 35 angiosperm genomes to test the performance of ERCnet on empirical data, including its sensitivity to user-defined analysis parameters such as input dataset size and branch-length measurement strategy. We investigated the overlap between ERCnet runs with different species samples to understand how species number and composition affect predicted interactions and to identify the protein sets that consistently exhibit ERC across angiosperms. Our systematic exploration of the performance of ERCnet provides a roadmap for design of future ERC analyses to predict functional interactions in a wide array of genomic datasets. ERCnet code is freely available at https://github.com/EvanForsythe/ERCnet.

genomics↗