Key Points
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Different classes of genome sequence, genome architectures, gene repertoires and molecular phenomes are subject to diverse evolutionary constraints that greatly vary in strength and in the nature of the underlying selective and neutral factors.
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Sequences coding for proteins and structural RNAs typically include the most strongly conserved sites in genomes.
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Most of the non-coding sequences are less strongly constrained than coding sequences, with the exception of some regulatory sites.
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Genome architecture is weakly constrained with the exception of the strong association seen between genes in operons, which is partly maintained by horizontal gene transfer.
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Principles of genome evolution widely differ between groups of organisms: prokaryotic genomes consist mostly of coding sequences and so are on average highly constrained; genomes of multicellular eukaryotes are much larger and contain large fractions of unconstrained, 'junk' DNA; and genomes of unicellular eukaryotes evolve under an intermediate regime that is closer to the prokaryote mode.
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Some molecular-phenomic features, such as the abundance of proteins encoded by orthologous genes, seem to be subject to surprisingly strong constraints.
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Evolutionary trajectories that lead to a particular phenotype are substantially constrained, limiting the potential of evolutionary tinkering.
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The overall level of constraint that affects a given evolving lineage depends on the intensity of selection: this is primarily determined by the characteristic effective population size, although selection is also strongly modulated by the lifestyle properties of the respective organisms.
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Despite the diversity of evolutionary constraints acting at different levels of biological organization, comparative-genomic and molecular-phenomic analyses reveal universal patterns that could be compatible with relatively simple, general models of evolution.
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The evolutionary constraints on genome and molecular-phenome evolution are complemented and partially offset by the robustness of biological systems, which is manifested at different levels and is likely to be an evolved feature.
Abstract
Multiple constraints variously affect different parts of the genomes of diverse life forms. The selective pressures that shape the evolution of viral, archaeal, bacterial and eukaryotic genomes differ markedly, even among relatively closely related animal and bacterial lineages; by contrast, constraints affecting protein evolution seem to be more universal. The constraints that shape the evolution of genomes and phenomes are complemented by the plasticity and robustness of genome architecture, expression and regulation. Taken together, these findings are starting to reveal complex networks of evolutionary processes that must be integrated to attain a new synthesis of evolutionary biology.
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References
Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, 1983).
Lynch, M. The Origins of Genome Architecture (Sinauer Associates, Sunderland, Massachusetts, 2007). A definitive presentation of the population-genetic perspective on genome evolution, with an emphasis on effective population size as the dominant factor of evolution and a non-adaptive origin of genomic complexity.
Loewe, L. A framework for evolutionary systems biology. BMC Syst. Biol. 3, 27 (2009).
Koonin, E. V. & Wolf, Y. I. Evolutionary systems biology: links between gene evolution and function. Curr. Opin. Biotechnol. 17, 481–487 (2006).
Yamada, T. & Bork, P. Evolution of biomolecular networks: lessons from metabolic and protein interactions. Nature Rev. Mol. Cell Biol. 10, 791–803 (2009).
Snell-Rood, E. C., Van Dyken, J. D., Cruickshank, T., Wade, M. J. & Moczek, A. P. Toward a population genetic framework of developmental evolution: the costs, limits, and consequences of phenotypic plasticity. Bioessays 32, 71–81 (2010).
Palsson, B. Metabolic systems biology. FEBS Lett. 583, 3900–3904 (2009).
Erwin, D. H. & Davidson, E. H. The evolution of hierarchical gene regulatory networks. Nature Rev. Genet. 10, 141–148 (2009).
Shabalina, S. A. & Kondrashov, A. S. Pattern of selective constraint in C. elegans and C. briggsae genomes. Genet. Res. 74, 23–30 (1999).
Margulies, E. H. et al. Analyses of deep mammalian sequence alignments and constraint predictions for 1% of the human genome. Genome Res. 17, 760–774 (2007).
Petersen, L., Bollback, J. P., Dimmic, M., Hubisz, M. & Nielsen, R. Genes under positive selection in Escherichia coli. Genome Res. 17, 1336–1343 (2007).
Muzzi, A., Moschioni, M., Covacci, A., Rappuoli, R. & Donati, C. Pilus operon evolution in Streptococcus pneumoniae is driven by positive selection and recombination. PLoS ONE 3, e3660 (2008).
Nielsen, R. et al. A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 3, e170 (2005).
Turner, L. M., Chuong, E. B. & Hoekstra, H. E. Comparative analysis of testis protein evolution in rodents. Genetics 179, 2075–2089 (2008).
Worth, C. L., Gong, S. & Blundell, T. L. Structural and functional constraints in the evolution of protein families. Nature Rev. Mol. Cell Biol. 10, 709–720 (2009).
