Uncovering the co-evolutionary network among prokaryotic genes

Genome-based assessment of antimicrobial resistance reveals the lineage specificity of resistance and resistance gene profiles in Riemerella anatipestifer from China

Riemerella anatipestifer (R. anatipestifer) is an important pathogen that causes severe systemic infections in domestic ducks, resulting in substantial economic losses for China's waterfowl industry. Controlling R. anatipestifer with antibiotics is extremely challenging due to its multidrug resistance. Notably, large-scale studies on antimicrobial resistance (AMR) and the corresponding genetic determinants in R. anatipestifer remain scarce. To solve this dilemma, more than 400 nonredundant R. anatipestifer isolates collected from 22 provinces in China between 1994 and 2021 were subjected to broth dilution antibiotic susceptibility assays, and their resistance-associated genetic determinants were characterized by whole-genome sequencing. While over 90% of the isolates was resistant to sulfamethoxazole, kanamycin, gentamicin, ofloxacin, norfloxacin, and trimethoprim, 88.48% of the isolates was resistant to the last-resort drug (tigecycline). Notably, R. anatipestifer resistance to oxacillin, norfloxacin, ofloxacin, and tetracycline was found to increase relatively over time. Genome-wide analysis revealed the alarmingly high prevalence of blaOXA-like (93.05%) and tet(X) (90.64%) genes and the uneven distribution of resistance genes among lineages. Overall, this study reveals a serious AMR situation regarding R. anatipestifer in China, with a high prevalence and high diversity of antimicrobial resistance genes, providing important data for the rational use of antibiotics in veterinary practice.IMPORTANCERiemerella anatipestifer (R. anatipestifer), an important waterfowl pathogen, has caused substantial economic losses worldwide, especially in China. Antimicrobial resistance (AMR) is a major challenge in controlling this pathogen. Although a few studies have reported antimicrobial resistance in R. anatipestifer, comprehensive data remain a gap. This study aims to address the lack of information on R. anatipestifer AMR and its genetic basis. By analyzing more than 400 isolates collected over two decades, this study reveals alarming levels of resistance to several antibiotics, including drugs of last resort. The study also revealed the lineage-specificity of resistance profiles and resistance gene profiles. Overall, this study provides new insights and updated data support for understanding AMR and its genetic determinants in R. anatipestifer.

Coevolution of the CDCA7-HELLS ICF-related nucleosome remodeling complex and DNA methyltransferases

5-Methylcytosine (5mC) and DNA methyltransferases (DNMTs) are broadly conserved in eukaryotes but are also frequently lost during evolution. The mammalian SNF2 family ATPase HELLS and its plant ortholog DDM1 are critical for maintaining 5mC. Mutations in HELLS, its activator CDCA7, and the de novo DNA methyltransferase DNMT3B, cause immunodeficiency-centromeric instability-facial anomalies (ICF) syndrome, a genetic disorder associated with the loss of DNA methylation. We here examine the coevolution of CDCA7, HELLS and DNMTs. While DNMT3, the maintenance DNA methyltransferase DNMT1, HELLS, and CDCA7 are all highly conserved in vertebrates and green plants, they are frequently co-lost in other evolutionary clades. The presence-absence patterns of these genes are not random; almost all CDCA7 harboring eukaryote species also have HELLS and DNMT1 (or another maintenance methyltransferase, DNMT5). Coevolution of presence-absence patterns (CoPAP) analysis in Ecdysozoa further indicates coevolutionary linkages among CDCA7, HELLS, DNMT1 and its activator UHRF1. We hypothesize that CDCA7 becomes dispensable in species that lost HELLS or DNA methylation, and/or the loss of CDCA7 triggers the replacement of DNA methylation by other chromatin regulation mechanisms. Our study suggests that a unique specialized role of CDCA7 in HELLS-dependent DNA methylation maintenance is broadly inherited from the last eukaryotic common ancestor.

Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes

Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method.

The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

Inverse Potts model improves accuracy of phylogenetic profiling

MOTIVATION

Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity metrics in phylogenetic profiling achieved high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. While previous studies reduced the evolutionary bias by considering a phylogenetic tree, few studies have analyzed the spurious correlation bias.

