Prevalence and Diversity of and Clinically

Relevant Antibiotic Resistance Genes

in Human Residences

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy (PhD)

EID AWDAH ALATAWI B.Med.Sc. (Medical Microbiology), Qassim University M.Med.Sc. (Laboratory Medicine), RMIT University

School of Science College of Science, Engineering and Health Royal Melbourne Institute of Technology University

March 2019

Declaration

DECLARATION

I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed.

EID AWDAH ALATAWI

13/03/2019

I

Dedication

DEDICATION

I would like to dedicate this work from the bottom of my

heart to my beloved parents for their prayers, support,

immense love and care throughout my life.

To my wife, Ohud, for her constant love and companionship, and for sharing the good and bad times during our overseas life.

To my children, Maysam and Muath, for bringing so

much joy and happiness into my life

To my brothers and sisters, for their love and

encouragement.

Hope this success make you all feel proud of me

II

Summary

SUMMARY

Human health can be positively or negatively affected by indoor microbiomes. with direct links found between indoor bacteria and outbreaks of infectious disease. Additionally, these indoor bacteria may harbour antibiotic resistance genes (ARGs). Several environments, including indoor environments such as hospitals have been found to be potential reservoirs of antibiotic resistance genes. The emergence and spread of antibiotic resistance genes in environmental and increases the risks to human health as these bacteria can be transmitted to humans through skin contact, inhalation and ingestion. Since people spend the majority of their lives indoors, they are constantly exposed to a diverse community of indoor bacteria. To better understand and evaluate the potential beneficial and/or adverse health impacts of bacterial communities that surround us and that are present in our homes, it is increasingly important to study these bacterial communities and their associated antibiotic resistances.

In this research project a total of 132 residential samples were obtained from four habitats (dust, kitchen surfaces, bathroom surfaces and drinking water samples) from eleven human residences and were used to investigate the variation in the structure, diversity and composition of bacterial communities within and between different houses and residential habitats, applying 16S ribosomal RNA gene next generation sequencing (NGS). Subsequently, Polymerase Chain Reaction (PCR) and quantitative real-time (Q-PCR) were used to determine the prevalence, distribution and abundance of 12 antibiotic resistance genes in human residences, namely: tetM and tetB, ermB, ampC, vanA, mecA, aac(6′)-Ie-aph(2"), sulII, catII, dfrA1, mcr-1 and blaNDM-1 encoding resistance to tetracycline, erythromycin, ampicillin, vancomycin, methicillin, aminoglycoside, sulfonamide, chloramphenicol, trimethoprim, colistin and carbapenem, respectively. The four most frequently detected antibiotic resistance genes namely: tetM, ermB, sulII and ampC genes were then sequenced using amplicon-based next generation sequencing to describe the diversity of these antibiotic resistance genes and to identify their likely bacterial taxonomic origins within human residences.

The structure, diversity and composition of bacterial communities was found to vary within and between different habitats in the human residences. These bacterial communities were found to be habitat-specific rather than house-specific. Nevertheless, within each residential

III

Summary habitat, samples clustered largely with respect to their house of origin. In addition, the residential dust showed the highest diversity whereas drinking water was found to be the least diverse habitat in human residences. Numbers of bacterial taxa identified ranged from 867±38 operational taxonomic units (OTU) in dust to 73±7 OTU in drinking water. Bacterial communities in human residences were primarily related to those from soil and human-related bacteria in dust, from soil, human skin, food and water in kitchen surface samples, and the bacteria in bathroom surface samples were mostly associated with those from either human skin or from water. The most abundant genera identified in this study included Acinetobacter and Pseudomonas (related to those from soil), Enhydrobacter, Chryseobacterium, Staphylococcus, Streptococcus and Corynebacterium (related to those from human body), Lactobacillus, Lactococcus and Bacillus (related to those from food) and Sphingomonas and Methylobacterium (related to those from water samples). Furthermore, in this study, potentially pathogenic bacteria have been detected in all residential habitats and houses which represent a major public health risk in our homes.

