Journal of Analytical and Applied Pyrolysis, 22 (1991) 29-38
29
Elsevier Science Publishers B.V., Amsterdam
The analysis of Listeria monocytogenes by pyrolysis-mass spectrometry Robert Kajioka a,* and Michael A. Noble b a Public Health Laboratories, Ontario Ministry of Health,
Toronto, Ontario MSWIR5 (Canada) b University of British Columbia, Vancouver, British Columbia (Canada)
(Received March 28, 1991; accepted in final form July 5, 1991)
ABSTRACT Following an outbreak of listeriosis in Atlantic Canada the infection in one patient was traced to a food source yielding Listeria monocytogenes belonging to the same serogroup, 4b, as the strain isolated from the patient. Although food has sometimes been suspected as a source, this correlation based on serological markers seemed unsatisfactory because serogroup 4b could comprise as much as 80% of the isolates from nature. In order to provide more substantive means of correlation the strains were investigated by generating their pyrolysis fingerprints scanned by mass spectrometry and analyzed by computerized pattern recognition techniques. The finer resolution offered by pyrolysis-mass spectrometry @y-MS) indicated that the strain obtained from the food source was not culpable. The differentiation possible by pyrolysis-mass spectrometry of various serotype 4b isolates and other advantages of the method are discussed. Food; listeria monocytogenes;
mass spectrometry;
pyrolysis.
INTRODUCTION
Previously a report appeared [l] that implicated coleslaw as the source of the agent, Listeria monocytogenes, responsible for a case of sepsis. Contrary to these earlier results the use of pyrolysis-mass spectrometry @y-MS) showed that the patient’s strain was not the same as the isolate from coleslaw. Listeria monocytogenes is a Gram-positive coccobacillus frequently associated with spontaneous and epidemic cases of perinatal sepsis as well as meningitis, and bacteremia in non-pregnant adults. Lately food has been indicted as harbouring this organism. When certain foods become implicated as the source of infection the problem becomes more than a public * Correspondence should be addressed to: Dr. R. Kajioka, Laboratory Services Branch, Ontario Ministry of Health, Box 9000, Terminal A, Toronto, Ontario M5W lR5, Canada. 01652370/91/$03.50
0 1991 - Elsevier Science Publishers B.V. All rights reserved
30
health issue. The correct identification of the culpable food source could play a useful role in prevention, whereas an incorrect identification could lead to costly and tragic economic consequences. Tracking the source of infection demands the most discriminating procedures. Interpretations based on serotyping of L. monocytogenes seemed inherently unreliable when a high proportion, sometimes as much as 80% [2] of clinical isolates belong to a common serogroup, 4b. Another scheme, phage typing, showed more promise 131.More recently multilocus enzyme focusing [4] has demonstrated excellent genetic subtyping capabilities. Neither of these methods could equal however, the rapidity of diagnosis by Py-MS. This paper describes the application of Py-MS to differentiate strains of L. monocytogenes from patients and food sources in tracking the source of infection. Meuzelaar et al. [5] demonstrated that the mass spectrum of products of controlled temperature pyrolysis of bacteria was sufficiently reproducible and contained enough information for the identification and subtyping of bacteria. They and many others have since confirmed the applicability of pyrolysis fingerprints to identification of clinically [6-lo], environmentally [ 1l] and industrially [ 121 important microorganisms. In our experience there have been no bacterial isolates with pyrolysis profiles in common, regardless of their subtyping identity by other methods. This seems to be more in line with the reality that undoubtedly there are component differences, both continuous (quantitative) and qualitative, between isolates whose similarity has been based on only one or a few markers. This was no less true for L. monocytogenes. An earlier study [6] demonstrated that they could be classified by their pyrograms into groups that correlated with serogroups. Our study required further differentiation within a serogroup. This was possible because there were quantitative as well as qualitative differences between strains of the same putative subtypes in the large amount of data generated by Py-MS from which lower orders of subgroups and any trend within a group could be distinguished by appropriate computerized pattern recognition techniques. On this basis the results with Py-MS were found to be at odds with the conclusions reached by other methods. The serology of L. monocytogenes as presently constituted appeared insufficiently discriminating for epidemiological purposes.
