Subjects

Abstract

The bacterium Escherichia coli O157:H7 is a worldwide threat to public health and has been implicated in many outbreaks of haemorrhagic colitis, some of which included fatalities caused by haemolytic uraemic syndrome1,2. Close to 75,000 cases of O157:H7 infection are now estimated to occur annually in the United States3. The severity of disease, the lack of effective treatment and the potential for large-scale outbreaks from contaminated food supplies have propelled intensive research on the pathogenesis and detection of E. coli O157:H7 (ref. 4). Here we have sequenced the genome of E. coli O157:H7 to identify candidate genes responsible for pathogenesis, to develop better methods of strain detection and to advance our understanding of the evolution of E. coli, through comparison with the genome of the non-pathogenic laboratory strain E. coli K-12 (ref. 5). We find that lateral gene transfer is far more extensive than previously anticipated. In fact, 1,387 new genes encoded in strain-specific clusters of diverse sizes were found in O157:H7. These include candidate virulence factors, alternative metabolic capacities, several prophages and other new functions—all of which could be targets for surveillance.

Main

Escherichia coli O157:H7 was first associated with human disease after a multi-state outbreak in 1982 involving contaminated hamburgers1. The strain EDL933 that we sequenced was isolated from Michigan ground beef linked to this incident, and has been studied as a reference strain for O157:H7. Figures 1 and 2 show the gene content and organization of the EDL933 genome, and compare it with the chromosome of the K-12 laboratory strain MG1655 (ref. 5). These strains last shared a common ancestor about 4.5 million years ago6. The two E. coli genomes revealed an unexpectedly complex segmented relationship, even in a preliminary examination7. They share a common ‘backbone’ sequence which is co-linear except for one 422-kilobase (kb) inversion spanning the replication terminus (Fig. 1). Homology is punctuated by hundreds of islands of apparently introgressed DNA—numbered and designated ‘K-islands’ (KI) or ‘O-islands’ (OI) in Fig. 2 (pdf file — 336K), where K-islands are DNA segments present in MG1655 but not in EDL933, and O-islands are unique segments present in EDL933.

Figure 1: Circular genome map of EDL933 compared with MG1655.
Figure 1

Outer circle shows the distribution of islands: shared co-linear backbone (blue); position of EDL933-specific sequences (O-islands) (red); MG1655-specific sequences (K-islands) (green); O-islands and K-islands at the same locations in the backbone (tan); hypervariable (purple). Second circle shows the G+C content calculated for each gene longer than 100 amino acids, plotted around the mean value for the whole genome, colour-coded like outer circle. Third circle shows the GC skew for third-codon position, calculated for each gene longer than 100 amino acids: positive values, lime; negative values, dark green. Fourth circle gives the scale in base pairs. Fifth circle shows the distribution of the highly skewed octamer Chi (GCTGGTGG), where bright blue and purple indicate the two DNA strands. The origin and terminus of replication, the chromosomal inversion and the locations of the sequence gaps are indicated. Figure created by Genvision from DNASTAR.

Figure 2: Detailed comparative map of the EDL933 and MG1655 genomes.
Figure 2

Please refer to the pdf version of this file (336K). The upper double bar in each tier shows the genome comparison in EDL933 coordinates, with segments shown in detail and colour coded as in Fig. 1. Segments shown below the blue bar represent K-islands (MG1655-specific sequence). Segments extending above the blue backbone bar represent O-islands (EDL933-specific sequence). Unique identifying names (KI and OI numbers) were assigned to all segments of more than 50 bp. Unnamed vertical black lines across the blue bar indicate segments of less than 50 bp. In the lower line of each tier, EDL933 genes are presented showing orientation, and are coloured by segment type. Genes spanning segment junctions are shown in pink. Some gene names are given to provide landmarks in the backbone regions, and the sequence gaps are indicated. The scale in base pairs marks the base of each tier. Map created by Genvision from DNASTAR.