Eight clinically relevant antibiotic resistance genes (namely: tetM, tetB, ermB, ampC, sulII, catII, mcr-1 and blaNDM-1) were detected in the 11 houses and with different frequencies and abundances within and between different human residences. The most commonly detected antibiotic resistance genes in human residences were tetM (75%) and ermB (55%) genes.

Moreover, antibiotic resistance genes such as blaNDM-1 and mcr-1 genes, which confer resistance to the antibiotics of last resort carbapenem and colistin, were detected with frequencies of 30% and 25%, respectively. The bathroom surfaces harboured the highest proportion (37%) of antibiotic resistance genes compared to dust (28%), kitchen surfaces (27%) and drinking water (11%). The ampC gene, which encodes β-lactam resistance, was found in drinking water with a very high relative abundance. In this study, a number of factors such as occupancy intensity and cleaning behaviours showed a possible influence on both diversity of bacterial communities and frequency of detection of antibiotic resistance genes. The diversity of antibiotic resistance genes was found to vary within and between different habitats in the human residences. Furthermore, antibiotic resistance genes were related to those from human-associated bacteria including those in potentially pathogenic bacteria such as Staphylococcus aureus, Streptococcus pneumoniae and . The association between antibiotic resistance genes identified in this study and those on mobile elements suggests horizontal gene transfer (HGT) of these ARGs between bacteria within

IV

Summary residential habitats. Multiple resistance genes related to those from potential pathogenic bacteria such as and were detected. Novel sequence variants were identified for the ampC gene. This research shows that human residences are a reservoir for antibiotic resistance genes. Overall, this research demonstrates that the home microbiome and antibiotic resistome will be a key driver for public health in human residences.

V

Acknowledgements

ACKNOWLEDGEMENTS

I am greatly indebted to my employer Tabuk University in Saudi Arabia for providing me a scholarship to conduct my Ph.D. and supporting me financially during my study. Thanks also go to the Saudi Arabian Cultural Mission (SACM) in Australia for their professional help and support.

I would like to express my sincere gratitude to my supervisor, Professor Mark Osborn for his continuous support during my Ph.D. study and for his patience, motivation, and immense knowledge. His feedback, advice, and guidance helped me during each stage of the research and writing of this thesis. I would also like to thank my second supervisor Dr. Taghrid Istivan for being so positive and always trying to help me.

I would like to thank all the staff/students of the Microbial Life Research Group especially, Dr. Slobodanka Stojkovic for her help with sequencing experiments, Dr. Esi Shahsavari for his help to use Rotor-gene Q machine, Taylor for his help with some bioinformatic programs for data analysis, and he always happy to help. I would like to thank Helen and Ray for providing resistant bacterial strains to use as positive controls. I would like to thank all the general/support staff at RMIT especially Dr. Lisa Dias, Emma Stockham, Leeanne Bickford, and Shannon Fernandes for their help. Special thanks to participants who voluntarily participated in this research project. I would also like to thank Fahad for his friendship and moral support. Especial thanks for my friend Sultan for every enjoyable and difficult time we shared together during our study overseas.

I am forever indebted to my parents, Awdah and Helalah for dedicating their life to take care of me. Words alone cannot express my sincere gratitude. I would like to thank my brothers and sisters especially Dr. Soliman for being a constant source of motivation and encouragement. I owe special thanks to my wife Ohud for being my pillar of strength even in the most stressful of times, and for sacrificing her time to look after our children and me. My daughter Maysam and my son Muath always with their smiles were being candles that lighting my way.

Lastly but most importantly, I owe everything to the Almighty God (Allah) without whose grace and guidance, any of this would be possible.

