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. 2013 May 21;8(5):e64997.
doi: 10.1371/journal.pone.0064997. Print 2013.

A model for the evolution of extremely fragmented macronuclei in ciliates

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A model for the evolution of extremely fragmented macronuclei in ciliates

David W Morgens et al. PLoS One. .

Abstract

While all ciliates possess nuclear dimorphism, several ciliates - like those in the classes Phyllopharyngea, Spirotrichea, and Armophorea - have an extreme macronuclear organization. Their extensively fragmented macronuclei contain upwards of 20,000 chromosomes, each with upwards of thousands of copies. These features have evolved independently on multiple occasions throughout ciliate evolutionary history, and currently no models explain these structures in an evolutionary context. In this paper, we propose that competition between two forces - the limitation and avoidance of chromosomal imbalances as a ciliate undergoes successive asexual divisions, and the costs of replicating massive genomes - is sufficient to explain this particular nuclear structure. We present a simulation of ciliate cell evolution under control of these forces, allowing certain features of the population to change over time. Over a wide range of parameters, we observe the repeated emergence of this unusual genomic organization found in nature. Although much remains to be understood about the evolution of macronuclear genome organization, our results show that the proposed model is a plausible explanation for the emergence of these extremely fragmented, highly polyploid genomes.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Flowchart of simulation.
Cycles continue for 100,000 iterations.
Figure 2
Figure 2. Simulation data for default values given in Table 1.
The top, middle, and bottom graph show copy number, X, elimination coefficient, E, and chromosome number, N, respectively versus number of iterations. Each value represents the average of the population, and each colored line represents a different trial, all with the same parameters. Note that due to the stochastic nature of our simulation we see significant variation between trails using the same parameters, yet the final result is consistent.

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