Deletions of chromosomal regions coding for fimbriae and hemolysins occur in vitro and in vivo in various extraintestinal Escherichia coli isolates

Current state and future prospects of Horizontal Gene Transfer detection

Artificial intelligence (AI) has been shown to be beneficial in a wide range of bioinformatics applications. Horizontal Gene Transfer (HGT) is a driving force of evolutionary changes in prokaryotes. It is widely recognized that it contributes to the emergence of antimicrobial resistance (AMR), which poses a particularly serious threat to public health. Many computational approaches have been developed to study and detect HGT. However, the application of AI in this field has not been investigated. In this work, we conducted a review to provide information on the current trend of existing computational approaches for detecting HGT and to decipher the use of AI in this field. Here, we show a growing interest in HGT detection, characterized by a surge in the number of computational approaches, including AI-based approaches, in recent years. We organize existing computational approaches into a hierarchical structure of computational groups based on their computational methods and show how each computational group evolved. We make recommendations and discuss the challenges of HGT detection in general and the adoption of AI in particular. Moreover, we provide future directions for the field of HGT detection.

Genomic Characterization of Extended-Spectrum β-Lactamase (ESBL) Producing E. coli Harboring bla OXA-1-catB3-arr-3 Genes Isolated From Dairy Farm Environment in China

Anthropogenic activities in the environment affect the ecosystem and can play an important role in selecting and spreading antibiotic-resistant bacteria (ARB) and genes (ARGs). The dairy farm environment may serve as a hotspot and reservoir for exchanging and spreading ARGs, but studies are scarce. Here, we investigated and characterized the extended-spectrum β-lactamase producing Escherichia coli strains recovered from the dairy farm environment co-harboring bla OXA-1, catB3, and arr-3 genes. The isolates were identified and characterized by PCR, antimicrobial susceptibility testing, conjugation assay, whole genome sequencing (WGS), and multiple bioinformatics tools. Seven E. coli strains co-harboring bla OXA-1, catB3, and arr-3 genes were identified which belonged to distinct sequence types (STs) and carried diverse plasmid replicon types. The conjugation assay revealed a successful transfer of bla OXA-1, catB3, and arr-3 genes into the recipient E. coli J53 with a co-conjugation frequency ranging from (2.25 ± 0.3) × 10-4 to (3.85 ± 0.3) × 10-3. Bioinformatics analysis of WGS revealed the diversity of acquired ARGs, conferring resistance to aminoglycosides, beta-lactams, quinolones, tetracyclines, macrolides, trimethoprim-sulfamethoxazole, phosphonic, phenicol, and rifamycin. The genetic environment analysis showed that aac(6')-Ib-cr-bla OXA-1-catB3-arr-3-qacE1-sul1 was the common genetic backbone among the seven E. coli strains. Among the mobile genetic elements, insertion sequences were the predominant elements as compared to transposons. The phylogenetic analysis demonstrated a close relationship between the E. coli of this study and other strains of human-animal-environment origin retrieved from the NCBI database. This study presented the whole genome-based characterization of E. coli strains carrying the bla OXA-1-catB3-arr-3 genes. It provided evidence that the dairy environment may harbor a variety of ARGs and act as a potential reservoir for their spread in the ecosystem. The results recommend the routine surveillance of ARGs carrying bacteria in dairy environments and the need for additional studies to understand the dissemination mechanism within One Health perspective to prevent their further spread.

Mosquitocidal toxin-like islands in Bacillus thuringiensis S2160-1 revealed by complete-genome sequence and MS proteomic analysis

Here, we present the whole genome sequence of Bt S2160-1, a potential alternative to the mosquitocidal model strain, Bti. One chromosome genome and four mega-plasmids were contained in Bt S2160-1, and 13 predicted genes encoding predicted insecticidal crystal proteins were identified clustered on one plasmid pS2160-1p2 containing two pathogenic islands (PAIs) designed as PAI-1 (Cry54Ba, Cry30Ea4, Cry69Aa-like, Cry50Ba2-like, Cry4Ca1-like, Cry30Ga2, Cry71Aa-like, Cry72Aa-like, Cry70Aa-like, Cyt1Da2-like and Vpb4C1-like) and PAI-2 (Cyt1Aa-like, and Tpp80Aa1-like). The clusters appear to represent mosquitocidal toxin islands similar to pathogenicity islands. Transcription/translation of 10 of the 13 predicted genes was confirmed by whole-proteome analysis using LTQ-Orbitrap LC-MS/MS. In summary, the present study identified the existence of a mosquitocidal toxin island in Bacillus thuringiensis, and provides important genomic information for understanding the insecticidal mechanism of B. thuringiensis.

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.

