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Keywords:

  • Bacterial interference;
  • carriage;
  • microbial flora;
  • Staphylococcus aureus

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

The potential role of a patient's resident microbial flora in the risk of acquiring multiresistant bacteria (MRB) during hospitalization is unclear. We investigated this role by cross-sectional study of 103 patients at risk of acquisition of Staphylococcus aureus (SA), resistant (MRSA) or not (MSSA) to methicillin, recruited in four French hospitals. The flora was analysed by an exhaustive culture-based approach combined with molecular and/or mass-spectrometry-based identification, and SA strain typing. Forty-three of the 53 SA-negative patients at entry were followed for up to 52 weeks: 19 (44.2%) remained negative for SA and 24 (55.8%) became positive, including 19 (79%) who acquired an MSSA, four (17%) who acquired an MRSA and one who acquired both (4%). Fifty-one different species were identified among the 103 patients, of which two, Corynebacterium accolens and Staphylococcus haemolyticus (p = 0.02-0.01), were more prevalent in the absence of SA. However, the same number of patients carrying or not these two species acquired an MSSA/MRSA during follow-up, regardless of antibiotic treatment received. Clustering analysis showed that the microbial flora was highly specific to each patient, and not predictive for acquisition of MSSA/MRSA or not. Patient-specific microbial resident flora is not predictive of SA acquisition.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

Antimicrobial resistance has become a central challenge in the control of infectious diseases, especially in hospitals [1, 2]. Strategies to prevent nosocomial infection will be more effective if they are guided by a comprehensive knowledge of risk factors determining the acquisition of these agents by the hospitalized patient. Such knowledge is especially pertinent when the same agents also exist in the community [3] or are widespread in some populations [3], rendering sourcing of the patient colonization more difficult.

Important risks factors, such as advanced age, underlying diseases and severity of illness, and inter-institutional transfer of patients proved to be common and universal whatever the agents [4]. However, the potential natural protection developed by hosts might also play an important role, by limiting the success of colonization by MRB in their own ecosystem. This question still needs to be addressed: studies examining in particular the role of host factors in SA acquisition are still scarce, especially with regards to SA nasal carriage [5, 6]. Two main mechanisms are putatively involved: host factors or immunity [7-9], whether acquired [10] or innate [11], and the barrier effect of the microbial resident flora (also termed bacterial interference) [12-16].

Uncertainties still persist regarding a potential barrier effect of the resident nostril microbial flora in preventing SA acquisition, despite the identification of several genus/species potentially involved [12-16]. The barrier effect of these genus/species during follow-up of naïve SA patients is still not known. In addition, published studies mostly involved healthy subjects only, rarely patients [16]. No longitudinal studies had been yet conducted on a hospitalized patient population who are also at risk of SA acquisition.

Here, we investigated the potential role of the resident nostril microbial flora in SA acquisition, by analysing the nostril flora of 103 handicapped patients with neurological disorders. These patients, hospitalized for a long period of time in four rehabilitation centres, often suffered from multiple infectious complications and, as a consequence, received many broad-spectrum antibiotics, providing a favourable environment for detecting stochastic acquisition of MSSA/MRSA and parameters involved. We undertook a systematic quantification of any isolated bacteria from a nasal swab taken from patients at inclusion and during the follow-up for patients' naïve for SA colonization at inclusion.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

Epidemiological study

Our study was performed from January 2008 to December 2010 as part of a clinical research programme named ASAR (detailed in Supplementary Data) in four French hospitals. Patients were asked and gave full consent to participate in the ASAR project. The first 103 recruited patients screened for nasal SA before inclusion, had their nasal swab also used for analysis of their microbial flora. This group comprised 50 patients that were positive and 53 patients that were naïve for SA carriage at entry. The 53 SA-naïve patients were then followed-up for 13 weeks if they did not acquire an SA and 52 weeks in the case of SA acquisition (Supplementary Figure 1).

Nasal swab collection and culture for SA detection

Alginate swabs, soaked beforehand in sterile physiological saline solution, were rotated five times around the inside of both nostrils while applying constant pressure. Swabs were then placed in Stuart's transport medium (500 μl, Transwab, Medical Wire and Equipment, Corsham, Wiltshire, England) and kept at room temperature until arrival at the Microbiology Laboratory (Raymond Poincaré Hospital, Garches, France).

A 100-μl aliquot was plated on selective and non-selective media for MSSA/MRSA isolation as detailed in Supplementary Data. The rest of the heavy dispersed suspension was then stored at −80°C for further use. Screening for MRSA and antimicrobial susceptibility testing were performed as detailed in Supplementary Data.

