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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) 

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 

6

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 

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 

15

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: 49 

2.1.2 Antibiotics sensitivities: Extended Spectrum Beta- lactamases 

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 

49

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 

cultures: 

2.3.1 Minimum Inhibitory Concentration (MIC) method: 62 

2.3.1.1 Agar incorporation method: 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 

62

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 

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 

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 

71 

74

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 

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 

cultures: 

3.3.1 Minimum Inhibitory Concentration (MIC) and Minimum 

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: 107 

3.4 Time Kill Curves 115 

3.4.1 Inhibition of test organisms by manuka honey monitored by 

optical density: 

3.4.2 Inhibition of test organisms by manuka honey monitored by total 119 

8 

80 

97 

97 

115

viable count 

3.4.2.1 Acinetobacter spp 120 

3 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 


3.5.1 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 


4.6 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. 


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. 


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 University- 

Oman) 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 

Tables Title 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 

in the project 

Table 2.2 Chemicals and Reagents used for electron microscopy 

experiment in the project 

Table 2.3 Chemicals and Reagents used for 2- dimensional 

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 

Table 2.6 

electrophoresis experiments in the project 

Thirty isolates of MDR Acinetobacter and their resistance 

pattern 

Table 2.7a Clinical isolates of E.coli & Klebsiella provided for this 

study 

Table 2.7b Clinical isolates of Citrobacter, Enterobacter & Serratia 

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 

40% (w/v) stock honey for MIC method 

Table 2.12 Preparation of tubes for MICs 65 

Table 2.13: Cultures and honey concentrations used in the time-kill 

curves 

15 

41 

42 

43 

45 

46 

47 

48 

64 

67

Index of Tables (Continued) 


Table 2.14 Reagents and buffers used in 2-D Electrophoresis 73 

Table 2.15: Quantity of proteins in Acinetobacter cells with and without 

Table 3.1 a 

& b: 

20% honey 

Identification and antibiotics sensitivities of 30 MDR 

Acinetobacter isolates 

Table 3.2: Confirmation of identity and antibiotics sensitivity including 

ESBL tests for 10 E.coli isolates. 


ESBL tests for 12 Klebsiella isolates 


Table 3.5: 

ESBL tests for 12 Citrobacter isolates. 

Confirmation of identity and antibiotics sensitivity including 

ESBL tests for15 Enterobacter isolates. 


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 

honey compared to manuka honey 

Table 3.9: Represent the identification of flora sources by pollen 

analysis 

Table 3.10: Amount of free radical and phenolic content in each honey 

samples 

16 

75 

81- 

82 

83 

84 

85 

86 

87 

93 

94 

96



Table 3.11: Susceptibility of Acinetobacter isolates against manuka 

honey 

Table 3.12: Sensitivity of 30 Acinetobacter isolates treated with manuka 

honey by broth dilution method 

Table 3.13: Sensitivity of 10 Klebsiella isolates to manuka honey tested 

by the broth dilution method 

Table 3.14: Sensitivity of 8 Serratia and 8 E.coli isolates to manuka 

honey using a broth dilution method 

Table 3.15: Susceptibility of 15 Enterobacter isolates to with manuka 

honey determined by broth dilution method 

Table 3.16: Susceptibility of 12 Citrobacter isolates to manuka honey 106 

Table 3.17: Susceptibility of 30 MDR Acinetobacter isolates against 4 

types of Omani honey using broth dilution method 

Table 3.18: Susceptibility of 12 Klebsiella isolates against 4 types of 

Omani honey using broth dilution method 

Table 3.19: Susceptibility of 10 E.coli isolates against 4 types of Omani 

honey using broth dilution method 

Table 3.20: Susceptibility of 15 Enterobacter isolates against 4 types of 


Table 3.21: Susceptibility of 12 Citrobacter isolates against 4 types of 


Table 3.22: Susceptibility of 8 Serratia isolates against 4 types of Omani 

honey using broth dilution method 

Table 3.23: Cultures and honey concentrations used in the time-kill 

curves assay 

17 

98 

100 

102 

103 

105 

107- 

108 

109 

110 

111 

112 

113 

115



Table 3.24: Decimal reduction dose (DRD) for each isolate after 5 h 

exposure to 2x respective MICs of manuka honey 

Table 3.25 Comparison of the mean viable cell count between non 

honey and honey treated cells using paired sample test (T- 

test). 

Table 3.26: Comparison of changes in cell sizes of isolates observed in 

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 

MIC of different honeys including manuka honey against six 

bacteria species. 

