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Use of a spectrophotometric bioassay for determination of microbial sensitivity to manuka honey
Journal of Microbiological Methods 64 (2006) 84 – 95 www.elsevier.com/locate/jmicmeth
Use of a spectrophotometric bioassay for determination of microbial sensitivity to manuka honey Thomas Patton*, John Barrett, James Brennan, Noel Moran Institute of Technology Sligo, IT Sligo, Ballinode, Sligo, Ireland Received 15 November 2004; received in revised form 5 April 2005; accepted 11 April 2005 Available online 24 June 2005
Abstract The antimicrobial activity of manuka honey has been well documented (Molan, 1992a,b,c, 1997) [Molan, P.C., 1992. The antibacterial activity of honey. 1: the nature of the antibacterial activity. Bee World 73 (1) 5–28; Molan, P.C., 1992. The antibacterial activity of honey. 2: variation in the potency of the antibacterial activity. Bee World 73 (2) 59–76; Molan, P.C., 1992. Medicinal uses for honey. Beekeepers Quarterly 26; Molan, P.C., 1997. Finding New Zealand honeys with outstanding antibacterial and antifungal activity. New Zealand Beekeeper 4 (10) 20–26]. The current bioassays for determining this antimicrobial effect employ a well diffusion (Ahn and Stiles, 1990) [Ahn, C., Stiles, M.E., 1990. Antibacterial activity of lactic acid bacteria isolated from vacuum-packed meats. Journal of Applied Bacteriology 69, 302–310], (Weston et al., 1999) [Weston, R.J., Mitchell, K.R., Allen, K.L., 1999. Antibacterial phenolic components of New Zealand manuka honey. J. Food Chem. 64, 295–301] or disc diffusion (Taormina et al., 2001) [Taormina, Peter J., Niemira, Brendan A., Beuchat, Larry R., 2001. Inhibitory activity of honey against food borne pathogens as influenced by the presence of hydrogen peroxide and level of antioxidant power. Int. J. Food Microbiol. 69, 217–225] assay using zones of inhibition as indicators of bacterial susceptibility. The development of a 24-h spectrophotometric assay employing 96-well microtiter plates, that is more sensitive and more amenable to statistical analysis than the assays currently employed, was undertaken. This simple and rapid assay permits extensive kinetic studies even in the presence of low honey concentrations, and is capable of detecting inhibitory levels below those recorded for well or disc diffusion assays. In this paper, we compare the assay to both well and disc diffusion assays. The results we obtained for the spectrophotometric method MIC values show that this method has greater sensitivity than the standard well and disc diffusion assays. In addition, inter- and intra-assay variance for this method was investigated, demonstrating the methods reproducibility and repeatability. D 2005 Elsevier B.V. All rights reserved. Keywords: Microtiter; Spectrophotometric; % Inhibition; Disc diffusion; Manuka honey
1. Introduction * Corresponding author. E-mail address: [email protected] (T. Patton). 0167-7012/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.mimet.2005.04.007
The nature of the antimicrobial activity of manuka honey has been extensively reviewed to date (Mundo
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
et al., 2004; Taormina et al., 2001, Weston et al., 1999; Molan, 1992a,b,c, 1997). The inhibitory activity has been attributed to several key properties of the honey including osmotic effect, naturally low pH, production of hydrogen peroxide (Weston et al., 1999; Willix et al., 1992), and also the presence of phenolic acids, lysozyme, and flavanoids (Cooper et al., 1999). With few exceptions, the majority of studies on manuka honey have used well or disc diffusion methods (Willix et al., 1992; Cooper et al., 2002) to evaluate and study activities. With these techniques, a sample material is placed into wells bored into an inoculated agar growth media, this diffuses into the agar around the well, and is assayed for an ability to produce a zone of inhibition. Alternatively, discs prepared from sterile absorbent material are soaked in test sample solution and placed onto an inoculated agar growth media. The samples diffuse into the agar around the disc and are assayed for an ability to produce a zone of inhibition. Inhibition zones are measured and used to determine the test material’s potency. Diffusion bioassays are comparatively basic and robust, but have limitations in their detection and quantification abilities at low concentrations and are open to subjective interpretation (Swenson et al., 1989). One major difficulty in honey research and applications is obtaining accurate and precise quantification using bioassays. The agar diffusion assay (ADA) is still the preferred method for honey bactericidal quantifications and is used for manuka honey production batch analysis and release procedures (Gribbles Analytical Laboratories). However, the subjective nature of this assay limits the interpretation of results. It is also time-consuming and laborious, requiring preparation and cooling of plates, boring of test wells in agar, and manual measurement of inhibition zones after 24 h of incubation. Results depend largely on technique and judgment, and the suggested precision cannot be obtained when the inhibition zone is unclear or not perfectly circular (Turcotte et al., 2004). The possibility of using infrared methods for quality analysis of honey has also been proposed (Lichtenberg-Kraag et al., 2002). Microbial growth, or inhibition of growth, can be detected using a variety of biological methods, including direct microscopic counts, absorbance (Bogdanov, 1997), and bioluminescence (Gellert, 1999), assays
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that incorporate a colorimetric (Gabrielson et al., 2002; Kuda et al., 2004) and fluorometric growth indicator, (Sykes, 1965) turbidity (Archer et al., 1996), dry weight, and zones of inhibition. New approaches for testing the susceptibilities of fungi are also being investigated (Lozano-Chiu et al., 1999). Efforts to standardise the variability between each method have also been made (Alderman and Smith, 2001). In this study, a spectrophotometric assay using 96well microtiter plates was compared to the standard methods of well/disc diffusion in order to evaluate the potential advantages of this bioassay for the evaluation of the antibacterial properties of manuka honey. Increased automation and throughput (efficiency) were also achieved by using the spectrophotometric assay, which can rapidly generate a large amount of data; detailed statistical analysis of results is also possible. This present study was also undertaken to investigate some of the aspects of the internal validity of the spectrophotometic protocol.
2. Materials and methods 2.1. Bacterial strains and growth conditions Bacillus cereus (NCIMB 8012), Escherichia coli (NCIMB 8545), Staphylococcus aureus (NCIMB 9518), and Candida albicans (NCIMB 3179) bacterial strains were grown on nutrient agar or nutrient broth. C. albicans was grown on sabaroud dextrose agar or in sabaroud dextrose broth. B. cereus was incubated at 30 8C. All other strains were incubated at 37 8C. Bacterial growth was monitored by measuring the culture optical density (OD) in a spectrophotometer (Anthos 2010) at a wavelength of 620 nm. 2.2. Honey preparation Comvita Manuka Care 18+ (UMF) honey was used in this study. Honey was stored in the dark at room temperature. The initial honey dilution preparation was a 50% vol/vol in nutrient broth. Twelve serial 1:2 dilutions of the 50% vol/vol preparation were made in nutrient broth for bacterial inhibition testing, giving the lowest concentration value of 0.01%. For assay of C. albicans, honey preparations of 50%,
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40%, 30%, 20%, and 10% vol/vol in sabaroud broth were prepared. 2.3. Disc diffusion Nutrient or sabaroud dextrose agar plates were inoculated by swabbing overnight cultures onto the surface of agar plates. Plates were allowed to stand at room temperature for 15 min before discs were applied. Sterile absorbent discs (8.2 mm diameter) were placed into honey concentrations for 10 min before being applied directly to inoculated agar plates. Control sterile discs and methylene blue dye-soaked discs were also applied. Plates were incubated for 24 h and zones of inhibition were measured using an Autodata automatic zone reader with a tolerance (F0.1 mm). The diameter of zones including the diameter of the discs (8.2 mm) was recorded. 2.4. Well diffusion assay The agar plates were prepared as for the disc assay. Wells 8.2 mm diameter were bored into the surface of the agar. One hundred and eighty microlitres of diluted honey was placed into each well. A control of methylene blue dye was used. Plates were incubated for 24 h and zones of inhibition were measured using an Autodata automatic zone reader. The diameter of zones, including the diameter of the well (8.2 mm), was recorded.
