• Escherichia coli O157:H7;
  • Acanthamoeba castellanii;
  • protozoa;
  • microarray;
  • transcriptome;
  • gene regulation


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Free-living protozoa, such as Acanthamoeba castellanii, are environmental hosts for pathogenic bacteria. Protozoa have been implicated in harboring pathogenic bacteria and enhancing virulence factors and antibiotic resistance. To better understand this relationship with Escherichia coli O157:H7, we characterized its transcriptome within A. castellanii compared with broth-grown organisms using two-color microarrays. Statistical analysis indicated that 969 genes were differentially expressed at P<0.018, with a false discovery rate of 1.9% and a fold change cutoff of 1.3 or greater. There were 655 upregulated transcripts that include 40 genes associated with virulence, of which 32 are encoded on O-islands, and include shiga toxin genes (stx1A, stx1B stx2A) and 14 genes involved in Type III secretion system components. Also included are SOS response genes such as lexA and recA, genes involved in or predicted to be involved in antibiotic resistance (rarD, macAB, marABR, mdtK, yojI, yhgN), the quorum-sensing operon lsrACDB, and the efe and feo iron-acquisition systems. There were 314 downregulated transcripts that included 19 transcripts associated with virulence, seven of which are encoded on O-islands. Our results demonstrate that a significant portion of the E. coli O157:H7 genome was differentially expressed as a result of the protozoan intracellular environment.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Escherichia coli O157:H7 causes food-borne illness in humans, with disease manifested as acute gastroenteritis and symptoms ranging from mild diarrhea to hemorrhagic colitis (Nataro & Kaper, 1998). A potentially fatal sequelae of E. coli O157:H7 infection, hemolytic uremic syndrome, is the leading cause of acute renal failure in children (Nataro & Kaper, 1998). While E. coli O157:H7 is pathogenic for many domestic and wild animals, asymptomatic, infected cattle serve as the major reservoir (Dean-Nystrom et al., 1998; Barkocy-Gallagher et al., 2004). Infected cattle are capable of shedding 102–105 CFU of E. coli O157:H7 per gram of feces (Wang et al., 1996; Campbell et al., 2001), and it can persist in manure and slurry (Kudva et al., 1998; Bolton et al., 1999; Lau & Ingham, 2001; Avery et al., 2005) and in soil, water, sediment, and animal carcasses for extended periods of time (Mead & Griffin, 1998). Thus, contamination of the soil and surface water with E. coli O157:H7 in the vicinity of infected cattle herds occurs at high frequency, making it the main source of contamination of nonmeat food products (McGee et al., 2002).

While E. coli O157:H7 is not thought of as an intracellular pathogen, it has been shown to survive within human macrophages for at least 24 h (Poirier et al., 2008) and in the soil protozoan Acanthamoeba polyphaga for at least 45 days (Barker et al., 1999). This bacterial–protozoal interaction has certain implications as protozoa are widely acknowledged as reservoirs for bacterial pathogens such as Legionella, Listeria, Campylobacter, Pseudomonas, Helicobacter, Mycobacterium, Coxellia, Salmonella, Staphylococcus, and the harboring of these pathogens within protozoa has been associated with increased survival and persistence in environment (King et al., 1988), increased virulence (Cirillo et al., 1994; Rasmussen et al., 2005), and increased resistance to antibiotics (Barker et al., 1995; Miltner & Bermudez, 2000). With this in mind, protozoa may serve as a vehicle for E. coli O157:H7 environmental persistence and transmission as well as preparing E. coli O157:H7 for enhanced survival during its journey through the rumen of cattle.

We sought to characterize the transcriptome of E. coli O157:H7 after exposure to the protozoan Acanthamoeba castellanii environment as a model for environmental and rumen exposure using microarrays to measure the transcriptional changes that occur in E. coli O157:H7 following uptake compared with standard planktonic growth conditions. Our results demonstrate that a significant portion of the E. coli O157:H7 genome, including many virulence-related genes, are differentially expressed as a result of the A. castellanii intracellular environment.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Strains and culture conditions

Escherichia coli O157:H7 EDL933 (ATCC 43895) was grown in Luria–Bertani (LB) broth at 37 °C. Following overnight incubation, these cultures were diluted 1 : 100 in LB broth and incubated with shaking for 2 h before use in the Acanthamoeba assay. Acanthamoeba castellanii (ATCC 30010) was grown in ATCC PYG712 broth at 30 °C.

