Bipartite graph analyses reveal interdomain LGT involving ultrasmall prokaryotes and their divergent, membrane-related proteins

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

The rise of diversity in metabolic platforms across the Candidate Phyla Radiation

BACKGROUND

A unifying feature of the bacterial Candidate Phyla Radiation (CPR) is a limited and highly variable repertoire of biosynthetic capabilities. However, the distribution of metabolic traits across the CPR and the evolutionary processes underlying them are incompletely resolved.

RESULTS

Here, we selected ~ 1000 genomes of CPR bacteria from diverse environments to construct a robust internal phylogeny that was consistent across two unlinked marker sets. Mapping of glycolysis, the pentose phosphate pathway, and pyruvate metabolism onto the tree showed that some components of these pathways are sparsely distributed and that similarity between metabolic platforms is only partially predicted by phylogenetic relationships. To evaluate the extent to which gene loss and lateral gene transfer have shaped trait distribution, we analyzed the patchiness of gene presence in a phylogenetic context, examined the phylogenetic depth of clades with shared traits, and compared the reference tree topology with those of specific metabolic proteins. While the central glycolytic pathway in CPR is widely conserved and has likely been shaped primarily by vertical transmission, there is evidence for both gene loss and transfer especially in steps that convert glucose into fructose 1,6-bisphosphate and glycerate 3P into pyruvate. Additionally, the distribution of Group 3 and Group 4-related NiFe hydrogenases is patchy and suggests multiple events of ancient gene transfer.

CONCLUSIONS

We infer that patterns of gene gain and loss in CPR, including acquisition of accessory traits in independent transfer events, could have been driven by shifts in host-derived resources and led to sparse but varied genetic inventories.

Exploring the evolutionary dynamics of Rhizobium plasmids through bipartite network analysis

The genus Rhizobium usually has a multipartite genome architecture with a chromosome and several plasmids, making these bacteria a perfect candidate for plasmid biology studies. As there are no universally shared genes among typical plasmids, network analyses can complement traditional phylogenetics in a broad-scale study of plasmid evolution. Here, we present an exhaustive analysis of 216 plasmids from 49 complete genomes of Rhizobium by constructing a bipartite network that consists of two classes of nodes, the plasmids and homologous protein families that connect them. Dissection of the network using a hierarchical clustering strategy reveals extensive variety, with 34 homologous plasmid clusters. Four large clusters including one cluster of symbiotic plasmids and two clusters of chromids carrying some truly essential genes are widely distributed among Rhizobium. In contrast, the other clusters are quite small and rare. Symbiotic clusters and rare accessory clusters are exogenetic and do not appear to have co-evolved with the common accessory clusters; the latter ones have a large coding potential and functional complementarity for different lifestyles in Rhizobium. The bipartite network also provides preliminary evidence of Rhizobium plasmid variation and formation including genetic exchange, plasmid fusion and fission, exogenetic plasmid transfer, host plant selection, and environmental adaptation.

Lateral Gene Transfer Shapes the Distribution of RuBisCO among Candidate Phyla Radiation Bacteria and DPANN Archaea

Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is considered to be the most abundant enzyme on Earth. Despite this, its full diversity and distribution across the domains of life remain to be determined. Here, we leverage a large set of bacterial, archaeal, and viral genomes recovered from the environment to expand our understanding of existing RuBisCO diversity and the evolutionary processes responsible for its distribution. Specifically, we report a new type of RuBisCO present in Candidate Phyla Radiation (CPR) bacteria that is related to the archaeal Form III enzyme and contains the amino acid residues necessary for carboxylase activity. Genome-level metabolic analyses supported the inference that these RuBisCO function in a CO₂-incorporating pathway that consumes nucleotides. Importantly, some Gottesmanbacteria (CPR) also encode a phosphoribulokinase that may augment carbon metabolism through a partial Calvin–Benson–Bassham cycle. Based on the scattered distribution of RuBisCO and its discordant evolutionary history, we conclude that this enzyme has been extensively laterally transferred across the CPR bacteria and DPANN archaea. We also report RuBisCO-like proteins in phage genomes from diverse environments. These sequences cluster with proteins in the Beckwithbacteria (CPR), implicating phage as a possible mechanism of RuBisCO transfer. Finally, we synthesize our metabolic and evolutionary analyses to suggest that lateral gene transfer of RuBisCO may have facilitated major shifts in carbon metabolism in several important bacterial and archaeal lineages.

Bipartite Network Analysis of Gene Sharings in the Microbial World

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.

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution

In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

MultiTwin: A Software Suite to Analyze Evolution at Multiple Levels of Organization Using Multipartite Graphs

The inclusion of introgressive processes in evolutionary studies induces a less constrained view of evolution. Network-based methods (like large-scale similarity networks) allow to include in comparative genomics all extrachromosomic carriers (like viruses, the most abundant biological entities on the planet) with their cellular hosts. The integration of several levels of biological organization (genes, genomes, communities, environments) enables more comprehensive analyses of gene sharing and improved sequence-based classifications. However, the algorithmic tools for the analysis of such networks are usually restricted to people with high programming skills. We present an integrated suite of software tools named MultiTwin, aimed at the construction, structuring, and analysis of multipartite graphs for evolutionary biology. Typically, this kind of graph is useful for the comparative analysis of the gene content of genomes in microbial communities from the environment and for exploring patterns of gene sharing, for example between distantly related cellular genomes, pangenomes, or between cellular genomes and their mobile genetic elements. We illustrate the use of this tool with an application of the bipartite approach (using gene family-genome graphs) for the analysis of pathogenicity traits in prokaryotes.

Bioinformatic Analysis Reveals Archaeal tRNATyr and tRNATrp Identities in Bacteria

The tRNA identity elements for some amino acids are distinct between the bacterial and archaeal domains. Searching in recent genomic and metagenomic sequence data, we found some candidate phyla radiation (CPR) bacteria with archaeal tRNA identity for Tyr-tRNA and Trp-tRNA synthesis. These bacteria possess genes for tyrosyl-tRNA synthetase (TyrRS) and tryptophanyl-tRNA synthetase (TrpRS) predicted to be derived from DPANN superphylum archaea, while the cognate tRNA^Tyr and tRNA^Trp genes reveal bacterial or archaeal origins. We identified a trace of domain fusion and swapping in the archaeal-type TyrRS gene of a bacterial lineage, suggesting that CPR bacteria may have used this mechanism to create diverse proteins. Archaeal-type TrpRS of bacteria and a few TrpRS species of DPANN archaea represent a new phylogenetic clade (named TrpRS-A). The TrpRS-A open reading frames (ORFs) are always associated with another ORF (named ORF1) encoding an unknown protein without global sequence identity to any known protein. However, our protein structure prediction identified a putative HIGH-motif and KMSKS-motif as well as many α-helices that are characteristic of class I aminoacyl-tRNA synthetase (aaRS) homologs. These results provide another example of the diversity of molecular components that implement the genetic code and provide a clue to the early evolution of life and the genetic code.