Essentiality Is a Strong Determinant of Protein Rates of Evolution during Mutation Accumulation Experiments in Escherichia coli

The overlooked evolutionary dynamics of 16S rRNA revises its role as the "gold standard" for bacterial species identification

The role of 16S rRNA has been and largely remains crucial for the identification of microbial organisms. Although 16S rRNA could certainly be described as one of the most studied sequences ever, the current view of it remains somewhat ambiguous. While some consider 16S rRNA to be a variable marker with resolution power down to the strain level, others consider them to be living fossils that carry information about the origin of domains of cellular life. We show that 16S rRNA is clearly an evolutionarily very rigid sequence, making it a largely unique and irreplaceable marker, but its applicability beyond the genus level is highly limited. Interestingly, it seems that the evolutionary rigidity is not driven by functional constraints of the sequence (RNA-protein interactions), but rather results from the characteristics of the host organism. Our results suggest that, at least in some lineages, Horizontal Gene Transfer (HGT) within genera plays an important role for the evolutionary non-dynamics (stasis) of 16S rRNA. Such genera exhibit an apparent lack of diversification at the 16S rRNA level in comparison to the rest of a genome. However, why it is limited specifically and solely to 16S rRNA remains enigmatic.

Impact of transient acquired hypermutability on the inter- and intra-species competitiveness of Pseudomonas aeruginosa

Once acquired, hypermutation is unrelenting, and in the long-term, leads to impaired fitness due to its cumulative impact on the genome. This raises the question of why hypermutators arise so frequently in microbial ecosystems. In this work, we explore this problem by examining how the transient acquisition of hypermutability affects inter- and intra-species competitiveness, and the response to environmental insults such as antibiotic challenge. We do this by engineering Pseudomonas aeruginosa to allow the expression of an important mismatch repair gene, mutS, to be experimentally controlled over a wide dynamic range. We show that high levels of mutS expression induce genomic stasis (hypomutation), whereas lower levels of induction lead to progressively higher rates of mutation. Whole-genome sequence analyses confirmed that the mutational spectrum of the inducible hypermutator is similar to the distinctive profile associated with mutS mutants obtained from the airways of people with cystic fibrosis (CF). The acquisition of hypermutability conferred a distinct temporal fitness advantage over the wild-type P. aeruginosa progenitor strain, in both the presence and the absence of an antibiotic selection pressure. However, over a similar time-scale, acquisition of hypermutability had little impact on the population dynamics of P. aeruginosa when grown in the presence of a competing species (Staphylococcus aureus). These data indicate that in the short term, acquired hypermutability primarily confers a competitive intra-species fitness advantage.

Purifying selection enduringly acts on the sequence evolution of highly expressed proteins in Escherichia coli

The evolutionary speed of a protein sequence is constrained by its expression level, with highly expressed proteins evolving relatively slowly. This negative correlation between expression levels and evolutionary rates (known as the E–R anticorrelation) has already been widely observed in past macroevolution between species from bacteria to animals. However, it remains unclear whether this seemingly general law also governs recent evolution, including past and de novo, within a species. However, the advent of genomic sequencing and high-throughput phenotyping, particularly for bacteria, has revealed fundamental gaps between the 2 evolutionary processes and has provided empirical data opposing the possible underlying mechanisms which are widely believed. These conflicts raise questions about the generalization of the E–R anticorrelation and the relevance of plausible mechanisms. To explore the ubiquitous impact of expression levels on molecular evolution and test the relevance of the possible underlying mechanisms, we analyzed the genome sequences of 99 strains of Escherichia coli for evolution within species in nature. We also analyzed genomic mutations accumulated under laboratory conditions as a model of de novo evolution within species. Here, we show that E–R anticorrelation is significant in both past and de novo evolution within species in E. coli. Our data also confirmed ongoing purifying selection on highly expressed genes. Ongoing selection included codon-level purifying selection, supporting the relevance of the underlying mechanisms. However, the impact of codon-level purifying selection on the constraints in evolution within species might be smaller than previously expected from evolution between species.

Evolutionary coupling range varies widely among enzymes depending on selection pressure

Recent studies proposed that enzyme-active sites induce evolutionary constraints at long distances. The physical origin of such long-range evolutionary coupling is unknown. Here, I use a recent biophysical model of evolution to study the relationship between physical and evolutionary couplings on a diverse data set of monomeric enzymes. I show that evolutionary coupling is not universally long-range. Rather, range varies widely among enzymes, from 2 to 20 Å. Furthermore, the evolutionary coupling range of an enzyme does not inform on the underlying physical coupling, which is short range for all enzymes. Rather, evolutionary coupling range is determined by functional selection pressure.

