Computational methods for predicting genomic islands in microbial genomes

Genomic Comparative of Pseudomonas aeruginosa Small Colony Variant, Mucoid and Non-mucoid Phenotypes Obtained from a Patient with Cystic Fibrosis During Respiratory Exacerbations

Pseudomonas aeruginosa, the most prevalent opportunistic pathogen in chronic obstructive pulmonary disease, associated with high morbidity and mortality in patients with cystic fibrosis (CF), is practically impossible to be eradicated from the airways in chronicity. Its extraordinary genomic plasticity is possibly associated with high antimicrobial resistance, virulence factors, and its phenotypic diversity. The occurrence of P. aeruginosa isolates promoting airway infection, showing mucoid, non-mucoid, and small colony variant (SCV) phenotypes, was observed simultaneously, in the present study, in sputum cultures obtained from a male CF young patient with chronic pulmonary infection for over a decade. The isolates belonged to a new ST (2744) were obtained in two moments of exacerbation of the respiratory disease, in which he was hospitalized. Genetic background and phenotypic analysis indicated that the isolates exhibited multi- and pan-antimicrobial resistant profiles, as well as non-susceptible to polymyxin and predominantly hypermutable (HPM) phenotypes. Whole genome sequencing showed variations in genome sizes, coding sequences and their determinants of resistance and virulence. The annotated genomes were compared for antimicrobial resistance, hypermutability, and SCV characteristics. We highlight the lack of reported genetic determinants of SCV emergence and HPM phenotypes, which can be explained in part due to the very short time between collections of isolates. To the best of our knowledge, this is the first report of genome sequencing of P. aeruginosa SCV from a CF patient in Brazil.

Using the pan-genomic framework for the discovery of genomic islands in the haloarchaeon Halorubrum ezzemoulense

In this study, we use pan-genomics to characterize the genomic variability of the widely dispersed halophilic archaeal species Halorubrum ezzemoulense (Hez). We include a multi-regional sampling of newly sequenced, high-quality draft genomes. The pan-genome graph of the species reveals 50 genomic islands that represent rare accessory genetic capabilities available to members. Most notably, we observe rearrangements that have led to the insertion/recombination/replacement of mutually exclusive genomic islands in equivalent genome positions ("homeocassettes"). These conflicting islands encode for similar functions, but homologs from islands located between the same core genes exhibit high divergence on the amino acid level, while the neighboring core genes are nearly identical. Both islands of a homeocassette often coexist in the same geographic location, suggesting that either island may be beyond the reach of selective sweeps and that these loci of divergence between Hez members are maintained and persist long term. This implies that subsections of the population have different niche preferences and rare metabolic capabilities. After an evaluation of the gene content in the homeocassettes, we speculate that these islands may play a role in the speciation, niche adaptability, and group selection dynamics in Hez. Though homeocassettes are first described in this study, similar replacements and divergence of genes on genomic islands have been previously reported in other Haloarchaea and distantly related Archaea, suggesting that homeocassettes may be a feature in a wide range of organisms outside of Hez.IMPORTANCEThis study catalogs the rare genes discovered in strains of the species Halorubrum ezzemoulense (Hez), an obligate halophilic archaeon, through the perspective of its pan-genome. These rare genes are often found to be arranged on islands that confer metabolic and transport functions and contain genes that have eluded previous studies. The discovery of divergent, but homologous islands occupying equivalent genome positions ("homeocassettes") in different genomes, reveals significant new information on genome evolution in Hez. Homeocassette pairs encode for similar functions, but their dissimilarity and distribution imply high rates of recombination, different specializations, and niche preferences in Hez. The coexistence of both islands of a homeocassette pair in multiple environments demonstrates that both islands are beyond the reach of selective sweeps and that these genome content differences between strains persist long term. The switch between islands through recombination under different environmental conditions may lead to a greater range of niche adaptability in Hez.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures

Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic diversity. Predicting their intercellular mobility is of utmost importance in the context of the emerging crisis of multidrug resistance. Here, we propose a large-scale classification method to categorize GIs according to their mobility profile and, subsequently, analyze their gene cargo. We based our classification decision scheme on a collection of mobility protein motif definitions available in publicly accessible databases. Our results show that the size distribution of GI classes correlates with their respective structure and complexity. Self-transmissible GIs are usually the largest, except in Bacillota and Actinomycetota, accumulate antibiotic and phage resistance genes, and favour the use of a tyrosine recombinase to insert into a host's replicon. Non-mobilizable GIs tend to use a DDE transposase instead. Finally, although tRNA genes are more frequently targeted as insertion sites by GIs encoding a tyrosine recombinase, most GIs insert in a protein-encoding gene. This study is a stepping stone toward a better characterization of mobile GIs in bacterial genomes and their mechanism of mobility.