dilution was applied to wells of a flat-bottom 96well microtiter plates with lid to prevent cross contamination (Costar, Corning Ltd. NY). Control wells received 200 Al of 5% culture inoculated nutrient or sabaroud broth. Optical density was determined in a spectrophotometer at 620 nm prior to incubation, T 0. Plates were incubated for 24 h in the dark on a Certomat MO orbital shaker at 100 rpm to prevent adherence and clumping. After 24 h, plates were again read in a spectrophotometer at 620 nm, T 24. 2.6. Analysis of results The OD for each replicate at T 0 was subtracted from the OD for each replicate at T 24. The adjusted OD of each control well was then assigned a value of 100% growth. The growth inhibition for the test wells at each dilution was determined using the formula: Percent Inhibition = 1 (OD test well/OD of corresponding control well) 100 for each row of the 96well plate (e.g., OD row 1, column 1, well 1 (test) was divided by the OD value of row 1, column 12, well 12 (control)). This yielded eight replicate inhibition values for each honey dilution. The variation associated with the average calculated inhibition values for replicate wells was determined as percent coefficient of variation (%CV). Where the resulting measurement recorded a negative inhibition value (growth promotion), this was reported as stimulation using the formula: Percent Growth ¼ ðOD test=OD controlÞ 100:
2.5. Spectrophotometric assay Honey dilutions were inoculated with test culture to give a 5% vol/vol concentration. Two hundred microlitres of each dilution in eight replicates per
Using this formula prevented a negative coefficient of variation being reported. The antibacterial detection limits for each method were compared. This is reported as minimum inhibi-
Table 1 Statistical analysis of turbidometric method inter/intra-assay response correlation/variation
S. aureus B. cereus E. coli C. albicans
Correlation coefficient
ANOVA (between groups)
ANCOVA (between groups)
% Inhibition * concentration of honey
Variable = % inhibition
Variable = % inhibition; covariate = concentration of honey
r value
p value
f value
p value
p value (days)
p value (test number)
p value (days * test number)
0.634 0.647 0.780 0.954
0.001 0.001 0.001 0.001
0.105 0.133 0.126 0.009
0.901 0.876 0.881 0.991
0.849 0.808 0.739 0.921
0.992 0.999 0.995 0.990
1.000 1.000 1.000 0.998
100.00
% Inhibition
60.00
40.00
40.00
20.00
20.00
-1.5
-1.0
0.1
0.6
1.1
1.6
0.00 -1.95 -20.00
Bacillus cereus
-0.95
-0.45
0.05
0.55
1.05
1.55
1.05
1.55
Log 10 conc honey (% v/v)
100.00
Bacillus cereus
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00 -1.95 -20.00
-1.45
Log 10 conc honey (% v/v)
100.00 800.00
-0.5
Escherichia coli
-40.00
-40.00
% Inhibition
80.00
60.00
0.00 -2.0 -20.00
Escherichia coli
100.00
-1.45
-0.95
-0.45
0.05
0.55
1.05
1.55
0.00 -1.95 -20.00
Candida albicans Candida albicans
-1.45
-0.95
-0.45
0.05
0.55
-40.00
-40.00 Log 10 conc honey (% v/v)
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
80.00
Staphylococcus aureus Staphylococcus aureus
Log 10 conc honey (% v/v)
Fig. 1. Dose–response curve for the antibacterial activity of manuka honey against species. The activity is expressed as the percentage inhibition of growth of the culture, with inhibition being determined by comparison to control well after 24 h of incubation. Error bars indicate the standard deviation of inter-assay results (n = 120).
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tory concentration, expressed as MIC0 (the highest concentration of test material which results in no inhibition of growth), MIC50 (concentration of test material which results in 50% inhibition of growth, also known as the median response), and MIC100 (the lowest concentration of test material which results in 100% inhibition of growth). For the diffusion assays, 100% inhibition was taken at the highest concentration of test material. The statistical significance for the spectrophotometric method was determined using SPSS. Specifically, correlation coefficient, ANCOVA, and ANOVA with Tukey post hoc analysis were determined (Table 1).
13.5%. The MIC50 value for E. coli was 5.6% vol/vol, B. cereus 2.00%. S. aureus 0.78%, and C. albicans 25.1%. The MIC100 value for E. coli was 12.5% vol/ vol, B. cereus 3.125%, S. aureus 6.25%, and C. albicans 40.0%, The reported results for each measurement are shown in Table 3 with standard deviation and %CV values. All data were statistically analysed for significance to determine overall differences between groups, for all measurements p N 0.05 (Table 1). 3.2. Well diffusion assay Antibacterial activity was determined by measuring the zones of inhibition. From these data, a dose– response plot was calculated by plotting the diameter of the zone squared versus the log of the honey concentration (Fig. 2). From these plots, the minimum detectable concentration (MIC0) value was calculated by extrapolation of the regression line, which intersects the squared diameter of the well plus 0.1 mm detection buffer (minimum detectable area of inhibition by the naked eye). The R 2 values are reported. B. cereus gave a linear response, although an exponential curve fitted best to S. aureus and E. coli. C. albicans failed to produce any zones detectable on all tests. The MIC0 result for E. coli was 6.3% vol/vol, B. cereus 10%, and S. aureus 3.7%. The MIC100 was taken as the highest concentration of honey used for the assay. The MIC50 was calculated as midway between these two values for E. coli (24.5% vol/vol): B. cereus 21.9% and S. aureus 22.6% (Table 2). The average zone of inhibition measurement values for each test concentration are shown together with the standard deviation and %CV (Table 4). The highest %CV
3. Results 3.1. Spectrophotometric assay The dose–response curves obtained from plotting the log of the concentration of honey against the resulting percent inhibition of bacterial growth are shown in Fig. 1. The concentration range of honey used for C. albicans was from 50% to 10% vol/vol, as this was shown to be the concentration range where an inhibitory effect could be observed. C. albicans was the only species that demonstrated a near-linear relationship between honey concentration and percent inhibition, with the regression analysis giving an R 2 value of 0.954. For B. cereus, S. aureus, and E. coli, the response curves were distinctly non-linear; the correlation coefficient values for the method are shown in Table 1. From the plotted results, the honey MIC values for each organism were determined (Table 2). The MIC0 value for E. coli 0.18% vol/vol, B. cereus 0.34%, S. aureus 0.05%, and C. albicans
Table 2 The concentration percent vol/vol of manuka honey required in growth medium in spectrophotometric assay (SA) and for well and disc diffusion assays to inhibit the growth of species of bacteria tested over a 24-h incubation period Bacterial species
Escherichia coli Staphylococcus aureus Bacillus cereus Candida albicans
MIC0 (% vol/vol)
MIC50 (% vol/vol)
MIC100 (% vol/vol)
Discs
Wells
SA
Discs
Wells
SA
Discs
Wells
SA
(5.4) (5.2) (11.5) N/A
(6.3) (3.7) (10.0) N/A
0.18 0.05 0.34 13.50
22.4 25.7 24.0 N/A
24.5 22.6 21.9 N/A
5.60 0.78 2.00 25.10
50 50 50 N/A
50 50 50 N/A
12.5 6.25 3.125 40.0
Values shown in parentheses were obtained by extrapolation.
Table 3 Spectrophotometric results used to plot dose–response curves as the percent inhibition vs. log10 concentration honey, X-bar, standard deviation (S.D.), and coefficient of variation (%CV) for each value Bacterial strain
0.012% vol/vol manuka honey
r-day (n = 120 used for graph) Bacillus cereus r-day (n = 120 used for graph)
X¯
0.098
0.391
X¯
S.D.
%CV
X¯
S.D.
%CV
%CV
X¯
%CV
X¯
102.60a 106.34a 106.82a 105.26
5.72 10.78 6.66 8.18
5.57 10.14 6.23 7.77
103.75a 107.06a 110.56a 107.12
8.70 6.57 11.54 9.51
8.38 6.14 10.44 8.88
97.05a 94.95a 97.83a 96.61
8.73 6.84 5.54 7.20
8.99 7.20 5.66 7.45
24.18 27.68 27.46 26.44
6.40 7.97 8.07 7.62
26.46 28.77 29.40 28.81
48.93 52.64 48.14 49.91
6.54 5.13 6.14 6.23
13.36 9.74 12.75 12.49
50.59 52.01 45.27 49.29
5.39 4.36 6.66 6.23
10.65 8.39 14.72 12.64
1 2 3 X¯
107.79a 110.84a 108.39a 109.00
9.40 8.41 11.65 10.12
8.72 7.59 10.75 9.28
131.01a 111.51a 125.08a 122.53
32.36 9.11 15.45 12.68
24.70 8.17 12.35 10.35
123.70a 111.21a 127.11a 120.67
14.68 7.96 12.04 13.64
11.87 7.16 9.47 11.30
117.72a 109.93a 113.08a 113.58
15.13 9.53 11.81 22.71
12.85 8.66 10.44 20.00
96.43a 96.63a 99.26a 97.44
10.52 10.54 9.24 9.92
10.91 10.91 9.31 10.18
25.32 25.32 16.12 22.26
10.50 10.50 9.46 9.94
41.49 41.49 58.69 44.67
1 2 3 X¯
107.79a 110.84a 108.39a 109.00a
5.70 5.40 5.30 5.47
5.29 4.87 4.89 5.02
131.01a 111.51a 125.08a 122.53a
6.20 7.40 6.20 6.60
4.73 6.64 4.96 5.39
123.70a 111.21a 127.11a 120.67a
5.50 8.20 6.80 6.83
4.45 7.37 5.35 5.66
117.72a 109.94a 113.08a 113.58a
6.70 7.60 6.30 6.87
5.69 6.91 5.57 6.05
96.43a 96.63a 99.26a 97.44a
7.40 6.70 5.20 6.43
7.67 6.93 5.24 6.60
4.55 5.37 5.67 5.20
3.70 3.10 4.20 3.67
81.32 57.76 74.07 70.57
S.D.
S.D.
