Evolution of Prokaryotes: A Kuhnian Scientific Revolution

The conviction, due to previous failures, that bacteriology and darwinism were incompatible, has postponed the application of molecular phylogenesis to bacteria. But once introduced, this new field has led to a profound revolution of this science. A stable classification of the bacteria is at last possible; a new domain, the Archae, as distant from the Bacteria as from the Eukarya, has been discovered; noncultivable new species can be identified from the environment. It may even be possible to unravel the pathway of early evolution after the common ancestor. However, controversies on this subject are already disregarded by the new paradigm. This revised version was published online in June 2006 with corrections to the Cover Date.     

Intensive Care Ethics in Evolution

The ethics of treating the seriously and critically ill have not been static throughout the ages. Twentieth century medicine has inherited from the nineteenth century a science which places an inappropriate weight on diagnosis over prognosis and management, combined with a seventeenth century duty to prolong life. However other earlier ethical traditions, both Hippocratic and Christian, respected both the limitations of medicine and emphasised the importance of prognosis. This paper outlines some of the historical precedents for the treatment of the critically ill, and also how the current paradigm limits clinical practice and causes ethical tensions. An understanding that other paradigms have been ethically acceptable in the past allows wider consideration and acceptance of alternatives for the future. However future alternatives will also have to address the role of technology, given its importance in this area of medicine.     

代谢途径的进化

生物的新陈代谢是通过一系列的代谢途径来实现的.这些调节精妙、相瓦协作的代谢途径是如何进化形成的一直是一个引人入胜的重要问题.自1945年有关该问题的第一个假说--"逆向进化假说"提出以来,迄今已发展出七种假说,包括"逆向进化假说"、"半酶理论"、"向前发展模型"、"酶的招募假说"、"多功能酶特化假说"、"整个代谢途径的复制假说"和"从头创造假说".其中最受关注的是"逆向进化假说"和"酶的招募假说",而最近提出的"多功能酶特化假说"由于有较好的理论基础和实验证据支持,也逐渐引起人们的关注.本文对这些假说逐一作了概述,并结合作者的相关研究工作,对该领域的研究现状和发展趋势进行了分析讨论和展望.     

Global Metabolic Reconstruction and Metabolic Gene Evolution in the Cattle Genome

The sequence of cattle genome provided a valuable opportunity to systematically link genetic and metabolic traits of cattle. The objectives of this study were 1) to reconstruct genome-scale cattle-specific metabolic pathways based on the most recent and updated cattle genome build and 2) to identify duplicated metabolic genes in the cattle genome for better understanding of metabolic adaptations in cattle. A bioinformatic pipeline of an organism for amalgamating genomic annotations from multiple sources was updated. Using this, an amalgamated cattle genome database based on UMD_3.1, was created. The amalgamated cattle genome database is composed of a total of 33,292 genes: 19,123 consensus genes between NCBI and Ensembl databases, 8,410 and 5,493 genes only found in NCBI or Ensembl, respectively, and 266 genes from NCBI scaffolds. A metabolic reconstruction of the cattle genome and cattle pathway genome database (PGDB) was also developed using Pathway Tools, followed by an intensive manual curation. The manual curation filled or revised 68 pathway holes, deleted 36 metabolic pathways, and added 23 metabolic pathways. Consequently, the curated cattle PGDB contains 304 metabolic pathways, 2,460 reactions including 2,371 enzymatic reactions, and 4,012 enzymes. Furthermore, this study identified eight duplicated genes in 12 metabolic pathways in the cattle genome compared to human and mouse. Some of these duplicated genes are related with specific hormone biosynthesis and detoxifications. The updated genome-scale metabolic reconstruction is a useful tool for understanding biology and metabolic characteristics in cattle. There has been significant improvements in the quality of cattle genome annotations and the MetaCyc database. The duplicated metabolic genes in the cattle genome compared to human and mouse implies evolutionary changes in the cattle genome and provides a useful information for further research on understanding metabolic adaptations of cattle.     

