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Parallels between phylogeny and ontogeny have been discussed for almost two centuries, and a number of theories have been proposed to explain such patterns (1). Especially elusive is the phylotypic stage, a phase during development where species within a phylum are particularly similar to each other (2-6). Although this has formerly been interpreted as a recapitulation of phylogeny (1), it is now thought to reflect an ontogenetic progression phase (2), where strong constraints on developmental regulation and gene interactions exist (2,3). Several studies have shown that genes expressed during this stage evolve at a slower rate, but it has so far not been possible to derive an unequivocal molecular signature associated with this stage (7-15). Here we use a combination of phylostratigraphy (16) and stage-specific gene expression data to generate a cumulative index that reflects the evolutionary age of the transcriptome at given ontogenetic stages. Using zebrafish ontogeny and adult development as a model, we find that the phylotypic stage does indeed express the oldest transcriptome set and that younger sets are expressed during early and late development, thus faithfully mirroring the hourglass model of morphological divergence (2,3). Reproductively active animals show the youngest transcriptome, with major differences between males and females. Notably, ageing animals express increasingly older genes. Comparisons with similar data sets from flies and nematodes show that this pattern occurs across phyla. Our results indicate that an old transcriptome marks the phylotypic phase and that phylogenetic differences at other ontogenetic stages correlate with the expression of newly evolved genes., The evolutionary origin of genes can be traced by similarity searches in genomes representing the whole tree of life. We have called this approach 'phylostratigraphy' and have shown that meaningful [...]

How distinct stem cell populations originate and whether there is a clear stem cell "genetic signature" remain poorly understood. Understanding the evolution of stem cells requires molecular profiling of stem cells in an animal at a basal phylogenetic position. In this study, using transgenic Hydra polyps, we reveal for each of the three stem cell populations a specific signature set of transcriptions factors and of genes playing key roles in cell type-specific function and interlineage communication. Our data show that principal functions of stem cell genes, such as maintenance of stemness and control of stem cell self-renewal and differentiation, arose very early in metazoan evolution. They are corroborating the view that stem cell types shared common, multifunctional ancestors, which achieved complexity through a stepwise segregation of function in daughter cells.

Background: Phylostratigraphy is a method used to correlate the evolutionary origin of founder genes (that is, functional founder protein domains) of gene families with particular macroevolutionary transitions. It is based on a model of genome evolution that suggests that the origin of complex phenotypic innovations will be accompanied by the emergence of such founder genes, the descendants of which can still be traced in extant organisms. The origin of multicellularity can be considered to be a macroevolutionary transition, for which new gene functions would have been required. Cancer should be tightly connected to multicellular life since it can be viewed as a malfunction of interaction between cells in a multicellular organism. A phylostratigraphic tracking of the origin of cancer genes should, therefore, also provide insights into the origin of multicellularity., Results: We find two strong peaks of the emergence of cancer related protein domains, one at the time of the origin of the first cell and the other around the time of the evolution of the multicellular metazoan organisms. These peaks correlate with two major classes of cancer genes, the 'caretakers', which are involved in general functions that support genome stability and the 'gatekeepers', which are involved in cellular signalling and growth processes. Interestingly, this phylogenetic succession mirrors the ontogenetic succession of tumour progression, where mutations in caretakers are thought to precede mutations in gatekeepers., Conclusions: A link between multicellularity and formation of cancer has often been predicted. However, this has not so far been explicitly tested. Although we find that a significant number of protein domains involved in cancer predate the origin of multicellularity, the second peak of cancer protein domain emergence is, indeed, connected to a phylogenetic level where multicellular animals have emerged. The fact that we can find a strong and consistent signal for this second peak in the phylostratigraphic map implies that a complex multi-level selection process has driven the transition to multicellularity.

Several thousand genes in the human genome have been linked to a heritable genetic disease. The majority of these appear to be nonessential genes (i.e., are not embryonically lethal when inactivated), and one could therefore speculate that they are late additions in the evolutionary lineage toward humans. Contrary to this expectation, we find that they are in fact significantly overrepresented among the genes that have emerged during the early evolution of the metazoa. Using a phylostratigraphic approach, we have studied the evolutionary emergence of such genes at 19 phylogenetic levels. The majority of disease genes was already present in the eukaryotic ancestor, and the second largest number has arisen around the time of evolution of multicellularity. Conversely, genes specific to the mammalian lineage are highly underrepresented. Hence, genes involved in genetic diseases are not simply a random subset of all genes in the genome but are biased toward ancient genes.

Macroevolutionary trends traditionally are studied by fossil analysis, comparative morphology or evo-devo approaches. With the availability of genome sequences and associated data from an increasing diversity of taxa, it is now possible to add an additional level of analysis: genomic phylostratigraphy. As an example of this approach, we use a phylogenetic framework and embryo expression data from Drosophila to show that grouping genes by their phylogenetic origin can uncover footprints of important adaptive events in evolution.

Hybridization of rRNAs to microarrays is a promising approach for prokaryotic and eukaryotic species identification. Typically, the amount of bound target is measured by fluorescent intensity and it is assumed that the signal intensity is directly related to the target concentration. Using thirteen different eukaryotic LSU rRNA target sequences and 7693 short perfect match oligonucleotide probes, we have assessed current approaches for predicting signal intensities by comparing Gibbs free energy (DeltaG degrees) calculations to experimental results. Our evaluation revealed a poor statistical relationship between predicted and actual intensities. Although signal intensities for a given target varied up to 70-fold, none of the predictors were able to fully explain this variation. Also, no combination of different free energy terms, as assessed by principal component and neural network analyses, provided a reliable predictor of hybridization efficiency. We also examined the effects of single-base pair mismatch (MM) (all possible types and positions) on signal intensities of duplexes. We found that the MM effects differ from those that were predicted from solution-based hybridizations. These results recommend against the application of probe design software tools that use thermodynamic parameters to assess probe quality for species identification. Our results imply that the thermodynamic properties of oligonucleotide hybridization are by far not yet understood.

Recent work has shown that Drosophila melanogaster genes with fast-evolving nonsynonymous sites have lower codon usage bias. This pattern has been attributed to interference between positive selection at nonsynonymous sites and weak selection on codon usage. Here we have looked for this correlation in a much larger and less biased dataset, comprising 630 gene pairs from D. melanogaster and D. yakuba. We confirmed that there is a negative correlation between the rate of nonsynonymous substitutions (d(N)) and codon bias in D. melanogaster. We then tested the interference hypothesis and other alternative explanations, including one involving gene expression. We found that d(N) indeed correlates with the level of gene expression. Given that gene expression is a strong determinant of codon bias, the relationship between d(N) and codon bias might be a by-product of gene expression. However, our tests show that none of the hypotheses we consider seem to explain the data fully.