Co-Evolution of Metabolism and Protein Sequences (original) (raw)
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Evolution of Enzymes in Metabolism: A Network Perspective
Journal of Molecular Biology, 2002
Several models have been proposed to explain the origin and evolution of enzymes in metabolic pathways. Initially, the retro-evolution model proposed that, as enzymes at the end of pathways depleted their substrates in the primordial soup, there was a pressure for earlier enzymes in pathways to be created, using the later ones as initial template, in order to replenish the pools of depleted metabolites. Later, the recruitment model proposed that initial templates from other pathways could be used as long as those enzymes were similar in chemistry or substrate specificity. These two models have dominated recent studies of enzyme evolution. These studies are constrained by either the small scale of the study or the artificial restrictions imposed by pathway definitions. Here, a network approach is used to study enzyme evolution in fully sequenced genomes, thus removing both constraints. We find that homologous pairs of enzymes are roughly twice as likely to have evolved from enzymes that are less than three steps away from each other in the reaction network than pairs of non-homologous enzymes. These results, together with the conservation of the type of chemical reaction catalyzed by evolutionarily related enzymes, suggest that functional blocks of similar chemistry have evolved within metabolic networks. One possible explanation for these observations is that this local evolution phenomenon is likely to cause less global physiological disruptions in metabolism than evolution of enzymes from other enzymes that are distant from them in the metabolic network.
The Origin of Modern Metabolic Networks Inferred from Phylogenomic Analysis of Protein Architecture
Proceedings of The National Academy of Sciences, 2007
Metabolism represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cellular life. Here we explore the origins and evolution of modern metabolism. Using phylogenomic information linked to the structure of metabolic enzymes, we sort out recruitment processes and discover that most enzymatic activities were associated with the nine most ancient and widely distributed protein fold architectures. An analysis of newly discovered functions showed enzymatic diversification occurred early, during the onset of the modern protein world. Most importantly, phylogenetic reconstruction exercises and other evidence suggest strongly that metabolism originated in enzymes with the P-loop hydrolase fold in nucleotide metabolism, probably in pathways linked to the purine metabolic subnetwork. Consequently, the first enzymatic takeover of an ancient biochemistry or prebiotic chemistry was related to the synthesis of nucleotides for the RNA world. enzyme activity ͉ evolution ͉ metabolism ͉ nucleotide metabolism T here is current interest in the processes underlying the biology of network because these offer insight into the organization and evolution of life (1). Cellular metabolism, one of the greatest achievements of science, is clearly the best-studied biological network. It represents a complex collection of enzymatic reactions and transport processes that convert metabolites into molecules capable of supporting cells and organisms. However, our knowledge of how modern metabolism originated and evolved is limited (2). One widely accepted hypothesis is that promiscuous catalytic activities in proteins provide a selective advantage and are recruited to perform new metabolic functions (3, 4). Considerable evidence supports a patchwork recruitment scenario in which recruited homologous enzymes are scattered over diverse pathways (2). For example, enzymes with ␣/ barrel fold structure that catalyze similar reactions occur across metabolic subnetworks (5, 6) and a small set of structural families dominates the small-molecule metabolism in Escherichia coli (7-10). The recruitment hypothesis assumes there is already an active enzymatic core with multifunctional enzymes from which proteins are drawn for metabolic innovation. Because history restricts the interplay between structure and function of metabolic enzymes, we here use evolutionary patterns in protein structure advantageously to study recruitment processes and metabolic network evolution.
Evolution of Domain Architectures and Catalytic Functions of Enzymes in Metabolic Systems
Genome Biology and Evolution, 2012
Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes. The reconstructed history reveals distinct patterns of evolutionary mechanisms between prokaryotic and eukaryotic networks. Although the evolutionary mechanisms in early ancestors of prokaryotes and eukaryotes are quite similar, more novel and duplicated domain compositions with identical catalytic functions arise along the eukaryotic lineage. In contrast, prokaryotic enzymes become more versatile by catalyzing multiple reactions with similar chemical operations. Moreover, different metabolic pathways are enriched with distinct network evolution mechanisms. For instance, although the pathways of steroid biosynthesis, protein kinases, and glycosaminoglycan biosynthesis all constitute prominent features of animal-specific physiology, their evolution of domain architectures and catalytic functions follows distinct patterns. Steroid biosynthesis is enriched with reaction creations but retains a relatively conserved repertoire of domain compositions and proteins. Protein kinases retain conserved reactions but possess many novel domains and proteins. In contrast, glycosaminoglycan biosynthesis has high rates of reaction/protein creations and domain recruitments. Finally, we elicit and validate two general principles underlying the evolution of DPR networks: 1) duplicated enzyme proteins possess similar catalytic functions and 2) the majority of novel domains arise to catalyze novel reactions. These results shed new lights on the evolution of metabolic systems.