Genomic Islands Identified in Highly Resistant Serratia sp. HRI: A Pathway to Discover New Disinfectant Resistance Elements

Molecular insights into the mechanisms of resistance to disinfectants are severely limited, together with the roles of various mobile genetic elements. Genomic islands are a well-characterised molecular resistance element in antibiotic resistance, but it is unknown whether genomic islands play a role in disinfectant resistance. Through whole-genome sequencing and the bioinformatic analysis of Serratia sp. HRI, an isolate with high disinfectant resistance capabilities, nine resistance islands were predicted and annotated within the genome. Resistance genes active against several antimicrobials were annotated in these islands, most of which are multidrug efflux pumps belonging to the MFS, ABC and DMT efflux families. Antibiotic resistance islands containing genes encoding for multidrug resistance proteins ErmB (macrolide and erythromycin resistance) and biclomycin were also found. A metal fitness island harbouring 13 resistance and response genes to copper, silver, lead, cadmium, zinc, and mercury was identified. In the search for disinfectant resistance islands, two genomic islands were identified to harbour smr genes, notorious for conferring disinfectant resistance. This suggests that genomic islands are capable of conferring disinfectant resistance, a phenomenon that has not yet been observed in the study of biocide resistance and tolerance.

Pathogenicity Islands in Uropathogenic Escherichia coli Clinical Isolate of the Globally Disseminated O25:H4-ST131 Pandemic Clonal Lineage: First Report from Egypt

Uropathogenic Escherichia coli (UPEC) is the main etiological agent of urinary tract infections (UTIs). The pathogenesis of UTIs relies upon UPEC's acquisition of virulence determinants that are commonly inserted into large chromosomal blocks which are termed 'pathogenicity islands' (PAIs). In this study, we investigated the virulence-associated genes embedded in the chromosome of a UPEC Egyptian strain, EC14142. Additionally, we present a detailed characterization of the PAIs in the EGY_EC14142 chromosome. The isolate displayed a multidrug-resistant phenotype, and whole genome sequencing indicated that it belonged to the globally disseminated O25:H4-ST131 pandemic lineage and the H30-Rx clade. EGY_EC14142 carried genes that are responsible for resistance to aminoglycosides, fluoroquinolones, extended-spectrum β-lactams, macrolides, folate pathway antagonists, and tetracyclines. It encoded five PAIs with a high similarity to PAI II536, PAI IV536, PAI V536, PAI-536-icd, and PAIusp. The genome analysis of EGY_EC14142 with other closely related UPEC strains revealed that they have a high nucleotide sequence identity. The constructed maximum-likelihood phylogenetic tree showed the close clonality of EGY_EC14142 with the previously published ST131 UPEC international isolates, thus endorsing the broad geographical distribution of this clone. This is the first report characterizing PAIs in a UPEC Egyptian strain belonging to the globally disseminated pandemic clone O25:H4-ST131.

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.

Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila

Aeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study was to determine in silico the contribution of genomic islands to A. hydrophila. The complete genomes of 17 A. hydrophila isolates, which were separated into two phylogenetic groups, were analyzed using a genomic island (GI) predictor. The number of predicted GIs and their characteristics varied among strains. Strains from group 1, which contains mainly fish pathogens, generally have a higher number of predicted GIs, and with larger size, than strains from group 2 constituted by strains recovered from distinct sources. Only a few predicted GIs were shared among them and contained mostly genes from the core genome. Features related to virulence, metabolism, and resistance were found in the predicted GIs, but strains varied in relation to their gene content. In strains from group 1, O Ag biosynthesis clusters OX1 and OX6 were identified, while strains from group 2 each had unique clusters. Metabolic pathways for myo-inositol, L-fucose, sialic acid, and a cluster encoding QueDEC, tgtA5, and proteins related to DNA metabolism were identified in strains of group 1, which share a high number of predicted GIs. No distinctive features of group 2 strains were identified in their predicted GIs, which are more diverse and possibly better represent GIs in this species. However, some strains have several resistance attributes encoded by their predicted GIs. Several predicted GIs encode hypothetical proteins and phage proteins whose functions have not been identified but may contribute to Aeromonas fitness. In summary, features with functions identified on predicted GIs may confer advantages to host colonization and competitiveness in the environment.

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/.

Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

Genomic islands are related to microbial adaptation and carry different genomic characteristics from the host. Therefore, many methods have been proposed to detect genomic islands from the rest of the genome by evaluating its sequence composition. Many sequence features have been proposed, but many of them have not been applied to the identification of genomic islands. In this paper, we present a scheme to predict genomic islands using the chi-square test and random forest algorithm. We extract seven kinds of sequence features and select the important features with the chi-square test. All the selected features are then input into the random forest to predict the genome islands. Three experiments and comparison show that the proposed method achieves the best performance. This understanding can be useful to design more powerful method for the genomic island prediction.

Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

Background

Genomic islands (GIs) play an important role in the chromosome diversity of Enterococcus. In the current study, we aimed to investigate the spread of GIs between Enterococcus strains and their correlation with antibiotic resistance genes (ARGs). Bitsliced Genomic Signature Indexes (BIGSI) were used to screen the NCBI Sequence Read Archive (SRA) for multiple resistant Enterococcus. A total of 37 pairs of raw reads were screened from 457,000 whole-genome sequences (WGS) in the SRA database, which come from 37 Enterococci distributed in eight countries. These raw reads were assembled for the prediction and analysis of GIs, ARGs, plasmids and prophages.

