Biology subjects

Deutekom, E. S.

Publications and source records attributed to Deutekom, E. S..

4 recordsLinked to original sources

bioRxiv2021

Phylogenetic profiling in eukaryotes: The effect of species, orthologous group, and interactome selection on protein interaction prediction

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.

bioinformatics↗

bioRxiv2020

Comparing orthology methods and their performance by recapitulating patterns of eukaryotic genome evolution.

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 regards to genome evolution in eukaryotes. Specifically, we investigate phylogenetic profile similarity (co-occurrence of complexes), the Last Eukaryotic Common Ancestors 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 implementationThe data and code underlying this article are available in github and/or upon reasonable request to the corresponding author: https://github.com/ESDeutekom/ComparingOrthologies. SummaryO_LIWe compared multiple orthology inference methods by looking at how well they perform in recapitulating multiple observations made in eukaryotic genome evolution. C_LIO_LICo-occurrence of proteins is predicted fairly well by most methods and all show similar behaviour when looking at loss numbers and dynamics. C_LIO_LIAll the methods show imperfect overlap when compared to manually curated orthologous groups and when compared to orthologous groups of the other methods. C_LIO_LIDifferences are compared between methods by looking at how the inferred orthologies represent a high-quality set of manually curated orthologous groups. C_LIO_LIWe conclude that all methods behave similar when describing general patterns in eukaryotic genome evolution. However, there are large differences within the orthologies themselves, arising from how a method can differentiate between distant homology, recent duplications, or classifying orthologous groups. C_LI

bioinformatics↗

bioRxiv2020

PhyRepID: a comparative phylogenomics approach for large-scale quantification of protein repeat evolution

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/.

bioinformatics↗

bioRxiv2017

Epigenome-associated phenotypic acclimatization to ocean acidification in a reef-building coral

Over the last century, the anthropogenic production of CO2 has led to warmer (+0.74 {degrees}C) and more acidic (-0.1 pH) oceans1, resulting in increasingly frequent and severe mass bleaching events worldwide that precipitate global coral reef decline2,3. To mitigate this decline, proposals to augment the stress tolerance of corals through genetic and non-genetic means have been gaining traction4. Work on model systems has shown that environmentally induced alterations in DNA methylation can lead to phenotypic acclimatization5,6. While DNA methylation has been observed in corals7-10, its potential role in phenotypic plasticity has not yet been described. Here, we show that, similar to findings in mice11, DNA methylation significantly reduces spurious transcription in the Red Sea coral Stylophora pistillata, suggesting the evolutionary conservation of this essential mechanism in corals. Furthermore, we find that DNA methylation also reduces transcriptional noise by fine-tuning the expression of highly expressed genes. Analysis of DNA methylation patterns of corals subjected to long-term pH stress showed widespread changes in pathways regulating cell cycle and body size. Correspondingly, we found significant increases in cell and polyp sizes that resulted in more porous skeletons, supporting the maintenance of linear extension rates under conditions of reduced calcification. These findings suggest an epigenetic component in phenotypic acclimatization, providing corals with an additional mechanism to cope with climate change.

ecology↗