EXPERIMENTAL
Materials
L. monocytogenes strains Strains analyzed in the present study were recovered during an outbreak of listeriosis in Atlantic Canada in 1981. The outbreak as well as the
31
methods for recovery and serotyping of the strains has been described [l]. Eight of the strains were selected for further study (Table 1). Strain 1 was recovered from the cerebral spinal fluid of one patient and strain 2 from the blood of another, both from Yarmouth, Nova Scotia. Strains 3 and 4 were recovered from the throat and blood, respectively, of a patient from Prince Edward Island. Strain 5 was recovered from the blood of a patient from Halifax, Nova Scotia and strain 6 was recovered from food in the refrigerator of this patient. Strains 7 and 8 were two additional isolates from unimplicated cabbage. All strains were identified as serotype 4b with the exception of strain 4 identified as serotype la. All strains were stored as lyophilized cultures derived from single colonies. Methods Sample preparation
After recovery from lyophilization, strains were grown on Mueller-Hinton agar at 37 OC for 24 h and then prepared for pyrolysis. Each culture was picked directly from the culture plate and suspended in spectroscopic grade methanol to provide a slurry. In order to reduce any effects of differences in weight 25 pg (dry weight) in 5 ~1 of each slurry were dried and pyrolyzed at 510 “C. All samples were pyrolyzed on the same day to minimize day-to-day variation. Pyrolysis mass spectrometry
The method of pyrolysis and the equipment used have been described elsewhere [lo]. The pyrolysis products were channelled on line to a quadrupole mass analyzer (Spectrel) for ion counting over mass range of e/z 15 to e/z 200 scanning eight channels (0.00125 volts/channel) per mass. The counts were collected into minicomputer (HPlOOOF) memory and stored permanently on disc. Data processing and analysis
The mass spectra were analyzed with the techniques available in ARTHUR, a package designed by Kowalski and his colleagues [13,14] for chemometrics. For normalization of the spectra all peak ion counts for each mass were expressed as a percentage of the total ion counts of the spectrum. The unequal weight carried by large values was minimized by scaling the mean ion count for each mass in the combined spectra to equal 0 and the standard deviations to equal 1. Outlying spectra within replicate sets of spectra were identified and deleted from further analysis. The latter approach was selected because it seemed undesirable to eliminate any mass values (features) at the preliminary stages. The selection of features with high ratios of external to internal variance [12,14,15] is a useful alternative
32
but in ARTHUR the latter would require the a priori categorizing of spectra that we were trying to avoid. The spectra selected for analysis were automatically drawn from storage and assembled into the ARTHUR file format by our program ISELN. Outlying spectra among each replicate set were identified by factor analysis (KAPRIN) aided by graphic display (VARVAR) using as coordinates the first y1 factors accounting for over 90% of the variance. In an additional effort at data reduction the spectra were analyzed in short consecutive portions of 10 masses in sequence. With the clustering within replicates as well as strain differentiation taken into account it was noted that most of the pattern discriminating information was contained in the range from m/t 26 to 100.
RESULTS
The eigenvalues extracted by principal component factor analysis (KAPRIN) of the spectral data dispersion matrix revealed that three factors primarily described the pyrograms of these Listeriu strains (Fig, 1). Factors 1 and 2 identified the main subdivisions of the strains, and appeared to reflect a continuous property change between most of the
I
2
B
II
6
klc+:r Nund
B
b
a
Fig. 1. Plot of factor number vs. its corresponding eigenvalue. The factors are ranked in descending order of magnitude of eigenvalues with the first factor having the highest. Most of the variance was accounted for in the first 3 factors. The eigenvalues/eigenvectors were computed with KAPRIN.