%CV
10.00 X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
115.04a 118.35a 109.61a 114.33a
9.59 9.18 8.17 9.63
8.33 7.75 7.46 8.42
24.51 20.34 20.36 21.74
12.26 8.55 6.82 9.61
50.02 42.04 33.51 44.21
75.71 77.08 69.25 74.01
6.08 3.54 3.24 5.60
8.03 4.59 4.68 7.57
98.51 98.18 95.42 97.37
1.33 0.96 1.77 1.96
1.35 0.98 1.86 2.01
99.80 100.57 99.57 99.98
2.03 1.74 1.90 1.93
2.04 1.73 1.91 1.93
Inter-day (n = 120 used for graph)
40.00
S.D.
Intra-day (n = 60) 1 2 3 X¯
30.00
S.D.
% vol/vol manuka honey
Candida albicans
20.00
%CV
X¯
0.195
50.00
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
r-day (n = 120 used for graph) Escherichia coli
0.49
1 2 3 X¯
Intra-day (n = 60) Staphylococcus aureus
0.024
Each data point represents the mean value recorded for five different samples over three different days. a Values reported as percent growth.
89
90
Bacterial strain 0.781 Intra-day (n = 60)
X¯
1.563 S.D.
%CV X¯
3.13 S.D. %CV X¯
6.25 S.D. %CV X¯
25.00
50.00
S.D. %CV X¯
S.D. %CV X¯
S.D. %CV X¯
S.D. %CV
17.64 10.85 22.33 19.36
96.86 96.57 98.11 97.18
2.36 2.72 2.14 2.49
2.43 2.81 2.18 2.56
97.13 95.20 98.60 96.98
2.28 5.06 3.16 3.91
2.34 5.31 3.20 4.03
95.50 94.51 97.86 95.96
4.15 6.99 4.86 5.60
4.34 7.39 4.96 5.84
93.42 93.48 96.76 94.55
6.65 4.66 8.20 6.79
7.12 4.99 8.48 7.18
20.12 20.12 22.15 21.75
54.88 54.88 58.39 56.05
5.85 5.85 6.90 7.03
10.65 10.65 11.81 12.54
101.69 101.69 102.00 101.79
1.30 1.30 0.88 1.81
1.27 1.27 0.86 1.78
102.91 102.91 103.21 103.01
2.25 2.25 1.42 1.67
2.19 2.19 1.38 1.62
103.50 103.50 105.94 104.31
3.39 3.39 2.08 3.08
3.28 3.28 1.96 2.95
4.10 100.64 4.30 2.93 99.59 2.63 3.86 100.59 7.60 3.63 100.27 4.84
4.27 2.64 7.56 4.83
101.39 100.74 104.40 102.18
5.40 4.06 5.50 4.99
5.33 4.03 5.27 4.88
97.33 96.88 102.95 99.05
7.03 6.43 5.40 6.29
7.22 6.64 5.25 6.35
98.69 99.56 102.53 100.26
6.47 5.14 4.20 5.27
6.56 5.16 4.10 5.26
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Staphylococcus 44.95 6.98 15.53 45.24 5.60 12.38 43.55 7.68 aureus 54.84 4.82 8.79 54.00 4.23 7.83 52.11 5.66 46.15 6.19 13.42 43.31 7.91 18.27 41.87 9.35 r-day (n = 120 48.65 7.47 15.35 47.52 7.65 16.09 45.84 8.87 used for graph) Escherichia 30.18 10.01 33.15 30.26 8.64 28.56 38.92 7.83 coli 30.18 10.01 33.15 30.26 8.64 28.56 38.92 7.83 21.32 8.62 40.41 26.77 7.71 28.79 35.43 7.85 r-day (n = 120 27.23 10.92 40.12 29.10 9.83 33.78 37.76 8.21 used for graph) Bacillus 16.54 4.10 24.79 27.11 5.87 21.64 95.07 3.90 cereus 17.76 4.70 26.46 24.94 5.77 23.12 97.19 2.85 16.51 5.40 32.71 25.55 4.30 16.83 101.10 3.90 r-day (n = 120 16.94 4.73 27.95 25.87 5.31 20.53 97.79 3.55 used for graph)
12.50
417.2
267.2 217.2 167.2 117.2 67.2 0.60
1.60
Log conc. of honey (%v/v) circle = disc triangle = well
367.2 317.2 267.2 217.2 167.2 117.2
1.10
y = 33.282e1.0634x R2 = 0.9721 y = 16.373e1.8781x R2 = 0.9964
67.2 0.50
Bacillus cereus 517.2 Mean zone diameter^2 (mm)
Mean zone diameter*2 (mm)
Mean zone diameter*2 (mm)
317.2
Staphylococcus aureus 467.2 417.2 367.2 317.2 267.2 217.2 167.2 117.2
67.2 0.9 1.00 1.50 Log conc. honey (% v/v)
circle = disc y = 22.904e1.568x R2 = 0.975 Triangle = well y = 28.582e1.6006x R2 = 0.9867
circle = disc triangle = well
1.1
1.3 1.5 1.7 Log conc honey (% v/v)
y = 514.86x - 475.02 y = 712.41x - 643.87
R2 = 0.9933 R2 = 0.9935
Fig. 2. Dose–response curves for the antibacterial activity of manuka honey against three species of bacteria. The activity is expressed as mean zone diameter squared vs. the log10 of percent concentration. (E) Well diffusion assay; (.) disc diffusion assay (n = 60); results of 3-day assay.
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Escherichia coli 367.2
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T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Table 4 Inter/intra-assay results from diffusion and disc methods, average, standard deviation (S.D.), and coefficient of variation (%CV) for each value are reported Diffusion assay
% vol/vol manuka honey
Inter-assay results
Method
50
25
12.5
Bacterial species
Inter-day (n = 20)
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
Escherichia coli
Wells
18.2 20.0 20.4 19.6 13.8 15.5 14.4 14.6 21.8 21.0 19.7 20.8 18.7 19.6 17.9 18.8 27.0 21.6 22.2 23.6 22.4 19.1 18.9 20.2
1.0 1.1 1.1 1.5 0.9 1.1 0.8 1.2 1.2 2.0 1.2 1.7 1.2 1.4 0.5 1.3 0.9 0.8 1.0 2.6 0.8 1.3 0.7 1.9
5.4 5.7 5.4 7.4 6.3 6.8 5.8 7.9 5.4 9.5 6.2 8.3 6.5 7.1 3.1 6.9 3.4 3.8 4.5 11.2 3.4 6.7 3.9 9.3
14.6 15.2 16.4 15.4 11.7 11.4 11.5 11.6 17.0 17.9 15.4 16.8 13.8 14.7 13.4 14.0 20.7 18.3 18.9 19.3 15.9 14.9 14.7 15.2
1.1 1.2 1.5 1.5 1.2 0.9 1.1 1.1 1.3 0.9 0.6 1.4 0.7 0.9 1.0 1.0 1.7 0.8 1.0 1.6 1.4 1.5 0.9 1.4
7.3 8.1 9.1 9.5 10.3 7.6 9.8 9.2 7.4 4.7 3.7 8.4 5.3 6.0 7.2 7.3 8.4 4.5 5.5 8.3 8.9 10.1 6.1 9.1
11.8 11.3 11.4 11.5 11.6 10.1 10.2 10.6 12.1 12.3 12.2 12.2 10.7 10.7 10.6 10.7 11.1 10.7 12.1 11.3 10.1 9.3 10.1 9.9
0.9 0.9 0.7 0.9 0.8 0.7 0.7 1.0 1.1 0.7 0.9 0.9 0.6 0.4 0.7 0.6 0.4 1.0 0.8 0.9 0.8 0.5 0.6 0.7
7.7 8.3 6.3 7.6 6.7 6.8 6.6 9.1 9.1 5.4 7.6 7.4 5.3 4.2 6.6 5.3 3.9 8.9 6.4 8.3 7.7 5.8 5.7 7.4
8.4 8.5 8.4 8.4 9.3 8.7 8.5 8.9 10.8 10.1 10.2 10.4 9.3 9.1 9.6 9.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1 0.2 0.2 0.2 0.4 0.5 0.7 0.6 0.5 0.7 0.5 0.6 0.3 0.4 0.5 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.5 2.6 2.6 2.3 4.3 5.3 7.7 6.9 4.2 7.1 4.6 6.2 3.0 3.9 4.8 4.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Intra-day (n = 60) Discs
Staphylococcus aureus
Intra-day (n = 60) Wells
Intra-day (n = 60) Discs
Bacillus cereus
Intra-day (n = 60) Wells
Intra-day (n = 60) Discs
Intra-day (n = 60)
1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯
6.25
Candida albicans is not shown as no inhibition was observed at the concentrations used.
observed was 11.2%, indicating good inter/intraassay reproducibility/repeatability. 3.3. Disc diffusion assay Antibacterial activity was again determined by measuring the zones of inhibition, as for the well diffusion assay. The values were plotted to produce the dose–response curves for each culture (Fig. 2). The MIC values were obtained as for the well diffusion assay. The MIC0 result for E. coli was 5.4% vol/vol, B. cereus 11.5%, and S. aureus 5.2%. The MIC100 was taken as the highest concentration of honey used for the assay. The MIC50 was calculated as midway between these two values. For E. coli, this was 22.4% vol/vol, for B. cereus 24.0%, and for S. aureus 25.7% (Table 2). The variations of zone measurements are reported (Table 4).