RESULTS

To reduce the spurious correlation bias, we developed metrics based on the inverse Potts model (IPM) for phylogenetic profiling. We also developed a metric based on both the IPM and a phylogenetic tree. In an empirical dataset analysis, we demonstrated that these IPM-based metrics improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several metrics, including the IPM-based metrics, had superior performance to a single metric.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/fukunagatsu/Ipm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evidence for Selection in the Abundant Accessory Gene Content of a Prokaryote Pangenome

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.

Public good exploitation in natural bacterioplankton communities

Bacteria often interact with their environment through extracellular molecules that increase access to limiting resources. These secretions can act as public goods, creating incentives for exploiters to invade and "steal" public goods away from producers. This phenomenon has been studied extensively in vitro, but little is known about the occurrence and impact of public good exploiters in the environment. Here, we develop a genomic approach to systematically identify bacteria that can exploit public goods produced during the degradation of polysaccharides. Focusing on chitin, a highly abundant marine biopolymer, we show that public good exploiters are active in natural chitin degrading microbial communities, invading early during colonization, and potentially hindering degradation. In contrast to in vitro studies, we find that exploiters and degraders belong to distant lineages, facilitating their coexistence. Our approach opens novel avenues to use the wealth of genomic data available to infer ecological roles and interactions among microbes.

Robustness of phylogenetic inference to model misspecification caused by pairwise epistasis

Likelihood-based phylogenetic inference posits a probabilistic model of character state change along branches of a phylogenetic tree. These models typically assume statistical independence of sites in the sequence alignment. This is a restrictive assumption that facilitates computational tractability, but ignores how epistasis, the effect of genetic background on mutational effects, influences the evolution of functional sequences. We consider the effect of using a misspecified site-independent model on the accuracy of Bayesian phylogenetic inference in the setting of pairwise-site epistasis. Previous work has shown that as alignment length increases, tree reconstruction accuracy also increases. Here, we present a simulation study demonstrating that accuracy increases with alignment size even if the additional sites are epistatically coupled. We introduce an alignment-based test statistic that is a diagnostic for pairwise epistasis and can be used in posterior predictive checks.

Mechanisms That Shape Microbial Pangenomes

Analyses of multiple whole-genome sequences from the same species have revealed that differences in gene content can be substantial, particularly in prokaryotes. Such variation has led to the recognition of pangenomes, the complete set of genes present in a species - consisting of core genes, present in all individuals, and accessory genes whose presence is variable. Questions now arise about how pangenomes originate and evolve. We describe how gene content variation can arise as a result of the combination of several processes, including random drift, selection, gain/loss balance, and the influence of ecological and epistatic interactions. We believe that identifying the contributions of these processes to pangenomes will need novel theoretical approaches and empirical data.

Horizontal Gene Transfer as a Source of Conflict and Cooperation in Prokaryotes

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.

A multi-scale coevolutionary approach to predict interactions between protein domains

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

Phylogenetic Clustering of Genes Reveals Shared Evolutionary Trajectories and Putative Gene Functions

Homologous genes in prokaryotes can be described using phylogenetic profiles which summarize their patterns of presence or absence across a set of genomes. Phylogenetic profiles have been used for nearly twenty years to cluster genes based on measures such as the Euclidean distance between profile vectors. However, most approaches do not take into account the phylogenetic relationships amongst the profiled genomes, and overrepresentation of certain taxonomic groups (i.e., pathogenic species with many sequenced representatives) can skew the interpretation of profiles. We propose a new approach that uses a coevolutionary method defined by Pagel to account for the phylogenetic relationships amongst target organisms, and a hierarchical-clustering approach to define sets of genes with common distributions across the organisms. The clusters we obtain using our method show greater evidence of phylogenetic and functional clustering than a recently published approach based on hidden Markov models. Our clustering method identifies sets of amino-acid biosynthesis genes that constitute cohesive pathways, and motility/chemotaxis genes with common histories of descent and lateral gene transfer.

