Isha R. Patel

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

BackgroundAdvances in microbial genomics and bioinformatics are offering greater insights into the emergence and spread of foodborne pathogens in outbreak scenarios. The Food and Drug Administration (FDA) has developed a genomics tool, ArrayTrackTM, which provides extensive functionalities to manage, analyze, and interpret genomic data for mammalian species. ArrayTrackTM has been widely adopted by the research community and used for pharmacogenomics data review in the FDA’s Voluntary Genomics Data Submission program.ResultsArrayTrackTM has been extended to manage and analyze genomics data from bacterial pathogens of human, animal, and food origin. It was populated with bioinformatics data from public databases such as NCBI, Swiss-Prot, KEGG Pathway, and Gene Ontology to facilitate pathogen detection and characterization. ArrayTrackTM’s data processing and visualization tools were enhanced with analysis capabilities designed specifically for microbial genomics including flag-based hierarchical clustering analysis (HCA), flag concordance heat maps, and mixed scatter plots. These specific functionalities were evaluated on data generated from a custom Affymetrix array (FDA-ECSG) previously developed within the FDA. The FDA-ECSG array represents 32 complete genomes of Escherichia coli and Shigella. The new functions were also used to analyze microarray data focusing on antimicrobial resistance genes from Salmonella isolates in a poultry production environment using a universal antimicrobial resistance microarray developed by the United States Department of Agriculture (USDA).ConclusionThe application of ArrayTrackTM to different microarray platforms demonstrates its utility in microbial genomics research, and thus will improve the capabilities of the FDA to rapidly identify foodborne bacteria and their genetic traits (e.g., antimicrobial resistance, virulence, etc.) during outbreak investigations. ArrayTrackTM is free to use and available to public, private, and academic researchers at http://www.fda.gov/ArrayTrack.

Investigating the global genomic diversity of Escherichia coli using a multi-genome DNA microarray platform with novel gene prediction strategies

BackgroundThe gene content of a diverse group of 183 unique Escherichia coli and Shigella isolates was determined using the Affymetrix GeneChip®E. coli Genome 2.0 Array, originally designed for transcriptome analysis, as a genotyping tool. The probe set design utilized by this array provided the opportunity to determine the gene content of each strain very accurately and reliably. This array constitutes 10,112 independent genes representing four individual E. coli genomes, therefore providing the ability to survey genes of several different pathogen types. The entire ECOR collection, 80 EHEC-like isolates, and a diverse set of isolates from our FDA strain repository were included in our analysis.ResultsFrom this study we were able to define sets of genes that correspond to, and therefore define, the EHEC pathogen type. Furthermore, our sampling of 63 unique strains of O157:H7 showed the ability of this array to discriminate between closely related strains. We found that individual strains of O157:H7 differed, on average, by 197 probe sets. Finally, we describe an analysis method that utilizes the power of the probe sets to determine accurately the presence/absence of each gene represented on this array.ConclusionsThese elements provide insights into understanding the microbial diversity that exists within extant E. coli populations. Moreover, these data demonstrate that this novel microarray-based analysis is a powerful tool in the field of molecular epidemiology and the newly emerging field of microbial forensics.

Erratum to: Genetic analysis of the roles of agaA

Correction: Genetic analysis of the roles of agaA, agaI, and agaS genes in the N-acetyl-D-galactosamine and D-galactosamine catabolic pathways in Escherichia coli strains O157:H7 and C After the publication of this work [1], it was brought to our attention that in Figure 1B it is erroneously shown that Gam is transported by EII Aga and Aga is transported by EII Gam. The correct depiction should be that Gam is transported by EII Gam and Aga is transported by EII Aga and the corrected figure is now shown in Figure 1. agaR kbaZ agaV agaW agaE agaF agaA agaS kbaY agaB agaC agaD agaI ?

Genomic Analysis of Immune Response against Vibrio cholerae Hemolysin in Caenorhabditis elegans

Vibrio cholerae cytolysin (VCC) is among the accessory V. cholerae virulence factors that may contribute to disease pathogenesis in humans. VCC, encoded by hlyA gene, belongs to the most common class of bacterial toxins, known as pore-forming toxins (PFTs). V. cholerae infects and kills Caenorhabditis elegans via cholerae toxin independent manner. VCC is required for the lethality, growth retardation and intestinal cell vacuolation during the infection. However, little is known about the host gene expression responses against VCC. To address this question we performed a microarray study in C. elegans exposed to V. cholerae strains with intact and deleted hlyA genes. Many of the VCC regulated genes identified, including C-type lectins, Prion-like (glutamine [Q]/asparagine [N]-rich)-domain containing genes, genes regulated by insulin/IGF-1-mediated signaling (IIS) pathway, were previously reported as mediators of innate immune response against other bacteria in C. elegans. Protective function of the subset of the genes up-regulated by VCC was confirmed using RNAi. By means of a machine learning algorithm called FastMEDUSA, we identified several putative VCC induced immune regulatory transcriptional factors and transcription factor binding motifs. Our results suggest that VCC is a major virulence factor, which induces a wide variety of immune response- related genes during V. cholerae infection in C. elegans.

