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Protein repeats consisting of domains or motifs are involved in key biological processes such as neural development, host-pathogen interactions, and speciation. Expansion and contraction of these repeats can strongly impact protein function as was shown for KNL1 and PRDM9. However, these known cases could only be identified manually and were previously incorrectly reported as conserved in large-scale analyses, because signatures of repeat evolution are difficult to resolve automatically.We developed PhyRepID to compare protein domain repeat evolution and analysed 4939 groups of orthologous proteins (OGs) from 14 vertebrate species. Our main contributions are 1) detecting a wide scope of repeats consisting of Pfam structural domains and motifs, 2) improving sensitivity and precision of repeat unit detection through optimization for the OGs, 3) using phylogenetic analysis to detect evolution within repeat regions. From these phylogenetic signals, we derived a “protein repeat duplication” (PRD) score that quantifies evolution in repeat regions and thereby enables large-scale comparison of protein families. Zinc finger repeats show remarkably fast evolution, comprising 25 of 100 fastest evolving proteins in our dataset, whilst cooperatively-folding domain repeats like beta-propellers are mostly conserved. Motif repeats have a similar PRD score distribution as domain repeats and also show a large diversity in evolutionary rates. A ranking based on the PRD score reflects previous manual observations of both highly conserved (CDC20) and rapidly evolving repeats (KNL1, PRDM9) and proposes novel candidates (e.g. AHNAK, PRX, SPATA31) showing previously undescribed rapid repeat evolution. PhyRepID is available on https://github.com/ivanbelzen/PhyRepID/.

Comparative genomics has proven a fruitful approach to acquire many functional and evolutionary insights into core cellular processes. Here it is argued that in order to perform accurate and interesting comparative genomics, one first and foremost has to be able to recognize, postulate, and revise different evolutionary scenarios. After all, these studies lack a simple protocol, due to different proteins having different evolutionary dynamics and demanding different approaches. The authors here discuss this challenge from a practical (what are the observations?) and conceptual (how do these indicate a specific evolutionary scenario?) viewpoint, with the aim to guide investigators who want to analyze the evolution of their protein(s) of interest. By sharing how the authors draft, test, and update such a scenario and how it directs their investigations, the authors hope to illuminate how to execute molecular evolution studies and how to interpret them. Also see the video abstract here https://youtu.be/VCt3l2pbdbQ.

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.

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Item does not contain fulltext 18 november 2016

SummaryTo understand regulatory systems, it would be useful to uniformly determine how different components contribute to the expression of all other genes. We therefore monitored mRNA expression genome-wide, for individual deletions of one-quarter of yeast genes, focusing on (putative) regulators. The resulting genetic perturbation signatures reflect many different properties. These include the architecture of protein complexes and pathways, identification of expression changes compatible with viability, and the varying responsiveness to genetic perturbation. The data are assembled into a genetic perturbation network that shows different connectivities for different classes of regulators. Four feed-forward loop (FFL) types are overrepresented, including incoherent type 2 FFLs that likely represent feedback. Systematic transcription factor classification shows a surprisingly high abundance of gene-specific repressors, suggesting that yeast chromatin is not as generally restrictive to transcription as is often assumed. The data set is useful for studying individual genes and for discovering properties of an entire regulatory system.

Oomycetes in the class Saprolegniomycetidae of the Eukaryotic kingdom Stramenopila have evolved as severe pathogens of amphibians, crustaceans, fish and insects, resulting in major losses in aquaculture and damage to aquatic ecosystems. We have sequenced the 63 Mb genome of the fresh water fish pathogen, Saprolegnia parasitica. Approximately 1/3 of the assembled genome exhibits loss of heterozygosity, indicating an efficient mechanism for revealing new variation. Comparison of S. parasitica with plant pathogenic oomycetes suggests that during evolution the host cellular environment has driven distinct patterns of gene expansion and loss in the genomes of plant and animal pathogens. S. parasitica possesses one of the largest repertoires of proteases (270) among eukaryotes that are deployed in waves at different points during infection as determined from RNA-Seq data. In contrast, despite being capable of living saprotrophically, parasitism has led to loss of inorganic nitrogen and sulfur assimilation pathways, strikingly similar to losses in obligate plant pathogenic oomycetes and fungi. The large gene families that are hallmarks of plant pathogenic oomycetes such as Phytophthora appear to be lacking in S. parasitica, including those encoding RXLR effectors, Crinkler's, and Necrosis Inducing-Like Proteins (NLP). S. parasitica also has a very large kinome of 543 kinases, 10% of which is induced upon infection. Moreover, S. parasitica encodes several genes typical of animals or animal-pathogens and lacking from other oomycetes, including disintegrins and galactose-binding lectins, whose expression and evolutionary origins implicate horizontal gene transfer in the evolution of animal pathogenesis in S. parasitica.