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: 

18 

132 

133 

173 

196- 

197 

208

Index of Figures 

Figure Title Page 

Figure 2.1: Calibration curve for protein determination in honey 

samples 

Figure 2.2: Standard curve for total phenolic content in honey samples 60 

Figure 2.3: Calibration curve of protein concentration in Acinetobacter 

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 

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 

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 

58 

75 

95 

121

Index of Figures (Continued) 


Figure 3.15: The effect of manuka honey on the viability of Serratia 

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 

minutes at x5,000 magnification 

Figure 3.23: SEM micrograph of Acinetobacter cells exposed to 20% 

(w/v) manuka honey of 0 minutes at x5,000 magnification 

Figure 3. 24: SEM micrograph of untreated cells of Acinetobacter after 

60 minutes at x5,000 magnification 


(w/v) manuka honey after 60 minutes at x5,000 

magnification 

Figure 3.26: SEM micrograph of untreated cells of Acinetobacter after 

90 minutes at x5,000 magnification 


(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 

131 

140 

140 

141 

141 

142 

142 

143



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 

Figure 3.30: SEM micrographs of untreated cells of Acinetobacter after 


respectively 

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 

Figure 3.32: SEM micrograph of untreated cells of E.coli after 30 


Figure 3.33: SEM micrograph of E.coli cells exposed to 30% (w/v) 

manuka honey after 30 minutes at x5,000 magnification 

Figure 3.34 : SEM micrograph of untreated cells of E.coli after 180 


Figure 3.35 : SEM micrograph of E.coli cells exposed to 30% (w/v) 


Figure 3.36: SEM micrographs of untreated cells of Klebsiella after 30 

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 

144 

146 

147 

149 

149 

150 

150 

152 

153



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 

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 

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 

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 

Figure 3.42: SEM micrographs of untreated cells of Enterobacter after 


respectively 

Figure 3.43: SEM micrographs of Enterobacter cells exposed to 30% 

(w/v) manuka honey after 30 minutes at x5,000 A & B 


Figure 3.44: SEM micrographs of untreated Enterobacter cells after 180 

minutes at x5,000 A & x20,000 B magnification 

respectively 

Figure 3.45: SEM micrographs of Enterobacter cells exposed to 30% 

(w/v) manuka honey after 180 minutes at x5,000 (A & B) 


Figure 3.46: SEM micrographs of untreated cells of Citrobacter after 30 

minutes at x5,000 A & x15,000 B magnification 

respectively 

22 

154 

155 

157 

158 

161 

162 

163 

164 

166



Figure 3.47: SEM micrographs of Citrobacter cells exposed to 20% 

(w/v) manuka honey after 30 minutes at x10,000 A & 


Figure 3.48: SEM micrographs of untreated Citrobacter cells after 180 

minutes at x5,000 (A & B) magnification respectively 

Figure 3.49: SEM micrographs of Citrobacter cells exposed to 20% 

(w/v) manuka honey after 180 minutes at x5,000 A & 


Figure 3.50: SEM micrograph of untreated cells of Serratia after 30 


Figure 3.51: SEM micrograph of Serratia cells exposed to 30% (w/v) 

manuka honey for 30 minutes at x 5,000 magnification 

Figure 3.52: SEM micrograph of untreated cells of Serratia after 180 


Figure 3.53: SEM micrograph of Serratia cells exposed to 30% (w/v) 


Figure 3.54: Transmission micrographs of untreated cells of 

Acinetobacter after 1 h incubation with isosensitest borth 

(ISB) at 16,000x magnification 

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. 

Figure 3.56: Transmission micrographs of untreated cells of 

Acinetobacter incubated with isosensitest borth (ISB) after 

3 h at 16,000x magnification 

23 

167 

168 

169 

171 

171 

172 

172 

175 

176 

177



Figure 3.57: Transmission micrographs of Acinetobacter incubated with 


honey after 3 h at 16,000x magnification 

Figure 3.58: Transmission micrographs of untreated cells of E.coli after 

1 h incubation with isosensitest borth (ISB) at 16,000x 

magnification 

Figure 3.59: Transmission micrographs of E.coli incubated with 



Figure 3.60: Transmission micrographs of untreated cells of E.coli after 

3 h incubation with isosensitest borth (ISB) at 16,000x & 

30,000x magnification 

Figure 3.61: Transmission micrographs of E.coli incubated with 



Figure 3.62: 2-D protein electrophoresis gel of Acinetobacter cells 

without honey treatment 

Figure 3.63: 2-D protein electrophoresis gel of Acinetobacter cells 

exposed to 20% (w/v) manuka honey 

Figure 4.1: Mean MIC and MBC (%w/v) for 30 Acinetobacter strain 

against 5 types of Hone 

Figure 4.2: Mean MIC and MBC (%w/v) for 12 Klebsiella strain 

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 

178 

180 

181 

182 

183 

185 

185 

198 

198 

199



Figure 4.4: Mean MIC & MBC (%w/v) for 12 Citrobacter strain 


Figure 4.5 Mean MIC and MBC (%w/v) for 15 Enterobacter strain 


Figure 4.6: Mean MIC and MBC (%w/v) for 8 Serratia strain against 5 


25 

199 

200 

200

AHL-N-acylhomoserine-lactone 

Abbreviations list 

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 β- 

lactamase 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 CTX- 

M 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 CTX- 

M 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 (AmpC- 

stable 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 β- 

lactamases 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 β- 

lactamases 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 (Amabile- 

Cuevas 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 30 o C for 

most strains, the optimum temperature for this bacterium at 33-35 o C (Winn et al., 

