Anti-CRISPR Anopheles mosquitoes inhibit gene drive spread under challenging behavioural conditions in large cages

CRISPR-based gene drives have the potential to spread within populations and are considered as promising vector control tools. A doublesex-targeting gene drive was able to suppress laboratory Anopheles mosquito populations in small and large cages, and it is considered for field application. Challenges related to the field-use of gene drives and the evolving regulatory framework suggest that systems able to modulate or revert the action of gene drives, could be part of post-release risk-mitigation plans. In this study, we challenge an AcrIIA4-based anti-drive to inhibit gene drive spread in age-structured Anopheles gambiae population under complex feeding and behavioural conditions. A stochastic model predicts the experimentally-observed genotype dynamics in age-structured populations in medium-sized cages and highlights the necessity of large-sized cage trials. These experiments and experimental-modelling framework demonstrate the effectiveness of the anti-drive in different scenarios, providing further corroboration for its use in controlling the spread of gene drive in Anopheles.

Reduced ambiguity and improved interpretability of bacterial genome-wide associations using gene-cluster-centric k-mers

The wide adoption of bacterial genome sequencing and encoding both core and accessory genome variation using k-mers has allowed bacterial genome-wide association studies (GWAS) to identify genetic variants associated with relevant phenotypes such as those linked to infection. Significant limitations still remain because of k-mers being duplicated across gene clusters and as far as the interpretation of association results is concerned, which affects the wider adoption of GWAS methods on microbial data sets. We have developed a simple computational method (panfeed) that explicitly links each k-mer to their gene cluster at base-resolution level, which allows us to avoid biases introduced by a global de Bruijn graph as well as more easily map and annotate associated variants. We tested panfeed on two independent data sets, correctly identifying previously characterized causal variants, which demonstrates the precision of the method, as well as its scalable performance. panfeed is a command line tool written in the python programming language and is available at https://github.com/microbial-pangenomes-lab/panfeed.

The bacterial genetic determinants of Escherichia coli capacity to cause bloodstream infections in humans

Escherichia coli is both a highly prevalent commensal and a major opportunistic pathogen causing bloodstream infections (BSI). A systematic analysis characterizing the genomic determinants of extra-intestinal pathogenic vs. commensal isolates in human populations, which could inform mechanisms of pathogenesis, diagnostic, prevention and treatment is still lacking. We used a collection of 912 BSI and 370 commensal E. coli isolates collected in France over a 17-year period (2000-2017). We compared their pangenomes, genetic backgrounds (phylogroups, STs, O groups), presence of virulence-associated genes (VAGs) and antimicrobial resistance genes, finding significant differences in all comparisons between commensal and BSI isolates. A machine learning linear model trained on all the genetic variants derived from the pangenome and controlling for population structure reveals similar differences in VAGs, discovers new variants associated with pathogenicity (capacity to cause BSI), and accurately classifies BSI vs. commensal strains. Pathogenicity is a highly heritable trait, with up to 69% of the variance explained by bacterial genetic variants. Lastly, complementing our commensal collection with an older collection from 1980, we predict that pathogenicity continuously increased through 1980, 2000, to 2010. Together our findings imply that E. coli exhibit substantial genetic variation contributing to the transition between commensalism and pathogenicity and that this species evolved towards higher pathogenicity.

Epistatic interactions between the high pathogenicity island and other iron uptake systems shape Escherichia coli extra-intestinal virulence

The intrinsic virulence of extra-intestinal pathogenic Escherichia coli is associated with numerous chromosomal and/or plasmid-borne genes, encoding diverse functions such as adhesins, toxins, and iron capture systems. However, the respective contribution to virulence of those genes seems to depend on the genetic background and is poorly understood. Here, we analyze genomes of 232 strains of sequence type complex STc58 and show that virulence (quantified in a mouse model of sepsis) emerged in a sub-group of STc58 due to the presence of the siderophore-encoding high-pathogenicity island (HPI). When extending our genome-wide association study to 370 Escherichia strains, we show that full virulence is associated with the presence of the aer or sit operons, in addition to the HPI. The prevalence of these operons, their co-occurrence and their genomic location depend on strain phylogeny. Thus, selection of lineage-dependent specific associations of virulence-associated genes argues for strong epistatic interactions shaping the emergence of virulence in E. coli.

