Download Honey as an antimicrobial agent against multi

Honey as an antimicrobial agent against
multi-drug resistant Gram negative
bacterial rods
By
Rahma Ali Saleh Al-Maaini
This dissertation is submitted in partial fulfilment of the
degree of MPhil in Biomedical Sciences
(Microbiology)
School of Health Sciences
University of Wales Institute Cardiff, UK
May 2011
1
Contents
Title
I
Contents
II
Declaration
XI
Dedication
XII
Acknowledgments
XIII
Poster Presentation
XIV
Index of Tables
XV
Index of Figures
XIX
Abbreviations
XXVI
Abstract
XXX
Chapter 1: Introduction
1
1.1 Antimicrobial Resistance:
2
1.2 Extended Spectrum Beta-Lactamases (ESBLs):
3
1.2.1 Classification of ESBL:
4
1.2.1.1 Functional Classification:
4
1.2.1.2 Molecular Classification:
4
1.2.2 Types of ESBL:
5
1.2.2.1 TEM-type ESBLs (class A)
5
1.2.2.2 SHV-type ESBLs (class A)
6
1.2.2.3 CTX-M type ESBLs (class A):
6
1.2.2.4 OXA-type ESBLs (class D)
7
1.2.2. 5 AmpC-type ESBLs (class C)
7
1.2.2.6 Carbapenemases (class A, B, D)
8
2
1.2.3 Emergence of ESBL:
9
1.2.4 Risk factor for ESBL:
9
1.2.5 Treatment of ESBLs:
10
1.3 Acinetobacter:
10
1.3.1 Taxonomy and Historical Features of Acinetobacter:
11
1.3.2 Laboratory Diagnosis:
12
1.3.3 Clinical features of Acinetobacter infections:
12
1.3.4 Pathogenesis of Acinetobacter infection:
14
1.3.4.1 The production of exopolysaccharide:
15
1.3.4.2 Quorum-sensing:
15
1.3.4.3 The property of adhesion to human epithelial cells via
15
the capsule or fimbriae.
1.3.4.4 Surface and mitochondrial porins:
16
1.3.5 Emergence of Resistance:
17
1.3.6 Carbapenem action on Acinetobacter:
18
1.3.7 Mechanisms of Carbapenem Resistance:
18
1.3.8 Acinetobatcer Treatment with Honey:
20
1.4 Alternative Antimicrobial Therapies:
21
1.4.1 Ancient Use of Honey as a Medicine:
22
1.4.2 Honey as a Modern Medicine:
22
1.4.3 Antimicrobial Activity of Honey:
24
1.4.4 Chemical composition of honey:
26
3
1.4.5 Factors Contributing Antibacterial Properties of Honey:
27
1.4.5.1 Osmotic effect:
28
1.4.5.2 Acidity
28
1.4.5.3 Hydrogen peroxide production
29
1.4.5.4 Non-peroxide Components
30
1.4.5.5 Antioxidant activity:
31
1.4.5 Manuka Honey:
34
1.4.6 Omani Honey:
36
1.5 Aims and Objectives:
39
Chapter 2: Materials & Methods:
40
2.1 Characterization of test organisms:
49
2.1.1 Confirmation of the identity of test
organisms:
2.1.2 Antibiotics sensitivities: Extended Spectrum Beta- lactamases
49
49
test (ESBLs):
2.1.2.1 Inoculum preparation:
49
2.1.2.2 Disc application:
49
2.1.2.3 Screening test for ESBL:
50
2.1.2.4 Phenotypic confirmatory test for ESBL:
50
2.1.2.5 ESBL/AmpC* confirmation test:
50
2.2 Characterization of honey samples:
51
4
2.2.1 Honey sample collection:
51
2.2.2 Bioassay of antibacterial activity:
51
2.2.2.1 Phenol standards preparation for bioassay:
52
2.2.2.3 Inoculum preparation:
52
2.2.2.4 Plate preparation:
52
2.2.2.5 Sample preparation:
53
2.2.2.6 Samples and standards application:
53
2.2.2.7 Zone measurement:
54
2.2.2.8 Calculation of antibacterial activity of honey:
54
2.2.3 Determination of pH:
55
2.2.4 Sugar and water content:
55
2.2.5 Hydroxymethylfurfural (HMF) concentration:
56
2.2.6 Protein content:
56
2.2.7 Colour:
58
2.2.8 Pollen analysis:
58
2.2.9 Total phenolic content:
59
2.2.9.1 Reagent/standard preparation:
59
2.2.9.2 Assay method:
60
2.2.10 Free radical activity of honey:
61
2.2.10.1 Reagent / Standard Preparations
61
5
2.2.10.2 Assay procedure:
62
2.3 Determination of antibacterial activity of honey samples against test
62
cultures:
2.3.1 Minimum Inhibitory Concentration (MIC) method:
2.3.1.1 Agar incorporation method:
62
62
2.3.1.1.1 Honey selection:
62
2.3.1.1.2 Inoculum preparation:
63
2.3.1.1.3 Plates preparation:
63
2.3.1.1.4 Honey preparation:
63
2.3.1.1.5 Plates reading (MIC determination):
64
2.3.1.2 Broth dilution method:
2.3.1.2.1 Honey selection:
65
2.3.1.2.2 Honey dilution:
65
2.3.2.1.3 Microtitre plate inoculation:
65
2.3.2.1.4 Visual inspection of MIC:
66
2.3.2.1.5 Spectrophotometric determination of MIC:
66
2.3.2 Minimum Bactericidal concentration (MBC) method:
66
2.4 Time Kill Curve Assay:
67
2.4.1 Strain selection:
67
2.4.2 Time-kill curve:
67
6
2.5 Effect of honey on bacterial structure:
68
2.5.1 Scanning Electron Microscopy (SEM):
68
2.5.1.1 Bacterial Selection for SEM
68
2.5.1.2 Preparation of cells in the exponential phase of growth:
69
2.5.1.3 Preparation of cells for scanning electron microscopy:
69
2.5.2 Transmission Electron Microscopy (TEM):
70
2.5.2.1 Bacterial Selection for TEM
70
2.5.2.2 Preparation of cells for transmission electron
71
microscopy:
2.5.2.3 Pallet trimming and sectioning:
71
2.5.2.4 Staining of thin sections
71
2.6 Effect of honey on bacterial proteins:
72
2.6.1 Two Dimensional Gel Electrophoresis:
72
2.6.1.1 Buffers preparation:
73
2.6.1.2 Cell preparation:
74
2.6.1.3 Protein determination of Acinetobacter extracts for each
74
extract:
2.6.1.4 Rehydration and sample application:
76
2.6.1.5 Rehydration in the PROTEAN® IEF Focusing Tray:
76
2.6.1.6 Equilibration and SDS-PAGE:
77
7
2.6.1.7 Staining and gel visualising:
77
2.7 Statistical analysis of the data:
78
Chapter 3: Results
79
3.1 Confirmations of the identity and antibiotics sensitivities of test
80
organisms:
3.2 Characterization of honey samples:
88
3.2.1 Determination of antibacterial activity:
88
3.2.2 Chemical & physical analysis of honey samples
91
3.2.2.1 Pollen analysis
94
3.2.2.2 Antioxidant activity assay:
96
3.3 Determination of antibacterial activity of honey samples against test
97
cultures:
3.3.1 Minimum Inhibitory Concentration (MIC) and Minimum
97
Bactericidal Concentration (MBC) of manuka honey:
3.3.1.1 Agar incorporation method:
97
3.3.1.2 Broth dilution method:
99
3.3.2 Sensitivity of MDR and ESBLs to Omani honey:
3.4 Time Kill Curves
107
115
3.4.1 Inhibition of test organisms by manuka honey monitored by
115
optical density:
3.4.2 Inhibition of test organisms by manuka honey monitored by total
8
119
viable count
3
3.4.2.1 Acinetobacter spp
120
3.4.2.2 E.coli
122
3.4.2.3 Klebsiella
124
3.4.2.4 Citrobacter
126
3.4.2.5 Enterobacter
128
3.4.2.6 Serratia
130
3.5 Effect of honey on bacterial structure:
134
3.5.1 Growth Curves:
134
3.5.2 Scanning Electron Microscopy (SEM):
138
3.5.2.1 SEM of Acinetobacter
138
3.5.2.2 SEM of E.coli
148
3.5.2.3 SEM of Klebsiella
151
3.5.2.4 SEM of Enterobacter
160
3.5.2.5 SEM of Citrobacter
165
3.5.2.6 SEM of Serratia
170
3.5.2 Transmission Electron Microscopy (TEM):
174
3.5.2.1 TEM for Acinetobacter:
174
3.5.2.2. TEM for E.coli:
179
9
3.6 Effect of honey on bacterial proteins:
184
3.6.1 Two Dimensional Gel Electrophoresis:
184
Chapter 4: Discussion
186
4.1 Antibacterial activity of honey samples:
187
4.2. Chemical & physical analysis of honey samples
189
4.3 Effect of honey samples against test cultures:
193
4.4 Inhibition of test organisms by manuka honey using time kill curve assay
201
4.5 Effect of honey on bacterial structure:
202
4.6 Further investigations:
213
Chapter 5: References
215
10
Declaration
This work has not previously been accepted in substance for any degree and is
not being concurrently submitted in candidature for any degree.
Signed ...................................................................... (candidate)
Date ..........................................................................
STATEMENT 1
This thesis is the result of my own investigations, except where otherwise
stated. Where correction services have been used, the extent and nature of the
correction is clearly marked in a footnote(s).
Other sources are acknowledged by footnotes giving explicit references. A
bibliography is appended
Signed ..................................................................... (candidate)
Date .........................................................................
STATEMENT 2
I hereby give consent for my thesis, if accepted, to be available for
photocopying and for inter-library loan, and for the title and summary to be
made available to outside organisations.
Signed ..................................................................... (candidate)
Date .........................................................................
I hereby give consent for my thesis, if accepted, to be available for
photocopying and for inter-library loans after expiry of a bar on access
approved by UWIC.
Signed ..................................................................... (candidate)
Date .............................
11
Dedication
This project is lovingly dedicated to my dear parents whose
legacy. I will treasure throughout my life. They thought me to
strive and do my best in all things that I undertake. They are
indeed my inspiration in doing my best in this endeavour. I
thank them for bringing me up the best way they could. I fully
dedicate this humble accomplishment of mine to them.
12
Acknowledgments
I wish to express my special and sincere thanks to my supervisor Prof. Rose Cooper
for all her help, supervision, guidance and valuable suggestions during the execution
of this project.
I owe my deepest gratitude to Mr. John Philips (dean of international office- UWIC)
to offer me a research scholarship.
I wish also to record my grateful thanks to Mr. Leighton Jenkins for his support and
guidance throughout the practical aspects of this dissertation. Special thanks to Dr.
Hann (Cardiff University- Cardiff) and Mr. Issa Al-Amri (Sultan Qaboos UniversityOman) for their assistant in using the facility of electron microscopy at their
Universities.
Great thanks to Dr. Charles Bakheit (Associate Prof. in Statistics- SQU) for his help
in statistical analysis.
I would like to express my appreciation to my family who support and encourage me
to do the best in writing this dissertation. Special thanks to my friends Shafiqa,
Suhaila, Kulthom and Rahima.
13
Presented posters
Al-Maaini, R. A, Cooper, R. and Burton, N. „Honey as an antimicrobial agent against
multi-drug resistant Gram negative bacterial rods‟, poster presented in the Society for
Applied Microbiology (SfAM) 2010 Summer Meeting, Brighton, 5-8 July 2010.
14
Index of Tables
Title
Tables
Page
Table 1.1
Honey composition
26
Table 1.2
Comparisons between peroxide and non-peroxide honey
33
Table 2.1
Media, Chemicals and Reagents used for general experiment
41
in the project
Table 2.2
Chemicals and Reagents used for electron microscopy
42
experiment in the project
Table 2.3
Chemicals
and
Reagents
used
for
2-
dimensional
43
electrophoresis experiment in the project
Table 2.4
Equipment used in general experiments in the project
44
Table 2.5
Equipment used in electron microscopy and 2-dimentional
45
electrophoresis experiments in the project
46
Table 2.6
Thirty isolates of MDR Acinetobacter and their resistance
pattern
Table 2.7a
Clinical isolates of E.coli & Klebsiella provided for this
47
study
Table 2.7b
Clinical isolates of Citrobacter, Enterobacter & Serratia
48
provided for this study
Table 2.8
List of honeys used in this project
51
Table 2.9
Preparation of phenol standards
52
Table 2.10
Preparation of gallic acid standard solutions
60
Table 2.11
Preparation of varying concentration of honey solution from
64
40% (w/v) stock honey for MIC method
Table 2.12
Preparation of tubes for MICs
Table 2.13: Cultures and honey concentrations used in the time-kill
curves
15
65
67
Index of Tables (Continued)
Title
Tables
Table 2.14
Reagents and buffers used in 2-D Electrophoresis
Table 2.15: Quantity of proteins in Acinetobacter cells with and without
Page
73
75
20% honey
Table 3.1 a Identification and antibiotics sensitivities of 30 MDR
& b:
Acinetobacter isolates
8182
Confirmation of identity and antibiotics sensitivity including
83
Table 3.2:
ESBL tests for 10 E.coli isolates.
Table 3.3:
Confirmation of identity and antibiotics sensitivity including
84
ESBL tests for 12 Klebsiella isolates
Table 3.4:
Confirmation of identity and antibiotics sensitivity including
85
ESBL tests for 12 Citrobacter isolates.
Table 3.5:
Confirmation of identity and antibiotics sensitivity including
86
ESBL tests for15 Enterobacter isolates.
Table 3.6:
Confirmation of identity and antibiotics sensitivity including
87
ESBL tests for 8 Serratia isolates.
Table 3.7:
Antibacterial activity of honey samples
91
Table 3.8:
Chemical & physical analysis of different types of Omani
93
honey compared to manuka honey
Table 3.9:
Represent the identification of flora sources by pollen
94
analysis
Table 3.10:
Amount of free radical and phenolic content in each honey
samples
16
96
Index of Tables (Continued)
Title
Tables
Table 3.11: Susceptibility of Acinetobacter isolates against manuka
Page
98
honey
Table 3.12:
Sensitivity of 30 Acinetobacter isolates treated with manuka
100
honey by broth dilution method
Table 3.13:
Sensitivity of 10 Klebsiella isolates to manuka honey tested
102
by the broth dilution method
Table 3.14:
Sensitivity of 8 Serratia and 8 E.coli isolates to manuka
103
honey using a broth dilution method
Table 3.15:
Susceptibility of 15 Enterobacter isolates to with manuka
105
honey determined by broth dilution method
Table 3.16:
Susceptibility of 12 Citrobacter isolates to manuka honey
Table 3.17:
Susceptibility of 30 MDR Acinetobacter isolates against 4 107-
Table 3.18:
106
types of Omani honey using broth dilution method
108
Susceptibility of 12 Klebsiella isolates against 4 types of
109
Omani honey using broth dilution method
Table 3.19:
Susceptibility of 10 E.coli isolates against 4 types of Omani
110
honey using broth dilution method
Table 3.20:
Susceptibility of 15 Enterobacter isolates against 4 types of
111
Omani honey using broth dilution method
Table 3.21:
Susceptibility of 12 Citrobacter isolates against 4 types of
112
Omani honey using broth dilution method
Table 3.22:
Susceptibility of 8 Serratia isolates against 4 types of Omani
113
honey using broth dilution method
Table 3.23: Cultures and honey concentrations used in the time-kill
curves assay
17
115
Index of Tables (Continued)
Tables
Title
Page
Table 3.24:
Decimal reduction dose (DRD) for each isolate after 5 h
132
exposure to 2x respective MICs of manuka honey
Table 3.25
Comparison of the mean viable cell count between non
133
honey and honey treated cells using paired sample test (Ttest).
Table 3.26:
Comparison of changes in cell sizes of isolates observed in
173
scanning electron microscopy between untreated and honey
treated cells (P value) (Mann-Whitney Test)
Table 4.1:
The physicochemical analysis of selected honeys tested
190
Table 4.2:
Comparison between previous studies and current study on
196197
MIC of different honeys including manuka honey against six
bacteria species.
Table 4.3:
Summary of the growth inhibition, killing rate and ultrastructure changes in EM for six species selected after
exposure to 2x MIC (%w/v) of manuka honey:
18
208
Index of Figures
Figure
Figure 2.1:
Title
Page
Calibration curve for protein determination in honey
58
samples
Figure 2.2:
Standard curve for total phenolic content in honey samples
60
Figure 2.3:
Calibration curve of protein concentration in Acinetobacter
75
with and without 20% manuka honey.
Figure 3.1:
A typical honey bioassay plate.
88
Figure 3.2:
A typical calibration curve of the bioassay
90
Figure 3.3:
Image of pollen present in Omani honey samples at 100x
95
magnification.
Figure 3.4:
The effect of manuka honey on the growth of Acinetobacter
116
Figure 3.5:
The effect of manuka honey on the growth of E.coli
116
Figure 3.6:
The effect of manuka honey on the growth of Klebsiella
117
Figure 3.7:
The effect of honey on the growth of Citrobacter
117
Figure 3.8:
The effect of honey on the growth of Enterobacter
118
Figure 3.9:
The effect of honey on the growth of Serratia
118
Figure 3.10:
The effect of manuka honey on the viability of
121
Acinetobacter.
Figure 3.11:
The effect of manuka honey on the viability of E.coli
123
Figure 3.12:
The effect of manuka honey on the viability of Klebsiella
125
Figure 3.13:
The effect of manuka honey on the viability of Citrobacter
127
Figure 3.14:
The effect of manuka honey on the viability of Enterobacter
129
19
Index of Figures (Continued)
Figure
Figure 3.15:
Title
Page
The effect of manuka honey on the viability of Serratia
131
Figure 3.16:
Growth curve of Acinetobacter in ISB
135
Figure 3.17:
Growth curve of E.coli in ISB
135
Figure 3.18:
Growth curve of Klebsiella in ISB
136
Figure 3.19:
Growth curve of Enterobacter in ISB
136
Figure 3.20:
Growth curve of Citrobacter in ISB
137
Figure 3.21:
Growth curve of Serratia in ISB
137
Figure 3.22:
SEM micrograph of untreated cells of Acinetobacter after 0
140
minutes at x5,000 magnification
Figure 3.23:
SEM micrograph of Acinetobacter cells exposed to 20%
140
(w/v) manuka honey of 0 minutes at x5,000 magnification
Figure 3. 24:
SEM micrograph of untreated cells of Acinetobacter after
141
60 minutes at x5,000 magnification
Figure 3.25:
SEM micrograph of Acinetobacter cells exposed to 20%
141
(w/v) manuka honey after 60 minutes at x5,000
magnification
Figure 3.26:
SEM micrograph of untreated cells of Acinetobacter after
142
90 minutes at x5,000 magnification
Figure 3.27:
SEM micrograph of Acinetobacter cells exposed to 20%
142
(w/v) manuka honey after 90 minutes at x5,000
magnification
Figure 3.28:
SEM micrographs of untreated cells of Acinetobacter after
150 minutes at x5,000 A & x20,000 B magnification
respectively
20
143
Index of Figures (Continued)
Figure
Title
Page
Figure 3.29:
SEM micrographs of Acinetobacter cells exposed to 20%
(w/v) manuka honey after 150 minutes at x5,000A,
x10,000B,
x20,000C
&
x25,000D
144
magnification
respectively
Figure 3.30:
SEM micrographs of untreated cells of Acinetobacter after
146
180 minutes at x5,000 A & x10,000 B magnification
respectively
Figure 3.31:
SEM micrographs of Acinetobacter cells exposed to 20%
147
(w/v) manuka honey after 180 minutes at x 5,000 (A) &
(B) magnification respectively
Figure 3.32:
SEM micrograph of untreated cells of E.coli after 30
149
minutes at x5,000 magnification
Figure 3.33:
SEM micrograph of E.coli cells exposed to 30% (w/v)
149
manuka honey after 30 minutes at x5,000 magnification
Figure 3.34 :
SEM micrograph of untreated cells of E.coli after 180
150
minutes at x5,000 magnification
Figure 3.35 :
SEM micrograph of E.coli cells exposed to 30% (w/v)
150
manuka honey after 180 minutes at x5,000 magnification
Figure 3.36:
SEM micrographs of untreated cells of Klebsiella after 30
152
minutes at x5,000 (A) & 15,000 (B) magnification
respectively
Figure 3.37:
SEM micrographs of untreated cells of Klebsiella after 180
minutes at x5,000 (A) & 15,000 (B) magnification
respectively
21
153
Index of Figures (Continued)
Figure
Figure 3.38 :
Title
SEM micrographs of Klebsiella cells
Page
exposed to 30%
manuka honey after 30 minutes at x5,000 (A) & 15,000 (B)
154
magnification respectively
Figure 3.39:
SEM micrographs of Klebsiella cells exposed to 30%
155
(w/v) manuka honey after 180 minutes at x5,000A,
x15,000B,C & x20,000D magnification
Figure 3.40:
SEM micrographs of Klebsiella cells
exposed to 40%
157
(w/v) Omani honey after 30 minutes at x5,000 A &
x15,000 B magnification respectively
Figure 3.41 :
SEM micrographs of Klebsiella cells
exposed to 40%
158
(w/v) Omani honey after 180 minutes at x5,000 A & x
20,000 B,C & D magnification respectively
Figure 3.42:
SEM micrographs of untreated cells of Enterobacter after
161
30 minutes at x5,000 A & x15,000 B magnification
respectively
Figure 3.43:
SEM micrographs of Enterobacter cells exposed to 30%
162
(w/v) manuka honey after 30 minutes at x5,000 A & B
magnification respectively
Figure 3.44:
SEM micrographs of untreated Enterobacter cells after 180
163
minutes at x5,000 A & x20,000 B magnification
respectively
Figure 3.45:
SEM micrographs of Enterobacter cells exposed to 30%
164
(w/v) manuka honey after 180 minutes at x5,000 (A & B)
magnification respectively
Figure 3.46:
SEM micrographs of untreated cells of Citrobacter after 30
minutes at x5,000 A & x15,000 B magnification
respectively
22
166
Index of Figures (Continued)
Figure
Title
Page
Figure 3.47:
SEM micrographs of Citrobacter cells exposed to 20%
(w/v) manuka honey after 30 minutes at x10,000 A &
167
x15,000 B magnification respectively
Figure 3.48:
SEM micrographs of untreated Citrobacter cells after 180
168
minutes at x5,000 (A & B) magnification respectively
Figure 3.49:
SEM micrographs of Citrobacter cells exposed to 20%
169
(w/v) manuka honey after 180 minutes at x5,000 A &
x15,000 B magnification respectively
Figure 3.50:
SEM micrograph of untreated cells of Serratia after 30
171
minutes at x5,000 magnification
Figure 3.51:
SEM micrograph of Serratia cells exposed to 30% (w/v)
171
manuka honey for 30 minutes at x 5,000 magnification
Figure 3.52:
SEM micrograph of untreated cells of Serratia after 180
172
minutes at x5,000 magnification
Figure 3.53:
SEM micrograph of Serratia cells exposed to 30% (w/v)
172
manuka honey after 180 minutes at x5,000 magnification
Figure 3.54:
Transmission
micrographs
of
untreated
cells
of
175
Acinetobacter after 1 h incubation with isosensitest borth
(ISB) at 16,000x magnification
Figure 3.55:
Transmission micrographs of Acinetobacter incubated with
176
isosensitest borth (ISB) containing 20% (w/v) manuka
honey for 1 h at 16,000x magnification.
Figure 3.56:
Transmission
micrographs
of
untreated
cells
of
Acinetobacter incubated with isosensitest borth (ISB) after
3 h at 16,000x magnification
23
177
Index of Figures (Continued)
Figure
Title
Figure 3.57:
Transmission micrographs of Acinetobacter incubated with
isosensitest borth (ISB) containing 20% (w/v) manuka
Page
178
honey after 3 h at 16,000x magnification
Figure 3.58:
Transmission micrographs of untreated cells of E.coli after
180
1 h incubation with isosensitest borth (ISB) at 16,000x
magnification
Figure 3.59:
Transmission micrographs of E.coli incubated with
181
isosensitest borth (ISB) containing 20% (w/v) manuka
honey after 1 h at 16,000x magnification
Figure 3.60:
Transmission micrographs of untreated cells of E.coli after
182
3 h incubation with isosensitest borth (ISB) at 16,000x &
30,000x magnification
Figure 3.61:
Transmission micrographs of E.coli incubated with
183
isosensitest borth (ISB) containing 20% (w/v) manuka
honey after 3 h at 16,000x magnification
Figure 3.62:
2-D protein electrophoresis gel of Acinetobacter cells
185
without honey treatment
Figure 3.63:
2-D protein electrophoresis gel of Acinetobacter cells
185
exposed to 20% (w/v) manuka honey
Figure 4.1:
Mean MIC and MBC (%w/v) for 30 Acinetobacter strain
198
against 5 types of Hone
Figure 4.2:
Mean MIC and MBC (%w/v) for 12 Klebsiella strain
198
against 5 types of honey
Figure 4.3:
Mean MIC and MBC (%w/v) for 10 E.coli strain against 5
types of
honey
24
199
Index of Figures (Continued)
Figure
Figure 4.4:
Title
Mean MIC & MBC (%w/v) for 12 Citrobacter strain
Page
199
against 5 types of honey
Figure 4.5
Mean MIC and MBC (%w/v) for 15 Enterobacter strain
200
against 5 types of honey
Figure 4.6:
Mean MIC and MBC (%w/v) for 8 Serratia strain against 5
types of honey
25
200
Abbreviations list
AHL-N-acylhomoserine-lactone
ATCC- American Type Culture Collection
APF - Antibacterial phenolic fraction
AIF- Apoptosis inducing factor
AMP- Ampicillin
AK- Amikacin
AZT- Aztreonam
BDMA- Benzyl dimethylamine
BSA- Bovine serum albumin
CDC- Centres for Disease Control
CAZ- Ceftazidime
CE – Cephradin
CFU- Colony forming unit
CHAPS--[3-cholamidopropyl)dimethylammonio]-1-propanesulfonate
CLA- Clavulanic acid
CLSI- Clinical and Laboratory Standards Institute
CRO- Ceftriaxone
CTX- Cefotaxime
CPM- Cefepem
DDSA- Dodecenyl Succinic Anhydride
DDT- DL-Dithiothreitol
DPPH- Di(4-tert-octylphenyl)-1-picryl-hydrazyl
DRD- Decimal reduction dose
26
ESBL- Extended spectrum beta lactamase
ERT- Ertapenem
EDTA- Ethylenediaminetetraacetic acid
EMB- Eosin Methylene Blue
FOX- Cefoxitin
GN- Gentamicin
HPA- Health Protection Agency
HMF- hydroxymetheyl furfural
IMP- Imipenem
IEF- Iso- electric focusing
ISB- Isosensitest broth
MALDI-TOF- MS- Matrix-assisted laser desorption ionisation-time-of-flight mass
spectroscopy
MBC- Minimum bactericidal concentration
MDR- Multi-drug resistant
MEM- Meropenem
MG or MGO- Methyglyoxal
MH- Mueller-Hinton
MIC- Minimum inhibitory concentration
MOPS- Morpholino-Propansulfonsaure acid
MRAB-C Imipenem resistant Acinetobacter baumannii- Carbapenemases
MRD- Maximum recovery diluent
MRSA- Methicillin resistant Staph. aureus
MSSA- Methicillin sensitive Staph. aureus
27
MW- Molecular weight
NB- Nutrient broth
NDM-1- New delhi metalobetalactamases
NCTC- National Collection Type Culture
NHB- National honey board
NPARU- National Pollen And Research Unit
OH- Omani honey
OMPs- Outer membrane proteins
OSO4- Osmium tetroxide
OXA- Oxacillinases
PBPs- Penicillin binding protein
pI- Iso- electric point
ROS- Reactive oxygen species
RTI- Respiratory tract infection
SEM- Scanning electron microscopy
SD- Standard deviation
SDS- Sodium dodecylsulphate
SDS- PAGE – Sodium dodecyl sulphate polyacrylamide gel electrophoresis
SHV- Sulphydryl variable
SXT- Septrin
TEM- Transmission electron microscopy
TAZ- Piperacillin-tazobactam
TSB- Tryptone Soya Broth
TVC- Total viable count
28
TEM- Temoneira
UTI- Urinary tract infection
UMF- Unique manuka factor
VRE- Vancomycin resistant Enterococcus
VSE- Vancomycin sensitive Enterococcus
V/V- Volume per volume
W/V- Weight per volume
29
Abstract
Honey has been shown to have therapeutic properties, which include
immunomodulatory and antibacterial activity in vitro and anti-inflammatory,
antipyretic and wound healing properties in vivo. A complex mix of factors such as
acidity, osmolality and hydrogen peroxide content contribute to antibacterial
activity. Unusually manuka honey has been shown to contain methylglyoxal which
is derived from nectar collected from the blossom of manuka trees and this confers
high antibacterial activity. Manuka honey is used in licensed wound dressings in
the UK. Its ability to inhibit staphylococci has been reported, but its efficacy with
Gram negative bacteria is less well documented. Since these bacteria are difficult to
control and commonly infect military wounds and burns, there is a need to
investigate their susceptibility to manuka honey. The main aim of this study is to
assess the antimicrobial potential of manuka honey against multi-drug resistant
(MDR) Gram negative rods with the potential to infect wounds. Eighty five clinical
isolates were tested in this study (30 MDR Acinetobacter and 55 extended spectrum
beta-lactamases [ESBL] producing members of the Enterobacteriaceae). The
minimum inhibitory concentration (MIC) of manuka honey for each isolate was
determined by agar incorporation and broth dilution methods, as well as the
minimum bactericidal concentration (MBC). The kinetics of inhibition of selected
isolates with high MIC values was monitored by total viable counts. Also,
ultrastructural changes in cell morphology were studied before and after exposure
to manuka honey using scanning (SEM) and transmission electron microscopy
(TEM). Electron micrographs were examined for structural changes, such as
altered shape, surface abnormalities and evidence of cell division.
Eight Omani honeys were assayed for their antibacterial activity using bioassay, MIC
and MBC methods. Omani honeys were also analysed for their chemical and
physical properties such as pH, protein, water and sugar contents,
hydroxymetheylfurfural (HMF), colour and antioxidant properties. Pollen analysis
was also used for identifying the flora origin of honey. All Omani honeys were found
to possess peroxide activity nonetheless it exhibited a bactericidal mode of activity
against all MDR and ESBLs tested. In addition honey analysis revealed
unadulterated and natural honey. A study of anti-radical activity and phenolic
contents demonstrated that Omani honey could be used to promote a rapid wound
healing and aid its antibacterial activity. The proximity of MIC and MBC values
indicates that manuka honey had a bactericidal mode of action against these isolates
and this was confirmed by the time to kill curves. The SEM and TEM of images of
representative isolates after treatment with manuka honey showed some physical
membrane damage, septa formation and irregular shape; whereas non honey treated
cells (control) did not appear to be obvious damage. In conclusion manuka honey
possesses strong antibacterial activity against the antibiotic-resistant wound
pathogens tested here and further investigation into cellular target sites is needed.
Both manuka and selected Omani honeys have clinical potential to inhibit pathogens
that commonly colonise wounds.
30
Chapter 1
Introduction
31
1.1 Antimicrobial Resistance:
An antimicrobial agent is a substance that inhibits or kills microbial growth;
unfortunately, the introduction of a new antimicrobial agent into clinical practice is
usually followed by the rapid emergence of resistance. Resistance in bacteria can be
intrinsic, because not all bacterial species are naturally sensitive to all antimicrobials,
or it can be acquired, based on genetic mutation or genetic transfer from other
organisms (Forbes et al., 2007). The production of drug-inactivating enzymes,
alteration of an active target site, acquirement of a target bypass system, decrease cell
permeability and an efflux pump in cell membrane are the five mechanisms which
admit microorganisms to acquire resistance through a biochemical basis (Sefton
2002). The mechanisms by which β lactam resistance is manifest in bacteria involve
enzymatic inactivation, or by altered receptors of penicillin binding proteins (Winn et
al., 2006).
Resistance to antimicrobial agents is not a new incident. Soon after penicillin has
established as an antibiotic in the 1940s, the rate of penicillin resistance had risen to
14%, and is over 90% for Staphylococcus aureus today (Aksoy 2007). In the past,
resistant strains were believed to be a problem confined to hospitals, however today
resistance has increased in the community (Aksoy 2007).
Urinary tract infections (UTI), respiratory tract infections (RTI), and tuberculosis are
considered to be problem diseases both in the community and the hospital setting
with regard to antimicrobial resistance. Misuse of antibiotics by healthcare
professionals, unprofessional doctors, poor drug quality, poor hygienic conditions
and inadequate surveillance measures account for the emergence of resistant bacteria.
All of these factors contribute to the spread of multidrug resistant (MDR) organisms,
32
such as methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant
Enterococcus species (VRE), Extended spectrum β-lactamase (ESBL) producing
Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii
(Madigan et al., 2009). According to the Centre for Disease Control and Prevention
(CDC), more than 70% of bacteria that are concidered now as a sources of hospital
acquired infections are resistant to at least one of the drugs that are most commonly
used to treat them (Aksoy 2007).
1.2 Extended Spectrum Beta-Lactamases (ESBLs):
ESBLs are enzymes that inactivate or hydrolyse β-lactam antibiotics by cleaving the
C-N bond on the β-lactam ring. Most of these enzymes are plasmid mediated; they
hydrolyze penicillins, cephalosporins, and aztreonam and are inhibited by βlactamase inhibitors, such as clavulanate, sulbactam and tazobactam, (Moland et al.,
2008).
Beta lactamases are commonly circulated in nature and are generally classified
according to the main compounds that they inactivate (e.g., as penicillinases or
cephalosporinases) (Baron 1996). The enzymes can be produced in either a
constitutive or an inducible manner. Infections with ESBLs can vary from urinary
tract infections (UTIs) to more complicated deadly sepsis (Bhattacharya 2006).
Many β-lactamase resistance genes of Gram negative bacteria are present on the
chromosome and some are carried on plasmids which can be transferred to other
organisms. A recently discovered transfer mechanism is the transposon which carries
genes between the chromosomes and the plasmids (Greenwood et al., 2007) and an
increasing number of β-lactamases have been associated with integrons. Today,
ESBL producing bacteria have spread worldwide and have emerged as significant
33
community-and hospital acquired pathogens (Murray et al., 2007). European
countries recorded higher incidence of ESBL producing Enterobacteriaceae than the
USA, especially for Klebsiella strains (Canton et al., 2008). More than 100 European
intensive care units (ICUs) were involved in a study on the prevalence of ESBLs in
Klebsiella. Sweden demonstrated the lowest prevalence of ESBL with 3%, whereas
Portugal obtained the highest value with 34% (Paterson & Bonomo 2005)
1.2.1 Classification of ESBL:
There are many different β -lactamases, and they can be differentiated according to
their substrate and inhibitor specificities, physical factors (pH, isoelectric point) and
immunological differences. These factors makes classification very difficult (Scholar
& Pratt 2000), but two schemes are currently used to classify β-lactamases, which are
the Bush-Medeiros-Jacopy system (functional classification) and the Ambler system
(molecular classification) (Murray et al., 2007).
1.2.1.1 Functional Classification:
A classification scheme for β-lactamases based on functional characteristics was
categorized on three major groups of enzymes. These enzymes have been defined by
the effects of their substrate and inhibitor product: group one cephalosporinases
which are inhibited by clavulanic acid; group two penicillinases, cephalosporinases
and broad spectrum β-lactamases are inhibited by active site-occupied β-lactamase
inhibitors and the group three metallo-β-lactamases which hydrolyze penicillins,
cephalosporins, and carbapenems and weakly inhibited by nearly all β-lactamases
inhibitors (Bush, Jacopy & Medeiros 1995)
1.2.1.2 Molecular Classification:
34
Beta-lactamases in this classification are depended on the nucleotide and amino acid
sequences built in these enzymes under the Ambler system. Four classes have been
recognized (A-D), which associate with the functional classification. Classes A, C,
and D have serine residues at the active site, similar to penicillin binding proteins
(PBPs), whereas class B or metallo-β-lactamases use a metallic ion, preferentially
zinc for their action (Amabile-Cuevas 2007; Murray et al., 2007).
1.2.2 Types of ESBL:
1.2.2.1 TEM-type ESBLs (class A)
TEM-1 is the most common β-lactamase found in Gram-negative bacteria; it was
first deteced from a patient named Temoneira in 1965. At least 90% of ampicillin
resistance in Escherichia coli was found to be due to TEM-1 production, which has
an iso-electric point (pI) of 5.4 (Amabile-Cuevas 2007). TEM-1 inactivate most
penicillins and first generation cephalosporins such as cephalothin, cefaclor and
cephaloridine but does not hydrolyse more stable cephalosporins, like cefotaxime,
cefuroxime, cefixime, ceftriaxone, cefepime and ceftazidime and the monobactam,
aztreonam (Moland et al., 2008).
There are two TEM-ESBL families, one derived from TEM-1 and other derived from
TEM-2. These enzymes are plasmid-mediated β-lactamases and can be produced by
several members of the Enterobacteriaceae. TEM enzymes involve more than 160
different ESBLs. TEM is commonly distributed because it seems to have emerged by
point mutations, and it is also easily created in the laboratory (Amabile-Cuevas,
2007). More than 100 derived of TEM β -lactamases have been identified on a world
wide basis (Greenwood et al., 2007)
35
1.2.2.2 SHV-type ESBLs (class A)
SHV is an abbreviation for sulphydryl variable (Paterson and Bonomo, 2005). The
SHV-1 β-lactamase has been most commonly found in Klebsiella pneumoniae
isolates and plasmid-mediated ampicillin resistance has accounted for more than 20%
of this enzyme in this species. This enzyme can also be produced by Citrobacter
diversus, Escherichia coli, and P. aeruginosa. SHV or a related gene is usually
incorporated into the bacterial chromosome, but it has also been found in plasmids
(Jacopy and Munoz-Price 2005). The majority of SHV family variant ESBLs
producers are differentiated by point mutation with the replacement of a serine for
glycine at position 238 (Bradford 2001). Recently outbreaks of SHV-producers have
been reported in Acinetobacter spp, and P. aeruginosa (Amabile-Cuevas 2007).
Currently, there are approximately 100 SHV- variants β-lactamases (Moland et al.,
2008).
1.2.2.3 CTX-M type ESBLs (class A):
CTX-M ESBLs are not mutated forms of broad spectrum β -lactamases but are
derived from chromosomal enzymes found in rare bacteria known as Kluyvera
species. The encoding gene of this enzyme is also commonly located on plasmids
with different sizes (7-160 kb). At present approximately half of the CTX-M
enzymes have been discovered to be plasmid encoded (Amabile-Cuevas 2007).
CTX-M was first detected in 1990 and it spread between Enterobacteriaceae such as
E.coli, K. pneumoniae, Salmonella, Shigella, Citrobacter freundii, Enterobacter and
Serratia marcescens (Murray et al., 2007). More than 90 molecular variants of CTXM have been described (Zong and Yu 2010). It is called CTX (a common
abbreviation for cefotaxime) because many CTX-M enzymes are able to hydrolyse
36
cefotaxime faster than ceftazidime; however, a few CTX-Ms significantly hydrolyse
ceftazidime (e.g., CTX-M-15, -19, -25 and -32). CTX-M carried on specific bacterial
genotypes was related to different geographical regions (Hawkey & Jones 2009).
CTX-M-14 and-15 seems to be the most pandemic genotypes in recent times
(Hawkey 2008). Moreover, these enzymes were more readily inhibited by
tazobactam compared to other β-lactamase inhibitors such as sulbactam and
clavulanate (Bradford 2001). This may explain the higher sensitivity of some CTXM producers to piperacillin-tazobactam than to cefepime (Amabile-Cuevas 2007).
1.2.2.4 OXA-type ESBLs (Class D)
OXA ESBLs are enzymes that hydrolyse oxacillin and cloxacillin. These ESBLs
occur commonly in the Enterobacteriaceae, Acinetobacter and P. aeruginosa. They
belong to molecular class D which dishtinguishes them from the TEM and SHV
enzymes (Murray et al., 2007). Although resistance to ceftazidime is a phenotypic
indicator in the detection of this type of ESBL, their detection can be difficult
because of the weak inhibition by clavulanate (Moland et al., 2008).
1.2.2. 5 AmpC-type ESBLs (Class C)
AmpC β-lactamases enzyme (also termed class C or group 1) are commonly isolated
from Gram-negative bacteria that are resistant to extended-spectrum cephalosporins.
This enzyme is usually determined on the chromosome of many Enterobacteriaceae
including Citrobacter, Serratia and Enterobacter species and usually has inducible
expression; whereas in E. coli it is not usually inducible, even if
it is hyper-
expressed. Thus, AmpC β-lactamase genes of Gram-negative bacteria have been
transferred onto plasmids and have spread worldwide by increasing their number and
variety (Hawkey 2008).
37
AmpC β-lactamases, compared to ESBLs are able to hydrolyse third generation
cephalosporins but are not inhibited by β-lactamase inhibitors such as clavulanic
acid, sulbactam and tazobactam (Murray et al., 2007). The detection of the ESBL
gene in the Enterobacteriaceae can be difficult if AmpC plasmids are present in the
same isolate, as well as ESBL gene. To avoid this cefepime or cefpirome (AmpCstable cephalosporins) can be used in combination with β-lactamases inhibitors such
as clavulanate or boronic acid, even though this detection is not 100% perfect when
using these combinations (Hawkey 2008).
1.2.2.6 Carbapenemases (class A, B, D)
Carbapenems have been suggested as the most effective treatment for the most
extended-spectrum-β-lactamases. However, increasing reports of carbapenemases
enzymes that are able to hydrolyse oxyimino-cephalosporins, cephamycins and
carbapenems makes treatment difficult (Murray et al., 2007). Carbapenemases
belong to three molecular classes: A, B and D.
Class A carbapenemases can hydrolyse imipenem but are inhibited by clavulanic
acid. Most of this class has chromosomal genes, but some are plasmid-mediated,
such as KPC-1 which is found mainly in K. pneumoniae. Class B such as IMP or
VIM group are not inhibited by clavulanic acid (Murray et al., 2007). Recently
Yong et al., (2009) was first to report a new subgroup case of class B of metallo βlactamases called NDM-1 isolated from New Delhi, India. This mobile gene was
detected on a plasmid and found mainly in K. pneumoniae. NDM-1 with 28 kDa can
hydrolyse all β-lactams antibiotics except aztreonam and it represents a potential
public health problem (Kumarasamy et al., 2010).
Class D carbapenamases are mostly found in Acinetobacter baumannii. However,
they can develop resistant to carbapenems if there is an alteration in a porin.
38
1.2.3 Emergence of ESBL:
The production of the TEM and SHV β-lactamases in plasmid mediated resistance
strains initiated many clinical problems after the introduction of ampicillin in the
1960s. In the early 1980s, third generation extended spectrum cephalosporins, such
as cefotaxime and ceftazidime had been developed and offered reliable treatment for
patients infected with Enterobacteriaceae. However, in the mid 1980s the first ESBL
producing organisms (resistant to third generation cephalosporins) were isolated.
Very rapidly mutations in amino acids in both TEM and SHV genes occurred, which
spread specifically between Klebsiella species and some strains of E. coli. As a
result in the emergence of the ESBL mutant, derivatives of these widely spread βlactamases that were capable of hydrolysing third generation cephalosporins and
monobactams appeared (Hawkey 2008). Both excessive use of antibiotics and
environmental conditions were therefore consider to be main reasons for the
emergence and spread of resistance. ESBL have also been reported in Enterobacter,
Salmonella, Proteus, Citrobacter, Morganella morganii, Serratia marcescens,
Shigella dysenteriae, Pseudomonas aeruginosa, Burkholderria cepacia and
Acinetobacter baumannii (Moland et al., 2008).
1.2.4 Risk factors for ESBL:
To determine the risk factors of ESBL producing organisms, several studies were
conducted in different countries. It was found that ESBL-producing strains were
associated with low patient outcome and crowded hospitals and were also linked to
improper first line treatment (Amabile-Cuevas 2007). The risk factors for most
ESBLs are excessive previous use of multiple ranges of antibiotics, including third
39
generation cephalosporins, cotrimoxazole and ciprofloxacin, severely ill patients
with prolonged hospital stay, or patients with indwelling medical devices such as the
presence of urinary catheters, placement of endotracheal tubes for more than 10 days
and old age (Hawkey 2008; Paterson & Bonomo 2005).
1.2.5 Treatment of ESBLs:
Due to high rates of resistance of ESBL-producing bacteria to fluoroquinolones, the
treatment can be complicated. If an ESBL-producing strain is detected in an isolate
of K. pneumoniae, E.coli or P. mirabilis, the CLSI recommends the laboratory to
report it as resistant to all penicillins, cephalosporins, and aztreonam, even if it
showed sensitivity (Moland et al., 2008).
Carbapenems have been recommended as the drug class selected for serious
infections caused by ESBL producing Enterobacteriaceae strains. However, in the
case of carbapenemases emergence, tigecycline revealed excellent potential activity
against ESBL producing Enterobacteriaceae and Acinetobacter (Hawkey 2008).
Recently, according to the survey of 104 isolates the presence of world wide a
resistant strain to tigecycline was not reported (Castanheira 2008). If CTX-M ESBL
is not prevalent, cefepime has been suggested as another treatment choice (AmabileCuevas 2007).
1.3 Acinetobacter:
During the last two decades, bacteria of the genus Acinetobacter have been selected
as one of the most important nosocomial pathogens, especially in ICU units. They
have been also involved in many infections such as bacteraemia, urinary tract
infection, pneumonia, skin and tissue infections and in secondary meningitis.
40
Acinetobacter spp are widespread in water and soil as free-living saprophytes
(Hawkey & Bergogne-Berezin 2006). Acinetobacter is now also involved in
aggressive situations such as war district zones or earthquake areas (Dallo and
Weitao 2010).
1.3.1 Taxonomy and Historical Features of Acinetobacter:
Acinetobacter was first described under the group of "Micrococcus calcoaceticus" by
Beijerinck in 1911. In 1956 these bacteria were then classified under the name of
Moraxella in France, but at the same time a group of French researchers had
recognised a genus named Acinetobacter. In 1968 a phenotypic study of 106 strains
was completed by Baumann which resulted in the recognition of only a single
species named Acinetobacter baummanii (Towner 1997). In the early 1970's, most
isolates of Acinetobacter were sensitive to many antimicrobial agents (Greenwood et
al., 2003). However during the same period many microbiologists in hospitals
noticed that these organisms were pathogenic and implicated in various nosocomial
infections. In 1986, Bovetand and Grimont were compeleted a basic subdivision of
the genus Acinetobacter and identified 12 genomic species by DNA-DNA
hybridization.
However, today there are at least 19 genomic species of
Acinetobacter (Murray et al., 2007).
Although, Acinetobacter baumannii is known to be the most clinically important
strains among species, there are other important nosocomial pathogens called the „A.
baumannii A. calcoaceticus complex‟ (referred to the genomospecies 1, 2, 3 and 13
of Tjernberg and Ursing) (Gerischer 2008). This complex is accountable for many
epidemic infections throughout the world because of the multi-resistant gene that it
contains. Other species are not very important because they are rarely involved in
41
outbreaks of human disease (Brauers et al., 2005; Joly-Guillou, 2005; Murray et al.,
2007).
1.3.2 Laboratory Diagnosis:
Morphologically, Acinetobacter are aerobic, non motile Gram-negative coccobacilli