Acanthamoeba assay

An estimate of A. castellanii cell numbers was obtained using a Coulter particle counter. Acanthamoeba castellanii cultures were centrifuged at 100 g for 5 min, resuspended in fresh PYG712 broth to a density of 2 × 106 cells mL−1. Wells within six-well cell culture plates were seeded with 1 mL of this suspension. After 2 h of incubation, E. coli O157:H7 cultures were centrifuged at 5000 g for 5 min, resuspended in PYG712 broth, and added to six-well cell culture plates at a multiplicity of infection of 1000 : 1 (bacteria : protozoa). Controls (planktonic growth) did not contain A. castellanii. Cultures were incubated at 30 °C (growth temperature of A. castellanii) for 30 min, and then gentamicin was added to wells containing A. castellanii to 100 μg μL−1 to eliminate extracellular bacteria (Alsam et al., 2006). After 2 h, A. castellanii cultures were centrifuged at 100 g for 5 min and resuspended in 1 mL of PYG712 broth containing 25 μg μL−1 of gentamicin to prevent the growth of extracellular bacteria. After an additional 2 hours, cultures were centrifuged at 10 000 g for 30 s, pellets were resuspended in 1 mL of ice-cold RNA stop solution (19% ethanol, 0.1% sodium dodecyl sulfate (SDS), 1% acidic phenol) (Bernstein et al., 2002), and incubated on ice for 30 min. Following centrifugation at 10 000 g for 5 min at 0 °C, the RNA was immediately extracted from the pellets. For determination of survival of intracellular E. coli O157:H7, cultures were generated in exactly the same way as cultures used for RNA extraction were generated. At the end of each time point, cultures were subjected to 0.1% SDS (final concentration) for 15 min to lyse A. castellanii and CFUs were determined. This level of SDS had no effect on the viability of E. coli O157:H7 grown planktonically (data not shown).

RNA isolation

RNA isolation, DNase treatment, subsequent purification, and determination of the absence of DNA was conducted as described previously (Carruthers & Minion, 2009). Samples were purified and concentrated using Millipore Microcon YM-30 columns. RNA integrity and purity (absence of eukaryotic ribosomal peaks) were determined using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), with all samples measured having an Agilent RNA integrity number of 9.0 or higher and were void of detectable eukaryotic rRNA peaks (data not shown). Samples were determined to be free of contaminating genomic DNA by the absence of a product after 30 rounds of PCR.

Microarrays and data analysis

The microarray used for these studies has been described (Carruthers & Minion, 2009). It is based on PCR products representing 4756 genes printed to Corning UltraGAPS substrates. Target generation, labeling, reaction clean-up, hybridization, and pre- and posthybridization washes were all conducted as described previously using Cy3 and Cy5 dyes (Oneal et al., 2008; Carruthers & Minion, 2009). Scanning, image segmentation, and normalization were conducted as described previously (Oneal et al., 2008). Cluster of orthologous groups of proteins (COGs) information was obtained from NCBI (

Microarray experimental design

Eighteen RNA samples, half from cells within A. castellanii and half from planktonic control cells, were used for the microarray study. A sample from each treatment was randomly paired with a sample from another treatment for hybridization on a two-color microarray substrate for a total of nine hybridizations. To account for differences in emission intensity of the Cy dyes (dye bias), dye assignments for control and treated samples were reversed for four of the arrays.

Semi-quantitative real-time (qRT)-PCR

qRT-PCR was preformed on the same samples used for microarray analysis using primer sets for eight genes (dnaK, espA, lpfD, macA, ompA, recA, stx1A, stx2A) to confirm significant transcriptional differences due to treatment. The Express One-Step SYBR GreenER kit (Invitrogen) was used for qRT-PCR with the Mx3005P QPCR System (Stratagene, La Jolla, CA) and mxpro 4.1 software. Reaction volume for each well totaled 15 μL and contained 3.69 μL of water, 7.5 μL qRT-PCR mix, 1.2 μL of each primer (Table 1) at 2.5 μM, 0.03 μL ROX, 1 μL of sample RNA, and 0.375 μL (75 U) SuperScript III. Six biological replicates for each treatment were randomly chosen for qRT-PCR validation and were run in duplicate. Gene btuD was used as a reference gene because it demonstrated no detectable differential expression due to treatment and had a small variance on the microarrays. The method described in Gallup & Ackermann (2006) was used for primer optimization, detection of inhibition, and troubleshooting of qRT-PCR. A four-point standard curve was constructed with duplicate samples of a collection of all RNAs (Stock 1) and used for the calculation of efficiencies for target genes and the reference gene (Gallup & Ackermann, 2006). The ISU equation was used to calculate fold change between treatment and control samples (Gallup & Ackermann, 2006), and the Student's t-test was used to determine significance of differences. Confidence threshold values that were greater than 2 SD from the mean were considered outliers and were not used in data analysis.