Phylogenetic distances are encoded in networks of interacting pathways
Bioinformatics/computer Applications in The Biosciences, 2008
Although metabolic reactions are unquestionably shaped by evolutionary processes, the degree to which the overall structure and complexity of their interconnections are linked to the phylogeny of species has not been evaluated in depth. Here, we apply an original metabolome representation, termed Network of Interacting Pathways or NIP, with a combination of graph theoretical and machine learning strategies, to address this question. NIPs compress the information of the metabolic network exhibited by a species into much smaller networks of overlapping metabolic pathways, where nodes are pathways and links are the metabolites they exchange. Results: Our analysis shows that a small set of descriptors of the structure and complexity of the NIPs combined into regression models reproduce very accurately reference phylogenetic distances derived from 16S rRNA sequences (10-fold crossvalidation correlation coefficient higher than 0.9). Our method also showed better scores than previous work on metabolism-based phylogenetic reconstructions, as assessed by branch distances score, topological similarity and second cousins score. Thus, our metabolome representation as network of overlapping metabolic pathways captures sufficient information about the underlying evolutionary events leading to the formation of metabolic networks and species phylogeny. It is important to note that precise knowledge of all of the reactions in these pathways is not required for these reconstructions. These observations underscore the potential for the use of abstract, modular representations of metabolic reactions as tools in studying the evolution of species.
BMC Bioinformatics, 2010
Background: Pathways provide topical descriptions of cellular circuitry. Comparing analogous pathways reveals intricate insights into individual functional differences among species. While previous works in the field performed genomic comparisons and evolutionary studies that were based on specific genes or proteins, whole genomic sequence, or even single pathways, none of them described a genomic system level comparative analysis of metabolic pathways. In order to properly implement such an analysis one should overcome two specific challenges: how to combine the effect of many pathways under a unified framework and how to appropriately analyze co-evolution of pathways.
MANET: tracing evolution of protein architecture in metabolic networks
BMC Bioinformatics, 2006
Background Cellular metabolism can be characterized by networks of enzymatic reactions and transport processes capable of supporting cellular life. Our aim is to find evolutionary patterns and processes embedded in the architecture and function of modern metabolism, using information derived from structural genomics. Description The Molecular Ancestry Network (MANET) project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism. Conclusion MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks.
Widespread Recruitment of Ancient Domain Structures in Modern Enzymes during Metabolic Evolution
2013
Protein domains sometime combine to form multidomain proteins and are acquired or lost in lineages of organisms. These processes are ubiquitous in modern metabolism. To sort out evolutionary patterns of domain recruitment, we developed an algorithm that derives the most plausible ancestry of an enzyme from structural and evolutionary annotations in the MANET database. We applied this algorithm to the analysis of 1,163 enzymes with structural assignments. We then counted the number of enzymes along a time series and analyzed enzyme distribution in organisms belonging to superkingdoms Archaea, Bacteria, and Eukarya. The generated timelines described the evolution of modern metabolic networks and showed an early build-up of metabolic activities associated with metabolism of nucleotides, cofactors, and vitamins, followed by enzymes involved in carbohydrate and amino acid metabolism. More importantly, we find that existing domain structures were pervasively co-opted to perform more modern enzymatic tasks, either singly or in combination with other domains. This occurred differentially in lineages of the superkingdoms as the world diversified and organisms adapted to various environments. Our results highlight the important role of recruitment and domain organization in metabolic evolution.
Network analysis of metabolic enzyme evolution in Escherichia coli
BMC Bioinformatics, 2004
The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc.
Genome evolution reveals biochemical networks and functional modules
Proceedings of The National Academy of Sciences, 2003
The analysis of completely sequenced genomes uncovers an astonishing variability between species in terms of gene content and order. During genome history, the genes are frequently rearranged, duplicated, lost, or transferred horizontally between genomes. These events appear to be stochastic, yet they are under selective constraints resulting from the functional interactions between genes. These genomic constraints form the basis for a variety of techniques that employ systematic genome comparisons to predict functional associations among genes. The most powerful techniques to date are based on conserved gene neighborhood, gene fusion events, and common phylogenetic distributions of gene families. Here we show that these techniques, if integrated quantitatively and applied to a sufficiently large number of genomes, have reached a resolution which allows the characterization of function at a higher level than that of the individual gene: global modularity becomes detectable in a functional protein network. In Escherichia coli, the predicted modules can be benchmarked by comparison to known metabolic pathways. We found as many as 74% of the known metabolic enzymes clustering together in modules, with an average pathway specificity of at least 84%. The modules extend beyond metabolism, and have led to hundreds of reliable functional predictions both at the protein and pathway level. The results indicate that modularity in protein networks is intrinsically encoded in present-day genomes. ‡ C.v.M., E.M.Z., and S.T. contributed equally to this work. ¶ To whom correspondence may be addressed.