An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays

Protein Subdomain Enrichment of NUP155 Variants Identify a Novel Predicted Pathogenic Hotspot

Functional variants in nuclear envelope genes are implicated as underlying causes of cardiopathology. To examine the potential association of single nucleotide variants of nucleoporin genes with cardiac disease, we employed a prognostic scoring approach to investigate variants of NUP155, a nucleoporin gene clinically linked with atrial fibrillation. Here we implemented bioinformatic profiling and predictive scoring, based on the gnomAD, National Heart Lung and Blood Institute-Exome Sequencing Project (NHLBI-ESP) Exome Variant Server, and dbNSFP databases to identify rare single nucleotide variants (SNVs) of NUP155 potentially associated with cardiopathology. This predictive scoring revealed 24 SNVs of NUP155 as potentially cardiopathogenic variants located primarily in the N-terminal crescent-shaped domain of NUP155. In addition, a predicted NUP155 R672G variant prioritized in our study was mapped to a region within the alpha helical stack of the crescent domain of NUP155. Bioinformatic analysis of inferred protein-protein interactions of NUP155 revealed over representation of top functions related to molecular transport, RNA trafficking, and RNA post-transcriptional modification. Topology analysis revealed prioritized hubs critical for maintaining network integrity and informational flow that included FN1, SIRT7, and CUL7 with nodal enrichment of RNA helicases in the topmost enriched subnetwork. Furthermore, integration of the top 5 subnetworks to capture network topology of an expanded framework revealed that FN1 maintained its hub status, with elevation of EED, CUL3, and EFTUD2. This is the first study to report novel discovery of a NUP155 subdomain hotspot that enriches for allelic variants of NUP155 predicted to be clinically damaging, and supports a role for RNA metabolism in cardiac disease and development.

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS.

Phenotypic and Genotypic Eligible Methods for Salmonella Typhimurium Source Tracking

Salmonellosis is one of the most common causes of foodborne infection and a leading cause of human gastroenteritis. Throughout the last decade, Salmonella enterica serotype Typhimurium (ST) has shown an increase report with the simultaneous emergence of multidrug-resistant isolates, as phage type DT104. Therefore, to successfully control this microorganism, it is important to attribute salmonellosis to the exact source. Studies of Salmonella source attribution have been performed to determine the main food/food-production animals involved, toward which, control efforts should be correctly directed. Hence, the election of a ST subtyping method depends on the particular problem that efforts must be directed, the resources and the data available. Generally, before choosing a molecular subtyping, phenotyping approaches such as serotyping, phage typing, and antimicrobial resistance profiling are implemented as a screening of an investigation, and the results are computed using frequency-matching models (i.e., Dutch, Hald and Asymmetric Island models). Actually, due to the advancement of molecular tools as PFGE, MLVA, MLST, CRISPR, and WGS more precise results have been obtained, but even with these technologies, there are still gaps to be elucidated. To address this issue, an important question needs to be answered: what are the currently suitable subtyping methods to source attribute ST. This review presents the most frequently applied subtyping methods used to characterize ST, analyses the major available microbial subtyping attribution models and ponders the use of conventional phenotyping methods, as well as, the most applied genotypic tools in the context of their potential applicability to investigates ST source tracking.

GenomeVIP: a cloud platform for genomic variant discovery and interpretation

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.

Differential distribution improves gene selection stability and has competitive classification performance for patient survival

A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription factors or epigenetic signals become deregulated, a change in expression variability (DV) of target genes is frequently observed. This suggests that assessing the importance of genes by either differential expression or variability alone potentially misses sets of important biomarkers that could lead to improved predictions and treatments. Here, we describe a new approach for assessing the importance of genes based on differential distribution (DD), which combines information from differential expression and differential variability into a unified metric. We show that feature ranking and selection stability based on DD can perform two to three times better than DE or DV alone, and that DD yields equivalent error rates to DE and DV. Finally, assessing genes via differential distribution produces a complementary set of selected genes to DE and DV, potentially opening up new categories of biomarkers.