The DNA cytosine methylome revealed two methylation motifs in the upstream regions of genes related to morphological and physiological differentiation in Streptomyces coelicolor A(3)2 M145

DNA methylation is an epigenetic modification detected in both prokaryotic and eukaryotic genomic DNAs. In bacteria, the importance of 5-methylcytosine (m5C) in gene expression has been less investigated than in eukaryotic systems. Through dot-blot analysis employing m5C antibodies against chromosomal DNA, we have previously demonstrated that m5C influences the differentiation of Streptomyces coelicolor A(3)2 M145 in solid sporulating and liquid non-sporulating complex media. Here, we mapped the methylated cytosines of the M145 strain growing in the defined Maltose Glutamate (MG) liquid medium. Sequencing of the M145 genome after bisulfite treatment (BS-sequencing) evidenced 3360 methylated cytosines and the two methylation motifs, GGCmCGG and GCCmCG, in the upstream regions of 321 genes. Besides, the role of cytosine methylation was investigated using the hypo-methylating agent 5'-aza-2'-deoxycytidine (5-aza-dC) in S. coelicolor cultures, demonstrating that m5C affects both growth and antibiotic biosynthesis. Finally, quantitative reverse-transcription polymerase-chain-reaction (RT-qPCR) analysis of genes containing the methylation motifs in the upstream regions showed that 5-aza-dC treatment influenced their transcriptional levels and those of the regulatory genes for two antibiotics. To the best of our knowledge, this is the first study that reports the cytosine methylome of S. coelicolor M145, supporting the crucial role ascribed to cytosine methylation in controlling bacterial gene expression.

ChemGAPP: A tool for Chemical Genomics Analysis and Phenotypic Profiling

MOTIVATION

High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data.

RESULTS

ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small, for small-scale screens and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the E. coli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small, demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type.

AVAILABILITY

ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

A Code of Ethics for Gene Drive Research

Gene drives hold promise for use in controlling insect vectors of diseases, agricultural pests, and for conservation of ecosystems against invasive species. At the same time, this technology comes with potential risks that include unknown downstream effects on entire ecosystems as well as the accidental or nefarious spread of organisms that carry the gene drive machinery. A code of ethics can be a useful tool for all parties involved in the development and regulation of gene drives and can be used to help ensure that a balanced analysis of risks, benefits, and values is taken into consideration in the interest of society and humanity. We have developed a code of ethics for gene drive research with the hope that this code will encourage the development of an international framework that includes ethical guidance of gene drive research and is incorporated into scientific practice by gaining broad agreement and adherence.

Major role of iron uptake systems in the intrinsic extra-intestinal virulence of the genus Escherichia revealed by a genome-wide association study

The genus Escherichia is composed of several species and cryptic clades, including E. coli, which behaves as a vertebrate gut commensal, but also as an opportunistic pathogen involved in both diarrheic and extra-intestinal diseases. To characterize the genetic determinants of extra-intestinal virulence within the genus, we carried out an unbiased genome-wide association study (GWAS) on 370 commensal, pathogenic and environmental strains representative of the Escherichia genus phylogenetic diversity and including E. albertii (n = 7), E. fergusonii (n = 5), Escherichia clades (n = 32) and E. coli (n = 326), tested in a mouse model of sepsis. We found that the presence of the high-pathogenicity island (HPI), a ~35 kbp gene island encoding the yersiniabactin siderophore, is highly associated with death in mice, surpassing other associated genetic factors also related to iron uptake, such as the aerobactin and the sitABCD operons. We confirmed the association in vivo by deleting key genes of the HPI in E. coli strains in two phylogenetic backgrounds. We then searched for correlations between virulence, iron capture systems and in vitro growth in a subset of E. coli strains (N = 186) previously phenotyped across growth conditions, including antibiotics and other chemical and physical stressors. We found that virulence and iron capture systems are positively correlated with growth in the presence of numerous antibiotics, probably due to co-selection of virulence and resistance. We also found negative correlations between virulence, iron uptake systems and growth in the presence of specific antibiotics (i.e. cefsulodin and tobramycin), which hints at potential “collateral sensitivities” associated with intrinsic virulence. This study points to the major role of iron capture systems in the extra-intestinal virulence of the genus Escherichia.

