Lack of correlation between sequence type and growth rate

In addition to resolving the discrepancies in species identification described above, strains

from the AFSSA collection were selected for analysis based on their growth rates or source. Clinical

isolates are overrepresented in the PubMLST Cronobacter database and this work increased the

number of environmental strains in the database [42]. Additionally, strains 05CHPL02, 05CHPL65,

05CHPL101bis, 05CHPL47, 05CHPL54, 05CHPL99, 05CHPL10, 05CHPL97, 05CHPL29 were

selected for analysis due to their growth rates, as reported by Miled-Bennour et al. [116]. Strains

05CHPL02, 05CHPL65, 05CHPL101bis have some of the slowest growth rates, while 05CHPL47,

05CHPL54, and 05CHPL99 are the fastest [116]. The remaining three strains were selected as they

have growth rates near the mean value observed by Miled-Bennour et al. [116]. In total, 32 strains

from the AFSSA collection were analysed by MLST (Table 4.2).

An examination of the AFSSA collection strains attempted to link the observed growth rates

STs were determined as described previously. The STs and growth rates for all strains used in this

analysis are shown in Table 4.8.

Though strains were selected to represent a range of growth rates, some STs and CCs

were more frequently observed among the strains utilized for this investigation. Therefore, statistical

analysis focused on the most common CCs observed; CC1 (n = 9) and CC4 (n = 6), both of which

have been linked to cases of human illness. The Wilcoxon rank sum test was used to determine if

the growth rates of the CC1 or CC4 strains differed significantly from the growth rates of the

remaining strains [164]. Calculations were carried out using the Statistics Computational Online

Resource (http://socr.ucla.edu/SOCR.html) [30].

Table 4.8. Growth rates and sequence types of strains from the AFSSA collection

Growth rate (hr-1₎a Strain Source 25° C 37° C ST CC 08HMPA08 Clinical 0.99 2.16 1 1 08HMPA09 Clinical 1.02 2.30 1 1 08HMPA10 Clinical 0.91 2.07 1 1 05CHPL10 PIF 1.00 1.95 1 1 05CHPL18 PIF 0.95 2.07 1 1 08HMPA11 PIF 0.93 2.17 1 1 05CHPL27 Environmental 1.02 1.90 1 1 05CHPL78 Environmental 0.92 2.20 1 1 07HMPA87F Environmental 0.99 2.00 391 1 05CHPL59 Collection strain 0.84 1.99 4 4 05CHPL101bis Environmental 0.63 1.46 4 4 05CHPL82 Environmental 0.95 2.23 4 4 05CHPL99 Environmental 0.97 2.60 4 4 05CHPL29 Environmental 0.99 2.06 255 4 05CHPL97 Environmental 0.99 2.03 295 4 05CHPL50 Environmental 0.91 1.88 8 8 07HMPA87B Environmental 0.97 1.89 21 21 07HMPA41A PIF 1.03 1.83 31 31 05CHPL53 Environmental 1.01 2.09 64 64 05CHPL56 Environmental 1.03 2.29 64 64 07HMPA93A Environmental 0.99 2.13 100 100 08HMPA06 Environmental 1.01 2.02 100 100 05CHPL54 Environmental 1.17 2.43 125 100 05CHPL65 Collection strain 0.76 1.63 256 --

a_{Growth rates determined by Miled-Bennour et al. (2010)}

No significant differences (p > 0.05) were identified between the growth rates of the CC1

strains and those of the remaining STs at either 25° C or 37° C. Similarly, no significant differences

(p > 0.05) were found when comparing the growth rates of the CC4 strains to the growth rates of the

other STs (including CC1) at either temperature. It is interesting to note that the slowest and fastest

growing strains at both temperatures belonged to CC4, highlighting the natural variation that exists

among even closely related C. sakazakii strains.

Though based on a small subset of strains, this analysis suggests that ST or CC is not

based on housekeeping genes [6]. While useful for typing and differentiating strains, these genes

are not responsible for specific, observable phenotypes [95]. Though strains belonging to the same

ST or CC are closely related, variations in phenotype are expected [95]. These observed differences

suggest that it may be possible to use DNA sequence-based analysis to further subdivide STs for

more accurate differentiation of strains. As more genome sequences become available, it will be

possible to better characterize strains and isolates. This could lead to the development of a variety

of typing schemes for different purposes, including characterization of strains and isolates based on

traits important for virulence or environmental survival [87, 111]. A proposed capsular typing scheme

is discussed in Chapter 6.

4.7. Conclusions

This chapter has highlighted some of the uses and benefits of MLST with regard to

Cronobacter species and their identification. It was demonstrated that this technique can be used for

identification of novel species and for characterization of outbreak strains. Unfortunately, while

MLST is useful for the differentiation of strains, it cannot necessarily be used for predicting

phenotypes. The attempt to correlate ST to growth rate highlights one of the major issues with

MLST: the use of housekeeping genes. The seven loci sequenced are not directly related to the

phenotype of the organism. They were selected because, as housekeeping genes, they are required

for the organism to remain alive. Thus, they will be present in all strains examined. This is beneficial

for differentiation of strains and phylogenetic analyses, but less reliable for characterization of

strains, particularly with regard to potential virulence.

While previous studies have linked ST4 strains to cases of neonatal meningitis, the genes

used to determine the ST are not directly responsible for the phenotype of these strains [55, 91, 95].

In order to better understand the Cronobacter genus and how certain species cause illness while

others do not, it is necessary to link genotype and phenotype together. In the future, it may be

possible to predict a strain’s phenotype from its genome sequence [87, 111]. Currently, individual genomic data can be used to guide laboratory analysis and particular genes can be linked to

observable phenotypes. This approach to characterization of Cronobacter species and strains will

CHAPTER 5

Linking genotype and phenotype for the characterization of