The traditional tree of life shows eukaryotes as a distinct lineage of living things, but many studies have suggested that the first eukaryotic cells were chimeric, descended from both Eubacteria ...(through the mitochondrion) and Archaebacteria. Eukaryote nuclei thus contain genes of both eubacterial and archaebacterial origins, and these genes have different functions within eukaryotic cells. Here we report that archaebacterium-derived genes are significantly more likely to be essential to yeast viability, are more highly expressed, and are significantly more highly connected and more central in the yeast protein interaction network. These findings hold irrespective of whether the genes have an informational or operational function, so that many features of eukaryotic genes with prokaryotic homologs can be explained by their origin, rather than their function. Taken together, our results show that genes of archaebacterial origin are in some senses more important to yeast metabolism than genes of eubacterial origin. This importance reflects these genes' origin as the ancestral nuclear component of the eukaryotic genome.
Horizontal gene transfer (HGT) is one of the most important processes in prokaryote evolution. The sharing of DNA can spread neutral or beneficial genes, as well as genetic parasites across ...populations and communities, creating a large proportion of the variability acted on by natural selection. Here, we highlight the role of HGT in enhancing the opportunities for conflict and cooperation within and between prokaryote genomes. We discuss how horizontally acquired genes can cooperate or conflict both with each other and with a recipient genome, resulting in signature patterns of gene co-occurrence, avoidance, and dependence. We then describe how interactions involving horizontally transferred genes may influence cooperation and conflict at higher levels (populations, communities, and symbioses). Finally, we consider the benefits and drawbacks of HGT for prokaryotes and its fundamental role in understanding conflict and cooperation from the gene-gene to the microbiome level.
Eukaryotes are traditionally considered to be one of the three natural divisions of the tree of life and the sister group of the Archaebacteria. However, eukaryotic genomes are replete with genes of ...eubacterial ancestry, and more than 20 mutually incompatible hypotheses have been proposed to account for eukaryote origins. Here we test the predictions of these hypotheses using a novel supertree-based phylogenetic signal-stripping method, and recover supertrees of life based on phylogenies for up to 5,741 single gene families distributed across 185 genomes. Using our signal-stripping method, we show that there are three distinct phylogenetic signals in eukaryotic genomes. In order of strength, these link eukaryotes with the Cyanobacteria, the Proteobacteria, and the Thermoplasmatales, an archaebacterial (euryarchaeotes) group. These signals correspond to distinct symbiotic partners involved in eukaryote evolution: plastids, mitochondria, and the elusive host lineage. According to our whole-genome data, eukaryotes are hardly the sister group of the Archaebacteria, because up to 83% of eukaryotic genes with a prokaryotic homolog have eubacterial, not archaebacterial, origins. The results reject all but two of the current hypotheses for the origin of eukaryotes: those assuming a sulfur-dependent or hydrogen-dependent syntrophy for the origin of mitochondria.
Abstract
A pangenome is the complete set of genes (core and accessory) present in a phylogenetic clade. We hypothesize that a pangenome’s accessory gene content is structured and maintained by ...selection. To test this hypothesis, we interrogated the genomes of 40 Pseudomonas species for statistically significant coincident (i.e., co-occurring/avoiding) gene patterns. We found that 86.7% of common accessory genes are involved in ≥1 coincident relationship. Further, genes that co-occur and/or avoid each other—but are not vertically inherited—are more likely to share functional categories, are more likely to be simultaneously transcribed, and are more likely to produce interacting proteins, than would be expected by chance. These results are not due to coincident genes being adjacent to one another on the chromosome. Together, these findings suggest that the accessory genome is structured into sets of genes that function together within a given strain. Given the similarity of the Pseudomonas pangenome with open pangenomes of other prokaryotic species, we speculate that these results are generalizable.
Abstract
Extensive microbial gene flows affect how we understand virology, microbiology, medical sciences, genetic modification, and evolutionary biology. Phylogenies only provide a narrow view of ...these gene flows: plasmids and viruses, lacking core genes, cannot be attached to cellular life on phylogenetic trees. Yet viruses and plasmids have a major impact on cellular evolution, affecting both the gene content and the dynamics of microbial communities. Using bipartite graphs that connect up to 149,000 clusters of homologous genes with 8,217 related and unrelated genomes, we can in particular show patterns of gene sharing that do not map neatly with the organismal phylogeny. Homologous genes are recycled by lateral gene transfer, and multiple copies of homologous genes are carried by otherwise completely unrelated (and possibly nested) genomes, that is, viruses, plasmids and prokaryotes. When a homologous gene is present on at least one plasmid or virus and at least one chromosome, a process of “gene externalization,” affected by a postprocessed selected functional bias, takes place, especially in Bacteria. Bipartite graphs give us a view of vertical and horizontal gene flow beyond classic taxonomy on a single very large, analytically tractable, graph that goes beyond the cellular Web of Life.