Results

The results showed that GIs were universal in Enterococcus, with an average of 3.2 GIs in each strain. Network analysis showed that frequent genetic information exchanges mediated by GIs occurred between Enterococcus strains. Seven antibiotic-resistant genomic islands (ARGIs) were found to carry one to three ARGs, mdtG, tetM, dfrG, lnuG, and fexA, in six strains. These ARGIs were involved in the spread of antibiotic resistance in 45.9% of the 37 strains, although there was no significant positive correlation between the frequency of GI exchanges and the number of ARGs each strain harboured (r = 0. 287, p = 0.085). After comprehensively analysing the genome data, we found that partial GIs were associated with multiple mobile genetic elements (transposons, integrons, prophages and plasmids) and had potential natural transformation characteristics.

Conclusions

All of these results based on genomic sequencing suggest that GIs might mediate the acquisition of some ARGs and might be involved in the high genome plasticity of Enterococcus through transformation, transduction and conjugation, thus providing a fitness advantage for Enterococcus hosts under complex environmental factors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02114-4.

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.

Significant association between genes encoding virulence factors with antibiotic resistance and phylogenetic groups in community acquired uropathogenic Escherichia coli isolates

BACKGROUND

Antibiotic resistance is an increasing phenomenon in many bacterial pathogens including uropathogenic Escherichia coli. Hypothetical anti-virulent agents could be a solution, but first clear virulence associated gene-pool of antibiotic resistant isolates have to be determined. The aim of this study is to investigate the significant associations between genes encoding VFs with antibiotic resistance and phylogenetic groups in UPEC isolates.

RESULTS

The majority of 248 UPEC isolates belonged to phylogenetic group B2 (67.3%). The maximum and minimum resistance was attributed to amoxicillin (90.3%) and both fosfomycin and imipenem (1.6%) respectively. 11.3% of isolates were resistant to all antibiotic agents except that of imipenem, nitrofurantoin and fosfomycin. These highly resistant isolates were placed only in group B2 and D. The most prevalent virulence gene was ompA (93.5%). The hlyA was the only virulence gene that was significantly more prevalent in the highly resistant isolates. The ompA, malX and hlyA genes were obviously more abundant in the antibiotic resistant isolates in comparison to susceptible isolates. The papC gene was associated with amoxicillin resistance (p-value = 0.006, odds ratio: 26.00).

CONCLUSIONS

Increased resistance to first line drugs prescribed for UTIs were detected in CA-UPEC isolates in our study.. Minimal resistance was observed against nitrofurantoin, fosfomycin and imipenem. Therefore, they are introduced for application in empirical therapy of UTIs. Fosfomycin may be the most effective antibiotic agent against highly resistant UPEC isolates. The presence of the ompA, malX and hlyA genes were significantly associated with resistance to different antibiotic agents. We assume that the ability of UPEC isolates to upgrade their antibiotic resistance capacity may occurs in compliance with the preliminary existence of specific virulence associated genes. But, more investigation with higher number of bacterial isolates, further virulence associated genes and comparison of gene pools from CA-UPEC isolates with HA-UPEC are proposed to confirm these finding and discovering new aspects of this association.

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.

Targeting Plasmids to Limit Acquisition and Transmission of Antimicrobial Resistance

Antimicrobial resistance (AMR) is a significant global threat to both public health and the environment. The emergence and expansion of AMR is sustained by the enormous diversity and mobility of antimicrobial resistance genes (ARGs). Different mechanisms of horizontal gene transfer (HGT), including conjugation, transduction, and transformation, have facilitated the accumulation and dissemination of ARGs in Gram-negative and Gram-positive bacteria. This has resulted in the development of multidrug resistance in some bacteria. The most clinically significant ARGs are usually located on different mobile genetic elements (MGEs) that can move intracellularly (between the bacterial chromosome and plasmids) or intercellularly (within the same species or between different species or genera). Resistance plasmids play a central role both in HGT and as support elements for other MGEs, in which ARGs are assembled by transposition and recombination mechanisms. Considering the crucial role of MGEs in the acquisition and transmission of ARGs, a potential strategy to control AMR is to eliminate MGEs. This review discusses current progress on the development of chemical and biological approaches for the elimination of ARG carriers.

2SigFinder: the combined use of small-scale and large-scale statistical testing for genomic island detection from a single genome

BACKGROUND

Genomic islands are associated with microbial adaptations, carrying genomic signatures different from the host. Some methods perform an overall test to identify genomic islands based on their local features. However, regions of different scales will display different genomic features.