Culture and quantification of the microbial flora

One hundred-microlitre aliquots were serially diluted up to 10−4 and plated on different selective and non-selective media (resulting in 16 conditions per patient, see supplementary figure 2), and incubated at 37°C in aerobic ± 5% CO2 and anaerobic conditions. Plates were read at 24 h and 48 h after incubation (see Supplementary Data).

MALDI-TOF and molecular species identification of isolated bacteria

Matrix-assisted laser desorption/ionisation-time-of-flight mass spectrometry (MALDI-TOF) and/or DNA sequencing were used for specific identification as previously described [17, 18].

Spa-typing of SA isolates

Spa typing was performed as previously described [19] and is summarized in Supplementary Data.

Clustering-based analyses of composition of nostril flora

For each patient, each of the species isolated from the microbial nostril flora, or of the 16 most represented species, was coded 1 when present or 0 when absent. The resulting flora composition fingerprints were then used in the Bionumerics 6.5 software (Applied Maths) as strings of categorical characters to construct minimum spanning trees, in order to analyse the clustering of SA and non-SA carriers according to the flora composition.

Statistical analysis

T-tests for independent samples, using R software, and logistic regression, using Stata software (Data analysis and Statistical Software, TX, USA), were utilized in univariate and multivariate models to evaluate probability for differences between patients who were SA carriers or non-carriers (see Supplementary Data); p  0.05 was considered statistically significant.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

Quantitative comparison of the SA+ and SA− patient population

The 103 patients included in the study were all disabled, requiring extensive medical and nursing care, leading to bacterial over-exposure (Table 1). Patients had bedsores and close regular contacts with nursing and physiotherapy staff, and underwent intermittent bladder catheterization and other invasive procedures several times per day. Each of these characteristics represents a high risk factor for the acquisition of MRB.

Table 1. Socio-demographic and therapeutic characteristics of the 103 patients
 S. aureus colonizationUnivariate analysis
 Yes (= 50) No (= 53)Total (n  = 103)
 MSSA (n  = 34)MRSA (n  = 16)  
  1. a

    Logistic regression between S. aureus-colonized patients (n = 50) and non-colonized patients (n = 53).

  2. b

    Mean.

  3. c

    Standard deviation.

Measurement n % n %NN%NOdds ratioIC 95 %p- valuea
Age (years)48.8b ± 17.7c49.5b ± 15.1c 47.8b ± 15.7c 0.770.24–2.490.92
        
Sex          0.78
Female926.5318.8121426.4260.880.36–2.14 
Male2573.51381.3383973.677Ref.  
Skin lesions          0.35
Yes1235.3743.8192547.2440.690.32–1.51 
No2264.7956.3312852.859Ref.  
Medical devices          0.40
Yes1132.4743.8181528.3331.430.62–3.27 
No2367.7956.3323871.770Ref.  
Allergy          0.30
Yes411.816.25917.0140.540.17–1.75 
No3088.21593.8454483.089Ref.  
Smoking          0.87
Yes411.8318.87713.2141.100.35–3.39 
No3088.21381.3434686.888Ref.  

Fifty out of the 103 patients were positive for SA carriage and 53 were negative. The 50 carriers comprised 36 MSSA (72%) and 14 MRSA (28%). SA isolates were classified into 15 different spa-types (Fig. 1). The most represented spa-types were t008, t777 and t002. MRSA types were represented by three spa-types only, of which a single one was MRSA specific (t6442).

image

Figure 1. Distribution of S. aureus spa-types in the nostril flora of patients. Minimum spanning tree (MST) analysis of 50 SA nostril isolates from patients based on spa-types. Each circle represents a spa-type, and the size of the circle corresponds to the number of isolates. Bold line connects spa-types that differ by one repeat unit motives. The fine line connects spa-types that differ by two repeat unit motives, bold dotted line indicates three repeat unit motives and other dotted lines more than three repeat unit motives. The red colour represents MSSA and purple is used for the characterization of MRSA.

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The number of species per nostril specimen was significantly higher among the 53 non-SA carriers (Table 2A). A similar trend towards a higher number of different species was observed among the 36 MSSA-positive patients compared with the 14 MRSA-positive patients, although this difference was not significant (p 0.15; Table 2B).