2006). They are oxidase-negative, catalase-positive, indole-negative and nitrate- 

negative. 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 (Bergogne- 

Berezin 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 & Bergogne- 

Berezin 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 β- 

lactams 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 OXA- 

type 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 (Viuda- 

Martos 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 water- 

soluble 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

lock 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 

Peroxide honey Non-peroxide honey 

Form H2O2 Polyphenolic compounds 

Effected by heat & 

light 

Effected by long 

storage 

Sensitive Thermostable 

Yes No 

Origin: Bee glucose oxidase Honey acidity, diastase & 

invertase enzymes 

Origin: Flora - Flavonoids, phenolic acids 

Mode of action active only when diluted, 

tissue repair 

63 

Antioxidant, MGO 

High level Bad- release of free radical Good- more scavenging 

(quenching properties) 

Example Almost all honey Manuka, Jelly bush honey

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 of manuka honey 

(http://www.molangoldstandard.co.nz/article, 2010). 

64

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

ecause 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 wide- 

ranged 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

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) 

Free radical – 257621 

71 

Sigma-Aldrich

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) 

72 

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

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 

salt dihydrate E6511 

73 

Sigma-Aldrich 

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

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 

74 

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 o C BINDER 

pH meter 3510 JENWAY 

Water refractometer Atago HHR-2N 

Sugar refractometer (40-85%) Bellingham & Stanley 

Water bath 37 o C/100 OLS200-Grant 

BioPak (Synergy UV) B0901

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 & 

Ready strip 11cm pH 3-10 BioRad 

Protean IEF cell & Tank BioRad 

75 

JEOL 1230 TEM- UK 

Refrigerated superspeed centrifuge Sorvell ® RC-5B 

Thirty isolates of Acinetobacter baumannii and Acinetobacter 

baumannii/calcoaceticus complex that were resistant to third generation 

cephalosporins (3 rd 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).

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 




























76

Fifty five clinical isolates belonging to the Enterobacteriaceae family that were resistant 

to third generation cephalosporins (3 rd 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 37 o C. 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 





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 



45. Klebsiella oxytoca CTX-M 



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 







62. Citrobacter farmeri Hyper ampC expression +TEM 


64. Enterobacter cloacae CTX-M 




68. Enterobacter hormaechi CTX-M 



71. Enterobacter aerogenes AMPC 

72. Enterobacter cloacae TEM 

73. Enterobacter cloacae Hyper ampC expression 





78. Serratia plymuthica CTX-M 

79. Serratia marcescens Hyper ampC expression 





84. Serratia marcescens Hyper ampC expression +SHV 


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 Beta- 

lactamases 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 piperacillin- 

tazobactam TZP 100/10 µg were applied for all isolates by disc dispenser. 

79

Appropriate discs (BD BBL TM 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 4 o C till one month. 

Table 2.9: Preparation of phenol standards 

Final Phenol Conc (%w/v) Volume of 10% 

Phenol/ml 

82 

Volume of H2O/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 

37 o C 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 

120 o C. After autoclaving, the agar was kept at 50 o C 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 50 o C. Then the agar was mixed thoroughly and

transfered into a large square assay plate (Corning 43110) on flat surface. When the 

agar had set, the plate was layed upside down at 4 o C 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 37 o C 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 (4 o C). 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 

37 o C 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 air- 

conditioned 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 re- 

centrifuged 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 (45 o C) 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) 

90 

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 

Absorbance at 750nm 

0.4 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

2.2.9.2 Assay method: 

Standard curve for total phenolic content 

y = 0.0013x 

R² = 0.9974 

0 50 100 150 200 250 300 

Concentration of gallic acid standard (ug/ml) 

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

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 37 o C 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 50 o C for 15 minutes. An automatic pipette was 

used to dispense 10 ml of nutrient agar into each of 22 universal containers and 

autoclaved at 121 o C for 15 minutes. After autoclaving, it was put in water bath at 50 o C 

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 

(w/v) 

Quantity of double 

strength N.A (ml) 

94 

Quantity of 40% 

stock honey (ml) 

Quantity of sterile 

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 37 o C 

incubator to remove excess moisture. The plates of varying honey concentration were 

then inoculated with 1 µl of each test organism (typically 1x10 5 cfu/ml) in triplicate 

using multipoint inoculator. The plates were placed in an incubator at 37 o C 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.


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) 

95 

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)

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 37 o C 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 37 o C 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 37 o C. 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 4 o C. 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 4 o C. 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 


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 


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 37 o C 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 37 o C 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 -20 o C. 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) 

105 

Volume of 

protein 

sample µl 

Volume of re- 

hydration 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.