The complete and closed genome of the facultative generalist Candidatus Endoriftia persephone from deep-sea hydrothermal vents

The mutualistic interactions between Riftia pachyptila and its endosymbiont Candidatus Endoriftia persephone (short Endoriftia) have been extensively researched. However, the closed Endoriftia genome is still lacking. Here, by employing single-molecule real-time sequencing we present the closed chromosomal sequence of Endoriftia. In contrast to theoretical predictions of enlarged and mobile genetic element-rich genomes related to facultative endosymbionts, the closed Endoriftia genome is streamlined with fewer than expected coding sequence regions, insertion-, prophage-sequences and transposase-coding sequences. Automated and manually curated functional analyses indicated that Endoriftia is more versatile regarding sulphur metabolism than previously reported. We identified the presence of two identical rRNA operons and two long CRISPR regions in the closed genome. Additionally, pangenome analyses revealed the presence of three types of secretion systems (II, IV and VI) in the different Endoriftia populations indicating lineage-specific adaptations. The in depth mobilome characterization identified the presence of shared genomic islands in the different Endoriftia drafts and in the closed genome, suggesting that the acquisition of foreign DNA predates the geographical dispersal of the different endosymbiont populations. Finally, we found no evidence of epigenetic regulation in Endoriftia, as revealed by gene screenings and absence of methylated modified base motifs in the genome. As a matter of fact, the restriction-modification system seems to be dysfunctional in Endoriftia, pointing to a higher importance of molecular memory-based immunity against phages via spacer incorporation into CRISPR system. The Endoriftia genome is the first closed tubeworm endosymbiont to date and will be valuable for future gene oriented and evolutionary comparative studies.

Genome sequencing of biocontrol strain Bacillus amyloliquefaciens Bam1 and further analysis of its heavy metal resistance mechanism

Plant growth-promoting rhizobacteria (PGPR) or Biocontrol strains inevitably encounter heavy metal excess stress during the product's processing and application. Bacillus amyloliquefaciens Bam1 was a potential biocontrol strain with strong heavy metal resistant ability. To understand its heavy metal resistance mechanism, the complete genome of Bam1 had been sequenced, and the comparative genomic analysis of Bam1 and FZB42, an industrialized PGPR and biocontrol strain with relatively lower heavy metal tolerance, was conducted. The comparative genomic analysis of Bam1 and the other nine B. amyloliquefaciens strains as well as one Bacillus velezensis (genetically and physiologically very close to B. amyloliquefaciens) was also performed. Our results showed that the complete genome size of Bam1 was 3.95 Mb, 4219 coding sequences were predicted, and it possessed the highest number of unique genes among the eleven analyzed strains. Nine genes related to heavy metal resistance were detected within the twelve DNA islands of Bam1, while only two of them were detected within the seventeen DNA islands of FZB42. When compared with B. amyloliquefaciens type strain DSM7, Bam1 lacked contig L, whereas FZB42 lacked contig D and I, as well as just possessed contig B with a very small size. Our results could also deduce that Bam1 promoted its essential heavy metal resistance mainly by decreasing the import and increasing the export of heavy metals with the corresponding homeostasis systems, which are regulated by different metalloregulators. While Bam1 promoted its non-essential heavy metal resistance mainly by the activation of some specific or non-specific exporters responding to different heavy metals. The variation of the genes related to heavy metal resistance and the other differences of the genomes, including the different number and arrangement of contigs, as well as the number of the heavy metal resistant genes in Prophages and Genomic islands, led to the significant different resistance of Bam1 and FZB42 to heavy metals.

Discovery of archaeal fusexins homologous to eukaryotic HAP2/GCS1 gamete fusion proteins

Sexual reproduction consists of genome reduction by meiosis and subsequent gamete fusion. The presence of genes homologous to eukaryotic meiotic genes in archaea and bacteria suggests that DNA repair mechanisms evolved towards meiotic recombination. However, fusogenic proteins resembling those found in gamete fusion in eukaryotes have so far not been found in prokaryotes. Here, we identify archaeal proteins that are homologs of fusexins, a superfamily of fusogens that mediate eukaryotic gamete and somatic cell fusion, as well as virus entry. The crystal structure of a trimeric archaeal fusexin (Fusexin1 or Fsx1) reveals an archetypical fusexin architecture with unique features such as a six-helix bundle and an additional globular domain. Ectopically expressed Fusexin1 can fuse mammalian cells, and this process involves the additional globular domain and a conserved fusion loop. Furthermore, archaeal fusexin genes are found within integrated mobile elements, suggesting potential roles in cell-cell fusion and gene exchange in archaea, as well as different scenarios for the evolutionary history of fusexins.

Performance assessment of genomic island prediction tools with an improved version of Design-Island

Genomic Islands (GIs) play an important role in the evolution and adaptation of prokaryotes. The origin and extent of ecological diversity of prokaryotes can be analyzed by comparing GIs across closely or distantly related prokaryotes. Understanding the importance of GI and to study the bacterial evolution, several GI prediction tools have been generated. An unsupervised method, Design-Island, was developed to identify GIs using Monte-Carlo statistical test on randomly selected segments of a chromosome. Here, in the present study Design-Island was modified with the incorporation of majority voting, multiple hypothesis testing correction. The performance of the modified version, Design-Island-II was tested and compared with the existing GI prediction tools. The performance assessment and benchmarking of the GI prediction tools require experimentally validated dataset, which is lacking. So, different datasets, generated or taken from literature were utilized to compare the sensitivity (SN), specificity (SP), precision (PPV) and accuracy (AC) of Design-Island-II. It showed substantial enhancement in term of SN, SP, PPV and AC, and significantly reduced the computation time of the algorithm. The performance of Design-Island-II has also been compared with several GI prediction tools using curated dataset of putative horizontally transferred genes. Design-Island-II showed the highest sensitivity and F1 score, comparable specificity, precision and accuracy in comparison to the other available methods. IslandViewer4 and Islander outperformed all the available methods in terms of AC and PPV respectively. Our study suggested Design-Island-II, IslandViewer4 and GIHunter among the top performing GI prediction tools considering both sensitivity and specificity of the methods.