"Protein" no longer means what it used to

Every biologist knows that the word protein describes a group of macromolecules essential to sustain life on Earth. As biologists, we are invariably trained under a protein paradigm established since the early twentieth century. However, in recent years, the term protein unveiled itself as an euphemism to describe the overwhelming heterogeneity of these compounds. Most of our current studies are targeted on carefully selected subsets of proteins, but we tend to think and write about these as representative of the whole population. Here we discuss how seeking for universal definitions and general rules in any arbitrarily segmented study would be misleading about the conclusions. Of course, it is not our purpose to discourage the use of the word protein. Instead, we suggest to embrace the extended universe of proteins to reach a deeper understanding of their full potential, realizing that the term encompasses a group of molecules very heterogeneous in terms of size, shape, chemistry and functions, i.e. the term protein no longer means what it used to.

Avoidance of protein unfolding constrains protein stability in long-term evolution

Every amino acid residue can influence a protein's overall stability, making stability highly susceptible to change throughout evolution. We consider the distribution of protein stabilities evolutionarily permittable under two previously reported protein fitness functions: flux dynamics and misfolding avoidance. We develop an evolutionary dynamics theory and find that it agrees better with an extensive protein stability data set for dihydrofolate reductase orthologs under the misfolding avoidance fitness function rather than the flux dynamics fitness function. Further investigation with ribonuclease H data demonstrates that not any misfolded state is avoided; rather, it is only the unfolded state. At the end, we discuss how our work pertains to the universal protein abundance-evolutionary rate correlation seen across organisms' proteomes. We derive a closed-form expression relating protein abundance to evolutionary rate that captures Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens experimental trends without fitted parameters.

Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli

Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski's long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein-protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.

Richard Dickerson, Molecular Clocks, and Rates of Protein Evolution

Proteins approximately behave as molecular clocks, accumulating amino acid replacements at a more or less constant rate. Nonetheless, each protein displays a characteristic rate of evolution: whereas some proteins remain largely unaltered over large periods of time, others can rapidly accumulate amino acid replacements. An article by Richard Dickerson, published in the first issue of the Journal of Molecular Evolution (J Mol Evol 1:26-45, 1971), described the first analysis in which the rates of evolution of many proteins were compared, and the differences were interpreted in the light of their function. When comparing the sequences of fibrinopeptides, hemoglobin, and cytochrome c of different species, he observed a linear relationship between the number of amino acid replacements and divergence time. Remarkably, fibrinopeptides had evolved fast, cytochrome c had evolved slowly, and hemoglobin exhibited an intermediate rate of evolution. As the Journal of Molecular Evolution celebrates its 50th anniversary, I highlight this landmark article and reflect on its impact on the field of Molecular Evolution.

Molecular Chaperones Accelerate the Evolution of Their Protein Clients in Yeast

Protein stability is a major constraint on protein evolution. Molecular chaperones, also known as heat-shock proteins, can relax this constraint and promote protein evolution by diminishing the deleterious effect of mutations on protein stability and folding. This effect, however, has only been stablished for a few chaperones. Here, we use a comprehensive chaperone–protein interaction network to study the effect of all yeast chaperones on the evolution of their protein substrates, that is, their clients. In particular, we analyze how yeast chaperones affect the evolutionary rates of their clients at two very different evolutionary time scales. We first study the effect of chaperone-mediated folding on protein evolution over the evolutionary divergence of Saccharomyces cerevisiae and S. paradoxus. We then test whether yeast chaperones have left a similar signature on the patterns of standing genetic variation found in modern wild and domesticated strains of S. cerevisiae. We find that genes encoding chaperone clients have diverged faster than genes encoding non-client proteins when controlling for their number of protein–protein interactions. We also find that genes encoding client proteins have accumulated more intraspecific genetic diversity than those encoding non-client proteins. In a number of multivariate analyses, controlling by other well-known factors that affect protein evolution, we find that chaperone dependence explains the largest fraction of the observed variance in the rate of evolution at both evolutionary time scales. Chaperones affecting rates of protein evolution mostly belong to two major chaperone families: Hsp70s and Hsp90s. Our analyses show that protein chaperones, by virtue of their ability to buffer destabilizing mutations and their role in modulating protein genotype–phenotype maps, have a considerable accelerating effect on protein evolution.