Bacterial isolates belonging to the genus Escherichia can be human commensals but also opportunistic pathogens, with the ability to cause extra-intestinal infection. There is therefore the need to identify the genetic elements that favour extra-intestinal virulence, so that virulent bacterial isolates can be identified through genome analysis and potential treatment strategies be developed. To reduce the influence of host variability on virulence, we have used a mouse model of sepsis to characterize the virulence of 370 strains belonging to the genus Escherichia, for which whole genome sequences were also available. We have used a statistical approach called Genome-Wide Association Study (GWAS) to show how the presence of genes that encode for iron scavenging are significantly associated with the propensity of a bacterial isolate to cause extra-intestinal infections. Taking advantage of previously generated growth data on a subset of the strains and its correlation to virulence we generated hypothesis on the relationship between iron scavenging and growth in the presence of various antimicrobials, which could have implications for developing new treatment strategies.

Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions

Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially.IMPORTANCE Being able to identify the genetic variants responsible for specific bacterial phenotypes has been the goal of bacterial genetics since its inception and is fundamental to our current level of understanding of bacteria. This identification has been based primarily on painstaking experimentation, but the availability of large data sets of whole genomes with associated phenotype metadata promises to revolutionize this approach, not least for important clinical phenotypes that are not amenable to laboratory analysis. These models of phenotype-genotype association can in the future be used for rapid prediction of clinically important phenotypes such as antibiotic resistance and virulence by rapid-turnaround or point-of-care tests. However, despite much effort being put into adapting genome-wide association study (GWAS) approaches to cope with bacterium-specific problems, such as strong population structure and horizontal gene exchange, current approaches are not yet optimal. We describe a method that advances methodology for both association and generation of portable prediction models.

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.

pyseer: a comprehensive tool for microbial pangenome-wide association studies

Summary

Genome-wide association studies (GWAS) in microbes have different challenges to GWAS in eukaryotes. These have been addressed by a number of different methods. pyseer brings these techniques together in one package tailored to microbial GWAS, allows greater flexibility of the input data used, and adds new methods to interpret the association results.

Availability and implementation

pyseer is written in python and is freely available at https://github.com/mgalardini/pyseer, or can be installed through pip. Documentation and a tutorial are available at http://pyseer.readthedocs.io.

Supplementary information

Supplementary data are available at Bioinformatics online.

A resource of variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure-based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

Comparison of Highly and Weakly Virulent Dickeya solani Strains, With a View on the Pangenome and Panregulon of This Species

Bacteria belonging to the genera Dickeya and Pectobacterium are responsible for significant economic losses in a wide variety of crops and ornamentals. During last years, increasing losses in potato production have been attributed to the appearance of Dickeya solani. The D. solani strains investigated so far share genetic homogeneity, although different virulence levels were observed among strains of various origins. The purpose of this study was to investigate the genetic traits possibly related to the diverse virulence levels by means of comparative genomics. First, we developed a new genome assembly pipeline which allowed us to complete the D. solani genomes. Four de novo sequenced and ten publicly available genomes were used to identify the structure of the D. solani pangenome, in which 74.8 and 25.2% of genes were grouped into the core and dispensable genome, respectively. For D. solani panregulon analysis, we performed a binding site prediction for four transcription factors, namely CRP, KdgR, PecS and Fur, to detect the regulons of these virulence regulators. Most of the D. solani potential virulence factors were predicted to belong to the accessory regulons of CRP, KdgR, and PecS. Thus, some differences in gene expression could exist between D. solani strains. The comparison between a highly and a low virulent strain, IFB0099 and IFB0223, respectively, disclosed only small differences between their genomes but significant differences in the production of virulence factors like pectinases, cellulases and proteases, and in their mobility. The D. solani strains also diverge in the number and size of prophages present in their genomes. Another relevant difference is the disruption of the adhesin gene fhaB2 in the highly virulent strain. Strain IFB0223, which has a complete adhesin gene, is less mobile and less aggressive than IFB0099. This suggests that in this case, mobility rather than adherence is needed in order to trigger disease symptoms. This study highlights the utility of comparative genomics in predicting D. solani traits involved in the aggressiveness of this emerging plant pathogen.

Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

Phenotype inference in an Escherichia coli strain panel

Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.

Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis

A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria.

Metabolic modelling reveals the specialization of secondary replicons for niche adaptation in Sinorhizobium meliloti

The genome of about 10% of bacterial species is divided among two or more large chromosome-sized replicons. The contribution of each replicon to the microbial life cycle (for example, environmental adaptations and/or niche switching) remains unclear. Here we report a genome-scale metabolic model of the legume symbiont Sinorhizobium meliloti that is integrated with carbon utilization data for 1,500 genes with 192 carbon substrates. Growth of S. meliloti is modelled in three ecological niches (bulk soil, rhizosphere and nodule) with a focus on the role of each of its three replicons. We observe clear metabolic differences during growth in the tested ecological niches and an overall reprogramming following niche switching. In silico examination of the inferred fitness of gene deletion mutants suggests that secondary replicons evolved to fulfil a specialized function, particularly host-associated niche adaptation. Thus, genes on secondary replicons might potentially be manipulated to promote or suppress host interactions for biotechnological purposes.

The genome of some bacteria consists of two or more chromosomes or replicons. Here, diCenzo et al. integrate genome-scale metabolic modelling and growth data from a collection of mutants of the plant symbiont Sinorhizobium meliloti to estimate the fitness contribution of each replicon in three environments.

Mixed Nodule Infection in Sinorhizobium meliloti-Medicago sativa Symbiosis Suggest the Presence of Cheating Behavior

In the symbiosis between rhizobia and legumes, host plants can form symbiotic root nodules with multiple rhizobial strains, potentially showing different symbiotic performances in nitrogen fixation. Here, we investigated the presence of mixed nodules, containing rhizobia with different degrees of mutualisms, and evaluate their relative fitness in the Sinorhizobium meliloti-Medicago sativa model symbiosis. We used three S. meliloti strains, the mutualist strains Rm1021 and BL225C and the non-mutualist AK83. We performed competition experiments involving both in vitro and in vivo symbiotic assays with M. sativa host plants. We show the occurrence of a high number (from 27 to 100%) of mixed nodules with no negative effect on both nitrogen fixation and plant growth. The estimation of the relative fitness as non-mutualist/mutualist ratios in single nodules shows that in some nodules the non-mutualist strain efficiently colonized root nodules along with the mutualist ones. In conclusion, we can support the hypothesis that in S. meliloti-M. sativa symbiosis mixed nodules are formed and allow non-mutualist or less-mutualist bacterial partners to be less or not sanctioned by the host plant, hence allowing a potential form of cheating behavior to be present in the nitrogen fixing symbiosis.

COLOMBOS v3.0: leveraging gene expression compendia for cross-species analyses

COLOMBOS is a database that integrates publicly available transcriptomics data for several prokaryotic model organisms. Compared to the previous version it has more than doubled in size, both in terms of species and data available. The manually curated condition annotation has been overhauled as well, giving more complete information about samples’ experimental conditions and their differences. Functionality-wise cross-species analyses now enable users to analyse expression data for all species simultaneously, and identify candidate genes with evolutionary conserved expression behaviour. All the expression-based query tools have undergone a substantial improvement, overcoming the limit of enforced co-expression data retrieval and instead enabling the return of more complex patterns of expression behaviour. COLOMBOS is freely available through a web application at http://colombos.net/. The complete database is also accessible via REST API or downloadable as tab-delimited text files.

Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome

Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial pangenomes.

The influence of transcriptional regulatory networks on the evolution of bacterial pangenomes has not yet been elucidated, even though the role of transcriptional regulation is widely recognized. Using the model symbiont Sinorhizobium meliloti we have predicted the regulatory targets of 41 transcription factors in 51 strains and 5 other rhizobial species, showing a correlation between regulon diversity and pangenome evolution, through upstream sequence diversity and accessory genome composition. We have also shown that genes not wired to the regulatory network are more likely to belong to the accessory genome, thus suggesting that inclusion in the regulatory circuits may be an indicator of gene conservation. We have also highlighted a series of transcription factors that preferentially regulate genes belonging to one of the three replicons of this species, indicating the presence of replicon-specific regulatory modules, with peculiar functional signatures. At the same time the chromid shares a significant part of the regulatory network with the chromosome, indicating an additional way by which this replicon integrates itself in the pangenome.

Mapping contigs using CONTIGuator

Obtaining bacterial genomic sequences has become a routine task in today's biology. The emergence of the comparative genomics approach has led to an increasing number of bacterial species having more than one strain sequenced, thus facilitating the annotation process. On the other hand, many genomic sequences are now left in the "draft" status, as a series of contigs, mainly for the labor-intensive finishing task. As a result, many genomic analyses are incomplete (e.g., in their annotation) or impossible to be performed (e.g., structural genomics analysis). Many approaches have been recently developed to facilitate the finishing process or at least to produce higher quality scaffolds; taking advantage of the comparative genomics paradigm, closely related genomes are used to align the contigs and determine their relative orientation, thus facilitating the finishing process, but also producing higher quality scaffolds. In this chapter we present the use of the CONTIGuator algorithm, which aligns the contigs from a draft genome to a closely related closed genome and resolves their relative orientation based on this alignment, producing a scaffold and a series of PCR primer pairs for the finishing process. The CONTIGuator algorithm is also capable of handling multipartite genomes (i.e., genomes having chromosomes and other plasmids), univocally mapping contigs to the most similar replicon. The program also produces a series of contig maps that allow to perform structural genomics analysis on the draft genome. The functionalities of the web interface, as well as the command line version, are presented.

MeDuSa: a multi-draft based scaffolder

MOTIVATION

Completing the genome sequence of an organism is an important task in comparative, functional and structural genomics. However, this remains a challenging issue from both a computational and an experimental viewpoint. Genome scaffolding (i.e. the process of ordering and orientating contigs) of de novo assemblies usually represents the first step in most genome finishing pipelines.

RESULTS

In this article we present MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalizes the scaffolding problem by means of a combinatorial optimization formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries. This makes usability and running time two additional important features of our method. Moreover, benchmarks and tests on real bacterial datasets showed that MeDuSa is highly accurate and, in most cases, outperforms traditional scaffolders. The possibility to use MeDuSa on eukaryotic datasets has also been evaluated, leading to interesting results.

Evaluation of the Performances of Ribosomal Database Project (RDP) Classifier for Taxonomic Assignment of 16S rRNA Metabarcoding Sequences Generated from Illumina-Solexa NGS

Here we report a benchmark of the effect of bootstrap cut-off values of the RDP Classifier tool in terms of data retention along the different taxonomic ranks by using Illumina reads. Results provide guidelines for planning sequencing depths and selection of bootstrap cut-off in taxonomic assignments.