The backbone comprises 4.1 megabases (Mb), which are clearly homologous between the two E. coli genomes. O-islands total 1.34 Mb of DNA and K-islands total 0.53 Mb. These lineage-specific segments are found throughout both genomes in clusters of up to 88 kb. There are 177 O-islands and 234 K-islands greater than 50 bp in length. Histograms (Fig. 3) show more intermediate and large islands in EDL933 than in MG1655. Only 14.7% (26/177) of the O-islands correspond entirely to intergene regions. The two largest are identical copies of a 106-gene island, both in the same orientation and adjacent to genes encoding identical transfer RNAs.

Figure 3: Histograms of lineage-specific segment lengths.
Figure 3

a, EDL933; b, MG1655. The frequencies for the smallest length class are truncated to emphasize the distribution of longer clusters.

Labelling lineage-specific segments ‘islands’ is an extension of the term ‘pathogenicity island’ now in common, albeit varied, use. The original term arose from observations that virulence determinants are often clustered in large genomic segments showing hallmarks of horizontal transfer8. However, we found K- and O-islands of all sizes with no obvious association with pathogenicity; conversely, genes probably associated with virulence are not limited to the largest islands.

Roughly 26% of the EDL933 genes (1,387/5,416) lie completely within O-islands. In 189 cases, backbone-island junctions are within predicted genes. We classified the EDL933 genes into the functional groups reported for the MG1655 genome5 and this is included in the annotation. Of the O-island genes, 40% (561) can be assigned a function. Another 338 EDL933 genes marked as unknowns lie within phage-related clusters and are probably remnants of phage genomes. About 33% (59/177) of the O-islands contain only genes of unknown function. Many classifiable proteins are related to known virulence-associated proteins from other E. coli strains or related enterobacteria.

Nine large O-islands (>15 kb) encode putative virulence factors: a macrophage toxin and ClpB-like chaperone (OI#7); a RTX-toxin-like exoprotein and transport system (OI#28); two urease gene clusters (OI#43 and #48); an adhesin and polyketide or fatty-acid biosynthesis system (OI#47); a type III secretion system and secreted proteins similar to the Salmonella–Shigella inv–spa host-cell invasion genes (OI#115); two toxins and a PagC-like virulence factor (OI#122); a fatty-acid biosynthesis system (OI#138); and the previously described locus of enterocyte effacement (OI#148)9. Among the large islands, four include a P4-family integrase and are directly adjacent to tRNAs (OI#43-serW, #48-serX, #122-pheV and #148- selC). Only the locus of enterocyte effacement and two of the lambdoid phages (see below) have as yet been experimentally associated with virulence in animal models.

Smaller islands that may be involved in virulence contain fimbrial biosynthesis systems, iron uptake and utilization clusters, and putative non-fimbrial adhesins. Many clusters have no obvious role in virulence, but may confer strain-specific abilities to survive in different niches. Examples include candidates for transporting diverse carbohydrates, antibiotic efflux, aromatic compound degradation, tellurite resistance and glutamate fermentation. Not all islands are expected to be adaptive. Some may represent neutral variation between strains. Still others may be deleterious but either have not yet been eliminated by selection or cannot be eliminated because of linkage constraints.

We identified 18 multigenic regions of the EDL933 chromosome related to known bacteriophages. Only one, the Stx2 Shiga toxin-converting phage BP-933W, is known to be capable of lytic growth and production of infectious particles10. We named the other EDL933 prophages cryptic prophage (CP) to indicate that they probably lack a full complement of functional phage genes. They vary in size from 7.5 kb (CP-933L) to 61.6 kb (BP-933W) and consist of a mosaic of segments similar to various bacteriophages, recalling the ‘modular’ phage genome hypothesis11. The two remaining physical gaps in the genome sequence correspond to prophage-related regions, and resolution of the sequence is complicated by extensive similarities to other prophage within this genome. The gap sizes and positions (4 kb and 54 kb) were determined from optical restriction maps. With only one exception, the EDL933 prophages and the eight cryptic prophages of MG1655 are all lineage-specific. Prophage Rac (MG1655) and CP-933R are similarly located in the backbone, and are sufficiently related to suggest a common prophage ancestor at the time that the strains diverged.

