The potential for non-adaptive origins of evolutionary innovations in central carbon metabolism

Environmental complexity is more important than mutation in driving the evolution of latent novel traits in E. coli

Recent experiments show that adaptive Darwinian evolution in one environment can lead to the emergence of multiple new traits that provide no immediate benefit in this environment. Such latent non-adaptive traits, however, can become adaptive in future environments. We do not know whether mutation or environment-driven selection is more important for the emergence of such traits. To find out, we evolve multiple wild-type and mutator E. coli populations under two mutation rates in simple (single antibiotic) environments and in complex (multi-antibiotic) environments. We then assay the viability of evolved populations in dozens of new environments and show that all populations become viable in multiple new environments different from those they had evolved in. The number of these new environments increases with environmental complexity but not with the mutation rate. Genome sequencing demonstrates the reason: Different environments affect pleiotropic mutations differently. Our experiments show that the selection pressure provided by an environment can be more important for the evolution of novel traits than the mutational supply experienced by a wild-type and a mutator strain of E. coli.

Novel traits without immediate fitness benefit evolve frequently but we don’t know whether mutation or environment-driven selection drives this evolution. Here, using experimental evolution of E. coli populations, the authors demonstrate the importance of selection in the evolution of latent novel traits.

Multiple novel traits without immediate benefits originate in bacteria evolving on single antibiotics

How new traits originate in evolution is a fundamental question of evolutionary biology. When such traits arise, they can either be immediately beneficial in their environment of origin, or they may become beneficial only in a future environment. Compared to immediately beneficial novel traits, novel traits without immediate benefits remain poorly studied. Here we use experimental evolution to study novel traits that are not immediately beneficial but that allow bacteria to survive in new environments. Specifically, we evolved multiple E. coli populations in five antibiotics with different mechanisms of action, and then determined their ability to grow in more than 200 environments that are different from the environment in which they evolved. Our populations evolved viability in multiple environments that contain not just clinically relevant antibiotics, but a broad range of antimicrobial molecules, such as surfactants, organic and inorganic salts, nucleotide analogues and pyridine derivatives. Genome sequencing of multiple evolved clones shows that pleiotropic mutations are important for the origin of these novel traits. Our experiments, which lasted fewer than 250 generations, demonstrate that evolution can readily create an enormous reservoir of latent traits in microbial populations. These traits can facilitate adaptive evolution in a changing world.

Information Theory Can Help Quantify the Potential of New Phenotypes to Originate as Exaptations

Exaptations are adaptive traits that do not originate de novo but from other adaptive traits. They include complex macroscopic traits, such as the middle ear bones of mammals, which originated from reptile jaw bones, but also molecular traits, such as new binding sites of transcriptional regulators. What determines whether a trait originates de novo or as an exaptation is unknown. I here use simple information theoretic concepts to quantify a molecular phenotype’s potential to give rise to new phenotypes. These quantities rely on the amount of genetic information needed to encode a phenotype. I use these quantities to estimate the propensity of new transcription factor binding phenotypes to emerge de novo or exaptively, and do so for 187 mouse transcription factors. I also use them to quantify whether an organism’s viability in one of 10 different chemical environment is likely to arise exaptively. I show that informationally expensive traits are more likely to originate exaptively. Exaptive evolution is only sometimes favored for new transcription factor binding, but it is always favored for the informationally complex metabolic phenotypes I consider. As our ability to genotype evolving populations increases, so will our ability to understand how phenotypes of ever-increasing informational complexity originate in evolution.

Syntrophy emerges spontaneously in complex metabolic systems

Syntrophy allows a microbial community as a whole to survive in an environment, even though individual microbes cannot. The metabolic interdependence typical of syntrophy is thought to arise from the accumulation of degenerative mutations during the sustained co-evolution of initially self-sufficient organisms. An alternative and underexplored possibility is that syntrophy can emerge spontaneously in communities of organisms that did not co-evolve. Here, we study this de novo origin of syntrophy using experimentally validated computational techniques to predict an organism’s viability from its metabolic reactions. We show that pairs of metabolisms that are randomly sampled from a large space of possible metabolism and viable on specific primary carbon sources often become viable on new carbon sources by exchanging metabolites. The same biochemical reactions that are required for viability on primary carbon sources also confer viability on novel carbon sources. Our observations highlight a new and important avenue for the emergence of metabolic adaptations and novel ecological interactions.

By exchanging resources, the members of a microbial community can survive in environments where individual species cannot. Despite the abundance of such syntrophy, little is known about its evolutionary origin. The predominant hypothesis is that syntrophy arises when originally independent organisms in the same community become interdependent by accumulating mutations. In this view, syntrophy arises when organisms co-evolve. In sharp contrast we find that different metabolism can interact syntrophically without a shared evolutionary history. We show that syntrophy is an inherent and emergent property of the complex chemical reaction networks that constitute metabolism.

Evolutionary design principles in metabolism

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

An enormous potential for niche construction through bacterial cross-feeding in a homogeneous environment

Microorganisms modify their environment by excreting by-products of metabolism, which can create new ecological niches that can help microbial populations diversify. A striking example comes from experimental evolution of genetically identical Escherichia coli populations that are grown in a homogeneous environment with the single carbon source glucose. In such experiments, stable communities of genetically diverse cross-feeding E. coli cells readily emerge. Some cells that consume the primary carbon source glucose excrete a secondary carbon source, such as acetate, that sustains other community members. Few such cross-feeding polymorphisms are known experimentally, because they are difficult to screen for. We studied the potential of bacterial metabolism to create new ecological niches based on cross-feeding. To do so, we used genome scale models of the metabolism of E. coli and metabolisms of similar complexity, to identify unique pairs of primary and secondary carbon sources in these metabolisms. We then combined dynamic flux balance analysis with analytical calculations to identify which pair of carbon sources can sustain a polymorphic cross-feeding community. We identified almost 10,000 such pairs of carbon sources, each of them corresponding to a unique ecological niche. Bacterial metabolism shows an immense potential for the construction of new ecological niches through cross feeding.