Current phylogenetic methods attempt to account for evolutionary rate variation across characters in a matrix. This is generally achieved by the use of sophisticated evolutionary models, combined ...with dense sampling of large numbers of characters. However, systematic biases and superimposed substitutions make this task very difficult. Model adequacy can sometimes be achieved at the cost of adding large numbers of free parameters, with each parameter being optimized according to some criterion, resulting in increased computation times and large variances in the model estimates. In this study, we develop a simple approach that estimates the relative evolutionary rate of each homologous character. The method that we describe uses the similarity between characters as a proxy for evolutionary rate. In this article, we work on the premise that if the character-state distribution of a homologous character is similar to many other characters, then this character is likely to be relatively slowly evolving. If the character-state distribution of a homologous character is not similar to many or any of the rest of the characters in a data set, then it is likely to be the result of rapid evolution. We show that in some test cases, at least, the premise can hold and the inferences are robust. Importantly, the method does not use a "starting tree" to make the inference and therefore is tree independent. We demonstrate that this approach can work as well as a maximum likelihood (ML) approach, though the ML method needs to have a known phylogeny, or at least a very good estimate of that phylogeny. We then demonstrate some uses for this method of analysis, including the improvement in phylogeny reconstruction for both deep-level and recent relationships and overcoming systematic biases such as base composition bias. Furthermore, we compare this approach to two well-established methods for reweighting or removing characters. These other methods are tree-based and we show that they can be systematically biased. We feel this method can be useful for phylogeny reconstruction, understanding evolutionary rate variation, and for understanding selection variation on different characters.
Pangenomes exhibit remarkable variability in many prokaryotic species, much of which is maintained through the processes of horizontal gene transfer and gene loss. Repeated acquisitions of ...near-identical homologs can easily be observed across pangenomes, leading to the question of whether these parallel events potentiate similar evolutionary trajectories, or whether the remarkably different genetic backgrounds of the recipients mean that postacquisition evolutionary trajectories end up being quite different. In this study, we present a machine learning method that predicts the presence or absence of genes in the
pangenome based on complex patterns of the presence or absence of other accessory genes within a genome. Our analysis leverages the repeated transfer of genes through the
pangenome to observe patterns of repeated evolution following similar events. We find that the presence or absence of a substantial set of genes is highly predictable from other genes alone, indicating that selection potentiates and maintains gene-gene co-occurrence and avoidance relationships deterministically over long-term bacterial evolution and is robust to differences in host evolutionary history. We propose that at least part of the pangenome can be understood as a set of genes with relationships that govern their likely cohabitants, analogous to an ecosystem's set of interacting organisms. Our findings indicate that intragenomic gene fitness effects may be key drivers of prokaryotic evolution, influencing the repeated emergence of complex gene-gene relationships across the pangenome.
Prokaryote pangenomes are dynamic entities Cummins, Elizabeth A; Hall, Rebecca J; McInerney, James O ...
Current opinion in microbiology,
April 2022, 2022-04-00, 20220401, Letnik:
66
Journal Article
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Prokaryote pangenomes are influenced heavily by environmental factors and the opportunity for gene gain and loss events. As the field of pangenome analysis has expanded, so has the need to fully ...understand the complexity of how eco-evolutionary dynamics shape pangenomes. Here, we describe current models of pangenome evolution and discuss their suitability and accuracy. We suggest that pangenomes are dynamic entities under constant flux, highlighting the influence of two-way interactions between pangenome and environment. New classifications of core and accessory genes are also considered, underscoring the need for continuous evaluation of nomenclature in a fast-moving field. We conclude that future models of pangenome evolution should incorporate eco-evolutionary dynamics to fully encompass their dynamic, changeable nature.
Bacterial genomes are rife with orphan biosynthetic gene clusters (BGCs) associated with secondary metabolism of unrealized natural product molecules. Often up to a tenth of the genome is predicted ...to code for the biosynthesis of diverse metabolites with mostly unknown structures and functions. This phenomenal diversity of BGCs coupled with their high rates of horizontal transfer raise questions about whether they are really active and beneficial, whether they are neutral and confer no advantage, or whether they are carried in genomes because they are parasitic or addictive. We previously reported that Salinispora bacteria broadly use the desferrioxamine family of siderophores for iron acquisition. Herein we describe a new and unrelated group of peptidic siderophores called salinichelins from a restricted number of Salinispora strains in which the desferrioxamine biosynthesis genes have been lost. We have reconstructed the evolutionary history of these two different siderophore families and show that the acquisition and retention of the new salinichelin siderophores co-occurs with the loss of the more ancient desferrioxamine pathway. This identical event occurred at least three times independently during the evolution of the genus. We surmise that certain BGCs may be extraneous because of their functional redundancy and demonstrate that the relative evolutionary pace of natural pathway replacement shows high selective pressure against retention of functionally superfluous gene clusters.
Networks allow the investigation of evolutionary relationships that do not fit a tree model. They are becoming a leading tool for describing the evolutionary relationships between organisms, given ...the comparative complexities among genomes.
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