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SUPPLEMENTARY MATERIAL FOR
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Transmission of Pseudomonas aeruginosa is infrequent in New Zealand cystic fibrosis
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clinics
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Jan Schmid, Lena J. Ling, Joyce L.S. Leung, Ningxin Zhang, John Kolbe, Alison W. Wesley,
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Graham D. Mills, P. Jeff Brown, David T. Jones, Richard T.R. Laing, Philip K. Pattemore,
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D. Robin Taylor, Keith Grimwood.
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SUPPLEMENTARY METHODS
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Selection of colonies for genotyping
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We randomly chose colonies irrespective of their morphology as this provided a better defined
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and more representative sample than a sampling strategy, which selected all colony morphotypes.
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This decision was based on earlier work [1, 2], which indicated differences in colony morphology
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were a poor predictor of differences in DNA type (reaffirmed in the present study; data not
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shown) and that choosing colonies for typing on the basis of morphology would introduce a bias
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that interfered with obtaining a representative sample of the major genotypes present, while not
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significantly increasing the genetic diversity of the sample.
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Calculation of genetic distances from P. aeruginosa genotyping patterns
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Pattern–based genetic distances between isolate pairs were calculated as the sum of differences in
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band number across all molecular weight intervals divided by 18, the number of molecular
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weight intervals. Note that in scoring patterns, it is difficult to exactly determine the number of
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bands in a molecular weight bracket if this number exceeds three. In order to increase the
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reproducibility of the method, the maximum count for each interval is set to three even if more
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bands appear to be present [3].
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SUPPLEMENTARY DATA
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Additional evidence against a correlation between genetic distance and degree of patient
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contact
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The analysis shown in Figure 3 of the manuscript may not be sufficiently sensitive if, while rare,
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the frequency of transmission within healthcare settings is nevertheless correlated with physical
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contact. If so, a large number of patient pairs where no transmission occurred could mask a
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significant increase in the small number of transmission events with increased contact. We
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therefore determined how the frequency of low genetic distance values differed between physical
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distance categories. We chose a genetic distance of 0.2 as our threshold, since the lower quartile
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of genetic distances in most physical distance categories > II in most clinics included 0.2, which
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would provide enough data points to observe a trend. Figure S2 shows there is no such trend of
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increasing frequency of distance values below 0.2 with increasing centre-related contact. Only
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Greenlane values in contact categories II-VI were consistently above the average for all hospitals
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and its own category VII average. We also tested whether among patients with the most centre-
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related contact, category II, had genetic distances between their isolates, which decreased with
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length of co-hospitalisation (Fig. S3; only 4 hospitals had sufficient data for this analysis). No
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such trend was found.
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A final control was necessary, as participants were not sampled at the same time leading to a
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possible underestimate of transmission frequency, especially if the elapsed time between
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sampling differed between patient categories. We would not be able to detect transmission of
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strains to patient A, sampled first, from sample B, sampled later, if transmission occurred after
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the sampling of patient A. If sampling time differences have this effect, this should be detectable
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as an increase of genetic distance between isolates with increasing length of the time between the
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sampling of two patients. There was no evidence for an increase in genetic distance with time in
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any of the clinics (data not shown). Thus differences in sampling time did not affect our analysis.
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REFERENCES
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1.
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polymorphic DNA typing of Pseudomonas aeruginosa isolates recovered from patients with
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cystic fibrosis. J Clin Microbiol 1996; 34: 1129-1135.
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2.
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an epidemiological marker for Pseudomonas aeruginosa. J Infect Dis 1987; 155: 119-126.
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3.
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J. Simple and inexpensive but highly discriminating method for computer-assisted DNA
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fingerprinting of Pseudomonas aeruginosa. J Clin Microbiol 2000; 38: 4445-4452.
Mahenthiralingam E, Campbell ME, Foster J, Lam JS, Speert DP. Random amplified
Ogle JW, Janda JM, Woods DE, Vasil ML. Characterization and use of a DNA probe as
Al-Samarrai TH, Zhang N, Lamont I, Martin L, Kolbe J, Wilsher M, Morris AJ, Schmid
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3
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SUPPLEMENTARY FIGURE LEGENDS
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Fig. S1. Gel images showing SalI digests (A) and PFGE patterns (B) of the Manchester (MAN)
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and pulsotype 2 strain and closely related New Zealand isolates. Clinics are as defined for Fig. 1.
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SalI patterns were determined on different gels and their images were aligned using molecular
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weight standards, which are routinely run adjacent to each sample lane. PFGE patterns of SpeI-
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digested genomic DNA shown are from the same gel. STD, molecular weight standards (Roche
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Diagnostics XV molecular weight standard in A and New England Biolabs Lambda Ladder
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PFGE marker in B); molecular weights of some of the standard bands are shown on the right.
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Fig. S2. Fraction of genetic distances below 0.2 between isolates from patient pairs in different
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physical distance categories in Starship (grey bars), Greenlane (black bars), Waikato (downward
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striped black bars), Palmerston North (downward striped grey bars) Wellington (empty bars),
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Christchurch (upward striped bars), and Dunedin (horizontal bars) CF clinics. The dashed line
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shows the average across all centres, giving each centre equal weight. See Table 1 and Fig.3 for
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definition of physical distance categories.
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Fig. S3. Genetic distance between pairs of isolates from co-hospitalised (category II) patients as a
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function of co-hospitalisation duration in Starship (A), Greenlane (B), Wellington (C), and
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Christchurch (D) hospitals. Starship and Greenlane are paediatric and adult CF clinics
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respectively. In Wellington suitable isolate pairs were available only from the paediatric ward. In
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(D) paediatric ward isolate pairs are marked with  and adult ward isolate pairs with .
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TABLE S1. Nearest neighbour analysis [3] assessing if isolates from a given clinic have other isolates from the same clinic as their
closest relatives more often than expected by chance.
Clinic
Number of patients with isolates from the same
Expected number of patients with isolates from the same
clinic as the closest relatives of their isolates a
clinic as the closest relatives of their isolates b
0.0
0.4
18.7 c
13.7
Waikato
0.2
0.2
Palmerston North
0.0
0.4
Wellington
3.2
3.3
Christchurch
1.7
2.6
Starship (Auckland)
Greenlane (Auckland)
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Dunedin
1.2
0.9
All
25.0
21.5
a
Often a patient’s isolates had several closest-related counterparts, some from patients at the same clinic and some from patients at
other clinics. In such cases the ratio for closest related counterparts from the same clinic to all closest related isolates was scored.
b
In the absence of centre-specific groups of strains, i.e. based on the ratio between the number of patients from a clinic and the number
of all patients in the study, this was calculated as: number of patients at a clinic x (number of patients at the clinic –1) ÷ (number of all
patients in study –1) [3].
c
The number of Greenlane patients' isolates having other Greenlane isolates as their closest counterparts is 35% higher than expected,
but the binominal probability of this deviation is 0.09 and the type I error-corrected level of significance after Bonferroni correction is
0.007 as observations and expectations were compared for 7 clinics.
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