A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination

Genome-Wide Association Studies (GWAS) in microbial organisms have the potential to vastly improve the way we understand, manage, and treat infectious diseases. Yet, microbial GWAS methods established thus far remain insufficiently able to capitalise on the growing wealth of bacterial and viral genetic sequence data. Facing clonal population structure and homologous recombination, existing GWAS methods struggle to achieve both the precision necessary to reject spurious findings and the power required to detect associations in microbes. In this paper, we introduce a novel phylogenetic approach that has been tailor-made for microbial GWAS, which is applicable to organisms ranging from purely clonal to frequently recombining, and to both binary and continuous phenotypes. Our approach is robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Thorough testing via application to simulated data provides strong support for the power and specificity of our approach and demonstrates the advantages offered over alternative cluster-based and dimension-reduction methods. Two applications to Neisseria meningitidis illustrate the versatility and potential of our method, confirming previously-identified penicillin resistance loci and resulting in the identification of both well-characterised and novel drivers of invasive disease. Our method is implemented as an open-source R package called treeWAS which is freely available at https://github.com/caitiecollins/treeWAS.

Measurable differences often exist within a microbial population, with important ecological or epidemiological consequences. Examples include differences in growth rates, host range, transmissibility, antimicrobial resistance, virulence, etc. Understanding the genetic factors involved in these phenotypic properties is a crucial aim in microbial genomics. A fundamental approach for doing so is to perform a Genome-Wide Association Study (GWAS), where genomes are compared to search for genetic markers systematically correlated with the property of interest. If this strategy were implemented naively in microbes, it could lead to spurious results due to the confounding effects of population structure and recombination. Here we present treeWAS, a new phylogenetic method to perform microbial GWAS that avoids these pitfalls. We show, using simulated datasets, that treeWAS is able to distinguish between genetic markers that are truly associated with the property of interest and those that are not. Furthermore, we demonstrate that treeWAS offers advantages in both sensitivity and specificity over alternative cluster-based and dimension-reduction techniques. We also showcase treeWAS in two applications to real datasets from N. meningitidis. We have developed an easy-to-use implementation of treeWAS in the R environment, which should be useful to a wide range of researchers in microbial genomics.

Efficient Gene Tree Correction Guided by Genome Evolution

MOTIVATIONS

Gene trees inferred solely from multiple alignments of homologous sequences often contain weakly supported and uncertain branches. Information for their full resolution may lie in the dependency between gene families and their genomic context. Integrative methods, using species tree information in addition to sequence information, often rely on a computationally intensive tree space search which forecloses an application to large genomic databases.

RESULTS

We propose a new method, called ProfileNJ, that takes a gene tree with statistical supports on its branches, and corrects its weakly supported parts by using a combination of information from a species tree and a distance matrix. Its low running time enabled us to use it on the whole Ensembl Compara database, for which we propose an alternative, arguably more plausible set of gene trees. This allowed us to perform a genome-wide analysis of duplication and loss patterns on the history of 63 eukaryote species, and predict ancestral gene content and order for all ancestors along the phylogeny.

AVAILABILITY

A web interface called RefineTree, including ProfileNJ as well as a other gene tree correction methods, which we also test on the Ensembl gene families, is available at: http://www-ens.iro.umontreal.ca/~adbit/polytomysolver.html. The code of ProfileNJ as well as the set of gene trees corrected by ProfileNJ from Ensembl Compara version 73 families are also made available.

Evolutionary assembly patterns of prokaryotic genomes

Evolutionary innovation must occur in the context of some genomic background, which limits available evolutionary paths. For example, protein evolution by sequence substitution is constrained by epistasis between residues. In prokaryotes, evolutionary innovation frequently happens by macrogenomic events such as horizontal gene transfer (HGT). Previous work has suggested that HGT can be influenced by ancestral genomic content, yet the extent of such gene-level constraints has not yet been systematically characterized. Here, we evaluated the evolutionary impact of such constraints in prokaryotes, using probabilistic ancestral reconstructions from 634 extant prokaryotic genomes and a novel framework for detecting evolutionary constraints on HGT events. We identified 8228 directional dependencies between genes and demonstrated that many such dependencies reflect known functional relationships, including for example, evolutionary dependencies of the photosynthetic enzyme RuBisCO. Modeling all dependencies as a network, we adapted an approach from graph theory to establish chronological precedence in the acquisition of different genomic functions. Specifically, we demonstrated that specific functions tend to be gained sequentially, suggesting that evolution in prokaryotes is governed by functional assembly patterns. Finally, we showed that these dependencies are universal rather than clade-specific and are often sufficient for predicting whether or not a given ancestral genome will acquire specific genes. Combined, our results indicate that evolutionary innovation via HGT is profoundly constrained by epistasis and historical contingency, similar to the evolution of proteins and phenotypic characters, and suggest that the emergence of specific metabolic and pathological phenotypes in prokaryotes can be predictable from current genomes.