Table 1.   qRT-PCR primers used to validate microarray results
PrimerSequence 5′–3′Tm (°C)

Microarray data accession

The microarray dataset can be accessed from the National Center for Biological Informatics Gene Expression Omnibus using Series accession number GSE16762 (


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Intracellular survival

Initially, we determined the survivability of E. coli O157:H7 in A. castellanii under the conditions of the microarray study (Fig. 1). Initial CFUs of E. coli O157:H7 began at 109 and fell 5 logs during the first 2 h before leveling off to 103–104 for the next 14 h (Fig. 1). The addition of gentamicin to the culture media after a 30-min ingestion period did not affect the viability of A. castellanii or the bacteria within (data not shown).


Figure 1.  Quantification of Escherichia coli O157:H7 in Acanthamoeba castellanii over time by plate count. Data represents mean±SE of three replicates.

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Microarrays were used to compare steady-state transcript levels of E. coli O157:H7 within A. castellanii to planktonic cultures to determine the effect of the intracellular environment. Based on the data from the internal survival curve, an incubation period of 4.5 h was chosen for the microarray study. This included an initial 30 min for A. castellanii engulfment of E. coli, 2 h for killing extracellular bacteria with gentamicin, and an additional 2 h for transcriptional activity to stabilize and allow dead bacteria to be degraded.

All RNA preparations fulfilled our criteria for integrity and purity and lacked contamination with A. castellanii RNA, demonstrating the effectiveness of the bacterial RNA isolation procedure. Also, preliminary studies using 2.5 μg of A. castellanii-labeled cDNA hybridized to the E. coli O157:H7 microarray showed minimal reactivity to E. coli-specific features (data not shown). This reduced the probability that low-level protozoa RNA contamination could introduce errors into our transcriptional analysis. Also, there was no indication from the Bioanalyzer results that degraded RNAs from dead or dying bacteria were present in the RNA preparations.

Statistical analysis indicated that 969 genes with an estimated fold change >1.3 demonstrated transcriptional differences with a P-value<0.018 and an estimated false discovery rate (FDR) of 1.9%. This represents 20% of the genes on the microarray and 17.5% of the genes in the genome and virulence plasmid. Significance and differences in transcript levels for all genes are depicted as a volcano plot seen in Fig. 2. Of the 969 genes differentially expressed, 655 genes were upregulated while 314 genes were downregulated. Differentially expressed genes involved in virulence are listed in Table 2. Table 3 lists differentially expressed genes associated with antibiotic resistance, the SOS response, and iron acquisition/metabolism. These genes cover 21COGs, as shown in Fig. 3. All statistically significant genes with P<0.05 are listed in Supporting Information, Table S1.


Figure 2.  Volcano plot of transcriptional differences of Escherichia coli O157:H7 within Acanthamoeba castellanii. Differences are plotted as Log2 fold change vs.−Log10P-value. The horizontal line represents a P-value of 0.01.

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Table 2.   Differentially expressed genes involved in virulence as annotated in the ERIC database*
  • *

    The ERIC database can be found at

  • FC, microarray estimate of fold change.

  • FC, qRT-PCR estimate of fold change.

  • §

    § Gene is encoded on an O-island.