RESULTS

We proposed here a novel method "2SigFinder ", the first combined use of small-scale and large-scale statistical testing for genomic island detection. The proposed method was tested by genomic island boundary detection and identification of genomic islands or functional features of real biological data. We also compared the proposed method with the comparative genomics and composition-based approaches. The results indicate that the proposed 2SigFinder is more efficient in identifying genomic islands.

CONCLUSIONS

From real biological data, 2SigFinder identified genomic islands from a single genome and reported robust results across different experiments, without annotated information of genomes or prior knowledge from other datasets. 2SigHunter identified 25 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats from 27 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats, and detected 101 Phage and 28 HEG out of 130 Phage and 36 HEGs in S. enterica Typhi CT18, which shows that it is more efficient in detecting functional features associated with GIs.

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.

Catabolism of Alkylphenols in Rhodococcus via a Meta-Cleavage Pathway Associated With Genomic Islands

The bacterial catabolism of aromatic compounds has considerable promise to convert lignin depolymerization products to commercial chemicals. Alkylphenols are a key class of depolymerization products whose catabolism is not well-elucidated. We isolated Rhodococcus rhodochrous EP4 on 4-ethylphenol and applied genomic and transcriptomic approaches to elucidate alkylphenol catabolism in EP4 and Rhodococcus jostii RHA1. RNA-Seq and RT-qPCR revealed a pathway encoded by the aphABCDEFGHIQRS genes that degrades 4-ethylphenol via the meta-cleavage of 4-ethylcatechol. This process was initiated by a two-component alkylphenol hydroxylase, encoded by the aphAB genes, which were upregulated ~3,000-fold. Purified AphAB from EP4 had highest specific activity for 4-ethylphenol and 4-propylphenol (~2,000 U/mg) but did not detectably transform phenol. Nevertheless, a ΔaphA mutant in RHA1 grew on 4-ethylphenol by compensatory upregulation of phenol hydroxylase genes (pheA1-3). Deletion of aphC, encoding an extradiol dioxygenase, prevented growth on 4-alkylphenols but not phenol. Disruption of pcaL in the β-ketoadipate pathway prevented growth on phenol but not 4-alkylphenols. Thus, 4-alkylphenols are catabolized exclusively via meta-cleavage in rhodococci while phenol is subject to ortho-cleavage. A putative genomic island encoding aph genes was identified in EP4 and several other rhodococci. Overall, this study identifies a 4-alkylphenol pathway in rhodococci, demonstrates key enzymes involved, and presents evidence that the pathway is encoded in a genomic island. These advances are of particular importance for wide-ranging industrial applications of rhodococci, including upgrading of lignocellulose biomass.

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.

MTGIpick allows robust identification of genomic islands from a single genome

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.

An efficient genomic signature ranking method for genomic island prediction from a single genome

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome. In simulations with alien fragments from artificial and real genomes, the proposed kurtosis-based ranking method efficiently selected the informative genomic signatures from a single genome, without annotated information of genomes or prior knowledge from other datasets. This understanding can be useful to design more powerful method for genomic island detection.

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.

Involvement of β-Carbonic Anhydrase Genes in Bacterial Genomic Islands and Their Horizontal Transfer to Protists

Genomic islands (GIs) are a type of mobile genetic element (MGE) that are present in bacterial chromosomes. They consist of a cluster of genes that produce proteins that contribute to a variety of functions, including, but not limited to, the regulation of cell metabolism, antimicrobial resistance, pathogenicity, virulence, and resistance to heavy metals. The genes carried in MGEs can be used as a trait reservoir in times of adversity. Transfer of genes using MGEs, occurring outside reproduction, is called horizontal gene transfer (HGT). Previous data have shown that numerous HGT events have occurred through endosymbiosis between prokaryotes and eukaryotes. β-Carbonic anhydrase (β-CA) enzymes play a critical role in the biochemical pathways of many prokaryotes and eukaryotes. We previously suggested the horizontal transfer of β-CA genes from plasmids of some prokaryotic endosymbionts to their protozoan hosts. In this study, we set out to identify β-CA genes that might have been transferred between prokaryotic and protist species through HGT in GIs. Therefore, we investigated prokaryotic chromosomes containing β-CA-encoding GIs and utilized multiple bioinformatics tools to reveal the distinct movements of β-CA genes among a wide variety of organisms. Our results identify the presence of β-CA genes in GIs of several medically and industrially relevant bacterial species, and phylogenetic analyses reveal multiple cases of likely horizontal transfer of β-CA genes from GIs of ancestral prokaryotes to protists.IMPORTANCE The evolutionary process is mediated by mobile genetic elements (MGEs), such as genomic islands (GIs). A gene or set of genes in the GIs is exchanged between and within various species through horizontal gene transfer (HGT). Based on the crucial role that GIs can play in bacterial survival and proliferation, they were introduced as environment- and pathogen-associated factors. Carbonic anhydrases (CAs) are involved in many critical biochemical pathways, such as the regulation of pH homeostasis and electrolyte transfer. Among the six evolutionary families of CAs, β-CA gene sequences are present in many bacterial species, which can be horizontally transferred to protists during evolution. This study shows the involvement of bacterial β-CA gene sequences in the GIs and suggests their horizontal transfer to protists during evolution.

