Rearrangements and chromosomal evolution

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Abstract

Comparisons of the genome sequences of related species suggests varying patterns of chromosomal rearrangements in different evolutionary lineages. In this review, I focus on the quantitative characterization of rearrangement processes and discuss specific inventories that have been compiled to date. Of particular interest are the statistical distribution of the lengths of inverted or locally transposed chromosome fragments (notably very short ones), inhomogeneities in susceptibility to evolutionary breakpoints in chromosomal regions, the relative importance of genome doubling in the history of multicellular eukaryotes, and of lateral transfer versus gene gain and loss in prokaryotes. These developments provide challenges to computational biologists to refine, revise and scale up mathematical models and algorithms for analyzing genome rearrangements.

Introduction

Analytical paradigms for studying genomic evolution have been in the making for several years 1., 2.•. With the availability of essentially complete genome sequences, these paradigms can now be tested, revised and scaled up. Pairs of closely related genomes sharing large conserved chromosomal segments provide clear evidence of the nature of individual rearrangement events, although in relatively small numbers. Larger sets of genomes within a family, class or order can provide data about the rate of rearrangement and how it varies, but because remaining conserved segments are short and subject to local ‘microrearrangements’, there will be ambiguity about details of individual events.

In this review, I bypass sequence-level processes that affect intensive genome variables such as base composition and codon usage, which are invariant under rearrangement operations, in favour of evolutionary events that can alter extensive variables such as gene order, genome size or ploidy.

Purely combinatorial algorithms for comparing genomes are essentially model-free, requiring no a priori quantitative characterization of the processes of genome rearrangement. It is possible to introduce various parameters into the optimizing criterion to increase realism, but this leads to algorithmically much harder problems. Nevertheless, given the large eukaryotic genome sequences that are available, answers to the following questions could aid in reconstructing the details of genomic evolution. With what frequency does each type of rearrangement operation occur in a given evolutionary lineage? What regions or sites on the chromosome are susceptible to a particular type of change? How large is the DNA segment inverted, transposed, deleted, translocated, or duplicated? Partial answers are now known for several lineages and I discuss how to integrate this information into inference about evolutionary genomics.

Section snippets

Rearrangement repertories, regions and rates in eukaryotes

Comparisons of parts of the Drosophila repleta genome with D. melanogaster [3] suggest an overall rate of ∼0.05 paracentric inversions (corresponding to 0.1 chromosomal breakages) per megabase per million years, with virtually no interchromosomal translocation. A similar regime has been reported for the genus Anopheles (mosquito) [4], although the more distant comparison between the fruit fly and the mosquito [5] does suggest considerable translocation. In comparing the nematode Caenorhabditis

Loss of signal in prokaryote comparison

With notable exceptions [31], comparative genomics studies of prokaryotes have not focused on genome-wide rates of rearrangement or sizes of conserved segments in the way eukaryotic comparisons have for the simple reason that gene adjacency is poorly conserved even among relatively closely related organisms. Gene content changes radically through both loss and horizontal acquisition, especially among pathogens, and even conserved groups of functionally related genes maintain their proximities

Analytical advances

In the field of combinatorial optimization, much attention has been devoted to improving the efficiency of the Hannenhalli–Pevzner algorithm for inferring the reversal (inversion) or translocation history between two genomes 36., 37., 38. and to generalizing this to take into account gene insertion and loss 39., 40., and paralogy [41]. Much effort has also gone into extending two-genome comparisons towards multiple genome analyses in the phylogenetic context [42], as reviewed in [43].

Several

Inhomogeneities

A survey of rearrangement breakpoints culled from tumour karyotypes and infertility tests showed that whereas somatic cell chromosomal aberrations tended to cluster arm-medially over the set of autosomal arms, with certain arms also showing strong telomeric concentrations, breakpoints in the genomes of normal translocation carriers showed a uniform random distribution, at least at the level of resolution determined by chromosome bands [51]. Algorithmic approaches to link tumour genome

Conclusions

The wealth of eukaryotic genome sequence and related comparative data now being produced are enabling the newly quantitative study of genome rearrangement data at the gene-order level and at finer resolutions. One of the more striking results is the variety of patterns of rearrangement, including what types of chromosomal change, where they occur, and how frequently, in different evolutionary lineages. Much attention has been paid to the prevalence and consequences of segmental duplication. At

References and recommended reading

Papers of particular interest, published within the annual period of review, have been highlighted as:

  • of special interest

  • ••

    of outstanding interest

Acknowledgements

This work supported in part by a grant from the Natural Sciences and Engineering Research Council of Canada. The author is a Fellow of the Program in Evolutionary Biology of the Canadian Institute for Advanced Research and holds the Canada Research Chair in Mathematical Genomics.

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