Subunits Stx1A and Stx1B of the second Shiga toxin of EDL933 (ref. 12) are encoded in the newly identified CP-933V. The position of the stx1 genes in a putative Q antiterminator-dependent transcript is analogous to the placement of the stx2 genes in BP-933W, although there are no tRNAs adjacent to stx1AB. Genes in this position should be expressed maximally during lytic growth. The relationship between Stx toxin expression and phage induction is important, because treatment of O157:H7 with macrolide and quinolone antibiotics increase expression of the toxins13,14. Clinical decisions regarding drug therapy are complicated by strain-specific variation in this response15, and reports in the literature (for example, refs 6 and 12) taken together suggest that the Stx phage status is variable among O157:H7 strains. Given the potential for recombination among the prophage reported here, this does not seem surprising. In addition, the stx locus in Shigella is known to lie within a cryptic prophage, inserted at a site different from either stx phage of EDL933 (ref. 16).

The MG1655 genome contains 528 genes (528/4,405 = 12%) not found in EDL933. About 57% (303) of these were classified into known functional groups and include genes, such as for ferric citrate utilization, that would suggest a role in virulence if identified in a pathogen. It is unclear whether these are remnants of a recent pathogenic ancestor, steps along a path leading to evolution of a new pathogen, indicators that K-12 strains may be pathogenic for non-human hosts, or completely unrelated to pathogenicity. There are 106 examples of O-islands and K-islands present at the same locations relative to the conserved chromosomal backbone. The two replichores in each strain are nearly equal in length despite the large number of insertion/deletion events necessary to generate the observed segmented structure between strains. Only a subset of islands is associated with elements likely to be autonomously mobile.

Each island might be ancestral and lost from the reciprocal genome; however, atypical base composition suggests that most islands are horizontal transfers of relatively recent origin from a donor species with a different intrinsic base composition. Restricting analysis to the 108 O-islands greater than 1 kb, 94% (101/108) are significantly different (χ2 > 7.815, P < 0.05) from the average base composition of shared backbone regions in the same replichore. The percentage drops very little with a Bonferroni correction for multiple tests (91/108; χ2 > 17.892, P < 0.05). Similar results are obtained for analysis of the third-codon position composition (Fig. 1). Still more islands may have originated as horizontal transfers but have been resident in genomes with a spectrum of mutation similar enough to E. coli to have obtained equilibrium nucleotide frequencies or at least obscure statistical significance17. Still other gene clusters may be horizontal transfers that predate the divergence of MG1655 and EDL933.

Single nucleotide polymorphisms (75,168 differences) are distributed throughout the homologous backbone. There are 3,574 protein-coding genes encoded in backbone, and the average nucleotide identity for orthologous genes is 98.4%. Many orthologues (3,181/3,574 = 89%) are of equal length in the two genomes, but only 25% (911) encode identical proteins. Table 1 shows the number of each type of polymorphism observed by codon position. As expected, most differences are synonymous changes at third-codon positions. Multiple mutations at the same site should be infrequent at this low level of divergence. Thus the co-occurrence matrix provides insight into the substitution pattern, despite uncertainty of the ancestral state. The overall ratio of transitions to transversions is close to 3:1. A bias towards a greater number of T↔C than A↔G transitions on the coding strand previously attributed to transcription-coupled repair is evident18. An additional bias was observed at third-codon positions. Thymidines are more frequently involved in tranversions than cytosines, and G↔T are the most frequent transversions for the coding strand. The reciprocal polymorphisms, C↔A, are not over-represented. This bias is consistent for genes on both the leading and lagging strands (data not shown) and is therefore not related to asymmetries in the replication process. One possible explanation is transcription-coupled repair of damage associated with oxidative stress. Oxidized products of guanine (2,2,4-triaminooxazolone and 7,8-dihydro-8-oxoguanine) lead to G→T transversions by mispairing with A, and two DNA glycosylases (MutY and Fpg) are responsible for mismatch resolution19. Preferential repair of these lesions on the transcribed strand has been observed in humans20, and a similar mechanism could account for the observed transversion bias on the coding strand in E. coli.

