Search

Insights into the evolution of ancestral complexes and pathways are generally achieved through careful and time-intensive manual analysis often using phylogenetic profiles of the constituent proteins. This manual analysis limits the possibility of including more protein-complex components, repeating the analyses for updated genome sets or expanding the analyses to larger scales. Automated orthology inference should allow such large-scale analyses, but substantial differences between orthologous groups generated by different approaches are observed. We evaluate orthology methods for their ability to recapitulate a number of observations that have been made with regard to genome evolution in eukaryotes. Specifically, we investigate phylogenetic profile similarity (co-occurrence of complexes), the last eukaryotic common ancestor’s gene content, pervasiveness of gene loss and the overlap with manually determined orthologous groups. Moreover, we compare the inferred orthologies to each other. We find that most orthology methods reconstruct a large last eukaryotic common ancestor, with substantial gene loss, and can predict interacting proteins reasonably well when applying phylogenetic co-occurrence. At the same time, derived orthologous groups show imperfect overlap with manually curated orthologous groups. There is no strong indication of which orthology method performs better than another on individual or all of these aspects. Counterintuitively, despite the orthology methods behaving similarly regarding large-scale evaluation, the obtained orthologous groups differ vastly from one another. Availability and implementation The data and code underlying this article are available in github and/or upon reasonable request to the corresponding author: https://github.com/ESDeutekom/ComparingOrthologies.

Hotspots of rapid genome evolution hold clues about human adaptation. We present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection in 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%). Our new procedure is stringent and reveals substantial artefacts (20% of initial predictions) that have inflated previous estimates. The final 331 positively selected genes (PSG) are strongly enriched for innate and adaptive immunity, secreted and cell membrane proteins (e.g. pattern recognition, complement, cytokines, immune receptors, MHC, Siglecs). We also find evidence for positive selection in reproduction and chromosome segregation (e.g. centromere-associated CENPO, CENPT), apolipoproteins, smell/taste receptors and mitochondrial proteins. Focusing on the virus–host interaction, we retrieve most evolutionary conflicts known to influence antiviral activity (e.g. TRIM5, MAVS, SAMHD1, tetherin) and predict 70 novel cases through integration with virus–human interaction data. Protein structure analysis further identifies positive selection in the interaction interfaces between viruses and their cellular receptors (CD4-HIV; CD46-measles, adenoviruses; CD55-picornaviruses). Finally, primate PSG consistently show high sequence variation in human exomes, suggesting ongoing evolution. Our curated dataset of positive selection is a rich source for studying the genetics underlying human (antiviral) phenotypes. Procedures and data are available at https://github.com/robinvanderlee/positive-selection.

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 http://bioinformatics.bio.uu.nl/john/syscilia/ciliacarta/. This work was supported by the European Community’s Seventh Framework Programme [241955, 278568 to MU and KB, 602273 to RS]; the Virgo consortium, funded by the Dutch government [FES0908 to TvD, RvdL and MAH]; the Netherlands Genomics Initiative [050-060-452 to TvD, RvdL and MAH]; the Canadian Institutes of Health Research [MOP-142243, MOP-82870 and PJT-156042 to MRL]; Michael Smith Foundation for Health Research to MRL and VLJ; Kidney Research Scientist Core Education and National Training fellowship to VLJ; The Foundation Fighting Blindness [PPA-0717-0719-RAD to UW, RR, and MU]; the Dutch Kidney Foundation “KOUNCIL” consortium [CP11.18 to RHG, PLB and RR]; The Deutsche Forschungsgemeinschaft [Excellence grant CellNetworks to RBR and QL, CRC1140 “Kidney Disease – From Genes to Mechanisms” to MS, collaborative research center grant SFB-1411 KIDGEM to MS]; Metakids Foundation to RS; the National Institute for Health Research to PLB and VH-H. PLB is an NIHR Senior Investigator; Radboudumc Hypatia Tenure Track Fellowship, Radboud Universiteit excellence fellowship, ERC starting grant TREATCilia, grant agreement no. 716344 to MS; and the Netherlands Organization for Scientific Research [NWO Vici-865.12.005 to RR].