Diversification and coevolution of the ghrelin/growth hormone secretagogue receptor system in vertebrates

The gut hormone ghrelin is involved in numerous metabolic functions, such as the stimulation of growth hormone secretion, gastric motility, and food intake. Ghrelin is modified by ghrelin O-acyltransferase (GOAT) or membrane-bound O-acyltransferase domain-containing 4 (MBOAT4) enabling action through the growth hormone secretagogue receptors (GHS-R). During the course of evolution, initially strong ligand/receptor specificities can be disrupted by genomic changes, potentially modifying physiological roles of the ligand/receptor system. Here, we investigated the coevolution of ghrelin, GOAT, and GHS-R in vertebrates. We combined similarity search, conserved synteny analyses, phylogenetic reconstructions, and protein structure comparisons to reconstruct the evolutionary history of the ghrelin system. Ghrelin remained a single-gene locus in all vertebrate species, and accordingly, a single GHS-R isoform was identified in all tetrapods. Similar patterns of the nonsynonymous (dN) and synonymous (dS) ratio (dN/dS) in the vertebrate lineage strongly suggest coevolution of the ghrelin and GHS-R genes, supporting specific functional interactions and common physiological pathways. The selection profiles do not allow confirmation as to whether ghrelin binds specifically to GOAT, but the ghrelin dN/dS patterns are more similar to those of GOAT compared to MBOAT1 and MBOAT2 isoforms. Four GHS-R isoforms were identified in teleost genomes. This diversification of GHS-R resulted from successive rounds of duplications, some of which remained specific to the teleost lineage. Coevolution signals are lost in teleosts, presumably due to the diversification of GHS-R but not the ghrelin gene. The identification of the GHS-R diversity in teleosts provides a molecular basis for comparative studies on ghrelin's physiological roles and regulation, while the comparative sequence and structure analyses will assist translational medicine to determine structure-function relationships of the ghrelin/GHS-R system.

Evolution of chemical diversity by coordinated gene swaps in type II polyketide gene clusters

Natural product biosynthetic pathways generate molecules of enormous structural complexity and exquisitely tuned biological activities. Studies of natural products have led to the discovery of many pharmaceutical agents, particularly antibiotics. Attempts to harness the catalytic prowess of biosynthetic enzyme systems, for both compound discovery and engineering, have been limited by a poor understanding of the evolution of the underlying gene clusters. We developed an approach to study the evolution of biosynthetic genes on a cluster-wide scale, integrating pairwise gene coevolution information with large-scale phylogenetic analysis. We used this method to infer the evolution of type II polyketide gene clusters, tracing the path of evolution from the single ancestor to those gene clusters surviving today. We identified 10 key gene types in these clusters, most of which were swapped in from existing cellular processes and subsequently specialized. The ancestral type II polyketide gene cluster likely comprised a core set of five genes, a roster that expanded and contracted throughout evolution. A key C24 ancestor diversified into major classes of longer and shorter chain length systems, from which a C20 ancestor gave rise to the majority of characterized type II polyketide antibiotics. Our findings reveal that (i) type II polyketide structure is predictable from its gene roster, (ii) only certain gene combinations are compatible, and (iii) gene swaps were likely a key to evolution of chemical diversity. The lessons learned about how natural selection drives polyketide chemical innovation can be applied to the rational design and guided discovery of chemicals with desired structures and properties.