 eivA§Z41951.3 Type III secretion apparatus protein
 eivE§Z41961.3 Putative secreted protein
 eivG§Z41971.3 Type III secretion apparatus protein
 escF§Z51031.4 LEE-encoded Type III secretion system component
 espA§Z51071.35.0Secreted protein EspA
 espD§Z51061.3 Secreted protein EspD
 espF§Z51001.3 LEE-encoded Type III secreted effector
 espM1§Z25651.3 Non-LEE-encoded Type III secreted effector
 espR4§Z30261.3 Predicted non-LEE-encoded Type III secreted effector
 espY1§Z00651.4 Non-LEE-encoded Type III secreted effector
 espY2§Z00781.7 Predicted non-LEE-encoded Type III secreted effector
 fmlA§Z22001.4 Major subunit of F9 fimbriae
 fmlB§Z22011.3 F9 fimbriae chaperone
 lomP§Z60281.3 Putative Lom-like outer membrane protein of cryptic prophage CP-933P
 lpfD§Z49661.3 Putative fimbrial protein
 lpfD2§Z52211.3 Putative fimbrial protein
 lpfE§Z49651.3 Putative fimbrial subunit
 lpxCZ01061.5 UDP-3-O-acyl N-acetylglucosamine deacetylase
 lpxKZ12611.8 Lipid A 4′ kinase
 nleA§Z60241.4 Non-LEE-encoded Type III secreted effector
 nleG2-3§Z21491.3 Predicted non-LEE-encoded Type III secreted effector
 nleG5-2§Z21511.3 Predicted non-LEE-encoded Type III secreted effector
 orf29§Z51021.3 LEE-encoded predicted Type III secretion system factor
 stx1A§Z33441.31.9Shiga-like toxin 1 subunit A
 stx1B§Z33431.52.2Shiga-like toxin I subunit B
 stx2A§Z14641.3 Shiga toxin II subunit A
 ycbQZ12861.8 Predicted fimbrial-like adhesin protein
 ydeAZ21731.4 Predicted arabinose transporter
 ydeUZ21961.7 C-terminal fragment of a predicted autotransporter
 ydhQZ26911.3 Conserved protein
 yfaLZ34871.4 Adhesin
 Z0635Z06351.3 Putative membrane spanning export protein
 Z1534§Z15341.3 Putative chaperone
 Z1544§Z15441.4 Putative acyl-carrier protein synthase
 Z1917§Z19171.9 Putative outer membrane protein of prophage CP-933X
 Z1964§Z19642.0 Putative iron compound ABC transporter, ATP-binding protein
 Z1965§Z19651.9 Putative iron compound ABC transporter/permease
 Z2146§Z21461.4 Putative outer membrane protein Lom precursor of prophage CP-933O
 Z4383§Z43831.5 Putative iron compound permease protein
 Z4385§Z43851.6 Putative ATP-binding protein
 csgAZ1676−1.3 Cryptic curlin major subunit
 epaO§Z4190−1.3 Type III secretion apparatus protein
 gadEZ4925−1.5 Acid-induced positive regulator of glutamate-dependent acid resistance
 mviMZ1705−1.3 Predicted oxidoreductase
 nleG2-2Z2339−1.4 Non-LEE-encoded Type III secreted effector
 nleG5-1Z2337−1.3 Non-LEE-encoded Type III secreted effector
 nleH1-1§Z0989−1.3 Non-LEE-encoded Type III secreted effector
 ompAZ1307−2.5−6.2Outer membrane protein A
 ompXZ1036−1.3 Outer membrane protein
 rorf1§Z5143−1.3 LEE-encoded Type III secretion system factor
 sapAZ2494−1.3 Predicted antimicrobial peptide transporter subunit
 sapFZ2500−1.3 Peptide transport system ATP-binding protein
 tolCZ4392−2.0 Transport channel
 toxBL7095−1.3 Putative cytotoxin
 yehBZ3277−1.3 Predicted outer membrane protein
 Z0609§Z0609−1.4 Predicted protein
 Z0615§Z0615−1.5 Putative RTX family exoprotein
 Z0634§Z0634−1.6 Putative cytoplasmic membrane export protein
 Z4192§Z4192−1.3 Hypothetical protein
Table 3.   Differentially expressed genes involved in antibiotic resistance, the SOS response, and iron acquisition/metabolism
  • *

    FC, microarray estimate of fold change.

  • FC, qRT-PCR estimate of fold change.