33
..IS,
FACTOR 1
m,”
SCORES
._. .Ie.,
Fig. 2. Principal component factor analysis (KAPRIN) of L. monocytogenes spectra in range m/z 26 to m/z 100. The repeat samples of a strain have been joined for the sake of clarity. The numbers correspond to the strains listed in Table 1. Note the dissimilarity of the food isolate strain 6 to clinical isolate strain 5 (patient D) compared with other strains even though it was the suspected agent of D’s infection.
strains 6 and 8 at opposite extremes (Fig. 2). The exceptions were strain 4 which had been identified as the only serogroup la member and strain 5. The latter was not closely related to any of the food isolates. The difference between strains 3 and 4 isolated from the same patient was confirmed by pyrolysis. Factor 3 was of less interest because it identified each strain as a separate group and was therefore ignored. Factor analysis repeated after deleting the spectra of strains 4 and 5 revealed two main subdivisions in the remaining strains comprised of food isolates 6, 7 and 8 on the one hand and clinical isolates 1, 2 and 3 on the other (Fig. 3). The results of hierarchal (cluster) analysis (HIER) were generally consistent with factor analysis, although some differences were evident (Fig. 4). The interpattern distances were computed as the Euclidean distances (DISTANCE) and organized into similarity clusters by HIER. Spectrum 3d seemed to tend towards being an outlier by factor analysis (Fig. 2) but was nevertheless retained for data analysis. It clustered with strain 2 in hierarchal analysis. A similar tendency was noted for this spectrum in factor analysis. Otherwise spectra within replicate sets showed strains
with
34
TABLE 1 Strains of L. monocytogenes analyzed by Py-MS Strain
Serovar
Source
1 2 3 4 5 6 7 8
4b 4b 4b la 4b 4b 4b 4b
Patient Patient Patient Patient Patient Food Food Food
a CSF = cerebrospinal
A B C C D
Specimen
Locale
Description
CSF a Blood Throat swab Blood Blood Coleslaw Cabbage Cabbage
Yarmouth Yarmouth Halifax Halifax Halifax Halifax Halifax Halifax
Meningitis Meningitis Epidemic Epidemic Sepsis Food from D
fluid.
the highest similarity values ranging for the most part between 0.75 to 0.65. Strain 5 from patient S.S. was the greatest distance from other strains, i.e. similarity value equal to 0. The strains were clustered mainly into two groups each of which showed an overall intra-group similarity of approximately 0.30. One group consisted of strains 2, 3, 4 and 6, and the other group of 1, 7 and 8. Strain 5 formed a separate group by itself.
. IV,? .“%a
FACTOR
1 SCORES
.,“m. .I,.
Fig. 3. Principal component factor analysis of pyrolysis spectra of L. monocytogenes after the spectra for strains 4 and 5 were deleted. The plot of factor one vs. factor two showed the division of the remaining spectra into two subgroups.
35 DISCUSSION It was evident from the distance between strain 5 and the rest of the serogroup 4b strains that serological similarity tended to mask more substantial strain differences. The pyrolysis mass spectra of all the strains were different from each other, although subgroups were present. Strains 1, 2, 3, 6, 7 and 8 differed primarily by a continuous difference implying different quantities of certain cellular component(s) between the strains. Further factor analysis divided these strains into two qualitatively different subgroups distinguished apparently by component presence or absence. Therefore, these two latter groups comprised of strains 1, 2 and 3 and strains 6, 7 and 8, respectively, along with strain 4 and strain 5 appeared to form 4 major subgroups distinguishable as a result of component differences. Strain 4 has been identified as serotype la. Notably, strains 6, 7 and 8, the isolates from food, formed a group separate from all the clinical
SIMILARITY STRAIN -la-lb-lC-4b-4c-40. -3d-2b-2c-2d-2a-3b-3c-30. -7c-7d-70. -7b-6a-6b-
l.00
90
.x0
.70
.xX)
VALUES SO
.40
.30
20
.I0
O.(x)
.30
20
.I0
0 00
-
II
I
I
-6C-
-6d-8b-8C-
-ad-8a-5b-5c-5d-
I
-
I .m
90
no
.7O
xi0
SIMILARITY
50
.40
VALUES
Fig. 4. Hierarchal analysis (HIER) of Euclidean distance calculations for L. monocytogenes’ pyrograms. Clustering is shown by the complete link method. Similarity matrix was constructed from a function of the ratio of inter-pattern distances to the greatest interpattern distance. The results confirm the distinct difference between strains 6 and 5. The clustering by distance conforms to the spatial proximity of the patterns given by principal component factor analysis, but fails to reveal the more subtle pattern behaviours shown by the latter technique.
