Gene mobility promotes the spread of resistance in bacterial populations

Theory predicts that horizontal gene transfer (HGT) expands the selective conditions under which genes spread in bacterial populations. Whereas vertically inherited genes can only spread by positively selected clonal expansion, mobile genetic elements can drive fixation of genes by infectious HGT. We tested this using populations of Pseudomonas fluorescens and the conjugative mercury resistance (Hg R) plasmid pQBR57. HGT expanded the selective conditions allowing the spread of Hg R: Chromosomal Hg R only increased in frequency under positive selection, whereas plasmid-encoded Hg R reached fixation with or without positive selection. Tracking plasmid dynamics over time revealed that the mode of Hg R inheritance varied across mercury environments. Under mercury selection, the spread of Hg R was driven primarily by clonal expansion while in the absence of mercury Hg R dynamics were dominated by infectious transfer. Thus, HGT is most likely to drive the spread of resistance genes in environments where resistance is useless

Soil aggregates as massively concurrent evolutionary incubators

Soil aggregation, a key component of soil structure, has mostly been examined from the perspective of soil management and the mediation of ecosystem processes such as soil carbon storage. However, soil aggregation is also a major factor to consider in terms of the fine-scale organization of the soil microbiome. For example, the physico-chemical conditions inside of aggregates usually differ from the conditions prevalent in the bulk soil and aggregates therefore increase the spatial heterogeneity of the soil. In addition, aggregates can provide a refuge for microbes against predation since their interior is not accessible to many predators. Soil aggregates are thus clearly important for microbial community ecology in soils (for example, Vos et al., 2013; Rillig et al., 2016) and for microbially driven biogeochemistry, and soil microbial ecologists are increasingly appreciating these aspects of soil aggregation. Soil aggregates have, however, so far been neglected when it comes to evolutionary considerations (Crawford et al., 2005) and we here propose that the process of soil aggregation should be considered as an important driver of evolution in the soil microbial community

Cohesive versus Flexible Evolution of Functional Modules in Eukaryotes

Although functionally related proteins can be reliably predicted from phylogenetic profiles, many functional modules do not seem to evolve cohesively according to case studies and systematic analyses in prokaryotes. In this study we quantify the extent of evolutionary cohesiveness of functional modules in eukaryotes and probe the biological and methodological factors influencing our estimates. We have collected various datasets of protein complexes and pathways in Saccheromyces cerevisiae. We define orthologous groups on 34 eukaryotic genomes and measure the extent of cohesive evolution of sets of orthologous groups of which members constitute a known complex or pathway. Within this framework it appears that most functional modules evolve flexibly rather than cohesively. Even after correcting for uncertain module definitions and potentially problematic orthologous groups, only 46% of pathways and complexes evolve more cohesively than random modules. This flexibility seems partly coupled to the nature of the functional module because biochemical pathways are generally more cohesively evolving than complexes

Identifying lineage effects when controlling for population structure improves power in bacterial association studies

Bacteria pose unique challenges for genome-wide association studies because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome1,2. Although methods developed for human studies can correct for strain structure3,4, this risks considerable loss-of-power because genetic differences between strains often contribute substantial phenotypic variability5. Here, we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3,144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence, our method pinpoints locus-specific effects where possible and boosts power by detecting lineage-level differences when fine-mapping is intractable

Predicting protein linkages in bacteria: Which method is best depends on task

<p>Abstract</p> <p>Background</p> <p>Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations.</p> <p>Results</p> <p>Using <it>Escherichia coli </it>K12 and <it>Bacillus subtilis</it>, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in <it>E. coli </it>K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in <it>E. coli </it>K12 and 88% (333/418)in <it>B. subtilis</it>. Comparing two versions of the <it>E. coli </it>K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction.</p> <p>Conclusion</p> <p>A common problem for computational methods is the generation of a large number of false positives that might be caused by an incomplete source of validation. By comparing two versions of a database, we demonstrated the dramatic differences on reported results. We used several benchmarks on which we have shown the comparative effectiveness of each prediction method, as well as provided guidelines as to which method is most appropriate for a given prediction task.</p

Frequency-dependent selection in vaccine-associated pneumococcal population dynamics

Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often cocirculate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, because the changes in population structure associated with the recent introduction of partial-coverage vaccines have substantially reduced pneumococcal disease. Here we show that pneumococcal lineages from multiple populations each have a distinct combination of intermediate-frequency genes. Functional analysis suggested that these loci may be subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Bayesian Computation generated reproducible estimates of the influence of NFDS on pneumococcal evolution, the strength of which varied between loci. Simulations replicated the stable frequency of lineages unperturbed by vaccination, patterns of serotype switching and clonal replacement. This framework highlights how bacterial ecology affects the impact of clinical interventions.Accessory loci are shown to have similar frequencies in diverse Streptococcus pneumoniae populations, suggesting negative frequency-dependent selection drives post-vaccination population restructuring