and are usually found in diploid shape or chains of different length. They are strictly
aerobic and grow simply on all common media at temperatures from 20 to 30oC for
most strains, the optimum temperature for this bacterium at 33-35oC (Winn et al.,
2006). They are oxidase-negative, catalase-positive, indole-negative and nitratenegative. Furthermore, the initial clue in recognising these bacteria is the appearance
of tiny (1.0x 0.7 µm) diplococci with the Gram stain (Koneman et al., 1997).
Colonies appear smooth, opaque, sometimes mucoid and slightly smaller than those
of members of the family Enterobacteriaceae on blood agar. Most strains appear
colourless, slightly pink or lavender in colour on MacConkey agar due to lactose
oxidation (Engelkirk 2007). The genus of Acinetobacter can be therefore subdivided
into two groups. Acinetobacter that are able to oxidise glucose are called
saccharolytic, with those that are unable called asaccharolytic (Engelkirk 2007).
Most glucose-oxidizing non-haemolytic clinical strains are A. baumannii, most
glucose-negative non-haemolytic ones are A. lwoffii, and most haemolytic ones are
A. haemolytic (Murray et al., 2007).
1.3.3 Clinical features of Acinetobacter infections:
Acinetobacter species particularly Acinetobacter baumannii, can cause many clinical
disorders, including pneumonia, secondary meningitis, bacteraemia, wound
infections in burn patients and UTI. They are also isolated from skin, throat and
42
many secretions of normal people and are part of the commensal flora (Hawkey &
Bergogne-Berezin 2006). Other species such as A. lwoffii, A. johnsonii, and A.
radioresistens, seem to be natural inhabitants of human skin and as commensals in
the oropharynx and vagina (Winn et al., 2006). They are considered as less resistant
to antibiotics and easier to eliminate. Two recent species have been described which
are associated with infections; these are A. ursingii and A. schindleri (BergogneBerezin et al., 2008).
Acinetobacter can only be obtained from soil, water, food and sewage (Towner
1997) and are also able to live for long periods in lifeless environments (Greenwood
et al., 2003). In the case of wound infections, bacteraemia within 3-5 days following
infection can often develop. In several large case series, 4-27% of all Acinetobacter
that caused bacteraemia occurred as a result of infected surgical or burn wounds
(Gillespie 2004; Hawkey & Bergogne-Berezin 2006). Such infections are often
difficult to treat because of the ability of Acinetobacter to become rapidly resistance
to
multiple
antibiotics,
including
aminoglycosides,
expanded-spectrum
cephalosporins, carbapenems and fluoroquinolones (Gerischer 2008).
Inappropriate or excessive use of antibiotics therapy (i.e third generation
cephalosporin), surgery, use of medical machinery (e.g. ventilators), insertion of
intravenous or urinary catheters, and prolonged hospital stay are all identified as risk
factors for colonization and infection with Acinetobacter (Hawkey & BergogneBerezin 2006). Soap and water hand washing and alcohol based gels, could therefore
reduce the spread of this strain (Joly-Guillou 2005).
Acinetobacter has been detected from a large selection of clinical samples, including
blood, urine, faeces, cerebrospinal fluid and sputum (Gillespie 2004). It is an
opportunistic pathogen and is commonly found in patient samples. However, serious
infection caused by Acinetobacter depends upon the site of infection as well as the
patient‟s immunity to infection (Murray et al., 2007). Furthermore, Chiang, et al.,
43
(2008) added increased serum creatinine level and malignancy as risk factors
associated with increased mortality in patients with bacteraemia caused by
Acinetobacter. A European survey of the main cause of nosocomial pneumonia
carried out in seven countries has established an overall incidence of approximately
10% for Acinetobacter (Hawkey & Bergogne-Berezin 2006). Also, during a study
period from 2003 to 2006 for over 270 patients admitted every year in a burn clinic,
an increased trend of Acinetobacter strains was confirmed (Babik et al., 2008).
Recently, over 21,000 American army personnel who were injured during the Iraq
war have suffered severe wound infections, mostly from resistant strains of
Acinetobacter baumanii (Murray et al., 2008). Scientists examined Iraq and Kuwait
soil for the presence of this pathogen but it proved negative. The source of this
outbreak could therefore be from European hospitals (Silberman 2007). However,
this bacterium caused bacteraemia, osteomyelitis and respiratory infections for the
soldiers.
Also this pathogen is able to form a biofilm which will reduce its
susceptibility to systemic antibiotics and make treatment more difficult (Dallo and
Weitao 2010). In this situation significant Acinetobacter infections have increased
worldwide and the outbreaks of resistant strains of Acinetobacter have been
described in the medical literature (Bergogne-Berezin et al., 2008).
1.3.4 Pathogenesis of Acinetobacter infection:
Bacteria produce many substances and molecules that allow them to survive and
grow in a host. These molecules are proteins, enzymes, capsules, toxins and surface
carbohydrates (Bergogne-Berezin et al., 2008). Acinetobacter spp. were thought to
be relatively low-grade pathogens, but a number of virulence factors have been
identified. These include:
44
1.3.4.1 The production of exopolysaccharide:
The presence of exopolysaccharide capsule helps in the protection of bacteria from
host defences. A capsule is produced by approximately 30% of Acinetobacter strains
and it consists of L-rhamnose, D-glucose, D-glucuronic acid, and D-mannose, which
make the cell surface of strains more hydrophilic (Joly-Guillou 2005; Hawkey &
Bergogne-Berezin 2006). Acinetobacter strains which produce exopolysaccharide are
known to be more dangerous than those without. This is because this capsule can
block the entry of complement to the bacterial cell wall and interrupt the alternative
pathway of complement activation (Joly-Guillou 2005).
1.3.4.2 Quorum-sensing:
Quorum-sensing is defined as the ability of bacteria to initiate the transcription of
certain genes only when a certain population density is reached. It is known as a
widely distributed regulatory mechanism in Gram-negative bacteria such as
Pseudomonas aeruginosa (Wilson et al., 2002). Acinetobacter isolates in the
stationary growth phase have demonstrated four different signal molecules of
quorum sensing involved in activating N-acylhomoserine-lactone (AHL) biosensors.
The AHLs system can therefore act as a main mechanism for auto-induction of
several virulence factors in an opportunistic pathogen such as Acinetobacter. This
process need to be studied for its clinical implications (Joly-Guillou 2005).
1.3.4.3
The
property
of
adhesion
to
human
epithelial
cells
via
the capsule or fimbriae.
The initial step in the infection process is the ability of bacteria to penetrate the host.
This step depends on the adherence capacity and the survival time of microorganisms
on mucosal surfaces of the host. Bacterial adherence involves the possession of
45
fimbriae, the production of capsular polysaccharides and cell wall components
(Bergogne-Berezin et al., 2008). Recently, two different types of adherence were
observed in A. baumannii to epithelial cells of human bronchial. The first one was
diffusing adherence of bacteria to the cell surface and the other type was a group of
clusters of bacteria that adhered to a localized area of the cell by producing small
colonies (Bergogne-Berezin et al., 2008).
1.3.4.4 Surface and mitochondrial porins:
On the outer membrane of the surface of Gram negative bacteria there are special
channels consisting of protein molecules called porins. Depending on bacterial
species, porins play a role in the maintenance of the cell structure, bacteriophage and
resistance mechanisms of antimicrobial agent (Bergogne-Berezin et al., 2008).
Surface porins allow the passive diffusion of low molecular weight components to
penetrate through this membrane. Large antibiotic molecules penetrate slowly, which
may account for the high antibiotic resistance of A.baumannii. For example, the
permeability of the outer membrane varies from one Gram negative species to
another; in Pseudomonas aeruginosa (which is extremely resistance to antibiotics)
the outer membrane is 100 times less permeable than in E.coli (Brooks et al., 2001).
A study showed the permeability of the outer membrane of Acinetobacter for a
cephalosporin was 2-7 times lower than in P.aeruginosa (Vila 1998).
Many considerations that involve host factors, the bacterial load, the virulence of
strains and the production of lipase enzymes such as butyrate esterase, caprylate
esterase and leucine arylamidase which may damage tissue lipids may play important
roles in initiating infection in colonised patients (Towner 2002).
46
1.3.5 Emergence of Resistance:
A.baumannii and related species have acquired resistance to multiple antibiotics
rather than being inherently resistant. When these species were first introduced as
pathogens to human, most strains were sensitive to ampicillin and cephalosporins. In
1975, less than 20% of these strains were resistant to ticarcillin. However, at the end
of the 1970s and early 1980s, A.baumannii caused increase resistance to second
generation cephalosporins which being used to control nosocomial infections. When
the third generation cephalosporins were first introduced, A.baumannii developed
resistance to cefotoxime and ceftazidime. This bacterium therefore has an excessive
acquired resistance to β-lactam drugs (Towner 1997). There are no specific treatment
guidelines for Acinetobacter spp. due to the large variation in antibiotic resistance.
To determine the best mode of treatment for a particular isolate, antimicrobial
susceptibility testing must be performed (Forbes 2007).
Although, carbapenems have been the drug of preference in the treatment of
Acinetobacter infections numerous reports in the medical and scientific literature
have documented resistant strains to carbapenems such as imipenem and meropenem
(Costa et al., 2000; Levin 2002; Perez et al., 2007).
According to the Health Protection Agency in the UK (HPA) a survey of 1,225 cases
of bacteraemia due to Acinetobacter spp were reported from England, Wales and
Northern Ireland in 2007, with total incidence rate of 2.2 per 100,000 populations. In
the same survey 12% of Acinetobacter spp were shown to be resistant to imipenem,
whereas the prevalence of ciprofloxacin and gentamicin resistance was 16% and 12%
respectively (HPA 2008)
Currently, several surveys have studied the prevalence, mode of transmission and
risk factors of multi-drug resistant A. baumannii in ICUs and burn clinics. They
reported an increased incidence of an outbreak clone of A. baumannii (Babik et al.,
47
2008; Bacakoglu et al., 2009; Barchitta et al., 2009; Cootz and Marra 2008; Fontana
et al., 2008).
1.3.6 Carbapenem action on Acinetobacter:
Carbapenem is the most effective broad spectrum antibiotic among all of the βlactams and imipenem is one of the most important carbapenems (Bergogne-Berezin
et al., 2008). Imipenem is an active agent against many organisms including Gram
positive and Gram negative aerobes and anaerobes. It is a bactericidal agent that kills
or destroys bacteria at 2-4 times the MIC for most species (Greenwood et al., 2003).
The initial step of drug action in destroying bacteria is drug binding to the cell wall
receptors known as penicillin binding proteins (PBPs). The transpeptidation process
is stopped and peptidoglycan synthesis is blocked after β-lactam drug binding to one
or more receptors. The next step involves the removal or suppression of autolytic
enzymes inhibitor in the cell wall. This activates the lytic enzyme which results in
lysis of cells leads to cell death (Brooks et al., 2001).
1.3.7 Mechanisms of Carbapenem Resistance:
There are several complex mechanisms and genetics of resistance acquired by this
species which involve several plasmid-borne β-lactamases and aminoglycoside
modifying enzymes, as well as variation in membrane permeability and alteration in
penicillin-binding proteins. The possession of these multiple mechanisms may be due
to the physiological capability of Acinetobacter and it can obtain DNA by
transformation in vivo (Finch 2003)
48
In the last few years resistant strain of A. baumannii to carbapenem have been
reported globally and known as imipenem and meropenem resistant A. baumannii
(IMRAB). The epidemic strains of IMRAB demonstrated three different mechanisms
of β-lactamases, these are: plasmid mediated enzymes (TEM-1), chromosomal
mediated enzyme (Noval OXA-type) and cephalosporinase ampC-type enzyme (Bou
et al., 2000)
Major carbapenem-resistant Acinetobacter species have metallo-enzyme and OXAtype enzymes (Bou et al., 2000). However, OXA-type β-lactamases which belong to
class D β-lactamase have a poor activity against carbapenems; such enzymes
discovered in A. baumannii isolates from Argentina, Belgium, Kuwait, Scotland,
Spain and Singapore. Many of this group of enzymes have been classified to form a
subgroup in class D β-lactamases, currently including the OXA-23, -24, -25, -26, -27
and -40 types (Song et al., 2004).
There are several factors that determine the acquisition of multi- resistance in
A. baumannii. One is the intrinsic resistance of microorganisms, due to low level
diffusion of certain antibiotics through the outer membrane because of low number
of porins present (Levin 2002). Another is due to the acquisition genetic elements;
there are 3 types of mobile genetic elements have been found in Acinetobacter.
These are plasmids, transposons and integrons (Vila 1998). The plasmids contains 3
resistance genes; genes encoding ß-lactamase TEM-1, TEM-2, and CARB-5 (Bou et
al., 2000). The plasmid encoded β-lactamases have attracted great attention in which
the resistance of this bacterium occurs by a single genetic event. However, this type
of resistance occurs mostly in the highly selective environment of the hospital
(Greenwood 2000). Integrons, which are chains of genes with a greater mobility to
transfer from one location of A.baumannii chromosome to another with help of a
transposon, carry this component (Vila 1998). This may be an important factor in the
49
ability of Acinetobacter species to survive in human and environmental reservoirs in
which the genes of resistance may be transferred (Vila 1998).
For multi-drug resistant Acinetobacter infections, several studies have demonstrated
clinical effectiveness of sulbactam in combination with ampicillin or cefoperazone.
The only effective antibacterial agent to this bacterium is colistin (Winn et al., 2006).
One report has demonstrated the efficiency and safety of colistin in patients with
Acinetobacter infection that was not susceptible to carbapenem and shown that 57%
of patients cured with colistin therapy, without prolonged neuro-muscular blockade
as a side effect of therapy (Torres et al., 2007)
Recently Enoch and his colleges (2008) completed a six month study of the outbreak
caused by multi-drug-carbapenem-resistant Acinetobacter baumanii (MRAB-C)
strain in UK hospitals. This outbreak would have increased the rate of mortality if
not controlled properly. However, isolation of the patients infected with MRAB-C,
education of staff dealing with those patients, early patient and environmental
screening, and effective hygiene all helped to control this outbreak. Also, patients
infected with MRAB-C were treated with colistin and tigecycline and improved.
1.3.8 Acinetobacter Treatment with Honey:
Due to the emergence of bacteria resistant to antibiotics, the bactericidal properties of
manuka honey have been extensively researched. Currently, few studies have
reported the antibacterial activity of honey against Acinetobacter. George & Cutting,
(2007) initiated an in-vitro study of the antibacterial activity of Medihoney against
130 clinical isolates of multi-drug resistant organisms including Acinetobacter. The
study showed that the concentration needed to inhibit the resistant Acinetobacter
strains was 8% (v/v) of honey.
50
More recently, a study was carried out using Malaysian tualang honey in comparison
to manuka honey against wound pathogens including Acinetobacter. The
antibacterial activity for both honeys was same with the MIC ranges between (11.25
& 12.5% w/v) (Tan et al., 2009).
Although Acinetobacter spp. have been found to be susceptible to honey, more
objective evidence derived from clinical trails and animal models to determine
whether honey has a similar antimicrobial effect in-vivo are required.
1.4 Alternative Antimicrobial Therapies:
As concerns about antimicrobial resistance increase, efforts of the pharmaceutical
industry to develop new drugs have diminished and the possibility of running out of
effective antimicrobial agents has increased. It takes 10-12 years for new antibiotic to
be developed and costs approximately £250 million for each one (Greenwood 2003).
Yet the chance of developing a new drug that will have excellent bactericidal
properties without causing the emergence of resistance is impossible. The
introduction and application of honey into clinical practice several years ago has put
attention on using it for treatment of various infections. An extensive review of
clinical studies by Molan suggested that honey might be successfully used as an
antimicrobial agent and also in promoting healing of wounds (Molan 2006).
1.4.1 Ancient Use of Honey as a Medicine:
Honey has been used for many thousands of years as a food, a medicine and it has
been incorporated into cosmetics. A large number of different cultures have
extensively used honey as a medicine for many disorders. It has been used in wound
51
care since the time of ancient Egyptians, as suggested by an inscription on a
Sumarian clay tablet. Honey was also mentioned in the Holy Koran, the Talmud, the
Bible, as well as the sacred books of India, China, Persia and Egypt (Zumla & Lulat
1989). All the clues point to the medicinal use of honey throughout human history.
1.4.2 Honey as a Modern Medicine:
In the last 30 years interest in the use of honey as a treatment agent has increased.
Most research that has been undertaken has focused on the employment of honey in
wound treatment (Ahmed et al., 2003; Berguman et al., 1983; Dumronglert, 1983;
Emarah, 1982; Haffejee & Moosa, 1985; Ingle et al., 2006; Wadi et al., 1987).
In 1988, Efem reported the first large clinical cohort study involving 59 patients who
had a variety of wounds such as Fournier‟s gangrene, burns and ulcers. The use of
honey on these patients resulted in successful wound healing and the clearance of
infection. In addition Subrahmanyam (1993, 1994, 1996, 1998) and Subrahmanyam
et al., 2001, 2003, 2007) reported several clinical trials on burns patients with honey
compared to various different treatments.
Clinically, many researchers have studied the uses of honey in wound management
and it has been reported to clear wound pathogens rapidly (Al-Waili and Saloom,
1999; Lusby et al., 2002), to stimulate immune response and to reduce inflammation
(Molan and Betts 2001; Tonks et al., 2003) and to support the debridement of
wounds by autolysis (Stephen-Haynes 2004). In addition, honey has been reported to
have a deodorising property on wounds, due to the oxidation of glucose by bacteria
resulting in production of lactic acid rather than malodorous compounds such as
ammonia, sulphur compounds and amines produced by the breakdown of amino
acids (Cooper 2005; Molan 2002; Stephen-Haynes 2004). Moreover, honey has been
used effectively on skin grafts (Schumacher 2004), diabetic foot ulcers (Eddy &
52
Gideonsen 2005), malignant ulcers (Simon et al., 2005) and abscesses (Okeniyi et
al., 2005). Some researchers have observed that honey promotes tissue regeneration
through the stimulation of angiogenesis and the growth of fibroblasts and epithelial
cells (Efem, 1988, 1993; Stephen-Haynes 2004; Subrahmanyam, 1994, 1998). Fast
healing can therefore minimise the need for skin grafts (Subrahmanyam 1998).
Recently, (Gethin et al., 2008) observed that the use of manuka honey as a wound
dressing reduced wound pH which in turn decreased protease activity, increased
fibroblast activity and released more oxygen from haemoglobin to promote rapid
wound healing.
Furthermore, after honey is applied to the wound, it forms a film of liquid between
the wound and the dressing that prevents the dressing from sticking to the wound,
reducing pain and not damaging the newly formed cells. As honey has no adverse
effects on tissue, it can be used on wounds safely and introduced into cavities and
sinuses to clear infection (Molan 2000).
Despite, extensive anecdotal evidence to support the topical used of honey in treating
wounds, systematic review of the clinical evidence has not been so supportive (Bardy
et al., 2008; Jull et al., 2008; Moore et al., 2001).
1.4.3 Antimicrobial Activity of Honey:
The antibacterial activity of honey was first identified by Van Ketel in 1892
(Dustmann 1979). After that several studies established the antibacterial activity of
honey against various bacterial pathogens and fungi (Cooper et al., 1999; Efem, et
al., 1992; Molan, 1992a; Molan, 1992b; Mulu et al., 2004; Lusby et al., 2005;
Wilkinson and Cavonagh 2005). It was shown that honey inhibits various bacterial
53
species. There are many reports of it being bacteriostatic and bactericidal (Alandejani
et al., 2009; Henriques et al., 2009; Molan 1992b).
Undiluted honey was shown to prevent the growth of Candida albicans and
demostrated potential as a topical treatment of external fungal infections such as
ringworm and superficial candidiases (Brady et al., 1997; Efem 1992; Irish et al.,
2006; Wahdan 1998).
Cooper et al., (1999) showed that honey has an effective antibacterial activity against
the major wound infecting species including Staphylococcus aureus. A year later the
sensitivity of multi-resistant strains of Burkholderia cepacia isolated from cystic
fibrosis patients to manuka honey at concentrations below 6% (v/v) was reported
(Cooper et al.,2000).
Cooper et al., (2002a) compared the antimicrobial activity of artificial honey ( a
sugar solution) and two natural honeys (manuka and pasture honey) against 18
strains of MRSA isolated from wounds, 20 strains of vancomycin-resistant
enterococci (VRE) and 7 strains of vancomycin-sensitive enterococci (VSE). The
study showed the minimum inhibitory concentration (MIC) which is the
concentration required to inhibit the growth for the natural honeys was below 10%
(v/v) and < 30% (v/v) with artificial honey for all strains. This means the
antibacterial activity of honey is not limited to osmolarity. Also in the same study it
was concluded that there was no difference in the MIC values between the sensitive
and resistant Gram positive strains. Furthermore, the antimicrobial activity of the
same two natural honeys was tested against 17 strains of Pseudomonas aeruginosa
isolated from burns. Both honeys maintained bactericidal activity when diluted more
than 10-fold (Cooper et al., 2002b). In addition, a study showed that manuka honey
(MIC 3.4±0.5% (v/v)) has a potential activity to control and prevent infection with
coagulase-negative staphylococci (French et al., 2005)
54
Nezeako & Hamdi, (2000) tested six commercial honey samples against control
organisms, Staph.aureus, E. coli, P. aeruginosa and various clinical isolates. They
found that some samples had high broad-spectrum antimicrobial activity which
resisted refrigeration temperature for six months and being boiled for 15 minutes. In
addition Mullai and Menon (2007) tested the antibacterial effect of different types of
honey against 150 strains of P. aeruginosa isolated from otitis media, diabetic foot
ulcers and burns wound. MIC of 20% (v/v), 11% (v/v) and 20% (v/v) were
determined from manuka, khadikraft and heather honey respectively.
Recently, Blair et al., (2009) studied the antimicrobial activity of Leptospermum
Medihoney with high levels of hydrogen peroxide-dependent activity or Comvita
manuka woundcare 18+ against MRSA, Acinetobacter and 6 strains of multi-drug
resistant Enterobacteriaceae. MICs ranged from 4 to 5% w/v, 6.0 to 9.3% w/v and
6.3 to 14.8% w/v respectively. This indicated that active Leptospermum honey is
potentially active against antibiotic-resistant clinical pathogens.
A survey was carried out with regard to 345 samples of New Zealand honey to assess
the antibacterial activity of honey. Four types of honey were shown to have high
antibacterial activity equivalent to phenol standard. In this assay, antibacterial
activity was not detected in almost all honeys when catalase was added to remove
hydrogen peroxide.
However, manuka and vipers bugloss honeys showed
measurable amount of hydrogen peroxide which is believed to aid antibacterial
activity (Allen et al., 1991).
1.4.4 Chemical composition of honey:
Honey is a combination of sugars, water, and other compounds (Table 1.1), the
specific composition depending largely on the mix of flowers foraged by the bees. It
55
has been reported that approximately 181 substances are present in honey (Terrab et
al., 2003).
Table 1.1: Honey composition
Substance
Percentages %
Water
17.1
Fructose
38.5
Glucose
31.0
Maltose and other reducing disaccharides
7.2
Sucrose
1.5
Tri-saccharides and other carbohydrates
4.2
Minerals, Vitamins & Enzymes
0.5
Sammataro and Avitabile (1998)
The composition of honey varies from one honey to another depending on several
factors. A major factor is the floral source, as the nectar from different plants will
contain different compositions of the main sugars and trace elements. These
compositions are influenced by soil type, climatic conditions (seasons) and the
environment surrounding the plant (Crane 1979).
It is recognized that some chemical changes occur when the nectar is transformed to
honey. These changes are mainly because of some bee enzymes deposited in the
honey. These enzymes are invertase that hydrolyses sucrose into glucose and
fructose, amylase or diastase enzymes and glucose oxidase that generate gluconic
acid and hydrogen peroxide from glucose in diluted honey. Other enzymes which are
also present in the honey are catalase and acid phosphatase (Sammataro and
Avitabile 1998).
56
In addition, honey contains several B vitamins such as riboflavin, niacin, folic acid,
and B6. Moreover, honey contains a number of minerals such as calcium, iron, zinc,
potassium, phosphorus, magnesium, selenium, chromium and manganese (Atrouse et
al., 2004; White 1975). Cooper and Jenkins, (2009) compared the antibacterial
activity of medical grade honey with 18 table honeys collected from different
regions. They found that all table honeys have less antibacterial activity compared to
medical grade honey. Moreover, all table honeys were non sterile and contained
various bacterial species such as mesophilic aerobic bacteria, coliforms and
clostridia.
1.4.5 Factors Contributing Antibacterial Properties of
Honey:
Until 1963, it was thought that the antimicrobial properties of honey were mainly
because of hydrogen peroxide, but further studies have indicated that other physical
factors like acidity, osmolarity (Molan 1992a) and electrical conductivity, and
chemical factors including volatile compounds (Yao et al., 2003), antioxidant
(Gheldoff et al., 2002; Henriques et al., 2006), beeswax, propolis and pollen (ViudaMartos et al., 2008) play a considerable role in antimicrobial activity.
1.4.5.1 Osmotic effect:
Honey is a super saturated solution of sugar (80%) and water (17%). The osmolarity
of honey inhibits microbial growth because of the strong interaction of sugar
molecules with water molecules thus, insufficient water molecules are available to
support microbial growth. This availability is known as water activity (aw). Water
involved in many metabolic processes in many organisms. Depending on the
57
permeability of cell membrane in each organism the water activity (aw) of many
bacterial species is vary between 0.94-0.99. The water activity of honey is 0.6
because of high sugar molecules and low water thus many species cannot grow in
that environment. Fungi can tolerate a lower aw than bacteria, so reports of antifungal
activity with diluted honey reveal that there are more factors involved than only the
sugar content of honey. Also, Staph aureus has a high tolerance of low aw (0.86) ie.
can tolerate high NaCl level but not high sugar therefore, it considered as one of the
most susceptible species to the antibacterial activity of honey (Molan 1992a).
1.4.5.2 Acidity
Honey is quite acidic; normally, it has an average pH of 3.9 (with a typical range of
3.2 to 4.5). It has been known that this acidity is a result the conversion of glucose to
gluconic acid with help of glucose oxidase enzyme (Molan 2001b). The optimum pH
for growth of many bacterial species is 7.2 – 7.4. However, the lowest pH value for
growth of some wound pathogens is 4.3 for E .coli and 4.4 for P. aeruginosa. The
low pH of honey is therefore important to slow down or inhibit bacterial growth
(Bogdanov 1996; Molan 2000a). Since 2001, the osmotic effect was thought to be
the main factor for antimicrobial activity (Molan 2001b). However, in 2005 a study
compared honey and sugar solution of same osmotic effect on coagulase negative
staphylococci. The study confirmed that antimicrobial properties are not exclusively
due to osmotic effect (French et al., 2005). It has been noted that the pH of honey
also generates and maintains good environment for fibroblast activity (Lusby et al.,
2002).
1.4.5.3 Hydrogen peroxide production
In 1919, Sackett reported that in diluted honey the antibacterial properties of honey
were increased. This is because when honey is diluted, hydrogen peroxide is released
58
with the help of an enzyme (glucose oxidase) that is found in honey (Molan 1992b).
This enzyme is secreted by the hypopharyngeal gland of bees and added to nectar
during honey formation (Borland 2000).
Glucose + H2O + O2
glucose oxidase
gluconic acid + H2O2
Hydrogen peroxide (H2O2) is considered to be one of the main factors in antibacterial
activity of honey. It is involved in cell multiplication in different cell types in the
body as certain a concentration of H2O2 can support epithelial cells and fibroblast
growth to repaire damage or injury (Burdon 1995). It also promotes wound healing
by regeneration of new capillaries (Tur et al., 1995). The enzyme (glucose oxidase)
is inactive in full strength honey due to the low pH, so the diluting action of fluids
produced by the wound is thought to activate glucose oxidase to produce hydrogen
peroxide. In addition, it stays in the honey during storage without losing activity.
Hydrogen peroxide was used for long time to disinfect wounds in hospitals. This
chemical causes damage to the tissues and inflammation due to free radical that is
produced. The levels of H2O2 in honey are around 1000 times lower than those
applied as antiseptic on wounds (Molan 2001b). As a result it does not inflame a
wound or damage the tissue (Bang et al., 2003). Weston (2000) suggested that the
level of H2O2 was related to floral source, and that it depended on the balance
between the production and destruction rate of H2O2. Destruction of H2O2 is due to
catalase which derives from both the pollen and the nectar of plants, and the amount
of catalase in different sources is variable. In addition, Brudzynski (2006) studied the
effect of H2O2 on the antibacterial activity of 42 honey samples from Canada. She
found that the antibacterial activity was correlated with production of H2O2 in honey.
59
1.4.5.4 Non-peroxide Components
Several efforts were made to identify the non-peroxide antibacterial components
present in the honey (Allen et al., 1991). Weston et al., (1999) separated the
antibacterial phenolic fraction (APF) from the honey which consisted of benzonic
acids, cinnamic acids and flavonoids. It was determined that APF plays a small role
in manuka honey as non-peroxide antibacterial component, therefore, there are other
factors which were need to be identified. Honey contains a variety of polyphenolic
compounds that may be capable of chelating metal ions and decreasing oxidation
(Gheldof et al., 2002). Two important classes of phenolic compounds are flavonoids
and phenolic acid which are known as natural antioxidants (Molan 1992a; Pyrznska
and Biesaga 2009; Yao et al., 2003). In a study performed by Wahdan (1998), two
phenolic acids were extracted for the first time; these were caffeic acid and ferulic
acid. Flavonoids had shown a range of biochemical and pharmacological actions,
which affect the inflammatory cells and the generation of inflammatory processes
(Viuda-Martos et al., 2008). The use of flavonoids in medicine is increasing due to
their ability to trap free radicals, to stimulate hormones and neurotransmitters, and to
inhibit specific enzymes which produce superoxide anions (Pyrznska and Biesaga
2009).
However, it has been identified that several organic components in the ether extract
of honey possess antibacterial activity; these include 3,5-dimethoxy-4-hydroxy
benzoic acid (syringic acid), and methyl 3,5-dimethoxy-4- hydroxy benzoate (methyl
syringate) (Russel et al., 1988). By using high performance liquid chromatography
(HPLC), some other flavonoids and phenolic acids have also been identified in
different honeys, for example, pinocembrin, pinobanksin and chrysin (Bogdanove et
al., 1989), gallic acid and abscisic acid (Yao et al., 2003) caffeic acid and ferulic acid
60
(Wahdan 1998), and vanillic acid, cinnamic acid, and benzoic acid (Weston et al.,
1999; Weston et al., 2000).
1.4.5.5 Antioxidant activity:
Antioxidants are substances that protect wound tissues from being damaged by
oxygen radicals. The free radicals may be produced by hydrogen peroxide and cause
cellular damage. Free radicals are involved in cell toxicity and can alter cell
biomolecules such as proteins, carbohydrates, lipids and nucleic acids causing cell
death (Nagai et al., 2001).
Gheldof et al., (2002) analysed the antioxidant activity in different honey fractions
and determined that most of the antioxidant components were found in the watersoluble fraction.
These include
gluconic
acid,
protein, ascorbic acid,
hydroxymethylfuraldehyde, and the combined activities of the enzymes glucose
oxidase, catalase and peroxidase. The same study also showed that the phenolic
compounds in honey contributed very significantly to its antioxidant capacity.
When honey is diluted the release of high levels of hydrogen peroxide may lead to
tissue damage by formation of free radicals such as hydroxyl and superoxide. Many
honeys including manuka honey have the ability to quench free radicals. This
property may play a role in reducing inflammation and chronic wound infection
(Henriques et al., 2006)
A recent study was completed by Van de Berg et al., (2008) with regard to the
antioxidant level in buckweat honey showing that this type of honey reduced the
level of reactive oxygen species (ROS) which affect the wound healing process.
Also, beside the low pH and high acidity buckwheat honey was shown to contain
high amounts of phenolic components that aid the antimicrobial mechanisms and
61
block the oxidative reaction system (Inoue et al., 2005). In addition, several reports
demonstrated the relationship between the antioxidant and the colour of honey,
where darker honey exhibited higher antioxidant content (Bogdanov et al., 2004;
Estevinho et al., 2008; Turkmen et al., 2006). It has been thought that non-hydrogen
peroxide activity in manuka honey may be due to plant derived components such as
flavonoids and phenolic compounds. Recently, two research groups have reported
that the activity of Leptospermum honeys correlates with the presence of
methyglyoxal (MG), an alpha-oxoaldehyde that reacts with macromolecules such as
DNA, RNA and proteins (Adams et al., 2008; Mavric et al., 2008). High amount of
MG was present in some manuka honey which is equivalent to the non-peroxide
activity. MG was, therefore, known as a bioactive complex responsible for the
antibacterial activity in manuka honey (Mavric et al., 2008).
Recently Atrott and Henle (2009) studied the presence of methylglyoxal in 61
samples of manuka honey. They found that the antibacterial activity ranged between
12.4% to 30.9% equivalent to phenol concentration.
More recently Kwakman et al., (2010) discovered an antibacterial bee peptide called
bee defensin-1 in honey. To date this peptide has been isolated only from a honey
used in the production of Revamil and it was confirmed that this protein exhibits
most of the antibacterial activity. The exact mechanism of bee defensin-1 on bacteria
is not yet known.
62
Table 1.2: Comparisons between peroxide and non-peroxide honey
Form
Effected by heat &
light
Effected by long
storage
Origin: Bee
Origin: Flora
Mode of action
Peroxide honey
Non-peroxide honey
H2O2
Polyphenolic compounds
Sensitive
Thermostable
Yes
No
glucose oxidase
Honey acidity, diastase &
invertase enzymes
-
Flavonoids, phenolic acids
active only when diluted, Antioxidant, MGO
tissue repair
High level
Bad- release of free radical Good- more scavenging
(quenching properties)
Example
Almost all honey
Manuka, Jelly bush honey
63
1.4.5 Manuka Honey:
Manuka honey comes from the tree (Leptospermum scoparium) and is currently
approved for therapeutic use in many countries. The honey mainly derived from
nectar taken from trees of L.scoparium variety is referred to as manuka; a closely
related shrub is Kunzea ericoides commonly referred to as kanuka. These are shrubs
which form bushes with height of 12-15 ft. They belong to the Myrtaceae family and
are native to New Zealand although other species of Leptospermum are found in
Australia.
A survey of 345 New Zealand honeys found that antibacterial activity of manuka
honey was retained in the presence of catalase, and was called non-peroxide activity,
while most other types of honey were found to be inactive when catalase was added.
The later honeys were called peroxide honeys. In this study antibacterial assays were
performed using phenol as a standard and Staphylococcus aureus as the test strain.
This developed a measure of activity relating to percentage of phenol which had the
same degree of antibacterial activity (Allen et al., 1991). This system was then
granted by New Zealand government as the UMF or „Unique Manuka Factor‟ to give
a standard for customers. It is now recommended to use honey with a UMF of 10 or
higher (equivalent activity of 10% phenol) to ensure sufficient activity circulate into
deeper tissues where severe wounds are involved. Higher UMF ratings indicate
higher levels of antibacterial activity (Molan 2001b). Recently, several rating
systems developed by increasing the companies that produce manuka honey.
Methylglyoxal (MG) and Molan Gold Standard are new registered trademarks from
different
organizations
to
rate
the
potency
(http://www.molangoldstandard.co.nz/article, 2010).
64
of
manuka
honey
Another study compared the effectiveness of manuka honey with other peroxide
honey against 7 Helicobacter pylori strains isolated from biopsies of stomach ulcers.
The MIC was 5% (v/v) for manuka honey & >40% (v/v) for other honey (Al Somal
et al., 1994).
Some studies have reported the in vitro effectiveness of manuka honey against
biofilms produced by 3 species; Pseudomonas aeruginosa, methicillin resistant
Staphylococcus aureus (MRSA) & methicillin sensitive
Staphylococcus aureus
(MSSA) with killing rate of 91%, 63% and 82%, respectively (Alandejani et al.,
2009; Okhiria et al., 2009).
The high level of methyl syringate in manuka honey was proved to scavenge
superoxide anion radicals (Inoue et al., 2005). In addition, Stephens and his
colleagues (2009) have analysed the phenolic components in manuka and kanuka
honeys. Both honeys have six phenolic acids in common as a primary component. In
the same study elevated levels of MG in manuka honey acted as a source of
antibacterial activity. Both of these components (MG and phenolic compounds)
could contribute to the bacterial effect of honey in medical use.
The conclusion from these observations is that certain honeys contain antimicrobial
factors such as methylglyoxal and bee defensin-1 in addition to sugar content, low
pH and hydrogen peroxide generation.
However, in order for honey to be accepted as an alternative to antimicrobial, it is
desirable to characterise the components that are responsible for its activity.
Although many constituents of the antibacterial activity of honey are known, such as
the sugars, pH, hydrogen peroxide, and more recently MG and bee defensin-1, these
do not account for the total antibacterial activity observed in many honeys. However,
complete identification of the antibacterial components has not been achieved
65
because of the complexity of honey itself and the possible interaction between
different substances present in the honey. Recently, a few studies concentrated on the
effect of honey against certain bacteria by looking into the intracellular change in
order to find the mechanism of action and the target site of the honey. They found
that 10% (w/v) of manuka honey was able to cause marked structural changes to
Gram positive S.aureus. The mechanism was not fully understood however; the
inhibition of the cell cycle with increased septa formation is obvious in this study and
is considered as one mechanism leading to cell death (Henriques et al., 2009). A
similar study with P. aeruginosa has demonstrated that manuka honey induced cell
lysis (Henriques et al., 2010).
1.4.6 Omani Honey:
Beekeeping has been practised since ancient times in Oman. Oman has a wideranged landscape characterized in its dried-up river beds, hills, plains and deserts in
which grow the plants and trees that supply the honey bee with the nutrients it
requires. Such trees are palm trees, coconut palms, cereals, limes, vegetables, sugar
cane, frankincense and gum trees (Al-Taie et al., 1999)
In particular, trees such as the Simr (Acacia tortilis), Sidr (Ziziphus spina-christi),
Ghaf (Prosopis cineraria), coconut palm, prickly pear and papaya trees make the
primary ingredients that give Omani honey its distinctive flavour (Al-Taie et al.,
1999). Honeycombs are formed within one and half months and harvested twice a
year, which is long enough not to be mixed with the date formation period. Acacia
tortilis take part in the production of honey, because it flowers at a different time of
year compared to other trees. This expands the season in which bees can collect
nectar and produce their honey. Total polyphenols, flavonoids and antioxidant levels
66
of raw honey samples from Acacia, have been evaluated. Phenolic content, expressed
as caffeic acid equivalents, ranged from 3 to 11 mg/100 g in Acacia. Total flavonoids
in 100 g Acacia honey were in the range of 0.45–1.01 mg CE (Blasa et al., 2006)
Two types of bees have been identified in Oman these are Apis mellifera and Apis
florae. The specialist beekeepers of northern Oman have build up a huge skill in
gaining honey and proliferating bee colonies in a controlled approach (Sajwani et al.,
2007a).
The distribution area of A. florae is commonly restricted to warm climates. In the
west, this species is found in the warmer parts of Oman, Iran and Pakistan, through
India and Sri Lanka (Akratanakul 1990). A recent 3 year melissopalynological study
of 48 Omani honey samples collected from 14 locations in the Muscat and Al Batina
regions of Oman was conducted. It was found that 32 honey samples among 48 are
monofloral types and the other 16 were multifloral. Overall 122 pollen types from 50
plant families were recognized (Sajwani et al., 2007a). In addition, sugar and protein
profiles of same Omani honeys were measured (Sajwani et al., 2007b)
Al-Jabri et al., (2003) compared the antibacterial activity of 16 honeys from different
parts of Oman, and 8 from different countries in Africa, against three organisms
Staph.aureus, E. coli, P. aeruginosa. It was found that Dhofar honey (Oman) and
eucalyptus honey (Uganda) had the highest level of activity against the three test
organisms. Another study was performed by the same group on the interaction of
honey and bovine milk against S.aureus (Al-Jabri et al., 2005b).
In addition, anti-Staphylococcal activity of thirty types of Omani honey was tested
alone and in combination with gentamicin. It was observed that thirteen of the Omani
honeys had high anti-Staphylococcus aureus activity. The killing rate for the best
honey was 38% of Staphylococcus aureus using 50% (w/v) concentration honey in
67
30 minutes. Gentamicin (4µg/ml) killed 70% of isolate; honey and gentamicin were
combined to give excellent killing rate at 92% in the same duration (Al-Jabri et al.,
2005a).
The first study of the ability of honey to prevent bacterial adherence in-vitro was
done by Al-Naqdy et al., (2005). Four different types of Omani honey were used in
this study of growth inhibition. Bacterial adherence was examined using Salmonella
enteritidis that had been incubated first with honey and then with intestinal epithelial
cells. Results showed decreases in the number of bacteria attached to the treated
epithelial cells from 25.6±6.5 to 6.7±3.3 bacteria per epithelial cell (P<0.001).
The efficacy of Oman honey against antibiotic resistant strains of wound pathogens
has not been investigated, and antibacterial activity has not been compared to that of
medical grade manuka honey
68
1.5 Aims and Objectives:
The aim of this study was to investigate the inhibition of MDR bacteria including
ESBLs by a medical grade honey and a selection of Omani honeys. The following
objectives were identified:
1. To investigate the effect of a medical grade manuka honey on MDR bacteria
possessing ESBLs by determining:

The MIC and MBC of manuka honey against selected organisms

The bactericidal activity of manuka honey using time-to-kill curves assay

The surface and intra-structural morphological changes of bacterial cells after
treatment with manuka honey using scanning and transmission electron
microscopy.

the physiological effect of manuka honey against selected organisms using
two-dimensional electrophoresis
2. To investigate the potential of Omani honeys for their use in wound care in
Oman by:

Characterizing the physical and chemical properties of selected Omani honey.

Investigating the antimicrobial activity of Omani honey against MDR and
ESBLs bacteria

Investigating the antioxidant potential activity of selected Omani honey
69
Chapter 2
Materials &
Methods
70
The materials used throughout this study are listed as chemicals and reagents (Table
2.1, 2.2 & 2.3), equipment and apparatus (Table 2.4 & 2.5), and MDR and ESBL
clinical isolates (Table 2.3 and 2.4, respectively).
Table 2.1: Media, Chemicals and Reagents used for general experiment in the
project:
Name of Chemical
Suppliers
Nutrient agar CM 0003
Oxoid
Mueller-Hinton agar (MH) CM0337
Oxoid
Eosin Methylene Blue agar (EMB) CM0069
Oxoid
Iso-sensitest broth (ISB) CM 0473
Oxoid
Tryptone Soya Broth (TSB)
Oxoid
Maximum recovery diluent (MRD)
Oxoid
Catalase 010M7010
Sigma-Aldrich
Nutrient broth (NB)
Oxoid
Phenol
Fisher
Staph aureus – Oxford strain- NCTC 6571
Selectrol
Bovine albumin serum (BSA)
Sigma
DPPH (2,2-Di(4-tert-octylphenyl)-1-picryl-hydrazyl)
Sigma-Aldrich
Free radical – 257621
71
Table 2.2: Chemicals and Reagents used for electron microscopy experiment in
the project:
Name of chemicals / reagents
Suppliers
Phosphate Buffered Saline (PBS)
Oxoid
Glutaraldehyde 3%
Fluka-Sigma
Osmium tetroxide 1% (OsO4)
Agar scientific
Ethanol
Sigma-Aldrich
Filter membrane (0.02mm pore size)
Agar scientific
Resin (no propylene oxide)
Agar scientific
Araldite resin CY212
Agar scientific
DDSA
Agar scientific
BDMM/propylene oxide
Agar scientific
Uranyl acetate
Sigma
Reynolds lead citrate
Sigma
72
Table 2.3: Chemicals and Reagents used for 2- dimensional electrophoresis
experiment in the project
Name of chemicals / reagents
Suppliers
DC protein assay reagent A - 8473
BioRad
DC protein assay reagent B - 500-0114
BioRad
2,4,6-Tris(2-pyridyl)-s-triazine- T1253
Sigma-Aldrich
Trizma®base (C4H11NO3) BCBC0475
Sigma-Aldrich
Iodoacetamide (C2H4INO) 030M5300
Sigma-Aldrich
MOPS (3-Morpholino-Propansulfonsaure acid – 69947 Fluka, Sigma-Aldrich
DL-Dithiothreitol (C4H10O2S2) D9163
Sigma-Aldrich
Trizma®hydrochloride (C4H11NO3) T3253
Sigma-Aldrich
Sodium dodecylsulfate (C12H25NaO4S) L3771
Sigma-Aldrich
CHAPS (C32H58N2O75) C9426
Sigma-Aldrich
Simply Blue™ Safe stain LC6060
Invitrogen
Ethylenediaminetetraacetic acid tetrasodium sodium
Sigma-Aldrich
salt dihydrate E6511
Glycerol G8773
Sigma-Aldrich
Sodium chloride NaCl
Fisher scientific
Urea (CH4 N2O) 46504
Sigma-Aldrich
Bromophenol Blue B0126
Sigma-Aldrich
Agarose- low melting point A9414
Sigma-Aldrich
73
Table 2.4: Equipment used in general experiments in the project:
Name of Materials
Suppliers
Automatic pipettes
Eppendorf
Volumetric flasks
Volac-UK
Conical flasks
PYREX-UK
Autoclave
LTE scientific
Micro-centrifuge
Sanyo-MSE
Weighing scale DP-300
Fisher brand
Microtitre plate/96wells Nunc (flat bottomed)
Fisher scientific Nuclon™
Surface
Bioassay plate
CORNING 43110
Multipoint inoculator
MAST
Spectrophotometer/Plate reader
MRX Revelation, Dynex
BBL Sens-Disc for ESBL
BBL
Oxoid Sens-Disc
Oxoid
Spectrophotometer
JENWAY 6705UV Vis
Disc dispenser
Oxoid
AmpC & ESBL ID set D68C
MAST Diagnostic-UK
Incubator 37 oC
BINDER
pH meter 3510
JENWAY
Water refractometer
Atago HHR-2N
Sugar refractometer (40-85%)
Bellingham & Stanley
Water bath 37 oC/100
OLS200-Grant
BioPak (Synergy UV)
B0901
74
Table 2.5: Equipment used in electron microscopy and 2-dimentional
electrophoresis experiments in the project:
Name of materials
Suppliers
Critical point dryer (CPD)
Balzers CPD-030
Gold sputter coater AE 1232
EM Scope UK
Scanning electron microscopy (SEM)
JEOL 5600V SEM- UK
Transmission electron microscopy (TEM)
Leica-Reichert- Jung UK &
JEOL 1230 TEM- UK
Ready strip™ 11cm pH 3-10
BioRad
Protean IEF cell & Tank
BioRad
Refrigerated superspeed centrifuge
Sorvell® RC-5B
Thirty
isolates
of
baumannii/calcoaceticus
Acinetobacter
complex
that
baumannii
were
resistant
and
to
Acinetobacter
third
generation
cephalosporins (3rd G cephalosporin) were kindly provided by the Department of
Medical Microbiology in the University of Wales Hospital at Heath Park, Cardiff.
Antibiotic sensitivities had already been evaluated in the hospital using antibiotic
sensitivity discs for cefotaxime (CTX 30µg), ceftazidime (CAZ 30µg), ceftriaxone
(CRO 30µg) & cefoxitin (FOX30µg). This information was provided with the
isolates (Table 2.6).
75
Table 2.6: Thirty isolates of MDR Acinetobacter
S. no.
Identification of isolates
1.
Acinetobacter baumannii /calcoaceticus complex
2.
Acinetobacter baumannii/calcoaceticus complex
3.
Acinetobacter baumannii
4.
Acinetobacter baumannii
5.
Acinetobacter baumannii
6.
Acinetobacter baumannii/calcoaceticus complex
7.
Acinetobacter baumannii/calcoaceticus complex
8.
Acinetobacter baumannii/calcoaceticus complex
9.
Acinetobacter baumannii/calcoaceticus complex
10.
Acinetobacter baumannii/calcoaceticus complex
11.
Acinetobacter baumannii
12.
Acinetobacter baumannii/calcoaceticus complex
13.
Acinetobacter baumannii
14.
Acinetobacter baumannii
15.
Acinetobacter baumannii/calcoaceticus complex
16.
Acinetobacter baumannii/calcoaceticus complex
17.
Acinetobacter baumannii/calcoaceticus complex
18.
Acinetobacter baumannii
19.
Acinetobacter baumannii
20.
Acinetobacter baumannii
21.
Acinetobacter baumannii
22.
Acinetobacter baumannii
23.
Acinetobacter baumannii
24.
Acinetobacter baumannii
25.
Acinetobacter baumannii
26.
Acinetobacter baumannii
27.
Acinetobacter baumannii
28.
Acinetobacter baumannii
29.
Acinetobacter baumannii
30.
Acinetobacter baumannii
76
Fifty five clinical isolates belonging to the Enterobacteriaceae family that were resistant
to third generation cephalosporins (3rd G cephalosporin) with their mechanisms of ESBL
production by molecular method were also provided for this study (Table 2.7a & 2.7b).
Isolates were stored at -80ºC on Protect beads until required. When needed, one bead
was introduced into 10 ml nutrient broth and incubated overnight at 37oC. The broth
culture was either used directly or streaked onto nutrient agar and incubated at 37ºC
overnight.
Table 2.7a: Clinical isolates of E.coli & Klebsiella provided for this study
Identification of isolates
Mechanisms
31.
Escherichia coli
CTX-M
32.
Escherichia coli
CTX-M
33.
Escherichia coli
CTX-M
34.
Escherichia coli
CTX-M
35.
Escherichia coli
CTX-M
36.
Escherichia coli
TEM
37.
Escherichia coli
TEM
38.
Escherichia coli
AMPC
39.
Escherichia coli
N.A
40.
Escherichia coli
N.A
41.
Klebsiella pneumoniae
SHV
42.
Klebsiella pneumoniae
CTX-M
43.
Klebsiella pneumoniae
CTX-M
44.
Klebsiella pneumoniae
CTX-M
45.
Klebsiella oxytoca
CTX-M
46.
Klebsiella pneumoniae
SHV
47.
Klebsiella pneumoniae
SHV
48.
Klebsiella oxytoca
SHV
49.
Klebsiella pneumoniae
Plasmidic ampC
50.
Klebsiella oxytoca
K1
51.
Klebsiella pneumoniae
N.A
NA = not available
77
Table 2.7b: Clinical isolates of Citrobacter, Enterobacter & Serratia provided for
this study
Identification of isolates
Mechanisms
52.
Citrobacter farmeri
CTX-M
53.
Citrobacter farmeri
Hyper ampC expression
54.
Citrobacter farmeri
Hyper ampC expression +CTX-M
55.
Citrobacter farmeri
Hyper ampC expression +TEM
56.
Citrobacter farmeri
Hyper ampC expression +CTX-M
57.
Citrobacter farmeri
Hyper ampC expression
58.
Citrobacter farmeri
Hyper ampC expression
59.
Citrobacter farmeri
Hyper ampC expression
60.
Citrobacter farmeri
Hyper ampC expression
61.
Citrobacter farmeri
Hyper ampC expression
62.
Citrobacter farmeri
Hyper ampC expression +TEM
63.
Citrobacter farmeri
Hyper ampC expression +CTX-M
64.
Enterobacter cloacae
CTX-M
65.
Enterobacter cloacae
CTX-M
66.
Enterobacter cloacae
CTX-M
67.
Enterobacter cloacae
CTX-M
68.
Enterobacter hormaechi
CTX-M
69.
Enterobacter cloacae
CTX-M
70.
Enterobacter cloacae
CTX-M
71.
Enterobacter aerogenes
AMPC
72.
Enterobacter cloacae
TEM
73.
Enterobacter cloacae
Hyper ampC expression
74.
Enterobacter cloacae
Hyper ampC expression
75.
Enterobacter cloacae
Hyper ampC expression
76.
Enterobacter cloacae
Hyper ampC expression
77.
Enterobacter cloacae
Hyper ampC expression
78.
Serratia plymuthica
CTX-M
79.
Serratia marcescens
Hyper ampC expression
80.
Serratia marcescens
Hyper ampC expression
81.
Serratia marcescens
Hyper ampC expression
82.
Serratia marcescens
Hyper ampC expression
83.
Serratia marcescens
Hyper ampC expression
84.
Serratia marcescens
Hyper ampC expression +SHV
85.
Serratia marcescens
Hyper ampC expression
78
2.1 Characterization of test organisms:
2.1.1 Confirmation of the identity of test organisms:
To confirm the identity of these isolates, each culture was tested with API 20E
(BioMerieux, Becton & Dickinson) according to the manufacturer‟s instructions. To
verify that the kits gave reliable results, two known organisms (Escherichia coli
NCTC 10410 and Klebsiella oxytoca NCTC 8167) were tested on each occasion.
2.1.2 Antibiotics sensitivities: Extended Spectrum Betalactamases test (ESBLs):
Antibiotic sensitivity of the clinical isolates was also confirmed and the method used
was that of Clinical and Laboratory Standard Institute (CLSI 2006).
2.1.2.1 Inoculum preparation:
Three to four similar colonies were selected from a pure overnight nutrient agar plate
culture and transferred with inoculation loop into 4-5 ml of nutrient broth and
incubated at 37°C for 2-6 h. The inocula were standardized photometrically to obtain
turbidity equivalent to the 0.5 McFarland standards.
2.1.2.2 Disc application:
A sterile cotton wool swab was dipped into the standardised inocula and excess
culture removed by pressing against the edge of the culture bottle. The entire agar
surface of a Mueller Hinton plate was streaked in three directions to produce an even
lawn plate. Then, the appropriate discs (Oxoid Sens-Disc) of carbapenem (imipenem
10µg and meropenem 10µg), third and fourth generation cephalosporins (cefotaxime
CTX 30µg, ceftazidime CAZ 30µg and cefepime FEP 30µg) respectively,
aminoglycosides (amikacin AK 30µg & gentamicin CN 10µg) and piperacillintazobactam TZP 100/10 µg were applied for all isolates by disc dispenser.
79
Appropriate discs (BD BBLTM Sens-Disc) of ceftazidime (CAZ 30µg), cefotaxime
(CTX 30µg), ceftazidime with clavulanic acid (CAZ/CLA 30/10µg) and cefotaxime
with clavulanic acid (CTX/CLA 30/10µg) were also dispensed for the 55
Enterobacteriaceae isolates. The plates were placed in the 37°C incubator within 15
minutes and incubated for 16-18 h. After incubation, the plates were examined and
zone diameters were recorded. Antibiotic sensitivity was determined by reference to
the (CLSI).
2.1.2.3 Screening test for ESBL:
If the zones for each of CAZ 30µg ≤ 22mm, CTX 30µg ≤ 27mm and CRO 30µg ≤
25mm, ESBL production was indicated.
2.1.2.4 Phenotypic confirmatory test for ESBL:
A confirmatory test was required for each of ceftazidime and cefotaxime in
combination with clavulanic acid.
For comparison with zone sizes without
clavulanic acid a ≥ 5 mm increase in zone diameter for either antimicrobial agent
tested in combination with clavulanic acid versus its zone when tested alone
confirmed an ESBL producer.
A positive control (ESBL producer) of Escherichia coli ATCC 13353 and a negative
control (non-ESBL producer) Escherichia coli NCTC 10418 were used for quality
control of this method.
2.1.2.5 ESBL/AmpC* confirmation test:
This test was used for the detection of AmpC and/or ESBL enzyme production. Four
discs were applied: cefpodoxime (10 µg), cefpodoxime (10µg) with ESBL inhibitor,
cefpodoxime (10µg) with AmpC inhibitor and cefpodoxime (10µg) with ESBL and
AmpC inhibitor. The interpretation of results was determined using software product
of ESBL/AmpC calculator (EAC) programme provided by MAST manufacturer.
80
2.2 Characterization of honey samples:
2.2.1 Honey sample collection:
A medical grade honey (manukacare 18+), which was kindly provided by Comvita
UK, was used throughout this study. Eight samples of raw and unprocessed Omani
honey (labelled OH-B to OH-I) were collected from different regions of Oman
(Table 2.8). The floral source was estimated by beekeepers. All samples were stored
at 4°C in the dark until tested.
Table 2.8: List of honeys used in this project:
Batch Number
Flora Source
Country
Regions
A
Manuka
New Zealand
-
OH-B
Multi-flora
Oman
Batina
OH-C
Honey dew
Oman
Dakhilyia
OH-D
Honey dew
Oman
Batina
OH-E
Honey dew
Oman
Dhahira
OH-F
Citrus
Oman
Sumail
OH-G
Honey dew
Oman
Batina
OH-H
Acacia
Oman
Sharqiya
OH-I
Market honey
Oman
Not known
2.2.2 Bioassay of antibacterial activity:
The antibacterial activity of all honey samples was determined by the method
described by Allen et al., (1991).
81
2.2.2.1 Phenol standards preparation for bioassay:
10% (w/v) of phenol (a reference antiseptic) (Fisher) was prepared by weighing 5 g of
phenol made up to 50 ml with deionised water. This solution of phenol was used to
prepare the standard solutions of 2%, 3%, 4%, 5%, 6% and 7% (Table 2.9).These
standards were stored at 4oC till one month.
Table 2.9: Preparation of phenol standards
Final Phenol Conc (%w/v)
Volume of 10%
Volume of H2O/ml
Phenol/ml
2%
2
8
3%
3
7
4%
4
6
5%
5
5
6%
6
4
7%
7
3
2.2.2.3 Inoculum preparation:
One bead of a freeze dried culture of Staph.aureus NCTC 6571(Selectrol) was
dispensed into 10 ml nutrient broth (Oxoid) using aseptic technique and incubated at
37oC for 18 h. Staph. aureus was universally used in antibiotic sensitivity testing.
2.2.2.4 Plate preparation:
Nutrient agar (Oxoid) was prepared by weighing 3.45g of nutrient agar and dissolving
in 150 ml of deionised water. The agar was mixed and sterilised by autoclaving at
120oC. After autoclaving, the agar was kept at 50oC in water bath for 30 minutes.
While waiting for the agar, the nutrient broth with Staph. aureus (working culture)
was adjusted to an absorbance of 0.5 measured at 540 nm in a Cecil spectrophotometer
using sterile nutrient broth as a blank. 100 µl of working culture of Staph. aureus was
added to the molten nutrient agar at 50oC. Then the agar was mixed thoroughly and
82
transfered into a large square assay plate (Corning 43110) on flat surface. When the
agar had set, the plate was layed upside down at 4oC and left overnight.
2.2.2.5 Sample preparation:
Eight universal containers were labelled as A1, B1, C1, D1, E1, F1, G1, H1 and I1 one
for each of the eight types of Omani honey used in this project. Stock honey solution
was prepared by adding 10g of well mixed honey to 10 ml of deionised water,
therefore the final honey concentration was 50% w/v. The solution was then placed in
a water bath at 37oC for 30 minutes to aid dissolving and mixing. To prepare further
dilutions, another eight universal containers were labelled as A2, B2, C2, D2, E2, F2,
G2, H2 and I2 and was transferred 1 ml of 50% honey (stock solution) to 1 ml of
deionised water to make the final concentration of 25% honey.
For non peroxide activity testing 0.02g of catalase from bovine liver (Sigma) was
made up to 10 ml with deionised water. The solution was mixed gently. Then eight
universal containers were labelled as A3, B3, C3, D3, E3, F3, G3, H3 and I3. 1 ml of
50% honey (stock honey) was transferred to these tubes aseptically and 1 ml of
catalase solution was added to these tubes to make 25% (w/w) honey and catalase
solution.
2.2.2.6 Samples and standards application:
The bioassay plate was placed over a quasi-latin square as a template after it was
removed from the cold room (4oC). A flamed, cooled 8 mm cork borer was used to cut
out 64 wells of the agar. Cut agar was removed with a sterile blade into a discard pot.
A 100 µl of each sample was added to each of 4 wells with the same number assigned
on the assay plate. Thus, each sample was tested in quadruplicate. Similarly, 100 µl
phenol standards (2%-7%) were tested in duplicate. The plate was then incubated at
37oC for 18 h.
83
2.2.2.7 Zone measurement:
After incubation, the plate was placed back over black paper to measure the diameter
of the zones of inhibition with digital calipers. Each zone of inhibition was measured
twice (horizontal and vertical reading) at 90°angles.
2.2.2.8 Calculation of antibacterial activity of honey:
After measurement of the clear zone around each phenol standard twice, the mean
diameter was calculated and squared. A standard graph was plotted of % (w/v) phenol
against the square of the mean diameter of the clear zone. A best straight line shape
was plotted using Cricket graph software and the equation of this line was used to
obtain the activity of each diluted honey sample. The diameter of the clear zone was
measured and squared. However, the dilution factor is correlation to the density of
honey thus this value was multiplied by a factor of 4.69. In short to obtain the dilution
from 50% w/w solution of honey, 10 ml of water were added to 10 g of honey. Ten g
of honey is actually equal to 7.41 ml where the average density of honey is 1.35 g/ml
i.e. 10/1.35 = 7.41. Therefore, 50% honey solution should be made by adding 10 ml of
water to 7.41 ml of honey which was actually equal to 42.56% (v/v) and 25% honey
solution was equal to 21.28% (v/v). To obtain the phenol percentage equivalent of full
strength honey it required to multiply by a factor of 4.69 i.e 100/21.28. Then the
activity of honey obtained from the assay was expressed as the equivalent phenol
concentration (% w/v) (Molan, personal communication).
84
2.2.3 Determination of pH:
This method was derived from the method described by the International Honey
Commission (Bogdanov, 2002).
All honey samples were removed from the fridge and kept at 20°C for 24 hrs in an airconditioned laboratory prior to testing. A 50% (w/v) solution of honey was made by
weighing 5g of honey and dissolved in ultrapure water (BioPak- B0901) to 10 ml. The
solution was gently shaken until the honey sample had completely dissolved. A
JENWAY instrument 3510 pH meter was calibrated at pH 4, 7 and 10 using reference
buffers (Fisher). After calibration the probe was inserting into the diluted sample. Each
sample was tested in triplicate. Between each sample the probe was rinsed under
running water and dried. Before testing the next sample, the probe was then placed in
deionised water.
2.2.4 Sugar and water content:
This method was established from the method described by the International Honey
Commission (Bogdanov 2002).
Sugar and water content were determined using a Bellingham & Stanley 40-85% sugar
refractometer and an Atago HHR-2N moisture refractometer. Sugar and moisture
content are usually tested at 20°C. Each of the honey samples were allowed to set to
20°C for 24 hours before the testing started. The whole honey sample was mixed with
a sterile spatula then a drop of honey was placed in the lens of each of the
refractometers. To confirm an equal distribution of the sample over the lens with no
air bubbles, the lids above the lens were carefully closed. To read the scale, the
refractometers were held directed to the light and the spot of the line was recorded.
The refractometers were cleaned with running water and dried with clean tissue
between each sample.
85
2.2.5 Hydroxymethylfurfural (HMF) concentration:
The method used was that of White & Rudji (1978).
Into 50 ml volumetric flasks, five grams from each of the honey samples were
weighed with a total of 25 ml of deionised water. Then 0.50 ml of Carrez solution I
(15 g potassium ferrocyanide in 100 ml of water) was added to each flask and mixed
well. After mixing, 0.50 ml of Carrez solution II (30 g zinc acetate in 100 ml of water)
was added to each flask. The solutions were diluted to volume with water and filtered
through Whatman filter paper, discarding the first 10 ml. Then 5 ml of the remaining
filtrate were added to each of two test tubes (18 x 150 mm). 5 ml of water were added
to the first tube labelled as sample, and 5 ml of 0.20% sodium bisulphite (breaks down
HMF) were added into second tube lablled as blank. Then a vortex mixer was used to
mix the solutions and the absorbance was read in a Cecil spectrophotometer at 284 and
336 nm. The HMF value was calculated using the following formula:
HMF (mg/Kg honey) = (A284 - A336) x 14.97 x 5/weight of sample in g.
2.2.6 Protein content:
This method was established from the DC Protein assay kit (BioRad) that is based on
the Lowry method for the determination of protein content in honey (White and Rudji
1978).
Cellulose dialysis tubing is a selective membrane which allows low molecular weight
molecules such as sugar to pass through this membrane. However, protein molecules
cannot cross the membrane and remain in the tubing because of the pore size of
membrane is smaller than the size of protein. The diameter of the inflated tube was 16
mm and a cut off of 12 kDa MWCO. The tubing was cut into lengths of 30-33 cm and
placed under running tap water to hydrate and open the tubing. Two knots were tied
86
once the tubing opened at one end and it was stored until required in a beaker of tap
water. To prepare the honey solution for dialysis, 5 g of honey was dissolved with 10
ml of deionised water into a small beaker. Honey solution was poured into a section of
the inflated tubing. To facilitate the transfer of the solution, the tubing was clipped to a
funnel. Then total volume of 10 ml of water were used to rinse the beaker twice and
washed out any residue. This water was transferred into the tubing. The air was
expelled from the dialysis sac and two knots tied above the liquid. The tubing was
inverted several times to ensure mixing of the solution. The dialysis sac was then kept
in a beaker with running tap water for 16 h. After dialysis was completed, A
Erlenymeyer flask (50 ml capacity) was weighed to 0.01 g and the weight recorded. A
dialysis sac was removed from the beaker and held over a funnel directed into a flask.
The lower end of the sac was cut with a blade, ensuring that the flow of solution
released was lead into the funnel. The remaining residual liquid was removed from the
tubing using fingers. To obtain the weight of the protein solution the flask was
reweighed. Five µl of these solutions (protein) were distributed into the wells of a 96
well microtitre plate. Then 25 µl of reagent A (alkaline copper tartrate) were added to
the wells. After that 200 µl of reagent B (dilute Folin reagent) were also added to the
same wells. The plate was then incubated at room temperature for 15 minutes and read
in a microtiter plate reader (DYNEX Revelation 4.21) at 620 nm. A calibration curve
of the best fit line was obtained from the standard solutions of bovine serum albumin
(Sigma) (Fig. 2.1).
The concentration of proteins in the honey samples was
determined from the equation of the calibration curve line. Each sample was repeated
in triplicate.
87
Figure 2.1: Calibration curve for protein determination in honey samples
2.2.7 Colour:
The colour of all honey samples was established using the optical density method
recommended by the National Honey Board. The absorbance of a 50% (w/v) honey
solution was determined at 560 nm in a Cecil spectrophotometer. To obtain the actual
absorbance value of the undiluted solution, the absorbance was multiplied by 2. Honey
were then categorised according to a Townsend classification system (1969).
2.2.8 Pollen analysis:
This method was derived from that of Loveaux et al. (1978).
Ten grams of honey were dissolved in 20 ml of deionised water and centrifuged in a
Sanyo-MSE centrifuge at 2500 rpm (3.8 G) for 10 mins. Then the supernatant was
decanted and re-suspended in 10 ml of deionised water. The solution was recentrifuged as above and the supernatant was decanted and discarded. The sediment
88
was re-suspended in 1 ml liquefied glycerine-gelatine, transferred to a glass slide using
a Pasteur pipette and mounted with a cover slip. Using a light microscope at least 100
pollen grains in each sample were identified, examined and counted. The samples
were classified according to the predominant pollen present in the sample (›45%
pollen grains). Advice about the identity of the pollen grains was kindly provided by
National Pollen And Research Unit (NPARU) at Worcester University.
2.2.9 Total phenolic content:
This method was used that described by Berreta et al. (2005).
2.2.9.1 Reagent/standard preparation:
A 10% (w/v) stock honey solution was prepared by weighing 0.5 g of each honey
sample and diluted with 5ml of warm (45oC) deionised water, then mixed for 5 mins.
A Folin-Ciocalteau reagent (1:10) was prepared by adding 10 ml of Folin reagent to
90 ml of deionised water.
Gallic acid standards (10-250 µg/ml): A stock solution of 0.005 M gallic acid (Sigma)
was prepared by weighing 0.017g of gallic acid and made up to 20 ml using
water:methanol solution (1:1).
A range of gallic acid standards (10-250 µg/ml) was prepared (Table 2. 10)
89
Table 2.10: Preparation of gallic acid standard solutions
Working standard
solution (µg/ml)
Standard stock
solution (µl)
Deionised water:
methanol (µl)
0
0
4000
10
47
3953
25
118
3882
50
235
3765
100
470
3530
150
705
3295
200
940
3060
250
1176
2824
Figure 2.2: Standard curve for total phenolic content in honey samples
Standard curve for total phenolic content
y = 0.0013x
R² = 0.9974
0.4
Absorbance at 750nm
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
50
100
150
200
250
300
Concentration of gallic acid standard (ug/ml)
2.2.9.2 Assay method:
100 µl of 10% of each honey solution was added to 1 ml of Folin-phenol reagent
(1:10) in a test tube. The mixture was mixed for 2 mins and incubated for 20 mins at
room temperature. After incubation, the absorbance was read at 750 nm against
reagent blank (100 ml of water:methanol) on a Cecil spectrophotometer. The
90
difference between this absorbance and the sample blank (honey solution with
deionised water) was calculated to get the net absorbance. The solutions with gallic
acid in the range of 10-250 µg/ml were used for calibration (standard graph). Total
phenolic content was expressed as µmol of gallic acid eq/kg honey. Each honey
sample was tested in duplicate and the average net absorbance was calculated (A).
Determination for honey colour interference with the assay:
Two honey solutions were made for each sample as follows:
A: 1000 µl distilled water: methanol + 100 µl honey solution
B: 1100 µl distilled water: methanol
The honey colour (H) was calculated as the difference in absorbance between A and B
at 750nm. Net absorbance (N) for each sample was calculated as follows: N = (A) –
(H), and this value (N) was used to determine phenolic content from the calibration
curve. The standard graph was plotted and phenolic content value for test was obtained
from the standard graph.
2.2.10 Free radical activity of honey:
This method was established from the method described by Chen et al. (2000) and
Aljadi & Kamaruddish (2004).
2.2.10.1 Reagent / Standard Preparations
DPPH (2,2-Di(4-tert-octylphenyl)-1-picryl-hydrazyl) 0.09 mg/ ml (Sigma) was
prepared by weighing 0.009 g of DPPH and made up to 100 ml with methanol.
Deionised water: methanol (1:1) was prepared by adding 5 ml of deionised water to 5
ml of methanol.
91
A 10 % (w/v) honey solution was made by weighing 5 g of honey and added to 5 ml
of warmed deionised water. The solution then mixed for 5 minutes.
2.2.10.2 Assay procedure:
To assay free radical activity 0.75 ml of each 10 % (w/v) honey solution was mixed
with 1.5 ml of 0.09 mg/ml DPPH in methanol in a test tube. Mixture was then
incubated at 25°C for 5 minutes. The absorbance was measured at 517 nm against a
test blank (honey solution + 1.5 of deionised water:methanol). The absorbance of 1.5
ml of DPPH radical control was measured with 0.75 ml of distilled water: methanol
against DPPH radical control blank (2.25 ml of distilled water: methanol). The anti
radical activity (ARA) was expressed as a percentage inhibition of DPPH radical by
honey and calculated as follows: Anti –radical Activity (ARA%)= {(A-B) / A} x 100
Where A= Average absorbance of DPPH radical without honey (DPPH control
absorbance) and B = Average absorbance of DPPH radical with honey (Test
absorbance).
2.3 Determination of antibacterial activity of honey
samples against test cultures:
2.3.1 Minimum Inhibitory Concentration (MIC) method:
2.3.1.1 Agar incorporation method:
2.3.1.1.1 Honey selection:
From the bioassay results, two honeys were selected according to their antibacterial
activity: Manuka honey with non peroxide activity equivalent to 18.0% (w/v) phenol
92
and Omani honey (B) with hydrogen peroxide activity equivalent to 17.7% (w/v)
phenol
2.3.1.1.2 Inoculum preparation:
One to five colonies of test organism were inoculated into 10 ml nutrient broth and
incubated at 37oC overnight. Subculture was performed for each isolate onto nutrient
agar plates to confirm the purity.
2.3.1.1.3 Plates preparation:
300 ml of double strength nutrient agar was prepared by weighing 14.8 g of nutrient
agar into 300 ml of deionised water. The mixture was mixed and boiled to dissolve the
agar then it was kept in a water bath at 50oC for 15 minutes. An automatic pipette was
used to dispense 10 ml of nutrient agar into each of 22 universal containers and
autoclaved at 121oC for 15 minutes. After autoclaving, it was put in water bath at 50oC
for 20 minutes.
2.3.1.1.4 Honey preparation:
A stock honey solution of 40% (w/v) was prepared either by adding 20 g of manuka
honey (A) to a volumetric flask, and making up to 50 ml with sterile deionised water,
or by adding 12 g of honey (B) to a volumetric flask, and making up to 30 ml with
sterile deionised water. Nutrient agar plates containing honey varying from 0 -10%
(w/v) were prepared (Table 2.11).
93
Table 2.11: Preparation of varying concentration of honey solution from 40% (w/v)
stock honey for MIC method
% of honey
Quantity of double
Quantity of 40%
Quantity of sterile
(w/v)
strength N.A (ml)
stock honey (ml)
water (ml)
0%
10
0
10.0
1%
10
0.5
9.5
2%
10
1.0
9
3%
10
1.5
8.5
4%
10
2
8
5%
10
2.5
7.5
6%
10
3
7
7%
10
3.5
6.5
8%
10
4
6
9%
10
4.5
5.5
10%
10
5
5
The plates were allowed to set at room temperature for 30 min and then placed in 37oC
incubator to remove excess moisture. The plates of varying honey concentration were
then inoculated with 1 µl of each test organism (typically 1x105 cfu/ml) in triplicate
using multipoint inoculator. The plates were placed in an incubator at 37oC for 24 h to
allow for growth.
2.3.1.1.5 Plates reading (MIC determination):
After overnight incubation, the growth was recorded for each dilution. Positive growth
was confluent growth within the inoculated area and negative growth was recorded
when no colonies where present. The MIC was deduced as the lowest concentration of
honey that inhibited growth, and the average for all assays on each test organism was
calculated.
94
2.3.1.2 Broth dilution method:
2.3.1.2.1 Honey selection:
Four honeys were selected according to their antibacterial activity: Manuka honey
with non peroxide activity equivalent to 20.0% (w/v) phenol and Omani honeys B, C,
G & F with hydrogen peroxide activity equivalent to 22.85, 21.8, 20.1 and 20.8 %
(w/v) phenol respectively.
2.3.1.2.2 Honey dilution:
40% (w/v) stock solutions of honey were prepared as described above and solutions
ranging from 6% to 15% honey diluted with iso-sensitest broth (ISB) were prepared as
shown in (Table 2.12).
Table 2.12: Preparation of tubes for MICs.
Honey Conc. (%w/v)
Quantity of 40%
honey (ml)
Quantity of ISB
(ml)
Final Volume
(ml)
6
0.225
1.275
1.5
7
0.262
1.237
1.5
8
0.3
1.2
1.5
9
0.337
1.163
1.5
10
0.375
1.125
1.5
11
0.412
1.08
1.5
12
0.45
1.05
1.5
13
0.487
1.012
1.5
14
0.525
0.975
1.5
15
0.562
0.937
1.5
2.3.2.1.3 Microtitre plate inoculation:
One 96 well microtitre plate was used for 3 test organisms and each organism was
tested in duplicate. 200 µl of each honey solution was dispensed to wells in row (A)
95
for honey control, rows (B&C) for isolate 1, rows (D&E) for isolate 2 and rows
(F&G) for isolate 3 in [columns 1-10]. 200 µl of ISB was dispensed to wells in rows
(A – G) and columns (11&12) only. 1 µl of isolates 1, 2 & 3 was added to wells in
rows B&C, D&E and F&G respectively in columns 1-11. After the wells have been
inoculated the plate was incubated at 37°C for 24h. This procedure was repeated for
all other isolates.
2.3.2.1.4 Visual inspection of MIC:
Plates were removed from the incubator and inspected visually for growth. Growth
was assessed by turbidity in the wells and comparing them to the negative control (ISB
only). Wells showing turbidity were considered to contain growth and marked as
positive. Wells showing no turbidity were assessed to have no growth and marked as
negative.
2.3.2.1.5 Spectrophotometric determination of MIC:
The microtitre plates were placed in a plate reader (DYNEX Revelation 4.21) and
optical density was measured at 405 nm filter for test and 620 nm filter as reference.
The printout of the reading were obtained and used to confirm the MIC.
2.3.2 Minimum Bactericidal concentration (MBC) method:
Wells that appeared to have no growth (no turbidity) by visual inspection were
streaked onto nutrient agar plates using sterile 10 µl plastic loops. The plates were
incubated at 37°C for 24 h. After incubation any growth on the plates was marked as
positive and no growth marked as negative. The plates with the lowest concentration
of honey showing no growth was recorded as the MBC.
96
2.4 Time Kill Curve Assay:
2.4.1 Strain selection:
One culture of each of the six types of bacterial strains was selected according to the
highest MIC and the MIC was approximately doubled as shown in (Table 2.13) below:
Table 2.13: Cultures and honey concentrations used in the time-kill curves
Strain ID
MIC (%w/v)
Test conc. used
Acinetobacter 15
9
20
E.coli 7 (2139)
13
30
Klebsiella 5 (3042)
13
30
Enterobacteria 9 (5865)
13
30
Citrobacter 2 (3031)
10
20
Serratia 4 (3036)
15
30
Manuka honey (Comvita) UMF 18 equivalent to 18 % (w/v) phenol was used in this
assay.
2.4.2 Time-kill curve:
An overnight broth culture of each test organism in 10 ml ISB was prepared by
inoculating a colony from a pure culture and incubating at 37oC for 18 h. After
incubation each culture was diluted with ISB to an optical density of 0.5 at 550 nm. 50
ml of sterile ISB was added to conical flask labelled as control for each isolate. A 50
ml of appropriate honey solution (approximately 2xMIC) was prepared in another
conical flask and labelled as test for each isolate. Then 5 ml from the diluted overnight
culture was transferred into each flask. The mixture was then incubated at 37°C in
shaking water bath. One ml from each flask were removed into a microcuvette and
absorbance was recorded at 550 nm every 30 mins for the first 2 h and every 1 hr until
4 h had passed. Finally, a graph of optical density versus time was plotted to allow the
97
exponential growth phase to be identified. Also, a total viable count for each collected
specimen was determined as follows:
20 µl of the specimen was diluted in a decimal dilution series from 10-1 to 10-7 using
maximum recovery diluent (Oxoid). For each dilution 3 x 20 µl aliquots were plated to
the respectively labelled seven segments of a nutrient agar plate. Following this, the
plates were left to dry before incubating for 24 h at 37°C. After incubation the colonies
in different sections were counted and recorded and used to calculate the number of
CFU/ml at each time point. Time-kill curves of total viable count versus time were
plotted.
A T-test was applied to establish the statistical difference between untreated cells and
honey treated cells in the selected 6 species.
2.5 Effect of honey on bacterial structure:
2.5.1 Scanning Electron Microscopy (SEM):
SEM is one of the multipurpose equipement that is used for the analysis and
examination of ultrastructure features of solid object. It allows the observer to see
entire specimen and larger part of an object (Williams and Carter 2009).
2.5.1.1 Bacterial Selection for SEM
The same test organisms as selected for time-kill assays were used for morphological
studies.
98
2.5.1.2 Preparation of cells in the exponential phase of growth:
1 ml of an overnight culture was inoculated into each of two flasks containing 50 ml of
ISB. The flasks were labelled as either control (no honey) or test (with honey). The
culture was incubated in a 37oC shaking water bath for 3 h when it was estimated that
cells would be in the exponential phase of growth. Honey solution (Table 2.13) was
then aseptically added into the flask labelled as test and the flasks continued to be
incubated with shaking at 37oC. The point at which the honey was added to the test
flasks was recorded as time 0. Absorbance was measured every 30 mins till 4 h had
passed (0, 30, 60, 90, 120, 150 and 180 mins at 550 nm) and 4 ml was removed at each
time point and distributed equally into 4 sterile eppendorff tubes. The tubes were
centrifuged at 13,000 rpm (19.89 G) for 2 mins the supernatant was decanted and the
pellet re-suspended in 250 µl of PBS. The contents from all tubes were combined into
1 ml tube, which was then centrifuged. The resulting pellet was re-suspended in 1 ml
of PBS and centrifuged again. After removing the supernatant, the pellet was fixed
with 750 µl of 3% glutaraldehyde for 1 h at 4oC. After fixation the cells were washed
twice (2 x 2 mins) with 1 ml PBS. Washed cells were suspended in PBS were then
stored on a shaker overnight at 4oC. At this point samples were transferred to Cardiff
University for electron microscopy.
2.5.1.3 Preparation of cells for scanning electron microscopy:
All samples were centrifuged at 13,000 rpm (19.89 G) for 2 mins. The resulting pellet
was re-suspended in 0.5 ml of 1% osmium tetroxide in PBS for 1 h. After the post
fixation, the cells were spun down as above and the osmium tetroxide solution was
decanted. The cells in pellet were dehydrated in a graded series of ethanol
concentrations as follows:
99
50%, 70%, 80% and 90% for 5 mins, 100% for 10 mins (twice). The samples were
added to small plastic containers (labelled by cutting an identification number into it)
that were fitted with 0.22 µm filter membrane attached to blotting paper to trap cells
for critical point drying. A small volume of ethanol was added to the containers taking
care not to let the sample completely dry. The dehydrated samples were dried for 16 h
under a high pressure (100 bars) in a critical point dryer (CPD 030; Balzers,
Lerchtenstein) using CO2. The dried samples were then mounted on metal stubs with
carbon – double sided - sticky tape and coated with gold in sputter-coater (EMscope
AE 1232, Kent, UK). Structural observations of cells size and shape were performed
using a scanning electron microscopy (JEOL 5600V SEM-UK) operated at 20 kV
accelerating voltage with magnifications ranging from 5000x to 10,000x
magnification. More than 2000 cells for each isolate were observed and examined.
The percentage of structural differences between untreated and treated cells was
determined using SMile View programme-JEOL (Version 2). The results were
analysed statistically for significance using the Mann-Witney test in Mini Tab
Statistical package (version 15).
2.5.2 Transmission Electron Microscopy (TEM):
TEM allow the observer to see the internal structure of an object with high
magnification and greater degree (Williams and Carter 2009).
2.5.2.1 Bacterial Selection for TEM
Two isolates were selected in this method. Acintobacter isolate no. 15 and E. coli
isolate no.7 with 20% (w/v) and 30% (w/v) of manuka honey respectively.
100
2.5.2.2 Preparation of cells for transmission electron microscopy:
Fixed cells with and without honey were prepared as described for scanning
microscopy in section 2.5.1.2 and 2.5.1.3 up until dehydration was completed. When
ethanol replacement was completed, pellets were released into a fresh 10 ml glass
container of 100% ethanol with a cocktail stick. The pellet was allowed to stand 15
minutes in 100% ethanol and then transferred to fresh 100% ethanol for further 5 min.
The pellet was transferred to 5 ml propylene oxide and left for 15 min. The pellet was
transferred to 50:50 propylene oxide/araldite resin (5 g of araldite resin and 5 g of
DDSA were preheated, mixed together and 0.15 g of BDMA/propylene oxide about 9