Comparative genome and evolution analysis of the locus of enterocyte effacement from enteropathogenic Escherichia coli Deng and its transcriptional response to ciprofloxacin

PURPOSE

In this study, we aimed to investigate the genomic characteristics and evolution of pathogenicity islands of an enteropathogenic Escherichia coli (EPEC) strain, and to obtain a transcriptional profile of EPEC under different concentrations of ciprofloxacin using microarray analysis.

METHODOLOGY

The complete EPEC Deng genome was sequenced and compared to genomes of 12 previously sequenced E. coli strains. A 180 min time course experiment was performed in which the effect of ciprofloxacin on EPEC Deng growth was evaluated. Microarray profiling was used to study the effect of varying ciprofloxacin pressure on genome-wide transcriptional expression. Differential expression of the genes identified using microarray data was confirmed using real-time quantitative reverse transcriptase PCR (RTQ). Target gene-defective recombineering strains were created to investigate the influence of the grlA gene on ciprofloxacin susceptibility.

RESULTS

Genomic comparisons revealed a close phylogenic relationship between EPEC Deng and E. coli strains O111_H_11128 and O26_H11_11368, with low genetic diversity among their type III secretion system genes and typically genetic variation in the map, tir, eae and espA genes of EPEC. It is noteworthy that 21 genes were down-regulated at all time points examined in the group exposed to 2 µg ml^-1 of ciprofloxacin. A grlA-mutant derivative with increased susceptibility to ciprofloxacin was discovered.

CONCLUSIONS

The present findings provide an overview of the phylogenetic characteristics of EPEC Deng and its transcriptional response to ciprofloxacin, further suggesting that GrlA may play a clinically important role in EPEC responses to ciprofloxacin.

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.

Recent insights into the tick microbiome gained through next-generation sequencing

The tick microbiome comprises communities of microorganisms, including viruses, bacteria and eukaryotes, and is being elucidated through modern molecular techniques. The advent of next-generation sequencing (NGS) technologies has enabled the genes and genomes within these microbial communities to be explored in a rapid and cost-effective manner. The advantages of using NGS to investigate microbiomes surpass the traditional non-molecular methods that are limited in their sensitivity, and conventional molecular approaches that are limited in their scalability. In recent years the number of studies using NGS to investigate the microbial diversity and composition of ticks has expanded. Here, we provide a review of NGS strategies for tick microbiome studies and discuss the recent findings from tick NGS investigations, including the bacterial diversity and composition, influential factors, and implications of the tick microbiome.

Zisland Explorer: detect genomic islands by combining homogeneity and heterogeneity properties

Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at http://cefg.uestc.edu.cn/Zisland_Explorer/ or http://tubic.tju.edu.cn/Zisland_Explorer/.

Bioinformatics comparisons of RNA-binding proteins of pathogenic and non-pathogenic Escherichia coli strains reveal novel virulence factors

Background

Pathogenic bacteria have evolved various strategies to counteract host defences. They are also exposed to environments that are undergoing constant changes. Hence, in order to survive, bacteria must adapt themselves to the changing environmental conditions by performing regulations at the transcriptional and/or post-transcriptional levels. Roles of RNA-binding proteins (RBPs) as virulence factors have been very well studied. Here, we have used a sequence search-based method to compare and contrast the proteomes of 16 pathogenic and three non-pathogenic E. coli strains as well as to obtain a global picture of the RBP landscape (RBPome) in E. coli.

Results

Our results show that there are no significant differences in the percentage of RBPs encoded by the pathogenic and the non-pathogenic E. coli strains. The differences in the types of Pfam domains as well as Pfam RNA-binding domains, encoded by these two classes of E. coli strains, are also insignificant. The complete and distinct RBPome of E. coli has been established by studying all known E. coli strains till date. We have also identified RBPs that are exclusive to pathogenic strains, and most of them can be exploited as drug targets since they appear to be non-homologous to their human host proteins. Many of these pathogen-specific proteins were uncharacterised and their identities could be resolved on the basis of sequence homology searches with known proteins. Detailed structural modelling, molecular dynamics simulations and sequence comparisons have been pursued for selected examples to understand differences in stability and RNA-binding.

Conclusions

The approach used in this paper to cross-compare proteomes of pathogenic and non-pathogenic strains may also be extended to other bacterial or even eukaryotic proteomes to understand interesting differences in their RBPomes. The pathogen-specific RBPs reported in this study, may also be taken up further for clinical trials and/or experimental validations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4045-3) contains supplementary material, which is available to authorized users.

