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Phylogenetic profiling in eukaryotes is of continued interest to study and predict the functional relationships between proteins. This interest is likely driven by the increased number of available diverse genomes and computational methods to infer orthologies. The evaluation of phylogenetic profiles has mainly focussed on reference genome selection in prokaryotes. However, it has been proven to be challenging to obtain high prediction accuracies in eukaryotes. As part of our recent comparison of orthology inference methods for eukaryotic genomes, we observed a surprisingly high performance for predicting interacting orthologous groups. This high performance, in turn, prompted the question of what factors influence the success of phylogenetic profiling when applied to eukaryotic genomes. Here we analyse the effect of species, orthologous group and interactome selection on protein interaction prediction using phylogenetic profiles. We select species based on the diversity and quality of the genomes and compare this supervised selection with randomly generated genome subsets. We also analyse the effect on the performance of orthologous groups defined to be in the last eukaryotic common ancestor of eukaryotes to that of orthologous groups that are not. Finally, we consider the effects of reference interactome set filtering and reference interactome species. In agreement with other studies, we find an effect of genome selection based on quality, less of an effect based on genome diversity, but a more notable effect based on the amount of information contained within the genomes. Most importantly, we find it is not merely selecting the correct genomes that is important for high prediction performance. Other choices in meta parameters such as orthologous group selection, the reference species of the interaction set, and the quality of the interaction set have a much larger impact on the performance when predicting protein interactions using phylogenetic profiles. These findings shed light on the differences in reported performance amongst phylogenetic profiles approaches, and reveal on a more fundamental level for which types of protein interactions this method has most promise when applied to eukaryotes. [ABSTRACT FROM AUTHOR]

In recent years it became clear that in eukaryotic genome evolution gene loss is prevalent over gene gain. However, the absence of genes in an annotated genome is not always equivalent to the loss of genes. Due to sequencing issues, or incorrect gene prediction, genes can be falsely inferred as absent. This implies that loss estimates are overestimated and, more generally, that falsely inferred absences impact genomic comparative studies. However, reliable estimates of how prevalent this issue is are lacking. Here we quantified the impact of gene prediction on gene loss estimates in eukaryotes by analysing 209 phylogenetically diverse eukaryotic organisms and comparing their predicted proteomes to that of their respective six-frame translated genomes. We observe that 4.61% of domains per species were falsely inferred to be absent for Pfam domains predicted to have been present in the last eukaryotic common ancestor. Between phylogenetically different categories this estimate varies substantially: for clade-specific loss (ancestral loss) we found 1.30% and for species-specific loss 16.88% to be falsely inferred as absent. For BUSCO 1-to-1 orthologous families, 18.30% were falsely inferred to be absent. Finally, we showed that falsely inferred absences indeed impact loss estimates, with the number of losses decreasing by 11.78%. Our work strengthens the increasing number of studies showing that gene loss is an important factor in eukaryotic genome evolution. However, while we demonstrate that on average inferring gene absences from predicted proteomes is reliable, caution is warranted when inferring species-specific absences. [ABSTRACT FROM AUTHOR]

The cilium is an essential organelle at the surface of mammalian cells whose dysfunction causes a wide range of genetic diseases collectively called ciliopathies. The current rate at which new ciliopathy genes are identified suggests that many ciliary components remain undiscovered. We generated and rigorously analyzed genomic, proteomic, transcriptomic and evolutionary data and systematically integrated these using Bayesian statistics into a predictive score for ciliary function. This resulted in 285 candidate ciliary genes. We generated independent experimental evidence of ciliary associations for 24 out of 36 analyzed candidate proteins using multiple cell and animal model systems (mouse, zebrafish and nematode) and techniques. For example, we show that OSCP1, which has previously been implicated in two distinct non-ciliary processes, causes ciliogenic and ciliopathy-associated tissue phenotypes when depleted in zebrafish. The candidate list forms the basis of CiliaCarta, a comprehensive ciliary compendium covering 956 genes. The resource can be used to objectively prioritize candidate genes in whole exome or genome sequencing of ciliopathy patients and can be accessed at . [ABSTRACT FROM AUTHOR]

The formation of proteins into stable protein complexes plays a fundamental role in the operation of the cell. The study of the degree of evolutionary conservation of protein complexes between species and the evolution of protein-protein interactions has been hampered by lack of comprehensive coverage of the high-throughput (HTP) technologies that measure the interactome. We show that new high-throughput datasets on protein co-purification in yeast have a substantially lower false negative rate than previous datasets when compared to known complexes. These datasets are therefore more suitable to estimate the conservation of protein complex membership than hitherto possible. We perform comparative genomics between curated protein complexes from human and the HTP data in Saccharomyces cerevisiae to study the evolution of co-complex memberships. This analysis revealed that out of the 5,960 protein pairs that are part of the same complex in human, 2,216 are absent because both proteins lack an ortholog in S. cerevisiae, while for 1,828 the cocomplex membership is disrupted because one of the two proteins lacks an ortholog. For the remaining 1,916 protein pairs, only 10% were never co-purified in the large-scale experiments. This implies a conservation level of co-complex membership of 90% when the genes coding for the protein pairs that participate in the same protein complex are also conserved. We conclude that the evolutionary dynamics of protein complexes are, by and large, not the result of network rewiring (i.e. acquisition or loss of co-complex memberships), but mainly due to genomic acquisition or loss of genes coding for subunits. We thus reveal evidence for the tight interrelation of genomic and network evolution. [ABSTRACT FROM AUTHOR]