Comparative evolutionary analysis of cell cycle proteins networks in fission and budding yeast

Fission yeast and budding yeast are the two distantly related species with common ancestors. Various studies have shown significant differences in metabolic networks and regulatory networks. Cell cycle regulatory proteins in both species have differences in structural as well as in functional organization. Orthologous proteins in cell cycle regulatory protein networks seem to play contemporary role in both species during the evolution but little is known about non-orthologous proteins. Here, we used system biology approach to compare topological parameters of orthologous and non-orthologous proteins to find their contributions during the evolution to make an efficient cell cycle regulation. Observed results have shown a significant role of non-orthologous proteins in fission yeast in maintaining the efficiency of cell cycle regulation with less number of proteins as compared to budding yeast.

Practical aspects of protein co-evolution

Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.

Mapping proteins in the presence of paralogs using units of coevolution

Background

We study the problem of mapping proteins between two protein families in the presence of paralogs. This problem occurs as a difficult subproblem in coevolution-based computational approaches for protein-protein interaction prediction.

Results

Similar to prior approaches, our method is based on the idea that coevolution implies equal rates of sequence evolution among the interacting proteins, and we provide a first attempt to quantify this notion in a formal statistical manner. We call the units that are central to this quantification scheme the units of coevolution. A unit consists of two mapped protein pairs and its score quantifies the coevolution of the pairs. This quantification allows us to provide a maximum likelihood formulation of the paralog mapping problem and to cast it into a binary quadratic programming formulation.

Conclusion

CUPID, our software tool based on a Lagrangian relaxation of this formulation, makes it, for the first time, possible to compute state-of-the-art quality pairings in a few minutes of runtime. In summary, we suggest a novel alternative to the earlier available approaches, which is statistically sound and computationally feasible.

Reconciliation-based detection of co-evolving gene families

Background

Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region). Their evolution may also be related if they are involved in the same protein complex or biological process. Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.

Results

We devise a simple, fast and accurate probability method based on species tree-gene tree reconciliations to detect when two gene families have co-evolved. Our method observes the number and location of predicted macro-evolutionary events, and estimates the probability of having the observed number of common events by chance.

Conclusions

Simulation studies confirm that our method effectively identifies co-evolving families. This opens numerous perspectives on genome-scale analysis where this method could be used to pinpoint co-evolving gene families and thus help to unravel ancestral genome arrangements or undocumented gene interactions.

CoPAP: Coevolution of presence-absence patterns

Evolutionary analysis of phyletic patterns (phylogenetic profiles) is widely used in biology, representing presence or absence of characters such as genes, restriction sites, introns, indels and methylation sites. The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence–absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at http://copap.tau.ac.il/.

Antibiotic resistance shaping multi-level population biology of bacteria

Antibiotics have natural functions, mostly involving cell-to-cell signaling networks. The anthropogenic production of antibiotics, and its release in the microbiosphere results in a disturbance of these networks, antibiotic resistance tending to preserve its integrity. The cost of such adaptation is the emergence and dissemination of antibiotic resistance genes, and of all genetic and cellular vehicles in which these genes are located. Selection of the combinations of the different evolutionary units (genes, integrons, transposons, plasmids, cells, communities and microbiomes, hosts) is highly asymmetrical. Each unit of selection is a self-interested entity, exploiting the higher hierarchical unit for its own benefit, but in doing so the higher hierarchical unit might acquire critical traits for its spread because of the exploitation of the lower hierarchical unit. This interactive trade-off shapes the population biology of antibiotic resistance, a composed-complex array of the independent "population biologies." Antibiotics modify the abundance and the interactive field of each of these units. Antibiotics increase the number and evolvability of "clinical" antibiotic resistance genes, but probably also many other genes with different primary functions but with a resistance phenotype present in the environmental resistome. Antibiotics influence the abundance, modularity, and spread of integrons, transposons, and plasmids, mostly acting on structures present before the antibiotic era. Antibiotics enrich particular bacterial lineages and clones and contribute to local clonalization processes. Antibiotics amplify particular genetic exchange communities sharing antibiotic resistance genes and platforms within microbiomes. In particular human or animal hosts, the microbiomic composition might facilitate the interactions between evolutionary units involved in antibiotic resistance. The understanding of antibiotic resistance implies expanding our knowledge on multi-level population biology of bacteria.