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 -70 o C 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 (3 rd G 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 

Identification of isolates Resistance Pattern 


/calcoaceticus complex 



111 

CAZ, CTX, CE, CXM, CIP, AMP 

CAZ, CTX, CXM, FOX, ERT, TAZ,AZT, 

AMP 

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 











CAZ, CTX, FOX, CN, TAZ, ERT, CIP 

CAZ, CTX, CN, FOX, TAZ, ERT, CIP, 

AMP 

CAZ, CTX, FOX, CXM, ERT, AZT, AMP 

CAZ, CTX, FOX, TAZ, ERT, AZT, AMP 

CAZ, CTX, CN, FOX, TAZ,ERT, CIP, AZT 

11. Acinetobacter baumannii CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT 



CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT 

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

Table 3.1b: Identification and antibiotics sensitivities of 15 MDR Acinetobacter 

isolates 

Identification of isolates Resistance Pattern 





CAZ, CTX, CXM, FOX, TAZ, ERT, CIP, SXT, CN 

CAZ, CTX, CN, FOX, TAZ, ERT, CIP, SXT 

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 









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 ESBL test ESBL/ 

AmpC * test 

CAZ CTX CPM TZP 

1. Escherichia coli R R R S ESBL (+) - 

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


for 11 Klebsiella isolates 

Isolate 

No. 


isolates 

Resistance Pattern ESBL 

test 


1. Klebsiella pneumoniae R R S S ESBL (+) - 


114 

ESBL 

/ 

Amp 

C * 

test 

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 (+) - 


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 (+) - 



test positive.


for 12 Citrobacter isolates. 


isolates 



test 

115 

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 


5. Citrobacter farmeri R R S R ESBL (-) AmpC 










test positive.


for 15 Enterobacter isolates. 


isolates 


test 


116 

ESBL/ 

AmpC * 

test 

1. Enterobacter cloacae R R S R ESBL (+) - 

2. Enterobacter cloacae R R S R ESBL (+) ESBL 



5. Enterobacter hormaechi 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 






R- Resistant, S – Sensitive 

All cephalosporin tested (CAZ, CTX & CPM) should be reported resistant if ESBL test 

positive.


for 8 Serratia isolates. 


isolates 


test 


117 

ESBL/ 

AmpC * test 

1. Serratia plymuthica R R S R ESBL (+) ESBL 

2. Serratia marcescens R R S R ESBL (-) AmpC 





7. 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.

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. 

118 

Zones of inhibition

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 (R 2 =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. 

Mean Diameter (mm) 

The data obtained from this method was repeated twice 

25 

20 

15 

10 

5 

0 

Phenol Standard Curve 

120 

y = 2.1643x + 5.3857 

R² = 0.9719 

0 1 2 3 4 5 6 7 8 

% (W/V) Phenol

Table 3.7: Antibacterial activity of honey samples 

Type of honey Total activity phenol 

equivalent %w/v 

121 

Non- peroxide activity 

phenol equivalent % 

w/v 

Manuka honey-(plate1) 20.4 20.6 



Mean ± SD(n) 21.3± 0.9 (3) 

OH-B (plate1) 21.5 

OH-B (plate2) 24.2 

Mean ± SD(n) 

22.9± 1.9 (2) 

OH-C 21.8 

20.0± 0.8 (3) 

3.7 

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 micro- 

organisms. 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) 

123 

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 12.5- 

19.5 

Mean ± 

SD (n) 

Manuka 


4.7± 

0.9(8) 

15.8± 

1.9(8) 

75-85 2.6-6.3 2.9-38.5 Extra light 

amber to 

Dark amber 

81.0± 

2.9(8) 

4.7± 

1.6(8) 

11.1± 

11.9(8) 

3.5 20 78 10 3 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 

Sample 

Beekeepers 

Identification 

National Pollen And Research Unit (NPARU) 

124 

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

Figure 3.3: Image of pollen present in Omani honey samples at 100x magnification. 

125 

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

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 (R 2 = 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) Mean 

MIC% 

(w/v) 

SD *(n) 

E.coli NCTC 10418 5 5 5 5 0 

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 









Mean of Means MIC 7.18 0.28 

*(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


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. 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

9. 

10. 

11. 

12. 

13. 

14. 

15. * 

Mean MIC 

± SD (n) 

Mean 

MBC ± SD 

(n) 

Mean MIC 

%(w/v) 

±SD(3) 

7 ±1 

7.3 ±1.5 

7 ±1 

6 ±0 

8 ±1 

7.5 ±1.5 

7 ±1 

7 ±0 

8 ±1 

8 ±0 

7 ±1 

8 ±1 

7 ±0 

6 ±1 

9 ±1.7 

Mean MBC 

%(w/v) 

±SD (3) 

7 ±1.7 

8 ±1.7 

8 ±1.7 

10 ±1 

10 ±0 

9 ±1 

9 ±1 

10 ±0 

9 ±0 

11 ±0 

10 ±1 

10 ±0 

9 ±1.7 

7 ±1 

11 ±1 

7.17 ± 0.7 (30) 

9.2 ± 1.3 (30) 

(*)The least susceptible strain to manuka honey 

(n) number of isolates tested, this expirement was done in triplicate 

130 

Isolates 

No. 

16. 

17. 

18. 

19. 

20. 

21. 

22. 

23. 

24. 

25. 

26. 

27. 

28. 

29. 

30. 