Table 1 Frequency of each type of single nucleotide polymorphism by codon position for 3,181 genes of equal length in EDL933 and MG1655

Some chromosomal regions are more divergent (‘hypervariable’) than the average homologous segment but encode a comparable set of proteins at the same relative chromosomal position. In the most extreme case (YadC), the MG1655 and EDL933 proteins exhibit only 34% identity. Four such loci encode known or putative fimbrial biosynthesis operons. Another encodes a restriction/modification system. Elevated divergence has been associated with positive selection at both these types of loci and among proteins that interact directly with the host9,21,22. Alternatively, hypervariable genes may result from locally elevated mutation rates or differential paralogue retention from an ancient tandem duplication.

Comparison of our observations with other genome-scale analyses of closely related strains or species supports the idea that enterobacterial genomes are particularly subject to recombinational evolution. Two Helicobacter pylori strains exhibited only 6–7% differential coding capacity despite showing less identity among orthologues (92.6%) than observed among these E. coli. Furthermore, almost half of the lineage-specific Helicobacter genes are clustered in a single region referred to as the plasticity zone23. Analyses of four Chlamydia genomes with orthologues that differ by as much as 19.5% show little evidence of horizontal transfer, and this is attributed to the inherent isolation of an obligate intracellular parasite24. Most lineage-specific genes are expansions of paralogous gene families. As in Helicobacter, many of the Chlamydia lineage-specific elements are clustered in a plasticity zone. Continuing genome projects will elucidate the generality of observations made from these comparisons of closely related organisms.

Together, our findings reveal a surprising level of diversity between two members of the species E. coli. Most differences in overall gene content are attributable to horizontal transfer, and offer a wealth of candidate genes that may be involved in pathogenesis. Base substitution has introduced variation into most gene products even among conserved regions of the two strains. Many of these differences can be exploited for development of highly sensitive diagnostic tools; but diagnostic utility will require a clearer understanding of the distribution of genetic elements in E. coli species as a whole. An independently isolated O157:H7 strain showed differences from EDL933 by restriction mapping25. Additional genome sequence data from other E. coli strains as well as functional characterization of gene products is necessary before the complex relationship between E. coli genotypes and phenotypes can be understood. Showing that disease-related traits are associated with predicted genes will require many areas of study including extensive testing in animal models that mimic symptoms of human infections, but the genome sequence offers a unique resource to help meet the challenge.

Methods

Clones and sequencing

 EDL933 was kindly provided by C. Kaspar, who obtained it from the American Type Culture Collection (ATCC 43895). The sequenced isolate has been redeposited at the ATCC and is available as ATCC 700927. Whole-genome libraries in M13Janus and pBluescript were prepared from genomic DNA as described for genome segments used in the K-12 genome project26. Random clones were sequenced using dye-terminator chemistry and data were collected on ABI377 and 3700 automated sequencers. Sequence data were assembled by Seqman II (DNASTAR). Finishing used sequencing of opposite ends of linking clones, several PCR-based techniques and primer walking. Whole-genome optical maps for restriction enzymes NheI and XhoI were prepared27 so that the ordering of contigs during assembly could be confirmed. Two gaps remain in the genome sequence. Extended exact matches pose a significant assembly challenge. The final determination of sequence for the 100-kb duplicated region was based on clones that span the junction between unique flanking sequences and the ends of the duplicated island, concordance of the two regions in optical restriction maps, excess random sequence coverage in the duplicated region, lack of polymorphism and confirmation of duplication of an internal segment by Southern blotting (data not shown).

