Investigating GC content in RRNA: a comparative phylogenetic approach

Catherine Waggoner Craig

The ribosomal RNA (rRNA) is the component that catalyzes the translation of proteins, implying strong functional constraints on rRNA evolution. One characteristic of rRNA that may experience strong selection is the proportion of Guanine (G) and Cytosine (C) nucleotides present in the sequence (GC bias). The wide variability of GC bias among regions of the rRNA sequence, and the relative similarity of GC content between relatives has led to the idea that GC bias in rRNA sequences may be adaptive. However, the tendency of related species to have similar traits even under random evolution (phylogenetic signal) can obscure the signature of adaptive trends. The test statistic, Blomberg's K, was calculated for the GC of each named region of the 18S and 28S genes. The goal was to determine which taxa resembled each other less than expected under the random (Brownian) expectation. It was expected that the highly variable GC bias in Expansion Segments (ES) would generate low phylogenetic signal, likely originating from divergent selection leading to unique GC content within lineages. Calculation of K for a 58 taxon data set reveals that both the variable ES and the more conserved core show low signal. Thus, findings presented here reinforce past studies which assert that the relative amount of trait variability cannot be used as a proxy for phylogenetic signal. While K cannot specifically identify the mode of trait evolution, quantifying phylogenetic signal can narrow the range of evolutionary regimes that maintain regional GC bias. Although many evolutionary processes can generate the same K value, incorporating a-priori knowledge about regional function can suggest the probability of one specific process over another. The ability of this study to determine regional GC bias is maintained in rRNA is limited because errors in the phylogenetic inference or in measurement of the trait can obscure true levels of phylogenetic signal. As more rRNA and protein sequences become available for both GC data and reference trees, a larger and more diverse data set can be constructed and used to answer increasingly complex evolutionary questions.

Investigating GC content in RRNA: a comparative phylogenetic approach

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