Pathoadaptive Mutations in Uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) are opportunistic human pathogens that primarily circulate as part of commensal intestinal microbiota. Though they have the ability to survive and proliferate in various urinary tract compartments, the urinary tract is a transient, occasional habitat for UPEC. Because of this, most of the UPEC traits have originally evolved to serve in intestinal colonization and transmission. Some of these bacterial traits serve as virulence factors - they are critical to or assist in survival of UPEC as pathogens, and the structure and/or function may be specialized for the infection. Other traits could serve as anti-virulence factors - they represent liability in the urinary tract and are under selection to be lost or inactivated during the infection. Inactivation, variation, or other changes of the bacterial genes that increase the pathogen's fitness during the infection are called pathoadaptive mutations. This chapter describes examples of pathoadaptive mutations in UPEC and provides rationale for their further in-depth study.

Microdiversification of a Pelagic Polynucleobacter Species Is Mainly Driven by Acquisition of Genomic Islands from a Partially Interspecific Gene Pool

Microdiversification of a planktonic freshwater bacterium was studied by comparing 37 Polynucleobacter asymbioticus strains obtained from three geographically separated sites in the Austrian Alps. Genome comparison of nine strains revealed a core genome of 1.8 Mb, representing 81% of the average genome size. Seventy-five percent of the remaining flexible genome is clustered in genomic islands (GIs). Twenty-four genomic positions could be identified where GIs are potentially located. These positions are occupied strain specifically from a set of 28 GI variants, classified according to similarities in their gene content. One variant, present in 62% of the isolates, encodes a pathway for the degradation of aromatic compounds, and another, found in 78% of the strains, contains an operon for nitrate assimilation. Both variants were shown in ecophysiological tests to be functional, thus providing the potential for microniche partitioning. In addition, detected interspecific horizontal exchange of GIs indicates a large gene pool accessible to Polynucleobacter species. In contrast to core genes, GIs are spread more successfully across spatially separated freshwater habitats. The mobility and functional diversity of GIs allow for rapid evolution, which may be a key aspect for the ubiquitous occurrence of Polynucleobacter bacteria.

IMPORTANCE

Assessing the ecological relevance of bacterial diversity is a key challenge for current microbial ecology. The polyphasic approach which was applied in this study, including targeted isolation of strains, genome analysis, and ecophysiological tests, is crucial for the linkage of genetic and ecological knowledge. Particularly great importance is attached to the high number of closely related strains which were investigated, represented by genome-wide average nucleotide identities (ANI) larger than 97%. The extent of functional diversification found on this narrow phylogenetic scale is compelling. Moreover, the transfer of metabolically relevant genomic islands between more distant members of the Polynucleobacter community provides important insights toward a better understanding of the evolution of these globally abundant freshwater bacteria.

Computational methods for predicting genomic islands in microbial genomes

Clusters of genes acquired by lateral gene transfer in microbial genomes, are broadly referred to as genomic islands (GIs). GIs often carry genes important for genome evolution and adaptation to niches, such as genes involved in pathogenesis and antibiotic resistance. Therefore, GI prediction has gradually become an important part of microbial genome analysis. Despite inherent difficulties in identifying GIs, many computational methods have been developed and show good performance. In this mini-review, we first summarize the general challenges in predicting GIs. Then we group existing GI detection methods by their input, briefly describe representative methods in each group, and discuss their advantages as well as limitations. Finally, we look into the potential improvements for better GI prediction.

The Floating (Pathogenicity) Island: A Genomic Dessert

Among the prokaryotic genomic islands (GIs) involved in horizontal gene transfer (HGT) are the classical pathogenicity islands, including the integrative and conjugative elements (ICEs), the gene-transfer agents (GTAs), and the staphylococcal pathogenicity islands (SaPIs), the primary focus of this review. While the ICEs and GTAs mediate HGT autonomously, the SaPIs are dependent on specific phages. The ICEs transfer primarily their own DNA, the GTAs exclusively transfer unlinked host DNA, and the SaPIs combine the capabilities of both. Thus the SaPIs derive their importance from the genes they carry (their genetic cargo) and the genes they move. They act not only as versatile high-frequency mobilizers but also as mediators of phage interference and consequently are major benefactors of their host bacteria.

Distribution of pathogenicity island markers in commensal and uropathogenic Escherichia coli isolates