Grishin, N. V., Wolf, Y. I. & Koonin, E. V. From complete genomes to measures of substitution rate variability within and between proteins. Genome Res. 10, 991–1000 (2000). An early study that suggests that the evolutionary rates of orthologous genes from diverse life forms follow a universal distribution, and that derives a link between intra-gene and across-gene distributions of evolutionary rates.
Nielsen, R. Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218 (2005).
Ohta, T. & Ina, Y. Variation in synonymous substitution rates among mammalian genes and the correlation between synonymous and nonsynonymous divergences. J. Mol. Evol. 41, 717–720 (1995).
Makalowski, W. & Boguski, M. S. Synonymous and nonsynonymous substitution distances are correlated in mouse and rat genes. J. Mol. Evol. 47, 119–121 (1998).
Ellegren, H. Comparative genomics and the study of evolution by natural selection. Mol. Ecol. 17, 4586–4596 (2008).
Drummond, D. A. & Wilke, C. O. The evolutionary consequences of erroneous protein synthesis. Nature Rev. Genet. 10, 715–724 (2009).
Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003). A seminal work that expounds the population-genetic perspective on the evolution of genomic complexity. The authors argue that genomic complexity is driven by weak purifying selection in populations with small Ne ; in such populations, slightly deleterious features, such as gene duplications or introns, cannot be efficiently eliminated. Collected data on Ne and genomic complexity in diverse life forms are shown to be compatible with this perspective, at least as a rough approximation.
Koonin, E. V. Evolution of genome architecture. Int. J. Biochem. Cell Biol. 41, 298–306 (2009).
Harrison, P. M. & Gerstein, M. Studying genomes through the aeons: protein families, pseudogenes and proteome evolution. J. Mol. Biol. 318, 1155–1174 (2002).
Monot, M. et al. Comparative genomic and phylogeographic analysis of Mycobacterium leprae. Nature Genet. 41, 1282–1289 (2009).
Darby, A. C., Cho, N. H., Fuxelius, H. H., Westberg, J. & Andersson, S. G. Intracellular pathogens go extreme: genome evolution in the Rickettsiales. Trends Genet. 23, 511–520 (2007).
Molina, N. & van Nimwegen, E. Universal patterns of purifying selection at noncoding positions in bacteria. Genome Res. 18, 148–160 (2008). A rigorous method for detecting purifying selection in groups of closely related prokaryotes was applied to the study of intergenic region evolution. Universal patterns of purifying selection were detected, and translation-initiation sites were found to be the elements subject to the strongest selective pressure.
Sella, G., Petrov, D. A., Przeworski, M. & Andolfatto, P. Pervasive natural selection in the Drosophila genome? PLoS Genet. 5, e1000495 (2009). A critical review of the evidence indicating that most sites in the fruitfly genome are subject to selection.
Waterston, R. H. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002).
Lunter, G., Ponting, C. P. & Hein, J. Genome-wide identification of human functional DNA using a neutral indel model. PLoS Comput. Biol. 2, e5 (2006).
Wright, S. I. & Andolfatto, P. The impact of natural selection on the genome: emerging patterns in Drosophila and Arabidopsis. Annu. Rev. Ecol. Syst. 39, 193–213 (2008).
Gossmann, T. I. et al. Genome wide analyses reveal little evidence for adaptive evolution in many plant species. Mol. Biol. Evol. 18 Mar 2010 (doi:10.1093/molbev/msq079).
Doolittle, W. F. & Sapienza, C. Selfish genes, the phenotype paradigm and genome evolution. Nature 284, 601–603 (1980).
Bowen, N. J. & Jordan, I. K. Exaptation of protein coding sequences from transposable elements. Genome Dyn. 3, 147–162 (2007).
Drake, J. A. et al. Conserved noncoding sequences are selectively constrained and not mutation cold spots. Nature Genet. 38, 223–227 (2006).
Shabalina, S. A., Ogurtsov, A. Y., Rogozin, I. B., Koonin, E. V. & Lipman, D. J. Comparative analysis of orthologous eukaryotic mRNAs: potential hidden functional signals. Nucleic Acids Res. 32, 1774–1782 (2004).
Proux, E., Studer, R. A., Moretti, S. & Robinson-Rechavi, M. Selectome: a database of positive selection. Nucleic Acids Res. 37, D404–D407 (2009).
Costa, F. F. Non-coding RNAs: new players in eukaryotic biology. Gene 357, 83–94 (2005).
Shabalina, S. A. & Koonin, E. V. Origins and evolution of eukaryotic RNA interference. Trends Ecol. Evol. 23, 578–587 (2008).
Carthew, R. W. & Sontheimer, E. J. Origins and mechanisms of miRNAs and siRNAs. Cell 136, 642–655 (2009).