The Evolution of Fungal Metabolic Pathways

Fungi contain a remarkable range of metabolic pathways, sometimes encoded by gene clusters, enabling them to digest most organic matter and synthesize an array of potent small molecules. Although metabolism is fundamental to the fungal lifestyle, we still know little about how major evolutionary processes, such as gene duplication (GD) and horizontal gene transfer (HGT), have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility. We examined the synteny and evolutionary history of 247,202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes. We found that gene clustering varied greatly with respect to metabolic category and lineage; for example, clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism, whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism. The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically, GD, which was an order of magnitude more abundant than HGT, was most frequently observed in Agaricomycetes, whereas HGT was much more prevalent in Pezizomycotina. The effect of HGT in some Pezizomycotina was particularly strong; for example, we identified 111 HGT events associated with the 15 Aspergillus genomes, which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum. Finally, the impact of GD within a metabolic category was typically consistent across all fungal lineages, whereas the impact of HGT was variable. These results indicate that GD is the dominant process underlying fungal metabolic diversity, whereas HGT is episodic and acts in a category- or lineage-specific manner. Both processes have a greater impact on clustered genes, suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity.     

Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution

A new method for the mathematical analysis of large metabolic networks is presented. Based on the fact that the occurrence of a metabolic reaction generally requires the existence of other reactions providing its substrates, series of metabolic networks are constructed. In each step of the corresponding expansion process those reactions are incorporated whose substrates are made available by the networks of the previous generations. The method is applied to the set of all metabolic reactions included in the KEGG database. Starting with one or more seed compounds, the expansion results in a final network whose compounds define the scope of the seed. Scopes of all metabolic compounds are calculated and it is shown that large parts of cellular metabolism can be considered as the combined scope of simple building blocks. Analyses of various expansion processes reveal crucial metabolites whose incorporation allows for the increase in network complexity. Among these metabolites are common cofactors such as NAD⁺, ATP, and coenzyme A. We demonstrate that the outcome of network expansion is in general very robust against elimination of single or few reactions. There exist, however, crucial reactions whose elimination results in a dramatic reduction of scope sizes. It is hypothesized that the expansion process displays characteristics of the evolution of metabolism such as the temporal order of the emergence of metabolic pathways. [Reviewing Editor : Dr. David Pollock]     

Proteome Evolution and the Metabolic Origins of Translation and Cellular Life

The origin of life has puzzled molecular scientists for over half a century. Yet fundamental questions remain unanswered, including which came first, the metabolic machinery or the encoding nucleic acids. In this study we take a protein-centric view and explore the ancestral origins of proteins. Protein domain structures in proteomes are highly conserved and embody molecular functions and interactions that are needed for cellular and organismal processes. Here we use domain structure to study the evolution of molecular function in the protein world. Timelines describing the age and function of protein domains at fold, fold superfamily, and fold family levels of structural complexity were derived from a structural phylogenomic census in hundreds of fully sequenced genomes. These timelines unfold congruent hourglass patterns in rates of appearance of domain structures and functions, functional diversity, and hierarchical complexity, and revealed a gradual build up of protein repertoires associated with metabolism, translation and DNA, in that order. The most ancient domain architectures were hydrolase enzymes and the first translation domains had catalytic functions for the aminoacylation and the molecular switch-driven transport of RNA. Remarkably, the most ancient domains had metabolic roles, did not interact with RNA, and preceded the gradual build-up of translation. In fact, the first translation domains had also a metabolic origin and were only later followed by specialized translation machinery. Our results explain how the generation of structure in the protein world and the concurrent crystallization of translation and diversified cellular life created further opportunities for proteomic diversification.     

蛋白质定向进化及其在微生物代谢调控中的应用

蛋白质定向进化是非理性改造蛋白质的一种有效方法。利用蛋白质定向进化技术可以改变代谢流,扩展或构建新的代谢途径,弱化或消除不必要或有害的代谢途径,从而达到提高某种代谢产物产率或降解有害物质的目的。蛋白质定向进化技术在代谢调控中的应用有效拓宽了代谢工程的应用范围。本文介绍了主要的蛋白质定向进化技术如易错PCR技术、DNA改组技术、交错延伸技术和临时模板随机嵌合技术等,评述了蛋白质定向进化技术对微生物细胞代谢中的关键蛋白进行定向改造,从而改善其代谢能力,调控微生物代谢等的应用。     

Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria

Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields – evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy, pharmaceuticals, chemical commodities, biomining, and bioremediation.     