4. Discussion When determining microbial susceptibility to inhibitory compounds, a decision must be made as to which method will be employed. The choice of method includes agar diffusion, disc diffusion, broth diffusion, or variants of these methods. The agar/disc diffusion method is regarded as the method of choice for inhibition tests (Lozano-Chiu et al., 1999; Gabhainn et al., 2004). This method has also become popular particularly in the area of antibiotic testing (Hewitt and Vincent, 1988). More recently, there has been an increased interest in microtiter plate assays (Kuda et al., 2004; Casey et al., 2004; Turcotte et al., 2004; Archer et al., 1996). Both the agar and disc diffusion methods can be unreliable in certain situations due to the subjectivity associated with visual determinations (Swenson et al., 1989; Piliouras et al., 2002). In addition, both these methods have asso-
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
ciated time, sample, and cost implications. With the publishing of the NCCLS M27 methodology (Rex et al., 2001) for antifungal susceptibility testing using photometric analysis, this has established the trend towards a turbidometric system for mould and yeast investigation methods. The inhibition of bacterial growth can be determined by simple single tube optical density measurement (Parente et al., 1995). The use of a 96-well microtiter plate assay system facilitates inhibition testing as it is less time-consuming and cheaper, and multiple samples can be tested on a single plate. The microtiter plate assay is also less subjective. The methods for disc and well diffusions were those used by previous studies (Weston et al., 2000; Willix et al., 1992; Allen et al., 1991; Lozano-Chiu et al., 1999; Gabhainn et al., 2004); however, the cultures were overlaid onto the agar surface instead of seeding the agar, so that the microorganisms were in an early growth phase enabling easier detection of inhibition (Casey et al., 2004). The methods for presenting results and determining MIC(s) were those adopted by previous studies (Weston et al., 1999). Developing the procedure for the spectrophotometric methodology was based on previous articles (Casey et al., 2004; Turcotte et al., 2004; Archer et al., 1996; Theunissen et al., 2001; Waites et al., 2003). The amount of bacterial growth occurring in the 24-h period was measured (end point) rather than the rate of growth, due to reported experiments that at low concentrations of honey some species are suppressed in the growth rate by having prolonged lag phases (Willix et al., 1992). As small changes in OD were repeatedly detected, reporting the percent inhibition using the formula enabled accurate measurement on normally undetectable changes in bioactivity. Reporting the minimum and median response concentrations was chosen along with 100% MIC as this is derived from a series of determinations as opposed to a single isolated point (Sykes, 1965). Results from the well, disc, and spectrophotometric assays show that the microorganisms vary in their sensitivity to manuka honey. This variation in sensitivity has been described previously (Mundo et al., 2004; Radwan et al., 1984). For the disc and well methods, the MIC values we obtained are similar to earlier work done on this material (Cooper et al., 2002). The MIC50 for the spectrophotometric method
93
correlates with previous work using a turbidometric method (Willix et al., 1992), albeit by a different technique of calculation. Overall, the MIC results were found to be similar to previously published material. No inhibition of the yeast was detected by either diffusion method. However, the spectrophotometric assay gave a near linear response demonstrating its sensitivity. The plots obtained for the bacterial species using the spectrophotometric method have a distinct nonlinear relationship. There is evidence of a (two-stage) process of inhibition (Snow and Manley-Harris, 2004), something that requires further investigation. The aim of the study was to determine if a spectrophotometric method could be used to detect inhibition by manuka honey. The method was compared with the two standard diffusion methods for comparison purposes. The variance associated with each method was evaluated; for the concentrations where inhibition for each method was reported, the CV values were found not to be significantly different when compared by ANCOVA ( p N 0.05). As the mean percent inhibition values fell to near zero or below, the method for reporting the %CV was modified from inhibition to growth to avoid reporting a negative value. Also due to the standard deviation remaining the same for the small and large percent inhibition values, the %CV gave a poor indication of validity for the method, typically in the inhibition range from 0% to 10%. This is a very small fraction of the overall region of detection, and the results for all other regions of detection are well within the accepted criteria for a biological assay. Compared to the two diffusion assays, the proposed spectrophotometric assay offers a number of advantages including: increased repeatability (the ability to validate assay variance was enhanced with the 96-well plates); increased sensitivity (The sensitivity of the spectrophotometric method is shown in Table 2. This method continuously reports MIC values at greater dilutions than that of diffusion assays.); ease of automation and reduced manipulation (the spectrophotometer was networked to a data analyzer, capable of real time data capture and interpretation); and removal of subjective observations of inhibition zones (as with all manual reported techniques, the incorporation of dhuman observationT is not a factor with the proposed method).
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The results obtained from the spectrophotometric method demonstrate an ability to determine inhibition. However, the method also permits the detection and quantification of stimulation. The enhanced growth of Candida by honey has been documented in previous articles (Theunissen et al., 2001). The ability of the honey to stimulate C. albicans was also observed during this study. We have also noted the stimulation of some cultures at low manuka honey concentrations using this method. We are currently elucidating the mechanism by which this stimulation occurs.
5. Conclusion From the inter/intra-assay results (repeatability– reproducibility), limit of detection, sensitivity, and ability to detect stimulation/inhibition, there is a large disparity between the evaluated methods. The spectrophotometric method consistently demonstrates an improvement over current methods.
Acknowledgments We would like to acknowledge the project sponsors B. Braun/Hospicare Ireland Ltd. and Enterprise Ireland. References Ahn, C., Stiles, M.E., 1990. Antibacterial activity of lactic acid bacteria isolated from vacuum-packed meats. Journal of Applied Bacteriology 69, 302 – 310. Alderman, D.J., Smith, P., 2001. Development of draft protocols of standard reference methods for antimicrobial agent susceptibility testing of bacteria associated with fish diseases. Journal of Aquaculture 196, 211 – 243. Allen, K.L., Molan, P.C., Reid, G.M., 1991. Survey of the antibacterial activity of some New Zealand honeys. The Journal of Pharmacy And Pharmacology 43, 817 – 822. Archer, M.H., Dillon, V.M., Campbell-Platt, G., Owens, J.D., 1996. Effect of diacetyl on growth rate of Salmonella typhimurium determined from detection times measured in a micro-well plate photometer. Journal of Food Control 7, 63 – 67. Bogdanov, S., 1997. Nature and origin of the antibacterial substances in honey. Lebensmittel-Wissenschaft und-Technologie 30, 748 – 753. Casey, J.T., O’Cleirigh, C., Walsh, P.K., O’Shea, D.G., 2004. Development of a robust microtiter plate-based assay method
for assessment of bioactivity. Journal of Microbiological Methods 58, 327 – 334. Cooper, R.A., Molan, P.C., Harding, K.G., 1999. Antibacterial activity of honey against strains of Staphylococcus aureus from infected wounds. Journal of Royal Society of Medicine 92, 283 – 285. Cooper, R.A., Molan, P.C., Harding, K.G., 2002. The sensitivity to honey of Gram-positive cocci of clinical significance isolated from wounds. Journal of Applied Microbiology 93, 857. Gabhainn, S.N., Bergh, Ø., Dixon, B., Donachie, L., Carson, J., Coyne, R., Curtin, J., Dalsgaard, I., Manfrin, A., Maxwell, G., Smith, P., 2004. The precision and robustness of published protocols for disc diffusion assays of antimicrobial agent susceptibility: an inter-laboratory study. Aquaculture 240 (1–4), 1 – 18. Gabrielson, J., Hart, M., Jarelo¨v, A., Ku¨hn, I., McKenzie, D., Mo¨llby, R., 2002. Evaluation of redox indicators and the use of digital scanners and spectrophotometer for quantification of microbial growth in microplates. Journal of Microbial Methods 50, 63 – 73. Gellert, G., 1999. Sensitivity and significance of luminescent bacteria in chronic toxicity testing based on growth and bioluminescence. Journal of Ecotoxicol. Environ. Safety 45, 87 – 91. Hewitt, W., Vincent, S., 1988. Theory and Application of Microbiological Assay. Academic Press, pp. 38 – 79. Kuda, T., Shimizu, K., Yano, T., 2004. Comparison of rapid and simple colorimetric microplate assays as an index of bacterial count. Journal of Food Control 15, 421 – 425. Lichtenberg-Kraag, B., Hedtke, C., Bienefeld, K., 2002. Infrared spectroscopy in routine quality analysis of honey. Journal of Apidologie 33, 327 – 337. Lozano-Chiu, M., Nelson, Page W., Paetznick, Victor L., Rex, John H., 1999. Disk diffusion method for determining susceptibilities of Candida spp. to MK-0991. Journal of Clinical Microbiology 37, 1625 – 1627. Molan, P.C., 1992a. The antibacterial activity of honey: 1. The nature of the antibacterial activity. Bee World 73 (1), 5 – 28. Molan, P.C., 1992b. The antibacterial activity of honey: 2. Variation in the potency of the antibacterial activity. Bee World 73 (2), 59 – 76. Molan, P.C., 1992c. Medicinal uses for honey. Beekeepers Quarterly, 26. Molan, P.C., 1997. Finding New Zealand honeys with outstanding antibacterial and antifungal activity. New Zealand Beekeeper 4 (10), 20 – 26. Mundo, Melissa A., Padilla-Zakour, Olga I., Worobo, Randy W., 2004. Growth inhibition of food borne pathogens and food spoilage organisms by select raw honeys. International Journal of Food Microbiology 97 (1), 1 – 8. Parente, E., Brienza, C., Moles, M., Ricciardi, A., 1995. A comparison of methods for the measurement of bacteriocin activity. Journal of Microbiological Methods 22, 95 – 108. Piliouras, P., Ulett, Glen C., Robert, G., Hirst, Robert E., 2002. A comparison of antibiotic susceptibility testing methods for cotrimoxazole with Burkholderia pseudomalle. International Journal of Antimicrobial Agents 19, 427 – 429.
