Correction: PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph.

[This corrects the article DOI: 10.1371/journal.pcbi.1007732.].

Tempo and mode of genome evolution in a 50,000-generation experiment.

Adaptation by natural selection depends on the rates, effects and interactions of many mutations, making it difficult to determine what proportion of mutations in an evolving lineage are beneficial. Here we analysed 264 complete genomes from 12 Escherichia coli populations to characterize their dynamics over 50,000 generations. The populations that retained the ancestral mutation rate support a model in which most fixed mutations are beneficial, the fraction of beneficial mutations declines as fitness rises, and neutral mutations accumulate at a constant rate. We also compared these populations to mutation-accumulation lines evolved under a bottlenecking regime that minimizes selection. Nonsynonymous mutations, intergenic mutations, insertions and deletions are overrepresented in the long-term populations, further supporting the inference that most mutations that reached high frequency were favoured by selection. These results illuminate the shifting balance of forces that govern genome evolution in populations adapting to a new environment.

MicroScope: an integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis.

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. Launched in 2005, the MicroScope platform (https://www.genoscope.cns.fr/agc/microscope) has been under continuous development and provides analysis for prokaryotic genome projects together with metabolic network reconstruction and post-genomic experiments allowing users to improve the understanding of gene functions. Here we present new improvements of the MicroScope user interface for genome selection, navigation and expert gene annotation. Automatic functional annotation procedures of the platform have also been updated and we added several new tools for the functional annotation of genes and genomic regions. We finally focus on new tools and pipeline developed to perform comparative analyses on hundreds of genomes based on pangenome graphs. To date, MicroScope contains data for >11 800 microbial genomes, part of which are manually curated and maintained by microbiologists (>4500 personal accounts in September 2019). The platform enables collaborative work in a rich comparative genomic context and improves community-based curation efforts.

MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data.

The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.

panRGP: a pangenome-based method to predict genomic islands and explore their diversity.

MOTIVATION: Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity. RESULTS: We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies. AVAILABILITY AND IMPLEMENTATION: The methods presented in the current work are available through the following software: https://github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https://github.com/axbazin/panrgp_supdata.

PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph.

The use of comparative genomics for functional, evolutionary, and epidemiological studies requires methods to classify gene families in terms of occurrence in a given species. These methods usually lack multivariate statistical models to infer the partitions and the optimal number of classes and don't account for genome organization. We introduce a graph structure to model pangenomes in which nodes represent gene families and edges represent genomic neighborhood. Our method, named PPanGGOLiN, partitions nodes using an Expectation-Maximization algorithm based on multivariate Bernoulli Mixture Model coupled with a Markov Random Field. This approach takes into account the topology of the graph and the presence/absence of genes in pangenomes to classify gene families into persistent, cloud, and one or several shell partitions. By analyzing the partitioned pangenome graphs of isolate genomes from 439 species and metagenome-assembled genomes from 78 species, we demonstrate that our method is effective in estimating the persistent genome. Interestingly, it shows that the shell genome is a key element to understand genome dynamics, presumably because it reflects how genes present at intermediate frequencies drive adaptation of species, and its proportion in genomes is independent of genome size. The graph-based approach proposed by PPanGGOLiN is useful to depict the overall genomic diversity of thousands of strains in a compact structure and provides an effective basis for very large scale comparative genomics. The software is freely available at https://github.com/labgem/PPanGGOLiN.

The Arginine Deiminase Operon Is Responsible for a Fitness Trade-Off in Extended-Spectrum-ß-Lactamase-Producing Strains of Escherichia coli.

We previously identified an operon involved in an arginine deiminase (ADI) pathway (arc operon) on a CTX-M-producing plasmid from an O102-ST405 strain of Escherichia coli As the ADI pathway was shown to be involved in the virulence of various Gram-positive bacteria, we tested whether the ADI pathway could be involved in the epidemiological success of extended-spectrum-ß-lactamase (ESBL)-producing E. coli strains. We studied two collections of human E. coli isolated in France (n = 493) and England (n = 1,509) and show that the prevalence of the arc operon (i) is higher in ESBL-producing strains (12.1%) than in nonproducers (2.5%), (ii) is higher in CTX-M-producing strains (16%) than in other ESBL producers (3.5%), and (iii) increased over time in ESBL-producing strains from 0% before 2000 to 43.3% in 2011 to 2012. The arc operon, found in strains from various phylogenetic backgrounds, is carried by IncF plasmids (85%) or chromosomes (15%) in regions framed by numerous insertion sequences, indicating multiple arrivals. Competition experiments showed that the arc operon enhances fitness of the strain in vitro in lysogeny broth with arginine. In vivo competition experiments showed that the arc operon is advantageous for the strain in a mouse model of urinary tract infection (UTI), whereas it is a burden in a mouse model of intestinal colonization. In summary, we have identified a trait linked to CTX-M-producing strains that is responsible for a trade-off between two main E. coli lifestyles, UTI and gut commensalism. This trait alone cannot explain the wide spread of ESBLs in E. coli but merits epidemiological surveillance.

