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

Abstract

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.

Adaptive Enrichment of a Thermophilic Bacterial Isolate for Enhanced Enzymatic Activity

“…Systems metabolic engineering is an alternative approach that is proving its worth for the design of novel biocatalysts with improved functionality. In the field of enzyme production, enzymes have been improved by directing their evolution in the laboratory using existing engineering technologies, which involve iterations of random mutagenesis or recombination followed by screening or selection [37], novel expression systems such as cell-surface display [38], modeling of bacterial metabolism [39], and improved fermentation methods [4]. However, unlike rational protein engineering techniques that focus on changing one enzyme property at a time, including directed modification of specific enzymes, ALE has the advantage of letting nonintuitive beneficial mutations occur in many different genes and regulatory regions simultaneously [40].…”

A Tale of Two Families: Whole Genome and Segmental Duplications Underlie Glutamine Synthetase and Phosphoenolpyruvate Carboxylase Diversity in Narrow-Leafed Lupin (Lupinus angustifolius L.)

“…Usually, genes encoding enzymes located at the top of the metabolic pathway are under stronger purifying selection than downstream ones [87]. An association between the selective pressure acting on a gene and the position of an encoded enzyme in the pathway was revealed in a wide metabolic context [88,89], including L. angustifolius genes encoding isoflavone synthase and acetyl-coenzyme A carboxylase [63,64]. A higher selection pressure acts on central and highly connected enzymes, enzymes with high metabolic flux, and enzymes catalyzing reactions that are difficult to bypass through alternative pathways [88].…”

“…An association between the selective pressure acting on a gene and the position of an encoded enzyme in the pathway was revealed in a wide metabolic context [88,89], including L. angustifolius genes encoding isoflavone synthase and acetyl-coenzyme A carboxylase [63,64]. A higher selection pressure acts on central and highly connected enzymes, enzymes with high metabolic flux, and enzymes catalyzing reactions that are difficult to bypass through alternative pathways [88]. Moreover, enzymes participating in primary metabolism are usually under a constant strong selective pressure, whereas enzymes performing specified metabolism are under weaker negative selection [89].…”

Molecular chaperones accelerate the evolution of their protein clients in yeast

“…The reasons for this diversity in rates of protein evolution are still a subject of intense debate (Rocha 2006;Alvarez-Ponce 2014;Zhang and Yang 2015). A number of factors have been shown to affect rates of evolution, including gene expression levels (Pál, et al 2001;Drummond, et al 2005), expression breadth in multicellular organisms (Duret and Mouchiroud 2000;Wright, et al 2004;Zhang and Li 2004;Alvarez-Ponce and Fares 2012), essentiality (Hurst and Smith 1999;Jordan, et al 2002;Aguilar-Rodríguez and Wagner 2018), duplicability (Nembaware, et al 2002;Yang, et al 2003;Pegueroles, et al 2013) and the number of protein-protein interactions (Fraser, et al 2002;Hahn and Kern 2005;Alvarez-Ponce and Fares 2012). However, a comprehensive understanding of which factors affect rates of protein evolution, their relative impacts on rates of evolution, and the molecular mechanisms underlying these impacts, is lacking.…”