Table 2. Number of bacterial species per patient nostril microbial flora in SA carrier vs. non-SA carrier (A) and in MSSA-positive vs. MRSA-positive patients (B)
A.Number of bacterial species present in nostril flora
S. aureus colonization012345678meanp-value
No (= 53)03147898133.920.002
Yes (= 50)1101212662102.9 
B.Number of bacterial species present in nostril flora
S. aureus colonization01234567 meanp-value
MSSA (= 34)171174211 2.60.15
MRSA (= 16)03152410 3.3 

Fifty-one different species were identified at the species level, independently of the SA status, with a majority of Gram-positive bacteria (89%). The mean CFU/ml of all isolated species was not significantly different between the 53 non-SA and 50 SA carriers (5.3 × 105 and 8.6 × 105, respectively; p 0.25).

The 50 SA carriers possessed less Gram-positive rods (p 0.031) and more Gram-positive cocci (p 0.019; Fig. 2a). The difference in Gram-positive rods was mostly attributable to the 36 MSSA-positive patients (p 0.015; Fig. 2b), and essentially reflected a lower proportion of Corynebacteriaceae in this group of patients, compared with the 53 non-SA carriers (p 0.05; Fig. 2c). A difference in Corynebacteriaceae frequency was also seen when comparing the 14 MRSA-positive patients and the 53 non-SA carriers, albeit not reaching significance. Likewise, the 36 MSSA-positive patients possessed more Gram-positive cocci compared with the 14 MRSA-positive patients (p 0.037) or the 53 non-SA carriers (p 0.002; Fig. 2b). At the genus level, the nostril composition of Staphylococcaceae differed only between the 36 MSSA-positive patients and the 53 non-SA carriers (p 0.002; Fig. 2c).

image

Figure 2. Composition of the microbial nostril flora varied between SA and non-SA carrier patients. (a) Representation of the composition of microbial nostril flora for SA and non-SA carrier patients. (b) Composition of microbial nostril flora differentiating patients with MSSA and MRSA. (c) Representation of bacterial families in nostril flora of MSSA, MRSA and non-carrier patients. Statistical analysis was performed with Fisher's exact test.

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Of interest, an increase in the presence of Enterobacteriaceae was observed only in the 14 MRSA carriers when compared with the 36 MSSA-positive patients (p 0.0047; Fig. 2c).

Species composition in SA and non-SA carriers

Analysis of the nostril microbial flora demonstrated a clear variety at the species level (Table 3), with an over-representation of Staphylococcus epidermidis (93.2%), and a good representation of Corynebacterium accolens (43.7%) and Propionibacterium spp (29.1%). Only C. accolens and Shaemolyticus were more significantly prevalent (but not exclusively) among the 50 non-SA patients (Table 3).

Table 3. Microbial flora composition of the 103 nostril swabs. Repartition per bacterial species
Bacterial species SANo SAUnivariate analysisMultivariate analysisa
   (= 50)(= 53)      
  N n % n %ORIC 95 %pORIC 95 %p
  1. a

    p (Hosmer Lemeshow) = 0.54

  2. Statistical analysis was performed with regression logistics in univariate and multivariate models.

  3. The robustness of this model was confirmed with the test of Hosmer and Lemeshow (p 0.54).

  4. OR, odds ratio. Ref. is reference and is equal to 1.

 C. accolens
Present451632.02954.70.390.17–0.870.020.32 Ref.0.14–0.750.01
Absent583468.02445.3Ref.     
 S. haemolyticus
Present1324.01121.00.160.33–0.760.010.12 Ref.0.24–0.600.01
Absent904896.04279.0Ref.     
Gram-positive bacteria
 S. epidermidis
Present964590.05196.20.350.07–1.910.23   
Absent7510.023.8Ref.     
 S. capitis
Present17816.0917.00.930.33–2.640.89   
Absent864284.04483.0Ref.     
 S. lugdunensis
Present16714.0917.00.790.27–2.320.67   
Absent874386.04483.0Ref.     
 S. hominis
Present1024.0815.00.230.47–1.160.08   
Absent934896.04585.0 Ref.     
 S. condimenti
Present412.036.00.340.034–3.380.36   
Absent994998.05094.0Ref.     
 S. pettenkoferi
Present12510.0713.00.730.21–2.470.61   
Absent914590.04687.0Ref.     
 C. propinquum
Present812.0713.20.130.16–1.130.06   
Absent954998.04686.8      
 C. tuberculosis
Present636.035.71.060.20–5.530.94   
Absent974794.05094.3      
 C. pseusodiphtericum
Present212.011.91.060.06–17.440.97   
Absent1014998.05298.1      
 Propionibacterium
Present301326.01732.00.740.31–1.750.50   
Absent733774.03638.0Ref.     
 E. faecalis
Present412.036.00.340.034–3.380.36   
Absent994998.05094.0Ref.     
 D. hominis
Present724.059.50.400.073–2.160.29   
Absent964896.04890.5Ref.     
 Actinomyces
Present324.012.02.170.19–24.70.53   
Absent1004896.05298.0 Ref.     
 Rothia
Present536.024.01.620.26–10.170.60   
Absent984794.05196.0Ref.     
 D. hominis
Present724.059.50.400.073–2.160.29   
Absent964896.04890.5Ref.     
Gram-negative bacteria 
 P. aeruginosa
Present612.059.50.190.22–1.730.14   
Absent974998.04890.5Ref.     
 E. coli
Present636.035.61.060.20–5.530.94   
Absent974794.05094.4Ref.     
 E. aerogenes
Present712.0611.00.160.18–1.370.10   
Absent964998.04789.0Ref.     
 Acinetobacter
Present324.012.02.170.19–24.70.53   
Absent1004896.05298.0Ref.     
 K. pneumoniae
Present324.012.02.170.19–24.70.53   
Absent1004896.05298.0Ref.     
 Proteus
Present636.036.01.060.20–5.530.94   
Absent974794.05094.0Ref.     