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95 Radwan, S., El-Esssay, A.A., Sarhan, M.M., 1984. Experimental evidence for the occurrence in honey of specific substances active against microorganisms. Zentralbatt Fur Mikrobiologie 139, 249 – 255. Rex, J.H., Pfaller, M.A., Walsh, T.J., Chaturvedi, V., EspinelIngroff, A., Ghannoum, M.A., Gosey, L.L., Odds, F.C., Rinaldi, M.G., Sheehan, D.J., Warnock, D.W., 2001. Antifungal susceptibility testing: practical aspects and current challenges. Journal of Clinical Microbiology Reviews 14, 643 – 658. Snow, M.J., Manley-Harris, M., 2004. On the nature of non-peroxide antibacterial activity in New Zealand manuka honey. Journal of Food Chemistry 84, 145 – 147. Swenson, J.M., Hill, BC., Thornsberry, C., 1989. Problems with the disk diffusion test for detection of vancomycin resistance in enterococci. Journal Of Clinical Microbiology 27, 2140 – 2142. Sykes, G., 1965. Disinfection and Sterilisation, 2nd edn. E.&F.N. Spon Ltd., London, p. 54. Taormina, Peter J., Niemira, Brendan A., Beuchat, Larry R., 2001. Inhibitory activity of honey against food borne pathogens as influenced by the presence of hydrogen peroxide and level of antioxidant power. International Journal of Food Microbiology 69, 217 – 225.
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Theunissen, F., Grobler, S., Gedalia, I., 2001. The antifungal action of three South African honeys on Candida albicans. Journal of Apidologie 32, 371 – 379. Turcotte, Carl, Lacroix, Christophe, Kheadr, Ehab, Grignon, Louis, Fliss, Ismaı¨l, 2004. A rapid turbidometric microplate bioassay for accurate quantification of lactic acid bacteria bacteriocins. International Journal of Food Microbiology 90, 283 – 293. Waites, Ken B., Crabb, Donna M., Duffy, Lnn B., 2003. Inhibitory and bactericidal activities of gemifloxacin and other antimicrobials against Mycoplasma pneumoniae. International Journal of Antimicrobial Agents 21, 574 – 577. Weston, R.J., Mitchell, Kevin R., Allen, Kerry L., 1999. Antibacterial phenolic components of New Zealand manuka honey. Journal of Food Chemistry 64, 295 – 301. Weston, Roderick J., Brocklebank, Lise K., Lu, Yinrong, 2000. Identification and quantitative levels of antibacterial components of some New Zealand honeys. Journal of Food Chemistry 70, 427 – 435. Willix, D.J., Molan, P.C., Harfoot, C.G., 1992. A comparison of the sensitivity of wound-infecting species of bacteria to the antibacterial activity of manuka honey and other honey. The Journal Of Applied Bacteriology 73, 388 – 394.
Use of a spectrophotometric bioassay for determination of microbial sensitivity to manuka honey Thomas Patton*, John Barrett, James Brennan, Noel Moran Institute of Technology Sligo, IT Sligo, Ballinode, Sligo, Ireland Received 15 November 2004; received in revised form 5 April 2005; accepted 11 April 2005 Available online 24 June 2005
Abstract The antimicrobial activity of manuka honey has been well documented (Molan, 1992a,b,c, 1997) [Molan, P.C., 1992. The antibacterial activity of honey. 1: the nature of the antibacterial activity. Bee World 73 (1) 5–28; Molan, P.C., 1992. The antibacterial activity of honey. 2: variation in the potency of the antibacterial activity. Bee World 73 (2) 59–76; Molan, P.C., 1992. Medicinal uses for honey. Beekeepers Quarterly 26; Molan, P.C., 1997. Finding New Zealand honeys with outstanding antibacterial and antifungal activity. New Zealand Beekeeper 4 (10) 20–26]. The current bioassays for determining this antimicrobial effect employ a well diffusion (Ahn and Stiles, 1990) [Ahn, C., Stiles, M.E., 1990. Antibacterial activity of lactic acid bacteria isolated from vacuum-packed meats. Journal of Applied Bacteriology 69, 302–310], (Weston et al., 1999) [Weston, R.J., Mitchell, K.R., Allen, K.L., 1999. Antibacterial phenolic components of New Zealand manuka honey. J. Food Chem. 64, 295–301] or disc diffusion (Taormina et al., 2001) [Taormina, Peter J., Niemira, Brendan A., Beuchat, Larry R., 2001. Inhibitory activity of honey against food borne pathogens as influenced by the presence of hydrogen peroxide and level of antioxidant power. Int. J. Food Microbiol. 69, 217–225] assay using zones of inhibition as indicators of bacterial susceptibility. The development of a 24-h spectrophotometric assay employing 96-well microtiter plates, that is more sensitive and more amenable to statistical analysis than the assays currently employed, was undertaken. This simple and rapid assay permits extensive kinetic studies even in the presence of low honey concentrations, and is capable of detecting inhibitory levels below those recorded for well or disc diffusion assays. In this paper, we compare the assay to both well and disc diffusion assays. The results we obtained for the spectrophotometric method MIC values show that this method has greater sensitivity than the standard well and disc diffusion assays. In addition, inter- and intra-assay variance for this method was investigated, demonstrating the methods reproducibility and repeatability. D 2005 Elsevier B.V. All rights reserved. Keywords: Microtiter; Spectrophotometric; % Inhibition; Disc diffusion; Manuka honey
1. Introduction * Corresponding author. E-mail address: [email protected] (T. Patton). 0167-7012/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.mimet.2005.04.007
The nature of the antimicrobial activity of manuka honey has been extensively reviewed to date (Mundo
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et al., 2004; Taormina et al., 2001, Weston et al., 1999; Molan, 1992a,b,c, 1997). The inhibitory activity has been attributed to several key properties of the honey including osmotic effect, naturally low pH, production of hydrogen peroxide (Weston et al., 1999; Willix et al., 1992), and also the presence of phenolic acids, lysozyme, and flavanoids (Cooper et al., 1999). With few exceptions, the majority of studies on manuka honey have used well or disc diffusion methods (Willix et al., 1992; Cooper et al., 2002) to evaluate and study activities. With these techniques, a sample material is placed into wells bored into an inoculated agar growth media, this diffuses into the agar around the well, and is assayed for an ability to produce a zone of inhibition. Alternatively, discs prepared from sterile absorbent material are soaked in test sample solution and placed onto an inoculated agar growth media. The samples diffuse into the agar around the disc and are assayed for an ability to produce a zone of inhibition. Inhibition zones are measured and used to determine the test material’s potency. Diffusion bioassays are comparatively basic and robust, but have limitations in their detection and quantification abilities at low concentrations and are open to subjective interpretation (Swenson et al., 1989). One major difficulty in honey research and applications is obtaining accurate and precise quantification using bioassays. The agar diffusion assay (ADA) is still the preferred method for honey bactericidal quantifications and is used for manuka honey production batch analysis and release procedures (Gribbles Analytical Laboratories). However, the subjective nature of this assay limits the interpretation of results. It is also time-consuming and laborious, requiring preparation and cooling of plates, boring of test wells in agar, and manual measurement of inhibition zones after 24 h of incubation. Results depend largely on technique and judgment, and the suggested precision cannot be obtained when the inhibition zone is unclear or not perfectly circular (Turcotte et al., 2004). The possibility of using infrared methods for quality analysis of honey has also been proposed (Lichtenberg-Kraag et al., 2002). Microbial growth, or inhibition of growth, can be detected using a variety of biological methods, including direct microscopic counts, absorbance (Bogdanov, 1997), and bioluminescence (Gellert, 1999), assays
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that incorporate a colorimetric (Gabrielson et al., 2002; Kuda et al., 2004) and fluorometric growth indicator, (Sykes, 1965) turbidity (Archer et al., 1996), dry weight, and zones of inhibition. New approaches for testing the susceptibilities of fungi are also being investigated (Lozano-Chiu et al., 1999). Efforts to standardise the variability between each method have also been made (Alderman and Smith, 2001). In this study, a spectrophotometric assay using 96well microtiter plates was compared to the standard methods of well/disc diffusion in order to evaluate the potential advantages of this bioassay for the evaluation of the antibacterial properties of manuka honey. Increased automation and throughput (efficiency) were also achieved by using the spectrophotometric assay, which can rapidly generate a large amount of data; detailed statistical analysis of results is also possible. This present study was also undertaken to investigate some of the aspects of the internal validity of the spectrophotometic protocol.