Rapid Genomic-Scale Analysis of Escherichia coli O104:H4 by Using High-Resolution Alternative Methods to Next-Generation Sequencing

ABSTRACT Two technologies, involving DNA microarray and optical mapping, were used to quickly assess gene content and genomic architecture of recent emergent Escherichia coli O104:H4 and related strains. In real-time outbreak investigations, these technologies can provide congruent perspectives on strain, serotype, and pathotype relationships. Our data demonstrated clear discrimination between clinically, temporally, and geographically distinct O104:H4 isolates and rapid characterization of strain differences.

A Proteomic and Transcriptomic Approach Reveals New Insight into β-methylthiolation of Escherichia coli Ribosomal Protein S12

β-methylthiolation is a novel post-translational modification mapping to a universally conserved Asp 88 of the bacterial ribosomal protein S12. This S12 specific modification has been identified on orthologs from multiple bacterial species. The origin and functional significance was investigated with both a proteomic strategy to identify candidate S12 interactors and expression microarrays to search for phenotypes that result from targeted gene knockouts of select candidates. Utilizing an endogenous recombinant E. coli S12 protein with an affinity tag as bait, mass spectrometric analysis identified candidate S12 binding partners including RimO (previously shown to be required for this post-translational modification) and YcaO, a conserved protein of unknown function. Transcriptomic analysis of bacterial strains with deleted genes for RimO and YcaO identified an overlapping transcriptional phenotype suggesting that YcaO and RimO likely share a common function. As a follow up, quantitative mass spectrometry additionally indicated that both proteins dramatically impacted the modification status of S12. Collectively, these results indicate that the YcaO protein is involved in β-methylthiolation of S12 and its absence impairs the ability of RimO to modify S12. Additionally, the proteomic data from this study provides direct evidence that the E. coli specific β-methylthiolation likely occurs when S12 is assembled as part of a ribosomal subunit.

A Microarray Based Approach for the Identification of Common Foodborne Viruses

An oligonucleotide array (microarray) incorporating 13,000 elements representing selected strains of hepatitis A virus (HAV), human coxsackieviruses A and B (CVA and CVB), genogroups I and II of Norovirus (NV), and human rotavirus (RV) gene segments 3,4,10, and 11 was designed based on the principle of tiling. Each oligonucleotide was 29 bases long, starting at every 5th base of every sequence, resulting in an overlap of 24 bases in two consecutive oligonucleotides. The applicability of the array for virus identification was examined using PCR amplified products from multiple HAV and CV strains. PCR products labeled with biotin were hybridized to the array, and the biotin was detected using a brief reaction with Cy3-labeled streptavidin, the array subjected to laser scanning, and the hybridization data plotted as fluorescence intensity against each oligonucleotide in the array. The combined signal intensities of all probes representing a particular strain of virus were calculated and plotted against all virus strains identified on a linear representation of the array. The profile of the total signal intensity identified the strain that is most likely represented in the amplified cDNA target. The results obtained with HAV and CV indicated that the hybridization profile thus generated can be used to identify closely related viral strains. This represents a significant improvement over current methods for virus identification using PCR amplification and amplicon sequencing.

Characterization of Shiga toxin subtypes and virulence genes in porcine Shiga toxin-producing Escherichia coli

Similar to ruminants, swine have been shown to be a reservoir for Shiga toxin-producing Escherichia coli (STEC), and pork products have been linked with outbreaks associated with STEC O157 and O111:H-. STEC strains, isolated in a previous study from fecal samples of late-finisher pigs, belonged to a total of 56 serotypes, including O15:H27, O91:H14, and other serogroups previously associated with human illness. The isolates were tested by polymerase chain reaction (PCR) and a high-throughput real-time PCR system to determine the Shiga toxin (Stx) subtype and virulence-associated and putative virulence-associated genes they carried. Select STEC strains were further analyzed using a Minimal Signature E. coli Array Strip. As expected, stx2e (81%) was the most common Stx variant, followed by stx1a (14%), stx2d (3%), and stx1c (1%). The STEC serogroups that carried stx2d were O15:H27, O159:H16 and O159:H-. Similar to stx2a and stx2c, the stx2d variant is associated with development of hemorrhagic colitis and hemolytic uremic syndrome, and reports on the presence of this variant in STEC strains isolated from swine are lacking. Moreover, the genes encoding heat stable toxin (estIa) and enteroaggregative E. coli heat stable enterotoxin-1 (astA) were commonly found in 50 and 44% of isolates, respectively. The hemolysin genes, hlyA and ehxA, were both detected in 7% of the swine STEC strains. Although the eae gene was not found, other genes involved in host cell adhesion, including lpfAO113 and paa were detected in more than 50% of swine STEC strains, and a number of strains also carried iha, lpfAO26, lpfAO157, fedA, orfA, and orfB. The present work provides new insights on the distribution of virulence factors among swine STEC strains and shows that swine may carry Stx1a-, Stx2e-, or Stx2d-producing E. coli with virulence gene profiles associated with human infections.

