In silico evolution of early metabolism

Biosynthetic Potential of a Novel Antarctic Actinobacterium Marisediminicola antarctica ZS314T Revealed by Genomic Data Mining and Pigment Characterization

Rare actinobacterial species are considered as potential resources of new natural products. Marisediminicola antarctica ZS314^T is the only type strain of the novel actinobacterial genus Marisediminicola isolated from intertidal sediments in East Antarctica. The strain ZS314^T was able to produce reddish orange pigments at low temperatures, showing characteristics of carotenoids. To understand the biosynthetic potential of this strain, the genome was completely sequenced for data mining. The complete genome had 3,352,609 base pairs (bp), much smaller than most genomes of actinomycetes. Five biosynthetic gene clusters (BGCs) were predicted in the genome, including a gene cluster responsible for the biosynthesis of C50 carotenoid, and four additional BGCs of unknown oligosaccharide, salinixanthin, alkylresorcinol derivatives, and NRPS (non-ribosomal peptide synthetase) or amino acid-derived compounds. Further experimental characterization indicated that the strain may produce C.p.450-like carotenoids, supporting the genomic data analysis. A new xanthorhodopsin gene was discovered along with the analysis of the salinixanthin biosynthetic gene cluster. Since little is known about this genus, this work improves our understanding of its biosynthetic potential and provides opportunities for further investigation of natural products and strategies for adaptation to the extreme Antarctic environment.

Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks

Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 10⁶ genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.

Biological evolution is driven by heritable, genetic alterations that affect the fitness of organisms. However, the pool of “fitter” variants (genotypes) is often restricted and it is not at all obvious how evolution finds its way from low-fitness to high-fitness genotypes in a complex, multidimensional “fitness landscapes” with many peaks (fit organisms) and valleys (unfit ones). To address this question we investigate how likely it is for biological evolution to find a way “uphill” from a lower-fitness organism to the best adapted organism. We discover that the accessibility of the fittest organism depends on the number of types of basic “units” used to encode genotypes. These units can be, for example, the four DNA nucleotides A,T,C,G, or the ∼20 amino acids used for synthesizing proteins, and the choice of the most appropriate unit is dictated by how the genotypes and the fitnesses are related—a relationship that researchers have begun to unveil only recently. We find that increasing the number of units strongly increases the probability that there will be at least one uphill path to the best-adapted genotype, and the number of evolutionary pathways leading to it. Our findings suggest that biological evolution can follow many more pathways than previously thought.

Lower glycolysis carries a higher flux than any biochemically possible alternative

The universality of many pathways of core metabolism suggests a strong role for evolutionary selection, but it remains unclear whether existing pathways have been selected from a large or small set of biochemical possibilities. To address this question, we construct in silico all possible biochemically feasible alternatives to the trunk pathway of glycolysis and gluconeogenesis, one of the most highly conserved pathways in metabolism. We show that, even though a large number of alternative pathways exist, the alternatives carry lower flux than the real pathway under typical physiological conditions. We also find that if physiological conditions were different, different pathways could outperform those found in nature. Together, our results demonstrate how thermodynamic and biophysical constraints restrict the biochemical alternatives that are open to evolution, and suggest that the existing trunk pathway of glycolysis and gluconeogenesis may represent a maximal flux solution.

The biochemical pathways of central carbon metabolism are highly conserved across all domains of life. Here, Court et al. use a computational approach to test all possible pathways of glycolysis and gluconeogenesis and find that the existing trunk pathways may represent a maximal flux solution selected for during evolution.