2. Materials and methods 2.1. Bacterial strains and growth conditions Bacillus cereus (NCIMB 8012), Escherichia coli (NCIMB 8545), Staphylococcus aureus (NCIMB 9518), and Candida albicans (NCIMB 3179) bacterial strains were grown on nutrient agar or nutrient broth. C. albicans was grown on sabaroud dextrose agar or in sabaroud dextrose broth. B. cereus was incubated at 30 8C. All other strains were incubated at 37 8C. Bacterial growth was monitored by measuring the culture optical density (OD) in a spectrophotometer (Anthos 2010) at a wavelength of 620 nm. 2.2. Honey preparation Comvita Manuka Care 18+ (UMF) honey was used in this study. Honey was stored in the dark at room temperature. The initial honey dilution preparation was a 50% vol/vol in nutrient broth. Twelve serial 1:2 dilutions of the 50% vol/vol preparation were made in nutrient broth for bacterial inhibition testing, giving the lowest concentration value of 0.01%. For assay of C. albicans, honey preparations of 50%,
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40%, 30%, 20%, and 10% vol/vol in sabaroud broth were prepared. 2.3. Disc diffusion Nutrient or sabaroud dextrose agar plates were inoculated by swabbing overnight cultures onto the surface of agar plates. Plates were allowed to stand at room temperature for 15 min before discs were applied. Sterile absorbent discs (8.2 mm diameter) were placed into honey concentrations for 10 min before being applied directly to inoculated agar plates. Control sterile discs and methylene blue dye-soaked discs were also applied. Plates were incubated for 24 h and zones of inhibition were measured using an Autodata automatic zone reader with a tolerance (F0.1 mm). The diameter of zones including the diameter of the discs (8.2 mm) was recorded. 2.4. Well diffusion assay The agar plates were prepared as for the disc assay. Wells 8.2 mm diameter were bored into the surface of the agar. One hundred and eighty microlitres of diluted honey was placed into each well. A control of methylene blue dye was used. Plates were incubated for 24 h and zones of inhibition were measured using an Autodata automatic zone reader. The diameter of zones, including the diameter of the well (8.2 mm), was recorded.
dilution was applied to wells of a flat-bottom 96well microtiter plates with lid to prevent cross contamination (Costar, Corning Ltd. NY). Control wells received 200 Al of 5% culture inoculated nutrient or sabaroud broth. Optical density was determined in a spectrophotometer at 620 nm prior to incubation, T 0. Plates were incubated for 24 h in the dark on a Certomat MO orbital shaker at 100 rpm to prevent adherence and clumping. After 24 h, plates were again read in a spectrophotometer at 620 nm, T 24. 2.6. Analysis of results The OD for each replicate at T 0 was subtracted from the OD for each replicate at T 24. The adjusted OD of each control well was then assigned a value of 100% growth. The growth inhibition for the test wells at each dilution was determined using the formula: Percent Inhibition = 1 (OD test well/OD of corresponding control well) 100 for each row of the 96well plate (e.g., OD row 1, column 1, well 1 (test) was divided by the OD value of row 1, column 12, well 12 (control)). This yielded eight replicate inhibition values for each honey dilution. The variation associated with the average calculated inhibition values for replicate wells was determined as percent coefficient of variation (%CV). Where the resulting measurement recorded a negative inhibition value (growth promotion), this was reported as stimulation using the formula: Percent Growth ¼ ðOD test=OD controlÞ 100:
2.5. Spectrophotometric assay Honey dilutions were inoculated with test culture to give a 5% vol/vol concentration. Two hundred microlitres of each dilution in eight replicates per
Using this formula prevented a negative coefficient of variation being reported. The antibacterial detection limits for each method were compared. This is reported as minimum inhibi-
Table 1 Statistical analysis of turbidometric method inter/intra-assay response correlation/variation
S. aureus B. cereus E. coli C. albicans
Correlation coefficient
ANOVA (between groups)
ANCOVA (between groups)
% Inhibition * concentration of honey
Variable = % inhibition
Variable = % inhibition; covariate = concentration of honey
r value
p value
f value
p value
p value (days)
p value (test number)
p value (days * test number)
0.634 0.647 0.780 0.954
0.001 0.001 0.001 0.001
0.105 0.133 0.126 0.009
0.901 0.876 0.881 0.991
0.849 0.808 0.739 0.921
0.992 0.999 0.995 0.990
1.000 1.000 1.000 0.998
100.00
% Inhibition
60.00
40.00
40.00
20.00
20.00
-1.5
-1.0
0.1
0.6
1.1
1.6
0.00 -1.95 -20.00
Bacillus cereus
-0.95
-0.45
0.05
0.55
1.05
1.55
1.05
1.55
Log 10 conc honey (% v/v)
100.00
Bacillus cereus
80.00
60.00
60.00
40.00
40.00
20.00
20.00
0.00 -1.95 -20.00
-1.45
Log 10 conc honey (% v/v)
100.00 800.00
-0.5
Escherichia coli
-40.00
-40.00
% Inhibition
80.00
60.00
0.00 -2.0 -20.00
Escherichia coli
100.00
-1.45
-0.95
-0.45
0.05
0.55
1.05
1.55
0.00 -1.95 -20.00
Candida albicans Candida albicans
-1.45
-0.95
-0.45
0.05
0.55
-40.00
-40.00 Log 10 conc honey (% v/v)
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
80.00
Staphylococcus aureus Staphylococcus aureus
Log 10 conc honey (% v/v)
Fig. 1. Dose–response curve for the antibacterial activity of manuka honey against species. The activity is expressed as the percentage inhibition of growth of the culture, with inhibition being determined by comparison to control well after 24 h of incubation. Error bars indicate the standard deviation of inter-assay results (n = 120).
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tory concentration, expressed as MIC0 (the highest concentration of test material which results in no inhibition of growth), MIC50 (concentration of test material which results in 50% inhibition of growth, also known as the median response), and MIC100 (the lowest concentration of test material which results in 100% inhibition of growth). For the diffusion assays, 100% inhibition was taken at the highest concentration of test material. The statistical significance for the spectrophotometric method was determined using SPSS. Specifically, correlation coefficient, ANCOVA, and ANOVA with Tukey post hoc analysis were determined (Table 1).
13.5%. The MIC50 value for E. coli was 5.6% vol/vol, B. cereus 2.00%. S. aureus 0.78%, and C. albicans 25.1%. The MIC100 value for E. coli was 12.5% vol/ vol, B. cereus 3.125%, S. aureus 6.25%, and C. albicans 40.0%, The reported results for each measurement are shown in Table 3 with standard deviation and %CV values. All data were statistically analysed for significance to determine overall differences between groups, for all measurements p N 0.05 (Table 1). 3.2. Well diffusion assay Antibacterial activity was determined by measuring the zones of inhibition. From these data, a dose– response plot was calculated by plotting the diameter of the zone squared versus the log of the honey concentration (Fig. 2). From these plots, the minimum detectable concentration (MIC0) value was calculated by extrapolation of the regression line, which intersects the squared diameter of the well plus 0.1 mm detection buffer (minimum detectable area of inhibition by the naked eye). The R 2 values are reported. B. cereus gave a linear response, although an exponential curve fitted best to S. aureus and E. coli. C. albicans failed to produce any zones detectable on all tests. The MIC0 result for E. coli was 6.3% vol/vol, B. cereus 10%, and S. aureus 3.7%. The MIC100 was taken as the highest concentration of honey used for the assay. The MIC50 was calculated as midway between these two values for E. coli (24.5% vol/vol): B. cereus 21.9% and S. aureus 22.6% (Table 2). The average zone of inhibition measurement values for each test concentration are shown together with the standard deviation and %CV (Table 4). The highest %CV
3. Results 3.1. Spectrophotometric assay The dose–response curves obtained from plotting the log of the concentration of honey against the resulting percent inhibition of bacterial growth are shown in Fig. 1. The concentration range of honey used for C. albicans was from 50% to 10% vol/vol, as this was shown to be the concentration range where an inhibitory effect could be observed. C. albicans was the only species that demonstrated a near-linear relationship between honey concentration and percent inhibition, with the regression analysis giving an R 2 value of 0.954. For B. cereus, S. aureus, and E. coli, the response curves were distinctly non-linear; the correlation coefficient values for the method are shown in Table 1. From the plotted results, the honey MIC values for each organism were determined (Table 2). The MIC0 value for E. coli 0.18% vol/vol, B. cereus 0.34%, S. aureus 0.05%, and C. albicans
Table 2 The concentration percent vol/vol of manuka honey required in growth medium in spectrophotometric assay (SA) and for well and disc diffusion assays to inhibit the growth of species of bacteria tested over a 24-h incubation period Bacterial species
Escherichia coli Staphylococcus aureus Bacillus cereus Candida albicans
MIC0 (% vol/vol)
MIC50 (% vol/vol)
MIC100 (% vol/vol)
Discs
Wells
SA
Discs
Wells
SA
Discs
Wells
SA
(5.4) (5.2) (11.5) N/A
(6.3) (3.7) (10.0) N/A
0.18 0.05 0.34 13.50
22.4 25.7 24.0 N/A
24.5 22.6 21.9 N/A
5.60 0.78 2.00 25.10
50 50 50 N/A
50 50 50 N/A
12.5 6.25 3.125 40.0
Values shown in parentheses were obtained by extrapolation.