Uropathogenic Escherichia coli (UPEC) isolates contain large genomic segments, termed pathogenicity islands (PAIs), that contribute to their virulence. A total of 150 UPEC and 50 commensal E. coli isolates from outpatients were investigated for antimicrobial susceptibility and the presence of eight PAI markers. One hundred ninety (95 %) isolates were resistant to one or more antimicrobial agents. The most frequent resistance found against amoxicillin (68 %), amoxicillin/clavulanic acid (55 %), aztreonam (50 %), trimethoprim/sulfamethoxazole (46 %) and tetracycline (43.5 %). Antimicrobial resistance among UPEC isolates was higher than that of commensals. PAI markers were detected in substantial percentage of commensal (88 %) and UPEC isolates (98.6 %) (P > 0.05). The most prevalent PAI marker among UPEC and commensal isolates was PAI IV536 (98.7 % UPEC vs. 84 % commensal). We found a high number of PAI markers such as PAI ICFT073, PAI IICFT073, PAI I536, PAI II536, PAI III536 and PAI IIJ96 significantly associated with UPEC. PAI III536 (21.3 %) and PAI IIJ96 (8 %) were detected only in the uropathogenic isolates. Several different combinations of PAIs were found among UPEC isolates. Comparison of PAIs among UPEC and commensal isolates showed that many UPEC isolates (79.3 %) carried two or more PAI markers, while 6 % of commensals had two PAI markers (P < 0.05). The most frequent combinations of PAI markers in UPEC isolates were PAI IV536 + PAI IICFT073 (18 %) and PAI IV536 + PAI ICFT073 + PAI IICFT073 (18 %). These results indicate that PAI markers are widespread among commensal and UPEC isolates and these commensal isolates may be reservoirs for transmission of these markers.

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

A Transmissible Plasmid-Borne Pathogenicity Island Confers Piscibactin Biosynthesis in the Fish Pathogen Photobacterium damselae subsp. piscicida

The fish pathogen Photobacterium damselae subsp. piscicida produces the siderophore piscibactin. A gene cluster that resembles the Yersinia high-pathogenicity island (HPI) encodes piscibactin biosynthesis. Here, we report that this HPI-like cluster is part of a hitherto-uncharacterized 68-kb plasmid dubbed pPHDP70. This plasmid lacks homologs of genes that mediate conjugation, but we found that it could be transferred at low frequencies from P. damselae subsp. piscicida to a mollusk pathogenic Vibrio alginolyticus strain and to other Gram-negative bacteria, likely dependent on the conjugative functions of the coresident plasmid pPHDP60. Following its conjugative transfer, pPHDP70 restored the capacity of a vibrioferrin mutant of V. alginolyticus to grow under low-iron conditions, and piscibactin became detectable in its supernatant. Thus, pPHDP70 appears to harbor all the genes required for piscibactin biosynthesis and transport. P. damselae subsp. piscicida strains cured of pPHDP70 no longer produced piscibactin, had impaired growth under iron-limited conditions, and exhibited markedly decreased virulence in fish. Collectively, our findings highlight the importance of pPHDP70, with its capacity for piscibactin-mediated iron acquisition, in the virulence of P. damselae subsp. piscicida. Horizontal transmission of this plasmid-borne piscibactin synthesis gene cluster in the marine environment may facilitate the emergence of new pathogens.

Islander: a database of precisely mapped genomic islands in tRNA and tmRNA genes

Genomic islands are mobile DNAs that are major agents of bacterial and archaeal evolution. Integration into prokaryotic chromosomes usually occurs site-specifically at tRNA or tmRNA gene (together, tDNA) targets, catalyzed by tyrosine integrases. This splits the target gene, yet sequences within the island restore the disrupted gene; the regenerated target and its displaced fragment precisely mark the endpoints of the island. We applied this principle to search for islands in genomic DNA sequences. Our algorithm identifies tDNAs, finds fragments of those tDNAs in the same replicon and removes unlikely candidate islands through a series of filters. A search for islands in 2168 whole prokaryotic genomes produced 3919 candidates. The website Islander (recently moved to http://bioinformatics.sandia.gov/islander/) presents these precisely mapped candidate islands, the gene content and the island sequence. The algorithm further insists that each island encode an integrase, and attachment site sequence identity is carefully noted; therefore, the database also serves in the study of integrase site-specificity and its evolution.

Galleria mellonella infection model demonstrates high lethality of ST69 and ST127 uropathogenic E. coli

Galleria mellonella larvae are an alternative in vivo model for investigating bacterial pathogenicity. Here, we examined the pathogenicity of 71 isolates from five leading uropathogenic E. coli (UPEC) lineages using G. mellonella larvae. Larvae were challenged with a range of inoculum doses to determine the 50% lethal dose (LD₅₀) and for analysis of survival outcome using Kaplan-Meier plots. Virulence was correlated with carriage of a panel of 29 virulence factors (VF). Larvae inoculated with ST69 and ST127 isolates (10⁴ colony-forming units/larvae) showed significantly higher mortality rates than those infected with ST73, ST95 and ST131 isolates, killing 50% of the larvae within 24 hours. Interestingly, ST131 isolates were the least virulent. We observed that ST127 isolates are significantly associated with a higher VF-score than isolates of all other STs tested (P≤0.0001), including ST69 (P<0.02), but one ST127 isolate (strain EC18) was avirulent. Comparative genomic analyses with virulent ST127 strains revealed an IS1 mediated deletion in the O-antigen cluster in strain EC18, which is likely to explain the lack of virulence in the larvae infection model. Virulence in the larvae was not correlated with serotype or phylogenetic group. This study illustrates that G. mellonella are an excellent tool for investigation of the virulence of UPEC strains. The findings also support our suggestion that the incidence of ST127 strains should be monitored, as these isolates have not yet been widely reported, but they clearly have a pathogenic potential greater than that of more widely recognised clones, including ST73, ST95 or ST131.

