Septins From Protists to People

Septin GTPases form nonpolar heteropolymers that play important roles in cytokinesis and other cellular processes. The ability to form heteropolymers appears to be critical to many septin functions and to have been a major driver of the high conservation of many septin domains. Septins fall into five orthologous groups. Members of Groups 1–4 interact with each other to form heterooligomers and are known as the “core septins.” Representative core septins are present in all fungi and animals so far examined and show positional orthology with monomer location in the heteropolymer conserved within groups. In contrast, members of Group 5 are not part of canonical heteropolymers and appear to interact only transiently, if at all, with core septins. Group 5 septins have a spotty distribution, having been identified in specific fungi, ciliates, chlorophyte algae, and brown algae. In this review we compare the septins from nine well-studied model organisms that span the tree of life (Homo sapiens, Drosophila melanogaster, Schistosoma mansoni, Caenorhabditis elegans, Saccharomyces cerevisiae, Aspergillus nidulans, Magnaporthe oryzae, Tetrahymena thermophila, and Chlamydomonas reinhardtii). We focus on classification, evolutionary relationships, conserved motifs, interfaces between monomers, and positional orthology within heteropolymers. Understanding the relationships of septins across kingdoms can give new insight into their functions.

The effect of mainland dynamics on data and parameter estimates in island biogeography

Understanding macroevolution on islands requires knowledge of the closest relatives of island species on the mainland. The evolutionary relationships between island and mainland species can be reconstructed using phylogenies, to which models can be fitted to understand the dynamical processes of colonisation and diversification. But how much information on the mainland is needed to gain insight into macroevolution on islands? Here we first test whether species turnover on the mainland and incomplete mainland sampling leave recognisable signatures in community phylogenetic data. We find predictable phylogenetic patterns: colonisation times become older and the perceived proportion of endemic species increases as mainland turnover and incomplete knowledge increase. We then analyse the influence of these factors on the inference performance of the island biogeography model DAISIE, a whole-island community phylogenetic model that assumes that mainland species do not diversify, and that the mainland is fully sampled in the phylogeny. We find that colonisation and diversification rate are estimated with little bias in the presence of mainland extinction and incomplete sampling. By contrast, the rate of anagenesis is overestimated under high levels of mainland extinction and incomplete sampling, because these increase the perceived level of island endemism. We conclude that community-wide phylogenetic and endemism datasets of island species carry a signature of mainland extinction and sampling. The robustness of parameter estimates suggests that island diversification and colonisation can be studied even with limited knowledge of mainland dynamics.

Evolution of two-component quorum sensing systems

Quorum sensing (QS) is a cell-to-cell communication system that enables bacteria to coordinate their gene expression depending on their population density, via the detection of small molecules called autoinducers. In this way bacteria can act collectively to initiate processes like bioluminescence, virulence and biofilm formation. Autoinducers are detected by receptors, some of which are part of two-component signal transduction systems (TCS), which comprise of a (usually membrane-bound) sensor histidine kinase (HK) and a cognate response regulator (RR). Different QS systems are used by different bacterial taxa, and their relative evolutionary relationships have not been extensively studied. To address this, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify all the QS HKs and RRs that are part of TCSs and examined their conservation across microbial taxa. We compared the combinations of the highly conserved domains in the different families of receptors and response regulators using the Simple Modular Architecture Research Tool (SMART) and KEGG databases, and we also carried out phylogenetic analyses for each family, and all families together. The distribution of the different QS systems across taxa, indicates flexibility in HK–RR pairing and highlights the need for further study of the most abundant systems. For both the QS receptors and the response regulators, our results indicate close evolutionary relationships between certain families, highlighting a common evolutionary history which can inform future applications, such as the design of novel inhibitors for pathogenic QS systems.

Fusexins, HAP2/GCS1 and Evolution of Gamete Fusion

Gamete fusion is the climax of fertilization in all sexually reproductive organisms, from unicellular fungi to humans. Similarly to other cell-cell fusion events, gamete fusion is mediated by specialized proteins, named fusogens, that overcome the energetic barriers during this process. In recent years, HAPLESS 2/GENERATIVE CELL-SPECIFIC 1 (HAP2/GCS1) was identified as the fusogen mediating sperm-egg fusion in flowering plants and protists, being both essential and sufficient for the membrane merger in some species. The identification of HAP2/GCS1 in invertebrates, opens the possibility that a similar fusogen may be used in vertebrate fertilization. HAP2/GCS1 proteins share a similar structure with two distinct families of exoplasmic fusogens: the somatic Fusion Family (FF) proteins discovered in nematodes, and class II viral glycoproteins (e.g., rubella and dengue viruses). Altogether, these fusogens form the Fusexin superfamily. While some attributes are shared among fusexins, for example the overall structure and the possibility of assembly into trimers, some other characteristics seem to be specific, such as the presence or not of hydrophobic loops or helices at the distal tip of the protein. Intriguingly, HAP2/GCS1 or other fusexins have neither been identified in vertebrates nor in fungi, raising the question of whether these genes were lost during evolution and were replaced by other fusion machinery or a significant divergence makes their identification difficult. Here, we discuss the biology of HAP2/GCS1, its involvement in gamete fusion and the structural, mechanistic and evolutionary relationships with other fusexins.