From pangenome to panphenome and back

The ability to relate genomic differences in bacterial species to their variability in expressed phenotypes is one of the most challenging tasks in today's biology. Such task is of paramount importance towards the understanding of biotechnologically relevant pathways and possibly for their manipulation. Fundamental prerequisites are the genome-wide reconstruction of metabolic pathways and a comprehensive measurement of cellular phenotypes. Cellular pathways can be reliably reconstructed using the KEGG database, while the OmniLog™ Phenotype Microarray (PM) technology may be used to measure nearly 2,000 growth conditions over time. However, few computational tools that can directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are available. In this chapter the use of the DuctApe software suite is presented, which allows the joint analysis of bacterial genomic and phenomic data, highlighting those pathways and reactions most probably associated with phenotypic variability. A case study on four Sinorhizobium meliloti strains is presented; more example datasets are available online.

High-throughput phenomics

Standard protocols are available in order to apply Phenotype MicroArray (PM) technology to characterize different groups of microorganisms. Nevertheless, there is the need to pay attention to several crucial steps in order to obtain high-quality and reproducible data from PM, such as the choice of the Dye mix, the type and concentration of the carbon source in metabolic experiments, the use of a buffered medium. A systematic research of auxotrophies in strains to be tested should be carefully evaluated before starting with PM experiments. Detailed protocols to obtain defined and reproducible phenotypic profiles for bacteria and yeasts are shown. Moreover, the innovative software opm R packages and DuctApe suite for the analysis of kinetic data produced by PM and panphenome description are reported.

Enly: Improving Draft Genomes through Reads Recycling

The reconstruction of the complete genome sequence of an organism is an important point for comparative, functional and evolutionary genomics. Nevertheless, overcoming the problems encountered while completing the sequence of an entire genome can still be demanding in terms of time and resources. We have developed Enly, a simple tool based on the iterative mapping of sequence reads at contig edges, capable to extend the genomic contigs deriving from high-throughput sequencing, especially those deriving by Newbler-like assemblies. Testing it on a set of de novo draft genomes led to the closure of up to 20% of the gaps originally present. Enly is cross-platform and most of the steps of its pipeline are parallelizable, making easy and fast to improve a draft genome resulting from a de novo assembly.

Permanent draft genome sequences of the symbiotic nitrogen fixing Ensifer meliloti strains BO21CC and AK58

Ensifer (syn. Sinorhizobium) meliloti is an important symbiotic bacterial species that fixes nitrogen. Strains BO21CC and AK58 were previously investigated for their substrate utilization and their plant-growth promoting abilities showing interesting features. Here, we describe the complete genome sequence and annotation of these strains. BO21CC and AK58 genomes are 6,985,065 and 6,974,333 bp long with 6,746 and 6,992 genes predicted, respectively.

DuctApe: a suite for the analysis and correlation of genomic and OmniLog™ Phenotype Microarray data

Addressing the functionality of genomes is one of the most important and challenging tasks of today's biology. In particular the ability to link genotypes to corresponding phenotypes is of interest in the reconstruction and biotechnological manipulation of metabolic pathways. Over the last years, the OmniLog™ Phenotype Microarray (PM) technology has been used to address many specific issues related to the metabolic functionality of microorganisms. However, computational tools that could directly link PM data with the gene(s) of interest followed by the extraction of information on gene-phenotype correlation are still missing. Here we present DuctApe, a suite that allows the analysis of both genomic sequences and PM data, to find metabolic differences among PM experiments and to correlate them with KEGG pathways and gene presence/absence patterns. As example, an application of the program to four bacterial datasets is presented. The source code and tutorials are available at http://combogenomics.github.io/DuctApe/.

The DivJ, CbrA and PleC system controls DivK phosphorylation and symbiosis in Sinorhizobium meliloti