Comparative Genomics Revealed Wide Intra-Species Genetic Heterogeneity and Lineage-Specific Genes of Akkermansia muciniphila

Akkermansia muciniphila has potential as a next-generation probiotic, but few previous studies attempted to analyze its intraspecies population diversity. In this study, we performed a comparative genomic analysis of 112 filtered genomes from the NCBI database. The populations formed three clades (A-C) on the phylogenetic tree, suggesting the existence of three genetic lineages though clades B and C were phylogenetically closer than clade A. The three clades also showed geographic-based clustering, different genetic characteristics, and clade-specific genes. Two putative functional genes (RecD2 and xerD) were specific to clade C due to genomic islands. These lineage-specific genes might be associated with differences in genomic features (number of phages/genomic islands, pan-core genome, recombination rate, genetic diversity) between genetic lineages. The carbohydrate utilization gene profile (particularly for glycolytic hydrolases and carbohydrate esterases) also varied between clades, suggesting different carbohydrate metabolism potential/requirements between genetic lineages. Our findings provide important implications for future research on A. muciniphila.

IMPORTANCEAkkermansia muciniphila has been widely accepted as part of the next generation of probiotics. However, most current studies on A. muciniphila have focused on the application of type strain BAA835^T in the treatment of diseases, while few studies have reported on the genomic specificity, population structure, and functional characteristics of A. muciniphila species. By comparing the genomes of 112 strains from NCBI which met the quality control conditions, we found that the A. muciniphila population could be divided into three main clades (clades A to C) and presented a certain regional aggregation. There are significant differences among the three clades in their genetic characteristics and functional genes (the type strain BAA835^T was located in clade A), especially in genes related to carbohydrate metabolism. It should be mentioned that probiotics should be a concept at the strain level rather than at the gut species level, so the probiotic properties of A. muciniphila need to be carefully interpreted.

Complete genome sequence of biocontrol strain Paenibacillus peoriae HJ-2 and further analysis of its biocontrol mechanism

Background

Paris polyphylla is a herb widely used in traditional Chinese medicine to treat various diseases. Stem rot diseases seriously affected the yield of P. polyphylla in subtropical areas of China. Therefore, cost-effective, chemical-free, eco-friendly strategies to control stem rot on P. polyphylla are valuable and urgently needed.

Results

In this paper, we reported the biocontrol efficiency of Paenibacillus peoriae HJ-2 and its complete genome sequence. Strain HJ-2 could serve as a potential biocontrol agent against stem rot on P. polyphylla in the greenhouse and field. The genome of HJ-2 consists of a single 6,001,192 bp chromosome with an average GC content of 45% and 5,237 predicted protein coding genes, 39 rRNAs and 108 tRNAs. The phylogenetic tree indicated that HJ-2 is most closely related to P. peoriae IBSD35. Functional analysis of genome revealed numerous genes/gene clusters involved in plant colonization, biofilm formation, plant growth promotion, antibiotic and resistance inducers synthesis. Moreover, metabolic pathways that potentially contribute to biocontrol mechanisms were identified.

Conclusions

This study revealed that P. peoriae HJ-2 could serve as a potential BCA against stem rot on P. polyphylla. Based on genome analysis, the genome of HJ-2 contains more than 70 genes and 12 putative gene clusters related to secondary metabolites, which have previously been described as being involved in chemotaxis motility, biofilm formation, growth promotion, antifungal activity and resistance inducers biosynthesis. Compared with other strains, variation in the genes/gene clusters may lead to different antimicrobial spectra and biocontrol efficacies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08330-0.

Identifying genomic islands with deep neural networks

Background

Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types.

Results

We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools.

Conclusions

We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm

BACKGROUND

Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes.

RESULTS

We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method.

CONCLUSIONS

Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

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.

Prophage loci predictor for bacterial genomes

This paper proposes a new algorithm for prophage loci prediction in bacteria. Prophages are defined in Bioinformatics as viral nucleotide sequences that are found intermixed with host nucleotide sequences in bacteria. The proposed algorithm uses machine learning patterns and processing methodologies in order to provide a highly efficient system for loci prediction, thereby reducing the time-space complexity required unlike others of its class. In the training phase, a pattern database is constructed from raw nucleotide sequences of both bacteria and viruses obtained from a training set. In the prediction phase, the aforementioned database is used along with Particle Swarm Optimization (PSO) to predict the probable loci of prophages in a test set of bacterial nucleotide sequences. Testing this method on raw sequences consisting of both partial and complete nucleotide sequences of various bacteria has yielded good results in predicting the loci of prophages in them. This algorithm and connected processes compare favorably in terms of predictive performance with others of its class such as PhiSpy and ProphET, while outperforming others in terms of raw processing speed, suggesting that a data-centric approach can yield comparable results while using a fraction of the resources.