Mean MIC 

%(w/v) 

± SD(3) 

6 ±0 

6 ±0 

8 ±1 

7 ±2.6 

8 ±2 

7 ±1.7 

7 ±1 

7 ±1 

7 ±1 

7 ±0 

7 ±1.7 

6 ±1 

7 ±0 

8 ±1 

8 ±0 

Mean MBC 

%(w/v) 

±SD (3) 

7 ±1 

7 ±0 

9 ±1 

9 ±1.7 

10 ±1.7 

10 ±2 

7 ±1 

9 ±0 

8 ±1 

10 ±0 

11 ±0 

10 ±1 

10 ±0 

10 ±1 

11 ±0

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 

Kleb-1 

Kleb-2 

Kleb-3 

Kleb-4 

Kleb-5* 

Kleb-6 

Mean 

MIC 

%(w/v) 

±SD(2) 

12 ±0 

8 ±1.4 

12 ±1.4 

12 ±0 

13 ±0 

12 ±0 

Mean 

MBC 

%(w/v) 

±SD(2) 

12 ±0 

10 ±0 

13 ±1.4 

13 ±1.4 

16 ±0 

13 ±0 

(*) 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 

Klebsiella 

Isolate 

Kleb-7 

Kleb-8 

Kleb-9 

Kleb-10 

Mean ± SD 

(n) 

Mean 

MIC 

%(w/v) 

±SD(2) 

12 ±1.4 

12 ±2.8 

12 ±0 

12 ±0 

11.7± 1.3 

(10) 

Mean 

MBC 

%(w/v) 

±SD(2) 

15 ±1.4 

12 ±2.8 

13 ±0 

12 ±1.4 

12.9± 1.7 

(10)

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 

Serratia-1 

Serratia -2 

Serratia -3 

Serratia -4* 

Serratia -5 

Serratia -6 

Serratia -7 

Serratia -8 

Mean ± SD 

(n) 

Mean MIC 

%(w/v) 

±SD(2) 

10 ±0 

15 ±0 

13 ±1.4 

15 ±1.4 

15 ±0 

14 ±0 

13 ±0 

14 ±0 

13.3 ± 1.7 

(8) 

Mean MBC 

%(w/v) 

±SD(2) 

12 ±0 

18 ±0 

22 ±1.4 

20 ±0 

16 ±0 

16 ±0 

14 ±0 

18 ±1.4 

17 ± 3.2 

(8) 

(*) 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 

E.COLI 

ISOLATES 

E.coli-1 

E.coli-2 

E.coli-3 

E.coli -4 

E.coli -5 

E.coli -6 

E.coli -7* 

E.coli -8 

Mean ± SD 

(n) 

Mean MIC 

%(w/v) 

±SD(2) 

10 ±1.4 

10 ±0 

10 ±0 

10 ±0 

10 ±0 

12 ±0 

13 ±0 

8 ±0 

10.4 ± 1.5 

(8) 

Mean MBC 

%(w/v) 

±SD(2) 

12 ±1.4 

10 ±1.4 

10 ±0 

10 ±0 

12 ±0 

12 ±0 

13 ±0 

8 ±0 

10.9 ± 1.6 

(8)

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. 

ISOLATES 

Enterobacter-1 








Mean 

MIC 

%(w/v) 

±SD(2) 

9 ±0 

9 ±0 

9 ±0 

13 ±0 

11 ±0 

9 ±0 

8 ±0 

8 ±0 

Mean 

MBC 

%(w/v) 

±SD(2) 

15 ±1.4 

13 ±0 

13 ±0 

14 ±0 

14 ±0 

11 ±1.4 

12 ±0 

11 ±0 

(*) least susceptible isolate 


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 

ISOLATES 

Enterobacter-9* 







Mean ± SD 

(n) 

Mean 

MIC 

%(w/v) 

±SD(2) 

13 ±0 

11 ±0 

11 ±0 

8 ±1.4 

11 ±0 

12 ±0 

10 ±0 

10.1± 1.7 

(15) 

Mean 

MBC 

%(w/v) 

±SD(2) 

14 ±0 

14 ±0 

14 ±0 

12 ±0 

14 ±0 

13 ±0 

13 ±0 

13.1± 1.2 

(15)

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 

Citrobacter 1 

Citrobacter 2 

Citrobacter 3 

Citrobacter 4 

Citrobacter 5* 

Citrobacter 6 

Citrobacter 7 

Mean 

MIC 

%(w/v) 

±SD(2) 

9 ±0 

10 ±1.4 

10 ±0 

10 ±0 

11 ±0 

10 ±0 

9 ±1.4 

Mean 

MBC 

%(w/v) 

±SD(2) 

12 ±0 

15 ±0 

14 ±0 

15 ±0 

15 ±0 

14 ±0 

11 ±0 

(*) least honey-susceptible isolate. 


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 

ISOLATES 

Citrobacter 8 

Citrobacter 9 

Citrobacter 10 

Citrobacter 11 

Citrobacter12 

Mean ± SD 

(n) 

Mean 

MIC 

%(w/v) 

±SD(2) 

9 ±0 

10 ±0 

10 ±0 

8 ±1.4 

10 ±0 

9.7± 0.8 

(12) 

Mean 

MBC 

%(w/v) 

±SD(2) 

11 ±1.4 

13 ±0 

15 ±0 

11 ±0 

14 ±0 

13.3± 1.7 

(12)

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. 