Genomic Analysis of Stress Response Against Arsenic in Caenorhabditis elegans

Arsenic, a known human carcinogen, is widely distributed around the world and found in particularly high concentrations in certain regions including Southwestern US, Eastern Europe, India, China, Taiwan and Mexico. Chronic arsenic poisoning affects millions of people worldwide and is associated with increased risk of many diseases including arthrosclerosis, diabetes and cancer. In this study, we explored genome level global responses to high and low levels of arsenic exposure in Caenorhabditis elegans using Affymetrix expression microarrays. This experimental design allows us to do microarray analysis of dose-response relationships of global gene expression patterns. High dose (0.03%) exposure caused stronger global gene expression changes in comparison with low dose (0.003%) exposure, suggesting a positive dose-response correlation. Biological processes such as oxidative stress, and iron metabolism, which were previously reported to be involved in arsenic toxicity studies using cultured cells, experimental animals, and humans, were found to be affected in C. elegans. We performed genome-wide gene expression comparisons between our microarray data and publicly available C. elegans microarray datasets of cadmium, and sediment exposure samples of German rivers Rhine and Elbe. Bioinformatics analysis of arsenic-responsive regulatory networks were done using FastMEDUSA program. FastMEDUSA analysis identified cancer-related genes, particularly genes associated with leukemia, such as dnj-11, which encodes a protein orthologous to the mammalian ZRF1/MIDA1/MPP11/DNAJC2 family of ribosome-associated molecular chaperones. We analyzed the protective functions of several of the identified genes using RNAi. Our study indicates that C. elegans could be a substitute model to study the mechanism of metal toxicity using high-throughput expression data and bioinformatics tools such as FastMEDUSA.

FDA Escherichia coli Identification (FDA-ECID) Microarray: a Pangenome Molecular Toolbox for Serotyping, Virulence Profiling, Molecular Epidemiology, and Phylogeny

ABSTRACT Most Escherichia coli strains are nonpathogenic. However, for clinical diagnosis and food safety analysis, current identification methods for pathogenic E. coli either are time-consuming and/or provide limited information. Here, we utilized a custom DNA microarray with informative genetic features extracted from 368 sequence sets for rapid and high-throughput pathogen identification. The FDA Escherichia coli Identification (FDA-ECID) platform contains three sets of molecularly informative features that together stratify strain identification and relatedness. First, 53 known flagellin alleles, 103 alleles of wzx and wzy, and 5 alleles of wzm provide molecular serotyping utility. Second, 41,932 probe sets representing the pan-genome of E. coli provide strain-level gene content information. Third, approximately 125,000 single nucleotide polymorphisms (SNPs) of available whole-genome sequences (WGS) were distilled to 9,984 SNPs capable of recapitulating the E. coli phylogeny. We analyzed 103 diverse E. coli strains with available WGS data, including those associated with past foodborne illnesses, to determine robustness and accuracy. The array was able to accurately identify the molecular O and H serotypes, potentially correcting serological failures and providing better resolution for H-nontypeable/nonmotile phenotypes. In addition, molecular risk assessment was possible with key virulence marker identifications. Epidemiologically, each strain had a unique comparative genomic fingerprint that was extended to an additional 507 food and clinical isolates. Finally, a 99.7% phylogenetic concordance was established between microarray analysis and WGS using SNP-level data for advanced genome typing. Our study demonstrates FDA-ECID as a powerful tool for epidemiology and molecular risk assessment with the capacity to profile the global landscape and diversity of E. coli. IMPORTANCE This study describes a robust, state-of-the-art platform developed from available whole-genome sequences of E. coli and Shigella spp. by distilling useful signatures for epidemiology and molecular risk assessment into one assay. The FDA-ECID microarray contains features that enable comprehensive molecular serotyping and virulence profiling along with genome-scale genotyping and SNP analysis. Hence, it is a molecular toolbox that stratifies strain identification and pathogenic potential in the contexts of epidemiology and phylogeny. We applied this tool to strains from food, environmental, and clinical sources, resulting in significantly greater phylogenetic and strain-specific resolution than previously reported for available typing methods.