Ponting, C. P., Oliver, P. L. & Reik, W. Evolution and functions of long noncoding RNAs. Cell 136, 629–641 (2009). A detailed review of long non-coding (macro) RNAs, a recently discovered class of mammalian genes that comprise a substantial part of the RNome.
Bertone, P. et al. Global identification of human transcribed sequences with genome tiling arrays. Science 306, 2242–2246 (2004).
Johnson, J. M., Edwards, S., Shoemaker, D. & Schadt, E. E. Dark matter in the genome: evidence of widespread transcription detected by microarray tiling experiments. Trends Genet. 21, 93–102 (2005).
Katzman, S. et al. Human genome ultraconserved elements are ultraselected. Science 317, 915 (2007). A rigorous demonstration of the exceptionally strong selection that affects ultraconserved elements of mammalian genomes that are located outside protein-coding genes.
Dermitzakis, E. T., Reymond, A. & Antonarakis, S. E. Conserved non-genic sequences — an unexpected feature of mammalian genomes. Nature Rev. Genet. 6, 151–157 (2005).
Elgar, G. Pan-vertebrate conserved non-coding sequences associated with developmental regulation. Brief. Funct. Genomic. Proteomic. 8, 256–265 (2009).
Bejerano, G. et al. Ultraconserved elements in the human genome. Science 304, 1321–1325 (2004).
Baira, E., Greshock, J., Coukos, G. & Zhang, L. Ultraconserved elements: genomics, function and disease. RNA Biol. 5, 132–134 (2008).
Koonin, E. V., Aravind, L. & Kondrashov, A. S. The impact of comparative genomics on our understanding of evolution. Cell 101, 573–576 (2000).
Wuchty, S. & Almaas, E. Evolutionary cores of domain co-occurrence networks. BMC Evol. Biol. 5, 24 (2005).
Basu, M. K., Carmel, L., Rogozin, I. B. & Koonin, E. V. Evolution of protein domain promiscuity in eukaryotes. Genome Res. 18, 449–461 (2008). A quantitative comparative analysis of promiscuous domains across eukaryotic lineages, including demonstration of a positive correlation between domain promiscuity and the strength of purifying selection.
Rogozin, I. B., Wolf, Y. I., Sorokin, A. V., Mirkin, B. G. & Koonin, E. V. Remarkable interkingdom conservation of intron positions and massive, lineage-specific intron loss and gain in eukaryotic evolution. Curr. Biol. 13, 1512–1517 (2003).
Roy., S. W. & Gilbert, W. The evolution of spliceosomal introns: patterns, puzzles and progress. Nature Rev. Genet. 7, 211–221 (2006).
Roy., S. W. & Penny, D. Patterns of intron loss and gain in plants: intron loss-dominated evolution and genome-wide comparison of O. sativa and A. thaliana. Mol. Biol. Evol. 24, 171–181 (2007).
Carmel, L., Wolf, Y. I., Rogozin, I. B. & Koonin, E. V. Three distinct modes of intron dynamics in the evolution of eukaryotes. Genome Res. 17, 1034–1044 (2007). A detailed analysis of differential dynamics of intron gain and loss across eukaryotic lineages reveals three distinct modes of evolution characterized by pervasive intron loss, equilibrium and relatively rare intron gain, respectively.
Carmel, L., Rogozin, I. B., Wolf, Y. I. & Koonin, E. V. Patterns of intron gain and conservation in eukaryotic genes. BMC Evol. Biol. 7, 192 (2007).
Koonin, E. V. & Wolf, Y. I. Genomics of Bacteria and Archaea: the emerging dynamic view of the prokaryotic world. Nucleic Acids Res. 36, 6688–6719 (2008).
Novichkov, P. S., Wolf, Y. I., Dubchak, I. & Koonin, E. V. Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes. J. Bacteriol. 191, 65–73 (2009). This study provides a comparative analysis of selective and neutral evolutionary processes between multiple bacterial and archaeal lineages. The article demonstrates high, variable rates of genome rearrangement and the lack of correlation between genome streamlining and selective constraints on sequence evolution.
Eisen, J. A., Heidelberg, J. F., White, O. & Salzberg, S. L. Evidence for symmetric chromosomal inversions around the replication origin in bacteria. Genome Biol. 1, research0011.1–research0011.9 (2000).
Zhou, F., Olman, V. & Xu, Y. Insertion sequences show diverse recent activities in Cyanobacteria and Archaea. BMC Genomics 9, 36 (2008).
Rogozin, I. B. et al. Connected gene neighborhoods in prokaryotic genomes. Nucleic Acids Res. 30, 2212–2223 (2002).
Ling, X., He, X. & Xin, D. Detecting gene clusters under evolutionary constraint in a large number of genomes. Bioinformatics 25, 571–577 (2009).