ml per sample and left overnight in the arbitrator. The infiltrated pellet were then
transferred into 5 ml of fresh resin (no propylene oxide) and left for 12 hours into the
arbitrator. After incubation, the pallets were transferred into moulds previously filled
with full strength resin. The moulds were then incubated in the oven for 3 days at
60°C.
2.5.2.3 Pallet trimming and sectioning:
The samples were trimmed in order to remove the excess resin around the samples and
to expose the pellet of cells. Using glass knives and ultratome III ultra thin sections
were cut and placed onto uncoated 3.00 mm copper grids.
2.5.2.4 Staining of thin sections
A paraffin sheet was placed onto either a ceramic tile or Petri dish. A drop of 2%
uranyl acetate solution was placed on the paraffin sheet. The grids were floated on the
drop by placing the thin sections side faced down ward. The grids were covered and
stained in a similar manner with Reynolds lead citrate for 5 minutes, then, washed
twice in distilled water. The grids were dried with filter paper. 12 blocks containing
101
Acinetobacter test & control were prepared (3 blocks at each time point). The same
was done also for E.coli. At least 8 grids from each sample were stained and
examined. Eleven TEM micrographs were taken for each sample at each time point.
Ten grids were captured by TEM under 16,000 x magnification and one under 32,000
x magnification using (TEM, Philips-PW60100, 10EM 208). A total of 400 cells for
each sample were observed and examined for structural differences between honey
treated cells and untreated cells, and the presence or absence of septa was recorded.
2.6 Effect of honey on bacterial proteins:
The effect of exposure to 20% (w/v) manuka honey on proteins was tested in selected
test organisms using 2-D gel electrophoresis.
2.6.1 Two Dimensional Gel Electrophoresis:
2.6.1.1 Buffers preparation:
The following reagents were prepared (Table 2.14)
102
Table 2.14: Reagents and buffers used in 2-D Electrophoresis
Re-hydration buffer
Per 10 ml
Urea 8 M
4.8 g
CHAPS 2% w/v
0.2 g
DDT 50 mM
0.08 g
Biolyte ampholytes 0.2% w/v
0.05 ml
Equilibration buffer 1
Per 10 ml
Urea 6 M
3.6 g
Tris-HCl, pH 8.8, 0.375M
0.592 g
SDS 2%
0.2 g
Glycerol 20%
2 ml
DTT 2% w/v
0.2 g
Equilibration buffer 2
Per 10 ml
Urea 6 M
3.6 g
Tris-HCl, pH 8.8, 0.375M
0.592 g
SDS 2%
0.2 g
Glycerol 20%
2 ml
Iodoacetamide 2.5% w/v
0.252 g
Overlay Agarose
0.04 g Bromophenol blue
100 ml water
1:20 of BPB in MOPS
Add 0.12 ml of agarose
MOPS running buffer
Per 1000 ml
MOPS
10.45 g
Tris base
6.06 g
SDS
1.00 g
EDTA
0.30 g
NaCl
Per 100 ml
20%
20 g
103
2.6.1.2 Cell preparation:
10 ml of TSB (Oxoid) was inoculated with 2-4 pure colonies of Acinetobacter and
incubated at 37oC for 18 h. 2 ml of overnight culture was added to 50 ml of ISB in a
500 ml flask and 2 ml was used to inoculate a second flask containing 50 ml ISB with
20% (w/v) manuka honey. The cultures were incubated at 37oC with shaking (100 rpm
for 24 h). After incubation, either 50 ml of TSB or 50 ml containing 20% manuka
honey was added to each respective culture and incubated for a further 3 h with
shaking (100 rpm) to ensure that the cells were in the exponential phase of growth.
Cells were harvested by centrifugation in a Sorvell centrifuge (10,000 rpm [15.3 G]
for 6 min) and the cells were re-suspended in 10 ml deionised water, the OD of the
resulting supernatant was recorded at 550 nm using water as a blank. The proteins
were extracted by sonicating (Jencons, Sonics VCX 500) with 500 watts power, 20
kHz frequency and 30% amplitude for 2.5 min. In every 30 seconds each sample was
tacked off to Eppendorff tube and kept on ice to cool and reduce air bubbles. The cells
were spun down in a microfuge on top speed (13,000 G for 4 mins). Then, the
supernatant was removed into sterile 1.5 ml Eppendorf tubes and stored at -20oC. A
protein determination was performed for each extract as described above.
2.6.1.3 Protein determination of Acinetobacter extracts for each extract:
Five µl of each sample to be analysed was dispensed into wells of a 96 well microtitre
plate, then 25 µl of reagent A (alkaline copper tartrate) were added followed by 200 µl
of reagent B (dilute Folin reagent). The plate was incubated at room temperature for
15 minutes and then read in a microtiter plate reader (DYNEX Revelation 4.21) at 620
nm. Suitable standard solutions (low standard 3mg/ml) of bovine serum albumin
(Sigma) were used and calibration curve obtained, to which a best-fit line was added
and the equation of this line was used to obtain the concentration of proteins in the
104
samples (Fig 2.3). Each sample was run in triplicate. The results from the protein
assay (Table 2.15) were used to calculate the value needed to give 150 µg of protein in
each sample for 2-D gel electrophoresis.
Table 2.15: Quantity of proteins in Acinetobacter cells with and without 20% honey
Protein sample of
Acinetobacter
Protein conc.
(mg/ml)
Volume of
protein
sample µl
Volume of rehydration buffer
µl
Control cells (no honey)
4.55
32
168
Test cells (20% honey)
4.2
35.7
164.3
Figure 2.3: Calibration curve of protein concentration in Acinetobacter with and
without 20% manuka honey.
105
2.6.1.4 Rehydration and sample application:
The rehydration tray was prepared and protein samples were pipetted as a line along
the edge of one of the rehydration channels, leaving 1 cm free at each end of the
channel and ensuring that there were no bubbles. The cover sheet was peeled from the
IPG strip using forceps. The strips were placed gel side down onto samples and ensure
„‟+‟‟ and pH range are clear and legible with no bubbles. The lid was placed back onto
the rehydration tray. Then the samples were kept for 60 min to be fully absorbed.
Mineral oil was dripped carefully over the plastic back of the IPG strip to fully cover
the strip. The rehydration tray was covered with the lid and left for 11-16 h (overnight)
to allow rehydration/loading to be fully completed.
2.6.1.5 Rehydration in the PROTEAN® IEF Focusing Tray:
A paper wick was placed over each of the electrodes using forceps, and 10 µl of
deionised water (18 megohoms-cm) was pipetted on each wick. Using forceps, the
IPG strip was removed from the rehydration channel and held vertically for 10
seconds to drain. The lower end of the strip was touched onto blotting paper. The IPG
strip was transferred to the prepared channel ensuring the gel was facing down and the
„‟+‟‟ sign is at the positive end of the tray. The IPG strip was covered with 2 ml of
fresh mineral oil ensuring there are no bubbles. The lid was put on the tray and the tray
was put in an iso-electric focusing cell. The IEF cell was programmed as per sheet
provided (35000 volt for 24 h); this was suitable for 11 cm pH 3-10. After completion,
the IPG strip was removed with forceps, and it was held vertically for 10 seconds to
drain, the lower end was touched onto blotting paper to remove the excess of mineral
oil. To ensure that all the oil was removed, the IPG strip was placed on dry filter paper
gel side up and plotted gently with wet filter paper. To store the gels at this point, the
IPG strip was transferred to a clean rehydration/equilibration tray (gel side up). The
106
tray was covered and wrapped in saran wrap and placed in a -70oC freezer. The gel
was defrosted for not more than 15 min before SDS-PAGE separation, as proteins will
migrate.
2.6.1.6 Equilibration and SDS-PAGE:
3 ml of equilibration buffer 1 (Table 2.14) was transferred to a lane in the rehydration
tray then the IPG strip was slid into the buffer (gel side up), ensuring there were no
bubbles. The lid was placed on the rehydration tray and put on an orbital shaker and
shacked gently for 30 min at room temperature. The used buffer 1 was decanted from
the squared end of the tray until the tray was vertical. 3 ml of equilibration buffer 2
was added to the strip and placed on an orbital shaker and shaken gently for 30 min at
room temperature. The used buffer 2 was decanted from the squared end of the tray
until the tray was vertical. The overlay agarose was made as mentioned above and
melted by gentle heating. A measuring cylinder was filled with MOPS buffer. Any
excess water from the SDS page gel was removed by pipetting and blotting gently.
The IPG strip was removed from the rehydration tray and dipped briefly into the
cylinder of running buffer. The IPG strip was placed gel facing forwarded onto the
back plate of the 2-D gel and pushed down carefully with forceps so that it made
contact with the gel. The IPG strip was covered with overlay agarose and allowed
setting for 10 mins. The gel was mounted in the electrophoresis criterion cell, and the
reservoirs were filled with 60 ml MOPS buffer and allowed for 55 min for the run to
complete (200 volts).
2.6.1.7 Staining and gel visualising:
The gel was placed in 100 ml ultrapure water and microwaved on full power for 1
min. The gel was shaken in an orbital shaker for 1 min, and then the water was
discarded. The last two steps were repeated twice. 20-30 ml of simplyBlue™ safe stain
107
was added to the gel and microwaved on high power for 45 seconds to 1 min. The gel
was placed on an orbital shaker and shaken gently for 5-10 mins before discarding the
stain. 100 ml of ultrapure water was added to the gel and placed on an orbital shaker
for 10 min, the water was discarded after use. 20 ml of 20% NaCl was added and the
gel was placed back onto the orbital shaker for 5-10 mins. After staining, the gel was
visualised using the UVP AutoChemi, gel system and analysed using PDQuest Basic
8.0 software.
2.7 Statistical analysis of the data:
In time-kill assay the statistical difference in cell count of two variables (honey
treatment and non honey treatment) in term of two factors (time and isolates) was
applied using T-test.
In SEM the percentage of structural differences (length of cells) between untreated and
treated cells was determined using SMile View programme-JEOL (Version 2). The
results were analysed statistically for significance using the Mann-Witney test in Mini
Tab Statistical package (version 15).
108
Chapter 3
Results
109
3.1 Confirmations of the identity and antibiotics sensitivities
of test organisms:
Before the effects of honey on test organisms were investigated, the identity of each
isolate was checked using BBL kits, and antibiotic susceptibilities were confirmed by
antibiotic disc sensitivity tests. Results for Acinetobacter, Escherichia coli, Klebsiella,
Citrobacter, Enterobacter and Serratia are presented in Tables 3.1, 3.2, 3.2, 3.4, 3.5
and 3.6 respectively.
Identification and antibiotics sensitivities of thirty multi-drug resistant isolates of
Acinetobacter baumannii and Acinetobacter baumannii/calcoaceticus complex
provided from University of Wales Hospital (Table 3.1a & 3.1b)
Identification and antibiotics sensitivity tests for the fifty six clinical isolates of
extended spectrum beta-lactamases (ESBL) of Enterobacteriaceae with resistant to
third generation cephalosporins (3rdG cephalosporin) that were provided from
University of Wales Hospital did confirm that all cultures conformed to their assigned
labels (Table 3.2 to 3.6)
110
Table 3.1a: Identification and antibiotics sensitivities of 15 MDR Acinetobacter
isolates
1.
Identification of isolates
Resistance Pattern
Acinetobacter baumannii
CAZ, CTX, CE, CXM, CIP, AMP
/calcoaceticus complex
2.
CAZ, CTX, CXM, FOX, ERT, TAZ,AZT,
AMP
Acinetobacter baumannii
/calcoaceticus complex
3.
Acinetobacter baumannii
CAZ, CTX, CXM, FOX, ERT,AZT, AMP
4.
Acinetobacter baumannii
CAZ, CTX, AK, CN, FOX, CXM, , AMP
5.
Acinetobacter baumannii
CAZ, CTX, FOX, CXM, ERT, AMP
6.
Acinetobacter baumannii
CAZ, CTX, FOX, CN, TAZ, ERT, CIP
/calcoaceticus complex
7.
CAZ, CTX, CN, FOX, TAZ, ERT, CIP,
AMP
Acinetobacter baumannii
/calcoaceticus complex
8.
CAZ, CTX, FOX, CXM, ERT, AZT, AMP
Acinetobacter baumannii
/calcoaceticus complex
9.
CAZ, CTX, FOX, TAZ, ERT, AZT, AMP
Acinetobacter baumannii
/calcoaceticus complex
10.
CAZ, CTX, CN, FOX, TAZ,ERT, CIP, AZT
Acinetobacter baumannii
/calcoaceticus complex
11.
Acinetobacter baumannii
CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT
12.
Acinetobacter baumannii
CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT
/calcoaceticus complex
13.
Acinetobacter baumannii
CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT
14.
Acinetobacter baumannii
CAZ, CTX, CN, FOX, TAZ, ERT, CIP, AZT
15.
Acinetobacter baumannii
CAZ, CTX, FOX, TAZ, ERT, CIP, AZT, CN
111
Table 3.1b: Identification and antibiotics sensitivities of 15 MDR Acinetobacter
isolates
16.
Identification of isolates
Resistance Pattern
Acinetobacter baumannii
CAZ, CTX, CXM, FOX, TAZ, ERT, CIP, SXT, CN
/calcoaceticus complex
17.
Acinetobacter baumannii
CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT
/calcoaceticus complex
18.
Acinetobacter baumannii
CAZ, CTX, FOX, TAZ, ERT, CN,CIP, SXT, AZT
19.
Acinetobacter baumannii
CAZ, CTX, FOX, CXM, ERT, AZT, AMP
20.
Acinetobacter baumannii
CAZ, CTX, FOX, CXM, AZT, ERT, AMP, SXT
21.
Acinetobacter baumannii
CAZ, CTX, FOX, CXM, AZT, ERT, AMP, SXT
22.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
23.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
24.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
25.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
26.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
27.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
28.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
29.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
30.
Acinetobacter baumannii
CAZ, CTX, CRO, CE, CXM, AK, CN,CIP, AMP
cefotaxime (CTX 30µg), ceftazidime (CAZ 30µg), cefoxitin (FOX 30µg), cefuroxime
(CXM 30µg), gentamicin (CN 10µg), ciprofloxacin (CIP 5µg), ampicillin (AMP
10µg), ertapenem (ERT 10µg), aztreonam (AZT 10µg), tazobactam (TAZ 110µg),
septrin (SXT 25µg), amikacin (AK 30µg). All isolates were sensitive to imipenem
(IMP 10µg), meropenem (MEM 10µg) & colistin (CT10µg).
112
Table 3.2: Confirmation of identity and antibiotics sensitivity including ESBL tests
for 10 E.coli isolates.
Isolate
No.
Identification of
isolates
Resistance Pattern
CAZ
CTX
CPM
TZP
ESBL test
ESBL/
AmpC* test
ESBL (+)
-
1.
Escherichia coli
R
R
R
S
2.
Escherichia coli
R
R
R
S
ESBL (+)
-
3.
Escherichia coli
R
R
S
S
ESBL (+)
-
4.
Escherichia coli
R
R
R
R
ESBL (+)
-
5.
Escherichia coli
R
R
R
R
ESBL (+)
ESBL
6.
Escherichia coli
R
R
S
S
ESBL (+)
AmpC
7.
Escherichia coli
R
R
S
R
ESBL (+)
ESBL
8.
Escherichia coli
R
R
S
R
ESBL (+)
-
9.
Escherichia coli
R
R
S
S
ESBL (+)
ESBL
10.
Escherichia coli
R
R
S
S
ESBL (+)
ESBL
R- Resistant, S - Sensitive
All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL
test positive.
113
Table 3.3: Confirmation of identity and antibiotics sensitivity including ESBL tests
for 11 Klebsiella isolates
Isolate
No.
Identification of
isolates
1.
Resistance Pattern
ESBL
test
ESBL
/
Amp
C*
test
CAZ
CTX
CPM
TZP
Klebsiella pneumoniae
R
R
S
S
ESBL (+)
-
2.
Klebsiella pneumoniae
R
R
S
S
ESBL (+)
-
3.
Klebsiella pneumoniae
R
R
R
R
ESBL (+)
ESBL
4.
Klebsiella pneumoniae
R
R
R
R
ESBL (+)
-
5.
Klebsiella oxytoca
R
R
R
R
ESBL (+)
-
6.
Klebsiella pneumoniae
R
R
S
S
ESBL (+)
-
7.
Klebsiella pneumoniae
R
R
S
S
ESBL (+)
ESBL
8.
Klebsiella oxytoca
R
R
S
R
ESBL (+)
-
9.
Klebsiella pneumoniae
R
R
S
R
ESBL (+)
ESBL
10.
Klebsiella oxytoca
R
R
S
R
ESBL (+)
-
11.
Klebsiella pneumoniae
R
R
R
S
ESBL (+)
-
R- Resistant, S - Sensitive
All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL
test positive.
114
Table 3.4: Confirmation of identity and antibiotics sensitivity including ESBL tests
for 12 Citrobacter isolates.
Identification of
isolates
Resistance Pattern
CAZ
CTX
CPM
TZP
ESBL
test
ESBL/
AmpC*
test
1.
Citrobacter farmeri
R
R
S
R
ESBL (+)
ESBL
2.
Citrobacter farmeri
R
R
S
S
ESBL (-)
AmpC
3.
Citrobacter farmeri
R
S
S
S
ESBL (-)
AmpC
4.
Citrobacter farmeri
R
R
S
S
ESBL (-)
AmpC
5.
Citrobacter farmeri
R
R
S
R
ESBL (-)
AmpC
6.
Citrobacter farmeri
R
R
S
R
ESBL (-)
AmpC
7.
Citrobacter farmeri
R
R
S
R
ESBL (-)
AmpC
8.
Citrobacter farmeri
R
R
S
S
ESBL (-)
AmpC
9.
Citrobacter farmeri
R
R
S
S
ESBL (-)
AmpC
10.
Citrobacter farmeri
R
R
S
R
ESBL (-)
AmpC
11.
Citrobacter farmeri
R
R
S
S
ESBL (-)
AmpC
12.
Citrobacter farmeri
R
R
S
R
ESBL (-)
AmpC
R- Resistant, S - Sensitive
All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL
test positive.
115
Table 3.5: Confirmation of identity and antibiotics sensitivity including ESBL tests
for 15 Enterobacter isolates.
Identification of
isolates
Resistance Pattern
CAZ
CTX
CPM
TZP
ESBL
test
ESBL/
AmpC*
test
1.
Enterobacter cloacae
R
R
S
R
ESBL (+)
-
2.
Enterobacter cloacae
R
R
S
R
ESBL (+)
ESBL
3.
Enterobacter cloacae
R
R
S
R
ESBL (+)
-
4.
Enterobacter cloacae
R
R
S
R
ESBL (+)
-
5.
Enterobacter hormaechi
R
R
S
R
ESBL (+)
-
6.
Enterobacter cloacae
R
R
S
R
ESBL (+)
-
7.
Enterobacter cloacae
R
R
S
R
ESBL (+)
ESBL
&
AmpC
8.
Enterobacter aerogenes
R
R
S
R
ESBL (+)
-
9.
Enterobacter cloacae
R
R
S
R
ESBL (+)
AmpC
10.
Enterobacter cloacae
R
R
S
R
ESBL (-)
AmpC
11.
Enterobacter cloacae
R
R
S
R
ESBL (-)
AmpC
12.
Enterobacter cloacae
R
R
S
R
ESBL (-)
AmpC
13.
Enterobacter cloacae
R
R
S
R
ESBL (+)
AmpC
14.
Enterobacter cloacae
R
R
S
R
ESBL (+)
AmpC
15.
Enterobacter cloacae
R
R
S
R
ESBL (+)
AmpC
R- Resistant, S – Sensitive
All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL test
positive.
116
Table 3.6: Confirmation of identity and antibiotics sensitivity including ESBL tests
for 8 Serratia isolates.
Identification of
isolates
Resistance Pattern
CAZ
CTX
CPM
TZP
ESBL
test
ESBL/
AmpC* test
1.
Serratia plymuthica
R
R
S
R
ESBL (+)
ESBL
2.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
3.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
4.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
5.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
6.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
7.
Serratia marcescens
R
R
S
R
ESBL (+)
AmpC
8.
Serratia marcescens
R
R
S
R
ESBL (-)
AmpC
R- Resistant, S – Sensitive
All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL test
positive.
ESBL/AmpC* confirmation test was used for the negative ESBL double-disk test.
cefotaxime (CTX 30µg), ceftazidime (CAZ 30µg), cefepem (FEP 30µg).
All isolates (MDR and ESBLs) were sensitive to imipenem (IMP 10µg) and
meropenem (MEM 10µg). The identity of these isolates was confirmed using API 20E
identification kit.
117
3.2 Characterization of honey samples:
3.2.1 Determination of antibacterial activity:
The antibacterial activity of honey samples was determined by the method of Allen et
al (1991). Essentially in this bioassay a reference strain of Staphylococcus aureus
(NCTC 6571) was seeded into agar, and the extent of inhibition induced by honey
samples were compared to that induced by phenol. An example of an incubated plate
is given in Fig 3.1.
Figure 3.1: A typical honey bioassay plate.
Zones of inhibition
118
The diameter of the zones of inhibition of the phenol standards were measured
(horizontal and vertical) and plotted against the phenol concentration. A linear
relationship between honey concentration and the diameter of the zone of inhibition
was determined and the equation (Fig 3.2) was used to calculate the potency relating
to phenol of the honey samples. Three bioassay plates were used in total, with the
manuka honey sample included on each plate for consistency. Each Omani honey
sample was tested in quadruplicate on a plate. Good agreement was found between the
three bioassay plates (R2=0.9482, 0.9657 & 0.9743 respectively)
Honey samples were diluted with and without catalase to determine whether the zones
of inhibition were due to the generation of hydrogen peroxide or not. Hence, total
activity was determined when the honey sample was diluted in deionised water, and
non-peroxide activity was calculated when the honey was diluted with catalase (Table
3.7).
119
Figure 3.2: A typical calibration curve of the bioassay.
Phenol Standard Curve
y = 2.1643x + 5.3857
R² = 0.9719
Mean Diameter (mm)
25
20
15
10
5
0
0
1
2
3
4
% (W/V) Phenol
The data obtained from this method was repeated twice
120
5
6
7
8
Table 3.7: Antibacterial activity of honey samples
Type of honey
Total activity phenol
Non- peroxide activity
equivalent %w/v
phenol equivalent %
w/v
Manuka honey-(plate1)
20.4
20.6
Manuka honey-(plate2)
22.1
20.2
Manuka honey-(plate3)
21.3
19.3
Mean ± SD(n)
21.3± 0.9 (3)
OH-B (plate1)
21.5
3.7
OH-B (plate2)
24.2
<2*
Mean ± SD(n)
22.9± 1.9 (2)
-
OH-C
21.8
3.7
OH-D
3.7
3.7
OH-E
13.3
< 2*
OH-F
20.8
< 2*
OH-G
20.1
< 2*
OH-H
10.1
< 2*
OH-I
< 2*
< 2*
20.0± 0.8 (3)
*No zone of inhibition was detected of the bioassay.
The antibacterial activity of the eight Omani honey samples used in this study varied
from < 2% to 22.9% (w/v) phenol equivalent, if the values obtained on plates 1 and 2
are averaged. The medical grade manuka honey sample used here was included in
each bioassay plate and demonstrated a non-peroxide honey with 20.0±0.8 (%w/v)
phenol equivalent, unlike the Omani honey samples. Seven of the eight Omani honey
samples exhibited peroxide activity, but none had marked non-peroxide activity.
Every Omani honey was tested in quadruplicate in each bioassay plate and OH-B
121
was tested on 2 plates. Omani honeys B & C demonstrated the highest total activity
of (22.9% & 21.8% (w/v) phenol equivalent) respectively. Omani honeys (F & G)
have almost same activity. There was no activity detected in Omani honey I.
3.2.2 Chemical & physical analysis of honey samples
As well as antibacterial activity, honeys were analysed for their
chemical and
physical characteristics. All Omani honey samples were tested for their pH, water,
sugar content, protein content, HMF level, colour, pollen and anti-oxidant activity
these were compred to reference manuka honey (Tables 3.8, 3.9 & 3.10). pH was
tested in triplicate using 50% (w/v) honey solutions and the mean was recorded. The
pH was ranged between 3.5 to 6.27 with mean of 4.76 ±0.93. This indicates that all
honeys fall within acidic range that able to inhibits the growth of most microorganisms. Sugar and water content were tested using refractometer. Mean sugar was
81%±2.86 with range between 75% and 85%. While mean water content was 15.8%
± 1.9 with range between 12.5% and 19.5%. Again low water and high sugar make
the survival of bacteria are imposible. HMF test was used to determine the quality
and history of honey or if it was subjected to heat treatment while processing (Sanz
et al., 2003). An elevated level of HMF was expected in the Omani honeys due to
the hot climate in Oman; nevertheless HMF level was not unduly elevated (Table
3.8). The mean range was 11.1±11.9 with a range between 2.9 and 38.5 mg/kg.
Conversely the mean protein level was quite high (4.7±1.5 mg/g) (Table 3.8)
compared to values previously established (White & Rudyj 1978). The colour of
each honey sample was primarily visually inspected and then the optical density of
50% (w/v) from each sample was read at 560 nm and worked out according to the
Townsend‟s classification (Townsend, 1969). The results were completely matched
122
with visual estimation. The colour of Omani honeys was ranged between extra light
amber and dark amber (Table 3.8). The darker honey the highest antibacterial
activity presents (Molan 1992). All parameters indicated that all honeys fell within
the ranges normally expected for pH, sugar, water content, protein and HMF from
National Honey Broad (NHB) (Table 3.8).
Table 3.8: Chemical & physical analysis of different types of Omani honey
compared to manuka honey
Honey
Sample
pH
Water
content
%(w/v)
Sugar
content
% (w/v)
Protein
(mg/g)
HMF
(mg/kg)
Colour
OH-B
4.6
15.1
82
5.8
5.7
Amber
OH-C
4.3
16
81
5.2
3.5
Amber
OH-D
4.7
15.1
82.5
2.6
7.1
Light amber
OH-E
3.9
15.3
82
6.2
2.9
Dark amber
OH-F
4.6
12.5
85
6.3
10.4
Dark amber
OH-G
6.2
17
80.5
2.6
38.5
Extra light
amber
OH-H
3.5
15.8
80
5.8
4.2
Dark amber
OH-I
5.9
19.5
75
3.5
16.7
Extra light
amber
Range
3.5-6.2
12.519.5
75-85
2.6-6.3
2.9-38.5
Extra light
amber to
Dark amber
Mean ±
4.7±
15.8±
81.0±
4.7±
11.1±
-
SD (n)
0.9(8)
1.9(8)
2.9(8)
1.6(8)
11.9(8)
Manuka
honey
3.5
20
78
10
3
123
Light amber
3.2.2.1 Pollen analysis
Floral sources reported by beekeepers did not completely match to confirmed
identities provided by the National Pollen And Research Unit (NPARU) at Worcester
university (Table 3.9). Images from each honey samples were taken and the grains
were classified according to the predominant pollen (›45% pollen grains) (Fig 3.3)
Table 3.9: Represent the identification of flora sources by pollen analysis
Honey
Beekeepers
National Pollen And Research Unit (NPARU)
Sample
Identification
Identification
OH-B
Multi-flora
Graminae (the grasses), Acacia (thorn trees, part of
mimosoideae family) Asteraceae (daisy/sunflower
family), Balsam (scented trees and shrubs), Tilia (lime)
OH-C
Honey dew
Calendula arvensis (marigold), Graminae, Mimosa
(subfamily of legume family), Myrtacae Eucalpyus,
rosaceae (rose) Graminae was very high
OH-D
Honey dew
little pollen was found on the slide
OH-E
Honey dew
Myrtacae eucalyptus, Asteraceae inc. Bellis (daisy) and
Ambrosia (ragweed) Fabaceae legume family) – specific
to this slide Brassica (cabbage /mustard /rape)
OH-F
Citrus
High proportions of Brassica types, Mimosa and Allium
OH-G
Honey dew
little pollen was found on the slide
OH-H
Acacia
High proportion of Acacia, Brassica and Ambrosia
OH-I
Honey dew
little pollen was found on the slide
124
Figure 3.3: Image of pollen present in Omani honey samples at 100x magnification.
OH-B
Lime
Graminae
Acacia
OH-C
Marigold
Graminae
Rose
OH-D
Not
Known
OH-E
Eucalyptus
Daisy
Brassica
OH-F
Brassica
Mimosa
Allium
OH-H
Acacia
125
3.2.2.2 Antioxidant activity assay:
All honey samples were analysed for antioxidant activity which included total
phenolic content and anti- radical activity (Table 3.10). The indication of linearity of
the calibration curve (R2= 0.997) was evident between 0 and 250 µg/ml of gallic acid
(Fig 2.2). From that equation total phenolic content was calculated for each honey
sample equivalent to gallic acid. A positive correlation between the antioxidant and
phenol content was found (Table 3.10) which suggests that a higher amount of
phenolic content present in honey can deactivate free radicals and thus provide
protection against diseases. Also differences in phenolic content between honeys
were observed marked with a mean of 89.7±64.8. OH-H which contain high
proportaion of acacia pollen and exhibit dark colour had the highest phenolic content
approaching 244.6 mg gallic acid /Kg of honey and approximately same antioxidant
activity as manuka honey. The least phenolic content and antioxidant activity was
observed in OH-I with 46.1eg/kg and 59.5% respectively. This is because the
antioxidant activity of honey varies and depends mainly on floral source, climate and
environment conditions.
Table 3.10: Amount of free radical and phenolic content in each honey samples
Honey sample
Anti- radical activity %
Phenol content (eq/kg)
OH-B
67.3
73.0
OH-C
68.1
80.7
OH-D
61.8
56.9
OH-E
64.7
63.0
OH-F
71.7
99.2
OH-G
62.6
53.8
OH-H
61.4
244.6
OH-I
59.5
46.1
Mean ±SD (n)
64.6±4.1
89.7±64.8
Manuka honey
61.1
65.4
126
3.3 Determination of antibacterial activity of honey samples
against test cultures:
3.3.1 Minimum Inhibitory Concentration (MIC) and Minimum
Bactericidal Concentration (MBC) of manuka honey:
Using sterile manuka MIC and MBC was determined for all of the clinical isolates.
Initially MICs were performed by agar incorporation (Table 3.11), but the method
was found to require large amounts of honey, it was time consuming and MBCs
could not be determined. A broth dilution method was therefore adopted.
3.3.1.1 Agar incorporation method:
In this assay, two honeys were selected according to initial bioassay results (Table
3.7). These were manuka honey and Omani honey-B. However, because Omani
honey did not give clear results and required too large an amount of honey to reach
the correct MIC, only manuka honey was tested here against 10 Acinetobacter
clinical isolates (Table 3.11).
127
Table 3.11: Susceptibility of Acinetobacter isolates against manuka honey
Isolates
MIC % (w/v)
E.coli NCTC 10418
5
5
5
Mean
MIC%
(w/v)
5
K. oxytoca NCTC 8167
9
9
9
9
0
Acinetobacter 1
7
7
7
7
0
Acinetobacter 2
7
8
8
7.6
0.57
Acinetobacter 3
7
7
7
7
0
Acinetobacter 4
6
6
6
6
0
Acinetobacter 5
8
8
8
8
0
Acinetobacter 6
7
7
8
7.3
0.57
Acinetobacter 7
7
7
7
7
0
Acinetobacter 8
7
7
7
7
0
Acinetobacter 9
7
7
8
7.3
0.57
Acinetobacter 10
7
7
9
7.6
1.15
7.18
0.28
Mean of Means MIC
SD *(n)
0
*(n) number of isolates tested, this expirement was done in triplicate
The MICs of 10 Acinetobacter isolates ranged between 6% (w/v) and 8% (w/v)
manuka honey. This indicates a medium variation of 2% (w/v) and standard
deviations showed good reproducibility. Clinical isolate four had the lowest MIC at
6% (w/v), whereas the highest MIC was for clinical isolate five at 8% (w/v). The
mode MIC was 7% (w/v) with four isolates inhibited at this concentration. The
overall mean average of MIC was 7.18 ± 0.28 % (w/v) manuka honey.
128
3.3.1.2 Broth dilution method:
The minimum inhibitory concentration (MIC) and Minimum Bactericidal
Concentration (MBC) for all MDR & ESBL clinical isolates against manuka can be
seen in Tables 3.12 to 3.16. A microtitre plate assay was preferred to the agar
incorporation method because of the small quantity of honey used and the ability to
perform replicate assays in the same plate. Besides that, MBC values could be easily
found by sub-culturing from non turbid wells onto nutrient agar plates. Also,
examining MIC/MBC ratios obtained by broth dilutions can be used as an indicator
of mode of inhibition (bacteriostatic or bactericidal). The antimicrobial agent
considered to have a bactericidal action is when MIC/MBC ratio < 4 while the
bacteriostatic action is when this ratio become >4 (Levison 2004).
From all
MIC/MBC observations (Table 3.12 to 3.16), manuka honey was considered to have
a bactericidal mode of action.
The mean MIC and MBC and standard deviation for 30 MDR stains of Acinetobacter
data derived from triplicate experiments were illustrated (Table 3.12)
129
Table 3.12: Sensitivity of 30 Acinetobacter isolates treated with manuka honey by
broth dilution method
Isolates
No.
Mean MIC
Mean MBC
%(w/v)
%(w/v)
±SD(3)
±SD (3)
1.
7 ±1
7 ±1.7
2.
7.3 ±1.5
3.
Isolates
No.
Mean MIC
Mean MBC
%(w/v)
%(w/v)
± SD(3)
±SD (3)
16.
6 ±0
7 ±1
8 ±1.7
17.
6 ±0
7 ±0
7 ±1
8 ±1.7
18.
8 ±1
9 ±1
4.
6 ±0
10 ±1
19.
7 ±2.6
9 ±1.7
5.
8 ±1
10 ±0
20.
8 ±2
10 ±1.7
6.
7.5 ±1.5
9 ±1
21.
7 ±1.7
10 ±2
7.
7 ±1
9 ±1
22.
7 ±1
7 ±1
8.
7 ±0
10 ±0
23.
7 ±1
9 ±0
9.
8 ±1
9 ±0
24.
7 ±1
8 ±1
10.
8 ±0
11 ±0
25.
7 ±0
10 ±0
11.
7 ±1
10 ±1
26.
7 ±1.7
11 ±0
12.
8 ±1
10 ±0
27.
6 ±1
10 ±1
13.
7 ±0
9 ±1.7
28.
7 ±0
10 ±0
14.
6 ±1
7 ±1
29.
8 ±1
10 ±1
15. *
9 ±1.7
11 ±1
30.
8 ±0
11 ±0
Mean MIC
± SD (n)
7.17 ± 0.7 (30)
Mean
MBC ± SD
(n)
9.2 ± 1.3 (30)
(*)The least susceptible strain to manuka honey
(n) number of isolates tested, this expirement was done in triplicate
130
The MIC range of 30 Acinetobacter isolates was between 6% (w/v) and 9% (w/v)
manuka honey and the MBC range was between 7% (w/v) and 11% (w/v) (Table
3.12). Considering that MICs are normally performed by doubling dilutions, these
differences are not large. Five clinical isolates (4, 14, 16, 17 & 27) have the lowest
MIC at 6% (w/v), whereas the lowest MBC at 7% (w/v) was also found in five
clinical isolates (1, 14, 16, 17 & 22). Hence isolates 14, 16 and 17 were the most
susceptible cultures to manuka honey. However, the highest MIC and, MBC was
observed in clinical isolate fifteen (15*) at 9% (w/v) and 11% (w/v) respectively
(Table 3.12). The difference between MIC and MBC was < 4 for all isolates which
means Acinetobacter exhibited bactericidal mode of inhibition with manuka honey.
The mode MIC was 7% (w/v) with almost half of isolates inhibited at this
concentration and the mode MBC was 10% (w/v) with eleven isolates killed at this
concentration. The overall mean average of MIC and MBC were 7.17±0.7 (%w/v)
and 9.2± 1.3 (%w/v) respectively (Table 3.12). This finding correlates with the
study published by Blair et al., (2009) who reported the MIC of Acinetobacter with
8.1±1.5 (%w/v) using Medihoney. In addition similar potency against Acinetobacter
(6-8 %v/v) has been reported by George & Cutting 2007.
The inhibition of 10 ESBLs Klebsiella isolates were also determined with manuka
honey and the mean MIC, MBC values were derived from duplicate expirements
with standard deviation (SD) obtained (Table 3.13)
131
Table 3.13: Sensitivity of 10 Klebsiella isolates to manuka honey tested by the broth
dilution method
Klebsiella
Isolate
Mean
MIC
Mean
MBC
%(w/v)
±SD(2)
%(w/v)
±SD(2)
Kleb-1
12 ±0
12 ±0
Kleb-2
8 ±1.4
Kleb-3
Klebsiella
Isolate
Mean
MIC
Mean
MBC
%(w/v)
±SD(2)
%(w/v)
±SD(2)
Kleb-7
12 ±1.4
15 ±1.4
10 ±0
Kleb-8
12 ±2.8
12 ±2.8
12 ±1.4
13 ±1.4
Kleb-9
12 ±0
13 ±0
Kleb-4
12 ±0
13 ±1.4
Kleb-10
12 ±0
12 ±1.4
Kleb-5*
13 ±0
16 ±0
Kleb-6
12 ±0
13 ±0
Mean ± SD
(n)
11.7± 1.3
(10)
12.9± 1.7
(10)
(*) The least susceptible isolate
(n) number of isolates tested, this expirement was repeated twice.
The MICs of 10 Klebsiella isolates were found to range between 8% & 13% (w/v)
manuka honey and between 10% & 16% (w/v) for MBC (Table 3.13). Although
these values extended over a slightly wider range than the values obtained with
Acinetobacter isolates, the variation was not large. Clinical isolate two demonstrated
the lowest MIC where 8% (w/v) manuka honey were required to inhibit this
bacterium and the highest MIC and MBC values were found in clinical isolate five at
13% (w/v) and 16% (w/v), respectively in the Klebsiella cohort. The mode MIC was
12% (w/v) manuka honey with eight isolates inhibited at this concentration and the
mode MBC was 13% (w/v) with four isolates killed at this concentration. The
MIC/MBC ratio was less than 4 for all Klebsiella isolates tested which proved the
bactericidal mode of manuka honey on this species. The overall mean average of
MIC and MBC were 11.7±1.3 & 12.9±1.7 (%w/v) respectively which also fairly
correlate with Blair et al., (2009) finding with MIC of Klebsiella 13±2.4 (%w/v).
132
Mean MIC and MBC (%w/v) values of manuka honey were determined for eight
ESBLs strains of Serratia and E.coli and the standard deviations (SD) were
calculated from duplicate expirements (Table 3.14)
Table 3.14: Sensitivity of 8 Serratia and 8 E.coli isolates to manuka honey using a
broth dilution method
SERRATIA
ISOLATES
Mean MIC
%(w/v)
±SD(2)
Mean MBC
%(w/v)
Serratia-1
10 ±0
12 ±0
Serratia -2
15 ±0
Serratia -3
E.COLI
ISOLATES
Mean MIC
%(w/v)
Mean MBC
%(w/v)
±SD(2)
±SD(2)
E.coli-1
10 ±1.4
12 ±1.4
18 ±0
E.coli-2
10 ±0
10 ±1.4
13 ±1.4
22 ±1.4
E.coli-3
10 ±0
10 ±0
Serratia -4*
15 ±1.4
20 ±0
E.coli -4
10 ±0
10 ±0
Serratia -5
15 ±0
16 ±0
E.coli -5
10 ±0
12 ±0
Serratia -6
14 ±0
16 ±0
E.coli -6
12 ±0
12 ±0
Serratia -7
13 ±0
14 ±0
E.coli -7*
13 ±0
13 ±0
Serratia -8
14 ±0
18 ±1.4
E.coli -8
8 ±0
8 ±0
Mean ± SD
(n)
13.3 ± 1.7
(8)
17 ± 3.2
(8)
Mean ± SD
(n)
10.4 ± 1.5
(8)
10.9 ± 1.6
(8)
±SD(2)
(*) Least susceptible isolates
(n) number of isolates tested, this expirement was repeated twice
The MIC values of 8 Serratia isolates ranged between 10% & 15% (w/v) and MBCs
were between 12% & 22% (w/v) for manuka honey. Although there were large
variations in the MIC and MBC ranges noted, manuka honey still demonstrated
bactericidal action (Table 3.14). Concentrations of 13.3 ± 1.7 % (w/v) were required
to inhibit test isolate. Serratia was considered to be the least susceptible isolates
against manuka honey among the other species tested.
Susceptibility of the E. coli isolates tested was similar to that of Klebsiella with
MICs against manuka honey varying between 8% & 13% (w/v) (Table 3.14). The
bactericidal action of honey also observed on E.coli where the MIC/MBC ratio was
133
very close (2 or less). It indicates that E.coli was inhibited and killed at the same
concentration.
There are several reports documenting the sensitivity of different honeys including
manuka honey against E.coli (Blair et al., 2009; George & Cutting 2007; Tan et al.,
2009; Sherlock et al., 2010). Some of them have an almost similar MIC value that
was determined in this project with 10 % (w/v) (Lusby et al., 2005). Unlike E.coli,
limited studies documented the sensitivity of honey against Serratia.
134
Table 3.15: Susceptibility of 15 Enterobacter isolates to manuka honey determined
by broth dilution method and the SD were determined from the duplicate
expirements.
Mean
MIC
Mean
MBC
Mean
MIC
Mean
MBC
%(w/v)
±SD(2)
%(w/v)
±SD(2)
%(w/v)
±SD(2)
%(w/v)
±SD(2)
Enterobacter-1
9 ±0
15 ±1.4
Enterobacter-9*
13 ±0
14 ±0
Enterobacter-2
9 ±0
13 ±0
Enterobacter-10
11 ±0
14 ±0
Enterobacter-3
9 ±0
13 ±0
Enterobacter-11
11 ±0
14 ±0
Enterobacter-4
13 ±0
14 ±0
Enterobacter-12
8 ±1.4
12 ±0
Enterobacter-5
11 ±0
14 ±0
Enterobacter-13
11 ±0
14 ±0
Enterobacter-6
9 ±0
11 ±1.4
Enterobacter-14
12 ±0
13 ±0
Enterobacter-7
8 ±0
12 ±0
Enterobacter-15
10 ±0
13 ±0
Enterobacter-8
8 ±0
11 ±0
Mean ± SD
(n)
10.1± 1.7
(15)
13.1± 1.2
(15)
ISOLATES
ISOLATES
(*) least susceptible isolate
(n) number of isolates tested, this expirement was repeated twice
The MICs of 15 Enterobacter isolates ranged between 8% & 13% (w/v) manuka
honey and MBCs were between 11% & 14% (w/v) (Table 3.15). The mode MIC &
MBC was 9% & 14% respectively. The sensitivity of Enterobacter was similar to
that of E.coli. The closest MIC, MBC ranges indicates the bactericidal mode of
manuka honey. These results were corresponded with a previous report (Lusby et al.,
2005), where the inhibitory concentration of Enterobacter was 10% (w/v). However,
this contrast with Tan et al., (2009) finding where manuka honey also used at
concentration of 20% (w/v) compared to our finding with 10.1± 1.7 (%w/v).
135
Table 3.16: Susceptibility of 12 Citrobacter isolates to manuka honey using broth
dilution method and the SD were determined from the duplicate expirements.
ISOLATES
Mean
MIC
Mean
MBC
ISOLATES
Mean
MIC
Mean
MBC
%(w/v)
%(w/v)
%(w/v)
%(w/v)
±SD(2)
±SD(2)
±SD(2)
±SD(2)
Citrobacter 1
9 ±0
12 ±0
Citrobacter 8
9 ±0
11 ±1.4
Citrobacter 2
10 ±1.4
15 ±0
Citrobacter 9
10 ±0
13 ±0
Citrobacter 3
10 ±0
14 ±0
Citrobacter 10
10 ±0
15 ±0
Citrobacter 4
10 ±0
15 ±0
Citrobacter 11
8 ±1.4
11 ±0
Citrobacter 5*
11 ±0
15 ±0
Citrobacter12
10 ±0
14 ±0
Citrobacter 6
10 ±0
14 ±0
Mean ± SD
(n)
9.7± 0.8
(12)
13.3± 1.7
(12)
Citrobacter 7
9 ±1.4
11 ±0
(*) least honey-susceptible isolate.
(n) number of isolates tested, this expirement was repeated twice
The inhibition ranges of 12 Citrobacter isolates between 8% & 11% (w/v) for MIC
and between 11% & 15% (w/v) for MBC. This indicates a medium variation of 3%
& 4% (w/v) for MIC and MBC respectively.
The overall average MIC of
Citrobacter was similar to that reported by Lusby et al., 2005 with 10 % (w/v) using
manuka honey and Blair et al., 2009 with 9.1±3 % (w/v) using medihoney.
136
3.3.2 Sensitivity of MDR and ESBLs to Omani honey:
The sensitivity of all of the clinical isolates was determined against the four Omani
honey samples with the highest total antibacterial activity detected (Table 3.7) using
the broth dilution method. MICs and MBCs % (w/v) was performed at once for all
85 MDR/ESBLs isolates, mean and SD was calculated (Tables 3.17 to 3.22). Similar
to manuka honey, it was deduced that Omani honey had a bactericidal mode of
action against test isolates because it was found that OH-B, OH-C and OH-F
exhibited relatively similar values for MIC and MBC for each species.
(The highlighted box indicates the lowest MIC mean± SD (highest susceptibility)
observed of that isolate among the other type of honey).
Table 3.17: Susceptibility of 30 MDR Acinetobacter isolates against 4 types of
Omani honey using broth dilution method.
Isolate
No.
OH-B
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
18
18
14
14
16
16
14
14
2.
14
14
16
18
18
20
18
18
3.
20
20
18
18
20
20
14
14
4.
41
18
18
20
20
22
16
16
5.
41
18
18
20
16
18
20
20
6.
41
16
18
18
22
24
14
14
7.
41
16
20
20
20
22
14
14
8.
18
20
16
22
20
22
16
16
9.
20
20
18
18
24
24
18
22
10.
20
20
18
18
18
18
18
18
11.
41
18
20
20
20
22
18
18
12.
41
16
18
22
20
22
20
20
137
13.
41
18
20
20
16
16
14
14
14.
41
18
16
16
24
24
20
20
15.
41
20
16
16
20
22
18
18
16.
41
20
20
20
22
24
18
18
17.
41
20
20
20
18
22
20
20
18.
41
20
18
20
18
22
18
18
19.
41
20
20
20
20
22
18
18
20.
41
18
20
20
22
>24
18
18
21.
20
20
20
20
24
>24
18
18
22.
16
18
18
22
24
>24
18
20
23.
14
14
18
22
20
22
20
20
24.
18
20
20
20
22
24
18
20
25.
18
20
18
18
24