36 Strain 5
Strain 6
Fig. 5. Representative
pyrograms of strains 5 and 6 pyrolyzed at 510 o C.
isolates. The two strains of particular interest, strain 5 from patient D and the isolate (strain 6) from the suspected food source belonging to D, were clearly not the same as judged by factor analysis of their pyrograms (Fig. 5), despite belonging to the same serogroup. The inescapable conclusion was that patient D was not infected by his own food, at least not by the strain picked for serotyping from the culture obtained from the food. It is worth noting that the strains within each subgroup remained distinguishable from each other. This demonstrates a potential use of our approach for grading relatedness or estimating drift. If, as suggested by the results with multilocus enzyme focusing [4], most clinically significant isolates of L. monocytogenes descended from a pathogenic clone that has now become geographically widespread and environmentally prevalent, epidemiological tracking will be difficult unless there has been adequate diversification of the clonal phenotype. The onus is on search for distinction rather than similarities. According to the history of the outbreaks, strains 1 and 2 and possibly 3 were suspected of being epidemiologically related. Factor analysis plots supported relatedness but not identity, implying that there was property divergence or, possibly, convergence, in the passage of strains from host to host through physiological environments exercising selective actions on survival and multiplication of the bacterial population. Although hierarchal analysis led to essentially the same conclusions as factor analysis we felt less confidence in it as a method of subgrouping. The magnitude of the calculated distance for a feature in a spectrum tended to carry, in our view, more weight than should be attributed to it in determining the clusters. This would be less problematic comparing strains with greater diversity. The low similarity values (0.75 to 0.65) for replicate
37
spectra would be greater if the Euclidean distance between strains were more pronounced. The questions posed in our study were answered by pyrolysis of a small number of strains, without reference to a classification scheme, instead of the large number typically required by other methods to determine, initially, the number of subgroups defined by the test marker(s). Three pyrotype subgroups of 4b were resolved with merely seven strains of this serotype. It would be interesting and useful, but not vital for these subgroups to correlate with those from other methods. The problem of untypeability frequently encountered with reagent techniques did not apply. A classification scheme based on pyrograms could be evolved with a large number of strains. Finger printing by Py-MS showed promise of finer resolution of subgroups than possible by current serology. Comparing other current methodologies with Py-MS, such as phage typing [l] and multilocus enzyme focusing [4], it seems likely, based on historical perspective, that each will contribute uniquely in resolving epidemiologic tracking problems. Finally, an attractive feature of Py-MS was its independence from the labour intensive preparation and stocking of an effective arsenal of reagent probes.
ACKNOWLEDGEMENTS
We thank Drs. F. Ashton, C. Krishnan, and K. Rozee for helpful criticism of the manuscript, Drs. A. Harper and D. Deuwer for advice on ARTHUR, Miss E. Patterson for her help in debugging it for the HPlOOOF, Mr. Noel Gordon for his able technical assistance, and Mrs. R. Vercillo and Mr. W. Vander Kolk and their respective staff for their assistance in the preparation of the manuscript.
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