Wolf, Y. I., Rogozin, I. B., Kondrashov, A. S. & Koonin, E. V. Genome alignment, evolution of prokaryotic genome organization, and prediction of gene function using genomic context. Genome Res. 11, 356–372 (2001).
Lawrence, J. Selfish operons: the evolutionary impact of gene clustering in prokaryotes and eukaryotes. Curr. Opin. Genet. Dev. 9, 642–648 (1999).
Rocha, E. P. The organization of the bacterial genome. Annu. Rev. Genet. 42, 211–233 (2008).
Osbourn, A. E. & Field, B. Operons. Cell. Mol. Life Sci. 66, 3755–3775 (2009).
Hurst, L. D., Pal, C. & Lercher, M. J. The evolutionary dynamics of eukaryotic gene order. Nature Rev. Genet. 5, 299–310 (2004).
Liao, B. Y. & Zhang, J. Coexpression of linked genes in Mammalian genomes is generally disadvantageous. Mol. Biol. Evol. 25, 1555–1565 (2008).
Lemons, D. & McGinnis, W. Genomic evolution of Hox gene clusters. Science 313, 1918–1922 (2006).
Wong, S. & Wolfe, K. H. Birth of a metabolic gene cluster in yeast by adaptive gene relocation. Nature Genet. 37, 777–782 (2005).
Eichler, E. E. & Sankoff, D. Structural dynamics of eukaryotic chromosome evolution. Science 301, 793–797 (2003).
Koonin, E. V. Comparative genomics, minimal gene-sets and the last universal common ancestor. Nature Rev. Microbiol. 1, 127–136 (2003). This article demonstrates the difference between the shrinking set of ubiquitously conserved orthologous genes and the larger minimal set of functional niches. Minimal gene sets are also examined in relation to different prokaryotic lifestyles.
Moya, A. et al. Toward minimal bacterial cells: evolution vs. design. FEMS Microbiol Rev. 33, 225–235 (2009). The latest update on minimal gene sets and the promise of synthetic biology for de novo synthesis of custom genomes.
Koonin, E. V. Orthologs, paralogs, and evolutionary genomics. Annu. Rev. Genet. 39, 309–338 (2005).
Mushegian, A. R. & Koonin, E. V. A minimal gene set for cellular life derived by comparison of complete bacterial genomes [see comments]. Proc. Natl Acad. Sci. USA 93, 10268–10273 (1996).
Charlebois, R. L. & Doolittle, W. F. Computing prokaryotic gene ubiquity: rescuing the core from extinction. Genome Res. 14, 2469–2477 (2004).
Koonin, E. V., Mushegian, A. R. & Bork, P. Non-orthologous gene displacement. Trends Genet. 12, 334–336 (1996).
Nilsen, T. W. & Graveley, B. R. Expansion of the eukaryotic proteome by alternative splicing. Nature 463, 457–463 (2010).
Lynch, M. & Conery, J. S. The evolutionary fate and consequences of duplicate genes. Science 290, 1151–1155 (2000).
Lespinet, O., Wolf, Y. I., Koonin, E. V. & Aravind, L. The role of lineage-specific gene family expansion in the evolution of eukaryotes. Genome Res. 12, 1048–1059 (2002).
Huynen, M. A. & van Nimwegen, E. The frequency distribution of gene family sizes in complete genomes. Mol. Biol. Evol. 15, 583–589 (1998). The authors report the discovery that the sizes of paralogous gene families follow a power-law-like distribution. They also present a simple model of gene family evolution.
Karev, G. P., Wolf, Y. I., Rzhetsky, A. Y., Berezovskaya, F. S. & Koonin, E. V. Birth and death of protein domains: a simple model of evolution explains power law behavior. BMC Evol. Biol. 2, 18 (2002).
Koonin, E. V., Wolf, Y. I. & Karev, G. P. The structure of the protein universe and genome evolution. Nature 420, 218–223 (2002). A discussion of non-adaptive models of genome evolution — in particular, how patterns of gene birth and death reproduce the observed size distributions of paralogous gene families.
Putnam, N. H. et al. Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science 317, 86–94 (2007).
Srivastava, M. et al. The Trichoplax genome and the nature of placozoans. Nature 454, 955–960 (2008).
Krylov, D. M., Wolf, Y. I., Rogozin, I. B. & Koonin, E. V. Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution. Genome Res. 13, 2229–2235 (2003).
Wang, X., Grus, W. E. & Zhang, J. Gene losses during human origins. PLoS Biol. 4, e52 (2006).