Antibiotic resistance associated
 fisZ46211.6 Global DNA-binding transcriptional dual regulator
 macAZ11151.6 Macrolide-specific efflux protein
 macBZ11161.6 Putative ATP-binding component of a transport system
 marAZ21701.4 DNA-binding transcriptional dual activator of multiple antibiotic resistance
 marBZ21691.5 Predicted protein
 marRZ21711.3 DNA-binding transcriptional repressor of multiple antibiotic resistance
 mdtKZ26901.3 Multidrug efflux system transporter
 rarDZ53401.3 Predicted chloramphenicol resistance permease
 yhgNZ47981.3 Predicted antibiotic transporter
 yojIZ34691.7 Fused predicted multidrug transport subunit
SOS response
 dinDZ50701.3 DNA-damage inducible protein
 dnaGZ44191.4 DNA primase
 ftsKZ12351.7 DNA-binding membrane protein required for chromosome resolution/partitioning
 grxAZ10761.7 Glutaredoxin 1, redox coenzyme for ribonucleotide reductase
 lexAZ56422.6 DNA-binding transcriptional repressor of SOS regulon
 nrdAZ34891.5 Ribonucleoside diphosphate reductase 1 α-subunit
 polBZ00681.5 DNA polymerase II
 recAZ40026.411.3DNA strand exchange and recombination
 recGZ50781.3 ATP-dependent DNA helicase
 recOZ38461.4 Gap repair protein
 recXZ40011.4 Inhibitor of RecA
 sulAZ13082.2 SOS cell division inhibitor
 umuCZ19472.0 DNA polymerase V, subunit C
Iron acquisition/metabolism
 dpsZ10344.6 Fe-binding and storage protein
 efeBZ15211.2 Redox component of a tripartite ferrous iron transporter
 efeOZ15201.7 Component of a tripartite ferrous iron transporter
 efeUZ15191.5 Ferrous iron permease
 entAZ07381.7 Enterobactin synthetase component A
 entBZ07371.6 Enterobactin synthetase component B
 entCZ07351.5 Enterobactin synthetase component C
 entDZ07231.7 Phosphopantetheinyltransferase component of enterobactin synthase multienzyme complex
 entEZ07361.8 Enterobactin synthetase component E
 entFZ07271.7 Enterobactin synthetase component F
 fepAZ07241.8 Iron-enterobactin outer membrane transporter
 fepBZ07342.0 Iron-enterobactin transporter subunit
 fepCZ07291.8 ATP-binding component of ferric enterobactin transport
 fepDZ07321.8 Ferric enterobactin transport
 fepEZ07281.5 Ferric enterobactin transport, regulator of length of O-antigen component of lipopolysaccharide chains
 fepGZ07311.8 Iron-enterobactin transporter subunit
 fesZ07251.6 Enterobactin/ferric enterobactin esterase
Quorum sensing
 glpDZ47862.1 Sn-glycerol-3-phosphate dehydrogenase
 lsrAZ21921.3 Fused AI2 transporter
 lsrBZ21891.6 AI2 transporter
 lsrCZ21911.6 AI2 transporter
 lsrDZ21901.7 AI2 transporter
 lsrKZ21942.1 A-2 kinase

Figure 3.  COGs of the differentially transcribed genes. White bars indicate downregulated genes; black bars, upregulated genes.

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To validate the microarray studies, eight genes were chosen for qRT-PCR analysis, six upregulated and two downregulated. btuD was used for the control as it did not show differential expression in the microarray study. In every case, the qRT-PCR results corroborated the microarray results with respect to direction of differential expression, as shown in Fig. 4. The degree of transcript difference measured by qRT-PCR was greater than that measured by microarray, as shown previously (Morey et al., 2006).


Figure 4.  Validation of microarray results by qRT-PCR. All results were significant at P<0.008.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Escherichia coli O157:H7 has adapted to two distinct habitats: the enteric environment of ruminants and the external environment, namely water, soil, and plant surfaces. It comes into contact with protozoa while in both the rumen and external water environments. During passage through the ruminant gastrointestinal tract, a series of environment shifts are encountered, including aerobe to anaerobiosis, protozoal uptake, rumen fluid, and large pH changes, to better prepare this pathogen for colonization of the lower gastrointestinal tract of cattle (Naylor et al., 2003). To better understand this path from a bacterial perspective, we sought to model individual segments starting with the uptake by protozoa. Ideally, this would involve isolation of protozoa from the rumen, but variability in the protozoa species populations, variability between animals, and the lack of protozoa free of internal bacterial, particularly E. coli, presents difficult problems in experimental design and interpretation of microarray data. Because E. coli O157:H7 encounters protozoa throughout its life cycle including environmental water sources, we chose to use the well-studied protozoan A. castellanii for these studies. In our estimation, this interaction is a pivotal point in the cycle of environmental contamination and cattle carriage of this important pathogen.

