Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

Mannakee, Brian K.; Gutenkunst, Ryan N.

doi:10.1371/journal.pgen.1006132

Cited by 4 publications

(2 citation statements)

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“…The first network is a model of the pyruvate branches in the bacterium Lactococcus lactis [30]. The following increasingly complex models based on glycolysis [31, 32] were selected because this pathway is found with conserved function across the tree of life and has been well studied [4, 33]. Furthermore, it has been shown that the structure of the enzymes involved changes across species, and variation emerges both within and between major lineages [34].…”

Section: Resultsmentioning

confidence: 99%

PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways

McCloskey,

Mammedova,

Liberles

2024

Preprint

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BackgroundMetabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype-phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function.ResultsA software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent witha prioriexpectations of fixation probabilities and systematic change in model parameters.ConclusionsThe PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available athttps://github.com/nmccloskey/PEMPS.

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Section: Resultsmentioning

confidence: 99%

PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways

McCloskey,

Mammedova,

Liberles

2024

Preprint

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“…Indeed, network centrality has been found to be one of the most important determinants of the rate of sequence evolution in organisms as diverse as humans (Alvarez-Ponce et al 2017 ) and Saccharomyces cerevisiae (Fraser et al 2002 ). Mannakee and Gutenkunst ( 2016 ) used systems modelling to develop a metric, dynamical influence, which measures functional importance of the protein within its interactions network. They found that this metric showed one of the strongest correlations with evolutionary rate, comparable with expression levels, even once covariates had been adjusted for.…”

Section: Background: Variables Influencing Sequence Constraintsmentioning

confidence: 99%

Variables Influencing Differences in Sequence Conservation in the Fission Yeast Schizosaccharomyces pombe

2021

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Which variables determine the constraints on gene sequence evolution is one of the most central questions in molecular evolution. In the fission yeast Schizosaccharomyces pombe, an important model organism, the variables influencing the rate of sequence evolution have yet to be determined. Previous studies in other single celled organisms have generally found gene expression levels to be most significant, with numerous other variables such as gene length and functional importance identified as having a smaller impact. Using publicly available data, we used partial least squares regression, principal components regression, and partial correlations to determine the variables most strongly associated with sequence evolution constraints. We identify centrality in the protein–protein interactions network, amino acid composition, and cellular location as the most important determinants of sequence conservation. However, each factor only explains a small amount of variance, and there are numerous variables having a significant or heterogeneous influence. Our models explain more than half of the variance in dN, raising the possibility that future refined models could quantify the role of stochastics in evolutionary rate variation.

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Metabolic determinants of enzyme evolution in a genome-scale bacterial metabolic network

Aguilar‐Rodríguez

Wagner

2018

Genome Biology and Evolution

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Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux—high substrate to product conversion rates—evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme’s role in the function of a metabolic network affects its evolution more than its place in the network’s structure. They highlight the value of a system-level perspective for studies of molecular evolution.

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Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

Cited by 4 publications

References 104 publications

PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways

PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways

Variables Influencing Differences in Sequence Conservation in the Fission Yeast Schizosaccharomyces pombe

Metabolic determinants of enzyme evolution in a genome-scale bacterial metabolic network

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