Sequence features and database searches

 Potential open reading frames (ORFs) were defined by GeneMark.hmm28. The GenPept118 protein and MG1655 protein and DNA databases were searched by each ORF using BLAST29. Annotations were created from the search output in which each gene was inspected, assigned a unique identifier, and its product classified by functional group5. Alternative start sites were chosen to conform to the annotated MG1655 sequence. Orthology was inferred when matches for EDL933 genes in the MG1655 database exceeded 90% nucleotide identity, alignments included at least 90% of both genes, and the MG1655 gene did not have an equivalent match elsewhere in the EDL933 genome. This list was supplemented by manual inspection of the protein-level matches in the complete GenPept database to include genes with lower similarities if they occurred within co-linear regions of the genomes. The genome sequence was compared with that of MG1655 by the maximal exact match (MEM) alignment utility, (B.M., manuscript in preparation) an adaptation of MUMmer30. This program was based on suffix arrays rather than suffix trees, and exact rather than unique matches, coupled with a custom anchored-alignment algorithm that extends sequence homology into the regions separating contiguous co-linear exact matches. Inferences on biases in polymorphism patterns are based on χ2 goodness-of-fit tests of a nested sequence of multinomial log–linear models. These predict symmetric elevated levels of A↔G, T↔C and G↔T polymorphisms, above a quasi-independent baseline generated from marginal frequencies in the co-occurrence matrix of synonymous third-codon differences. Further information may be found at our Website http://www.genome.wisc.edu/, including a Genome Browser displaying a comparative map of EDL933 and K-12.

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Acknowledgements

We thank T. Forsythe, M. Goeden, H. Kijenski, B. Leininger, J. McHugh, B. Peterson, G. Peyrot, D. Sands, P. Soni, E. Travanty and other members of the University of Wisconsin genomics team for their expert technical assistance. This work was funded by grants from the NIH (NIAID and NCHGR), the University of Wisconsin Graduate School and the RMHC to F.R.B., the NIH (NCHGR, NIAID) to D.C.S., HHMI/OTKA to G.P., an Alfred P. Sloan/DOE Fellowship to B.M., a CDC/APHL Fellowship to P.S.E., and an Alfred P. Sloan/NSF Fellowship to N.T.P. Sixteen University of Wisconsin undergraduates participated in this work and particular thanks are due to A. Byrnes for web-site development to complement this project, and to A. Darling for programming.

Author information

Affiliations

  1. Genome Center of Wisconsin,

    • Nicole T. Perna
    • , Galex Yen
    • , David C. Schwartz
    •  & Frederick R. Blattner
  2. Department of Animal Health and Biomedical Sciences

    • Nicole T. Perna
  3. Laboratory of Genetics

    • Guy Plunkett III
    • , Valerie Burland
    • , Bob Mau
    • , Jeremy D. Glasner
    • , Debra J. Rose
    • , George F. Mayhew
    • , Peter S. Evans
    • , Jason Gregor
    • , Heather A. Kirkpatrick
    • , Jeremiah Hackett
    • , Sara Klink
    • , Adam Boutin
    • , Ying Shao
    • , Leslie Miller
    • , Erik J. Grotbeck
    • , N. Wayne Davis
    • , Konstantinos D. Potamousis
    • , Jennifer Apodaca
    • , David C. Schwartz
    •  & Frederick R. Blattner
  4. Department of Chemistry

    • Alex Lim
    • , Eileen T. Dimalanta
    • , Konstantinos D. Potamousis
    • , Jennifer Apodaca
    •  & David C. Schwartz
  5. Department of Biostatistics

    • Thomas S. Anantharaman
  6. Department of Medical Microbiology and Immunology, University of Wisconsin, Madison, 53706 , Wisconsin, USA

    • Rodney A. Welch
  7. Institute of Biochemistry, Biological Research Center, Szeged, H-6701, Hungary

    • György Pósfai
  8. Cereon Genomics, LLC, 45 Sidney Street, Cambridge, 02139, Massachusetts, USA

    • Jieyi Lin

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Correspondence to Nicole T. Perna.

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https://doi.org/10.1038/35054089

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