EffectiveDB--updates and novel features for a better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems

Protein secretion systems play a key role in the interaction of bacteria and hosts. EffectiveDB (http://effectivedb.org) contains pre-calculated predictions of bacterial secreted proteins and of intact secretion systems. Here we describe a major update of the database, which was previously featured in the NAR Database Issue. EffectiveDB bundles various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, Type IV secretion peptides, eukaryotic-like domains and subcellular targeting signals in the host. Beyond the analysis of arbitrary protein sequence collections, the new release of EffectiveDB also provides a ‘genome-mode’, in which protein sequences from nearly complete genomes or metagenomic bins can be screened for the presence of three important secretion systems (Type III, IV, VI). EffectiveDB contains pre-calculated predictions for currently 1677 bacterial genomes from the EggNOG 4.0 database and for additional bacterial genomes from NCBI RefSeq. The new, user-friendly and informative web portal offers a submission tool for running the EffectiveDB prediction tools on user-provided data.

BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors

Gram-negative pathogenic bacteria inject type III secreted effectors (T3SEs) into host cells to sabotage their immune signaling networks. Because T3SEs constitute a meeting-point of pathogen virulence and host defense, they are of keen interest to host-pathogen interaction research community. To accelerate the identification and functional understanding of T3SEs, we present BEAN 2.0 as an integrated web resource to predict, analyse and store T3SEs. BEAN 2.0 includes three major components. First, it provides an accurate T3SE predictor based on a hybrid approach. Using independent testing data, we show that BEAN 2.0 achieves a sensitivity of 86.05% and a specificity of 100%. Second, it integrates a set of online sequence analysis tools. Users can further perform functional analysis of putative T3SEs in a seamless way, such as subcellular location prediction, functional domain scan and disorder region annotation. Third, it compiles a database covering 1215 experimentally verified T3SEs and constructs two T3SE-related networks that can be used to explore the relationships among T3SEs. Taken together, by presenting a one-stop T3SE bioinformatics resource, we hope BEAN 2.0 can promote comprehensive understanding of the function and evolution of T3SEs.

Online resources for genomic analysis using high-throughput sequencing

The availability of high-throughput sequencing has created enormous possibilities for scientific discovery. However, the massive amount of data being generated has resulted in a severe informatics bottleneck. A large number of tools exist for analyzing next-generation sequencing (NGS) data, yet often there remains a disconnect between these research tools and the ability of many researchers to use them. As a consequence, several online resources and communities have been developed to assist researchers with both the management and the analysis of sequencing data sets. Here we describe the use and applications of common file formats for coding and storing genomic data, consider several web-accessible open-source resources for the visualization and analysis of NGS data, and provide examples of typical analyses with links to further detailed exercises.

A comparison of conventional methods for the quantification of bacterial cells after exposure to metal oxide nanoparticles

Background

Due to potential interference of nanoparticles on bacterial quantification, there is a challenge to develop a fast, accurate and reproducible method for bacterial quantification. Currently various bacterial quantification methods are used by researchers performing nanoparticles study, but there has been no efficacy evaluation of these methods. Here we study interference of nanoparticles on three most commonly used conventional bacterial quantification methods, including colony counting to determine the colony-forming units (CFU), spectrophotometer method of optical density (OD) measurement, and flow cytometry (FCM).

Results

Three oxide nanoparticles including ZnO, TiO₂, and SiO₂ and four bacterial species including Salmonella enterica serovar Newport, Staphylococcus epidermidis, Enterococcus faecalis, and Escherichia coli were included in the test. Results showed that there is no apparent interference of the oxide nanoparticles on quantifications of all four bacterial species by FCM measurement; CFU counting is time consuming, less accurate and not suitable for automation; and the spectrophotometer method using OD measurement was the most unreliable method to quantify and detect the bacteria in the presence of the nanoparticles.