24
20
20
26.
18
18
18
20
24
>24
18
18
27.
18
18
16
16
22
22
18
18
28.
18
18
18
20
20
20
18
18
29.
18
18
16
20
20
22
18
18
30.
18
20
20
20
22
22
18
18
16.6±
18.4±
18.1±
19.2±
20.5±
21.8±
17.6±
17.8±
2
1.7
1.6
1.9
2.4
2.3
1.9
2.1
Mean
± SD
(30)
(n) number of isolates tested, this expirement was done once
138
Table 3.18: Susceptibility of 11 Klebsiella isolates against 4 types of Omani honey
using broth dilution method.
Isolate
No.
OH-B
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
12
14
14
14
20
24
14
18
2.
18
18
18
20
22
26
18
20
3.
20
20
20
20
24
28
20
24
4.
20
22
20
20
26
28
20
22
5.
20
22
20
20
24
26
18
22
6.
16
16
18
18
22
24
18
20
7.
20
24
20
22
24
28
20
22
8.
16
20
18
18
20
24
16
18
9.
14
16
18
20
20
22
18
20
10.
14
16
18
18
20
24
16
20
11.
16
16
18
18
22
24
18
22
12.
20
22
20
22
24
26
20
22
17.2±3
18.8±
3.2
18.5±
1.7
19± 2
22± 2
25±
1.9
18±
1.9
20.8±
1.8
Mean
± SD
(11)
(n) number of isolates tested, this expirement was done once
139
Table 3.19: Susceptibility of 10 E.coli isolates against 4 types of Omani honey using
broth dilution method.
Isolate
No.
OH-B
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
18
20
20
20
22
24
20
20
2.
20
20
20
20
24
24
22
22
3.
20
22
20
20
26
26
22
24
4.
16
18
18
20
20
22
18
18
5.
20
22
20
20
26
28
22
22
6.
20
20
20
20
24
24
18
20
7.
18
18
18
18
22
24
18
20
8.
18
20
18
22
22
24
18
20
9.
20
20
20
20
26
28
20
22
10.
18
18
20
20
24
26
18
20
18.8±
1.3
19.8±
1.4
19.4±1
20±
0.9
23.6±2
25±
1.9
19.6±
1.8
20.8±
1.6
Mean ±
SD (10)
(n) number of isolates tested, this expirement was done once
140
Table 3.20: Susceptibility of 15 Enterobacter isolates against 4 types of Omani
honey using broth dilution method.
Isolate
No.
OH-B1
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
22
22
18
24
22
26
18
22
2.
20
20
20
22
26
30
20
24
3.
22
>24
20
22
30
>30
20
24
4.
22
24
20
22
26
28
20
24
5.
22
>24
20
20
26
30
20
22
6.
22
22
18
20
24
24
18
22
7.
22
22
20
22
26
>30
18
20
8.
24
24
22
22
30
30
24
24
9.
>24
>24
24
24
30
>30
22
24
10.
22
24
22
24
30
>30
22
28
11.
22
>24
20
24
26
30
20
22
12.
22
22
22
22
30
>30
20
20
13.
22
>24
20
22
22
24
22
24
14.
22
>24
18
22
24
24
18
18
15.
20
20
18
22
24
24
18
20
21.6±
22.9±
20.1±
22.2±
26.4±
28±
20±
22.5±
0.9
1.5
1.7
1.3
2.9
2.7
1.8
2.4
Mean
±SD
(15)
(n) number of isolates tested, this expirement was done once
141
Table 3.21: Susceptibility of 12 Citrobacter isolates against 4 types of Omani honey
using broth dilution method.
Isolate
No.
OH-B
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
22
>24
20
22
28
>28
18
18
2.
24
24
20
22
>28
>28
22
24
3.
18
22
16
18
28
>28
16
18
4.
16
16
14
16
28
>28
16
16
5.
20
24
16
18
>28
>28
16
16
6.
18
18
16
18
>28
>28
16
18
7.
16
18
14
18
28
>28
14
16
8.
18
18
16
20
26
>28
14
18
9.
16
16
14
16
28
>28
14
16
10.
18
18
16
18
22
24
14
16
11.
16
16
14
16
28
>28
16
18
12.
16
16
14
18
24
26
18
20
18.1±
2.6
18.5 ±
3.2
15.8±
2.1
18.3
±2
26 ±
2.5
27.5±
1.4
16.1±
2.3
17.8±
2.3
Mean
± SD
(12)
(n) number of isolates tested, this expirement was done once
142
Table 3.22: Susceptibility of 8 Serratia isolates against 4 types of Omani honey
using broth dilution method.
Isolate
No.
OH-B
OH-C
OH-G
OH-F
MIC
MBC
MIC
MBC
MIC
MBC
MIC
MBC
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
(%w/v)
1.
24
>24
18
20
>28
>28
18
20
2.
20
22
20
24
>28
>28
20
24
3.
22
24
20
24
>28
>28
20
24
4.
20
22
20
26
>28
>28
14
26
5.
20
22
20
24
>28
>28
14
28
6.
20
24
22
24
>28
>28
14
28
7.
18
20
20
22
>28
>28
20
24
8.
18
18
22
26
>28
>28
16
16
20.2±
1.9
21.7±
2.1
20.2±
1.2
23.7±
1.9
>28
>28
17±
2.8
23.7±
4
Mean ±
SD (8)
(n) number of isolates tested, this expirement was done once
According to the above tables Acinetobacter and Citrobacter were the most sensitive
(low MIC recorded) isolates to Omani honeys and to manuka honey as well (lowest
MIC recorded). Acinetobacter were inhibited with Omani honeys B with MIC 16.6
(%w/v) (Table 3.17). The remaining two Omani honeys C & F with MIC 15.8 &
16.6 (%w/v) respectively were more inhibitory to Citrobacter (Table 3.21). Serratia
were less susceptible to most honeys tested with MIC ranged between 17 - >28
(%w/v) (Table 3.22).
Thus it was difficult to state which is the most effective honey because each species
responded in a different way. However, OH-G had the highest MIC/MBC ratio
among the other types of honey. But OH-B had lowest MIC values reported for
Acinetobacter, E.coli and Klebsiella so can be considered to be the most effective
143
(Table 3.17-3.19), whereas OH-C and OH-F were more effective against
Citrobacter, Enterobacter and Serratia (Table 3.20-3.22).
The susceptibility of isolates tested was recorded against manuka honey and 4 types
of Omani honey tested according to above tables were:
Acinetobacter > Citrobacter > Klesiella > E.coli > Enterobacter > Serratia
144
3.4 Time Kill Curves
One representative isolate from each of the different cohorts of clinical test species
(the least susceptible one) was selected to investigate the kinetics of inhibition with
manuka honey. Bactericidal concentrations of manuka honey were required in each
experiment for each isolate. Approximately doubled MIC values of honey
concentration were therefore utilised (Table 3.23).
Table 3.23: Cultures and honey concentrations used in the time-kill curves assay
Test organism
Strain No.
MIC (%w/v)
Test conc. used
2x MIC (%w/v)
Acinetobacter
15
9
20
E.coli
7
13
30
Klebsiella
5
13
30
Enterobacteria
9
13
30
Citrobacter
2
10
20
Serratia
4
15
30
3.4.1 Inhibition of test organisms by manuka honey monitored by
optical density:
Initially the optical density of each isolate incubated in isosensitest broth for 24 hours
with and without a bactericidal concentration of manuka honey was plotted against
time (Fig 3.4 to 3.9). As shown in Figures 3.4 to 3.9, no increase in optical density
observed over the 24 h exposure to honey thus was interpreted as complete inhibition
of growth of these six isolates at test concentration of manuka honey used. A steady
increase in optical density was observed during 24 h for all six isolates without the
addition of honey was expected.
145
Figure 3.4: The effect of manuka honey (%w/v) on the growth of Acinetobacter
The growth curve of Acinetobacter
2.2
Optical density at 550 nm
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
1000
1200
1400
1600
Time (minutes)
Acinetobacter without honey
Acinetobacter with 20% (w/v) manuka honey
Figure 3.5: The effect of manuka honey (%w/v) on the growth of E.coli
The growth curve of E.coli
2
Optical density at 550nm
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
1000
1200
1400
Time (minutes)
E.coli without honey
E.coli with 30% (w/v) manuka honey
146
1600
Figure 3.6: The effect of manuka honey (%w/v) on the growth of Klebsiella
The growth curve of Klebsiella
2
Optical density at 550nm
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
1000
1200
1400
1600
Time (minutes)
Klebsiella without honey
Klebsiella with 30% manuka honey
Figure 3.7: The effect of manuka honey (%w/v) on the growth of Citrobacter
The growth curve of Citrobacter
1.8
Optical density at 550 nm
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
1000
1200
1400
Time (minutes)
Citrobacter without honey
Citrobacter with 20% (w/v) manuka honey
147
1600
Figure 3.8: The effect of manuka honey (%w/v) on the growth of Enterobacter
The growth curve of Enterobacter
Optical density at 550 nm
3.5
3
2.5
2
1.5
1
0.5
0
0
200
400
600
800
1000
1200
1400
1600
Time (minutes)
Enterobacter without honey
Enterobacter with 30% (w/v) manuka honey
Figure 3.9: The effect of manuka honey (%w/v) on the growth of Serratia
The growth curve of Serratia
2.2
Optical density at 550 nm
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
200
400
600
800
1000
1200
1400
Time (minutes)
Serratia without honey
Serratia with 30% (w/v) manuka honey
148
1600
3.4.2 Inhibition of test organisms by manuka honey monitored by
total viable count
Time-kill curves were repeated for each representative test organism as above,
except that viability was monitored by total viable count (TVC) using the surface
drop method (Miles & Misra 1938). For total culturable counts decimal dilutions
from 10-1 to 10-7 were determined. The lowest dilution that gives a reasonable count
was selected and calculated the colony forming units (CFU) to determine the actual
viable cells in 1 ml. The mean data (Log10 CFU/ml) against time were plotted for
each isolate on the graph.
A linear regression analysis for each set of data was
performed. Time-kill curves were obtained for Acinetobacter (Fig 3.10), E.coli (Fig
3.11), Klebsiella (Fig 3.12), Enterobacter (Fig 3.13), Citrobacter (Fig 3.14) and
Serratia (Fig 3.15). Each graph illustrated the difference in viability between the test
(with honey treatment) and control (without honey treatment). The activity of
manuka honey against all strains demonstrated by maximal bacterial killing and
surviving bacteria were counted at 0, 30, 60, 90, 120, 180, 240 and 300 minutes
incubated at 37oC. From figures bellow it was observed that manuka honey at 2 times
the MIC (Table 3.23) was bactericidal for all strains tested up to 5 h. Log reduction
(LD) were calculated for each isolate up to 5 h exposure to manuka honey (Table
3.24). T-test was applied to establish the statistical difference between untreated cells
and honey treated cells in the selected 6 species (Table 3.25).
149
3.4.2.1 Acinetobacter
Acinetobacter cultivated without honey demonstrated a typical growth curve and
reached the stationary phase after 5 h incubation with broth only (Fig 3.10).
However, the test organism incubated with 2x MIC demonstrated rapid loss of
viability. Therefore, 20% manuka honey resulted in 1.44-log10 reduction in CFU/ml
compared to the starting inoculum (≈99% killing of Acinetobacter) at 5 h incubation
(Table 3.24). Although honey treated Acinetobacter cells demonstrated the slowest
killing rate among the selected isolates, there was a marked difference in viable cell
count between Acinetobacter with and without honey (Table 3.25).
150
Figure 3.10: The effect of manuka honey on the viability of Acinetobacter.
Time -to-kill curve of Acinetobacter
Number of bacterial cells Log 10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
Acinetobacter without honey
800
1000
1200
1400
Time/Minutes
Acinetobacter with 20% (w/v) manuka honey
This data was done in triplicate
151
1600
3.4.2.2 E.coli
The time-kill curve clearly shows an increase in number of E.coli cells without
honey treatment (Fig 3.11). However in honey treated cells a reduction in the number
of E.coli survivors were observed over time by 1.97-log10 reduction in CFU/ml at 5 h
incubation with 30% (w/v) manuka honey (Table 3.24).
In other words, 30%
manuka honey was expected to be capable of almost killing 99% (equivalent 2-log10
reduction) of the population of E.coli within 5 h and to yield an undetectable
bacterial count (<500 cfu /ml) at 24 h incubation. The variations between non honey
and honey treated were highly significant P= 0.00 (Table 3.25) using T-test analysis.
152
Figure 3.11: The effect of manuka honey on the viability of E.coli.
Time -to-kill curve of E.coli
Number of bacterial cells log10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Time (minutes)
E.coli without honey
E.coli with 30% (w/v) manuka honey
This data was done in triplicate
153
1400
1600
3.4.2.3 Klebsiella
Klebsiella cultivated without honey demonstrated a typical growth curve as the
isolates presented above (Fig 3.12). However, the test organism incubated with
2xMIC manuka honey demonstrated a marked decrease with 5 x105 cfu/ml after 24
hours exposure. Hence 30% manuka honey achieved a 1.7-log10 reduction (≈99%
killing) in Klebsiella population at 5 h. The cells continued to decrease till it reached
< 500 CFU in 1 ml at 24 h (Fig 3.12). Addition of 30% manuka honey therefore
caused significant difference in the viability of Klebsiella population (Table 3.25).
154
Figure 3.12: The effect of manuka honey on the viability of Klebsiella
Time -to-kill curve of Klebsiella
Number of bacterial cells log10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Time (minutes)
Klebsiella without honey
This data was done in triplicate
155
Klebsiella with 30% (w/v) manuka honey
1400
1600
3.4.2.4 Citrobacter
Citrobacter incubated with broth alone only showed increase in cell number for the
first 120 minutes then there were no significant change in colony counts in each time
ponit with incubation beyond 24 h. Viable counts of Citrobacter with 20% (w/v)
manuka honey showed markedly declined of colony forming unit from 1x108 cfu/ml
(eight Log CFU) to below 5.5x104 cfu/ml (five Log CFU). A time of exposure to
honey from 4 to 5 h was enough to reduce 99.9% (3-log10 reduction) of the
population of Citrobacter (Fig 3.13). Citrobacter incubated with 20% (w/v) manuka
honey demostrated the greatested bactericidal activity at 5 h incubation with >3-log10
killing unit (Table 3.24). The change in cell count in Citrobacter between non honey
and honey treatment was statistically significant (Table 3.25).
156
Figure 3.13: The effect of manuka honey on the viability of Citrobacter
Time-to-kill curve of Citrobacter
Number of bacterial cells Log10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Time (minutes)
Citrobacter without honey
This data was done in triplicate
157
Citrobacter with 20% (w/v) manuka honey
1400
1600
3.4.2.5 Enterobacter
The number of untreated cells increased with time as shown below (Fig 3.14). The
total number of live Enterobacter cells significantly decreased when exposed to 30%
manuka honey (Table 3.25). These differences were statistically significant (Table
3.25). A slow drop in numbers can be observed over time from 55 x106 just below
(eight Log CFU) to 1.5 x106 just below (six Log CFU). Manuka honey with twice
MIC concentration produced 1.57-log10 decrease in viable counts at 5 h (Fig 3.14).
158
Figure 3.14: The effect of manuka honey on the viability of Enterobacter
Time -to- kill curve of Enterobacter
Number of bacterial cells Log 10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Time (minutes)
Enterobacter without honey
This data was done in triplicate
159
Enterobacter with 30% manuka honey
1400
1600
3.4.2.6 Serratia
An increased number of colony forming units of Serratia was observed as shown by
the trend line in the growth curve without honey, while number of cells decreased
with time after exposure to 30% manuka honey with 1.87-log10 reduction in CFU/ml
(≈99% killing). There was marked bacterial killing at 5 h incubation with honey. The
cells continued to decrease till <500 colonies in 1 ml up to 24 hrs were found (Fig
3.15). The mean difference in number of colony forming units between non honey
and honey treated Serratia were statistically significant (P <0.05) (Table 3.25).
160
Figure 3.15: The effect of manuka honey on the viability of Serratia
Time-to-kill curve of Serratia
Number of bacterial cell Log 10 CFU/ml
12
10
8
6
4
2
0
0
200
400
600
800
1000
1200
Time (minutes)
Serratia without honey
Serratia with 30% (w/v) manuk honey
This data was done in triplicate
161
1400
1600
The Log Reduction (LR) was calculated for each isolate by subtracting the Log CFU
at zero time and the Log CFU at 5 h incubation with 2x respective MICs of manuka
honey. The higher LR, the faster was the killing rate (Table 3.24). Acinetobacter was
the most susceptible MDR when tested with MIC, but Citrobacter were inhibited at
a faster rate (Table 3.24).
Table 3.24: Log reduction (LR) for each isolate after 5 h exposure to 2x respective
MICs of manuka honey
Isolate
LogCFU/ml + honey
LR
At 0 h
At 5 h
Acinetobacter
7.04
5.6
1.44
E.coli
7.97
6
1.97
Klebsiella
7.39
5.69
1.7
Enterobacter
7.74
6.17
1.57
Citrobacter
8
4.74
3.26
Serratia
7.87
6
1.87
Sensitivity profile of six tested strains against honey at each concentration according
to the table above is:
Citrobacter > E.coli > Serratia > Klebsiella > Enterobacter > Acinetobacter
162
All isolates showed statistically significant differences in the mean of cell count
between honey treated and untreated cells P <0.05 using T-test analysis (Table 3.25).
Serratia determined the higher differences in cell count between non-honey and
honey treated cells.
Table 3.25: Comparison of the mean viable cell count between non honey and honey
treated cells using paired sample test (T-test).
Isolate ID
Paired mean difference
(Non honey-honey)
P- value
Acinetobacter
11.57
<0.005
E.coli
8.86
<0.005
Klebsiella
8.90
0.001
Enterobacter
10.33
<0.005
Citrobacter
9.10
<0.005
Serratia
12.19
<0.005
163
3.5 Effect of honey on bacterial structure:
Electron microscopy is an essential tool to observe the ultrastructural and physical
changes that occur to cells after adding certain agents. Details of surface structures
and bacterial morphology aid an understanding of the action of various drugs on
bacteria. In this project both scanning and transmission electron microscopy was
used to determine membrane integrity, morphological changes of cells, electron
density of the cytoplasm and evidence of cell division before and after exposure to
honey.
3.5.1 Growth Curves:
The exponential cells were collected after 3 h incubation at 37°C in ISB as indicated
by the arrows in each of the growth curves for Acinetobacter (Fig 3.16), E.coli (Fig
3.17), Klebsiella (Fig 3.18), Enterobacter (Fig 3.19), Citrobacter (Fig 3.20), and
Serratia (Fig 3.21). The exponential phase was selected because of active growth of
cells was expected to result in many proteins and enzymes being synthesized. These
molecules are considered to be the main target site for various inhibitors and thereby
help in identifying the action of an antimicrobial agent. Biosynthetic pathways in
stationary phase bacteria are more likely to reflect secondary metabolism, rather than
central pathways.
Exponential phase cultures were treated with and without
approximately twice MIC concentrations of honey and monitored for 180 minutes.
164
Figure 3.16: Growth curve of Acinetobacter in ISB
Growth Curve of Acinetobacter
Optical Density at 550 nm
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
700
600
700
Time (minutes)
Figure 3.17: Growth curve of E.coli in ISB
Growth Curve of E.coli
Optical Density at 550 nm
1.2
1
0.8
0.6
0.4
0.2
0
0
100
200
300
400
Time/minutes
165
500
Figure 3.18: Growth curve of Klebsiella in ISB
Growth curve for Klebsiella
2
Optical Density at 550 nm
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
100
200
300
400
500
600
700
Time (minutes)
Figure 3.19: Growth curve of Enterobacter in ISB
Growth curve for Enterobacter
Optical Density at 550 nm
1.2
1
0.8
0.6
0.4
0.2
0
0
100
200
300
400
Time (minutes)
166
500
600
700
Figure 3.20: Growth curve of Citrobacter in ISB
Growth curve for Citrobacter
Optical Density at 550 nm
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
100
200
300
400
500
600
700
Time (minutes)
Figure 3.21: Growth curve of Serratia in ISB
Growth curve for Serratia
1.8
Optical Density at 550 nm
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0
100
200
300
400
Time(minutes)
167
500
600
700
3.5.2 Scanning Electron Microscopy (SEM):
3.5.2.1 SEM of Acinetobacter
For Acinetobacter an average of ten SEM micrographs with a magnification of 5,000
(for cell count) and two micrographs with a magnification of 20,000 (for cellular
details) were taken for each time point. In total 30 SEM micrographs were taken for
untreated honey samples (control) and 50 micrographs for treated honey samples.
However only representative images for 0, 60, 90, 150 and 180 time points are
shown here.
SEM micrographs of untreated cells of Acinetobacter incubated with broth only at
time 0 were observed to have a regular rod shaped cell with a smooth surface for
95% of the cells and cell length was 3 µm (Fig. 3.22); normal cell size is between 1.5
to 2.5 µm (Gillespie and Hawkey 2006). However, after 3 h incubation in liquid
media this bacterium became enlarged and appeared as long bacilli (Fig. 3.24, 3.26,
3.28) with mean cell size increased to 5.5 µm. This bacterium normally tends to be a
bacillus in shape when it incubated for long time in liquid media and/or if collected
in exponential growth phase (Murray et al., 2007). Septated cells were also observed
with no surface changes and no debris.
In the honey treated sample, the majority of cells (94%) appeared unchanged for the
first 30 minutes exposure to manuka honey (Fig 3.23). From 60 minutes onwards
lots of debris was observed, suggesting that some leakage of cellular material or lysis
had taken place. Also clumping and cell aggregation increased (Fig. 3.25, 3.27 &
3.29). There were significant changes in the cell length of Acinetobacter after 1 h
exposed to 20% (w/v) manuka honey compared to untreated honey (p = 0.0216)
168
(Table 3.26). The length of individual cells treated with honey appeared to be shorter
than those without honey, which in turn resulted in decreased cellular volume.
In addition an increased number of septa were seen after 150 minutes incubation
with 20% (w/v) manuka honey (Fig 3.29). Formation of elongated cells seemed to
be decreased (P = 0.025) (Table 3.26) which again would lead to decrease in cellular
volume. An interesting effect seen in this stage was that multiple cell division
constriction sites were apparent on some filaments of cells, suggesting that cell
separation had not been completed (Fig 3.29 b, c & d). Also more fragments of
debris were present in honey- treated cells than in untreated cells incubated for the
same time (Fig 3.28 b). A significant change occurred after 3 hrs incubation where
marked damage observed in cell surface. It appeared that treated cells entirely
diffused and lysed (Fig 3.31) compared to control cells.
The statistical analysis of morphological changes in untreated and treated honey cells
are shown in Table (3.26)
169
Figure 3.22: SEM micrograph of untreated cells of Acinetobacter after 0 minutes at
x5,000 magnification
Figure 3.23: SEM micrograph of Acinetobacter cells exposed to 20% (w/v) manuka
honey after 0 minutes at x5,000 magnification
170
Figure 3.24: SEM micrograph of untreated cells of Acinetobacter after 60 minutes at
x5,000 magnification
Figure 3.25: SEM micrograph of Acinetobacter cells exposed to 20% (w/v) manuka
honey
after
60
minutes
at
x5,000
magnification
Clumps
Debris
171
Figure 3.26: SEM micrograph of untreated cells of Acinetobacter after 90 minutes at
x5,000 magnification
Figure 3.27: SEM micrograph of Acinetobacter cells exposed to 20%
manuka honey after 90 minutes at x5,000 magnification
Debris
Clumps
172
(w/v)
Figure 3.28: SEM micrographs of untreated cells of Acinetobacter after 150 minutes
at x5,000 A & x20,000 B magnification respectively
A
Single
division
B
173
Figure 3.29: SEM micrographs of Acinetobacter cells exposed to 20%
(w/v)
manuka honey after 150 minutes at x5,000A, x10,000B, x20,000C & x25,000D
magnification respectively
Debris
A
Multiple
division
sites
B
174
Cell division
C
Multiple septa
in each cell
D
175
Figure 3.30: SEM micrographs of untreated cells of Acinetobacter after 180
minutes at x5,000 A & x10,000 B magnification respectively
A
B
176
Figure 3.31: SEM micrographs of Acinetobacter cells exposed to 20%
(w/v)
manuka honey after 180 minutes at x 5,000 (A) & (B) magnification respectively
Cell lysis
A
B
177
3.5.2.2 SEM of E.coli
For E.coli an average of four SEM micrographs with a magnification of 5,000 (for
cell count) and one micrograph with a magnification of 20,000 (for cellular details)
were taken for each time point. In total 20 SEM micrographs were taken for
untreated honey samples and 30 micrographs for treated honey samples. However,
due to small variation in cell morphology between each time point, only
representative images collected at 30 and 180 time points were shown here.
SEM micrographs of control cells of E.coli incubated with broth only were shown to
have rods with regular structure and normal size after 3 h incubation in liquid media
(Fig 3.32 & 3.34)
In honey treated cells there was no change in shape and size at 0 min (not shown) (P
>0.05). There was no significant difference of cell length between treated honey
sample and untreated one at most of the time point (P> 0.05) (Table 3.26). However,
deformation of cells was observed after 3 hrs incubation with 30% manuka honey
(P= 0.025) (Fig 3.35, white arrows). Also it was observed that the cells exposed to
honey aggregated together by forming extended strand materials (filament) shape
around each cell probably cytoplasmic consistuent (Fig 3.33 & 3.35). Septated cells
were not observed in this stage.
178
Figure 3.32: SEM micrograph of untreated cells of E.coli after 30 minutes at x5,000
magnification
Normal cells
Figure 3.33: SEM micrograph of E.coli cells exposed to 30% (w/v) manuka honey
after 30 minutes at x5,000 magnification
Cells with
clumps
179
Figure 3.34 : SEM micrograph of untreated cells of E.coli after 180 minutes at
x5,000 magnification
Figure 3.35 : SEM micrograph of E.coli cells exposed to 30% (w/v) manuka honey
after 180 minutes at x5,000 magnification
Filament
shapes
180
3.5.2.3 SEM of Klebsiella
Klebsiella was treated with 30% manuka honey and 40% Omani honey (OH-B). An
average of eight SEM micrographs with a magnification of 5,000 (for cell count) and
two micrographs with a magnification of 15,000 and 20,000 (for cellular details)
were collected for analysis of both types of honey at each time point. In total 20
SEM micrographs were taken for untreated honey samples and 60 micrographs for
treated honey samples. However, only 30 and 180 time points were shown here.
SEM micrographs of Klebsiella incubated with broth alone (no honey) were found to
have a conventional rod shape after 3 h incubation in liquid media with approximate
size of 0.6 to 6 µm long (Fig 3.36 & 3.37). In SEM micrographs of honey treated
cells cell size and shape did not appear to be significantly different to untreated cells
after 30 min (Fig 3.38 & 3.40) for both honeys (P = > 0.05). However, cell size
significantly increased between 1 to 3 hours exposure for both honeys (P = < 0.05)
(Table 3.26). In addition rough cell surfaces were observed at the same time points
(Fig 3.39 b,c,d manuka honey) and (Fig 3.41 b,c,d Omani honey). There few septa
present with no debris observed.
181
Figure 3.36: SEM micrographs of untreated cells of Klebsiella after 30 minutes at
x5,000 (A) & 15,000 (B) magnification respectively
Normal cells
A
B
182
Figure 3.37: SEM micrographs of untreated cells of Klebsiella after 180 minutes at
x5,000 (A) & 15,000 (B) magnification respectively
A
Normal cells
B
183
Figure 3.38 : SEM micrographs of Klebsiella cells exposed to 30% manuka honey
after 30 minutes at x5,000 (A) & 15,000 (B) magnification respectively
A
B
184
Figure 3.39: SEM micrographs of Klebsiella cells exposed to 30% (w/v) manuka
honey after 180 minutes at x5,000A, x15,000B,C & x20,000D magnification
A
Rough
surfaces
B
185
Rough surface
C
D
186
Figure 3.40: SEM micrographs of Klebsiella cells exposed to 40% (w/v) Omani
honey after 30 minutes at x5,000 A & x15,000 B magnification respectively
A
B
187
Figure 3.41 : SEM micrographs of Klebsiella cells exposed to 40% (w/v) Omani
honey after 180 minutes at x5,000 A & x 20,000 B,C & D magnification respectively
A
Rough
surface
B
188
C
D
189
3.5.2.4 SEM of Enterobacter
Similarly for Enterobacter an average of four SEM micrographs with a
magnification of 5,000 (to do cell counts or cell sizes) and one micrograph with a
magnification of 20,000 (for examination of cellular surface details) were taken for
each time point. In total 20 SEM micrographs were taken for untreated honey
samples and 30 micrographs for treated honey samples. However, only 30 and 180
time points were shown here.
Again, regular bacilli shaped cells with no surface irregularities were observed in
untreated sample (Fig. 3.42). After exposure to 30% (w/v) manuka honey lots of
mesh like material between cells was obvious as a result of cells aggregation (Fig.
3.43 & 3.45). There were no significant difference in cell size between untreated and
treated honey samples at any time point (P = > 0.05) (Table 3.26).
190
Figure 3.42: SEM micrographs of untreated cells of Enterobacter after 30 minutes
at x5,000 A & x15,000 B magnification respectively
Normal cells
A
B
191
Figure 3.43: SEM micrographs of Enterobacter cells exposed to 30%
manuka honey after 30 minutes at x5,000 A & B magnification respectively
Cells with clumps
A
B
192
(w/v)
Figure 3.44: SEM micrographs of untreated Enterobacter cells after 180 minutes at
x5,000 A & x20,000 B magnification respectively
Filter membrane
Normal cells
A
B
193
Figure 3.45: SEM micrographs of Enterobacter cells exposed to 30% (w/v) manuka
honey after 180 minutes at x5,000 (A & B) magnification respectively
Mesh like structure
A
B
194
3.5.2.5 SEM of Citrobacter
For Citrobacter an average of four SEM micrographs with a magnification of 5,000
(for cell count) and one micrograph with a magnification of 20,000 (for cellular
details) were taken at each time point. In total 20 SEM micrographs were taken for
untreated honey samples and 30 micrographs for treated honey samples. However,
only 30 and 180 time points are shown here.
Regular bacilli shaped cells with approximate size 2 to 6 µm long with smooth
surfaces were observed in untreated cells (Fig. 3.46). After exposure to 20% (w/v)
manuka honey, the surface of bacterial cells seemed to be rougher, maybe as a result
of outer membrane damage (Fig. 3.47 & 3.49). Also, at higher magnification black
areas were present on the surface of some cells (Fig. 3.49 b) which was suggestive of
pore formation. There were significant difference in cell size between untreated and
treated honey samples at 30, 90 and 180 minutes time point (P ≤ 0.05) (Table 3.26).
195
Figure 3.46: SEM micrographs of untreated cells of Citrobacter after 30 minutes at
x5,000 A & x15,000 B magnification respectively
A
Normal cells
B
196
Figure 3.47: SEM micrographs of Citrobacter cells exposed to 20% (w/v) manuka
honey after 30 minutes at x10,000 A & x15,000 B magnification respectively
A
B
197
Figure 3.48: SEM micrographs of untreated Citrobacter cells after 180 minutes at
x5,000 (A & B) magnification respectively
A
B
198
Figure 3.49: SEM micrographs of Citrobacter cells exposed to 20% (w/v) manuka
honey after 180 minutes at x5,000 A & x15,000 B magnification respectively
Filter membrane
A
Pore
formation
B
199
3.5.2.6 SEM of Serratia
For Serratia an average of four SEM micrographs with a magnification of 5,000 (for
cell count) and one micrograph with a magnification of 20,000 (for cellular details)
were taken at each time point. In total 20 SEM micrographs were taken for untreated
honey samples and 30 micrographs for treated honey samples. However, only 30 and
180 time points are shown here.
Regular bacilli shaped cells with approximate size 0.9 to 2 µm long with smooth
surfaces were observed in untreated sample (Fig. 3.50). After exposure to 30% (w/v)
manuka honey for 30 minutes cells surfaces were noticeably uneven and cell debris
seemed to be present, suggesting that some cells had lysed. Blebs were also seen on
some honey treated cells (Fig 3.51). After 180 minutes exposure to 30 % (w/v)
manuka honey, elongated cells were significantly present (p = 0.0002) with cells
aggregation (Table 3.26). Extracellular material was observed, suggesting that
biofilm formation had been initiated (Fig. 3.53). Septated cells were not observed at
this stage.
200
Figure 3.50: SEM micrograph of untreated cells of Serratia after 30 minutes at
x5,000 magnification
Figure 3.51: SEM micrograph of Serratia cells exposed to 30% (w/v) manuka
honey for 30 minutes at x 5,000 magnification
Filter membrane
201
Figure 3.52: SEM micrograph of untreated cells of Serratia after 180 minutes at
x5,000 magnification
Figure 3.53: SEM micrograph of Serratia cells exposed to 30% (w/v) manuka
honey after 180 minutes at x5,000 magnification
202
Table 3.26: Comparison of changes in cell length (μm) of isolates observed in scanning electron microscopy between untreated and honey
treated cells, respectively, after 240 min. (P value <0.05) (Mann-Whitney Test)
Time point
Acinetobacter
(n=20)
E.coli
(n=20)
Manuka honey
Klebsiella (n=20)
Manuka
Omani
Manuka honey
Citrobacter
(n=20)
Enterobacter
(n=20)
Serratia
(n=20)
Manuka honey
Manuka honey
Manuka honey
0
0.85
0.85
NT
NT
0.41
NT
NT
30
NT
NT
0.104
0.38
0.0046
0.79
0.623
60
0.0216
NT
NT
NT
0.733
NT
NT
90
NT
NT
0.140
0.037
0.064
NT
0.049
120
NT
0.82
0.037
0.0091
NT
NT
NT
180
0.025
0.025
NT
NT
0.053
0.67
0.0002
240
NT
0.909
0.0173
0.0046
1.00
NT
NT
n= number of cells observed
P value = <0.05 expressed in bold
NT = Not tested
203
3.5.2 Transmission Electron Microscopy (TEM):
3.5.2.1 TEM for Acinetobacter:
TEM micrographs of untreated cells of Acinetobacter revealed regularly shaped
cocco-bacilli with entire margins after 60 minutes incubation in ISB (blue arrows)
with a few elongated cells were also seen (Fig 3.54). After 3 hrs cells did not appear
to have major change in cell shape or size (Fig 3.56). However, distinct
morphological changes had occurred in honey treated cells after 60 minutes
incubation with 20% (w/v) manuka honey in ISB. These changes took place in the
cell membrane, which was observed to be incomplete and indistinct in some cells
(Fig 3.55, red arrows) compared to the well defined cell membrane seen in untreated
cells (Fig 3.54, blue arrows). Moreover, some cells presented with black deposit
possibly mineral and some with empty vacuoles inside some cells (Fig. 3.55). The
presence of cellular debris indicated lysis of cells (Fig. 3.55 & 3.57). The number of
cells with septa was increased following treatment with 20% (w/v) manuka honey
(Fig 3.57 black arrows).
204
Figure 3.54: Transmission micrographs of untreated cells of Acinetobacter
after 1 h incubation with isosensitest broth (ISB) at 16,000x magnification (A, B, C)
Cocco-bacilli shape
A
300 nm x16,000
Define cell wall
B
300 nm x16,000
C
300 nm x16,000
205
Figure 3.55: Transmission micrographs of Acinetobacter incubated with isosensitest
borth (ISB) containing 20% (w/v) manuka honey for 1 h at 16,000x magnification.
Debri
s
Hole
s
300 nm x16,000
A
Black deposit
B
300nm x16,000
C
300nm x16,000
206
Figure 3.56: Transmission micrographs of untreated cells of Acinetobacter
incubated with isosensitest broth (ISB) after 3 h at 16,000x(A,B) & 32,000x(C)
A
300nm x16,000
B
300nm
C
300nm
207
x16,000
x32,000
Figure 3.57: Transmission micrographs of Acinetobacter incubated with isosensitest
broth (ISB) containing 20% (w/v) manuka honey after 3 h at 16,000x magnification
A
300nm x16,000
Debris
B
300nm
x16,000
Septa
C
300nm
208
x32,000
3.5.2.2. TEM for E.coli:
TEM images of untreated cells of E.coli did not appear to show obviously damaged
or abnormal shaped and sized cells. Nevertheless empty spaces or holes were
observed in some cells (Fig 3.58 & 3.60).
Honey-treated cells of E.coli with 30% (w/v) manuka honey demonstrated gaps
between the cell membrane and the cytoplasm which seemed to have caused the
membranes to shrink away from the cytoplasm (Fig 3.59). Also deformed cell
membrane could be seen in some images.
Very interestingly, due to membrane rupture the intracellular components had leaked
out from the cells after honey treatment and formed empty cells with black deposit
inside (Fig 3.59). After 3 hrs incubation with 30% (w/v) manuka honey these
samples also demonstrated cells with irregular shapes with relatively less electron
dense material compared to untreated cells (Fig 3.61). Septated cells were not
observed in this stage.
Unfortunately it was not possible to process all of the ESBL organisms for structural
changes induced by honey.
209
Figure 3.58: Transmission micrographs of untreated cells of E.coli after 1 h
incubation with isosensitest borth (ISB) at 16,000x magnification
Hole
A
300nm x16,000
B
300nm
300nm
C
210
x16,000
x16,000
Figure 3.59: Transmission micrographs of E.coli incubated with isosensitest borth
(ISB) containing 20% (w/v) manuka honey after 1 h at 16,000x magnification
Empty cell with deposit
A
300nm
x16,000
Lysed cell
Empty cell with deposit
B
300nm
x16,000
Empty cell with deposit
C
Deformed cells
300nm
211
x16,000
Figure 3.60: Transmission micrographs of untreated cells of E.coli after 3 h
incubation with isosensitest broth (ISB) at 16,000x(A) & 32,000x (B,C).
Holes
A
300nm x16,000
B
300nm x32,000
Define cell wall
C
300nm x32,000
212
Figure 3.61: Transmission micrographs of E.coli incubated with isosensitest broth
(ISB) containing 20% (w/v) manuka honey after 3 h at 16,000x magnification
Irregular cells
A
300nm x16,000
Less electron dense
B
300nm x16,000
C
300nm x16,000
213
3.6 Effect of honey on bacterial proteins:
3.6.1 Two Dimensional Gel Electrophoresis:
This technique is able to separate proteins using two distinct steps: the isoelectric
focusing (IEF) separates proteins according to their isoelectric point (pI) and SDSpolyacrylamide gel electrophoresis (SDS-PAGE) separates proteins according to
their molecular weight (MW). Each resulting spot from a 2-D gel can be matched to
a single protein in the sample. Then it can be analysed using PDQuest Basic 8.0
software to determine the number and position of spots. Besides obtaining protein
the pI and molecular weight identification, this method detects the amount of each
protein present. The proteome analysis of Acinetobacter using 2-D electrophoresis
was carried out to establish the differences in protein expression between treated and
untreated honey cells. This test was performed in Acinetobacter only because the
electron microscopy results of this bacterium were shown more physiological
damage. The software that was mention previously was able to detect the number of
spots present in each test gel (honey treatment) (Fig 3.63) and compared it to control
gel (no honey) (Fig 3.62). This allows for identifying those spots whether up or
down regulated by comparing their expression between these gels.
A total of 10 gels (control & test) were run successfully according to the method
described above, but despite repeated attempts, unfortunately the effect on protein
expression within this bacterium after exposure to 20% manuka honey was not
possible at this stage. This techniques offers promise in investigating the effects of
honey on ESBLs, but further work in optimising the conditions is needed. It has
proved valuable in UWIC in investigating the effect of manuka honey on MRSA
(Jenkins et al., 2011).
214
Figure 3.62: 2-D protein electrophoresis gel of Acinetobacter cells without honey
treatment
Protein spots
Figure 3.63: 2-D protein electrophoresis gel of Acinetobacter cells exposed to 20%
(w/v) manuka honey
215
Chapter 4
Discussion
216
“The therapeutic potential of uncontaminated, pure honey is grossly underutilized. It
is widely available in most communities and although the mechanism of action of
several of its properties remain obscure and needs further investigation, the time has
now come for conventional medicine to lift the blinds off this „traditional remedy‟
and give it its due recognition” (Zumla and Lulat 1989).
This study was carried out to investigate the efficacy of Omani honey and manuka
honey in the inhibition of Gram negative multi-drug resistant organisms implicated
in wound infections. The antibacterial potency of each honey was determined by
bioassay method, MIC and MBC, time-kill study and electron microscopy. Each
experiment provided some insight into possible inhibitory mechanisms of honey on
microorganisms.
4.1 Antibacterial activity of honey samples:
For many years local, natural and untreated honey was previously used in different
countries for their antibacterial activity against different types of bacteria
(Alandejani et al., 2009; Al-Jabri et al., 2003; Al-Waili, 2004; Basson and Grobler
2008; Wadi et al., 1987).
In order to quantify the antibacterial activity of a honey using the agar well diffusion
method, a bioassay in which activity was compared to phenol as a reference was
developed in New Zealand (Allen et al., 1991). Phenol is a chemical that was used as
an antiseptic in surgical procedures in the nineteenth century. It has been used as a
reference for the evaluation of the potency of many antiseptics, but it is not usually
used for wound treatment because of its hazardous effect in damaging cells and
tissues. It could be therefore argued that it is not a suitable standard to measure the
217
activity of honey, because topical treatments for wounds must be non-cytotoxic, as
well as antibacterial. Nevertheless it has been used in several studies.