Sinorhizobium meliloti is a soil bacterium that invades the root nodules it induces on Medicago sativa, whereupon it undergoes an alteration of its cell cycle and differentiates into nitrogen-fixing, elongated and polyploid bacteroid with higher membrane permeability. In Caulobacter crescentus, a related alphaproteobacterium, the principal cell cycle regulator, CtrA, is inhibited by the phosphorylated response regulator DivK. The phosphorylation of DivK depends on the histidine kinase DivJ, while PleC is the principal phosphatase for DivK. Despite the importance of the DivJ in C. crescentus, the mechanistic role of this kinase has never been elucidated in other Alphaproteobacteria. We show here that the histidine kinases DivJ together with CbrA and PleC participate in a complex phosphorylation system of the essential response regulator DivK in S. meliloti. In particular, DivJ and CbrA are involved in DivK phosphorylation and in turn CtrA inactivation, thereby controlling correct cell cycle progression and the integrity of the cell envelope. In contrast, the essential PleC presumably acts as a phosphatase of DivK. Interestingly, we found that a DivJ mutant is able to elicit nodules and enter plant cells, but fails to establish an effective symbiosis suggesting that proper envelope and/or low CtrA levels are required for symbiosis.

Replicon-dependent bacterial genome evolution: the case of Sinorhizobium meliloti

Many bacterial species, such as the alphaproteobacterium Sinorhizobium meliloti, are characterized by open pangenomes and contain multipartite genomes consisting of a chromosome and other large-sized replicons, such as chromids, megaplasmids, and plasmids. The evolutionary forces in both functional and structural aspects that shape the pangenome of species with multipartite genomes are still poorly understood. Therefore, we sequenced the genomes of 10 new S. meliloti strains, analyzed with four publicly available additional genomic sequences. Results indicated that the three main replicons present in these strains (a chromosome, a chromid, and a megaplasmid) partly show replicon-specific behaviors related to strain differentiation. In particular, the pSymB chromid was shown to be a hot spot for positively selected genes, and, unexpectedly, genes resident in the pSymB chromid were also found to be more widespread in distant taxa than those located in the other replicons. Moreover, through the exploitation of a DNA proximity network, a series of conserved “DNA backbones” were found to shape the evolution of the genome structure, with the rest of the genome experiencing rearrangements. The presented data allow depicting a scenario where the pSymB chromid has a distinctive role in intraspecies differentiation and in evolution through positive selection, whereas the pSymA megaplasmid mostly contributes to structural fluidity and to the emergence of new functions, indicating a specific evolutionary role for each replicon in the pangenome evolution.

The success of acinetobacter species; genetic, metabolic and virulence attributes

An understanding of why certain Acinetobacter species are more successful in causing nosocomial infections, transmission and epidemic spread in healthcare institutions compared with other species is lacking. We used genomic, phenotypic and virulence studies to identify differences between Acinetobacter species. Fourteen strains representing nine species were examined. Genomic analysis of six strains showed that the A. baumannii core genome contains many genes important for diverse metabolism and survival in the host. Most of the A. baumannii core genes were also present in one or more of the less clinically successful species. In contrast, when the accessory genome of an individual A. baumannii strain was compared to a strain of a less successful species (A. calcoaceticus RUH2202), many operons with putative virulence function were found to be present only in the A. baumannii strain, including the csu operon, the acinetobactin chromosomal cluster, and bacterial defence mechanisms. Phenotype microarray analysis showed that compared to A. calcoaceticus (RUH2202), A. baumannii ATCC 19606^T was able to utilise nitrogen sources more effectively and was more tolerant to pH, osmotic and antimicrobial stress. Virulence differences were also observed, with A. baumannii ATCC 19606^T, A. pittii SH024, and A. nosocomialis RUH2624 persisting and forming larger biofilms on human skin than A. calcoaceticus. A. baumannii ATCC 19606^T and A. pittii SH024 were also able to survive in a murine thigh infection model, whereas the other two species were eradicated. The current study provides important insights into the elucidation of differences in clinical relevance among Acinetobacter species.

Plant-bacteria association and symbiosis: are there common genomic traits in alphaproteobacteria?