Gene Duplication and Phenotypic Changes in the Evolution of Mammalian Metabolic Networks

Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines.     

On the Evolution of New Metabolic Functions in Diploid Organisms

Evolution of lactose utilization via the ebg system of Escherichia coli requires both structural gene (ebgA) and regulatory gene (ebgR) mutations. Because evolution of new metabolic functions in diploids might be subject to constraints not present in haploid organisms, merodiploid strains carrying a wild-type and an evolved ebgA allele, or a wild-type and an evolved ebgR allele were constructed. I show that heterozygosity at ebgA does not significantly affect the selective advantage of the evolved ebgA allele; whereas heterozygosity at ebgR eliminates the selective advantage of the evolved ebgR allele. It is suggested that, in diploid organisms, evolution of new functions for systems under negative control would be very difficult.     

Evolution under Fluctuating Environments Explains Observed Robustness in Metabolic Networks

A high level of robustness against gene deletion is observed in many organisms. However, it is still not clear which biochemical features underline this robustness and how these are acquired during evolution. One hypothesis, specific to metabolic networks, is that robustness emerges as a byproduct of selection for biomass production in different environments. To test this hypothesis we performed evolutionary simulations of metabolic networks under stable and fluctuating environments. We find that networks evolved under the latter scenario can better tolerate single gene deletion in specific environments. Such robustness is underlined by an increased number of independent fluxes and multifunctional enzymes in the evolved networks. Observed robustness in networks evolved under fluctuating environments was “apparent,” in the sense that it decreased significantly as we tested effects of gene deletions under all environments experienced during evolution. Furthermore, when we continued evolution of these networks under a stable environment, we found that any robustness they had acquired was completely lost. These findings provide evidence that evolution under fluctuating environments can account for the observed robustness in metabolic networks. Further, they suggest that organisms living under stable environments should display lower robustness in their metabolic networks, and that robustness should decrease upon switching to more stable environments.     

Human Breath Analysis May Support the Existence of Individual Metabolic Phenotypes

The metabolic phenotype varies widely due to external factors such as diet and gut microbiome composition, among others. Despite these temporal fluctuations, urine metabolite profiling studies have suggested that there are highly individual phenotypes that persist over extended periods of time. This hypothesis was tested by analyzing the exhaled breath of a group of subjects during nine days by mass spectrometry. Consistent with previous metabolomic studies based on urine, we conclude that individual signatures of breath composition exist. The confirmation of the existence of stable and specific breathprints may contribute to strengthen the inclusion of breath as a biofluid of choice in metabolomic studies. In addition, the fact that the method is rapid and totally non-invasive, yet individualized profiles can be tracked, makes it an appealing approach.     

The Role of Protein Conformational Switches in Pharmacology: Its Implications in Metabolic Reprogramming and Protein Evolution

Besides pharmacogenomics and drug dynamics, pharmacological properties of a drug could also arise from protein conformational switches. These switches would arise from the following mechanisms: (a) slight shifts away from a protein’s native conformation induced by mutation, (b) changes in the protein’s environment allowing for structural rearrangements to form hitherto unknown conformations, (c) parsing the protein into foldable polypeptide fragment(s) by either proteolysis of the native structure or (d) perturbation of the native conformation to generate polypeptide fragment(s). These switches are modulated by changes in the protein’s matrix properties such as pH, temperature, ligands—their nature, concentration and complexes; micronutrients, oxidant/antioxidant status and metabolic products within the functional environment of the protein. The pharmacological implications of these are discussed in light of polypharmacology arising from protein isomerism, cross-pharmacology, possible decreases in both the expressible and expressed protein population and metabolic reprogramming—and ultimately, how these factors relate to diseases. Further implications include variational drug toxicity and drug response idiosyncrasies. Another important consequence is that the “whole life” history of the individual would play an active role in that individual’s response to disease severity and drug response up to that very moment and is prone to variations with changes in pre-disposing factors.     