Evolution of communication protocols using an artificial regulatory network

I describe the Utrecht Machine (UM), a discrete artificial regulatory network designed for studying how evolution discovers biochemical computation mechanisms. The corresponding binary genome format is compatible with gene deletion, duplication, and recombination. In the simulation presented here, an agent consisting of two UMs, a sender and a receiver, must encode, transmit, and decode a binary word over time using the narrow communication channel between them. This communication problem has chicken-and-egg structure in that a sending mechanism is useless without a corresponding receiving mechanism. An in-depth case study reveals that a coincidence creates a minimal partial solution, from which a sequence of partial sending and receiving mechanisms evolve. Gene duplications contribute by enlarging the regulatory network. Analysis of 60,000 sample runs under a variety of parameter settings confirms that crossover accelerates evolution, that stronger selection tends to find clumsier solutions and finds them more slowly, and that there is implicit selection for robust mechanisms and genomes at the codon level. Typical solutions associate each input bit with an activation speed and combine them almost additively. The parents of breakthrough organisms sometimes have lower fitness scores than others in the population, indicating that populations can cross valleys in the fitness landscape via outlying members. The simulation exhibits back mutations and population-level memory effects not accounted for in traditional population genetics models. All together, these phenomena suggest that new evolutionary models are needed that incorporate regulatory network structure.

Increments and duplication events of enzymes and transcription factors influence metabolic and regulatory diversity in prokaryotes

In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.

Microbial evolution in vivo and in silico: methods and applications

Microbial evolution has been extensively studied in the past fifty years, which has lead to seminal discoveries that have shaped our understanding of evolutionary forces and dynamics. It is only recently however, that transformative technologies and computational advances have enabled a larger in-scale and in-depth investigation of the genetic basis and mechanistic underpinnings of evolutionary adaptation. In this review we focus on the strengths and limitations of in vivo and in silico techniques for studying microbial evolution in the laboratory, and we discuss how these complementary approaches can be integrated in a unifying framework for elucidating microbial evolution.

Early evolution of efficient enzymes and genome organization

Background

Cellular life with complex metabolism probably evolved during the reign of RNA, when it served as both information carrier and enzyme. Jensen proposed that enzymes of primordial cells possessed broad specificities: they were generalist. When and under what conditions could primordial metabolism run by generalist enzymes evolve to contemporary-type metabolism run by specific enzymes?

Results

Here we show by numerical simulation of an enzyme-catalyzed reaction chain that specialist enzymes spread after the invention of the chromosome because protocells harbouring unlinked genes maintain largely non-specific enzymes to reduce their assortment load. When genes are linked on chromosomes, high enzyme specificity evolves because it increases biomass production, also by reducing taxation by side reactions.

Conclusion

The constitution of the genetic system has a profound influence on the limits of metabolic efficiency. The major evolutionary transition to chromosomes is thus proven to be a prerequisite for a complex metabolism. Furthermore, the appearance of specific enzymes opens the door for the evolution of their regulation.

Reviewers

This article was reviewed by Sándor Pongor, Gáspár Jékely, and Rob Knight.

Excess mutual catalysis is required for effective evolvability

It is widely accepted that autocatalysis constitutes a crucial facet of effective replication and evolution (e.g., in Eigen's hypercycle model). Other models for early evolution (e.g., by Dyson, Gánti, Varela, and Kauffman) invoke catalytic networks, where cross-catalysis is more apparent. A key question is how the balance between auto- (self-) and cross- (mutual) catalysis shapes the behavior of model evolving systems. This is investigated using the graded autocatalysis replication domain (GARD) model, previously shown to capture essential features of reproduction, mutation, and evolution in compositional molecular assemblies. We have performed numerical simulations of an ensemble of GARD networks, each with a different set of lognormally distributed catalytic values. We asked what is the influence of the catalytic content of such networks on beneficial evolution. Importantly, a clear trend was observed, wherein only networks with high mutual catalysis propensity (p(mc)) allowed for an augmented diversity of composomes, quasi-stationary compositions that exhibit high replication fidelity. We have reexamined a recent analysis that showed meager selection in a single GARD instance and for a few nonstationary target compositions. In contrast, when we focused here on compotypes (clusters of composomes) as targets for selection in populations of compositional assemblies, appreciable selection response was observed for a large portion of the networks simulated. Further, stronger selection response was seen for high p(mc) values. Our simulations thus demonstrate that GARD can help analyze important facets of evolving systems, and indicate that excess mutual catalysis over self-catalysis is likely to be important for the emergence of molecular systems capable of evolutionlike behavior.