Wolf, Y. I., Novichkov, P. S., Karev, G. P., Koonin, E. V. & Lipman, D. J. The universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages. Proc. Natl Acad. Sci. USA 106, 7273–7280 (2009). This is the definitive demonstration of the universal character of the approximately log-normal distribution of the evolutionary rate of orthologous genes. The distribution of genes by age also follows a similar pattern. The article presents a simple, non-adaptive model according to which the universal distribution of gene-loss rates is a fundamental feature of genome evolution.
Pal, C., Papp, B. & Hurst, L. D. Highly expressed genes in yeast evolve slowly. Genetics 158, 927–931 (2001).
Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008). A comprehensive analysis of the anticorrelation between evolution rate and expression of protein-coding genes in a variety of model organisms. This is a definitive presentation of the mistranslation-induced misfolding hypothesis of protein evolution.
Pal, C., Papp, B. & Lercher, M. J. An integrated view of protein evolution. Nature Rev. Genet. 7, 337–348 (2006).
Grosjean, H. & Fiers, W. Preferential codon usage in prokaryotic genes: the optimal codon–anticodon interaction energy and the selective codon usage in efficiently expressed genes. Gene 18, 199–209 (1982).
Lipman, D. J. & Wilbur, W. J. Interaction of silent and replacement changes in eukaryotic coding sequences. J. Mol. Evol. 21, 161–167 (1984).
Hershberg, R. & Petrov, D. A. Selection on codon bias. Annu. Rev. Genet. 42, 287–299 (2008).
Zhou, T., Weems, M. & Wilke, C. O. Translationally optimal codons associate with structurally sensitive sites in proteins. Mol. Biol. Evol. 26, 1571–1580 (2009).
Lobkovsky, A. E., Wolf, Y. I. & Koonin, E. V. Universal distribution of protein evolution rates as a consequence of protein folding physics. Proc. Natl Acad. Sci. USA 107, 2983–2988 (2010). The universal distribution of evolutionary rates among orthologues is reproduced under a simple model of protein folding and under the assumption that misfolding is the only source of fitness cost in protein evolution.
Wolf, Y. I., Carmel, L. & Koonin, E. V. Unifying measures of gene function and evolution. Proc. Biol. Sci. 273, 1507–1515 (2006). A systematic analysis of correlations between evolutionary and molecular phenomic variables leads to the idea of 'gene status', according to which genes with a high expression level, a large number of physical or regulatory interactions and high values of other phenomic variables evolve slowly and are rarely lost in the course of evolution.
Jordan, I. K., Wolf, Y. I. & Koonin, E. V. No simple dependence between protein evolution rate and the number of protein–protein interactions: only the most prolific interactors tend to evolve slowly. BMC Evol. Biol. 3, 1 (2003).
Bloom, J. D. & Adami, C. Evolutionary rate depends on number of protein–protein interactions independently of gene expression level: response. BMC Evol. Biol. 4, 14 (2004).
de Silva, E. et al. The effects of incomplete protein interaction data on structural and evolutionary inferences. BMC Biol. 4, 39 (2006).
Jordan, I. K., Wolf, Y. I. & Koonin, E. V. Duplicated genes evolve slower than singletons despite the initial rate increase. BMC Evol. Biol. 4, 22 (2004).
Khaitovich, P. et al. A neutral model of transcriptome evolution. PLoS Biol. 2, e132 (2004).
Jordan, I. K., Marino-Ramirez, L., Wolf, Y. I. & Koonin, E. V. Conservation and coevolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21, 2058–2070 (2004).
Denver, D. R. et al. The transcriptional consequences of mutation and natural selection in Caenorhabditis elegans. Nature Genet. 37, 544–548 (2005).
Jordan, I. K., Marino-Ramirez, L. & Koonin, E. V. Evolutionary significance of gene expression divergence. Gene 345, 119–126 (2005).
Liao, B. Y. & Zhang, J. Evolutionary conservation of expression profiles between human and mouse orthologous genes. Mol. Biol. Evol. 23, 530–540 (2006).
Gilad, Y., Oshlack, A. & Rifkin, S. A. Natural selection on gene expression. Trends Genet. 22, 456–461 (2006).
Schrimpf, S. P. et al. Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol. 7, e48 (2009).
Weiss, M., Schrimpf, S., Hengartner, M. O., Lercher, M. J. & von Mering, C. Shotgun proteomics data from multiple organisms reveals remarkable quantitative conservation of the eukaryotic core proteome. Proteomics 10, 1297–1306 (2010). This work extends the pioneering study reported in reference 108. The authors applied quantitative, highly accurate proteomic methods to reveal that the abundance of orthologous proteins is — unexpectedly — highly correlated among distantly related model organisms.
Wolf, Y. I., Gopich, I. V., Lipman, D. J. & Koonin, E. V. Relative contributions of intrinsic structural-functional constraints and translation rate to the evolution of protein-coding genes. Genome Biol. Evol. 17 Mar 2010 (doi:10.1093/gbe/evq010).
Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nature Rev. Genet. 5, 101–113 (2004).
Bergmann, S., Ihmels, J. & Barkai, N. Similarities and differences in genome-wide expression data of six organisms. PLoS Biol. 2, e9 (2004).
Tsaparas, P., Marino-Ramirez, L., Bodenreider, O., Koonin, E. V. & Jordan, I. K. Global similarity and local divergence in human and mouse gene co-expression networks. BMC Biol. 6, 70 (2006).
Jordan, I. K., Katz, L. S., Denver, D. R. & Streelman, J. T. Natural selection governs local, but not global, evolutionary gene coexpression networks in Caenorhabditis elegans. BMC Syst. Biol. 2, 96 (2008).
Lynch, M. The evolution of genetic networks by non-adaptive processes. Nature Rev. Genet. 8, 803–813 (2007). A model of the evolution of biological networks that shows how characteristic network properties could evolve through non-adaptive processes of mutation, drift and recombination.
Kassen, R. Toward a general theory of adaptive radiation: insights from microbial experimental evolution. Ann. N. Y. Acad. Sci. 1168, 3–22 (2009).
Jacob, F. Evolution and tinkering. Science 196, 1161–1166 (1977). A seminal conceptual analysis emphasizing the importance of contingency in evolution: evolution is construed as a bricolage that makes use of pre-existing states and is fundamentally unpredictable.
Mani, G. S. & Clarke, B. C. Mutational order: a major stochastic process in evolution. Proc. R. Soc. Lond. B 240, 29–37 (1990).
Weinreich, D. M., Delaney, N. F., Depristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006). A key study on the landscape of protein evolution that revealed an unexpected level of constraint on evolutionary trajectories, apparently caused by interactions between mutations (epistasis).
Novais, A. et al. Evolutionary trajectories of b-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance. PLoS Pathog. 6, e1000735 (2010).
Barrick, J. E. & Lenski, R. E. Genome-wide mutational diversity in an evolving population of Escherichia coli. Cold Spring Harb. Symp. Quant. Biol. 23 Sep 2009 (doi: 10.1101/sqb.2009.74.018). A summary of a series of long-term, extensive studies of bacterial populations in controlled experimental conditions. The studies revealed that evolutionary trajectories are affected by an interplay between contingency and constraint.
Stanek, M. T., Cooper, T. F. & Lenski, R. E. Identification and dynamics of a beneficial mutation in a long-term evolution experiment with Escherichia coli. BMC Evol. Biol. 9, 302 (2009).
Blount, Z. D., Borland, C. Z. & Lenski, R. E. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl Acad. Sci. USA 105, 7899–7906 (2008).
Stewart, C. B., Schilling, J. W. & Wilson, A. C. Adaptive evolution in the stomach lysozymes of foregut fermenters. Nature 330, 401–404 (1987).
Yokoyama, R. & Yokoyama, S. Convergent evolution of the red- and green-like visual pigment genes in fish, Astyanax fasciatus, and human. Proc. Natl Acad. Sci. USA 87, 9315–9318 (1990).
Zhang, J. Parallel adaptive origins of digestive RNases in Asian and African leaf monkeys. Nature Genet. 38, 819–823 (2006).
Li, Y., Liu, Z., Shi, P. & Zhang, J. The hearing gene Prestin unites echolocating bats and whales. Curr. Biol. 20, R55–R56 (2010).
Mustonen, V. & Lassig, M. Fitness flux and ubiquity of adaptive evolution. Proc. Natl Acad. Sci. USA. 107, 4248–4253 (2010). A reformulation of the principles of population genetics analogous to the transition from classic to non-equilibrium thermodynamics. The concept of fitness is replaced by fitness flux, and fitness landscape becomes a time-dependent seascape.
Lynch, M. The frailty of adaptive hypotheses for the origins of organismal complexity. Proc. Natl Acad. Sci. USA 104 (Suppl. 1), 8597–8604 (2007).
Lynch, M. The origins of eukaryotic gene structure. Mol. Biol. Evol. 23, 450–468 (2006).
Irimia, M., Penny, D. & Roy., S. W. Coevolution of genomic intron number and splice sites. Trends Genet. 23, 321–325 (2007). A comparative analysis of splice sites showing that intron-poor organisms possess highly conserved splice sites that adhere to a strict consensus, whereas intron-rich genomes contain weak splice sites. A crucial corollary is that the evolution of alternative splicing is conditioned on relatively inefficient splice sites that are prevalent in organisms with weak selective pressure.
Irimia, M. & Roy, S. W. Evolutionary convergence on highly-conserved 3′ intron structures in intron-poor eukaryotes and insights into the ancestral eukaryotic genome. PLoS Genet. 4, e1000148 (2008).
