posted 17. March 2004 14:36                    

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Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae


MANOLIS KELLIS1,2, BRUCE W. BIRREN1 & ERIC S. LANDER1,3

1 The Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02138, USA
2 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts 02139, USA
3 Whitehead Institute for Biomedical Research, Cambridge, Massachusetts 02139, USA

Correspondence and requests for materials should be addressed to M.K. (manoli@mit.edu) and E.S.L. (lander@broad.mit.edu). The GenBank accession number for K. waltii is AADM01000000.

Abstract
Whole-genome duplication followed by massive gene loss and specialization has long been postulated as a powerful mechanism of evolutionary innovation. Recently, it has become possible to test this notion by searching complete genome sequence for signs of ancient duplication. Here, we show that the yeast Saccharomyces cerevisiae arose from ancient whole-genome duplication, by sequencing and analysing Kluyveromyces waltii, a related yeast species that diverged before the duplication. The two genomes are related by a 1:2 mapping, with each region of K. waltii corresponding to two regions of S. cerevisiae, as expected for whole-genome duplication. This resolves the long-standing controversy on the ancestry of the yeast genome, and makes it possible to study the fate of duplicated genes directly. Strikingly, 95% of cases of accelerated evolution involve only one member of a gene pair, providing strong support for a specific model of evolution, and allowing us to distinguish ancestral and derived functions.[/b]

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posted 17. March 2004 23:42                   

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WGD followed by massive gene loss and gene specialization offers an important path for large-scale evolutionary innovation. Compared
to multiple independent duplications and divergence of individual genes or segments, WGD may be more efficient and may offer great opportunities for coordinated evolution. Although organisms clearly can undergo WGD resulting in complete polyploids (as evidenced by existing tetraploid species of plants and animals 34–37), it has been unclear whether WGD can then be followed by massive genome reshaping to yield diploids with expanded gene content.
Our analysis of K. waltii definitively proves that S. cerevisiae is the descendant of an ancient WGD, as originally proposed by Wolfe 3 on
the basis of subtle genomic patterns. Comparison with K. waltii allows rigorous study of the many evolutionary innovations that arose in this dramatic event. We note that a similar analysis of the Ashbya gossypii genome has been carried out (P. Philippsen, personal communication). The results here suggest that it may also be fruitful to search for similar genomic signatures of ancient WGD in other organisms 38–40. It will be interesting to see just how far such distant echoes of genomic upheaval may be traced.

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posted 20. March 2004 15:23                   

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How does robustness to mutation arise through evolution? The simplest naturally occurring object capable of evolution is a single polymer molecule, known as RNA. An important fact about RNA folding is that many sequences do not change their shape when certain positions are mutated. Fontana and colleagues discovered (computationally) that sequences with the same shape are organized into mutationally connected networks called ``neutral networks''. An evolving population can drift on such a neutral network until a more advantageous shape comes within mutational reach. This enables evolutionary change to occur that otherwise would have been impossible. In this way neutrality is seen to be both a buffer against change and an enabler of change.

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By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix–loop–helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

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Abstract
The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein–protein interactions is highly heterogeneous, with many proteins having few edges while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well known organisms are reproduced and suggest that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model.

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Eventually, this allows an information theoretic interpretation of evolutionary dynamics:
The information that is given by the selection or non-selection of solutions is implicitly
accumulated by evolutionary dynamics and exploited for further search. This information is
stored in the way phenotypes are represented. In that way evolution implicitly learns about
the problem by adapting its genetic representations accordingly.

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In terms of information theory, sigma-evolution minimizes the Kullback-Leibler divergence between the exploration distribution and the exponential (Boltzmann) fitness distribution, and minimizes the entropy of exploration. It describes the accumulation of information given by the fitness distribution into genetic representations.

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As a benefit we can formalize the relation and conceptual similarity between natural evolution, evolutionary algorithms, and the generic heuristic search scheme that we introduced as a basic information theoretic paradigm of what it means to “learn the structure of a problem and exploit it for future exploration.” All three of them are based on a process of accumulating information—in a more or less explicit way. Concepts of self-adaptation or derandomized adaptation of probabilistic search strategies in evolutionary computation become comparable to the evolution of exploration strategies in natural evolution.

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The theorem was formalized and generalized in many ways. Schuhmacher, Vose, & Whitley
(2001) generalized it to be valid for any subsets of problems that are closed under permutation.
Igel & Toussaint (2003c, 2003b) derived sucient and necessary conditions for No Free Lunch when
the probability of permutations is not equally but arbitrarily distributed. In that paper we also argued that generally the necessary conditions for No Free Lunch are hardly ever fulfilled.

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However, in natural evolution mutation operators are not designed by some intelligence. A central question arises: What does it mean to “learn” about the problem structure and exploit it? How in principle can evolution realize this? The answer we will give is that the implicit process of the evolution of genetic representations allows for the self-adaptation of the “search strategy” (i.e., the phenotypic variability induced by mutation and recombination). To some degree, this process has been overlooked in the context of evolutionary algorithms because complex, non-trivial (to be rigorously defined later) genetic representations (genotype phenotype mappings) have been neglected by theoreticians. This chapter tries to fill this gap and propose a theoretical framework for evolution in the case of complex genotype-phenotype mappings focusing at the evolution of
phenotypic variability. The next section lays the first cornerstone by clarifying what it means to
learn about a problem structure.

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What's more, the evolutionary algorithms employing these fitness functions were "no prior knowledge" algorithms. "No prior knowledge" simply means that the algorithm has no additional information for finding a solution other than what it gets from the fitness function. In general, arbitrary, unconstrained, maximal classes of fitness functions each seem to have a No Free Lunch theorem for which evolutionary algorithms cannot, on average, outperform blind search.

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To be sure, fitness in biology varies with time. As organisms evolve and the environment changes, what the environment deems fit changes as well. But what exactly constrains the transition from one fitness landscape or function to the next? If there is no constraint, then we are in the position of Wolpert and Macready's Theorem 2, with evolutionary algorithms proceeding independently of their progress to solution and thus unable to outperform blind search (which means that even with 3.5 billion years of evolution, it's going to be vastly improbable that the evolutionary algorithm approaches a solution). Conveniently, Orr doesn't tell us what constrains the transitions. Presumably nature, unprogrammed and unguided, spontaneously gives rise to the right and needed transitions between successive fitness landscapes, thereby ensuring a form of complexity-increasing evolution. But that is precisely what needs to be explained. Yet for Orr there is no problem, only boundless optimism.

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