Conclusion

In summary, FCM measurement proved to be the best method, which is suitable for rapid, accurate and automatic detection of bacteria in the presence of the nanoparticles.

Defining a comprehensive verotype using electronic health records for personalized medicine

The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the integration of genotype, phenotype, and disease subtype (eg, specific glucose value pattern in patients with diabetes) information; analyzing the value of the 'verotype' concept for personalized medicine; and outlining the potential for using network-based approaches to reverse engineer clinical disease subtypes.

Characterization of intracellular growth regulator icgR by utilizing transcriptomics to identify mediators of pathogenesis in Shigella flexneri

Shigella species Gram-negative bacteria which cause a diarrheal disease, known as shigellosis, by invading and destroying the colonic mucosa and inducing a robust inflammatory response. With no vaccine available, shigellosis annually kills over 600,000 children in developing countries. This study demonstrates the utility of combining high-throughput bioinformatic methods with in vitro and in vivo assays to provide new insights into pathogenesis. Comparisons of in vivo and in vitro gene expression identified genes associated with intracellular growth. Additional bioinformatics analyses identified genes that are present in S. flexneri isolates but not in the three other Shigella species. Comparison of these two analyses revealed nine genes that are differentially expressed during invasion and that are specific to S. flexneri. One gene, a DeoR family transcriptional regulator with decreased expression during invasion, was further characterized and is now designated icgR, for intracellular growth regulator. Deletion of icgR caused no difference in growth in vitro but resulted in increased intracellular replication in HCT-8 cells. Further in vitro and in vivo studies using high-throughput sequencing of RNA transcripts (RNA-seq) of an isogenic ΔicgR mutant identified 34 genes that were upregulated under both growth conditions. This combined informatics and functional approach has allowed the characterization of a gene and pathway previously unknown in Shigella pathogenesis and provides a framework for further identification of novel virulence factors and regulatory pathways.

One test microbial diagnostic microarray for identification of Mycoplasma mycoides subsp. mycoides and other Mycoplasma species

The present study describes the use of microarray technology for rapid identification and differentiation of Mycoplasma mycoides subsp. mycoides from other mycoplasmas that may be pathogenic to ruminants, including those of the Mycoplasma mycoides cluster, genetically and antigenically strictly correlated with Mycoplasma mycoides subsp. mycoides. A microarray containing genetic sequences of 55 different bacterial species from Acholeplasma, Mycoplasma, Spiroplasma and Ureaplasma genera was constructed. Sequences to genes of interest were collected in FASTA format from NCBI. The collected sequences were processed with OligoPicker software. Oligonucleotides were then checked for their selectivity with BLAST searches in GenBank. The microarray was tested with ATCC/NCTC strains of Mycoplasma spp. of veterinary importance in ruminants including Mycoplasma belonging to the mycoides cluster as well as Mycoplasma mycoides subsp. mycoides and Mycoplasma mycoides subsp. capri field strains. The results showed that but one ATCC/NCTC reference strains hybridized with their species-specific sequences showed a profile/signature different and distinct from each other. The heat-map of the hybridization results for the nine genes interrogated for Mycoplasma mycoides subsp. mycoides demonstrated that the reference strain Mycoplasma mycoides subsp mycoides PG1 was positive for all of the gene sequences spotted on the microarray. CBPP field, vaccine and reference strains were all typed to be M. mycoides subsp. mycoides, and seven of the nine strains gave positive hybridization results for all of the nine genes. Two Italian strains were negative for some of the genes. Comparison with non-Mycoplasma mycoides subsp. mycoides reference strains showed some positive signals or considerable homology to Mycoplasma mycoides subsp. mycoides genes. As expected, some correlations were observed between the strictly genetically and antigenically correlated Mycoplasma mycoides subsp. mycoides and Mycoplasma mycoides subsp. capri strains. Specifically, we observed that some Italian Mycoplasma mycoides subsp. mycoides strains were positive for two out of the three Mycoplasma mycoides subsp. capri genes, differently from what has been observed for other European or African Mycoplasma mycoides subsp. mycoides strains. This study highlighted the use of microarray technology as a simple and effective method for a single-step identification and differentiation of Mycoplasma mycoides subsp. mycoides from other mycoplasmas that may be pathogenic to ruminants, including those of the Mycoplasma mycoides cluster, genetically and antigenically strictly correlated with Mycoplasma mycoides subsp. mycoides. The opportunity to discriminate several mycoplasmas in a single analysis enhances diagnostic rapidity and may represent a useful tool to screen occasionally mycoplasmas affecting animal farming in territories where diagnostic laboratory support is limited. The heat-map of the hybridization results of the comparative genomic hybridizations DNA-designed chip clearly indicates that the microarray performs well for the identification of the tested Mycoplasma mycoides subsp. mycoides reference and field strains, discriminating them from other mycoplasmas.