A New Zealand study of 345 honey samples found that non-peroxide activity
presented only in 25 honey samples which were identified as either manuka or
viper‟s bugloss (Allen et al., 1991). The same bioassay was used to survey
antibacterial activity in 139 Welsh honeys and it was found that not all honeys had a
detectable amount of total activity, and non-peroxide activity was not detected
(Wheat 2004). Also, a study of 30 Portuguese honeys showed only 7 honeys
possessed non-peroxide activity with 11.5 % (w/v) phenol equivalent (Henriques et
al., 2005). Twenty years from Allen et al., another New Zealand survey on the
antibacterial activity of 477 Australian honey samples found that 80 samples (16.8%)
exhibited non-peroxide activity (Irish et al., 2011)
From the bioassay most Omani honey tested in this project had a certain amount of
antibacterial activity (total activity) which ranged between < 2% and 22.8% (w/v)
phenol equivalent where 2% (w/v) is the lowest phenol standard able to cause a zone
of inhibition. However, none of them demonstrated detectable activity when tested
for non-peroxide activity, ie incubation with catalase. All the antibacterial activity of
Omani honey on dilution was therefore related to the formation of H2O2 and they can
be called peroxide honeys. Manuka honey used in this study possessed an equal
amount of antibacterial activity both total activity with 21.3±0.85 and non-peroxide
activity with 21.4± 0.8 (% w/v) phenol equivalent. Manuka honey therefore had nonperoxide type activity and this confirmed the deduction of Allen, Molan and Reid
(1991).
218
Non-peroxide honey shown to be effective in wound management. According to
many studies on the antibacterial activity of different types of honey, manuka honey
has been shown to be the most effective honey and because anti-bacterial activity on
dilution was entirely due to the non-peroxide components that are found in this
honey, this gives manuka honey an advantage over peroxide honeys (Al Somal et al.,
1994; Molan and Russell 1988; Snow and Manley-Harris 2003).
There are several rating systems recently developed to describe the quality and
effectiveness of non-peroxide honey as an anti-microbial agent for wound treatment.
Unique Manuka Factors (UMF) was the first rating system identified to denote the
potency of honey. Manuka honey with UMF activity of ten or above (10 +) (same
potency of 10% phenol equivalent) is the most acceptable for therapeutic use.
Methylglyoxal (MGO) and the Molan Gold Standard are new registered trademark
from
different
organizations
to
rate
the
potency
of
manuka
honey
(http://www.molangoldstandard.co.nz/article).
4.2. Chemical & physical analysis of honey samples
Different types of local honey from different origins were studied for chemical
compositions and antioxidant activity (Aljadi and Kamaruddin 2004; Al-Mamary et
al., 2002; Beretta et al.,2005; Estevinho et al., 2008). However, the physicochemical
analysis of Omani honey including the phenolic acid and anti-radical activity was not
well studied. Determination of honey compositions and its source of origin aid in
identifying its biochemical properties. Omani honey was traditionally assessed by
sensory tests such as taste, colour, aroma and texture which make the detection of
honey quality more difficult. Generally natural honey is variable in its composition
mainly in pH value, ash, water and sugar content.
219
The selected Omani honeys were analysed for their sugar, water, pH, HMF, colour,
protein, antioxidant and pollen. The values were compared to the reference National
Honey Board (NHB) (Table 4.1) and most parameters were found to be within the
NHB range.
Table 4.1: The physicochemical analysis of selected honeys tested
Honey Sample
pH
Water
content
%(w/v)
Sugar
content
% (w/v)
Protein
(mg/g)
HMF
(mg/kg)
Antiradical
activity%
Phenol
content
(eq/kg)
NHB
3.5-5.5
17.2
82.4
0.7
15
N/A
N/A
Mean ±
4.7±
15.8±
81±
4.7±
11.1±
64.6±
89.7±
SD (n) of
Omani honey
0.9
1.9
2.8
1.5
11.9
4.1
64.8
Manuka
honey
3.5
20
78
0.3
3
61.1
65.4
N/A (not available)
To date there has been only one research study that was conducted in Sultan Qaboos
University in Oman in which two parameters (sugar fractions and protein content) in
51 Omani honeys were analysed. These honeys collected from Muscat and Al-Batina
regions during summer and winter seasons between 1999- 2002. The mean sugar
was recorded as 67.41% and the mean protein was as low as 2 ± 1 mg/g. The same
study also compared the level of these two factors in summer and winter honeys, and
in multifloral and unifloral honeys. It was found the level of sugars in honeys
collected in summer was higher than winter honey whereas no difference in sugar
between multifloral and unifloral honeys was found. However the protein content
was not affected by either season or the type of flora (Sajwani et al., 2007b). This
study, therefore, has provided detailed information about selected Omani honeys.
220
Sugars are the major content of honey, however when these sugars (glucose and
fructose) broken down, the HMF released. An increase level of HMF in honey
samples occur when honey is stored for long periods at high temperature. Using
improper storage temperature or breakdown of sugars by acid hydrolase, therefore
releases high amount of HMF which could be used an indicator of honey quality
(Badawy et al., 2004; Sanz et al., 2003).
Honey is known to possess antioxidant activity which is one of main therapeutic
benefits of honey in trapping the free radicals and promoting wound healing (Molan
1992b). At an injury or wound site the activity of free radicals is increased. Topical
application of honey to the injury site is expected to mop up the free radicals and so
promote rapid healing (Subrahmanyam et al., 2003; Henriques et al., 2005). Omani
honey exhibited high levels of anti-radical activity (64.6± 4.1%) compared to tualang
honey (41.3±0.78%) (Mohammed et al., 2010).
Phenolic compounds in honey have been considered as possible indicators of the
botanical origin of honey (Al-Mamary et al., 2002; Yao et al., 2003).
Honey acts a natural antioxidant and it is known that the colour of honey is related to
antioxidants. Dark honey has been determined to contain more phenolic components
and so more antioxidant potency than the lighter ones (Bogdanov et al., 2004;
Estevinho et al., 2008; Pyrzynska & Biesaga 2009). There are more than 500
polyphenolic compounds already known in honey where phenolic acid and
flavonoids are most predominant (Anklam 1998). These compounds are identified by
high performance liquid chromatography and electrophoresis (Pyrzynska & Biesaga
2009).
221
The colour of honey is the most obvious physical parameter that is observed directly
by users. It varies with botanical origins, age, storage condition and the amount of
pollen present. The colour can vary from water white, amber brown shade to almost
black. There are two common classification systems used for colour grading, the
Pfund and Townsend classifications. These systems depend on optical assessment to
obtain the intensity of colour (Bogdanov et al., 2004). A new method has been
introduced using reflectance spectroscopy for colour classification (Terrab et al.,
2002a)
The colour of each Omani honey was estimated initially by visual assessment, then it
was tested by determining the optical density of 50% (w/v) honey at 560 nm and the
colour was classified according to Townsend system (Townsend 1969). The results
obtained from visual assessment completely matched the Townsend system. The
colour of most honey samples ranged from extra light amber to dark amber even
black. This means that Omani honeys exhibited a dark colour, which is also visually
obvious in all honeys sold in Oman. Elevated level of anti-radical activity with
phenolic content and antioxidant in Omani honeys highlight the important of these
compounds in human health to fight infections and in nutrition as a deitery
supplement of vitamins.
Eight honey samples collected for this project were produced in four different
regions of Oman. An estimated flora source for each honey sample was provided by
the beekeepers. Pollen grains were analysed and full identification of flora source
was kindly provided from National Pollen And Research Unit (NPARU) at
Worcester University (Table 3.9). Five honey samples showed that nectar sources
came from mixed flora sources (multi-flora). These are Graminae (the grasses),
222
Acacia (thorn trees), Myrtacae eucalyptus and Brassica types. However, the
remaining three honeys were not identified because of little pollen present in the
slides and may have been honeydew honeys. This demonstrated that the beekeepers
were not always correct in their assumptions about floral origin of honey due to lack
materials that could used in identifying pollen.
The quality of honey is based on their flora source which is determined by pollen
analysis, and chemical composition. The latter is more accurate and specific in
analysis of various components that present in honey and it involves identifying the
flora origin of honey. The price of honey is depended on its quality.
4.3 Effect of honey samples against test cultures:
Several reports have been published regarding the antimicrobial activity of different
types of honey against variety of organisms including Staphylococcus (MRSA &
MSSA), Pseudomonas aeruginosa, Streptococcus and anaerobes (Cooper et al.,
1999; Maedaa et al., 2008; Mullai and Menon 2005, 2007) but little was documented
for Gram negative Enterobacteriaceae especially MDR and ESBL producing
organisms before this study. Here the antibacterial activity of five types of selected
honey was tested against six species of MDR organisms that usually cause wound
infections. These were Acinetobacter, E.coli, Klebsiella, Citrobacter, Enterobacter
and Serratia. Eighty seven isolates in total were tested for MIC and MBC against
these five types of honey including manuka honey.
MIC is a measurement of the quantity of honey required for bacterial inhibition. To
determine the MIC of honey samples against selected bacteria two methods were
223
used: agar incorporation method and broth dilution method using 96 well microtiter
plates. The results in latter method are determined by visual inspection of turbidity
and confirmed by using spectrophotometric assay. Initially agar incorporation was
used for 10 Acinetobacter isolates against manuka honey. The mean MIC results
showed 7.18 (%w/v) compared to 7.17 (%w/v) using broth dilution methods.
The nature of agar incorporation method is basic but it has some limitations in the
results as it does not report MBC values and limits the number of test replicates.
Also it is time consuming, required large amount of samples and extensive plate
preparation was also needed. There were no differences in the results between two
techniques though the broth dilution technique presents many advantages including:
smaller volume of samples used, rapid and cheaper, allows more replicates in the
same plate, produce large amount of statistical data and determines the MBC value
(Patton et al., 2006). Recently, a microtiter plate assay has been widely used in
antibiotic testing and bioactivity (Casey et al., 2004; Kuda et al., 2004). Thus
because of these advantages, the broth dilution method was used for all other isolates
instead.
The bactericidal activity of any antimicrobial agent is usually more desirable than
agents with bacteriostatic activity. The antimicrobial agent considered to have a
bactericidal action is when MIC/MBC ratio < 4 while the bacteriostatic action is
when this ratio become >4 (Levison 2004). From the MIC and MBC results it was
observed that all honeys exhibit bactericidal mode of action against all isolates tested
in this project. Different honey reacts differently on each isolate and at different
concentrations. It is therefore difficult to determine which honey has a more potent
activity than others. However, manuka honey remained the most effective against
224
major wound pathogens, consistently giving lower MIC values for all of the test
organisms.
According to the summarized charts below (Fig. 4.1- 4.6) Acinetobacter isolates
were more sensitive (low MIC recorded) to 3 types of honey (manuka honey, and
Omani honeys B & G) with MICs of 7.17, 16.6 and 20.5 (%w/v) respectively. The
remaining two Omani honeys (C & F) with MICs of 15.8 and 16.6 (%w/v)
respectively were more active against Citrobacter. Serratia cultures were less
susceptible to most of the honeys tested with MICs ranging between 13.3-28
(%w/v).
As previously mentioned the antimicrobial activity of honey against MDR Gram
negative bacilli was not well documented in the medical literature. However, in some
circumstances the MIC values determined from this study were comparable to the
MICs reported earlier (Table 4.2). However, comparisons were limited by variations
in methodology and the honeys utilised.
In Oman honey has been used for thousand of years in the treatment of many
diseases. In this study only small number of honey samples were tested and this does
not account for all of the types present in Oman. Few studies of Omani honey were
reported on their antibacterial activity. It was documented that Omani honey has a
broad spectrum activity against Staphylococcus aureus, E. coli and P. aeruginosa
(Al-Jabri et al., 2003, 2005b). In addition the combination between honey and
antibiotics was first initiated by Al-Jabri et al., (2005a) who found that Omani honey
had a synergistic effect with aminoglycosides. However, analysis of Omani honeys
needs to be more extended by collecting larger number of samples, and determing
the effect of honey on other antimicrobial groups.
225
Table 4.2: Comparison between previous studies and current study on MIC of different honeys including manuka honey against six bacteria
species.
MIC % (w/v) previous studies
Manuka
honey
Acinetobacter
E.coli
Klebsiella
Other
honey
Authors
N/A
8.1±1.5
(Blair et al., 2009)
12.5
11.25
(Tan et al., 2009)
N/A
6-8
(George & Cutting 2007)*
N/A
7.5±0.8
(Blair et al., 2009)
17.5
22.5
(Tan et al., 2009)
10
20
(Lusby et al., 2005)
12.5
12.5
(Sherlock et al., 2010)*
N/A
6-8
(George & Cutting 2007)*
N/A
13±2.4
(Blair et al., 2009)
<20
<20
(Lusby et al., 2005)
N/A
6-8
(George & Cutting 2007)*
Comments
MIC % (w/v) this study
Manuka
honey
Omani honey (OH)
OH-B
OH-C
OH-D
OH-F
7.17± 0.7
16.6± 2
18.1± 1.6
20.5± 2.4
17.6± 1.9
Omani honey has
higher MIC than
medihoney, tualang
and some Australian
honey
10.4± 1.5
18.8±
1.3
19.4±1
23.6±2
19.6±1.8
OH has lower MIC
than tualang and
some Australian
honey
11.7±1 .3
17.2±3
18.5±1.7
22± 2
18± 1.9
OH has lower MIC
than some Australian
honey
Continue from Table 4.1
226
MIC % (w/v) Previous studies
Manuka
honey
Citrobacter
Enterobacter
Serratia
Other
honey
Authors
N/A
9.1±3
(Blair et al., 2009)
10
20
(Lusby et al., 2005)
N/A
11.7±1.8
(Blair et al., 2009)
20
25
(Tan et al., 2009)
10
20
(Lusby et al., 2005)
N/A
6
(George & Cutting
2007)*
N/A
14.8±0.5
(Blair et al., 2009)
0
0
(Lusby et al., 2005)
Comments
MIC % (w/v) this study
Manuka
honey
Omani honey (OH)
OH-B
OH-C
OH-D
OH-F
9.7± 0.8
18.1±2.6
15.8±2.1
26 ± 2.5
16.1± 2.3
OH has lower MIC
than some Australian
honey
10.1± 1.7
21.6±0.9
20.1±1.7
26.4±2.9
20±1.8
OH has same activity
as Australian honey
and lower MIC than
tualang honey but
higher than medihoney
13.3± 1.7
20.2± 1.9
20.2± 1.2
>28
17± 2.8
OH has higher MIC
than medihoney
N/A (not available)
Blair et al., 2009 used medihoney (Australia) by agar incorporation method (%w/v), Tan et al., 2009 used local tualang honey (Malaysian) by broth dilution
method (%w/v), Lusby et al., 2005 used some Australian honey by agar incorporation (%w/v), Sherlock et al., 2010 used manuka and ulmo honey by
microtitire plate (% v/v). George & Cutting 2007 used medihoney (Australia) by agar incorporation method (%v/v).
227
Figure 4.1: Mean MIC and MBC (%w/v) for 30 Acinetobacter strain against 5 types
of honey
Susceptibility of 30 Acinetobacter strains against
5 types of honey
% bacterial inhibition
25
20
18.4
15
18.1
16.6
10
19.2
20.5
21.8
17.6 17.8
11
7.17
5
0
MH-A
OH-B
OH-C
OH-G
OH-F
Honey types
Mean MIC (W/V%)
Mean MBC (W/V%)
Figure 4.2: Mean MIC and MBC (%w/v) for 12 Klebsiella strain against 5 types of
honey
Susceptibility of12 Klebsiella strains against 5
types of honey
30
% bacterial inhibition
25
25
22
20
17.2
15
10
11.7
18.5 19.2
18.8
20.8
18
12.9
5
0
MH- A
OH-B
OH-C
OH-G
Honey types
Mean MIC (W/V%)
228
Mean MBC (W/V%)
OH-F
Figure 4.3: Mean MIC and MBC (%w/v) for 10 E.coli strain against 5 types of
honey
Susceptibility of 10 E.coli strains against 5 types
of honey
30
% bacterial inhibition
25
23.6
20
18.8
19.8
25
19.4 20
19.6
20.8
15
10
10.4 10.9
5
0
MH- A
OH-B
OH-C
OH-G
OH-F
Honey type
Mean MIC (W/V%)
Mean MBC (W/V%)
Figure 4.4: Mean MIC and MBC (%w/v) for 12 Citrobacter strain against 5 types of
honey
% bacterial inhibition
30
Susceptibility of 12 Citrobacter strains
against 5 types of honey
25
26
27.5
20
18.1 18.5
15
18.3
17.8
16.1
15.8
13.3
10
9.7
5
0
MH- A
OH-B
OH-C
OH-G
Honey type
Mean MIC (W/V%)
229
Mean MBC (W/V%)
OH-F
Figure 4.5 Mean MIC and MBC (%w/v) for 15 Enterobacter strain against 5 types
of honey
Susceptibility of 15 Enterobacter strains against
5 types of honey
% bacterial inhibition
30
25
26.4
20
21.6
22.9
28
22.5
22.2
20.1
20
15
13.1
10
10.1
5
0
MH- A
OH-B
OH-C
OH-G
OH-F
Honey type
Mean MIC (W/V%)
Mean MBC (W/V%)
Figure 4.6: Mean MIC and MBC (%w/v) for 8 Serratia strain against 5 types of
honey
Susceptibility of 8 Serratia strains against 5
types of honey
% bacterial inhibition
30
28 28
25
23.75
20
20.2
23.7
20.2
17
15
10
21.7
17
13.3
5
0
MH- A
OH-B
OH-C
OH-G
Honey type
Mean MIC (W/V%)
230
Mean MBC (W/V%)
OH-F
4.4 Inhibition of test organisms by manuka honey
using time kill curve assay
Time-kill curve studies are used to measure the killing rate of organism by
antibiotics using time and concentration. Total cell count is define as the total
number of both dead and living cells in the a sample, whereas total viable count
(TVC) is define as the number of living cells (Singleton 2004).
A T test analysis was applied to establish the statistical difference in cell count
between honey treated cells and untreated cells in the selected species under study.
P values of less than 0.05 were considered to be statistically significant.
A bactericidal activity is defined as in vitro activity of 3 log reduction in the cfu/ml
or 99.9% killing over a specific period of time (Shrivastava et al., 2009). Killing
measurement was made in this study by the actual decrease in viable counts at 5 h
for each species.
To maintain and minimize the impact of time- kill variables on test results several
factors should be considered when performing time-kill studies. These variations
affect the results of the assay and/or interpretation of the results. These factors are:
first, the initial or starting inoculum of (104-106 cfu/ml) should be applied. Second,
the samples should be incubated at 37°C in shaking water bath. Third, the assay
should be continued up to 24 h (Klepser et al., 1998). In this study all these
conditions were applied in the time-kill assays and there were significant differences
between treated and untreated honey samples (P<0.05) (Table 3.25).
231
Addition of honey to an exponential phase culture leads to an obvious reduction for
bactericidal population over a period of time (Fig. 3.10 to 3.15). E.coli and Serratia
showed reductions in population of 1.97 and 1.87 log10 cfu/ml respectively (99%
growth inhibition) within 5 h. However, Citrobacter was inhibited faster because it
exhibited the highest reduction in population than other species tested within 5 h
with > 99.9% killing rate (it exceeded 3 log10 cfu/ml reduction). The initial
concentration of Acinetobacter and Enterobacter were 7 and 7.74 log10 cfu/ml and
were reduced to 5.6 and 6.17 log10 cfu/ml respectively within 5 h exposure to
manuka honey. These isolates were considered to be the least susceptible species
among those tested here (Table 3.24). This study has therefore shown that manuka
honey exhibited bactericidal activity within 5 hours.
4.5 Effect of honey on bacterial structure:
Shapes of various bacteria can be observed by light microscopy, including cocci,
rods, spiral or cubes. The development of electron microscopes provides new
insights into bacterial ultrastructural studies and bacterial organisation. Scanning
electron microscopy (SEM) provides a three-dimensional view of cellular structures
and information about their surface topography. Transmission electron microscopy
(TEM) examines the external and internal bacterial structures (Hobot 2002). Details
of surface structures and bacterial morphology aid to study the action of various
drugs on bacteria.
The mechanism of antimicrobial effects of honey is not yet fully understood. The
effects of honey on bacteria could be complicated because of the complexity of
232
honey chemistry. Nevertheless, observation of the bacterial structures and
morphological variation present valuable knowledge of complete understanding of
antimicrobial action of honey on bacteria.
From the EM images of the selected isolates all species undergo morphological
changes after exposure to honey, however the nature of changes seen was different in
each species. Primary research on the action of manuka honey on Gram positive and
negative bacteria using electron microscopy sequentially identifying the mode of
action and the target site was initiated by research team at UWIC. Manuka honey
with Staphylococcus aureus affected the cell division process by the production of
septated cells which were unable to complete cell division (Henriques et al., 2009).
However, cell destruction and lysis were observed in Pseudomonas aeruginosa
which affected the structure of the cell wall (Henriques et al., 2009, 2010). It was
therefore documented that both species responded differently to certain
concentration of manuka honey.
Typical prokaryotic bacterial structure consists of a cell wall, cytoplasmic
membrane, ribosomes, inclusions, chromosome, plasmids, pili or fimbrie, and
possibly a capsule. Each structure has a specific function in regulating nutrition and
protection processes (Hobot 2002). Due to previous observations on Pseudomonas
aeruginosa which demonstrated that the cell surface was markedly changed by
exposure to honey (Henriques et al., 2010), it seemed logical to concentrate on the
outer layer of the Gram negative bacteria studied here, especially the cell wall and
cytoplasmic membrane.
The cell wall of Gram negative bacteria is important for maintaining the bacterial
shape. It allows for the passage of the macromolecules from the outside environment
233
to the cytoplasmic membrane to the cytoplasm. If the cell wall is ruptured or
damaged, the cytoplasmic contents may be lost and eventually cause lysis of bacteria
(Sussman 2002). The cell wall consists of an outer membrane layer and inner
peptidoglycan layer. The important component of the outer membrane is a group of
proteins referred to as porins that permit the passage of small macromolecules, such
as glucose or maltose to the cell interior. These porins can open or close their pores
under cellular control. The peptidoglycan (murein) is responsible for maintaining
the cell shape and confers rigidity. It also prevents the osmotic pressure of the
cytoplasm from bursting the cell and is involved in cell division (Konig et al., 2010).
The structure of bacterial cells exposed to inhibitory concentrations of manuka
honey (2x MIC values) was compared to untreated cells using scanning electron
microscopy. The cells were collected in exponential growth phase where the cell
growth are fast and most of the proteins and enzymes are produced in that phase
(Madigan et al., 2009) which could be an indication of
target site of the
antimicrobial action of honey.
Images of treated Acinetobacter suggested increased cellular debris which is an
indication of cell lyses. However, the most remarkable change was increased
formation of constrictions suggesting septa in the individual cell (four septa in each
cell) (Fig 3.29). Instead of cells dividing by binary fission they seemed to undergo
multiple fission without cell separation, which normally only occurs in eukaryotic
cells. It could be the cells attempt to adapt to the high exposure of osmotic
concentration as a kind of stress response. However, after 1 h exposure to honey
onward the cells become significantly shorter compared to untreated cells which led
to a decrease in cellular volume (p = 0.0216).
234
In TEM, the cell membrane was interrupted and septated cells were also observed
with honey. The presence of dark areas in some cells that could be mineral deposit
also evidences cells abnormalities (Fig 3.55).
In hypertonic solution (in this case
honey) water molecules move from low concentration (inside bacteria) to high
concentration and this results in decreased internal pressure and shrinkage of cells. In
this case the high sugar concentration which is believed to aid the antimicrobial
effect of honey causes noticeable membrane damage. In addition the minimum pH
limit for most organisms to be viable is pH 5. pH value below this level may inhibit
the metabolism of most enzymes, alter fatty acids that responsible for cell wall
synthesis and protein denaturation may also occur which consequently leads to cell
damage (Booth 1985; Cotter and Hill 2003). This could reduce the size of bacterial
cells and lead to decrease cell volume. This may have been the reason why a
significant decrease in cells size of Acinetobacter from 1 to 3 h exposed to 20%
(w/v) manuka honey compared to untreated honey (Table 3. 26) was noted.
In normal division the size and volume of cells are increased before cell separate to
form new daughter cells but the opposite appears to happen here. Acinetobacter cells
in cultures exposed to honey were shortened and filamented, suggesting that
incomplete cell division had occurred. The total number of culturable cells decreased
immediately after exposure to honey. This was also confirmed by the data from time
to kill curves experiments (Fig 3.10).
In SEM images of E.coli treated cells, aggregation of cells is seen with strands of
material extending from honey treated cells (Fig 3.35). The most noticeable change
was seen in TEM images where the intracellular components leaked out from the
honey treated cells (cell lysed) due to loss of membrane integrity and formed empty
235
cells (Fig 3.59). Some treated cells also demonstrated irregular shapes with relatively
less electron dense material compared to untreated cells and the cytoplasmic material
had contracted away from the cell boundaries. All of these changes support the
suggestion of an effect of honey on bacterial cell membrane as a target site in E.coli.
Lysis of cells due to alteration in membrane integrity prevents bacterial growth and
multiplication and consequently inhibits normal cells division (Cotter and Hill 2003).
Also, by supporting the results from time kill study, the total number of cells were
decreased as shown by growth curve (Fig 3.5) and number of culturable cells were
also decreased (Fig 3.11). Septated cells were not observed in either SEM or TEM
images. However, it does not mean that it was not present, it was possible that it
might have been failed to be spotted through the ultrathin sectioning or due to
improper orientation of the section which reduce the chance of septum location.
However, cells of Klebsiella treated with manuka and Omani honey had a noticeably
rougher cell surface, compared to untreated cells (Fig 3.36). In addition, changes in
cells size significantly increased for both types of honeys (p= < 0.05). This suggests
that outer cell surfaces might have been affected by honey (Fig 3.39b,c,d, & Fig
3.41b,c,d)
Using SEM with Citrobacter exposed to 20% manuka honey revealed a significant
change in cell size between untreated and treated honey samples at 30, 90 and 180
minutes time point (p = ≤ 0.05). In addition, the surface of bacterial cells becomes
rougher, perhaps as a result of outer membrane damage (Fig 3.47). The possibility of
formation of pore was observed by black areas in the cell surface (Fig 3.49b). This
indicates that honey could enter the cells though that pore and cause cell destruction.
The total number of culturable cells showed marked decrease in colony forming unit
236
with more than (3-log10 reduction). This means 99.9% of the Citrobacter population
were killed. Citrobacter therefore gave the greatest bactericidal response compared
to the other species tested (Table 3.24).
SEM images of Enterobacter showed increased formation of a net like structure in
honey treated cells after 30 minutes of exposure compared to the background of the
untreated cells (Fig 3.45). This could be formed from lysed cells or from leaked
cellular products. Thus comparable to expectation from time- kill curve (Fig 3.14),
the total number of culturable cell count decreased immediately with time after
exposure to honey. There were no significant difference in cell size between
untreated and treated honey samples at each time point tested (P > 0.05) (Table 3.25)
Serratia treated with 30% manuka honey revealed some changes in cell surface and
increased incidence of cellular debris as a result of cell lyses (Fig 3.51). Increased
elongated cells was significant (P = 0.0002) (Fig 3.53). No septa were observed in
this stage.
237
Table 4.3: Summary of the growth inhibition, killing rate and ultra-structure changes in EM for six species selected after exposure to 2x MIC
(%w/v) of manuka honey:
Honey
Conc.
2xMIC
Growth
inhibition
by MIC
% (w/v)
Killing
rate by
time-kill*
Acinetobacter
20%
9
E.coli
30%
Klebsiella
SEM
TEM
Site of cell change
Septa
formation
Debris (cell
lysed)
6
Shorter cells with
clumps
High
High
loss of cell membrane, cellular debris,
septated cells and black deposit
13
2
Extended strand
materials around the
cells
Not seen
Not seen
Excess loss of membrane integrity,
irregular shape, empty cells and less
electron dense
30%
13
4
Cell size, rough surface
Few
Not seen
Not done
Citrobacter
20%
11
1
Cell size, rough
surface, black deposit
Not seen
Not seen
Not done
Enterobacter
30%
13
5
No change
Few
High
Not done
Serratia
30%
15
3
Cell surface & size
Not seen
Moderate
Not done
Killing rate by time-kill curve* (1-6) = (1) killing at faster rate, (6) slowest killing rate
238
To date there is little research on the effect of honey on multidrug resistant
Acinetobatcter and Enterobacteriaceae in general. However, no medical research was
conducted up to electron microscope level to find out the ultra-structural change of
these species against honey using EM in particular. It is difficult therefore to prove
the mechanism of the inhibitory effects of honey on bacteria using this approach
alone. A proteomic approach is likely to give more useful information on the
proteins that are affected by honey, but there was insufficient time to develop the
protocols in this study. However, in manuka honey cellular protein extraction of
Gram positive cocci (Staph aureus) was recently studied using 2-D electrophoresis.
The cells showed down regulation in protein synthesis after exposure to 10% (w/v)
manuka honey. The protein spot was identified using MALDI-TOF-MS techniques
as universal stress protein A (Jenkins et al., 2011). This will give an expectation on
the different proteomic profile of honey treated Gram negative bacteria. That same
research on the mechanisms of action of manuka honey on MRSA stated that the
manuka honey affected the process of cell division during septa formation. It was
recognised that decreased level of FtsZ enzyme, which is involved in cell division,
leads to disruption of the cell cycle after honey treatment and caused cell death
(Jenkins et al., 2011).
Nonetheless, microarrays would also give valuable information on how gene
expression in Gram negative bacteria is affected by honey.
Some researchers suggested that the antimicrobial activity was due to the presence of
high amount of tetracycline derivatives, peroxidases, phenols, amylases, ascorbic
acid and fatty acid (Nzeako and Hamdi 2000). Nevertheless, this pilot project has
clearly shown that even the most antibiotic-resistance bacteria succumb to relatively
239
low concentrations of honey in the laboratory and clinical studies are urgently
needed. The most feasible suggestion on the action of honey on Gram negative
bacteria is the alteration in cell wall or cell membrane integrity. The observation of
cell lysis suggested that there were significant changes in cell wall and plasma
membrane integrity (possibly by increased formation of pores) allowing honey to
enter cells and cellular contents to escape (Table 4.3). Then the action of antioxidant,
phenolic acid, MGO and other factors may be expected to contribute to the process
of cell destruction, but the extent of each influence is as yet unknown. Another line
of evidence to support this suggestion is that honey can act upon the structure of a
biological membrane to disrupt it enough to allow the diffusion of protons into a
liposome or bacterial cell (Grovell 2006). Very recently, Kwakman and his
colleagues who identified the bee defensin-1 for the first time studied the
antimicrobial activity of two important honeys (manuka and Revamil honeys)
against different species. Also, the quantity of bee defensin-1, MGO and H2O2 were
analysed for both honeys. It was found Revamil killed bacteria rapidly because of
unique presence of bee defensin-1 and H2O2 which create ion channels in lipid
bilayers and increase permeability of outer and inner biological membranes. Where
as manuka honey retained its activity even with higher dilution, the presence of high
sugar, low pH, high concentration of MGO (44 fold higher than Revamil) and other
cationic and non- cationic bactericidal factors aid in the bactericidal activity of
manuka honey (Kwakman et al., 2011).
Defensins are small, cysteine rich broad spectrum antibacterial peptides found in
human neutrophils. Their antimicrobial activity on Gram negative and Gram positive
bacteria, Mycobacteria, fungi and enveloped virus was documented in-vitro (Kagan
et al., 1994).
240
The effect of defensin peptide was first reported morphologically on Staph. aureus
using TEM and caused major changes in the cytoplasmic membrane (Shimoda et al.,
1995). It is known to increase permeability of the cell wall in Gram negative
bacteria, causing cytoplasmic contents to leak out. Its effect on E.coli causes damage
of outer and inner membranes by rapid permeabilization of the plasma membrane
leading to cell death (Lichtenstein 1991). Defensin was therefore proven to cause
destruction in the biological membrane. However its effect on human tumour cells
revealed that the primary target is the plasma membrane. Generally, antibacterial
peptides affect several target sites on bacteria such as binding to intracellular
compounds (DNA, RNA and proteins) or by bacterial enzymes inhibition (Patrzykat
et al., 2002). The evidence that the effect of bee defensin-1 is similar to human
defensin will provide a good indication in the mechanism of action of honey
containing that peptide on bacteria.
Therefore, honey contains compounds that inhibit bacteria to develop resistance.
Until now no report demonstrated that honey can develop resistance even in
excessive use (Blair et al., 2009; Cooper et al., 2010)
The statistics of war wound infection in wounded soldiers during military operations
(2003-2005) in Iraq had increased compared to previous operation in Korea,
Vietnam and the Persian Gulf. This has caused increase prevalence of MDR
Acinetobacter calcoaceticus-baumannii complex isolated from wound and
subsequently caused bacteraemia (Murray et al., 2008). Most investigations by
medical military research suggested that this is a nosocomial infection but their
source was not known. Despite careful procedure in the selection of antibiotic
therapy in such outbreaks there will be limited options of drug therapy available to
241
treat this infection (Davis et al., 2005). In addition global increase in antibiotic
resistant organisms causes major problems in controlling the emergence of resistance
(Hawkey and Jones 2009). Currently an increase of aggressive situations worldwide
i.e war in Libya and earthquake in Japan may cause increased prevalence of resistant
strain of Acinetobacter. This highlights the necessirly to develop alternative
treatments to eliminate such infections. As honey has several effective properties in
wound healing including anti-inflammatory properties, providing moist environment
around the wound, non toxic, reduce exudates and oedema, painless, debriding agent
and ease of application (Molan 2006). In addition rapid wound healing due to honey
treatment reduces hospital stays especially in chronic wound patients and reduces the
time required for doctors and nurses until the wound heals. This results in a decrease
in financial cost of the health care system. Thus it could be used as a perfect
treatment.
The main conclusion obtained from this study is that the antimicrobial effect of
honey is most probably due to several factors controlling its activity. Thus the
mechanisms of action of honey could have various target sites such as proteins,
enzymes and DNA.
242
4.6 Further investigations:
There is much evidence of antibacterial activity of using honey in the topical
treatment of infected wounds, however further assumptions needs to be given to its
systemic application and therapeutic properties in order to improve the use of this
product in clinical and systemic infections especially with increase prevalence of
multi drug resistant bacteria.
This pilot study of the Omani honeys needs more comprehensive research with
different types of honeys collected from different parts in Oman in order to establish
whether all honeys possess both antibacterial and healing properties. This could
improve the identification of the most appropriate type of honey to be used for
wound care in Oman particularly after isolation of highly resistant NDM gene of
Klebsiella pneumonia for the first time in Oman (Poirel et al., 2010). Also using an
advanced method to identify and study the nature of other components in Omani
honey could aid its antimicrobial effect such as, MGO, glucose oxidase enzyme,
antioxidant and bee defensin-1.
Further study on the idenfication of bee defensin-1 in other honey samples to
determine its effect on Gram postive and negative bacteria and to prove its
simillarity effect as human defensin is needed. Omani honeys could be tested for bee
defensin.
Further study on the action of different types of honey to identify the intracellular
morphological changes using electron microscopy is required. Investigation of the
molecules that leaked out from the cells after exposure to honey such as DNA,
potassium ions or other cell organell using leakage study. Furthermore, extensive
243
study on the alteration of Gram negative bacterial cell wall structure after honey
treatment by invistigating the synthesis and degradation of murien and/or expression
of murein hydrolase and peptidoglycan hydrolase enzymes is needed. These
enzymes are involved in cell cycle by controlling the breakdown of cell wall
components before cytokinesis and cell separation occurs. However, in Gram
positive (MRSA) these enzymes showed reduced activity after honey treatment
(Jenkins et al., 2011). By investigating these effects, a better understanding of the
effect of manuka honey on cell cycle of Gram negative bacterial will be achieved.
Large scale analysis on proteome expression using 2-D electrophoresis and kinetics
of RNA using radioactive isotopes in different organisms, provides important
information about the mode of action. Another method using MALDI-TOF- MS
techniques for the identity of protein after the detetcion of down regulated or
upregulated protiens in the proteome analysis in 2-D electrophoresis. Microarray
methods could be used to explain the reproducibility of gene expression on honey
treated cells compared to non honey treated cells.
To determine whether honey has simillar activity in vivo, further clinical trials on
wounds infected with MDR Acinetobacter or other ESBLs and treated with different
types of honey must be conducted. With the current difficulties in treating ESBL
infections in wounds, honey does seem to offer real potential in eradicating these
pathogens.
244
Chapter 5
References
245