A clustering approach for analysing nostril microbial flora of SA and non-SA carriers at inclusion

We used a clustering-like approach to identify signatures that might allow the prediction of SA carriage. The minimum-spanning tree obtained (with exclusion of SA) demonstrated a great diversity, with at least 80 different patterns among the 103 patients (Fig. 3a). None was correlated with the SA carrier status, as several microbial flora compositions were identical between some SA + or SA− carriers and no obvious groupings of different but related strain compositions (i.e. differing by one or two species at most) were detected according to the carrier status (i.e. no visible trend for grouping identically coloured circles in Fig. 3a). This was also shown by the observation that, when SA was included in the analysis, it separated almost perfectly the SA+ and SA− carriers, reflecting the fact that SA presence actually represented the sole major ‘allele’ shared by the SA+ population and systematically absent from the SA− population (Supplementary Figure 3A). A similar trend was observed when using only the 16 most prevalent species in order to reduce the weight of ‘absent species’ alleles in composition fingerprints (Fig. 3b and Supplementary Figure 3B).

image

Figure 3. Nostril flora profile excluding the presence of SA. A minimum spanning tree (MST) analysis was generated by BioNumerics software (version 6.5; Applied Maths) and based on the microbial composition of nostril flora of SA carrier and non-SA carrier patients. The red colour represents nostril floral with MSSA, purple represents nostril flora with MRSA and green represents nostril flora without S. aureus. (a) Repartition of nostril flora with the 51 represented species and excluding the SA presence in the algorithm. (b) Repartition of nostril flora with only the 16 most prevalent bacterial species excluding the presence of SA in the algorithm.

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Nostril microbial flora and SA acquisition during follow-up

Twenty-four out of 43 patients (55.8%) (10 exclusions, supplementary figure 1) became SA positive, with a medium time for acquisition of 7 weeks, and 19 out of 43 patients (44.2%) did not acquire SA during the 13-week follow-up. A similar therapeutic strategy was observed in both populations (not shown). Nineteen patients out of the 24 acquired an MSSA (79%), four an MRSA (17%) and one both an MSSA and MRSA (4%). In total, spa typing among the 35 SASM and 7 SARM isolates obtained for these 24 patients during follow-up demonstrated 13 different spa-types, with similar predominant spa-types seen at inclusion.

We compared the microbial flora between inclusion (S0) and 1 week before acquisition (S−1), between S−1 and acquisition (S), and between S and 1 week after acquisition of SA (S+1). As in the above situation, univariate and multivariate analysis and clustering analysis did not identify significant differences among flora compositions allowing prediction of SA, MSSA or MRSA carriage at any step. Groups were too small to achieve significance (not shown).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

The potential role of the resident nasal microbial flora in preventing or favouring SA establishment in the nares remains unclear. In particular, this potential role had not previously been examined in high-risk patients facing a long hospitalization and high antibiotic pressure.

Our results show that in such a patient population, resident flora are diverse and tend to be strongly individual-dependent, and none of the resident flora (other than SA) allows differentiation between SA-positive and SA-negative patients, and prediction of acquisition of sensitive or resistant SA.