MIC 

(%w/v) 

OH-B OH-C OH-G OH-F 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

137 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%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

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 

Mean 

± SD 

(30) 

16.6± 

2 

18.4± 

1.7 

18.1± 

1.6 

19.2± 

1.9 

138 

20.5± 

2.4 

(n) number of isolates tested, this expirement was done once 

21.8± 

2.3 

17.6± 

1.9 

17.8± 

2.1

Table 3.18: Susceptibility of 11 Klebsiella isolates against 4 types of Omani honey 

using broth dilution method. 

Isolate 

No. 

Mean 

± SD 

(11) 

MIC 

(%w/v) 


MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

139 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%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

Table 3.19: Susceptibility of 10 E.coli isolates against 4 types of Omani honey using 

broth dilution method. 

Isolate 

No. 

Mean ± 

SD (10) 

MIC 

(%w/v) 


MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

140 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%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

Table 3.20: Susceptibility of 15 Enterobacter isolates against 4 types of Omani 

honey using broth dilution method. 

Isolate 

No. 

MIC 

(%w/v) 

OH-B1 OH-C OH-G OH-F 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

141 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%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 

Mean 

±SD 

(15) 

21.6± 

0.9 

22.9± 

1.5 

20.1± 

1.7 

22.2± 

1.3 

26.4± 

2.9 


28± 

2.7 

20± 

1.8 

22.5± 

2.4

Table 3.21: Susceptibility of 12 Citrobacter isolates against 4 types of Omani honey 


Isolate 

No. 

MIC 

(%w/v) 


MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

142 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%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 

Mean 

± SD 

(12) 

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

Table 3.22: Susceptibility of 8 Serratia isolates against 4 types of Omani honey 


Isolate 

No. 

MIC 

(%w/v) 


MBC 

(%w/v) 

MIC 

(%w/v) 

MBC 

(%w/v) 

143 

MIC 

(%w/v) 

MBC 

(%w/v) 

MIC 

(%w/v) MBC 

(%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 

Mean ± 

SD (8) 

20.2± 

1.9 

21.7± 

2.1 

20.2± 

1.2 

23.7± 

1.9 


>28 >28 17± 

2.8 

23.7± 

4 

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

(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 

145 

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 


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.

Optical density at 550 nm 

Figure 3.4: The effect of manuka honey (%w/v) on the growth of Acinetobacter 

2.2 

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 

Figure 3.5: The effect of manuka honey (%w/v) on the growth of E.coli 

Optical density at 550nm 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

The growth curve of Acinetobacter 

Time (minutes) 

Acinetobacter without honey Acinetobacter with 20% (w/v) manuka honey 

The growth curve of E.coli 

0 200 400 600 800 1000 1200 1400 1600 


E.coli without honey E.coli with 30% (w/v) manuka honey 

146

Figure 3.6: The effect of manuka honey (%w/v) on the growth of Klebsiella 

Optical density at 550nm 

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 

Figure 3.7: The effect of manuka honey (%w/v) on the growth of Citrobacter 


1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

The growth curve of Klebsiella 


Klebsiella without honey Klebsiella with 30% manuka honey 

The growth curve of Citrobacter 

0 200 400 600 800 1000 1200 1400 1600 


Citrobacter without honey Citrobacter with 20% (w/v) manuka honey 

147

Figure 3.8: The effect of manuka honey (%w/v) on the growth of Enterobacter 


3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 200 400 600 800 1000 1200 1400 1600 

Figure 3.9: The effect of manuka honey (%w/v) on the growth of Serratia 


2.2 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

The growth curve of Enterobacter 


Enterobacter without honey Enterobacter with 30% (w/v) manuka honey 

The growth curve of Serratia 

0 200 400 600 800 1000 1200 1400 1600 


Serratia without honey Serratia with 30% (w/v) manuka honey 

148


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 37 o C. 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

This data was done in triplicate 

Number of bacterial cells Log 10 CFU/ml 

Figure 3.10: The effect of manuka honey on the viability of Acinetobacter. 

12 

10 

8 

6 

4 

2 

0 

Time -to-kill curve of Acinetobacter 

0 200 400 600 800 1000 1200 1400 1600 

Time/Minutes 

Acinetobacter without honey Acinetobacter with 20% (w/v) manuka honey 

151

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 (


Figure 3.11: The effect of manuka honey on the viability of E.coli. 

Number of bacterial cells log10 CFU/ml 

12 

10 

8 

6 

4 

2 

0 

Time -to-kill curve of E.coli 

0 200 400 600 800 1000 1200 1400 1600 

153 


E.coli without honey E.coli with 30% (w/v) manuka honey

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 x10 5 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 

Number of bacterial cells log10 CFU/ml 

12 

10 

8 

6 

4 

2 

0 

Time -to-kill curve of Klebsiella 

0 200 400 600 800 1000 1200 1400 1600 


Klebsiella without honey Klebsiella with 30% (w/v) manuka honey 

155

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 1x10 8 cfu/ml 

(eight Log CFU) to below 5.5x10 4 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 