The Evolution of Control and Distribution of Adaptive Mutations in a Metabolic Pathway

In an attempt to understand whether it should be expected that some genes tend to be used disproportionately often by natural selection, we investigated two related phenomena: the evolution of flux control among enzymes in a metabolic pathway and properties of adaptive substitutions in pathway enzymes. These two phenomena are related by the principle that adaptive substitutions should occur more frequently in enzymes with greater flux control. Predicting which enzymes will be preferentially involved in adaptive evolution thus requires an evolutionary theory of flux control. We investigated the evolution of enzyme control in metabolic pathways with two models of enzyme kinetics: metabolic control theory (MCT) and Michaelis–Menten saturation kinetics (SK). Our models generate two main predictions for pathways in which reactions are moderately to highly irreversible: (1) flux control will evolve to be highly unequal among enzymes in a pathway and (2) upstream enzymes evolve a greater control coefficient then those downstream. This results in upstream enzymes fixing the majority of beneficial mutations during adaptive evolution. Once the population has reached high fitness, the trend is reversed, with the majority of neutral/slightly deleterious mutations occurring in downstream enzymes. These patterns are the result of three factors (the first of these is unique to the MCT simulations while the other two seem to be general properties of the metabolic pathways): (1) the majority of randomly selected, starting combinations of enzyme kinetic rates generate pathways that possess greater control for the upstream enzymes compared to downstream enzymes; (2) selection against large pools of intermediate substrates tends to prevent majority control by downstream enzymes; and (3) equivalent mutations in enzyme kinetic rates have the greatest effect on flux for enzymes with high levels of flux control, and these enzymes will accumulate adaptive substitutions, strengthening their control. Prediction 1 is well supported by available data on control coefficients. Data for evaluating prediction 2 are sparse but not inconsistent with this prediction.THEORETICAL research on the process of adaptation has focused primarily on describing the size and number of genetic changes underlying phenotypic change (Fisher 1930; Kimura 1983; Orr 1998, 2002, 2003). By contrast, comparatively little theoretical attention has been given to the question of whether certain genes or types of genes are preferentially involved in the process of adaptation. Yet the current debate over the relative importance of regulatory vs. structural genes in morphological evolution (Hoekstra and Coyne 2007; Stern and Orgogozo 2008) clearly indicates that this question is of interest to evolutionary biologists.One situation in which this question is pertinent is the evolution of characters that are influenced by the concentration of end products of metabolic pathways. Often change in end-product concentration can be achieved by substitutions in any one of several genes in the pathway. One example is the intensity of floral pigmentation. To a first approximation, final pigment concentration, and hence color intensity, can be viewed as being determined by the flux rate down the pigment biosynthetic pathway for a fixed time corresponding to the duration of floral development. More generally, any situation in which flux rate determines phenotype is likely to fall in this category. In such situations, metabolic control theory (MCT) (Kacser and Burns 1973) and similar approaches (Heinrich and Rapoport 1974; Savageau 1976) indicate that changes in flux can be achieved by changing the activity of any enzyme in the pathway. We seek to determine whether and, if so, why enzymes differ in the probability that they contribute to evolutionary change in pathway flux.It has been suggested as a general principle that enzymes with the greatest control over flux will be disproportionately involved in such evolutionary change (Hartl et al. 1985; Eanes 1999; Watt and Dean 2000). This argument is based on the theoretical expectation that the probability of fixation of an advantageous allele is roughly proportional to its selection coefficient (Hedrick 2000). Since mutations equivalent in terms of enzyme kinetic properties will have greater effects on flux, and hence on fitness, in enzymes with greater metabolic control, mutations in those enzymes will be substituted preferentially.While this argument is likely sound, it simply pushes back the question of which genes evolve preferentially to the question of which enzymes are expected to have greatest control over flux. Although we are unaware of any theoretical attempts to model the evolution of flux control, many authors have speculated about where in pathways control is expected to be highest.Kacser and Burns (1973) hypothesized that the magnitudes of flux control exerted by different enzymes may be very similar. This hypothesis was based on the result from MCT that in linear pathways, overall flux control can be shared by all enzymes. Since metabolic pathways often consist of many enzymes, each would be expected to have only a limited potential to influence flux. Subsequent theoretical analysis of this hypothesis