Eukaryotic translation elongation factor 1A (eEF1A) domain I from S. cerevisiae is required but not sufficient for inter-species complementation

Ethanolamine phosphoglycerol (EPG) is a protein modification attached exclusively to eukaryotic elongation factor 1A (eEF1A). In mammals and plants, EPG is linked to conserved glutamate residues located in eEF1A domains II and III, whereas in the unicellular eukaryote Trypanosoma brucei, only domain III is modified by a single EPG. A biosynthetic precursor of EPG and structural requirements for EPG attachment to T. brucei eEF1A have been reported, but nothing is known about the EPG modifying enzyme(s). By expressing human eEF1A in T. brucei, we now show that EPG attachment to eEF1A is evolutionarily conserved between T. brucei and Homo sapiens. In contrast, S. cerevisiae eEF1A, which has been shown to lack EPG is not modified in T. brucei. Furthermore, we show that eEF1A cannot functionally complement across species when using T. brucei and S. cerevisiae as model organisms. However, functional complementation in yeast can be obtained using eEF1A chimera containing domains II or III from other species. In contrast, yeast domain I is strictly required for functional complementation in S. cerevisiae.

Application of Discrete Fourier Inter-Coefficient Difference for Assessing Genetic Sequence Similarity

Digital signal processing (DSP) techniques for biological sequence analysis continue to grow in popularity due to the inherent digital nature of these sequences. DSP methods have demonstrated early success for detection of coding regions in a gene. Recently, these methods are being used to establish DNA gene similarity. We present the inter-coefficient difference (ICD) transformation, a novel extension of the discrete Fourier transformation, which can be applied to any DNA sequence. The ICD method is a mathematical, alignment-free DNA comparison method that generates a genetic signature for any DNA sequence that is used to generate relative measures of similarity among DNA sequences. We demonstrate our method on a set of insulin genes obtained from an evolutionarily wide range of species, and on a set of avian influenza viral sequences, which represents a set of highly similar sequences. We compare phylogenetic trees generated using our technique against trees generated using traditional alignment techniques for similarity and demonstrate that the ICD method produces a highly accurate tree without requiring an alignment prior to establishing sequence similarity.