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The insect chemosensory repertoires of Odorant Receptors (ORs) and Gustatory Receptors (GRs) together represent one of the largest families of ligand-gated ion channels. Previous analyses have identified homologous ‘Gustatory Receptor-Like’ (GRL) proteins across Animalia, but the evolutionary origin of this novel class of ion channels is unknown. We describe a survey of unicellular eukaryotic genomes for GRLs, identifying several candidates in fungi, protists and algae that contain many structural features characteristic of animal GRLs. The existence of these proteins in unicellular eukaryotes, together with ab initio protein structure predictions, provide evidence for homology between GRLs and a family of uncharacterized plant proteins containing the DUF3537 domain. Together, our analyses suggest an origin of this protein superfamily in the last common eukaryotic ancestor.

Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at

Sexual reproduction consists of genome reduction by meiosis and subsequent gamete fusion. The presence of genes homologous to eukaryotic meiotic genes in archaea and bacteria suggests that DNA repair mechanisms evolved towards meiotic recombination. However, fusogenic proteins resembling those found in gamete fusion in eukaryotes have so far not been found in prokaryotes. Here, we identify archaeal proteins that are homologs of fusexins, a superfamily of fusogens that mediate eukaryotic gamete and somatic cell fusion, as well as virus entry. The crystal structure of a trimeric archaeal fusexin (Fusexin1 or Fsx1) reveals an archetypical fusexin architecture with unique features such as a six-helix bundle and an additional globular domain. Ectopically expressed Fusexin1 can fuse mammalian cells, and this process involves the additional globular domain and a conserved fusion loop. Furthermore, archaeal fusexin genes are found within integrated mobile elements, suggesting potential roles in cell-cell fusion and gene exchange in archaea, as well as different scenarios for the evolutionary history of fusexins. Sexual reproduction in eukaryotes involves gamete fusion, mediated by fusogenic proteins. Here, the authors identify fusogenic protein homologs encoded within mobile genetic elements in archaeal genomes, solve the crystal structure of one of the proteins, and show that its ectopic expression can fuse mammalian cells, suggesting potential roles in cell-cell fusion and gene exchange.

An analysis of publicly available viral genomes explores the evolutionary dynamics of host jumps and shows that humans are as much a source of viral spillover events to other animals as they are recipients.

The surge in genome data, with ongoing efforts aiming to sequence 1.5 M eukaryotes in a decade, could revolutionize genomics, revealing the origins, evolution and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets. Here, addressing this, ‘FastOMA’ provides linear scalability for orthology inference, enabling the processing of thousands of eukaryotic genomes within a day. FastOMA maintains the high accuracy and resolution of the well-established Orthologous Matrix (OMA) approach in benchmarks. FastOMA is available via GitHub at https://github.com/DessimozLab/FastOMA/ . FastOMA achieves fast and accurate orthology inference, with linear scalability.

Accurate determination of the evolutionary relationships between genes is a foundational challenge in biology. Homology—evolutionary relatedness—is in many cases readily determined based on sequence similarity analysis. By contrast, whether or not two genes directly descended from a common ancestor by a speciation event (orthologs) or duplication event (paralogs) is more challenging, yet provides critical information on the history of a gene. Since 2009, this task has been the focus of the Quest for Orthologs (QFO) Consortium. The sixth QFO meeting took place in Okazaki, Japan in conjunction with the 67th National Institute for Basic Biology conference. Here, we report recent advances, applications, and oncoming challenges that were discussed during the conference. Steady progress has been made toward standardization and scalability of new and existing tools. A feature of the conference was the presentation of a panel of accessible tools for phylogenetic profiling and several developments to bring orthology beyond the gene unit—from domains to networks. This meeting brought into light several challenges to come: leveraging orthology computations to get the most of the incoming avalanche of genomic data, integrating orthology from domain to biological network levels, building better gene models, and adapting orthology approaches to the broad evolutionary and genomic diversity recognized in different forms of life and viruses.

