Cracking the nut: geographical adjacency of sister taxa supports vicariance in a polytomic salamander clade in the absence of node support.

The urodelan genus Lyciasalamandra, which inhabits a relatively small area along the southern Turkish coast and some Aegean islands, provides an outstanding example of a diverse but phylogenetically unresolved taxon. Molecular trees contain a single basal polytomy that could be either soft or hard. We here use the information of nuclear (allozymes) and mitochondrial (fractions of the 16S rRNA and ATPase genes) datasets in combination with area relationships of lineages to resolve the phylogenetic relationships among Lyciasalamandra species in the absence of sufficient node support. We can show that neither random processes nor introgressive hybridization can be invoked to explain that the majority of pairs of sister taxa form geographically adjacent units and interpret that this pattern has been shaped by vicariant events. Topology discordance between mitochondrial and nuclear trees mainly refers to an affiliation of L. helverseni, a taxon restricted to the Karpathos archipelago, to the western-most and geographically proximate mainland taxon in the nuclear tree, while in the organelle tree it turns out to be the sister lineage to the geographically most distant eastern clade. As this discordance cannot be explained by long-branch attraction in either dataset we suppose that oversea dispersal may have accounted for a second colonization of the Karpathos archipelago. It may have initiated introgression and selection driven manifestation of alien eastern mitochondrial genomes on a western nuclear background. Our approach of testing for area relationships of sister taxa against the null hypothesis of random distribution of these taxa seems to be especially helpful in phylogenetic studies where traditional measures of phylogenetic branch support fail to reject the null hypothesis of a hard polytomy.

Tumour class prediction and discovery by microarray-based DNA methylation analysis.

Aberrant DNA methylation of CpG sites is among the earliest and most frequent alterations in cancer. Several studies suggest that aberrant methylation occurs in a tumour type-specific manner. However, large-scale analysis of candidate genes has so far been hampered by the lack of high throughput assays for methylation detection. We have developed the first microarray-based technique which allows genome-wide assessment of selected CpG dinucleotides as well as quantification of methylation at each site. Several hundred CpG sites were screened in 76 samples from four different human tumour types and corresponding healthy controls. Discriminative CpG dinucleotides were identified for different tissue type distinctions and used to predict the tumour class of as yet unknown samples with high accuracy using machine learning techniques. Some CpG dinucleotides correlate with progression to malignancy, whereas others are methylated in a tissue-specific manner independent of malignancy. Our results demonstrate that genome-wide analysis of methylation patterns combined with supervised and unsupervised machine learning techniques constitute a powerful novel tool to classify human cancers.