Table 3 Spectrophotometric results used to plot dose–response curves as the percent inhibition vs. log10 concentration honey, X-bar, standard deviation (S.D.), and coefficient of variation (%CV) for each value Bacterial strain
0.012% vol/vol manuka honey
r-day (n = 120 used for graph) Bacillus cereus r-day (n = 120 used for graph)
X¯
0.098
0.391
X¯
S.D.
%CV
X¯
S.D.
%CV
%CV
X¯
%CV
X¯
102.60a 106.34a 106.82a 105.26
5.72 10.78 6.66 8.18
5.57 10.14 6.23 7.77
103.75a 107.06a 110.56a 107.12
8.70 6.57 11.54 9.51
8.38 6.14 10.44 8.88
97.05a 94.95a 97.83a 96.61
8.73 6.84 5.54 7.20
8.99 7.20 5.66 7.45
24.18 27.68 27.46 26.44
6.40 7.97 8.07 7.62
26.46 28.77 29.40 28.81
48.93 52.64 48.14 49.91
6.54 5.13 6.14 6.23
13.36 9.74 12.75 12.49
50.59 52.01 45.27 49.29
5.39 4.36 6.66 6.23
10.65 8.39 14.72 12.64
1 2 3 X¯
107.79a 110.84a 108.39a 109.00
9.40 8.41 11.65 10.12
8.72 7.59 10.75 9.28
131.01a 111.51a 125.08a 122.53
32.36 9.11 15.45 12.68
24.70 8.17 12.35 10.35
123.70a 111.21a 127.11a 120.67
14.68 7.96 12.04 13.64
11.87 7.16 9.47 11.30
117.72a 109.93a 113.08a 113.58
15.13 9.53 11.81 22.71
12.85 8.66 10.44 20.00
96.43a 96.63a 99.26a 97.44
10.52 10.54 9.24 9.92
10.91 10.91 9.31 10.18
25.32 25.32 16.12 22.26
10.50 10.50 9.46 9.94
41.49 41.49 58.69 44.67
1 2 3 X¯
107.79a 110.84a 108.39a 109.00a
5.70 5.40 5.30 5.47
5.29 4.87 4.89 5.02
131.01a 111.51a 125.08a 122.53a
6.20 7.40 6.20 6.60
4.73 6.64 4.96 5.39
123.70a 111.21a 127.11a 120.67a
5.50 8.20 6.80 6.83
4.45 7.37 5.35 5.66
117.72a 109.94a 113.08a 113.58a
6.70 7.60 6.30 6.87
5.69 6.91 5.57 6.05
96.43a 96.63a 99.26a 97.44a
7.40 6.70 5.20 6.43
7.67 6.93 5.24 6.60
4.55 5.37 5.67 5.20
3.70 3.10 4.20 3.67
81.32 57.76 74.07 70.57
S.D.
S.D.
%CV
10.00 X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
115.04a 118.35a 109.61a 114.33a
9.59 9.18 8.17 9.63
8.33 7.75 7.46 8.42
24.51 20.34 20.36 21.74
12.26 8.55 6.82 9.61
50.02 42.04 33.51 44.21
75.71 77.08 69.25 74.01
6.08 3.54 3.24 5.60
8.03 4.59 4.68 7.57
98.51 98.18 95.42 97.37
1.33 0.96 1.77 1.96
1.35 0.98 1.86 2.01
99.80 100.57 99.57 99.98
2.03 1.74 1.90 1.93
2.04 1.73 1.91 1.93
Inter-day (n = 120 used for graph)
40.00
S.D.
Intra-day (n = 60) 1 2 3 X¯
30.00
S.D.
% vol/vol manuka honey
Candida albicans
20.00
%CV
X¯
0.195
50.00
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
r-day (n = 120 used for graph) Escherichia coli
0.49
1 2 3 X¯
Intra-day (n = 60) Staphylococcus aureus
0.024
Each data point represents the mean value recorded for five different samples over three different days. a Values reported as percent growth.
89
90
Bacterial strain 0.781 Intra-day (n = 60)
X¯
1.563 S.D.
%CV X¯
3.13 S.D. %CV X¯
6.25 S.D. %CV X¯
25.00
50.00
S.D. %CV X¯
S.D. %CV X¯
S.D. %CV X¯
S.D. %CV
17.64 10.85 22.33 19.36
96.86 96.57 98.11 97.18
2.36 2.72 2.14 2.49
2.43 2.81 2.18 2.56
97.13 95.20 98.60 96.98
2.28 5.06 3.16 3.91
2.34 5.31 3.20 4.03
95.50 94.51 97.86 95.96
4.15 6.99 4.86 5.60
4.34 7.39 4.96 5.84
93.42 93.48 96.76 94.55
6.65 4.66 8.20 6.79
7.12 4.99 8.48 7.18
20.12 20.12 22.15 21.75
54.88 54.88 58.39 56.05
5.85 5.85 6.90 7.03
10.65 10.65 11.81 12.54
101.69 101.69 102.00 101.79
1.30 1.30 0.88 1.81
1.27 1.27 0.86 1.78
102.91 102.91 103.21 103.01
2.25 2.25 1.42 1.67
2.19 2.19 1.38 1.62
103.50 103.50 105.94 104.31
3.39 3.39 2.08 3.08
3.28 3.28 1.96 2.95
4.10 100.64 4.30 2.93 99.59 2.63 3.86 100.59 7.60 3.63 100.27 4.84
4.27 2.64 7.56 4.83
101.39 100.74 104.40 102.18
5.40 4.06 5.50 4.99
5.33 4.03 5.27 4.88
97.33 96.88 102.95 99.05
7.03 6.43 5.40 6.29
7.22 6.64 5.25 6.35
98.69 99.56 102.53 100.26
6.47 5.14 4.20 5.27
6.56 5.16 4.10 5.26
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Staphylococcus 44.95 6.98 15.53 45.24 5.60 12.38 43.55 7.68 aureus 54.84 4.82 8.79 54.00 4.23 7.83 52.11 5.66 46.15 6.19 13.42 43.31 7.91 18.27 41.87 9.35 r-day (n = 120 48.65 7.47 15.35 47.52 7.65 16.09 45.84 8.87 used for graph) Escherichia 30.18 10.01 33.15 30.26 8.64 28.56 38.92 7.83 coli 30.18 10.01 33.15 30.26 8.64 28.56 38.92 7.83 21.32 8.62 40.41 26.77 7.71 28.79 35.43 7.85 r-day (n = 120 27.23 10.92 40.12 29.10 9.83 33.78 37.76 8.21 used for graph) Bacillus 16.54 4.10 24.79 27.11 5.87 21.64 95.07 3.90 cereus 17.76 4.70 26.46 24.94 5.77 23.12 97.19 2.85 16.51 5.40 32.71 25.55 4.30 16.83 101.10 3.90 r-day (n = 120 16.94 4.73 27.95 25.87 5.31 20.53 97.79 3.55 used for graph)
12.50
417.2
267.2 217.2 167.2 117.2 67.2 0.60
1.60
Log conc. of honey (%v/v) circle = disc triangle = well
367.2 317.2 267.2 217.2 167.2 117.2
1.10
y = 33.282e1.0634x R2 = 0.9721 y = 16.373e1.8781x R2 = 0.9964
67.2 0.50
Bacillus cereus 517.2 Mean zone diameter^2 (mm)
Mean zone diameter*2 (mm)
Mean zone diameter*2 (mm)
317.2
Staphylococcus aureus 467.2 417.2 367.2 317.2 267.2 217.2 167.2 117.2
67.2 0.9 1.00 1.50 Log conc. honey (% v/v)
circle = disc y = 22.904e1.568x R2 = 0.975 Triangle = well y = 28.582e1.6006x R2 = 0.9867
circle = disc triangle = well
1.1
1.3 1.5 1.7 Log conc honey (% v/v)
y = 514.86x - 475.02 y = 712.41x - 643.87
R2 = 0.9933 R2 = 0.9935
Fig. 2. Dose–response curves for the antibacterial activity of manuka honey against three species of bacteria. The activity is expressed as mean zone diameter squared vs. the log10 of percent concentration. (E) Well diffusion assay; (.) disc diffusion assay (n = 60); results of 3-day assay.
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Escherichia coli 367.2
91
92
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
Table 4 Inter/intra-assay results from diffusion and disc methods, average, standard deviation (S.D.), and coefficient of variation (%CV) for each value are reported Diffusion assay
% vol/vol manuka honey
Inter-assay results
Method
50
25
12.5
Bacterial species
Inter-day (n = 20)
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
X¯
S.D.