Abstract Protein posttranslational modifications add great sophistication to biological systems. Citrullination, a key regulatory mechanism in human physiology and pathophysiology, is enigmatic from an evolutionary perspective. Although the citrullinating enzymes peptidylarginine deiminases (PADIs) are ubiquitous across vertebrates, they are absent from yeast, worms, and flies. Based on this distribution PADIs were proposed to have been horizontally transferred, but this has been contested. Here, we map the evolutionary trajectory of PADIs into the animal lineage. We present strong phylogenetic support for a clade encompassing animal and cyanobacterial PADIs that excludes fungal and other bacterial homologs. The animal and cyanobacterial PADI proteins share functionally relevant primary and tertiary synapomorphic sequences that are distinct from a second PADI type present in fungi and actinobacteria. Molecular clock calculations and sequence divergence analyses using the fossil record estimate the last common ancestor of the cyanobacterial and animal PADIs to be less than 1 billion years old. Additionally, under an assumption of vertical descent, PADI sequence change during this evolutionary time frame is anachronistically low, even when compared with products of likely endosymbiont gene transfer, mitochondrial proteins, and some of the most highly conserved sequences in life. The consilience of evidence indicates that PADIs were introduced from cyanobacteria into animals by horizontal gene transfer (HGT). The ancestral cyanobacterial PADI is enzymatically active and can citrullinate eukaryotic proteins, suggesting that the PADI HGT event introduced a new catalytic capability into the regulatory repertoire of animals. This study reveals the unusual evolution of a pleiotropic protein modification.

Genes which are involved in the same biological processes tend to co-evolve. Thus, metabolic pathways, protein complexes, and other kinds of protein protein interactions can be inferred by looking for correlated patterns of gene retention and loss across the tree of life, a technique called phylogenetic profiling. Recent methodological developments on phylogenetic profiling have focused on scalability improvements to take advantage of the rapidly accumulating genomic data. However, state-of-the-art methods assume that the correlation resulting from co-evolving proteins is uniform across all species considered. This is reasonable for interactions already present at the root of the species considered, but less so for ones that emerge in more recent lineages. To address this challenge and take advantage of recent developments in deep learning methods, we introduce a phylogenetic profiling method which processes large gene co-phylogenies using neural networks. We show that post-processing conventional phylogenetic profiles using deep neural networks can improve predictions, but requires onerous training on specific phylogenies. Overcoming this limitation by taking the topology of the species tree as an input, Graph Neural Networks are shown to outperform all other methods when interaction detection is not centered on just one species of interest, while also predicting when interactions appeared and in which taxa they are present. Conclusion: Graph Neural Networks constitute a promising new approach for phylogenetic profiling. Our work is a first foray into dynamic phylogenetic profiling or the reconstruction of pairwise protein interaction across time. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/DessimozLab/HogProf/tree/master/pyprofiler/notebooks/Graphnet

Many proteins exist in the so-called “twilight zone” of sequence alignment, where low pairwise sequence identity makes it difficult to determine homology and phylogeny 1, 2. As protein tertiary structure is often more conserved 3, recent advances in ab initio protein folding have made structure-based identification of putative homologs feasible 4–6. However, structural screening and phylogenetics are in their infancy, particularly for twilight zone proteins. We present a pipeline for the identification and characterization of distant homologs, and apply it to 7-transmembrane domain ion channels (7TMICs), a protein group founded by insect Odorant and Gustatory receptors. Previous sequence and limited structure-based searches identified putatively-related proteins, mainly in other animals and plants 7–10. However, very few 7TMICs have been identified in non-animal, non-plant taxa. Moreover, these proteins’ remarkable sequence dissimilarity made it uncertain if disparate 7TMIC types (Gr/Or, Grl, GRL, DUF3537, PHTF and GrlHz) are homologous or convergent, leaving their evolutionary history unresolved. Our pipeline identified thousands of new 7TMICs in archaea, bacteria and unicellular eukaryotes. Using graph-based analyses and protein language models to extract family-wide signatures, we demonstrate that 7TMICs have structure and sequence similarity, supporting homology. Through sequence and structure-based phylogenetics, we classify eukaryotic 7TMICs into two families (Class-A and Class-B), which are the result of a gene duplication predating the split(s) leading to Amorphea (animals, fungi and allies) and Diaphoretickes (plants and allies). Our work reveals 7TMICs as a cryptic superfamily with origins close to the evolution of cellular life. More generally, this study serves as a methodological proof of principle for the identification of extremely distant protein homologs.