VI

Abbreviations

ABBREVIATIONS

Abbreviation Description % Percent °C Degrees Celsius µm Micrometer aa Amino acid ACP Acyl carrier protein Amp Ampicillin ANOSIM Analysis of Similarities ANOVA Analysis of one-way variance ARGs Antibiotic resistance genes BLAST Basic local alignment search tool Blastp Protein-protein BLAST bp Base pair C- Negative gel electrophoresis control C+ Positive gel electrophoresis control CA California Cat Chloramphenicol acetyltransferase CDC Centre for Disease Control and Prevention CE Cell equivalent CFU Colony forming units Cm2 Square centimeters CRE Carbapenem-resistant CTns Conjugative transposons D Dust samples dfrA1 Trimethoprim-insensitive dihydrofolate reductase DMSO Dimethyl sulfoxide DNA Deoxyribonucleic acid dNTP Deoxynucleotide triphosphate DW Drinking water samples E Simpson’s evenness ERM Erythromycin F Forward primer FunGene Functional gene pipeline g Gram Gb Gigabit GHAP Greenfield hybrid analysis pipeline H House H' Shannon’s diversity H1N1 Hemagglutinin 1 neuraminidase 1

VII

Abbreviations

HGT Horizontal gene transfer HPC Heterotrophic plate counts HREC Human research ethics committee HTS High-throughput sequencing IQR Interquartile range kg Kilogram L Liter m2 Square meters MCR-1 Mobilized colistin resistance MecA Methicillin MEGAN MEtaGenome ANalyzer MgCl2 Magnesium chloride MGEs Mobile genetic elements MG-RAST Metagenomic Rapid Annotation using Subsystem Technology ml Milliliter mM Millimole MRSA Methicillin-resistant Staphylococcus aureus MSSA Methicillin-sensitive Staphylococcus aureus n Number NCBI National Centre for Biotechnology Information NDM-1 New Delhi metallo-beta-lactamase ng Nanogram NGS Next generation sequencing NH4 Ammonium NLV Norwalk-like virus nm Nanometre nM Nanomolar nMDS Non-Metric Multidimensional Scaling NSW New South Wales ORF Open reading frames OTU Operational taxonomic units P p-value or probability value PBPs Penicillin binding proteins Pc Strong promoter PCA Principal component analysis PCR Polymerase chain reaction PERMANOVA Permutational multivariate analysis of variance PhiX A control library for Illumina sequencing runs pmol Picomole Q-PCR Quantitative PCR R Reverse Primer RDP Ribosomal database project RefSeq Reference sequence database

VIII

Abbreviations

RNA Ribonucleic acid rRNA Ribosomal RNA S- Negative control for surface samples SA South Australia SB Bathroom surface samples sdH2O Sterile deionized water SE Standard error SIMPER Similarity percentage SK Kitchen surface samples Sul Sulfonamide TA Annealing temperature TAE Tris-acetate-EDTA TET Tetracycline Tm Melting point tRNA Transfer ribonucleic acid U Unit USA United States of America UTI Urinary tract infections UV Ultraviolet V Voltage van Vancomycin VRE Vancomycin resistant enterococci VRSA Vancomycin- resistant Staphylococcus aureus W Watt WA West Australia WGS Whole genome shotgun sequencing WHO World health organization β Beta μl Microlitre μM Micromole