Genomic Characteristics of Invasive Streptococcus pneumoniae Serotype 1 in New Caledonia Prior to the Introduction of PCV13

Streptococcus pneumoniae serotype 1 is a common cause of global invasive pneumococcal disease. In New Caledonia, serotype 1 is the most prevalent serotype and led to two major outbreaks reported in the 2000s. The pneumococcal conjugate vaccine 13 (PCV13) was introduced into the vaccination routine, intending to prevent the expansion of serotype 1 in New Caledonia. Aiming to provide a baseline for monitoring the post-PCV13 changes, we performed a whole-genome sequence analysis on 67 serotype 1 isolates collected prior to the PCV13 introduction. To highlight the S. pneumoniae serotype 1 population structure, we performed a multilocus sequence typing (MLST) analysis revealing that NC serotype 1 consisted of 2 sequence types: ST3717 and the highly dominant ST306. Both sequence types harbored the same resistance genes to beta-lactams, macrolide, streptogramin B, fluoroquinolone, and lincosamide antibiotics. We have also identified 36 virulence genes that were ubiquitous to all the isolates. Among these virulence genes, the pneumolysin sequence presented an allelic profile associated with disease outbreaks and reduced hemolytic activity. Moreover, recombination hotspots were identified in 4 virulence genes and more notably in the cps locus (cps2L), potentially leading to capsular switching, a major mechanism of the emergence of nonvaccine types. In summary, this study represents the first overview of the genomic characteristics of S. pneumoniae serotype 1 in New Caledonia prior to the introduction of PCV13. This preliminary description represents a baseline to assess the impact of PCV13 on serotype 1 population structure and genomic diversity.

Pathogenicity Factors of Genomic Islands in Intestinal and Extraintestinal Escherichia coli

Escherichia coli is a versatile bacterial species that includes both harmless commensal strains and pathogenic strains found in the gastrointestinal tract in humans and warm-blooded animals. The growing amount of DNA sequence information generated in the era of "genomics" has helped to increase our understanding of the factors and mechanisms involved in the diversification of this bacterial species. The pathogenic side of E. coli that is afforded through horizontal transfers of genes encoding virulence factors enables this bacterium to become a highly diverse and adapted pathogen that is responsible for intestinal or extraintestinal diseases in humans and animals. Many of the accessory genes acquired by horizontal transfers form syntenic blocks and are recognized as genomic islands (GIs). These genomic regions contribute to the rapid evolution, diversification and adaptation of E. coli variants because they are frequently subject to rearrangements, excision and transfer, as well as to further acquisition of additional DNA. Here, we review a subgroup of GIs from E. coli termed pathogenicity islands (PAIs), a concept defined in the late 1980s by Jörg Hacker and colleagues in Werner Goebel's group at the University of Würzburg, Würzburg, Germany. As with other GIs, the PAIs comprise large genomic regions that differ from the rest of the genome by their G + C content, by their typical insertion within transfer RNA genes, and by their harboring of direct repeats (at their ends), integrase determinants, or other mobility loci. The hallmark of PAIs is their contribution to the emergence of virulent bacteria and to the development of intestinal and extraintestinal diseases. This review summarizes the current knowledge on the structure and functional features of PAIs, on PAI-encoded E. coli pathogenicity factors and on the role of PAIs in host-pathogen interactions.

Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Salmonella Pathogenicity Island 1 (SPI-1): The Evolution and Stabilization of a Core Genomic Type Three Secretion System

Salmonella Pathogenicity Island 1 (SPI-1) encodes a type three secretion system (T3SS), effector proteins, and associated transcription factors that together enable invasion of epithelial cells in animal intestines. The horizontal acquisition of SPI-1 by the common ancestor of all Salmonella is considered a prime example of how gene islands potentiate the emergence of new pathogens with expanded niche ranges. However, the evolutionary history of SPI-1 has attracted little attention. Here, we apply phylogenetic comparisons across the family Enterobacteriaceae to examine the history of SPI-1, improving the resolution of its boundaries and unique architecture by identifying its composite gene modules. SPI-1 is located between the core genes fhlA and mutS, a hotspot for the gain and loss of horizontally acquired genes. Despite the plasticity of this locus, SPI-1 demonstrates stable residency of many tens of millions of years in a host genome, unlike short-lived homologous T3SS and effector islands including Escherichia ETT2, Yersinia YSA, Pantoea PSI-2, Sodalis SSR2, and Chromobacterium CPI-1. SPI-1 employs a unique series of regulatory switches, starting with the dedicated transcription factors HilC and HilD, and flowing through the central SPI-1 regulator HilA. HilA is shared with other T3SS, but HilC and HilD may have their evolutionary origins in Salmonella. The hilA, hilC, and hilD gene promoters are the most AT-rich DNA in SPI-1, placing them under tight control by the transcriptional repressor H-NS. In all Salmonella lineages, these three promoters resist amelioration towards the genomic average, ensuring strong repression by H-NS. Hence, early development of a robust and well-integrated regulatory network may explain the evolutionary stability of SPI-1 compared to T3SS gene islands in other species.