%CV
Escherichia coli
Wells
18.2 20.0 20.4 19.6 13.8 15.5 14.4 14.6 21.8 21.0 19.7 20.8 18.7 19.6 17.9 18.8 27.0 21.6 22.2 23.6 22.4 19.1 18.9 20.2
1.0 1.1 1.1 1.5 0.9 1.1 0.8 1.2 1.2 2.0 1.2 1.7 1.2 1.4 0.5 1.3 0.9 0.8 1.0 2.6 0.8 1.3 0.7 1.9
5.4 5.7 5.4 7.4 6.3 6.8 5.8 7.9 5.4 9.5 6.2 8.3 6.5 7.1 3.1 6.9 3.4 3.8 4.5 11.2 3.4 6.7 3.9 9.3
14.6 15.2 16.4 15.4 11.7 11.4 11.5 11.6 17.0 17.9 15.4 16.8 13.8 14.7 13.4 14.0 20.7 18.3 18.9 19.3 15.9 14.9 14.7 15.2
1.1 1.2 1.5 1.5 1.2 0.9 1.1 1.1 1.3 0.9 0.6 1.4 0.7 0.9 1.0 1.0 1.7 0.8 1.0 1.6 1.4 1.5 0.9 1.4
7.3 8.1 9.1 9.5 10.3 7.6 9.8 9.2 7.4 4.7 3.7 8.4 5.3 6.0 7.2 7.3 8.4 4.5 5.5 8.3 8.9 10.1 6.1 9.1
11.8 11.3 11.4 11.5 11.6 10.1 10.2 10.6 12.1 12.3 12.2 12.2 10.7 10.7 10.6 10.7 11.1 10.7 12.1 11.3 10.1 9.3 10.1 9.9
0.9 0.9 0.7 0.9 0.8 0.7 0.7 1.0 1.1 0.7 0.9 0.9 0.6 0.4 0.7 0.6 0.4 1.0 0.8 0.9 0.8 0.5 0.6 0.7
7.7 8.3 6.3 7.6 6.7 6.8 6.6 9.1 9.1 5.4 7.6 7.4 5.3 4.2 6.6 5.3 3.9 8.9 6.4 8.3 7.7 5.8 5.7 7.4
8.4 8.5 8.4 8.4 9.3 8.7 8.5 8.9 10.8 10.1 10.2 10.4 9.3 9.1 9.6 9.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.1 0.2 0.2 0.2 0.4 0.5 0.7 0.6 0.5 0.7 0.5 0.6 0.3 0.4 0.5 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.5 2.6 2.6 2.3 4.3 5.3 7.7 6.9 4.2 7.1 4.6 6.2 3.0 3.9 4.8 4.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Intra-day (n = 60) Discs
Staphylococcus aureus
Intra-day (n = 60) Wells
Intra-day (n = 60) Discs
Bacillus cereus
Intra-day (n = 60) Wells
Intra-day (n = 60) Discs
Intra-day (n = 60)
1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯ 1 2 3 X¯
6.25
Candida albicans is not shown as no inhibition was observed at the concentrations used.
observed was 11.2%, indicating good inter/intraassay reproducibility/repeatability. 3.3. Disc diffusion assay Antibacterial activity was again determined by measuring the zones of inhibition, as for the well diffusion assay. The values were plotted to produce the dose–response curves for each culture (Fig. 2). The MIC values were obtained as for the well diffusion assay. The MIC0 result for E. coli was 5.4% vol/vol, B. cereus 11.5%, and S. aureus 5.2%. The MIC100 was taken as the highest concentration of honey used for the assay. The MIC50 was calculated as midway between these two values. For E. coli, this was 22.4% vol/vol, for B. cereus 24.0%, and for S. aureus 25.7% (Table 2). The variations of zone measurements are reported (Table 4).
4. Discussion When determining microbial susceptibility to inhibitory compounds, a decision must be made as to which method will be employed. The choice of method includes agar diffusion, disc diffusion, broth diffusion, or variants of these methods. The agar/disc diffusion method is regarded as the method of choice for inhibition tests (Lozano-Chiu et al., 1999; Gabhainn et al., 2004). This method has also become popular particularly in the area of antibiotic testing (Hewitt and Vincent, 1988). More recently, there has been an increased interest in microtiter plate assays (Kuda et al., 2004; Casey et al., 2004; Turcotte et al., 2004; Archer et al., 1996). Both the agar and disc diffusion methods can be unreliable in certain situations due to the subjectivity associated with visual determinations (Swenson et al., 1989; Piliouras et al., 2002). In addition, both these methods have asso-
T. Patton et al. / Journal of Microbiological Methods 64 (2006) 84–95
ciated time, sample, and cost implications. With the publishing of the NCCLS M27 methodology (Rex et al., 2001) for antifungal susceptibility testing using photometric analysis, this has established the trend towards a turbidometric system for mould and yeast investigation methods. The inhibition of bacterial growth can be determined by simple single tube optical density measurement (Parente et al., 1995). The use of a 96-well microtiter plate assay system facilitates inhibition testing as it is less time-consuming and cheaper, and multiple samples can be tested on a single plate. The microtiter plate assay is also less subjective. The methods for disc and well diffusions were those used by previous studies (Weston et al., 2000; Willix et al., 1992; Allen et al., 1991; Lozano-Chiu et al., 1999; Gabhainn et al., 2004); however, the cultures were overlaid onto the agar surface instead of seeding the agar, so that the microorganisms were in an early growth phase enabling easier detection of inhibition (Casey et al., 2004). The methods for presenting results and determining MIC(s) were those adopted by previous studies (Weston et al., 1999). Developing the procedure for the spectrophotometric methodology was based on previous articles (Casey et al., 2004; Turcotte et al., 2004; Archer et al., 1996; Theunissen et al., 2001; Waites et al., 2003). The amount of bacterial growth occurring in the 24-h period was measured (end point) rather than the rate of growth, due to reported experiments that at low concentrations of honey some species are suppressed in the growth rate by having prolonged lag phases (Willix et al., 1992). As small changes in OD were repeatedly detected, reporting the percent inhibition using the formula enabled accurate measurement on normally undetectable changes in bioactivity. Reporting the minimum and median response concentrations was chosen along with 100% MIC as this is derived from a series of determinations as opposed to a single isolated point (Sykes, 1965). Results from the well, disc, and spectrophotometric assays show that the microorganisms vary in their sensitivity to manuka honey. This variation in sensitivity has been described previously (Mundo et al., 2004; Radwan et al., 1984). For the disc and well methods, the MIC values we obtained are similar to earlier work done on this material (Cooper et al., 2002). The MIC50 for the spectrophotometric method
93
correlates with previous work using a turbidometric method (Willix et al., 1992), albeit by a different technique of calculation. Overall, the MIC results were found to be similar to previously published material. No inhibition of the yeast was detected by either diffusion method. However, the spectrophotometric assay gave a near linear response demonstrating its sensitivity. The plots obtained for the bacterial species using the spectrophotometric method have a distinct nonlinear relationship. There is evidence of a (two-stage) process of inhibition (Snow and Manley-Harris, 2004), something that requires further investigation. The aim of the study was to determine if a spectrophotometric method could be used to detect inhibition by manuka honey. The method was compared with the two standard diffusion methods for comparison purposes. The variance associated with each method was evaluated; for the concentrations where inhibition for each method was reported, the CV values were found not to be significantly different when compared by ANCOVA ( p N 0.05). As the mean percent inhibition values fell to near zero or below, the method for reporting the %CV was modified from inhibition to growth to avoid reporting a negative value. Also due to the standard deviation remaining the same for the small and large percent inhibition values, the %CV gave a poor indication of validity for the method, typically in the inhibition range from 0% to 10%. This is a very small fraction of the overall region of detection, and the results for all other regions of detection are well within the accepted criteria for a biological assay. Compared to the two diffusion assays, the proposed spectrophotometric assay offers a number of advantages including: increased repeatability (the ability to validate assay variance was enhanced with the 96-well plates); increased sensitivity (The sensitivity of the spectrophotometric method is shown in Table 2. This method continuously reports MIC values at greater dilutions than that of diffusion assays.); ease of automation and reduced manipulation (the spectrophotometer was networked to a data analyzer, capable of real time data capture and interpretation); and removal of subjective observations of inhibition zones (as with all manual reported techniques, the incorporation of dhuman observationT is not a factor with the proposed method).
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The results obtained from the spectrophotometric method demonstrate an ability to determine inhibition. However, the method also permits the detection and quantification of stimulation. The enhanced growth of Candida by honey has been documented in previous articles (Theunissen et al., 2001). The ability of the honey to stimulate C. albicans was also observed during this study. We have also noted the stimulation of some cultures at low manuka honey concentrations using this method. We are currently elucidating the mechanism by which this stimulation occurs.
5. Conclusion From the inter/intra-assay results (repeatability– reproducibility), limit of detection, sensitivity, and ability to detect stimulation/inhibition, there is a large disparity between the evaluated methods. The spectrophotometric method consistently demonstrates an improvement over current methods.
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