Transcriptional modulation of enterotoxigenic Escherichia coli virulence genes in response to epithelial cell interactions

Enterotoxigenic Escherichia coli (ETEC) strains are a leading cause of morbidity and mortality due to diarrheal illness in developing countries. There is currently no effective vaccine against these important pathogens. Because genes modulated by pathogen-host interactions potentially encode putative vaccine targets, we investigated changes in gene expression and surface morphology of ETEC upon interaction with intestinal epithelial cells in vitro. Pan-genome microarrays, quantitative reverse transcriptase PCR (qRT-PCR), and transcriptional reporter fusions of selected promoters were used to study changes in ETEC transcriptomes. Flow cytometry, immunofluorescence microscopy, and scanning electron microscopy were used to investigate alterations in surface antigen expression and morphology following pathogen-host interactions. Following host cell contact, genes for motility, adhesion, toxin production, immunodominant peptides, and key regulatory molecules, including cyclic AMP (cAMP) receptor protein (CRP) and c-di-GMP, were substantially modulated. These changes were accompanied by visible changes in both ETEC architecture and the expression of surface antigens, including a novel highly conserved adhesin molecule, EaeH. The studies reported here suggest that pathogen-host interactions are finely orchestrated by ETEC and are characterized by coordinated responses involving the sequential deployment of multiple virulence molecules. Elucidation of the molecular details of these interactions could highlight novel strategies for development of vaccines for these important pathogens.

Shigella flexneri effectors OspE1 and OspE2 mediate induced adherence to the colonic epithelium following bile salts exposure

Shigella flexneri is a Gram-negative pathogen that invades the colonic epithelium. While invasion has been thoroughly investigated, it is unknown how Shigella first attaches to the epithelium. Previous literature suggests that Shigella utilizes adhesins that are induced by environmental signals, including bile salts, encountered in the small intestine prior to invasion. We hypothesized that bile would induce adherence factors to facilitate attachment to colonic epithelial cells. To test our hypothesis, S. flexneri strain 2457T was subcultured in media containing bile salts, and the ability of the bacteria to adhere to the apical surface of polarized T84 epithelial cells was measured. We observed a significant increase in adherence, which was absent in a virulence plasmid-cured strain and a type-III secretion system mutant. Microarray expression analysis indicated that the ospE1/ospE2 genes were induced in the presence of bile, and bile-induced adherence was lost in a ΔospE1/ΔospE2 mutant. Further studies demonstrated that the OspE1/OspE2 proteins were localized to the bacterial outer membrane following exposure to bile salts. The data presented are the first demonstration that the OspE1/OspE2 proteins promote initial adherence to the intestinal epithelium. The adhesins required for Shigella attachment to the colonic epithelium may serve as ideal targets for vaccine development.