Typing methods based on whole genome sequencing data

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Serratia liquefaciens FG3 isolated from a metallophyte plant sheds light on the evolution and mechanisms of adaptive traits in extreme environments

Serratia liquefaciens strain FG3 (SlFG3), isolated from the flower of Stachytarpheta glabra in the Brazilian ferruginous fields, has distinctive genomic, adaptive, and biotechnological potential. Herein, using a combination of genomics and molecular approaches, we unlocked the evolution of the adaptive traits acquired by S1FG3, which exhibits the second largest chromosome containing the largest conjugative plasmids described for Serratia. Comparative analysis revealed the presence of 18 genomic islands and 311 unique protein families involved in distinct adaptive features. S1FG3 has a diversified repertoire of genes associated with Nonribosomal peptides (NRPs/PKS), a complete and functional cluster related to cellulose synthesis, and an extensive and functional repertoire of oxidative metabolism genes. In addition, S1FG3 possesses a complete pathway related to protocatecuate and chloroaromatic degradation, and a complete repertoire of genes related to DNA repair and protection that includes mechanisms related to UV light tolerance, redox process resistance, and a laterally acquired capacity to protect DNA using phosphorothioation. These findings summarize that SlFG3 is well-adapted to different biotic and abiotic stress situations imposed by extreme conditions associated with ferruginous fields, unlocking the impact of the lateral gene transfer to adjust the genome for extreme environments, and providing insight into the evolution of prokaryotes.

VCGIDB: A Database and Web Resource for the Genomic Islands from Vibrio Cholerae

Vibrio cholerae is the causative agent of cholera, which is a severe, life-threatening diarrheal disease. The current seventh pandemic has not been eradicated and the outbreak is still ongoing around the world. The evolution of the pandemic-causing strain has been greatly influenced by lateral gene transfer, and the mechanisms of acquisition of pathogenicity in V. cholerae are mainly involved with genomic islands (GIs). Thus, detecting GIs and their comprehensive information is necessary to understand the continuing resurgence and newly emerging pathogenic V. cholerae strains. In this study, 798 V. cholerae strains were tested using the GI-Scanner algorithm, which was developed to detect candidate GIs and identify them in a comparative genomics approach. The algorithm predicted 435 highly possible genomic islands, and we built a database, called Vibrio cholerae Genomic Island Database (VCGIDB). This database shows advanced results that were acquired from a large genome set using phylogeny-based predictions. Moreover, VCGIDB is a highly expendable database that does not require intensive computation, which enables us to update it with a greater number of genomes using a novel genomic island prediction method. The VCGIDB website allows the user to browse the data and presents the results in a visual manner.

Preliminary safety assessment of a new Bacteroides fragilis isolate

The novel commensal strain of Bacteroides fragilis HCK-B3 isolated from a healthy Chinese donor was discovered beneficial effects of attenuating lipopolysaccharides-induced inflammation. In order to contribute to the development of natural next-generation probiotic strains, the safety assessment was carried out with in vitro investigations of its morphology, potential virulence genes and antimicrobial resistance, and an in vivo acute toxicity study based on both healthy and immunosuppressed mice by cyclophosphamide injection. Consequently, the potential virulence genes in the genome of B. fragilis HCK-B3 have yet been identified as toxicity-associated. The absence of plasmids prevents the possibility of transferring antibiotic resistance features to other intestinal commensals. No intracorporal pathogenic properties were observed according to the body weight, hematological and liver parameters, cytokine secretions and tissue integrity. In addition, B. fragilis HCK-B3 performed alleviations on part of the side effects caused by the cyclophosphamide treatment. Thus, the novel strain of B. fragilis HCK-B3 was confirmed to be non-toxigenic and did not display adverse effects in both healthy and immune-deficient mice at a routinely applicable dose.

Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes

High-throughput shotgun metagenomics sequencing has enabled the profiling of myriad natural communities. These data are commonly used to identify gene families and pathways that were potentially gained or lost in an environment and which may be involved in microbial adaptation. Despite the widespread interest in these events, there are no established best practices for identifying gene gain and loss in metagenomics data. Horizontal gene transfer (HGT) represents several mechanisms of gene gain that are especially of interest in clinical microbiology due to the rapid spread of antibiotic resistance genes in natural communities. Several additional mechanisms of gene gain and loss, including gene duplication, gene loss-of-function events, and de novo gene birth are also important to consider in the context of metagenomes but have been less studied. This review is largely focused on detecting HGT in prokaryotic metagenomes, but methods for detecting these other mechanisms are first discussed. For this article to be self-contained, we provide a general background on HGT and the different possible signatures of this process. Lastly, we discuss how improved assembly of genomes from metagenomes would be the most straight-forward approach for improving the inference of gene gain and loss events. Several recent technological advances could help improve metagenome assemblies: long-read sequencing, determining the physical proximity of contigs, optical mapping of short sequences along chromosomes, and single-cell metagenomics. The benefits and limitations of these advances are discussed and open questions in this area are highlighted.

Improved genomic island predictions with IslandPath-DIMOB

Motivation

Genomic islands (GIs) are clusters of genes of probable horizontal origin that play a major role in bacterial and archaeal genome evolution and microbial adaptability. They are of high medical and industrial interest, due to their enrichment in virulence factors, some antimicrobial resistance genes and adaptive metabolic pathways. The development of more sensitive but precise prediction tools, using either sequence composition-based methods or comparative genomics, is needed as large-scale analyses of microbial genomes increase.