Irimia, M. et al. Complex selection on 5′ splice sites in intron-rich organisms. Genome Res. 19, 2021–2027 (2009).
Lynch, M. Streamlining and simplification of microbial genome architecture. Annu. Rev. Microbiol. 60, 327–349 (2006).
Wagner, A. Robustness, evolvability, and neutrality. FEBS Lett. 579, 1772–1778 (2005).
Dobrindt, U. et al. Analysis of genome plasticity in pathogenic and commensal Escherichia coli isolates by use of DNA arrays. J. Bacteriol. 185, 1831–1840 (2003).
Lozada-Chavez, I., Janga, S. C. & Collado-Vides, J. Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 34, 3434–3445 (2006).
Kazakov, A. E. et al. Comparative genomics of regulation of fatty acid and branched-chain amino acid utilization in proteobacteria. J. Bacteriol. 191, 52–64 (2009).
Wagner, A. Neutralism and selectionism: a network-based reconciliation. Nature Rev. Genet. 9, 965–974 (2008). A conceptual perspective on (nearly) neutral networks that reconciles the neutralistic and adaptationist paradigms of evolution by showing how initially neutral mutations form the basis for subsequent adaptation.
Masel, J. & Siegal, M. L. Robustness: mechanisms and consequences. Trends Genet. 25, 395–403 (2009).
Bergman, A. & Siegal, M. L. Evolutionary capacitance as a general feature of complex gene networks. Nature 424, 549–552 (2003).
Levy, S. F. & Siegal, M. L. Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol. 6, e264 (2008). An experimental demonstration of the unexpectedly large number of evolution capacitors among yeast genes, a finding that validates the theoretical predictions of reference 141.
Wang, Z. & Zhang, J. Abundant indispensable redundancies in cellular metabolic networks. Genome Biol. Evol. 2009, 23–33 (2009).
Koonin, E. V. Darwinian evolution in the light of genomics. Nucleic Acids Res. 37, 1011–1034 (2009).
Frank, S. A. The common patterns of nature. J. Evol. Biol. 22, 1563–1585 (2009).
Wilkins, A. S. Between 'design' and 'bricolage': genetic networks, levels of selection, and adaptive evolution. Proc. Natl Acad. Sci. USA 104 (Suppl. 1), 8590–8596 (2007).
Resch, A. M. et al. Widespread positive selection in synonymous sites of mammalian genes. Mol. Biol. Evol. 24, 1821–1831 (2007).
Parsch, J., Novozhilov, S., Saminadin-Peter, S. S., Wong, K. M. & Andolfatto, P. On the utility of short intron sequences as a reference for the detection of positive and negative selection in Drosophila . Mol. Biol. Evol. 27, 1226–1234 (2010).
Ellegren, H., Smith, N. G. & Webster, M. T. Mutation rate variation in the mammalian genome. Curr. Opin. Genet. Dev. 13, 562–568 (2003).
Charlesworth, J. & Eyre-Walker, A. The McDonald–Kreitman test and slightly deleterious mutations. Mol. Biol. Evol. 25, 1007–1015 (2008).
Eyre-Walker, A. & Keightley, P. D. Estimating the rate of adaptive molecular evolution in the presence of slightly deleterious mutations and population size change. Mol. Biol. Evol. 26, 2097–2108 (2009).
Hurst, L. D. The Ka/Ks ratio: diagnosing the form of sequence evolution. Trends Genet. 18, 486–487 (2002).
van Nimwegen, E. Scaling laws in the functional content of genomes. Trends Genet. 19, 479–484 (2003). A key study that reveals distinct scaling laws for different functional classes of genes and their virtual universality across a broad range of taxa.
Molina, N. & van Nimwegen, E. Scaling laws in functional genome content across prokaryotic clades and lifestyles. Trends Genet. 25, 243–247 (2009).
Maslov, S., Krishna, S., Pang, T. Y. & Sneppen, K. Toolbox model of evolution of prokaryotic metabolic networks and their regulation. Proc. Natl Acad. Sci. USA 106, 9743–9748 (2009). A simple model of evolution of metabolic networks that explains the universal scaling laws for regulators and enzymes.
Lipman, D. J. & Wilbur, W. J. Modelling neutral and selective evolution of protein folding. Proc. Biol. Sci. 245, 7–11 (1991).
Drummond, D. A., Bloom, J. D., Adami, C., Wilke, C. O. & Arnold, F. H. Why highly expressed proteins evolve slowly. Proc. Natl Acad. Sci. USA 102, 14338–14343 (2005).
Kramer, E. B. & Farabaugh, P. J. The frequency of translational misreading errors in E. coli is largely determined by tRNA competition. RNA 13, 87–96 (2007).