IX

Table of Contents

TABLE OF CONTENTS

DECLARATION...... I

DEDICATION…...... II

SUMMARY……...... III

ACKNOWLEDGEMENTS ...... VI

ABBREVIATIONS ...... VII

TABLE OF CONTENTS ...... X

LIST OF TABLES ...... XVI

LIST OF FIGURES ...... XVIII

CHAPTER 1: INTRODUCTION ...... 1

1.1 The indoor microbiome and human health ...... 1

1.2 The microbiome of the indoor environment ...... 2

1.2.1 The microbiome of indoor dust ...... 4

1.2.2 The microbiome of indoor surfaces ...... 5

1.2.3 The drinking water microbiome ...... 6

1.3 The Effects of occupants on the indoor microbiomes ...... 9

1.3.1 Human occupants ...... 9

1.3.2 Animal occupants ...... 10

1.4 Antibiotics ...... 10

1.5 Major classes of antibiotics and mode of actions ...... 12

1.5.1 Inhibitors of cell wall biosynthesis ...... 14

1.5.2 Inhibitors of cell membrane function ...... 15

1.5.3 Inhibitors of protein biosynthesis ...... 15

1.5.4 Inhibitors of nucleic acid biosynthesis ...... 17 X

Table of Contents

1.5.5 Inhibitors of metabolite synthesis ...... 17

1.5.6 Inhibitors of fatty acid biosynthesis ...... 18

1.6 Antibiotic resistance ...... 18

1.7 Mechanisms of antibiotic resistance ...... 19

1.7.1 Intrinsic (passive) antibiotic resistance ...... 19

1.7.2 Active antibiotic resistance ...... 19

1.7.3 Acquisition of antibiotic resistance ...... 21

1.7.3.1 Horizontal gene transfer ...... 22

1.7.3.2 Mobile genetic elements (MGEs) ...... 23

1.8 The clinical significance of antibiotic resistance...... 25

1.9 Antibiotic resistance in the environment ...... 26

1.10 Sources of antibiotic resistance in the environment ...... 27

1.10.1 The impact of agricultural uses of antibiotics ...... 28

1.10.2 The impact of human use of antibiotics...... 29

1.11 Antibiotic resistance in the indoor environment ...... 30

1.11.1 Antibiotic resistance in indoor dust ...... 30

1.11.2 Antibiotic resistance in indoor surfaces...... 30

1.11.3 Antibiotic resistance in drinking water ...... 31

1.12 Molecular approaches for characterisation of the indoor microbiome and resistome ...... 32

1.12.1 16S Ribosomal RNA gene sequence analysis ...... 33

1.12.2 Quantitative real-time polymerase chain reaction technique ...... 34

1.13 Aims and hypotheses of the project ...... 35

CHAPTER 2: GENERAL MATERIALS AND METHODS...... 37

2.1 MATERIALS ...... 37

2.1.1 Bacterial strains ...... 37

XI

Table of Contents

2.1.2 Residential samples ...... 38

2.1.2.1 Sources of residential samples ...... 38

2.1.2.2 Residential questionnaire ...... 38

2.2 Methods ...... 41

2.2.1 Methods for collecting samples from human residences...... 41

2.2.2 Description of collection and processing of samples from human residences ...... 41

2.2.2.1 Residential dust sampling ...... 41

2.2.2.2 Residential surface sampling ...... 42

2.2.2.3 Drinking water sampling...... 43

2.2.3 Oligonucleotides primers ...... 44

2.2.3.1 Preparation of primers ...... 47

2.2.4 Nucleic acid (bacterial DNA) extraction samples ...... 47

2.2.5 DNA extraction from bacterial colonies ...... 47

2.2.6 PCR amplification of extracted DNA ...... 48

2.2.7 PCR amplification of the 16S rRNA gene...... 48

2.2.8 PCR amplification of antibiotic resistance genes ...... 50

2.2.9 Agarose gel electrophoresis ...... 51

2.2.10 Construction of qPCR Standards ...... 51

2.2.11 Quantitative Polymerase Chain Reaction ...... 52

2.2.12 Target amplicon library preparation and DNA sequencing...... 53

2.2.13 Sequence reads processing ...... 54

2.2.14 Data analysis ...... 54

2.2.15 Statistical analysis ...... 55

CHAPTER 3: VARIATION IN BACTERIAL COMMUNITY STRUCTURE AND DIVERSITY IN HUMAN RESIDENCES USING 16S RIBOSOMAL RNA GENE SEQUENCING ...... 56

3.1 Introduction ...... 56

XII

Table of Contents

3.2 Results ...... 60

3.2.1 Amplification of 16S rRNA genes using the Polymerase Chain Reaction ...... 60

3.2.2 Quality of Illumina Miseq DNA sequence data ...... 61

3.2.3 Structure and diversity of bacterial communities in human residences ...... 63

3.2.3.1 Alpha diversity, richness and evenness of bacterial communities in human residences ...... 63

3.2.3.2 Bacterial community similarity in human residences ...... 69

3.2.3.3 Potential outliers ...... 75

3.2.3.4 Variation in bacterial community structure by sampling type ...... 77

3.2.3.5 SIMPER analysis of differences in bacterial community structure based on Bray-Curtis similarity indices in household communities by site and type of sample ...... 78