Tuning fresh: radiation through rewiring of central metabolism in streamlined bacteria

Most free-living planktonic cells are streamlined and in spite of their limitations in functional flexibility, their vast populations have radiated into a wide range of aquatic habitats. Here we compared the metabolic potential of subgroups in the Alphaproteobacteria lineage SAR11 adapted to marine and freshwater habitats. Our results suggest that the successful leap from marine to freshwaters in SAR11 was accompanied by a loss of several carbon degradation pathways and a rewiring of the central metabolism. Examples for these are C1 and methylated compounds degradation pathways, the Entner–Doudouroff pathway, the glyoxylate shunt and anapleuretic carbon fixation being absent from the freshwater genomes. Evolutionary reconstructions further suggest that the metabolic modules making up these important freshwater metabolic traits were already present in the gene pool of ancestral marine SAR11 populations. The loss of the glyoxylate shunt had already occurred in the common ancestor of the freshwater subgroup and its closest marine relatives, suggesting that the adaptation to freshwater was a gradual process. Furthermore, our results indicate rapid evolution of TRAP transporters in the freshwater clade involved in the uptake of low molecular weight carboxylic acids. We propose that such gradual tuning of metabolic pathways and transporters toward locally available organic substrates is linked to the formation of subgroups within the SAR11 clade and that this process was critical for the freshwater clade to find and fix an adaptive phenotype.This work was supported by the Swedish Research Council (Grant Numbers 2012-4592 to AE and 2012-3892 to SB) and the Communiy Sequencing Programme of the US Department of Energy Joint Genome Institute. The work conducted by the US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported under Contract No. DE-AC02-05CH11231

Network Evolution of Body Plans

Segmentation in arthropod embryogenesis represents a well-known example of body plan diversity. Striped patterns of gene expression that lead to the future body segments appear simultaneously or sequentially in long and short germ-band development, respectively. Regulatory genes relevant for stripe formation are evolutionarily conserved among arthropods, therefore the differences in the observed traits are thought to have originated from how the genes are wired. To reveal the basic differences in the network structure, we have numerically evolved hundreds of gene regulatory networks that produce striped patterns of gene expression. By analyzing the topologies of the generated networks, we show that the characteristics of stripe formation in long and short germ-band development are determined by Feed-Forward Loops (FFLs) and negative Feed-Back Loops (FBLs) respectively. Network architectures, gene expression patterns and knockout responses exhibited by the artificially evolved networks agree with those reported in the fly Drosophila melanogaster and the beetle Tribolium castaneum. For other arthropod species, principal network architectures that remain largely unknown are predicted.Comment: 35 pages, 4 figures and 1 tabl

Enzyme sequestration as a tuning point in controlling response dynamics of signalling networks

Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications

The Vein Patterning 1 (VEP1) Gene Family Laterally Spread through an Ecological Network

Lateral gene transfer (LGT) is a major evolutionary mechanism in prokaryotes. Knowledge about LGT— particularly, multicellular— eukaryotes has only recently started to accumulate. A widespread assumption sees the gene as the unit of LGT, largely because little is yet known about how LGT chances are affected by structural/functional features at the subgenic level. Here we trace the evolutionary trajectory of VEin Patterning 1, a novel gene family known to be essential for plant development and defense. At the subgenic level VEP1 encodes a dinucleotide-binding Rossmann-fold domain, in common with members of the short-chain dehydrogenase/reductase (SDR) protein family. We found: i) VEP1 likely originated in an aerobic, mesophilic and chemoorganotrophic α-proteobacterium, and was laterally propagated through nets of ecological interactions, including multiple LGTs between phylogenetically distant green plant/fungi-associated bacteria, and five independent LGTs to eukaryotes. Of these latest five transfers, three are ancient LGTs, implicating an ancestral fungus, the last common ancestor of land plants and an ancestral trebouxiophyte green alga, and two are recent LGTs to modern embryophytes. ii) VEP1's rampant LGT behavior was enabled by the robustness and broad utility of the dinucleotide-binding Rossmann-fold, which provided a platform for the evolution of two unprecedented departures from the canonical SDR catalytic triad. iii) The fate of VEP1 in eukaryotes has been different in different lineages, being ubiquitous and highly conserved in land plants, whereas fungi underwent multiple losses. And iv) VEP1-harboring bacteria include non-phytopathogenic and phytopathogenic symbionts which are non-randomly distributed with respect to the type of harbored VEP1 gene. Our findings suggest that VEP1 may have been instrumental for the evolutionary transition of green plants to land, and point to a LGT-mediated ‘Trojan Horse’ mechanism for the evolution of bacterial pathogenesis against plants. VEP1 may serve as tool for revealing microbial interactions in plant/fungi-associated environments