Phylogenetics in evolution

Phylogenetics was born along with Darwin’s theory of evolution. ‘At the beginning, the study of evolutionary relationships between living beings was based exclusively on the comparison of the morphological features of extant or fossilised animals,’ points out Denis Baurain. Scientists thus compared the external structure of organisms in order to determine their evolutionary affinities. At the end of the 1960s, molecular phylogenetics picked up the baton, and specialists in phylogenetics now use biological macromolecules such as DNA, RNA or proteins, the fundamental components of all living beings, to establish their evolutionary relationships.

The genes used to make a comparison have to be selected carefully as the different regions of the DNA of a cell do not all evolve at the same rate. To study the relationships between living beings on a large time scale it is necessary to use genes for which the observed mutation rate from one species to another is low. More often than not, this means genes ensuring vital functions and for which the pressure of selection is strong, such as those involved in the synthesis of proteins.

Up until recently, molecular phylogenetics was restricted to comparing one region of DNA across several species in order to position them in relation to each other on a phylogenetic tree. ‘Since 8 or 9 years ago, a new variant of phylogenetics has made an appearance and is more and more used to draw up the Tree of Life. It goes by the name of phylogenomics,’ adds Denis Baurain. The new methods of genome sequencing have enabled a drastic increase in the number of genes available to carry out studies of molecular phylogenetics. Today, the availability of complete genomes, and especially of transcriptomes (the ensemble of messenger RNAs) obtained at very low cost, makes possible the joint analysis of hundreds of genes for which there exists a homology from one species to another. ‘In principle, comparing several genes in parallel in several species is all the more reliable the greater the number of genes compared and the larger the sample of species,’ continues the scientist. Of course, these large data sets require huge computational power, such as the one offered by NIC3, the ULg’s powerful computing cluster.