3.2.4 Taxonomic composition of household microbiome communities ...... 80

3.2.4.1 Rare bacterial biosphere ...... 80

3.2.4.2 Taxonomic composition of household bacterial communities ...... 82

3.2.4.2.1 Phylum composition of household bacterial communities ...... 82

3.2.4.2.2 Class and family composition of household bacterial communities ...... 84

3.2.4.2.3 Genus composition of household bacterial communities ...... 90

3.2.4.2.4 Variation of the composition of bacterial communities between human residences ...... 97

3.2.5 Potential pathogens ...... 100

3.2.6 Negative controls ...... 103

3.3 Discussion ...... 105

CHAPTER 4: MOLECULAR IDENTIFICATION AND QUANTIFICATION OF ANTIBIOTIC RESISTANCE GENES IN HUMAN RESIDENCES ..... 119

4.1 Introduction ...... 119

4.2 Results ...... 124

XIII

Table of Contents

4.2.1 Detection of antibiotic resistance genes in human residences using the polymerase chain reaction ...... 124

4.2.1.1 Comparison of the prevalence of detected antibiotic resistance genes in different habitats ...... 131

4.2.1.2 Comparison of the prevalence of detected antibiotic resistance genes across different human residences ...... 132

4.2.2 Quantification of antibiotic resistance genes in human residences using quantitative PCR ...... 133

4.2.2.1 Quantification of 16S rRNA genes across different habitats ...... 133

4.2.2.2 Quantification of the abundance of antibiotic resistance genes in residential habitats ...... 136

4.2.2.3 Quantification of different antibiotic resistance genes in residential dust ..... 145

4.2.2.4 Quantification of different antibiotic resistance genes in residential surfaces ...... 146

4.2.2.5 Quantification of different antibiotic resistance genes in drinking water ...... 148

4.2.2.6 Comparison of the abundance of antibiotic resistance genes within and between different houses and habitats ...... 150

4.3 Discussion ...... 153

CHAPTER 5: DIVERSITY AND TAXONOMIC ORIGIN OF CLINICALLY RELEVANT ANTIBIOTIC RESISTANCE GENES IN HUMAN RESIDENCES ...... 162