A dispensable paralog of succinate dehydrogenase subunit C mediates standing resistance towards a subclass of SDHI fungicides in Zymoseptoria tritici

Succinate dehydrogenase inhibitor (SDHI) fungicides are widely used for the control of a broad range of fungal diseases. This has been the most rapidly expanding fungicide group in terms of new molecules discovered and introduced for agricultural use over the past fifteen years. A particular pattern of differential sensitivity (resistance) to the stretched heterocycle amide SDHIs (SHA-SDHIs), a subclass of chemically-related SDHIs, was observed in naïve Zymoseptoria tritici populations not previously exposed to these chemicals. Subclass-specific resistance was confirmed at the enzyme level but did not correlate with the genotypes of the succinate dehydrogenase (SDH) encoding genes. Mapping and characterization of the molecular mechanisms responsible for standing SHA-SDHI resistance in natural field isolates identified a gene paralog of SDHC, termed ZtSDHC3, which encodes for an alternative C subunit of succinate dehydrogenase, named alt-SDHC. Using reverse genetics, we showed that alt-SDHC associates with the three other SDH subunits, leading to a fully functional enzyme and that a unique Qp-site residue within the alt-SDHC protein confers SHA-SDHI resistance. Enzymatic assays, computational modelling and docking simulations for the two SQR enzymes (altC-SQR, WT_SQR) enabled us to describe enzyme-inhibitor interactions at an atomistic level and to propose rational explanations for differential potency and resistance across SHA-SDHIs. European Z. tritici populations displayed a presence (20–30%) / absence polymorphism of ZtSDHC3, as well as differences in ZtSDHC3 expression levels and splicing efficiency. These polymorphisms have a strong impact on SHA-SDHI resistance phenotypes. Characterization of the ZtSDHC3 promoter in European Z. tritici populations suggests that transposon insertions are associated with the strongest resistance phenotypes. These results establish that a dispensable paralogous gene determines SHA-SDHIs fungicide resistance in natural populations of Z. tritici. This study paves the way to an increased awareness of the role of fungicidal target paralogs in resistance to fungicides and demonstrates the paramount importance of population genomics in fungicide discovery.

Zymoseptoria tritici is the causal agent of Septoria tritici leaf blotch (STB) of wheat, the most devastating disease for cereal production in Europe. Multiple succinate dehydrogenase inhibitor (SDHI) fungicides have been developed and introduced for the control of STB. We report the discovery and detailed characterization of a paralog of the C subunit of the SDH enzyme conferring standing resistance towards the SHA-SDHIs, a particular chemical subclass of the SDHIs. The SDHC paralog is characterized by its presence/absence, expression and alternative splicing polymorphisms, which in turn influence resistance levels. The identified mechanisms exemplify the importance of population genomics for the discovery and rational design of the most adapted solutions.

Chaperonin overproduction and metabolic erosion caused by mutation accumulation in Escherichia coli

Bacterial cells adapting to a constant environment tend to accumulate mutations in portions of their genome that are not maintained by selection. This process has been observed in bacteria evolving under strong genetic drift, and especially in bacterial endosymbionts of insects. Here, we study this process in hypermutable Escherichia coli populations evolved through 250 single-cell bottlenecks on solid rich medium in a mutation accumulation experiment that emulates the evolution of bacterial endosymbionts. Using phenotype microarrays monitoring metabolic activity in 95 environments distinguished by their carbon sources, we observe how mutation accumulation has decreased the ability of cells to metabolize most carbon sources. We study if the chaperonin GroEL, which is naturally overproduced in bacterial endosymbionts, can ameliorate the process of metabolic erosion, because of its known ability to buffer destabilizing mutations in metabolic enzymes. Our results indicate that GroEL can slow down the negative phenotypic consequences of genome decay in some environments.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

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.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Essentiality, conservation, evolutionary pressure and codon bias in bacterial genomes

Essential genes constitute the core of genes which cannot be mutated too much nor lost along the evolutionary history of a species. Natural selection is expected to be stricter on essential genes and on conserved (highly shared) genes, than on genes that are either nonessential or peculiar to a single or a few species. In order to further assess this expectation, we study here how essentiality of a gene is connected with its degree of conservation among several unrelated bacterial species, each one characterised by its own codon usage bias. Confirming previous results on E. coli, we show the existence of a universal exponential relation between gene essentiality and conservation in bacteria. Moreover, we show that, within each bacterial genome, there are at least two groups of functionally distinct genes, characterised by different levels of conservation and codon bias: i) a core of essential genes, mainly related to cellular information processing; ii) a set of less conserved nonessential genes with prevalent functions related to metabolism. In particular, the genes in the first group are more retained among species, are subject to a stronger purifying conservative selection and display a more limited repertoire of synonymous codons. The core of essential genes is close to the minimal bacterial genome, which is in the focus of recent studies in synthetic biology, though we confirm that orthologs of genes that are essential in one species are not necessarily essential in other species. We also list a set of highly shared genes which, reasonably, could constitute a reservoir of targets for new anti-microbial drugs.

Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

The rates of evolution of the proteins of any organism vary across orders of magnitude. A primary factor influencing rates of protein evolution is expression. A strong negative correlation between expression levels and evolutionary rates (the so-called E-R anticorrelation) has been observed in virtually all studied organisms. This effect is currently attributed to the abundance-dependent fitness costs of misfolding and unspecific protein-protein interactions, among other factors. Secreted proteins are folded in the endoplasmic reticulum, a compartment where chaperones, folding catalysts, and stringent quality control mechanisms promote their correct folding and may reduce the fitness costs of misfolding. In addition, confinement of secreted proteins to the extracellular space may reduce misinteractions and their deleterious effects. We hypothesize that each of these factors (the secretory pathway quality control and extracellular location) may reduce the strength of the E-R anticorrelation. Indeed, here we show that among human proteins that are secreted to the extracellular space, rates of evolution do not correlate with protein abundances. This trend is robust to controlling for several potentially confounding factors and is also observed when analyzing protein abundance data for 6 human tissues. In addition, analysis of mRNA abundance data for 32 human tissues shows that the E-R correlation is always less negative, and sometimes nonsignificant, in secreted proteins. Similar observations were made in Caenorhabditis elegans and in Escherichia coli, and to a lesser extent in Drosophila melanogaster, Saccharomyces cerevisiae and Arabidopsis thaliana. Our observations contribute to understand the causes of the E-R anticorrelation.

Chance and necessity in the genome evolution of endosymbiotic bacteria of insects

An open question in evolutionary biology is how does the selection–drift balance determine the fates of biological interactions. We searched for signatures of selection and drift in genomes of five endosymbiotic bacterial groups known to evolve under strong genetic drift. Although most genes in endosymbiotic bacteria showed evidence of relaxed purifying selection, many genes in these bacteria exhibited stronger selective constraints than their orthologs in free-living bacterial relatives. Remarkably, most of these highly constrained genes had no role in the host–symbiont interactions but were involved in either buffering the deleterious consequences of drift or other host-unrelated functions, suggesting that they have either acquired new roles or their role became more central in endosymbiotic bacteria. Experimental evolution of Escherichia coli under strong genetic drift revealed remarkable similarities in the mutational spectrum, genome reduction patterns and gene losses to endosymbiotic bacteria of insects. Interestingly, the transcriptome of the experimentally evolved lines showed a generalized deregulation of the genome that affected genes encoding proteins involved in mutational buffering, regulation and amino acid biosynthesis, patterns identical to those found in endosymbiotic bacteria. Our results indicate that drift has shaped endosymbiotic associations through a change in the functional landscape of bacterial genes and that the host had only a small role in such a shift.

Phylogenomic resolution of the bacterial genus Pantoea and its relationship with Erwinia and Tatumella

Investigation of the evolutionary relationships between related bacterial species and genera with a variety of lifestyles have gained popularity in recent years. For analysing the evolution of specific traits, however, a robust phylogeny is essential. In this study we examined the evolutionary relationships among the closely related genera Erwinia, Tatumella and Pantoea, and also attempted to resolve the species relationships within Pantoea. To accomplish this, we used the whole genome sequence data for 35 different strains belonging to these three genera, as well as nine outgroup taxa. Multigene datasets consisting of the 1039 genes shared by these 44 strains were then generated and subjected to maximum likelihood phylogenetic analyses, after which the results were compared to those using conventional multi-locus sequence analysis (MLSA) and ribosomal MLSA (rMLSA) approaches. The robustness of the respective phylogenies was then explored by considering the factors typically responsible for destabilizing phylogenetic trees. We found that the nucleotide datasets employed in the MLSA, rMLSA and 1039-gene datasets contained significant levels of homoplasy, substitution saturation and differential codon usage, all of which likely gave rise to the observed lineage specific rate heterogeneity. The effects of these factors were much less pronounced in the amino acid dataset for the 1039 genes, which allowed reconstruction of a fully supported and resolved phylogeny. The robustness of this amino acid tree was also supported by different subsets of the 1039 genes. In contrast to the smaller datasets (MLSA and rMLSA), the 1039 amino acid tree was also not as sensitive to long-branch attraction. The robust and well-supported evolutionary hypothesis for the three genera, which confidently resolved their various inter- and intrageneric relationships, represents a valuable resource for future studies. It will form the basis for studies aiming to understand the forces driving the divergence and maintenance of lineages, species and biological traits in this important group of bacteria.