Parallel evolution of non-homologous isofunctional enzymes in methionine biosynthesis.

Experimental validation of enzyme function is crucial for genome interpretation, but it remains challenging because it cannot be scaled up to accommodate the constant accumulation of genome sequences. We tackled this issue for the MetA and MetX enzyme families, phylogenetically unrelated families of acyl-L-homoserine transferases involved in L-methionine biosynthesis. Members of these families are prone to incorrect annotation because MetX and MetA enzymes are assumed to always use acetyl-CoA and succinyl-CoA, respectively. We determined the enzymatic activities of 100 enzymes from diverse species, and interpreted the results by structural classification of active sites based on protein structure modeling. We predict that >60% of the 10,000 sequences from these families currently present in databases are incorrectly annotated, and suggest that acetyl-CoA was originally the sole substrate of these isofunctional enzymes, which evolved to use exclusively succinyl-CoA in the most recent bacteria. We also uncovered a divergent subgroup of MetX enzymes in fungi that participate only in L-cysteine biosynthesis as O-succinyl-L-serine transferases.

MicroScope in 2017: an expanding and evolving integrated resource for community expertise of microbial genomes.

The annotation of genomes from NGS platforms needs to be automated and fully integrated. However, maintaining consistency and accuracy in genome annotation is a challenging problem because millions of protein database entries are not assigned reliable functions. This shortcoming limits the knowledge that can be extracted from genomes and metabolic models. Launched in 2005, the MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Effective comparative analysis requires a consistent and complete view of biological data, and therefore, support for reviewing the quality of functional annotation is critical. MicroScope allows users to analyze microbial (meta)genomes together with post-genomic experiment results if any (i.e. transcriptomics, re-sequencing of evolved strains, mutant collections, phenotype data). It combines tools and graphical interfaces to analyze genomes and to perform the expert curation of gene functions in a comparative context. Starting with a short overview of the MicroScope system, this paper focuses on some major improvements of the Web interface, mainly for the submission of genomic data and on original tools and pipelines that have been developed and integrated in the platform: computation of pan-genomes and prediction of biosynthetic gene clusters. Today the resource contains data for more than 6000 microbial genomes, and among the 2700 personal accounts (65% of which are now from foreign countries), 14% of the users are performing expert annotations, on at least a weekly basis, contributing to improve the quality of microbial genome annotations.

GROOLS: reactive graph reasoning for genome annotation through biological processes.

BACKGROUND: High quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation. RESULTS: We developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented. CONCLUSIONS: GROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.

Expansion of the SOS regulon of Vibrio cholerae through extensive transcriptome analysis and experimental validation.

BACKGROUND: The SOS response is an almost ubiquitous response of cells to genotoxic stresses. The full complement of genes in the SOS regulon for Vibrio species has only been addressed through bioinformatic analyses predicting LexA binding box consensus and in vitro validation. Here, we perform whole transcriptome sequencing from Vibrio cholerae treated with mitomycin C as an SOS inducer to characterize the SOS regulon and other pathways affected by this treatment. RESULTS: Comprehensive transcriptional profiling allowed us to define the full landscape of promoters and transcripts active in V. cholerae. We performed extensive transcription start site (TSS) mapping as well as detection/quantification of the coding and non-coding RNA (ncRNA) repertoire in strain N16961. To improve TSS detection, we developed a new technique to treat RNA extracted from cells grown in various conditions. This allowed for identification of 3078 TSSs with an average 5'UTR of 116 nucleotides, and peak distribution between 16 and 64 nucleotides; as well as 629 ncRNAs. Mitomycin C treatment induced transcription of 737 genes and 28 ncRNAs at least 2 fold, while it repressed 231 genes and 17 ncRNAs. Data analysis revealed that in addition to the core genes known to integrate the SOS regulon, several metabolic pathways were induced. This study allowed for expansion of the Vibrio SOS regulon, as twelve genes (ubiEJB, tatABC, smpA, cep, VC0091, VC1190, VC1369-1370) were found to be co-induced with their adjacent canonical SOS regulon gene(s), through transcriptional read-through. Characterization of UV and mitomycin C susceptibility for mutants of these newly identified SOS regulon genes and other highly induced genes and ncRNAs confirmed their role in DNA damage rescue and protection. CONCLUSIONS: We show that genotoxic stress induces a pervasive transcriptional response, affecting almost 20% of the V. cholerae genes. We also demonstrate that the SOS regulon is larger than previously known, and its syntenic organization is conserved among Vibrio species. Furthermore, this specific co-localization is found in other γ-proteobacteria for genes recN-smpA and rmuC-tatABC, suggesting SOS regulon conservation in this phylum. Finally, we comment on the limitations of widespread NGS approaches for identification of all RNA species in bacteria.