Results

IslandPath-DIMOB, a leading GI prediction tool in the IslandViewer webserver, has now been significantly improved by modifying both the decision algorithm to determine sequence composition biases, and the underlying database of HMM profiles for associated mobility genes. The accuracy of IslandPath-DIMOB and other major software has been assessed using a reference GI dataset predicted by comparative genomics, plus a manually curated dataset from literature review. Compared to the previous version (v0.2.0), this IslandPath-DIMOB v1.0.0 achieves 11.7% and 5.3% increase in recall and precision, respectively. IslandPath-DIMOB has the highest Matthews correlation coefficient among individual prediction methods tested, combining one of the highest recall measures (46.9%) at high precision (87.4%). The only method with higher recall had notably lower precision (55.1%). This new IslandPath-DIMOB v1.0.0 will facilitate more accurate studies of GIs, including their key roles in microbial adaptability of medical, environmental and industrial interest.

Availability and implementation

IslandPath-DIMOB v1.0.0 is freely available through the IslandViewer webserver {{http://www.pathogenomics.sfu.ca/islandviewer/}} and as standalone software {{https://github.com/brinkmanlab/islandpath/}} under the GNU-GPLv3.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genomic characterization of Escherichia coli LCT-EC001, an extremely multidrug-resistant strain with an amazing number of resistance genes

BACKGROUND

Multidrug resistance is a growing global public health threat with far more serious consequences than generally anticipated. In this study, we investigated the antibiotic resistance and genomic traits of a clinical strain of Escherichia coli LCT-EC001.

RESULTS

LCT-EC001 was resistant to 16 kinds of widely used antibiotics, including fourth-generation cephalosporins and carbapenems. In total, up to 68 determinants associated with antibiotic resistance were identified, including 8 beta-lactamase genes (notably producing ESBLs and KPCs), 31 multidrug efflux system genes, 6 outer membrane transport system genes, 4 aminoglycoside-modifying enzyme genes, 10 two-component regulatory system genes, and 9 other enzyme or transcriptional regulator genes, covering nearly all known drug-resistance mechanisms in E. coli. More than half of the resistance genes were located close to mobile genetic elements, such as plasmids, transposons, genomics islands, and insertion sequences. Phylogenetic analysis revealed that this strain may have evolved from E. coli K-12 but is a completely new MLST type.

CONCLUSIONS

Antibiotic resistance was extremely severe in E. coli LCT-EC001, mainly due to mobile genetic elements that allowed the gain of a large quantity of resistance genes. The antibiotic resistance genes of E. coli LCT-EC001 can probably be transferred to other bacteria. To the best of our knowledge, this is the first report of a strain of E. coli which has such a large amount of antibiotic resistance genes. Apart from providing an E. coli reference genome with an extremely high multidrug-resistant background for future analyses, this work also offers a strategy for investigating the complement and characteristics of genes contributing to drug resistance at the whole-genome level.

VRprofile: gene-cluster-detection-based profiling of virulence and antibiotic resistance traits encoded within genome sequences of pathogenic bacteria

VRprofile is a Web server that facilitates rapid investigation of virulence and antibiotic resistance genes, as well as extends these trait transfer-related genetic contexts, in newly sequenced pathogenic bacterial genomes. The used backend database MobilomeDB was firstly built on sets of known gene cluster loci of bacterial type III/IV/VI/VII secretion systems and mobile genetic elements, including integrative and conjugative elements, prophages, class I integrons, IS elements and pathogenicity/antibiotic resistance islands. VRprofile is thus able to co-localize the homologs of these conserved gene clusters using HMMer or BLASTp searches. With the integration of the homologous gene cluster search module with a sequence composition module, VRprofile has exhibited better performance for island-like region predictions than the other widely used methods. In addition, VRprofile also provides an integrated Web interface for aligning and visualizing identified gene clusters with MobilomeDB-archived gene clusters, or a variety set of bacterial genomes. VRprofile might contribute to meet the increasing demands of re-annotations of bacterial variable regions, and aid in the real-time definitions of disease-relevant gene clusters in pathogenic bacteria of interest. VRprofile is freely available at http://bioinfo-mml.sjtu.edu.cn/VRprofile.

PhageWeb - Web Interface for Rapid Identification and Characterization of Prophages in Bacterial Genomes

This study developed a computational tool with a graphical interface and a web-service that allows the identification of phage regions through homology search and gene clustering. It uses G+C content variation evaluation and tRNA prediction sites as evidence to reinforce the presence of prophages in indeterminate regions. Also, it performs the functional characterization of the prophages regions through data integration of biological databases. The performance of PhageWeb was compared to other available tools (PHASTER, Prophinder, and PhiSpy) using Sensitivity (Sn) and Positive Predictive Value (PPV) tests. As a reference for the tests, more than 80 manually annotated genomes were used. In the PhageWeb analysis, the Sn index was 86.1% and the PPV was approximately 87%, while the second best tool presented Sn and PPV values of 83.3 and 86.5%, respectively. These numbers allowed us to observe a greater precision in the regions identified by PhageWeb while compared to other prediction tools submitted to the same tests. Additionally, PhageWeb was much faster than the other computational alternatives, decreasing the processing time to approximately one-ninth of the time required by the second best software. PhageWeb is freely available at http://computationalbiology.ufpa.br/phageweb.

