Abstract
The evolution of spliceosomal introns remains poorly understood. Although many approaches have been used to infer intron evolution from the patterns of intron position conservation, the results to date have been contradictory. In this paper, we address the problem using a novel maximum likelihood method, which allows estimation of the frequency of intron insertion target sites, together with the rates of intron gain and loss. We analyzed the pattern of 10,044 introns (7,221 intron positions) in the conserved regions of 684 sets of orthologs from seven eukaryotes. We determined that there is an average of one target site per 11.86 base pairs (bp) (95% confidence interval, 9.27 to 14.39 bp). In addition, our results showed that: (i) overall intron gains are ~25% greater than intron losses, although specific patterns vary with time and lineage; (ii) parallel gains account for ~18.5% of shared intron positions; and (iii) reacquisition following loss accounts for ~0.5% of all intron positions. Our results should assist in resolving the long-standing problem of inferring the evolution of spliceosomal introns.
Highlights
Twenty-eight years have passed since the discovery of spliceosomal introns [1], but their evolution remains poorly understood
Since our dataset includes representatives from all three groups (Drosophila melanogaster and Anopheles gambiae as arthropods, Caenorhabditis elegans as a nematode, and Homo sapiens as a deuterostome), we first tested both hypotheses using the pattern of intron position conservation
To the best of our knowledge, ours is the first estimate of frequency of target sites, and can be accounted for by two hypotheses
Summary
Frontier Science Research Center, University of Miyazaki, Kiyotake, Miyazaki, Japan. The evolution of spliceosomal introns remains poorly understood. Roy and Gilbert [6,7] applied an ML method to the above mentioned dataset of eight eukaryotes (excluding one species) Their results were somewhat surprising: the genes of the crown ancestor were rich in introns (about two-thirds the density found in humans), and many lineages exhibited a notable excess of intron losses over gains. Qiu et al [8] applied a Bayesian network method, based on the ML principle, to infer the evolution of introns in ten gene families containing a total of 677 sequences Their results suggest that many of the intron positions shared across various species are the result of independent gains, and are not due to conservation of intron position
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