Evolution of the Cell

Cells are life’s basic building blocks, and there is no more profound question than how they came to be. What made this murky subject accessible is the invention of methods to sequence nucleic acids and proteins, and to infer evolutionary relationships from those sequences. It seems that all living things share a common ancestry in LUCA (the Last Universal Common Ancestor), a shadowy entity thought to have lived nearly 4 billion years ago. LUCA’s nature has been much debated, but she appears to have been a cell of sorts endowed with membranes, metabolic networks, a usable energy source and the machinery to express and reproduce genetic information. The earliest known event in cell history was the divergence of Archaea from Bacteria, about 3.5 billion years ago. Eukaryotic cells, more closely allied with Archaea than with Bacteria, appear much later, some 2 billion years ago. Their origin remains one of life’s mysteries, but the evidence currently favors a fusion or merger of an early archaeon with a bacterium; the latter became the ancestor of mitochondria, and played a major role in cell evolution. Eukaryotic cells of the contemporary kind emerged over hundreds of million years. Prominent events included a second instance of intracellular symbiosis, this time with a cyanobacterium, that introduced photosynthesis into the eukaryotic universe and initiated the plant lineage. Eukaryotic cells are the building blocks of all higher organisms. Just what has given the eukaryotic order an edge is yet another of life’s stubborn mysteries.

TopHap: Rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

Motivation: Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of SARS-CoV-2 strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites and millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate phylogenetic inference of resolvable phylogenetic features. Results: We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. To assess topological robustness, we develop a bootstrap resampling strategy that resamples genomes spatiotemporally. The application of TopHap to build a phylogeny of 68,057 genomes (68KG) produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major variants of concern. Availability: TopHap is available on the web at https://github.com/SayakaMiura/TopHap.

GENPPI: standalone software for creating protein interaction networks from genomes

BackGround Bacterial genomes are being deposited into online databases at an increasing rate. Genome annotation represents one of the first efforts to understand organisms and their diseases. Some evolutionary relationships capable of being annotated only from genomes are conserved gene neighbourhoods (CNs), phylogenetic profiles (PPs), and gene fusions. At present, there is no standalone software that enables networks of interactions among proteins to be created using these three evolutionary characteristics with efficient and effective results. Results We developed GENPPI software for the ab initio prediction of interaction networks using predicted proteins from a genome. In our case study, we employed 50 genomes of the genus Corynebacterium. Based on the PP relationship, GENPPI differentiated genomes between the ovis and equi biovars of the species Corynebacterium pseudotuberculosis and created groups among the other species analysed. If we inspected only the CN relationship, we could not entirely separate biovars, only species. Our software GENPPI was determined to be efficient because, for example, it creates interaction networks from the central genomes of 50 species/lineages with an average size of 2200 genes in less than 40 min on a conventional computer. Moreover, the interaction networks that our software creates reflect correct evolutionary relationships between species, which we confirmed with average nucleotide identity analyses. Additionally, this software enables the user to define how he or she intends to explore the PP and CN characteristics through various parameters, enabling the creation of customized interaction networks. For instance, users can set parameters regarding the genus, metagenome, or pangenome. In addition to the parameterization of GENPPI, it is also the user’s choice regarding which set of genomes they are going to study. Conclusions GENPPI can help fill the gap concerning the considerable number of novel genomes assembled monthly and our ability to process interaction networks considering the noncore genes for all completed genome versions. With GENPPI, a user dictates how many and how evolutionarily correlated the genomes answer a scientific query.

Genetic diversity among queen bee, worker bees and larvae in terms of retrotransposon movements

Objective Honeybee (Apis mellifera L.) is a model organism, contributing significant effect on global ecology by pollination and examining due to its social behaviour. Methods In this study, barley-specific Sukkula and Nikita retrotransposons were analysed using IRAP (Inter-Retrotransposon Amplification Polymorphism) marker technique, and the relationships between retrotransposon movements and development were also investigated in three different colonies of the Caucasian bee (Apis mellifera caucasica). Furthermore, transposon sequences belonging to Apis mellifera, Bombus terrestris, Triticum turgidum and Hordeum vulgare were also examined to figure out evolutionary relationships. Results For this purpose, a queen bee, five worker bees, and five larvae from each colony were studied. Both retrotransposons were found in all samples in three colonies with different polymorphism ratios (0-100% for Nikita and 0-67% for Sukkula). We also determined polymorphisms in queen-worker (0-83% for Nikita, 0-63% for Sukkula), queen-larvae (0-83% for Nikita, 0-43% for Sukkula) and worker-larvae comparisons (0-100% for Nikita, 0-63% for Sukkula) in colonies. Moreover, close relationships among transposons found in plant and insect genomes as a result of in silico evaluations to verify experimental results. Conclusion This work could be one of the first studies to analyse plant-specific retrotransposons’ movements in honeybee genome. Results are expected to understand evolutionary relationships in terms of horizontal transfer of transposons among kingdoms.