Lifting the mask: identification of new small molecule inhibitors of uropathogenic Escherichia coli group 2 capsule biogenesis

Uropathogenic Escherichia coli (UPEC) is the leading cause of community-acquired urinary tract infections (UTIs), with over 100 million UTIs occurring annually throughout the world. Increasing antimicrobial resistance among UPEC limits ambulatory care options, delays effective treatment, and may increase overall morbidity and mortality from complications such as urosepsis. The polysaccharide capsules of UPEC are an attractive target a therapeutic, based on their importance in defense against the host immune responses; however, the large number of antigenic types has limited their incorporation into vaccine development. The objective of this study was to identify small-molecule inhibitors of UPEC capsule biogenesis. A large-scale screening effort entailing 338,740 compounds was conducted in a cell-based, phenotypic screen for inhibition of capsule biogenesis in UPEC. The primary and concentration-response assays yielded 29 putative inhibitors of capsule biogenesis, of which 6 were selected for further studies. Secondary confirmatory assays identified two highly active agents, named DU003 and DU011, with 50% inhibitory concentrations of 1.0 µM and 0.69 µM, respectively. Confirmatory assays for capsular antigen and biochemical measurement of capsular sugars verified the inhibitory action of both compounds and demonstrated minimal toxicity and off-target effects. Serum sensitivity assays demonstrated that both compounds produced significant bacterial death upon exposure to active human serum. DU011 administration in mice provided near complete protection against a lethal systemic infection with the prototypic UPEC K1 isolate UTI89. This work has provided a conceptually new class of molecules to combat UPEC infection, and future studies will establish the molecular basis for their action along with efficacy in UTI and other UPEC infections.

Role of capsule and O antigen in the virulence of uropathogenic Escherichia coli

Urinary tract infection (UTI) is one of the most common bacterial infections in humans, with uropathogenic Escherichia coli (UPEC) the leading causative organism. UPEC has a number of virulence factors that enable it to overcome host defenses within the urinary tract and establish infection. The O antigen and the capsular polysaccharide are two such factors that provide a survival advantage to UPEC. Here we describe the application of the rpsL counter selection system to construct capsule (kpsD) and O antigen (waaL) mutants and complemented derivatives of three reference UPEC strains: CFT073 (O6:K2:H1), RS218 (O18:K1:H7) and 1177 (O1:K1:H7). We observed that while the O1, O6 and O18 antigens were required for survival in human serum, the role of the capsule was less clear and linked to O antigen type. In contrast, both the K1 and K2 capsular antigens provided a survival advantage to UPEC in whole blood. In the mouse urinary tract, mutation of the O6 antigen significantly attenuated CFT073 bladder colonization. Overall, this study contrasts the role of capsule and O antigen in three common UPEC serotypes using defined mutant and complemented strains. The combined mutagenesis-complementation strategy can be applied to study other virulence factors with complex functions both in vitro and in vivo.

Identifying pathogenicity islands in bacterial pathogenomics using computational approaches

High-throughput sequencing technologies have made it possible to study bacteria through analyzing their genome sequences. For instance, comparative genome sequence analyses can reveal the phenomenon such as gene loss, gene gain, or gene exchange in a genome. By analyzing pathogenic bacterial genomes, we can discover that pathogenic genomic regions in many pathogenic bacteria are horizontally transferred from other bacteria, and these regions are also known as pathogenicity islands (PAIs). PAIs have some detectable properties, such as having different genomic signatures than the rest of the host genomes, and containing mobility genes so that they can be integrated into the host genome. In this review, we will discuss various pathogenicity island-associated features and current computational approaches for the identification of PAIs. Existing pathogenicity island databases and related computational resources will also be discussed, so that researchers may find it to be useful for the studies of bacterial evolution and pathogenicity mechanisms.

Postgenomic analysis of bacterial pathogens repertoire reveals genome reduction rather than virulence factors

In the pregenomic era, the acquisition of pathogenicity islands via horizontal transfer was proposed as a major mechanism in pathogen evolution. Much effort has been expended to look for the contiguous blocks of virulence genes that are present in pathogenic bacteria, but absent in closely related species that are nonpathogenic. However, some of these virulence factors were found in nonpathogenic bacteria. Moreover, and contrary to expectation, pathogenic bacteria were found to lack genes (antivirulence genes) that are characteristic of nonpathogenic bacteria. The availability of complete genome sequences has led to a new era of pathogen research. Comparisons of genomes have shown that the most pathogenic bacteria have reduced genomes, with less ribosomal RNA and unorganized operons; they lack transcriptional regulators but have more genes that encode protein toxins, toxin-antitoxin (TA) modules, and proteins for DNA replication and repair, when compared with less pathogenic close relatives. These findings questioned the paradigm of virulence by gene acquisition and put forward the notion of genomic repertoire of virulence.