Prediction system for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis fingerprints

A classification model is presented for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis (PFGE) fingerprints. The classification model was developed using random forest and support vector machine algorithms and was then applied to a database of 45,923 PFGE patterns, randomly selected from all submissions to CDC PulseNet from 2005 to 2010. The patterns selected included the top 20 most frequent serotypes and 12 less frequent serotypes from various sources. The prediction accuracies for the 32 serotypes ranged from 68.8% to 99.9%, with an overall accuracy of 96.0% for the random forest classification, and ranged from 67.8% to 100.0%, with an overall accuracy of 96.1% for the support vector machine classification. The prediction system improves reliability and accuracy and provides a new tool for early and fast screening and source tracking of outbreak isolates. It is especially useful to get serotype information before the conventional methods are done. Additionally, this system also works well for isolates that are serotyped as "unknown" by conventional methods, and it is useful for a laboratory where standard serotyping is not available.

Analysis of global transcriptional profiles of enterotoxigenic Escherichia coli isolate E24377A

Enterotoxigenic Escherichia coli (ETEC) is an important pathogenic variant (pathovar) of E. coli in developing countries from a human health perspective, causing significant morbidity and mortality. Previous studies have examined specific regulatory networks in ETEC, although little is known about the global effects of inter- and intrakingdom signaling on the expression of virulence and colonization factors in ETEC. In this study, an E. coli/Shigella pan-genome microarray, combined with quantitative reverse transcriptase PCR (qRT-PCR) and RNA sequencing (RNA-seq), was used to quantify the expression of ETEC virulence and colonization factors. Biologically relevant chemical signals were combined with ETEC isolate E24377A during growth in either Luria broth (LB) or Dulbecco's modified Eagle medium (DMEM), and transcription was examined during different phases of the growth cycle; chemical signals examined included glucose, bile salts, and preconditioned media from E. coli/Shigella isolates. The results demonstrate that the presence of bile salts, which are found in the intestine and thought to be bactericidal, upregulates the expression of many ETEC virulence factors, including heat-stable (estA) and heat-labile (eltA) enterotoxin genes. In contrast, the ETEC colonization factors CS1 and CS3 were downregulated in the presence of bile, consistent with findings in studies of other enteric pathogens. RNA-seq analysis demonstrated that one of the most differentially expressed genes in the presence of bile is a unique plasmid-encoded AraC-like transcriptional regulator (peaR); other previously unknown genetic elements were found as well. These results provide transcriptional targets and putative mechanisms that should help improve understanding of the global regulatory networks and virulence expression in this important human pathogen.

Genomic characterization of enteroaggregative Escherichia coli from children in Mali

Background. Enteroaggregative Escherichia coli (EAEC) is a cause of epidemic and sporadic diarrhea, yet its role as an enteric pathogen is not fully understood.

Methods. We characterized 121 EAEC strains isolated in 2008 as part of a case-control study of moderate to severe acute diarrhea among children 0–59 months of age in Bamako, Mali. We applied multiplex polymerase chain reaction and comparative genome hybridization to identify potential virulence factors among the EAEC strains, coupled with classification and regression tree modeling to reveal combinations of factors most strongly associated with illness.

Results. The gene encoding the autotransporter protease SepA, originally described in Shigella species, was most strongly associated with diarrhea among the EAEC strains tested (odds ratio, 5.6 [95% confidence interval, 1.92–16.17]; P = .0006). In addition, we identified 3 gene combinations correlated with diarrhea: (1) a clonal group positive for sepA and a putative hemolysin; (2) a group harboring the EAST-1 enterotoxin and the flagellar type H33 but no other previously identified EAEC virulence factor; and (3) a group carrying several of the typical EAEC virulence genes.

Conclusion. Our data suggest that only a subset of EAEC strains are pathogenic in Mali and suggest that sepA may serve as a valuable marker for the most virulent isolates.