The chimeric nature of the genomes of marine magnetotactic coccoid-ovoid bacteria defines a novel group of Proteobacteria.

Magnetotactic bacteria (MTB) are a group of phylogenetically and physiologically diverse Gram-negative bacteria that synthesize intracellular magnetic crystals named magnetosomes. MTB are affiliated with three classes of Proteobacteria phylum, Nitrospirae phylum, Omnitrophica phylum and probably with the candidate phylum Latescibacteria. The evolutionary origin and physiological diversity of MTB compared with other bacterial taxonomic groups remain to be illustrated. Here, we analysed the genome of the marine magneto-ovoid strain MO-1 and found that it is closely related to Magnetococcus marinus MC-1. Detailed analyses of the ribosomal proteins and whole proteomes of 390 genomes reveal that, among the Proteobacteria analysed, only MO-1 and MC-1 have coding sequences (CDSs) with a similarly high proportion of origins from Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria and Gammaproteobacteria. Interestingly, a comparative metabolic network analysis with anoxic network enzymes from sequenced MTB and non-MTB successfully allows the eventual prediction of an organism with a metabolic profile compatible for magnetosome production. Altogether, our genomic analysis reveals multiple origins of MO-1 and M. marinus MC-1 genomes and suggests a metabolism-restriction model for explaining whether a bacterium could become an MTB upon acquisition of magnetosome encoding genes.

Contrasting effects of historical contingency on phenotypic and genomic trajectories during a two-step evolution experiment with bacteria.

BACKGROUND: The impact of historical contingency, i.e. the past evolutionary history of a population, on further adaptation is mostly unknown at both the phenotypic and genomic levels. We addressed this question using a two-step evolution experiment. First, replicate populations of Escherichia coli were propagated in four different environmental conditions for 1000 generations. Then, all replicate populations were transferred and propagated for further 1000 generations to a single new environment. RESULTS: Using this two-step experimental evolution strategy, we investigated, at both the phenotypic and genomic levels, whether and how adaptation in the initial historical environments impacted evolutionary trajectories in a new environment. We showed that both the growth rate and fitness of the evolved populations obtained after the second step of evolution were contingent upon past evolutionary history. In contrast however, the genes that were modified during the second step of evolution were independent from the previous history of the populations. CONCLUSIONS: Our work suggests that historical contingency affects phenotypic adaptation to a new environment. This was however not reflected at the genomic level implying complex relationships between environmental factors and the genotype-to-phenotype map.

Synonymous Genetic Variation in Natural Isolates of Escherichia coli Does Not Predict Where Synonymous Substitutions Occur in a Long-Term Experiment.

Synonymous genetic differences vary by more than 20-fold among genes in natural isolates of Escherichia coli. One hypothesis to explain this heterogeneity is that genes with high levels of synonymous variation mutate at higher rates than genes with low synonymous variation. If so, then one would expect to observe similar mutational patterns in evolution experiments. In fact, however, the pattern of synonymous substitutions in a long-term evolution experiment with E. coli does not support this hypothesis. In particular, the extent of synonymous variation across genes in that experiment does not reflect the variation observed in natural isolates of E. coli. Instead, gene length alone predicts with high accuracy the prevalence of synonymous changes in the experimental populations. We hypothesize that patterns of synonymous variation in natural E. coli populations are instead caused by differences across genomic regions in their effective population size that, in turn, reflect different histories of recombination, horizontal gene transfer, selection, and population structure.

