The ortholog conjecture is untestable by the current gene ontology but is supported by RNA sequencing data

A pluralistic account of homology: adapting the models to the data

Defining homologous genes is important in many evolutionary studies but raises obvious issues. Some of these issues are conceptual and stem from our assumptions of how a gene evolves, others are practical, and depend on the algorithmic decisions implemented in existing software. Therefore, to make progress in the study of homology, both ontological and epistemological questions must be considered. In particular, defining homologous genes cannot be solely addressed under the classic assumptions of strong tree thinking, according to which genes evolve in a strictly tree-like fashion of vertical descent and divergence and the problems of homology detection are primarily methodological. Gene homology could also be considered under a different perspective where genes evolve as “public goods,” subjected to various introgressive processes. In this latter case, defining homologous genes becomes a matter of designing models suited to the actual complexity of the data and how such complexity arises, rather than trying to fit genetic data to some a priori tree-like evolutionary model, a practice that inevitably results in the loss of much information. Here we show how important aspects of the problems raised by homology detection methods can be overcome when even more fundamental roots of these problems are addressed by analyzing public goods thinking evolutionary processes through which genes have frequently originated. This kind of thinking acknowledges distinct types of homologs, characterized by distinct patterns, in phylogenetic and nonphylogenetic unrooted or multirooted networks. In addition, we define “family resemblances” to include genes that are related through intermediate relatives, thereby placing notions of homology in the broader context of evolutionary relationships. We conclude by presenting some payoffs of adopting such a pluralistic account of homology and family relationship, which expands the scope of evolutionary analyses beyond the traditional, yet relatively narrow focus allowed by a strong tree-thinking view on gene evolution.

The evolution of the ribosome biogenesis pathway from a yeast perspective

Ribosome biogenesis is fundamental for cellular life, but surprisingly little is known about the underlying pathway. In eukaryotes a comprehensive collection of experimentally verified ribosome biogenesis factors (RBFs) exists only for Saccharomyces cerevisiae. Far less is known for other fungi, animals or plants, and insights are even more limited for archaea. Starting from 255 yeast RBFs, we integrated ortholog searches, domain architecture comparisons and, in part, manual curation to investigate the inventories of RBF candidates in 261 eukaryotes, 26 archaea and 57 bacteria. The resulting phylogenetic profiles reveal the evolutionary ancestry of the yeast pathway. The oldest core comprising 20 RBF lineages dates back to the last universal common ancestor, while the youngest 20 factors are confined to the Saccharomycotina. On this basis, we outline similarities and differences of ribosome biogenesis across contemporary species. Archaea, so far a rather uncharted domain, possess 38 well-supported RBF candidates of which some are known to form functional sub-complexes in yeast. This provides initial evidence that ribosome biogenesis in eukaryotes and archaea follows similar principles. Within eukaryotes, RBF repertoires vary considerably. A comparison of yeast and human reveals that lineage-specific adaptation via RBF exclusion and addition characterizes the evolution of this ancient pathway.

Primate genome architecture influences structural variation mechanisms and functional consequences

Although nucleotide resolution maps of genomic structural variants (SVs) have provided insights into the origin and impact of phenotypic diversity in humans, comparable maps in nonhuman primates have thus far been lacking. Using massively parallel DNA sequencing, we constructed fine-resolution genomic structural variation maps in five chimpanzees, five orang-utans, and five rhesus macaques. The SV maps, which are comprised of thousands of deletions, duplications, and mobile element insertions, revealed a high activity of retrotransposition in macaques compared with great apes. By comparison, nonallelic homologous recombination is specifically active in the great apes, which is correlated with architectural differences between the genomes of great apes and macaque. Transcriptome analyses across nonhuman primates and humans revealed effects of species-specific whole-gene duplication on gene expression. We identified 13 gene duplications coinciding with the species-specific gain of tissue-specific gene expression in keeping with a role of gene duplication in the promotion of diversification and the acquisition of unique functions. Differences in the present day activity of SV formation mechanisms that our study revealed may contribute to ongoing diversification and adaptation of great ape and Old World monkey lineages.

Functional and evolutionary implications of gene orthology

Orthologues and paralogues are types of homologous genes that are related by speciation or duplication, respectively. Orthologous genes are generally assumed to retain equivalent functions in different organisms and to share other key properties. Several recent comparative genomic studies have focused on testing these expectations. Here we discuss the complexity of the evolution of gene-phenotype relationships and assess the validity of the key implications of orthology and paralogy relationships as general statistical trends and guiding principles.

A novel method for cross-species gene expression analysis

Background

Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.

Results

In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.

Conclusions

The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.