Adams, C. J., Boult, C. H., Deadman, B. J., Farr, J. M., Grainger, M. N. C.,
Manley-Harris M., Snow, M. J. (2008) Isolation by HPLC and
characterisation of the bioactive fraction of New Zealand manuka
(Leptospermum scoparium) honey, Carbohydrate Research, 343(4), pp, 651659.

Ahmed, A. K. J., Hoekstra, M. J., Hage, J. J. and Karim, R. B. (2003) Honeymedicated dressing: transformation of an ancient remedy, Annals of Plastic
Surgery, 50, pp, 143–148.

Akratanakul, P. (1990) Beekeeping in Asia; Honeybees of the Genus Apis,
Food and Agriculture Organization of the United Nations, FAO Agricultural
Services Bulletin68/4, Rome, Italy

Aksoy, D. Y., Tanriover, M. D. and Unal, S. (2007) Antimicrobial resistance,
in Gould, I. M. and Van Deer Meer, J. W. M. (ed.) Antibiotic policies:
fighting resistance, Springer Sciences.

Alandejani, T., Marsan, J., Ferris, W. and Chen, F. (2009) Effectiveness of
honey on Staphylococcus aureus and Pseudomonas aeruginosa biofilms,
Otolaryngology-Head and Neck Surgery, 139 (1), pp, 107-111.

Al-Jabri, A. A., Al-Hosni, S. A., Nzeako, B. C., Al-Mahrooqi, Z. H. and
Nsanze, H. (2005a) Antibacterial activity of Omani honey alone and in
combination with gentamicin, Saudi Medical Journal, 26 (5), pp, 767-771.

Al-Jabri, A. A., Al-Hosni, S. A., Nzeako, B. C. and Nsanze, H. (2005b)
Antistaphylococcal activity of Omani honey in combination with bovine
milk, British Journal of Biomedical Science, 62 (2), pp, 92-93.
246

Al-Jabri, A. A., Nzeako, B. C., Al-Mahrooqi, Z. H., Al-Naqdi, A. and
Nsanze, H. (2003) In vitro antibacterial activity of Omani and African honey,
British Journal of Biomedical Science, 60 (1), pp, 1-4

Al-Jadi, A. M. and Kamaruddin, M.Y. (2004) Evaluation of the phenolic
contents and antioxidant capacities of two Malaysian floral honeys, Food
Chemistry, 85, pp, 513-518.

Al-Naqdy, A., Al-Jabri, A. A., Al-Mahrooqi, Z. H., Nzeako, B. C. and
Nsanze, H. (2005) Inhibition effect of honey on the adherence of Salmonella
to intestinal epithelial cells in vitro, International Journal of Food
Microbiology, 103, pp, 347-351.

Allen, K. L., Molan, P. C. and Reid, G. M. (1991) A survey of the
antibacterial activity of some New Zealand honeys, Journal of Pharmacy
Pharmacology, 43 (12), pp, 817-822.

Al-Mamary, M., Al-Meeri, A. and Al-Habori M. (2002) Antioxidant
activities and total phenolic of different types of honey, Nutrition Research,
22, pp, 1041-1047.

Al Somal, N., Coley, K. E., Molan, P. C. and Hancock, B. M (1994)
Susceptibility of Helicobacter pylori to the antibacterial activity of manuka
honey, Journal of the Royal Society of Medicine, 87, pp, 9-12.

Al-Taie, H., Pickersgill, J., and Al-Taie, N. (1999) Oman, A comprehensive
Guide to the Sultanate of Oman, 2nd edition, Al-Roya Publishing, Oman.

Al- Waili, N. S. and Saloom, K. Y. (1999) Effects of topical honey on postoperative wound infections due to gram positive and gram negative bacteria
following caesarean sections and hysterectomies, European Journal of
Medical Research, 4, pp, 126-30.
247

Al- Waili, N. S. (2004) Investigating the antimicrobial activity of natural
honey and its effects on the pathogenic bacterial infections of surgical wound
and conjunctiva, Journal of Medicinal Food, 7 (2), pp, 210-222.

Amabile-Cuevas, C. F. (2007) Antimicrobial Resistance in Bacteria, Horizon
Bioscience, UK.

Anklam, E. (1998). A review of the analytical methods to determine the
geographical and botanical origin of honey, Food Chemistry, pp, 63, 549562.

Atrott, J. and Henle, T. (2009) Methylglyoxal in Manuka Honey -Correlation
with Antibacterial Properties, Czech Journal Food Science, 27, Special Issue,
S163-S165.

Atrouse, O. M., Oran, S. A. and Al-Abbadi, S. Y. (2004) Chemical analysis
and identification of pollen grains from different Jordanian honey samples,
International Journal of Food Science and Technology, 39 (4): 413-417.

Babik, J. and Bodnarova, L. (2008) Acinetobacter- serious danger for burn
patients, Acta Chir Plast, 50 (1), pp, 27-32.

Bacakoglu, F. and Korkmaz, E. P. (2009) Multidrug-resistant Acinetobacter
baumannii infection in respiratory intensive care unit, Microbiology Bul, 43
(4), pp, 575-585.

Badawy, O.F., Shafii, S.S., Tharwat, E.E. and Kamal, A.M. (2004)
Antibacterial activity of bee honey and its therapeutic usefulness against
Escherichia coli O157:H7 and Salmonella typhimurium infection, Rev. sci.
tech. Off. int. Epiz, 23 (3), pp, 1011-1022
248

Bang, L. M., Buntting, C. and Molan, P.C. (2003) The effect of dilution on
the rate of hydrogen peroxide production in honey and its implications for
wound healing, J Alternative Complement Med, 9 (2), pp, 267-273

Barchitta, M., Cipresso, R., Giaquinta, L., Romeo, M. A., Denaro, C.,
Pennisi, C. and Agodi, A. (2009) Acquisition and spread of Acinetobacter
baumannii and Stenotrophomonas maltophilia in intensive care patients,
International Journal of Hygiene and Environmental Health, 212 (3), pp,
330-337.