Whitehead, D. J., Wilke, C. O., Vernazobres, D. & Bornberg-Bauer, E. The look-ahead effect of phenotypic mutations. Biol. Direct 3, 18 (2008). A modelling study that demonstrates the possibility of evolutionary capacitation through synergistic interactions between mutations and errors of transcription and translation (phenotypic mutations).
Acknowledgements
The authors thank A. Lobkovsky for providing part of the data used in the figure in Box 3 and T. Senkevich for critical reading of the manuscript. We apologize to the many colleagues whose work is not cited here because of space constraints. The authors' research is funded by the Intramural Research Program of the US Department of Health and Human Services (National Library of Medicine, US National Institutes of Health).
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Glossary
- Robustness
-
The ability to maintain a phenotype or function in the presence of internal or external perturbations.
- Purifying selection
-
(Also known as negative or stabilizing selection.) Mode of natural selection that eliminates deleterious mutations and preserves the status quo; in protein-coding genes, it is manifested as Ka/Ks << 1.
- Non-synonymous substitutions
-
Nucleotide substitutions in protein-coding genes that lead to amino acid changes in the encoded protein.
- Synonymous substitutions
-
Nucleotide substitutions in protein-coding genes that occur in synonymous positions of codons and accordingly do not lead to amino acid changes in the encoded protein.
- Positive selection
-
(Also known as directional or Darwinian selection.) Mode of natural selection that increases the frequency of initially rare beneficial alleles in a population; in protein-coding genes, this often leads to Ka > Ks.
- Ultraconserved elements
-
Sequences in animal genomes that have retained their identity throughout long evolutionary spans, such as the entire course of vertebrate evolution.
- Evolutionary domains
-
Distinct units of gene/protein evolution that form combinations with varying degrees of evolutionary stability. Evolutionary domains may or may not correspond to structural domains (that is, an evolutionary domain could encompass one or more structural domains).
- Promiscuous domain
-
A protein domain that combines with diverse other domains in numerous proteins, providing malleable connections in interaction and regulatory networks and complexes.
- Orthologues
-
Genes that evolved from a single ancestral gene in the last common ancestor of the compared genomes (in contrast to paralogues).
- Selfish operon concept
-
A hypothesis according to which the presence of the same or similar operons in different prokaryotes is due more to the horizontal transfer of operons as distinct units than to selection for co-expression and co-regulation. When a transferred piece of DNA includes an entire operon consisting of genes encoding a complete pathway or functional system, the chances of fixation dramatically increase.
- Minimal gene set for cellular life
-
The minimal set of genes that is sufficient to maintain a functional cell.
- Non-orthologous gene displacement
-
The utilization of unrelated or distantly related (not orthologous) genes for the same function.
- Toolbox model of evolution
-
A model according to which enzymes for utilizing new metabolites, together with their dedicated regulators, are added (primarily by horizontal gene transfer) to a progressively versatile reaction network. Because of the growing complexity of the pre-existing network that provides enzymes for intermediate reactions, the ratio of regulators to regulated genes grows steadily.
- Paralogous gene families
-
Gene families that evolved by duplication.
- Neutral sequence network
-
A network of sequences connected by effectively single-step mutation distances (although not necessarily by single replacements), and in which there is a negligible fitness difference between neighbours.
- Evolutionary anticipation
-
(Also known as the look-ahead effect.) A scenario for the evolution of complex traits that require multiple mutations. In this scenario, the fixation of the final, beneficial mutation that leads to the emergence of the complex feature is enabled by a preceding random mutational walk over the neutral sequence network or by phenotypic mutations, such as mistranslation.
- Experimental evolution
-
The evolution of organisms with precisely defined genetic backgrounds and known evolutionary histories under controlled laboratory conditions.
- Epistasis
-
When non-allelic genes interact to produce a joint phenotype that differs from the one that would have been produced if the two genes had acted independently.
- Pleiotropy
-
Describes the multiple functions or mutation consequences of a single gene.
- Fitness landscape
-
A multidimensional surface defining the relationships between the fitness and the genotype spaces.
- Fitness seascape
-
A generalization of the concept of a fitness landscape, in which the dependence of fitness on sequence evolves over time.
- Effective population size
-
The size of an idealized panmictic population whose evolutionary behaviour is equivalent to that of the analysed population.
- Pathogenicity islands
-
Large clusters of genes in bacterial genomes that are typically transferred horizontally and contain pathogenicity determinants.
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Koonin, E., Wolf, Y. Constraints and plasticity in genome and molecular-phenome evolution. Nat Rev Genet 11, 487–498 (2010). https://doi.org/10.1038/nrg2810
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DOI: https://doi.org/10.1038/nrg2810
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