Number of bacterial cells Log10 CFU/ml 

12 

10 

8 

6 

4 

2 

0 

Time-to-kill curve of Citrobacter 

0 200 400 600 800 1000 1200 1400 1600 

157 


Citrobacter without honey Citrobacter with 20% (w/v) manuka honey

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 x10 6 just below 

(eight Log CFU) to 1.5 x10 6 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 

Number of bacterial cells Log 10 CFU/ml 

12 

10 

8 

6 

4 

2 

0 

Time -to- kill curve of Enterobacter 

0 200 400 600 800 1000 1200 1400 1600 

159 


Enterobacter without honey Enterobacter with 30% manuka honey

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

Figure 3.15: The effect of manuka honey on the viability of Serratia 

Number of bacterial cell Log 10 CFU/ml 

12 

10 

8 

6 

4 

2 

0 

0 200 400 600 800 1000 1200 1400 1600 


Time-to-kill curve of Serratia 


Serratia without honey Serratia with 30% (w/v) manuk honey 

161

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


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 

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 

Figure 3.17: Growth curve of E.coli in ISB 


1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

Growth Curve of Acinetobacter 


Growth Curve of E.coli 

0 100 200 300 400 500 600 700 

Time/minutes 

165

Figure 3.18: Growth curve of Klebsiella in ISB 


2 

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 

Figure 3.19: Growth curve of Enterobacter in ISB 


1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

Growth curve for Klebsiella 


Growth curve for Enterobacter 

0 100 200 300 400 500 600 700 


166

Figure 3.20: Growth curve of Citrobacter in ISB 


1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 100 200 300 400 500 600 700 

Figure 3.21: Growth curve of Serratia in ISB 


1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

Growth curve for Citrobacter 


Growth curve for Serratia 

0 100 200 300 400 500 600 700 

Time(minutes) 

167

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 3.25: SEM micrograph of Acinetobacter cells exposed to 20% (w/v) manuka 


171 

Debris 

Clumps



Figure 3.27: SEM micrograph of Acinetobacter cells exposed to 20% (w/v) 


Debris 

172 

Clumps

Figure 3.28: SEM micrographs of untreated cells of Acinetobacter after 150 minutes 

at x5,000 A & x20,000 B magnification respectively 

A 

B 

Single 

division 

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 


A 

B 

Multiple 

division 

sites 

174 

Debris

C 

D 

Cell division 

Multiple septa 

in each cell 

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 

A 

B 

Cell lysis 

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 

179 

Cells with 

clumps

Figure 3.34 : SEM micrograph of untreated cells of E.coli after 180 minutes at 


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 

A 

B 

Normal cells 

182

Figure 3.37: SEM micrographs of untreated cells of Klebsiella after 180 minutes at 

x5,000 (A) & 15,000 (B) magnification respectively 

A 

B 

Normal cells 

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 

B 

Rough 

surfaces 

185

C 

D 

Rough surface 

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 

B 

Rough 

surface 

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 

A 

B 

Normal cells 

191

Figure 3.43: SEM micrographs of Enterobacter cells exposed to 30% (w/v) 

manuka honey after 30 minutes at x5,000 A & B magnification respectively 

A 

B 

Cells with clumps 

192

Figure 3.44: SEM micrographs of untreated Enterobacter cells after 180 minutes at 

x5,000 A & x20,000 B magnification respectively 

A 

B 

Normal cells 

Filter membrane 

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 

A 

B 

Mesh like structure 

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 

B 

A 

196 

Normal cells

Figure 3.47: SEM micrographs of Citrobacter cells exposed to 20% (w/v) manuka 


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 


A 

B 


Pore 

formation 

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 


Figure 3.51: SEM micrograph of Serratia cells exposed to 30% (w/v) manuka 

honey for 30 minutes at x 5,000 magnification 


201

Figure 3.52: SEM micrograph of untreated cells of Serratia after 180 minutes at 


Figure 3.53: SEM micrograph of Serratia cells exposed to 30% (w/v) manuka 


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

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 

B 

A 

C 

205 

Define cell wall 

300 nm x16,000 

300 nm x16,000 

300 nm x16,000

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. 

A 

Black deposit 

B 

C 

Hole 

s 

206 

Debri 

s 

300 nm x16,000 

300nm x16,000 

300nm x16,000

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) 

B 

C 

A 

207 

300nm x16,000 

300nm x16,000 

300nm 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 

B 

C 

Septa 

208 

300nm x16,000 

Debris 

300nm x16,000 

300nm 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 

A 

B 

C 

210 

Hole 

300nm x16,000 

300nm x16,000 

300nm 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 

A 

B 

C 

Lysed cell 

Deformed cells 

Empty cell with deposit 

211 



300nm x16,000 

300nm x16,000 

300nm 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). 

A 

B 

C 

Holes 

212 

Define cell wall 

300nm x16,000 

300nm x32,000 

300nm x32,000

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 

B 

A 

C 

Irregular cells 

Less electron dense 

213 

300nm x16,000 

300nm x16,000 

300nm x16,000

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 SDS- 

polyacrylamide 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 non- 

peroxide 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) 

220 

Protein 

(mg/g) 

HMF 

(mg/kg) 

Anti- 

radical 

activity% 

Phenol 

content 

(eq/kg) 

NHB 3.5-5.5 17.2 82.4 0.7 15 N/A N/A 

Mean ± 

SD (n) of 

Omani honey 

Manuka 


N/A (not available) 

4.7± 

0.9 

15.8± 

1.9 

81± 

2.8 

4.7± 

1.5 

11.1± 

11.9 

64.6± 

4.1 

89.7± 

64.8 

3.5 20 78 0.3 3 61.1 65.4 

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.