Bardy, J., Slevin, N. J., Mais, K. L. and Molassiotis, A. A. (2008) Systematic
review of honey uses and its potential value within oncology care, Journal of
Clinical Nursing, 17(19), pp, 2604-2623.

Baron, S. (1996) Baron's Medical Microbiology, (ed), 4th edition, Galveston,
University of Texas Medical Branch, ISBN 0-9631172-1-14

Basson, N. J. and Grobler,S. R. (2008) Antimicrobial activity of two South
African honeys produced from indigenous Leucospermum cordifolium and
Erica species on selected micro-organisms, BMC Complementary and
Alternative Medicine, 8:41, doi:10.1186/1472-6882-8-41.

Beretta, G., Granata, P., Ferrero, M., Orioli, M. and Facino, R. M. (2005)
Standardization of antioxidant properties of honey by a combination of
spectrophotometric/fluorimetric assays and chemometrics,
Anal Chim
Acta,533, pp, 185-191

Bergogne-Bérézin,
Acinetobacter:
E.,
Biology
Friedman,
and
H.
and
Bendinelli,
M.
Pathogenesis
(Infectious
Agent
Pathogenesis), Springer Sciences and Business Media.
249
(2008)
and

Bergman, A., Yanai, J., Weiss, J., Bell, D. and David, M. P. (1983)
Acceleration of wound healing by topical application of honey: An animal
model, Am J Surg, 145, pp, 374-376.

Bhattacharya, S. (2006) ESBL- from petri dish to the patient, Indian J
Medical Microbiology, 24(1), pp, 20-24.

Blair, S. E., Cokecetin, N. N., Harry, E. J. and Carter, D. A. (2009) The
unusual antibacterial activity of medical-grade Leptospermum honey:
antibacterial spectrum, resistance and transcriptome analysis, Eur J Clin
Microbial Infect Disease, Springer.

Blasa, M., Candiracci, M., Accorsi, A., Piacentini, M. P., Albertini, M. C.
and Piatti, E. (2006) Raw Millefiori honey is packed full of antioxidants,
Food Chemistry, 97 (2), 217-222.

Bogdanov, S. (1997) Nature and origin of the antibacterial substances in
honey, Lebensm-Wiss. U- Technology, 30, pp, 748-753.

Bogdanov, S. (2002) Harmonized methods of the International Honey
Commission, Swiss Bee Research Centre, FAM, Liebefeld, CH-3003 Bern,
Switzerland.

Bogdanov, S., Rieder, K., and Ruegg, M. (1989) Determination of
pinocembrin in honey using HPLC, J Apicultural Research, 28, pp, 55-57

Bogdanov, S., Ruoff, K. and Oddo. L. (2004) Physico-chemical methods for
charactirisation of unifloral honeys: a review, Apidologie, 35, S5-S17.

Borland, M. (2000) Honey proves a sweet remedy in wound care, Nursing
Review (Oct)
250

Bou, G., Oliver, A. and Martinez-Beltrán, J. (2000) OXA-24, a novel class D
β-lactamase with carbapenemase activity in an Acinetobacter baumannii
clinical strain, Antimicrobial Agents and Chemotherapy 44, pp, 1556–1561.

Booth, I. R. (1985) Regulation of Cytoplasmic pH in Bacteria,
Microbiological Reviews, 49 (4), pp, 359-378.

Bradford, P. A. (2001) Extended-spectrum β-lactamases in the 21st century:
characterization, epidemiology, and detection of this important resistance
threat, Clinical Microbiology Reviews, 14 (4), pp, 933-951.

Brady, N. F., Molan, P. C. and Harfoot, C. G. (1996) The sensitivity of
dermatophytes to the antimicrobial activity of manuka honey and other
honey, Pharm. Science, 2(10), pp, 471-473.

Brauers, J., Frank, U., Kresken, M., Rodloff, A. C. and Seifert, H. (2005)
Activities of various beta-lactams and beta-lactam/beta-lactamase inhibitor
combinations against
Acinetobacter baumannii and Acinetobacter DNA
group 3 strains, Clinical Microbiology and Infection; 11,pp, 24-30.

Brooks, G. F., Butel, J. S. and Morse, S. A. (2001) Medical Microbiology,
Twenty two edition, Lange, USA.

Brudzynski, K. (2006) Effect of hydrogen peroxide on antibacterial activities
of Canadian honeys, Canadian Journal of Microbiology, 52, 1228-1237.

Burdon, R. H. (1995) Superoxide and hydrogen peroxide in relation to
mammalian cell proliferation, Free Rad Biological Medicine, 18, pp, 775-794

Bush, K., Jacoby, G. A., Medeiros, A. A. (1995) A functional classification
scheme for beta-lactamases and its correlation with molecular structure,
Antimicrobial Agents and Chemotherapy, 39, 1211-1233.
251

Canton, R., Novais, A., Valverde, A., Machado, E., Peixe, L., Baquero, F.
and Coque, T. M. (2008) Prevalence and spread of extended- spectrum betalactamase producing Enterobacteriaceae in Europe, Clinical Microbiology &
Infectious Diseases, 14 (1), pp, 144-153

Casey, J. T., O‟Cleirigh, C., Walsh, P. K. and O‟Shea, D. G. (2004)
Development of a robust microtiter plate-based assay method for assessment
of bioactivity, Journal of Microbiological Methods, 58, pp, 327-334.

Castanheira, M., Sader, H. S., Deshpande, L. M et al. (2008) Antimicrobial
activity of tigecycline and other broad spectrum antimicrobials tested against
serine
carbapenemase-
and
metallo-b-lactamaseproducing
Enterobacteriaceae: report from the SENTRY Antimicrobial surveillance
Program, Antimicrobial Agents Chemotherapy, 52, pp, 570-573.

Chen, L., Mehta, A., Berenbaum, M., Zangerl, A. R. and Engeseth, N. J.
(2000) Honeys from different floral sources as inhibitors of enzymatic
browning in fruit and vegetable homogenates, Journal of Agriculture and
Food Chemistry, 48, pp, 4997-5000.

Chiang, D. H. and Wang, C. C. (2008) Risk factors for mortality in patients
with Acinetobacter baumannii bloodstream infection with genotypic species
identification, Journal of Microbiology and Immunological Infection, 41(5),
pp, 397-402.

CLSI antimicrobial susceptibility testing standards M2-A9 and M7-A7
(2008) Performance standard for antimicrobial susceptibility testing;
Eighteen informational supplement, Vol 28, No 1
252

Cooper, R. (2005) The Antimicrobial Activity of Honey, in White, R.,
Cooper, R. and Molan, P. C. (ed.) Honey: A modern Wound Management
product, Aberdeen, UK.

Cooper, R. A., Halas, E. and Molan, P. C. (2002b) The efficacy of honey in
inhibiting strains of Pseudomonas aeruginosa from infected burns, J Burn
Care Rehabil, 23(6):366-370.

Cooper, R. A., Molan, P. C. and Harding, K. G. (2002a) The sensitivity to
honey of Gram-positive cocci of clinical significance isolated from wounds, J
Appl Microbiol, 93, pp, 857-863.

Cooper R. A & Jenkins L, (2009) Comparison between Medical Grade
Honey and table honeys in relation to antimicrobial efficacy, Wound,
21(2):29-36.

Cooper, R. A., Molan, P. C. and Harding, K. G. (1999) Antibacterial activity
of honey against strains of Staphylococcus aureus from infected wounds.
Journal of the Royal Society of Medicine 92 (6), pp, 283-285.

Cooper, R.A., Jenkins, L., Henriques, A. F., Duggan, R.S. and Burton. N. F.
(2010) „Absence of bacterial resistance to medical-grade manuka honey‟,
Eur J Clin Microbiol Infect Dis, 29(10), pp, 1237-12341.

Cooper, R. A., Wigley, P. and Burton, N. F. (2000) Susceptibility of multiresistant strains of Burkholderia cepacia to honey,
Letters in Applied
Microbiology, 31 (1), pp, 20-24.

Cootz, T. D. and Marra, A. (2008) Acinetobacter baumannii: an emerging
multidrug-resistant threat, Expert Review of Anti Infection Therapy, 6 (3), pp,
309-325.
253

Costa, S. F., Woodcook, J. and Gill, M. (2000) Outer-membrane protein
pattern and detection of beta-lactamases in clinical isolates of imipenemresistant Acinetobatcer baumannii from Brazil, International Journal of
Antimicrobial Agent, 13, pp, 175-182.

Cotter, P. D and Hill, C. (2003) Surviving the Acid Test: Responses of GramPositive Bacteria to Low pH, Microbiology and Molecular Biology Reviews,
67(3), pp, 429-453. DOI: 10.1128/MMBR.

Crane, E. (1979) Honey: A Comprehensive Survey, Heinemann, London, UK.

Crovell, K. (2006) Comparison of the effect of manuka honey and its main
constituent organic acid on artificial membranes, BSC (hons) BMS,
University of Wales Institute, Cardiff.

Dallo, S. F. and Weitao, T. (2010) Insights into Acinetobacter war-wound
infections, biofilms, and control, Advances in skin and wound care, 23 (4),
pp, 169-174.

Davis, K. A., Moran, K. A., McAllister, C. K. and Gray, P. J. (2005)
Multidrug-Resistant Acinetobacter Extremity Infections in Soldiers, Journal
of Emerging Infectious Diseases, 11(8), pp, 1218-1224.

Dumronglert, E. (1983) A follow-up study of chronic wound healing dressing
with pure natural honey, J Nat Res Counc Thail, 15(2), pp, 39-66.

Dustmann, J. H. (1979) Antibacterial effect of honey, Apiacta, 14 (1), pp, 711.

Eddy, J. J. and Gideonsen, M. D. (2005) Topical honey for diabetic foot
ulcers, J Fam Pract, 54(6), pp, 533-535.

Efem, S. E. (1988) Clinical observations on the wound healing properties of
honey, Br. J. Surg, 75, pp, 679-681.
254

Efem, S. E., Udoh, K. T. and Iwara, C. I. (1992) The antimicrobial spectrum
of honey and its clinical significance, Infection, 20(4), pp, 227-229.

Efem, S. E. (1993) Recent advances in the management of Fournier's
gangrene: Preliminary observations, Surgery, 113 (2), pp, 200-204.

Emarah, M. H. (1982) A clinical study of the topical use of bee honey in the
treatment of some occular diseases, Bull Islamic Med, 2(5), pp, 422-425.

Engelkirk, P. G. (2007) Laboratory Diagnosis of Infectious Diseases:
Essential of Diagnostic Microbiology, Philadelphia, USA, Lippincott
Williams & Wilkins.

Enoch, D. A., Summers, C., Brown, N. M, Moore, L., Gillham, M. I.,
Burnstein, R. M., Thaxter, R. et al., (2008), Investigation and management of
an outbreak of multidrug-carbapenem-resistant Acinetobacter baumannii in
Cambridge, UK, Journal of Hospital Infection, 70, pp, 109-118.

Estevinho, L., Pereira, A. P., Moreira, L., Dias, L. G. and Pereira, L. (2008)
Antioxidant and antimicrobial effects of phenolic compounds extracts on
Northeast Portugal honey, Food and Chemical Toxicology, 46, pp, 37743779.

Finch, R. G., Greenwood, D., Norrby, S. R. and Whitley, R. J. (2003)
Antibiotics and Chemotherapy: Anti-infective Agent and their use in Therapy,
Eight edition, Churchil Livingstone, UK.

Fontana, C. and Favaro, M. (2008) Acinetobacter baumannii in intensive care
unit: a noval system to study clonal relationship among the isolates, BMC
Infectious disease, 8, pp,79.
255

Forbes, B. A., Sahm, D. F., Weissfeld, A. S., Bailey, W. R., Trevino, E. A.
and Scott, E. G. (2007) Bailey & Scotts diagnostic microbiology, Twelve
edition, London, UK, Elsevier Mosby.

French, V. M., Cooper, R. A. and Molan, P. C. (2005) The antibacterial
activity of honey against coagulase-negative staphylococci, J Antimicrobial
Chemotherapy, 56(1), pp, 228-231.

Gheldof, N., Wang, X. H, and Engeseth, N. J. (2002) Identification and
quantification of antioxidant components of honeys from various floral
sources, Journal of Agricultural and Food Chemistry, 50, (21), pp, 58705877.

George, N. M. and Cutting, K. F. (2007) Antibacterial honey (MedihoneyTM):
in-vitro activity against clinical isolates of MRSA, VRE, and other multiresistant gram negative, Wounds, 19 (9): 231-336.

Gerisher, U. (2008) Acinetobacter Molecular Biology, Caister Academic
Press, UK

Gethin, G.T., Cowman, S. and Conroy, R.M. (2008) The impact of Manuka
honey dressings on the surface pH of chronic wounds, International Wound
Journal, 5, pp, 185–194. doi:10.1111/j.1742-481X.2007.00424.x

Gillespie, S. H. (2004) Management of Multiple Drug Resistant Infections.
New York, USA, Humana Press.

Greenwood, D. (2000) Antimicrobial Chemotherapy, Forth edition, Oxford
University press, USA

Greenwood, D., Finch, R. G., Whintley, R. J. (2003) Antibiotic and
Chemotherapy, 8th edition, Edinburgh, Churchill Livingstone.
256

Greenwood, D., Finch, P., Davey, P. and Wilcox, M. (2007) Antimicrobial
Chemotherapy, Fifth edition, Oxford University press, UK.

Haffejee, I. E. and Moosa, A. (1985) Honey in the treatment infantile
gastroenteritis, British Medical Journal, 290, pp, 1866-1867.

Hawkey, P. M. (2008) The growing burden of antimicrobial resistance,
Journal of Antimicrobial Chemotherapy, 62, Suppl. 1, i1–i9.

Hawkey, P. and Bergogne-Berezin, E. (2006) Acinetobacter spp. in Gillespie,
S. H. and Hawkey, P. M. (ed.) Principle and Practical of Clinical
Bacteriology, second edition, John Wiley & Sons, Ltd, UK.

Hawkey, P. M. and Jones, A. M. (2009) The changing epidemiology of
resistance, Journal of Antimicrobial Chemotherapy, 64, Suppl 1:i3-10

Health Protection Agency, (2008) Antimicrobial Resistance and Prescribing
in England, Wales and Northern Ireland, London: Health Protection Agency
(HPA), July.

Henriques, A., Burton, N. and Cooper, R. (2005) Antibacterial activity of
selected Portuguese honeys, Journal of Apicultural Research, 44(3), pp, 119123.

Henriques, A., Jackson, S., Cooper, R. and Burton, N. (2006) Free radical
production and quenching in honeys with wound healing potential, Journal
of Antimicrobial Chemotherapy, 58, pp, 773–777.

Henriques, A., Jenkins, R., Burton, N. and Cooper, R. (2009) The
intracellular effects of manuka honey of Staphylococcus aureus, Eur J Clin
Microbial Infect Disease, Springer Sciences.
257

Henriques, A., Jenkins, R., Burton, N. and Cooper, R. (2010) The effects of
manuka honey on the structure of Pseudomonas aeruginosa, Eur J Clin
Microbial Infect Disease, Springer.

Hobot, J. A. (2002) Bacterial ultrastructure, , in Sussman, M. (ed.) Molecular
medical microbiology, Vol 2, Cambridge University press, UK.

Ingle, R., Levin, J. and Polinder, K. (2006) Wound healing with honey- a
randomised controlled trial, South African Medical Journal, 96(9), pp, 831–
835.

Irish, J., Blair, S. and Carter, D. A. (2011) The Antibacterial Activity of
Honey Derived from Australian Flora, PLoS ONE, 6(3), e18229.
doi:10.1371/journal.pone.0018229

Irish, J., Carter, D. A., Shokohi, T. and Blair, S. E. (2006) Honey has an
antifungal effect against Candida species, Medical Mycology, 44, pp, 289291.

Jacoby, G. A. and Munoz-Price, L. S. (2005) The new beta lactamases, New
England Journal Medicine, 352, pp, 380-391

Jenkins, R., Burton, N. and Cooper, R. (2011) Effect of manuka honey on the
expression
of
universal
stress
protein
A
in
methicillin-resistant
Staphylococcus aureus, International Journal of antimicrobial agents, 37 (4),
pp, 373-376.

Joly-Guillou, M. L. (2005) Clinical impact and pathogenicity of
Acinetobacter, Clinical Microbiology and Infection, CMI, 11, pp, 868-873.

Jul, A. B., Rodgers, A. and Walker, N. (2009) Honey as a topical treatment
for
wounds,
(Review),
The
http://www.thecochranelibrary.com
258
Cochrane
Library,
Issue
4,

Kagan, B.L., Ganz, T. and Lehrer, R. I. (1994) Defensins: a family of
antimicrobial and cytotoxic peptides, Toxicology, 87 (1-3), pp, 131-149.

Klepser, M., Ernst, E. J., Lewis, R. E., Ernst, M. E. and Pfaller, M. A. (1998)
Influence of Test Conditions on Antifungal Time-Kill Curve Results:
Proposal for Standardized Methods, Antimicrobial Agent and Chemotherapy,
42 (5), pp, 1207-1212.

Knoneman, E. W., Alen, S. D., Janda, W. M., Schereckrenberger, P. C. and
Winn, W. C. (1997) Colour atlas and Text book of Diagnostic Microbiology,
Fifth edition, Lippincott, USA.

Konig, H., Claus, H. and Varma, A. (2010) Prokayotic cell wall compunds:
Structure and biochemistry, Springer.

Kuda, T., Shimizu, K., Yano, T. (2004) Comparision of rapid and simple
colorimetric microplate assays as an index of bacterial count, Journal of
Food Control, 15, pp, 421-425.

Kumarasamy, K.K., Toleman, M. A. and Walsh, T. R. (2010) emergence of a
new antibiotic resistance mechanism in India, Pakistan, and the UK: A
Molecular, biological, and epidemiological study, Lancet Infection Disease,
10, pp, 597-602.

Kwakman, P. H. S., Johannes, P. C., Van den Akker, J. P., Ahmet, G.,
Aslami, H., Binnekade, J. M., et al. (2008) Medical-grade honey kills
antibiotic-resistant bacteria in-vitro and eradicates skin colonization, Clin
Infect Dis,46, pp 1677-1682. DOI: 10.1086/587892.

Kwakman, P. H. S., Velde, A. A., de Boer, L., Speijer, D., VandenbrouckeGrauls C. M. and Zaat, S. A. J. (2010) How honey kills bacteria, The FASEB
Journal article, 24, pp, 2576-2582.
259

Kwakman, P. H. S., te Velde, A. A., de Boer, L., Vandenbroucke-Grauls, C.
M. and Zaat, S. A. (2011) Two Major Medicinal Honeys Have Different
Mechanisms of Bactericidal Activity, PLoS ONE , 6(3): e17709.
doi:10.1371/journal.pone.0017709

Levin, A. S. (2001) Multiresistant Acinetobacter infections: a role for
sulbactam combinations in overcoming an emerging worldwide problem,
Clinical Microbiology Infection, 8, pp, 144-153.

Levison, M. E. (2004) Pharmacodynamics of antimicrobial drugs, Infectious
Disease Clinics of North America, 18, pp, 451-465.

Lichtenstein, A. (1991) Mechanism of mammalian cell lyses mediated by
peptide defensins: Evidence for an initial alteration of the plasma membrane,
J. Clin. Invest. 88, pp, 93-100.

Loveaux, J., Maurizio, A. and Vorwohl, G. (1978) Methods of
Melissopalynology, Bee World, 59, pp, 139-157.

Lusby, P. E. (2002) Honey: a potent agent for wound healing?, Journal of
Wound, Ostomy and Continence Nursing, 29(6), pp, 295–300.

Lusby, P. E., Coombes, A. L. and Wilkinson, J. M. (2005) Bacterial activity
of different honeys against pathogenic bacteria, Archives of medical
Research, 36, 464-467.

Madigan, M. T., Martinko, J. M., Dunlap, P. V. and Clark, D. P. (2009)
Brock, Biology of Microorganisms, 12th edition, Pearson International
Edition, USA.

Maedaa, Y. B., Loughreya, A., Earlec, P., Millara, C. et al, (2008)
Antibacterial activity of honey against community-associated methicillin-
260
resistant Staphylococcus aureus (CA-MRSA), Complementary Therapies in
Clinical Practice 14, pp, 77–82.

Mavric, E., Wittmann, S., Barth, G., and Henle, T. (2008) Identification and
quantification of methylglyoxal as the dominant antibacterial constituent of
manuka (Leptospermum scoparium) honeys from New Zealand, Mol Nutr
Foods Res, 52(4), pp, 483-489.

Mohamed, M.1., Sirajudeen, K.N., Swamy, M., Yaacob, N.S. and Sulaiman,
S.A. (2010) Studies on the antioxidant properties of Tualang honey of
Malaysia, Afr. J. Trad CAM, 7 (1), pp, 59 – 63, ISSN 0189-6016©2009.

Molan gold standard (http://www.molangoldstandard.co.nz/article).

Molan, P. C. (1992a) The antibacterial activity of honey. 1. The nature of the
antibacterial activity, Bee World, 73(1), pp, 5-28.

Molan, P. C. (1992b) The antibacterial activity of honey. 2. Variation in the
potency of the antibacterial activity, Bee World, 73(2), pp, 59-76.

Molan, P. C. (1999) The role of honey in the management of wounds,
Journal of Wound Care, 8(8), pp, 423- 426

Molan, P. C. (2000a) Selection of honey for use as a wound dressing,
Primary Intention, 8 (3), pp, 87-92.

Molan, P. C. (2001b) Why honey is effective as a medicine. 2. The scientific
explanation of its effects, Bee World, 82 (1), pp, 22-40.

Molan, P. C. (2002) Re-introducing honey in the management of wounds and
ulcers: theory and practice, Ostomy Wound Management, 48, pp, 28-40.

Molan, P. C. (2006) The evidence supporting the use of honey in wound
healing, International Journal of Lower Extremity Wounds, 5(1), pp, 40–54.
DOI: 10.1177/15347346052860145: 40
261

Molan, P. C. and Betts, J. A. (2001) Dressing wounds with honey, NZ
Nursing Review (March issue), pp, 19-20.

Molan, P. C. and Russell, K. M. (1988) Non-peroxide antibacterial activity in
some New Zealand honeys, J Apic Res, 27, pp, 62–67.

Moland, E. S., Kim, S. Y., Hong, S. G. and Thomson, K. S. (2008). Newer
beta lactamases: Clinical and laboratory implications, Part 1, Clinical
Microbiology Newsletter, 30(10), pp, 71-77.

Moore, O. A., Smith, L. A., Campbell, F., Seers, K., McQuay, H. J. and
Moore, R. A. (2001) Systematic review of the use of honey as a wound
dressing, BMC, Complement Alternative Medicine, 1:2.

Mullai, V. and Menon, T. (2005) Antibacterial activity of honey against
Pseudomonas aeruginosa, Indian Journal of Pharmacology, 37, pp, 403

Mullai V, Menon T. (2007) Bacterial activity of different types of honey
against clinical and environment isolates of Pseudomonas aeruginosa, The
Journal of Alternative and Complementary Medicine, 13 (4), pp, 439–441,
DOI: 10.1089/acm.2007.6366.

Mulu, A., Tessema, B. and Derbie, F. (2004) In vitro assessment of the
antimicrobial potential of honey on common human pathogens, Ethiopian
Jorunal of Health Dev. 18(2), pp, 107-111.

Murray, C., Scott, P., Moran, K. and Craft, D. (2008) US army experience
with Acinetobacter in operation Iraqi freedom, in Bergogne-Bérézin, E.,
Friedman, H. and Bendinelli, M. (ed.) Acinetobacter: Biology and
Pathogenesis (Infectious Agent and Pathogenesis), Springer Sciences and
Business Media.
262

Murray, P. R., Baron, E. J., Jorgensen, J. and Landry, M. L. (2007) Manual
of Clinical Microbiology, Nine edition, Washington, USA, ASM Press.

Nagai, T., Sakai, M., Inoue, R., Inoue, H. and Suzuki, N. (2001)
Antioxidative activities of some commercially honeys, royal jelly and
propolis, Food Chemistry, 75, pp, 237-240.

National
Honey
Board
(NHB),
Honey
technical
information,
http://www.honey.com/nhb/technical/.

Nzeako, B. C. and Hamdi, J. (2000) Anti microbial potential of honey on
some microbial isolates, Journal of Science Research Medicine, 2, pp, 75-79.

Okeniyi, J. A., Olubanjo, O., Ogunlesi, T. A. and Oyelami, O. A. (2005)
Comparesion of healing of incised abscess wound with honey and EUSOL
dressing, Journal of Alternative and Complementary Medicine, 11 (3), pp,
511-513.

Okhiria, O. A., Henriques, A. F., Burton, N. F., Peters, A. and Cooper R. A.
(2009) Honey modulates biofilms of Pseudomonas aeruginosain a time and
dose dependent manner, Journal of ApiProduct and ApiMedical Science, 1
(1), pp, 6- 10.

Paterson, D. L. and Bonomo, R. A. (2005) Extended spectrum β-lactamase: a
clinical update, Clinical Microbiology Reviews, 18 (4), pp, 657-686.

Patrzykat, A., Friedrich, C. L., Zhang, L., Mendoza, V. and Hancock, R.E.
(2002) Sublethal Concentrations of Pleurocidin-Derived Antimicrobial
Peptides Inhibit Macromolecular Synthesis in Escherichia coli, Antimicrobial
Agent
and
Chemotherapy,
46
10.1128/AAC.46.3.605–614.2002.
263
(3),
pp,
605-614.
DOI:

Patton, T., Barrett, J., Bernnan, J. and Moran, N. (2006) Use of a
spectrophotometric bioassay for determination of microbial sensitivity to
manuka honey, Journal of Microbiological Methods, 64, pp, 84-95.

Perez, F., Hujer, A. M., Hujer, K. M., Decker, B. K., Rather, P.N and
Bonomo, R.A. (2007) The Global Challenge of Multidrug Resistant (MDR)
Acinetobacter baumannii, Antimicrobial Agents and Chemotherapy, 51 (10),
pp, 3471–3484, doi:10.1128/AAC.01464-06.

Poirel, L., Al-Maskari, Z., Al-Rashdi, F., Bernabeu, S. and Nordmann, P.
(2010) NDM-1- producing Klebsiella pneumoniae isolated in the Sultanate of
Oman, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkq428.

Pyrznska, K. and Biesaga, M.
(2009) Analysis of phenolic acid and
flavonoids in honey, Trends in Analytical Chemistry, 28 (7), pp, 893-902.

Russell, K. M., Molan, P. C., Wilkins, A. L. and Holland, P. T. (1988) The
identification of some antibacterial constituents of New Zealand manuka
honey, Journal of Agriculture Food Chemistry, 38, pp, 10-13.

Sajwani, A., Farooq, S. A., Eltayeb, E. A. and Bryant, V. (2007a)
Melissopalynological studies from Oman, Palynology, 31, pp, 63-79.

Sajwani, A., Eltayeb, E. A., Farooq, S. A., and Patzelt, A. (2007b) Sugar and
protein profiles of Omani honey from Muscat and Batina regions of Oman,
International Journal of Food Properties, 10, pp, 675-690.

Sammataro, D. and Avitabile, A. (1998) The beekeeper’s handbook, third
edition, Cornell University Press, USA.

Sanz, M.L., del Castillo, M.D., Corzo, N. and Olano, A. (2003) 2Furoylmethyl amino acids and hydroxymethylfurfural as indicators of honey
quality, Journal of Agriculture Food Chemistry, 51 (15), pp, 4278-4283.
264

Scholar, E. M. and Pratt, W. B. (2000) The Antimicrobial Drugs, Second
edition, Oxford University press, UK.

Schumacher, H. H. (2004) Use of medical honey in patients with chronic
venous leg ulcers after split-skin grafting. Journal of Wound Care, 13, pp,
451-452.

Sefton, A. M. (2002) Mechanisms of antimicrobial resistance: Their clinical
relevance in the new millennium, Drugs, 62, pp, 557-566

Sherlock, O., Athman, D. A., Power, D. A., Gethin. G., Cowman, S, et al.
(2010) Comparison of the antimicrobial activity of ulmo honey from Chile
and manuka honey against methicillin-resistant Staphylococcus aureus,
Escherichia coli and Pseudomonas aeruginosa, BMC Complement Alternat
Med, 10, pp, 47.

Shimoda, M., OHKI, K., Shimamoto, Y. and Kohashi, O. (1995)
Morphology of Defensin-Treated Staphylococcus aureus, Infection and
Immunology, 63 (8), pp, 2886-2891.

Shrivastava, S. M., Kumar, S. and Chaudhary, M. (2009) Time-kill curve
studies of Ampucare against Escherichia coli, Staphylococcus aureus,
Klebsiella pneuminiae and Proteus vulgaris, Reseach Journal of medicinal
plant, ISSN 1819-3455

Silberman, S. (2007) The invisible enemy, WIRED, 15(2), on-line edition

Singleton, P. (2004) Bacteria, in Biology, Biotechnology and Medicine, Sixth
edition, John Wiley & Sons, Ltd, Spain.

Simon, A., Sofka, K.,Wiszniewsky, G., et al. (2005) Wound care with
antibacterial honey (Medihoney) in pediatric hematology-oncology, Support
Care Cancer
265

Snow, M. J. and Harris, M. M. (2003) Analytical, Nutritional and Clinical
Methods On the nature of non-peroxide antibacterial activity in New Zealand
manuka honey, Food Chemistry, 84 (1), pp, 145-147

Song, Y., Yang, Q., Kong, H. and Zhong, B. (2004) Typing and
characterization of carbapenem resistance Acinetobacter calcoaceticusbaumannii complex in a Chinese hospital, Journal of Medical Microbiology,
53, pp, 653-656.

Stephen-Haynes, J. (2004) Evaluation of honey-impregnated tulle dressing in
primary care, British Journal of Community Nursing, 9 (6) Suppl, S21-27.

Stephens, J. M., Schlothauer, R. C., Morris, B. D., Yang, D., Fearnley, L. and
Loomes, K. M. (2009) Phenolic compounds and methylglyoxal in some New
Zealand manuka and Kanuka honeys, Food Chemistry, 120 (1), pp, 78-86.

Subrahmanyam, M. (1993) Honey impregnated gauze versus polyurethane
film (OpSite®) in the treatment of burns-a prospective randomised study,
Burn Journal of Plastic Surgry, 46(4) pp, 322-323.

Subrahmanyam, M. (1994) Honey-impregnated gauze versus amniotic
membrane in the treatment of burns, Burns, 20(4), pp, 331-333.

Subrahmanyam, M. (1996) Addition of antioxidants and polyethylene glycol
4000 enhances the healing property of honey in burns, Annual Burns Fire
Disasters, 9(2), pp, 93-95.

Subrahmanyam, M. (1996) Honey dressing versus boiled potato peel in the
treatment of burns: a prospective randomized study, Burns, 22(6), pp, 491493.
266

Subrahmanyam, M. (1998) A prospective randomised clinical and
histological study of superficial burn wound healing with honey and silver
sulfadiazine, Burns, 24(2), pp, 157-161.

Subrahmanyam, M., Hemmady. A., Pawar, S. G. (2001) Antibacterial
activity of honey on bacteria isolated from wounds, Annals of Burns and Fire
Disasters, XIV

Subrahmanyam, M., Shahapure, A. G., Nagane, N.S., Bhagwat, V. R. and
Ganu, J. V. (2003) Free radical control-the main mechanism of the action of
honey in burns, Annals of Burns and Fire Disaster, XVI, pp, 135–137.

Subrahmanyam, M. (2007) Topical application of honey for burn wound
treatment-An overview, Annals of Burns and Fire Disasters, XX (3).

Sussman, M. (ed.) Molecular medical microbiology, Vol 2, Cambridge
University press, UK.

Tan, H. T., Abdul Rahman, R., Gan, S. H., Halim, A. S., Hassan, A.,
Sulaiman, A. and Kaur, K. (2009) The antibacterial properties of Malaysian
tualang honey against wound and enteric microorganisms in comparison to
manuka honey, BMC Complementary and Alternative Medicine, 9, pp, 34

Terrab, A., Diez, M. J. and Heredia, F. J. (2002a) Chromatic charactrisation
of Moroccan honeys by diffuse reflectance tristimulus colorimetry-Non
uniform and uniform colour spaces, Food Sciences Tech Int, 8, pp, 189-195.

Tonks, A. J., Cooper, R. A., Jones, K. P., et al. (2003) Honey stimulates
inflammatory cytokine production from monocytes, Cytokine, 21(5), pp, 242247.
267

Torres, M. V., Munoz, P. and Bouza, E. (2007) Hospital-Acquired
Pneumonia, in Gould, I. M. and Van Deer Meer, J. W. M. (ed.) Antibiotic
policies: fighting resistance, Springer Sciences

Towner, K. J. (1997) Clinical importance and antibiotic resistance of
Acinetobacter spp, Journal of Medical Microbiology, 46, pp, 721-746.

Towner, K. J. (2002) Acinetobacter, in Sussman, M. (ed.) Molecular medical
microbiology, Vol 2, Cambridge University press, UK.

Townsend, G. F. (1969) Optical density as a means of colour classification of
honey, Journal Apic. Research, 8 (1), pp, 29-36.

Tur, E., Bolton, L. and Constantine, B. E. (1995) Topical hydrogen peroxide
treatment of ischemic ulcers in the guinea pig: Blood recruitment in multiple
skin sites, Journal Am Academic Dermatology, 33 (2) pt 1, pp, 217-221

Turkmen, N., Sari, F., Poyrazoglu, E. S. and Velioglu, Y. S. (2006) Effect of
prolonged heating on antioxidant activity and colour of honey, Food
Chemistry, 95 (4), pp, 653-657.

Van den Berg, A. J., Van den Worm, E., Van Ufford, H. C., Halkes, S.B,
Hoekstra, M. J. and Beukelman ,C. J. (2008) An in vitro examination of the
antioxidant and anti-inflammatory properties of buckwheat honey, J Wound
Care, 17 , pp, 172-78.

Vila, J. (1998) Mechanisms of antimicrobial resistance in Acinetobacter
baumannii, Review of Medical Microbiology, 9, pp, 87–97.

Viuda-Martos, M., Ruiz-Navajas, Y., Fernandez-Lopez, J. and PerezAlvarez, J. A. (2008) Function properties of honey, propolis, and royal jelly,
Food Sciences, 73 (9), pp, 117-124.
268

Wadi, M., Al-Amin, H., Farouq, A., Kashef, H. and Khaled, S. A. (1987)
Sudanese bee honey in the treatment of suppurating wounds, Arab Medico,
3, pp, 16-18.

Wahdan, H. A. (1998) Causes of the antimicrobial activity of honey,
Infection, 26 (1), pp, 26-30.

Weston, R.J. (2000) The contribution of catalase and other natural products
to the antibacterial activity of honey: a review, Food Chemistry, 71, pp, 235 –
239.

Weston, R. J., Mitchell, K. R. and Allen, K. L. (1999) Antibacterial phenolic
components of New Zealand honey, Food Chemistry, 64, pp, 295-301.

Wheat, E. J. (2004) The antibacterial activity of Welsh honeys, Mphil,
University of Wales Institute, Cardiff.

White, J. W. (1975) Composition of honey, in: Crane, E. (ed.) Honey: a
Comprehensive Survey, London: Heinemann, pp, 157-206.

White, J. W. and Rudji, O. N. (1978) The protein content of honey, Journal
Apic. Research, 17 (4), pp, 234-238.

Williams, D. B and Carter, C. B. (2009) Transmission electron microscopy. A
text book for materials science, 2nd edition, Springer.

Wilkinson, J. M. and Cavanagh, H. M. (2005) Antimicrobial activity of 13
honeys against Escherichia coli and Pseudomonas aeruginosa, Journal of
Medicinal Food, 8 (1), pp, 100-103.

Wilson, M., McNab, R. and Hendeson, B. (2002) Bacterial disease
mechanisms- An introduction to cellular
University press, UK
269
microbiology, Cambridge

Winn, W. C., Koneman, E. W., Allen, S. D., Janda, W. M., Schreckenberger,
P. C., Procop, G. W. and Woods, G. L. (2006) Koneman’s Colour Atlas and
text book of Diagnostic Microbiology, Sixth edition, Lippincott Williams &
Wilkins, Philadelphia, USA.

Yao, L., Datta, N., Barberan, F. A., Ferreres, F., Martos, I. and Singanusong,
R. (2003) Flavonoids, phenolic acids and abscisic acid in Australian and New
Zealand Leptosperdium honeys, Food chemistry, 80.

Yong, D., Toleman, M. A., Giske, C. G., Cho, S. H., Sundman, K., Lee, K.
and Walsh, T. R. (2009) Characterization of a new metallo-Lactamase gene,
blaNDM-1, and a novel erythromycin esterase gene carried on a unique
genetic structure in Klebsiella pneumoniae sequence type 14 from India,
Antimicrobial Agent and Chemotherapy, 53(12), pp, 5046-5054.

Zong and Yu. (2010) Escherichia coli carrying the blaCTX-M-15 gene of
ST648, J Med Microbiol, 59, pp,1536-1537

Zumla, A. and Lulat, A. (1989) Honey- a remedy rediscovered, Journal of
the Royal Society of Medicine, 82, pp, 384-385
270