The transcript levels of the SOS response regulator lexA and several LexA-regulated genes were upregulated in E. coli O157:H7 within A. castellanii (Table 3). Transcripts of genes involved in the SOS response but regulated independently of LexA (recX, nrdA, dnaG) were also upregulated. Superoxide dismutase, sodC, was upregulated, which indicates that E. coli responded to an oxidative stress. To counteract the stress associated with internalization, transcripts associated with the SOS response were upregulated (Table 3). Similar regulation of the stress and SOS responses has been observed in E. coli O157:H7 within human macrophages (Poirier et al., 2008).

Although iron is essential for growth, free iron is limiting in vivo (Andrews et al., 2003). Transcripts of genes involved in the biosynthesis of the siderophore enterobactin (entABCE, entD, entF) were upregulated as were the iron–enterobactin transport system encoded by fepA, fepB, fepCDG, and fepE, and the iron uptake system efeBO and efeU, while the transcript that encodes the iron storage protein Dps was downregulated 4.6-fold. These results indicate that E. coli O157:H7 may selectively regulate genes required for iron assimilation and not storage within A. castellanii. This is a different set of iron uptake genes found to be regulated in human macrophages (Poirier et al., 2008). These results are the first demonstration of iron regulation at the transcriptional level by a bacterial pathogen inside a protozoan.

Although lipopolysaccharides and OmpA play a crucial role in E. coli K1–A. castellanii interactions (Alsam et al., 2006), transcription of ompA was downregulated in our study, while transcription of genes involved in lipopolysaccharides synthesis and modification were upregulated.

A recent study has implicated autoinducer-2 (AI-2) in the regulation of certain virulence genes in E. coli O157:H7 (Bansal et al., 2008). The AI-2 transporter and kinase-encoding transcript lsrACDB responsible for the uptake of AI-2 were upregulated, which in turn may have upregulated locus of enterocyte effacement (LEE)-encoded virulence genes, iron acquisition/metabolism genes, certain fimbrae genes (lpfD, lpfD2, lpfE, ycbQ, ydeA), and colanic acid biosynthesis genes (Bansal et al., 2008). glpD, which has been shown to prevent lsr repression by metabolizing glycerol-3-phosphate (Xavier & Bassler, 2005), was upregulated. These results together imply that E. coli O157:H7 may be involved in AI-2-mediated quorum sensing within A. castellanii.

Previous studies have shown a link between the maintenance and expression of bacterial virulence genes involved in human and animal infections and bacterial–protozoal interactions (Molmeret et al., 2005; Rasmussen et al., 2005). Multiple transcripts associated with virulence, as annotated in the ERIC database (Greene et al., 2007), were upregulated in our microarray study, including stx1A, stx1B, and stx2A. Shiga toxins have been shown to play a role in E. coli O157:H7 survival within grazing protozoa, and it has been postulated that the maintenance of shiga toxin genes is important for the protozoal-bacterial interaction and not the mammalian host interaction (Steinberg & Levin, 2007).

The role of the LEE-encoded Type III secretion system (T3SS) in the development of E. coli O157:H7 attaching and effacing lesions and translocation of effectors is well documented (Knutton et al., 1998; Roe et al., 2003; Tobe et al., 2006). Our microarray analysis indicated that genes that comprise part of the LEE-encoded T3SS (espAD, escF) on the transcript expADB-escF-Z5102-Z5104 were upregulated. This result was further confirmed by qRT-PCR analysis estimating the upregulation of espA to be 5.1-fold. A second T3SS described in E. coli O157:H7 (Ideses et al., 2005) that shares homology with the Salmonella pathogenicity island 1 (eivCAEGF) was also upregulated. Two LEE-encoded and seven non-LEE-encoded T3SS translocated factor transcripts were upregulated.

In summary, the analysis of transcript changes in E. coli O157:H7 during its interaction with A. castellanii show that E. coli upregulates genes involved in the response to various stressful environments including iron deprivation and oxidative stress. These results are purely based on transcript levels and need to be confirmed at the protein level. The phenotype changes required for protozoa survival may also include changes in the surface architecture and the translocation of effector molecules by T3SS. Some virulence genes were also upregulated, which suggests that protozoa may serve as the environment that selects for and helps to maintain virulence genes that result in colonization and disease outbreaks in mammalian populations.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We would like to thank Dr Greg Phillips for providing A. castellanii. This study was funded in part by a contract to F.C.M. from the United States Department of Agriculture (Specific Cooperative Agreement 58-3625-2-127).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1. Differentially expressed genes of Escherichia coli O157:H7 within Acanthamoeba.

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FML_2098_sm_tables1.doc1574KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.