Biodiversity can emerge in a completely homogeneous environment from populations with initially genetically identical individuals. This striking observation comes from experimental evolution of bacteria, which create new ecological niches when they excrete nutrient-rich waste products that can sustain the life of other bacteria. It is difficult to estimate the potential of any one organism for such metabolic niche construction experimentally, because it is challenging to screen for novel metabolic abilities on a large scale. We therefore used experimentally validated models of bacterial metabolism to predict how many novel niches organisms like Escherichia coli can construct, if a novel niche must be able to sustain a stable community of microbes that differ in the nutrients they consume. We identify thousands of such niches. They differ in their primary carbon source and a secondary carbon source that is excreted by some microbes and used by others. Because we restricted ourselves to chemically simple environments, we may even have underestimated the enormous potential of microbes for niche construction.

Non-adaptive origins of evolutionary innovations increase network complexity in interacting digital organisms

The origin of evolutionary innovations is a central problem in evolutionary biology. To what extent such innovations have adaptive or non-adaptive origins is hard to assess in real organisms. This limitation, however, can be overcome using digital organisms, i.e. self-replicating computer programs that mutate, evolve and coevolve within a user-defined computational environment. Here, we quantify the role of the non-adaptive origins of host resistance traits in determining the evolution of ecological interactions among host and parasite digital organisms. We find that host resistance traits arising spontaneously as exaptations increase the complexity of antagonistic host-parasite networks. Specifically, they lead to higher host phenotypic diversification, a larger number of ecological interactions and higher heterogeneity in interaction strengths. Given the potential of network architecture to affect network dynamics, such exaptations may increase the persistence of entire communities. Our in silico approach, therefore, may complement current theoretical advances aimed at disentangling the ecological and evolutionary mechanisms shaping species interaction networks.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.

Factors driving metabolic diversity in the budding yeast subphylum

Background

Associations between traits are prevalent in nature, occurring across a diverse range of taxa and traits. Individual traits may co-evolve with one other, and these correlations can be driven by factors intrinsic or extrinsic to an organism. However, few studies, especially in microbes, have simultaneously investigated both across a broad taxonomic range. Here we quantify pairwise associations among 48 traits across 784 diverse yeast species of the ancient budding yeast subphylum Saccharomycotina, assessing the effects of phylogenetic history, genetics, and ecology.

Results

We find extensive negative (traits that tend to not occur together) and positive (traits that tend to co-occur) pairwise associations among traits, as well as between traits and environments. These associations can largely be explained by the biological properties of the traits, such as overlapping biochemical pathways. The isolation environments of the yeasts explain a minor but significant component of the variance, while phylogeny (the retention of ancestral traits in descendant species) plays an even more limited role. Positive correlations are pervasive among carbon utilization traits and track with chemical structures (e.g., glucosides and sugar alcohols) and metabolic pathways, suggesting a molecular basis for the presence of suites of traits. In several cases, characterized genes from model organisms suggest that enzyme promiscuity and overlapping biochemical pathways are likely mechanisms to explain these macroevolutionary trends. Interestingly, fermentation traits are negatively correlated with the utilization of pentose sugars, which are major components of the plant biomass degraded by fungi and present major bottlenecks to the production of cellulosic biofuels. Finally, we show that mammalian pathogenic and commensal yeasts have a suite of traits that includes growth at high temperature and, surprisingly, the utilization of a narrowed panel of carbon sources.

Conclusions

These results demonstrate how both intrinsic physiological factors and extrinsic ecological factors drive the distribution of traits present in diverse organisms across macroevolutionary timescales.

Electronic supplementary material

The online version of this article (10.1186/s12915-018-0498-3) contains supplementary material, which is available to authorized users.

Constraint and Contingency Pervade the Emergence of Novel Phenotypes in Complex Metabolic Systems

An evolutionary constraint is a bias or limitation in phenotypic variation that a biological system produces. We know examples of such constraints, but we have no systematic understanding about their extent and causes for any one biological system. We here study metabolisms, genomically encoded complex networks of enzyme-catalyzed biochemical reactions, and the constraints they experience in bringing forth novel phenotypes that allow survival on novel carbon sources. Our computational approach does not limit us to analyzing constrained variation in any one organism, but allows us to quantify constraints experienced by any metabolism. Specifically, we study metabolisms that are viable on one of 50 different carbon sources, and quantify how readily alterations of their chemical reactions create the ability to survive on a novel carbon source. We find that some metabolic phenotypes are much less likely to originate than others. For example, metabolisms viable on D-glucose are 1835 times more likely to give rise to metabolisms viable on D-fructose than on acetate. Likewise, we observe that some novel metabolic phenotypes are more contingent on parental phenotypes than others. Biochemical similarities among carbon sources can help explain the causes of these constraints. In addition, we study metabolisms that can be produced by recombination among 55 metabolisms of different bacterial strains or species, and show that their novel phenotypes are also contingent on and constrained by parental genotypes. To our knowledge, our analysis is the first to systematically quantify the incidence of constrained evolution in a broad class of biological system that is central to life and its evolution.