However, we observed variations of composition among patient groups, though not in terms of abundance. We did not see any significant CFU count differences for any isolated species between SA+ and SA− patients. Instead, the main significant difference was in the number of bacterial species present in the nostril per specimen, with more numerous and different species in the absence of SA carriage. This result is consistent with the expected competition in the nostril flora, where the absence of a species is compensated for by the presence of other species.

Corynebacterium accolens and S. haemolyticus were found to be present in the resident flora in the absence of SA at a significantly higher level. Identification of C. accolens has not been carried out at the species level in previous studies using phenotypic or metagenomic methods [12, 13, 16] but could be achieved here by using Maldi-Tof and rpoB sequence analysis [17]. C. accolens, a resident of the upper respiratory tract, shows satellite growth around SA streaks on blood agar [20, 21]. It is also a recently isolated pathogen, described in a recent case of a breast abscess [22] and pelvic osteomyelitis [23].

Staphylococcus haemolyticus was the exclusive Gram-positive cocci present in non SA-carriers. S. haemolyticus is highly prevalent in the hospital environment [24] and is the second most frequent SCN isolated from blood cultures [24]. S. haemolyticus is notorious for its multiresistance [25-27] and the presence of many antibiotic resistance genes in its genome [28]. Additionally, S. haemolyticus possess several factors helping colonization of the human host [28]. These elements are thus consistent with its frequent isolation from hospitalized patients in the present study.

We also observed differences between MSSA and MRSA carriers, not investigated previously [12-16]. In fact, MSSA carrier flora differed from MRSA carrier flora, mainly by the Gram-positive cocci composition, which represented a difference almost equal to the difference observed between MSSA and non-SA carriers.

However, despite these unequal distributions among patient groups, our follow-up design allowed us to show that the number of patients acquiring SA during follow-up and with or without C. accolens or S. haemolyticus was not significantly different. This result, not found previously, confirms that a unique time-point comparison does not predict a role in favour of SA colonization for C. accolens and/or S. haemolyticus or otherwise. None of the differences related to antimicrobial consumption or to microbial flora composition before detection of SA carriage allowed the prediction of subsequent SA acquisition. This absence of a predictive influence points to a major role of factors other than the microbial flora in SA acquisition; these might include the patient's local environment and/or the immune status of the carrier.

This study has some limitations. We used exhaustive culture-based methods for bacterial identification, but not a metagenomic approach to identify non-cultivable species. However, the latter approaches, especially when targeting 16S rDNA amplification/sequencing or correlating the respective frequencies of sequence reads from each organism and the amount of organism, have many drawbacks for the identification and quantification of the microbial species present in a microbial flora, as reported recently [29]. In contrast, we were able to quantify globally and individually each species detected in the nostril flora, and did not see any significant CFU count differences for any isolated species from both SA+ and SA− patients. We even detected several bacterial species with a concentration in the range of 102 CFU/ml (not shown), below the usual detection thresholds of metagenomic approaches [16]. In addition, we note that none of the major species previously found by metagenomic analysis of nostril flora [16] was missed in our study, indicating the sensitivity of our serial dilutions and the relevant choice of culture media. Compared with classical metagenomics solely targeting 16S rDNA, we were likely to be more precise in terms of species identification by using a combination of several molecular targets and MALDI-TOF-based analysis [17, 18], leading, for example, to the identification of Corynebacteriaceae at a species level. We believe therefore that our approach might serve as a potential reference standard for further development of metagenomics to characterize the dynamics of nostril flora.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

We acknowledge the great contribution of Dr Ben Marshall (Southampton University Hospitals Trust, UK).

Fundings

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information

This work was supported by the French Programme Hospitalier de Recherche Clinique Régional 2006 (PHRC AOR06009) and Pasteur Institute, Paris, France (Programme Psion and PTR 225). ASA received a PhD fellowship from the Direction Générale de l'Armement (DGA, 2009–2012). Part of this work was presented at the French Society for Microbiology Meeting, 2010, Marseille, France.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Fundings
  9. Transparency Declaration
  10. References
  11. Supporting Information
FilenameFormatSizeDescription
clm12208-sup-0001-FigS1.docxWord document65KFigure S1. Flow chart of study population.
clm12208-sup-0002-FigS2.docxWord document6145KFigure S2. Plating technique of nostril swabs and bacterial identification.
clm12208-sup-0003-FigS3.docxWord document59KFigure S3. Nostril flora profile including the SA presence.
clm12208-sup-0004-TableS1.docxWord document14KTable S1. List of oligonucleotides used in the respective ID PCR panels.
clm12208-sup-0005-DataS1.docxWord document15KData S1. Materials and methods.

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