demonstrated that a given flux is consistent with many different flux-control distributions, including, at one extreme, equal flux control by all enzymes and, at the other extreme, major control by one or a few enzymes and little control for all others (Mazat et al. 1996). However, the question of which of these possibilities, if any, are likely to be favored by selection has not been addressed.Another hypothesis, the epistatic or synergistic principle, predicts that control will be vested in a single enzyme at any given time, but will shift over time among enzymes (Dykhuizen et al. 1987; Keightley 1989; Bost et al. 2001) According to this hypothesis, starting from equal control among enzymes in the pathway, selection to increase (or decrease) flux will cause the activity of one enzyme to increase (decrease). This change results in a decrease (increase) in flux control for the enzyme that changes and an increase (decrease) in control for the other pathway enzymes, causing control to be unequally shared. While this argument seems plausible, there has been no analysis of whether over time all enzymes have an equal chance of having elevated control.Finally, Eanes'' (1999) review of enzyme polymorphisms found that control is often centered in enzymes at pathway branch points, which constitute the most upstream enzymes of their specific branch. Flowers et al. (2007) also demonstrated that branching enzymes tend to exhibit more adaptive substitutions than downstream enzymes as would be expected under the principle that evolutionary change will be concentrated in enzymes with the largest control coefficients. In addition, evolutionary changes in these enzymes may be favored because they allow organisms to modify flux allocation to alternate functions and track environmental fluctuations. This suggestion is supported by the “branch point effect,” a theoretical demonstration that control coefficients can dramatically shift between enzymes depending on the kinetic rates of the two competing enzymes (LaPorte et al. 1984). However, this study does not address the question of how the distribution of control is likely to evolve, but describes only which distributions of control are mathematically possible. Thus, Eanes (1999, p. 318) concludes his review, stating “all enzymes in [a] contributing pathway may not be equal; determining the rule[s] for these inequalities should be a major goal in studies of enzyme polymorphism.”A control coefficient (CC) indicates the degree to which flux through a pathway is altered by a small change in the activity of an enzyme (see appendix; this is equivalent to the sensitivity coefficient of Kacser and Burns 1973). The “rules” governing the distribution of control coefficients are determined by the biological evolution of metabolic systems. While research demonstrates that there are many possible distributions of control coefficients, none has examined which of these is most likely to evolve. The optimization of metabolic systems has been explored in detail (Heinrich et al. 1991, 1997; Heinrich and Schuster 1998). In these studies, however, the investigators employ as optimization criteria maximizing flux, maximizing transient times, or minimizing metabolic intermediates, criteria whose biological and evolutionary relevance is unclear.In an effort to understand how control is expected to be shared among enzymes and predict which enzymes are most likely to contribute to adaptive genetic changes, we present two models of the evolution of flux control in a simple linear pathway. The first model employs the framework of MCT. Although there have been many challenges to the MCT framework (Savageau 1976, 1992; Cornish-Bowden 1989; Savageau and Sorribas 1989), it should be made clear that our aim is not to construct a precisely parameterized model of a particular biological system, but to use this generalized framework to address a single, critically ignored question: What are the rules governing how control will evolve to be distributed among enzymes? The use of the MCT framework to address questions in evolutionary genetics is firmly established, with investigation focused on the molecular basis of dominance (Kacser and Burns 1981; Keightley 1996a; Phadnis and Fry 2005; but see Bagheri and Wagner 2004), the relationship between metabolic flux and fitness (Dykhuizen et al. 1987; Szathmary 1993), the amount of additive and nonadditive genetic variance in metabolic systems (Keightley 1989), whether this variation can be explained by mutation–selection balance (Clark 1991), and patterns of response of quantitative traits to selection (Keightley 1996b). The second model we examine, saturation kinetics (SK), is based on Michealis–Menten kinetics and enables us to relax one major assumption of MCT: that enzymes are far from saturation.Here we limit our analysis to linear pathways as an initial attempt to examine these issues. We find that for such pathways control coefficients will generally evolve to be unequal; that the magnitude of this inequality depends on the thermodynamic properties, rather than the kinetic properties, of each reaction step; that upstream enzymes tend to evolve higher control coefficients than downstream enzymes; and that upstream enzymes fix advantageous mutations in greater numbers, and those mutations have larger effects than in downstream enzymes.