Oomycetes in the class Saprolegniomycetidae of the Eukaryotic kingdom Stramenopila have evolved as severe pathogens of amphibians, crustaceans, fish and insects, resulting in major losses in aquaculture and damage to aquatic ecosystems. We have sequenced the 63 Mb genome of the fresh water fish pathogen, Saprolegnia parasitica. Approximately 1/3 of the assembled genome exhibits loss of heterozygosity, indicating an efficient mechanism for revealing new variation. Comparison of S. parasitica with plant pathogenic oomycetes suggests that during evolution the host cellular environment has driven distinct patterns of gene expansion and loss in the genomes of plant and animal pathogens. S. parasitica possesses one of the largest repertoires of proteases (270) among eukaryotes that are deployed in waves at different points during infection as determined from RNA-Seq data. In contrast, despite being capable of living saprotrophically, parasitism has led to loss of inorganic nitrogen and sulfur assimilation pathways, strikingly similar to losses in obligate plant pathogenic oomycetes and fungi. The large gene families that are hallmarks of plant pathogenic oomycetes such as Phytophthora appear to be lacking in S. parasitica, including those encoding RXLR effectors, Crinkler's, and Necrosis Inducing-Like Proteins (NLP). S. parasitica also has a very large kinome of 543 kinases, 10% of which is induced upon infection. Moreover, S. parasitica encodes several genes typical of animals or animal-pathogens and lacking from other oomycetes, including disintegrins and galactose-binding lectins, whose expression and evolutionary origins implicate horizontal gene transfer in the evolution of animal pathogenesis in S. parasitica.

Lettuce downy mildew (Bremia lactucae) is a rapidly adapting oomycete pathogen affecting commercial lettuce cultivation. Oomycetes are known to use a diverse arsenal of secreted proteins (effectors) to manipulate their hosts. Two classes of effector are known to be translocated by the host: the RXLRs and Crinklers. To gain insight into the repertoire of effectors used by B. lactucae to manipulate its host, we performed massively parallel sequencing of cDNA derived from B. lactucae spores and infected lettuce (Lactuca sativa) seedlings. From over 2.3 million 454 GS FLX reads, 59 618 contigs were assembled representing both plant and pathogen transcripts. Of these, 19 663 contigs were determined to be of B. lactucae origin as they matched pathogen genome sequences (SOLiD) that were obtained from >270 million reads of spore-derived genomic DNA. After correction of cDNA sequencing errors with SOLiD data, translation into protein models and filtering, 16 372 protein models remained, 1023 of which were predicted to be secreted. This secretome included elicitins, necrosis and ethylene-inducing peptide 1-like proteins, glucanase inhibitors and lectins, and was enriched in cysteine-rich proteins. Candidate host-translocated effectors included 78 protein models with RXLR effector features. In addition, we found indications for an unknown number of Crinkler-like sequences. Similarity clustering of secreted proteins revealed additional effector candidates. We provide a first look at the transcriptome of B. lactucae and its encoded effector arsenal.

The taxonomic class of oomycetes contains numerous pathogens of plants and animals but is related to nonpathogenic diatoms and brown algae. Oomycetes have flexible genomes comprising large gene families that play roles in pathogenicity. The evolutionary processes that shaped the gene content have not yet been studied by applying systematic tree reconciliation of the phylome of these species. We analyzed evolutionary dynamics of ten Stramenopiles. Gene gains, duplications, and losses were inferred by tree reconciliation of 18,459 gene trees constituting the phylome with a highly supported species phylogeny. We reconstructed a strikingly large last common ancestor of the Stramenopiles that contained ∼10,000 genes. Throughout evolution, the genomes of pathogenic oomycetes have constantly gained and lost genes, though gene gains through duplications outnumber the losses. The branch leading to the plant pathogenic Phytophthora genus was identified as a major transition point characterized by increased frequency of duplication events that has likely driven the speciation within this genus. Large gene families encoding different classes of enzymes associated with pathogenicity such as glycoside hydrolases are formed by complex and distinct patterns of duplications and losses leading to their expansion in extant oomycetes. This study unveils the large-scale evolutionary dynamics that shaped the genomes of pathogenic oomycetes. By the application of phylogenetic based analyses methods, it provides additional insights that shed light on the complex history of oomycete genome evolution and the emergence of large gene families characteristic for this important class of pathogens.

The taxonomic class of oomycetes contains numerous pathogens of plants and animals but is related to nonpathogenic diatoms and brown algae. Oomycetes have flexible genomes comprising large gene families that play roles in pathogenicity. The evolutionary processes that shaped the gene content have not yet been studied by applying systematic tree reconciliation of the phylome of these species. We analyzed evolutionary dynamics of ten Stramenopiles. Gene gains, duplications, and losses were inferred by tree reconciliation of 18,459 gene trees constituting the phylome with a highly supported species phylogeny. We reconstructed a strikingly large last common ancestor of the Stramenopiles that contained ∼10,000 genes. Throughout evolution, the genomes of pathogenic oomycetes have constantly gained and lost genes, though gene gains through duplications outnumber the losses. The branch leading to the plant pathogenic Phytophthora genus was identified as a major transition point characterized by increased frequency of duplication events that has likely driven the speciation within this genus. Large gene families encoding different classes of enzymes associated with pathogenicity such as glycoside hydrolases are formed by complex and distinct patterns of duplications and losses leading to their expansion in extant oomycetes. This study unveils the large-scale evolutionary dynamics that shaped the genomes of pathogenic oomycetes. By the application of phylogenetic based analyses methods, it provides additional insights that shed light on the complex history of oomycete genome evolution and the emergence of large gene families characteristic for this important class of pathogens.

Information on protein-protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at http://string.embl.de/