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. 

Acinetobacter 

MIC % (w/v) previous studies MIC % (w/v) this study Comments 

Manuka 


Other 


Authors Manuka 


226 

Omani honey (OH) 

OH-B OH-C OH-D OH-F 

N/A 8.1±1.5 (Blair et al., 2009) 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 

12.5 11.25 (Tan et al., 2009) 

medihoney, tualang 

and some Australian 

N/A 6-8 (George & Cutting 2007) 


* 

E.coli N/A 7.5±0.8 (Blair et al., 2009) 10.4± 1.5 18.8± 

1.3 

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) * 

19.4±1 23.6±2 19.6±1.8 OH has lower MIC 

than tualang and 

some Australian 


Klebsiella N/A 13±2.4 (Blair et al., 2009) 11.7±1 .3 17.2±3 18.5±1.7 22± 2 18± 1.9 OH has lower MIC 

than some Australian 

MIC % (w/v) Previous studies MIC % (w/v) this study Comments 

Manuka 


Other 


Authors Manuka 


227 

Omani honey (OH) 

OH-B OH-C OH-D OH-F 

Citrobacter N/A 9.1±3 (Blair et al., 2009) 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 

10 20 (Lusby et al., 2005) 


Enterobacter N/A 11.7±1.8 (Blair et al., 2009) 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 

20 25 (Tan et al., 2009) 

and lower MIC than 

tualang honey but 

10 20 (Lusby et al., 2005) 

higher than medihoney 

N/A 6 (George & Cutting 

2007) * 

Serratia N/A 14.8±0.5 (Blair et al., 2009) 13.3± 1.7 20.2± 1.9 20.2± 1.2 >28 17± 2.8 OH has higher MIC 

than medihoney 

0 0 (Lusby et al., 2005) 

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).

Figure 4.1: Mean MIC and MBC (%w/v) for 30 Acinetobacter strain against 5 types 

of honey 

25 

20 

15 

10 

5 

0 

7.17 

11 

18.4 

16.6 

19.2 

18.1 

Figure 4.2: Mean MIC and MBC (%w/v) for 12 Klebsiella strain against 5 types of 


% bacterial inhibition 


30 

25 

20 

15 

10 

5 

0 

Susceptibility of 30 Acinetobacter strains against 

5 types of honey 

228 

21.8 

20.5 

17.6 17.8 

MH-A OH-B OH-C OH-G OH-F 

12.9 

11.7 

Honey types 

Mean MIC (W/V%) Mean MBC (W/V%) 

Susceptibility of12 Klebsiella strains against 5 


18.8 

17.2 

18.5 

19.2 

MH- A OH-B OH-C OH-G OH-F 

Honey types 

22 


25 

18 

20.8

Figure 4.3: Mean MIC and MBC (%w/v) for 10 E.coli strain against 5 types of 



10.4 10.9 

19.8 

18.8 

19.4 

Figure 4.4: Mean MIC and MBC (%w/v) for 12 Citrobacter strain against 5 types of 



30 

25 

20 

15 

10 

5 

0 

30 

25 

20 

15 

10 

5 

0 

Susceptibility of 10 E.coli strains against 5 types 


229 

20 

23.6 

25 

20.8 

19.6 


9.7 

13.3 

Honey type 


Susceptibility of 12 Citrobacter strains 


18.1 18.5 18.3 

15.8 

26 

27.5 

17.8 

16.1 


Honey type 

Mean MIC (W/V%) Mean MBC (W/V%)

Figure 4.5 Mean MIC and MBC (%w/v) for 15 Enterobacter strain against 5 types 


10.1 

13.1 

22.9 

21.6 

22.2 

20.1 

Figure 4.6: Mean MIC and MBC (%w/v) for 8 Serratia strain against 5 types of 




30 

25 

20 

15 

10 

5 

0 

30 

25 

20 

15 

10 

5 

0 

Susceptibility of 15 Enterobacter strains against 

5 types of honey 

13.3 

230 

26.4 


17 

Honey type 


Susceptibility of 8 Serratia strains against 5 


23.75 

21.7 

20.2 20.2 


Honey type 

28 

28 

28 


20 

17 

22.5 

23.7

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 (10 4 -10 6 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

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. 


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 * 

Site of cell change Septa 

formation 

Acinetobacter 20% 9 6 Shorter cells with 

clumps 

E.coli 30% 13 2 Extended strand 

materials around the 

cells 

238 

SEM TEM 

Debris (cell 

lysed) 

High High loss of cell membrane, cellular debris, 

septated cells and black deposit 

Not seen Not seen Excess loss of membrane integrity, 

irregular shape, empty cells and less 

electron dense 

Klebsiella 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

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 

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