Comparative Analysis of Genomic Island Prediction Tools

Tools for genomic island prediction use strategies for genomic comparison analysis and sequence composition analysis. The goal of comparative analysis is to identify unique regions in the genomes of related organisms, whereas sequence composition analysis evaluates and relates the composition of specific regions with other regions in the genome. The goal of this study was to qualitatively and quantitatively evaluate extant genomic island predictors. We chose tools reported to produce significant results using sequence composition prediction, comparative genomics, and hybrid genomics methods. To maintain diversity, the tools were applied to eight complete genomes of organisms with distinct characteristics and belonging to different families. Escherichia coli CFT073 was used as a control and considered as the gold standard because its islands were previously curated in vitro. The results of predictions with the gold standard were manually curated, and the content and characteristics of each predicted island were analyzed. For other organisms, we created GenBank (GBK) files using Artemis software for each predicted island. We copied only the amino acid sequences from the coding sequence and constructed a multi-FASTA file for each predictor. We used BLASTp to compare all results and generate hits to evaluate similarities and differences among the predictions. Comparison of the results with the gold standard revealed that GIPSy produced the best results, covering ~91% of the composition and regions of the islands, followed by Alien Hunter (81%), IslandViewer (47.8%), Predict Bias (31%), GI Hunter (17%), and Zisland Explorer (16%). The tools with the best results in the analyzes of the set of organisms were the same ones that presented better performance in the tests with the gold standard.

Pilot Safety Evaluation of a Novel Strain of Bacteroides ovatus

Bacteroides ovatus ELH-B2 is considered as a potential next-generation probiotic due to its preventive effects on lipopolysaccharides-associated inflammation and intestinal microbiota disorders in mice. To study safety issues associated with B. ovatus ELH-B2, we conducted comprehensive and systematic experiments, including in vitro genetic assessments of potential virulence and antimicrobial resistance genes, and an in vivo acute toxicity study of both immunocompetent and immunosuppressed mice via cyclophosphamide treatment. The results indicated that this novel strain is non-toxigenic, fragilysin is not expressed, and most of potential virulence genes are correlated with cellular structures such as capsular polysaccharide and polysaccharide utilizations. The antibiotic resistance features are unlikely be transferred to other intestinal microorganisms as no plasmids nor related genomic islands were identified. Side effects were not observed in mice. B. ovatus ELH-B2 also alleviated the damages caused by cyclophosphamide injection.

Complete Genome Sequence of Industrial Biocontrol Strain Paenibacillus polymyxa HY96-2 and Further Analysis of Its Biocontrol Mechanism

Paenibacillus polymyxa (formerly known as Bacillus polymyxa) has been extensively studied for agricultural applications as a plant-growth-promoting rhizobacterium and is also an important biocontrol agent. Our team has developed the P. polymyxa strain HY96-2 from the tomato rhizosphere as the first microbial biopesticide based on P. polymyxa for controlling plant diseases around the world, leading to the commercialization of this microbial biopesticide in China. However, further research is essential for understanding its precise biocontrol mechanisms. In this paper, we report the complete genome sequence of HY96-2 and the results of a comparative genomic analysis between different P. polymyxa strains. The complete genome size of HY96-2 was found to be 5.75 Mb and 5207 coding sequences were predicted. HY96-2 was compared with seven other P. polymyxa strains for which complete genome sequences have been published, using phylogenetic tree, pan-genome, and nucleic acid co-linearity analysis. In addition, the genes and gene clusters involved in biofilm formation, antibiotic synthesis, and systemic resistance inducer production were compared between strain HY96-2 and two other strains, namely, SC2 and E681. The results revealed that all three of the P. polymyxa strains have the ability to control plant diseases via the mechanisms of colonization (biofilm formation), antagonism (antibiotic production), and induced resistance (systemic resistance inducer production). However, the variation of the corresponding genes or gene clusters between the three strains may lead to different antimicrobial spectra and biocontrol efficacies. Two possible pathways of biofilm formation in P. polymyxa were reported for the first time after searching the KEGG database. This study provides a scientific basis for the further optimization of the field applications and quality standards of industrial microbial biopesticides based on HY96-2. It may also serve as a reference for studying the differences in antimicrobial spectra and biocontrol capability between different biocontrol agents.

Comparative and phylogenetic analysis of a novel family of Enterobacteriaceae-associated genomic islands that share a conserved excision/integration module

Genomic Islands (GIs) are DNA regions acquired through horizontal gene transfer that encode advantageous traits for bacteria. Many GIs harbor genes that encode the molecular machinery required for their excision from the bacterial chromosome. Notably, the excision/integration dynamics of GIs may modulate the virulence of some pathogens. Here, we report a novel family of GIs found in plant and animal Enterobacteriaceae pathogens that share genes with those found in ROD21, a pathogenicity island whose excision is involved in the virulence of Salmonella enterica serovar Enteritidis. In these GIs we identified a conserved set of genes that includes an excision/integration module, suggesting that they are excisable. Indeed, we found that GIs within carbapenem-resistant Klebsiella pneumoniae ST258 KP35 and enteropathogenic Escherichia coli O127:H6 E2348/69 are excised from the bacterial genome. In addition to putative virulence factors, these GIs encode conjugative transfer-related proteins and short and full-length homologues of the global transcriptional regulator H-NS. Phylogenetic analyses suggest that the identified GIs likely originated in phytopathogenic bacteria. Taken together, our findings indicate that these GIs are excisable and may play a role in bacterial interactions with their hosts.

