Insights into the Emergent Bacterial Pathogen Cronobacter spp., Generated by Multilocus Sequence Typing and Analysis

Abstract

Cronobacter spp. (previously known as Enterobacter sakazakii) is a bacterial pathogen affecting all age groups, with particularly severe clinical complications in neonates and infants. One recognized route of infection being the consumption of contaminated infant formula. As a recently recognized bacterial pathogen of considerable importance and regulatory control, appropriate detection, and identification schemes are required. The application of multilocus sequence typing (MLST) and analysis (MLSA) of the seven alleles atpD, fusA, glnS, gltB, gyrB, infB, and ppsA (concatenated length 3036 base pairs) has led to considerable advances in our understanding of the genus. This approach is supported by both the reliability of DNA sequencing over subjective phenotyping and the establishment of a MLST database which has open access and is also curated; http://www.pubMLST.org/cronobacter. MLST has been used to describe the diversity of the newly recognized genus, instrumental in the formal recognition of new Cronobacter species (C. universalis and C. condimenti) and revealed the high clonality of strains and the association of clonal complex 4 with neonatal meningitis cases. Clearly the MLST approach has considerable benefits over the use of non-DNA sequence based methods of analysis for newly emergent bacterial pathogens. The application of MLST and MLSA has dramatically enabled us to better understand this opportunistic bacterium which can cause irreparable damage to a newborn baby's brain, and has contributed to improved control measures to protect neonatal health.

Keywords: Cronobacter; MLSA; MLST; ST4; sequence typing.

Figure 1

Maximum likelihood tree based on the concatenated sequences (3036 bp) of the seven LST loci for the genus Cronobacter. The STs and the corresponding species are indicated at the tip of each branch. The tree is drawn to scale using MEGA5, with 1000 bootstrap replicates.

Figure 2

Maximum Likelihood tree of the Cronobacter MLST dataset indicating ranges of the hypothetical divergence dates of each species node measured in millions of years before the present. The tree has been drawn to scale using MEGA5. The bases of the triangles indicate the number of isolates used for the analysis, while the heights indicate the diversity of each branch. C. condimenti with the single isolate has been excluded from this analysis. Reproduced from Joseph et al. (2012b).

Figure 3

Neighbor-net of allele sequence alignment for (A) fusA and (B) gltB generated for the Cronobacter MLST dataset indicating diversity and recombination events. The figure has been drawn to scale using Splitstree4. The formation of parallelograms indicate possible recombination events.

Figure 4

Relationship between the clinically significant C. sakazakii ST4 clonal complex and ST8. The threshold for the output was set to triple locus variation. The black lines denote the SLVs; while the gray lines indicate the TLVs. ST4 is the founder clone of the clonal complex.

Figure 5

Population snapshot of the Cronobacter MLST database generated using the goeBURST algorithm, indicating the clonal complexes and the breakdown of the species of the strains. The threshold for the output was set to triple locus variation. The dominant STs are represented by the circles with larger diameters.

Figure 6

Population snapshot of the Cronobacter MLST database generated using goeBURST, indicating the clonal complexes and the diversity of the strains based on country of isolation. The threshold for the output was set to triple locus variation. The dominant STs are represented by the circles with larger diameters.

Figure 7

Population snapshot of the Cronobacter MLST database generated using the goeBURST algorithm, indicating the clonal complexes and the diversity of the sources of the strains. The threshold for the output was set to triple locus variation. The dominant STs are represented by the circles with larger diameters.