Molecular and comparative analysis of Salmonella enterica Senftenberg from humans and animals using PFGE, MLST and NARMS

BACKGROUND

Salmonella species are recognized worldwide as a significant cause of human and animal disease. In this study the molecular profiles and characteristics of Salmonella enterica Senftenberg isolated from human cases of illness and those recovered from healthy or diagnostic cases in animals were assessed. Included in the study was a comparison with our own sequenced strain of S. Senfteberg recovered from production turkeys in North Dakota. Isolates examined in this study were subjected to antimicrobial susceptibility profiling using the National Antimicrobial Resistance Monitoring System (NARMS) panel which tested susceptibility to 15 different antimicrobial agents. The molecular profiles of all isolates were determined using Pulsed Field Gel Electrophoresis (PFGE) and the sequence types of the strains were obtained using Multi-Locus Sequence Type (MLST) analysis based on amplification and sequence interrogation of seven housekeeping genes (aroC, dnaN, hemD, hisD, purE, sucA, and thrA). PFGE data was input into BioNumerics analysis software to generate a dendrogram of relatedness among the strains.

RESULTS

The study found 93 profiles among 98 S. Senftenberg isolates tested and there were primarily two sequence types associated with humans and animals (ST185 and ST14) with overlap observed in all host types suggesting that the distribution of S. Senftenberg sequence types is not host dependent. Antimicrobial resistance was observed among the animal strains, however no resistance was detected in human isolates suggesting that animal husbandry has a significant influence on the selection and promotion of antimicrobial resistance.

CONCLUSION

The data demonstrates the circulation of at least two strain types in both animal and human health suggesting that S. Senftenberg is relatively homogeneous in its distribution. The data generated in this study could be used towards defining a pathotype for this serovar.

ArrayTrack: a free FDA bioinformatics tool to support emerging biomedical research--an update

ArrayTrack™is a Food and Drug Administration (FDA) bioinformatics tool that has been widely adopted by the research community for genomics studies. It provides an integrated environment for microarray data management, analysis and interpretation. Most of its functionality for statistical, pathway and gene ontology analysis can also be applied independently to data generated by other molecular technologies. ArrayTrack has been undergoing active development and enhancement since its inception in 2001. This review summarises its key functionalities, with emphasis on the most recent extensions in support of the evolving needs of FDA's research programmes. ArrayTrack has added capability to manage, analyse and interpret proteomics and metabolomics data after quantification of peptides and metabolites abundance, respectively. Annotation information about single nucleotide polymorphisms and quantitative trait loci has been integrated to support genetics-related studies. Other extensions have been added to manage and analyse genomics data related to bacterial food-borne pathogens.

Genomic characterization of asymptomatic Escherichia coli isolated from the neobladder

The replacement of the bladder with a neobladder made from ileal tissue is the prescribed treatment in some cases of bladder cancer or trauma. Studies have demonstrated that individuals with an ileal neobladder have recurrent colonization by Escherichia coli and other species that are commonly associated with urinary tract infections; however, pyelonephritis and complicated symptomatic infections with ileal neobladders are relatively rare. This study examines the genomic content of two E. coli isolates from individuals with neobladders using comparative genomic hybridization (CGH) with a pan-E. coli/Shigella microarray. Comparisons of the neobladder genome hybridization patterns with reference genomes demonstrate that the neobladder isolates are more similar to the commensal, laboratory-adapted E. coli and a subset of enteroaggregative E. coli than they are to uropathogenic E. coli isolates. Genes identified by CGH as exclusively present in the neobladder isolates among the 30 examined isolates were primarily from large enteric isolate plasmids. Isolations identified a large plasmid in each isolate, and sequencing confirmed similarity to previously identified plasmids of enteric species. Screening, via PCR, of more than 100 isolates of E. coli from environmental, diarrhoeagenic and urinary tract sources did not identify neobladder-specific genes that were widely distributed in these populations. These results taken together demonstrate that the neobladder isolates, while distinct, are genomically more similar to gastrointestinal or commensal E. coli, suggesting why they can colonize the transplanted intestinal tissue but rarely progress to acute pyelonephritis or more severe disease.