Alignment-Free Sequence Analysis and Applications

Genome and metagenome comparisons based on large amounts of next generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been applied to many problems including the comparison of gene regulatory regions, genome sequences, metagenomes, binning contigs in metagenomic data, identification of virus-host interactions, and detection of horizontal gene transfers. We provide an updated review of these applications and other related developments of word-count based approaches for alignment-free sequence analysis.

GI-Cluster: Detecting genomic islands via consensus clustering on multiple features

The accurate detection of genomic islands (GIs) in microbial genomes is important for both evolutionary study and medical research, because GIs may promote genome evolution and contain genes involved in pathogenesis. Various computational methods have been developed to predict GIs over the years. However, most of them cannot make full use of GI-associated features to achieve desirable performance. Additionally, many methods cannot be directly applied to newly sequenced genomes. We develop a new method called GI-Cluster, which provides an effective way to integrate multiple GI-related features via consensus clustering. GI-Cluster does not require training datasets or existing genome annotations, but it can still achieve comparable or better performance than supervised learning methods in comprehensive evaluations. Moreover, GI-Cluster is widely applicable, either to complete and incomplete genomes or to initial GI predictions from other programs. GI-Cluster also provides plots to visualize the distribution of predicted GIs and related features. GI-Cluster is available at https://github.com/icelu/GI_Cluster.

Assessing the benefits of horizontal gene transfer by laboratory evolution and genome sequencing

BACKGROUND

Recombination is widespread across the tree of life, because it helps purge deleterious mutations and creates novel adaptive traits. In prokaryotes, it often takes the form of horizontal gene transfer from a donor to a recipient bacterium. While such transfer is widespread in natural communities, its immediate fitness benefits are usually unknown. We asked whether any such benefits depend on the environment, and on the identity of donor and recipient strains. To this end, we adapted Escherichia coli to two novel carbon sources over several hundred generations of laboratory evolution, exposing evolving populations to various DNA donors.

RESULTS

At the end of these experiments, we measured fitness and sequenced the genomes of 65 clones from 34 replicate populations to study the genetic changes associated with adaptive evolution. Furthermore, we identified candidate de novo beneficial mutations. During adaptive evolution on the first carbon source, 4-Hydroxyphenylacetic acid (HPA), recombining populations adapted better, which was likely mediated by acquiring the hpa operon from the donor. In contrast, recombining populations did not adapt better to the second carbon source, butyric acid, even though they suffered fewer extinctions than non-recombining populations. The amount of DNA transferred, but not its benefit, strongly depended on the donor-recipient strain combination.

CONCLUSIONS

To our knowledge, our study is the first to investigate the genomic consequences of prokaryotic recombination and horizontal gene transfer during laboratory evolution. It shows that the benefits of recombination strongly depend on the environment and the foreign DNA donor.

Background Adjusted Alignment-Free Dissimilarity Measures Improve the Detection of Horizontal Gene Transfer

Horizontal gene transfer (HGT) plays an important role in the evolution of microbial organisms including bacteria. Alignment-free methods based on single genome compositional information have been used to detect HGT. Currently, Manhattan and Euclidean distances based on tetranucleotide frequencies are the most commonly used alignment-free dissimilarity measures to detect HGT. By testing on simulated bacterial sequences and real data sets with known horizontal transferred genomic regions, we found that more advanced alignment-free dissimilarity measures such as CVTree and d2* that take into account the background Markov sequences can solve HGT detection problems with significantly improved performance. We also studied the influence of different factors such as evolutionary distance between host and donor sequences, size of sliding window, and host genome composition on the performances of alignment-free methods to detect HGT. Our study showed that alignment-free methods can predict HGT accurately when host and donor genomes are in different order levels. Among all methods, CVTree with word length of 3, d2* with word length 3, Markov order 1 and d2* with word length 4, Markov order 1 outperform others in terms of their highest F₁-score and their robustness under the influence of different factors.

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

Background

Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.

Results

We have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.

Conclusions

xenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2038-0) contains supplementary material, which is available to authorized users.

Four genes essential for recombination define GInts, a new type of mobile genomic island widespread in bacteria

Integrases are a family of tyrosine recombinases that are highly abundant in bacterial genomes, actively disseminating adaptive characters such as pathogenicity determinants and antibiotics resistance. Using comparative genomics and functional assays, we identified a novel type of mobile genetic element, the GInt, in many diverse bacterial groups but not in archaea. Integrated as genomic islands, GInts show a tripartite structure consisting of the ginABCD operon, a cargo DNA region from 2.5 to at least 70 kb, and a short AT-rich 3' end. The gin operon is characteristic of GInts and codes for three putative integrases and a small putative helix-loop-helix protein, all of which are essential for integration and excision of the element. Genes in the cargo DNA are acquired mostly from phylogenetically related bacteria and often code for traits that might increase fitness, such as resistance to antimicrobials or virulence. GInts also tend to capture clusters of genes involved in complex processes, such as the biosynthesis of phaseolotoxin by Pseudomonas syringae. GInts integrate site-specifically, generating two flanking direct imperfect repeats, and excise forming circular molecules. The excision process generates sequence variants at the element attachment site, which can increase frequency of integration and drive target specificity.