Alphaproteobacteria show a great versatility in adapting to a broad range of environments and lifestyles, with the association between bacteria and plants as one of the most intriguing, spanning from relatively unspecific nonsymbiotic association (as rhizospheric or endophytic strains) to the highly species-specific interaction of rhizobia. To shed some light on possible common genetic features in such a heterogeneous set of plant associations, the genomes of 92 Alphaproteobacteria strains were analyzed with a fuzzy orthologs-species detection approach. This showed that the different habitats and lifestyles of plant-associated bacteria (soil, plant colonizers, symbiont) are partially reflected by the trend to have larger genomes with respect to nonplant-associated species. A relatively large set of genes specific to symbiotic bacteria (73 orthologous groups) was found, with a remarkable presence of regulators, sugar transporters, metabolic enzymes, nodulation genes and several genes with unknown function that could be good candidates for further characterization. Interestingly, 15 orthologous groupspresent in all plant-associated bacteria (symbiotic and nonsymbiotic), but absent in nonplant-associated bacteria, were also found, whose functions were mainly related to regulation of gene expression and electron transport. Two of these orthologous groups were also detected in fully sequenced plant-associated Betaproteobacteria and Gammaproteobacteria. Overall these results lead us to hypothesize that plant-bacteria associations, though quite variable, are partially supported by a conserved set of unsuspected gene functions.

CONTIGuator: a bacterial genomes finishing tool for structural insights on draft genomes

Recent developments in sequencing technologies have given the opportunity to sequence many bacterial genomes with limited cost and labor, compared to previous techniques. However, a limiting step of genome sequencing is the finishing process, needed to infer the relative position of each contig and close sequencing gaps. An additional degree of complexity is given by bacterial species harboring more than one replicon, which are not contemplated by the currently available programs. The availability of a large number of bacterial genomes allows geneticists to use complete genomes (possibly from the same species) as templates for contigs mapping.

Here we present CONTIGuator, a software tool for contigs mapping over a reference genome which allows the visualization of a map of contigs, underlining loss and/or gain of genetic elements and permitting to finish multipartite genomes. The functionality of CONTIGuator was tested using four genomes, demonstrating its improved performances compared to currently available programs.

Our approach appears efficient, with a clear visualization, allowing the user to perform comparative structural genomics analysis on draft genomes. CONTIGuator is a Python script for Linux environments and can be used on normal desktop machines and can be downloaded from http://contiguator.sourceforge.net.

Exploring the symbiotic pangenome of the nitrogen-fixing bacterium Sinorhizobium meliloti

Background

Sinorhizobium meliloti is a model system for the studies of symbiotic nitrogen fixation. An extensive polymorphism at the genetic and phenotypic level is present in natural populations of this species, especially in relation with symbiotic promotion of plant growth. AK83 and BL225C are two nodule-isolated strains with diverse symbiotic phenotypes; BL225C is more efficient in promoting growth of the Medicago sativa plants than strain AK83. In order to investigate the genetic determinants of the phenotypic diversification of S. meliloti strains AK83 and BL225C, we sequenced the complete genomes for these two strains.

Results

With sizes of 7.14 Mbp and 6.97 Mbp, respectively, the genomes of AK83 and BL225C are larger than the laboratory strain Rm1021. The core genome of Rm1021, AK83, BL225C strains included 5124 orthologous groups, while the accessory genome was composed by 2700 orthologous groups. While Rm1021 and BL225C have only three replicons (Chromosome, pSymA and pSymB), AK83 has also two plasmids, 260 and 70 Kbp long. We found 65 interesting orthologous groups of genes that were present only in the accessory genome, consequently responsible for phenotypic diversity and putatively involved in plant-bacterium interaction. Notably, the symbiosis inefficient AK83 lacked several genes required for microaerophilic growth inside nodules, while several genes for accessory functions related to competition, plant invasion and bacteroid tropism were identified only in AK83 and BL225C strains. Presence and extent of polymorphism in regulons of transcription factors involved in symbiotic interaction were also analyzed. Our results indicate that regulons are flexible, with a large number of accessory genes, suggesting that regulons polymorphism could also be a key determinant in the variability of symbiotic performances among the analyzed strains.

Conclusions

In conclusions, the extended comparative genomics approach revealed a variable subset of genes and regulons that may contribute to the symbiotic diversity.