The revisited genome of Pseudomonas putida KT2440 enlightens its value as a robust metabolic chassis.

By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al., ) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data-let alone metabolic modeling and other nowadays common systems biology approaches-allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state-of-the-art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein-coding genes and re-annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

Revealing the hidden functional diversity of an enzyme family.

Millions of protein database entries are not assigned reliable functions, preventing the full understanding of chemical diversity in living organisms. Here, we describe an integrated strategy for the discovery of various enzymatic activities catalyzed within protein families of unknown or little known function. This approach relies on the definition of a generic reaction conserved within the family, high-throughput enzymatic screening on representatives, structural and modeling investigations and analysis of genomic and metabolic context. As a proof of principle, we investigated the DUF849 Pfam family and unearthed 14 potential new enzymatic activities, leading to the designation of these proteins as ß-keto acid cleavage enzymes. We propose an in vivo role for four enzymatic activities and suggest key residues for guiding further functional annotation. Our results show that the functional diversity within a family may be largely underestimated. The extension of this strategy to other families will improve our knowledge of the enzymatic landscape.

Genomic and physiological analysis reveals versatile metabolic capacity of deep-sea Photobacterium phosphoreum ANT-2200.

Bacteria of the genus Photobacterium thrive worldwide in oceans and show substantial eco-physiological diversity including free-living, symbiotic and piezophilic life styles. Genomic characteristics underlying this variability across species are poorly understood. Here we carried out genomic and physiological analysis of Photobacterium phosphoreum strain ANT-2200, the first deep-sea luminous bacterium of which the genome has been sequenced. Using optical mapping we updated the genomic data and reassembled it into two chromosomes and a large plasmid. Genomic analysis revealed a versatile energy metabolic potential and physiological analysis confirmed its growth capacity by deriving energy from fermentation of glucose or maltose, by respiration with formate as electron donor and trimethlyamine N-oxide (TMAO), nitrate or fumarate as electron acceptors, or by chemo-organo-heterotrophic growth in rich media. Despite that it was isolated at a site with saturated dissolved oxygen, the ANT-2200 strain possesses four gene clusters coding for typical anaerobic enzymes, the TMAO reductases. Elevated hydrostatic pressure enhances the TMAO reductase activity, mainly due to the increase of isoenzyme TorA1. The high copy number of the TMAO reductase isoenzymes and pressure-enhanced activity might imply a strategy developed by bacteria to adapt to deep-sea habitats where the instant TMAO availability may increase with depth.

RNase J depletion leads to massive changes in mRNA abundance in Helicobacter pylori.

Degradation of RNA as an intermediate message between genes and corresponding proteins is important for rapid attenuation of gene expression and maintenance of cellular homeostasis. This process is controlled by ribonucleases that have different target specificities. In the bacterial pathogen Helicobacter pylori, an exo- and endoribonuclease RNase J is essential for growth. To explore the role of RNase J in H. pylori, we identified its putative targets at a global scale using next generation RNA sequencing. We found that strong depletion for RNase J led to a massive increase in the steady-state levels of non-rRNAs. mRNAs and RNAs antisense to open reading frames were most affected with over 80% increased more than 2-fold. Non-coding RNAs expressed in the intergenic regions were much less affected by RNase J depletion. Northern blotting of selected messenger and non-coding RNAs validated these results. Globally, our data suggest that RNase J of H. pylori is a major RNase involved in degradation of most cellular RNAs.

Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load.

Mutations are the ultimate source of heritable variation for evolution. Understanding how mutation rates themselves evolve is thus essential for quantitatively understanding many evolutionary processes. According to theory, mutation rates should be minimized for well-adapted populations living in stable environments, whereas hypermutators may evolve if conditions change. However, the long-term fate of hypermutators is unknown. Using a phylogenomic approach, we found that an adapting Escherichia coli population that first evolved a mutT hypermutator phenotype was later invaded by two independent lineages with mutY mutations that reduced genome-wide mutation rates. Applying neutral theory to synonymous substitutions, we dated the emergence of these mutations and inferred that the mutT mutation increased the point-mutation rate by ∼150-fold, whereas the mutY mutations reduced the rate by ∼40-60%, with a corresponding decrease in the genetic load. Thus, the long-term fate of the hypermutators was governed by the selective advantage arising from a reduced mutation rate as the potential for further adaptation declined.

A new network representation of the metabolism to detect chemical transformation modules.

BACKGROUND: Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS). RESULTS: RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation. CONCLUSIONS: This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.