5.1 Introduction ...... 162

5.2 Results ...... 166

5.2.1 Quality of Illumina Miseq sequence data ...... 166

5.2.2 Alpha diversity and richness of antibiotic resistance genes in human residences. 166

5.2.2.1 Diversity and richness of antibiotic resistance genes in different habitats .... 166

5.2.2.2 Diversity and richness of translated tetM gene sequences in different habitats ...... 170

XIV

Table of Contents

5.2.2.3 Diversity and richness of translated ermB gene sequences in different habitats ...... 173

5.2.2.4 Diversity and richness of translated sulII gene sequences in different habitats ...... 175

5.2.2.5 Diversity and richness of translated ampC gene sequences in different habitats ...... 177

5.2.3 The rare biosphere ...... 179

5.2.4 Taxonomic diversity of antibiotic resistance gene sequences in human residences … ...... 181

5.2.4.1 Taxonomic diversity of translated tetM gene sequences in different habitats ...... 181

5.2.4.2 Taxonomic diversity of translated ermB gene sequences in different habitats ...... 188

5.2.4.3 Taxonomic diversity of translated sulII gene sequences in different habitats 193

5.2.4.4 Taxonomic diversity of translated ampC gene sequences in different habitats ...... 197

5.3 Discussion ...... 200

CHAPTER 6: General Discussion ...... 204

REFERENCES...... 214

APPENDICES… ...... 256

1.1 Appendix A1 ...... 256

2.1 Appendix A2 ...... 257

3.1 Appendix A3 ...... 258

4.1 Appendix A4 ...... 259

5.1 Appendix A5 ...... 261

XV

List of Tables

LIST OF TABLES

Table 1.1 Major classes of antibiotics ...... 13 Table 2.1 Bacterial strains used as positive controls for PCR amplification studies...... 37 Table 2.2 Summary of questionnaire meta-data collected from residential participants...... 39 Table 2.3 Oligonucleotides used in this study ...... 44 Table 2.5 qPCR master mix components...... 52 Table 3.1 Quality statistics of Illumina MiSeq amplicon sequencing run of 16S rRNA genes 61 Table 3.2 Estimates of observed OTUs, Chaol richness for OTUs, observed , Chaol richness for species ...... 64 Table 3.3 Estimated overall Shannon-Wiener diversity index, Simpson diversity index and Pielou’s Evenness ...... 67 Table 3.4 Sequence run data of different type of samples from household ecosystems which were considered potential outliers...... 75 Table 3.5 ANOSIM of a Bray-Curtis similarity matrix based on OTUs at ≥ 97% identity of bacterial communities ...... 77 Table 3.6 SIMPER analysis (%) of dissimilarity matrix based on OTUs at ≥ 97% identity of bacterial communities between different habitats ...... 79 Table 3.7 SIMPER analysis (%) based on a Bray-Curtis similarity matrix based on OTUs at ≥ 97% identity of bacterial communities of household ecosystems ...... 79 Table 3.8 Prevalence and proportion of potentially pathogenic bacteria in human residence communities ...... 101 Table 4.1 Detection of antibiotic resistance genes by endpoint PCR in house dust samples . 127 Table 4.2 Detection of antibiotic resistance genes by endpoint PCR in kitchen surface samples ...... 128 Table 4.3 Detection of antibiotic resistance genes by endpoint PCR in bathroom surface samples ...... 129 Table 4.4 Detection of antibiotic resistance genes by endpoint PCR in drinking water samples ...... 130 Table 4.5 Comparisons of P-values from one-way ANOVA test for abundance of 16S rRNA and antibiotic resistance genes ...... 135 Table 5.1 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of tetM gene ...... 171

XVI

List of Tables

Table 5.2 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of ermB gene ...... 174 Table 5.3 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of SulII gene ...... 176 Table 5.4 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of ampC gene ...... 178 Table 5.5 Closest related TetM protein sequences in the RefSeq protein database ...... 182 Table 5.6 Closest related TetM protein sequences in the RefSeq protein database ...... 184 Table 5.7 Closest related TetM protein sequences in the RefSeq protein database ...... 186 Table 5.8 Closest related ErmB protein sequences in the RefSeq protein database ...... 189 Table 5.9 Closest related ErmB protein sequences in the RefSeq protein database ...... 191 Table 5.10 Closest related ErmB protein sequences in the RefSeq protein database ...... 192 Table 5.11 Closest related SulII protein sequences in the RefSeq protein database ...... 194 Table 5.12 Closest related SulII protein sequences in the RefSeq protein database ...... 195 Table 5.13 Closest related SulII protein sequences in the RefSeq protein database ...... 196 Table 5.14 Closest related AmpC protein sequences in the RefSeq protein database ...... 198 Table 5.15 Closest related AmpC protein sequences in the RefSeq protein database ...... 199

XVII

List of Figures

LIST OF FIGURES

Figure 1.1 Timeline showing the history of discovery of major classes of antimicrobial and antibiotics. Data from Lewis, 2013. Graphic by author ...... 11 Figure 1.2 Active antibiotic resistance mechanisms ...... 21 Figure 1.3 Mechanisms of horizontal gene transfer. a. Transformation: uptake of naked DNA from the environment. b. Transduction: transfer of genes from a donor to a recipient bacterial cell by a bacteriophage. c. Conjugation: transfer of genes between cells that are in physical contact with one another...... 23 Figure 3.1 Agarose gel electrophoresis of PCR amplified 16S rRNA genes ...... 60 Figure 3.2 Rarefaction curves of 16S rRNA gene sequences assigned to OTUs at ≥ 97% identity...... 62 Figure 3.3 Rarefaction curves for 16S rRNA gene sequences with ≥ 97% identity to sequences from known species ...... 62 Figure 3.4 Box and whisker plots of diversity indexes for household bacterial communities 66 Figure 3.5 Principal Component Analysis (PCA) based on similarity between bacterial communities at genus level ...... 70 Figure 3.6 Non-metric multidimensional (nMDS) representation of the household bacterial communities ...... 70 Figure 3.7 Neighbour-Joining analysis showing variation in dust bacterial community structure...... 71 Figure 3.8 Neighbour-Joining analysis showing variation in kitchen bacterial community structure...... 72 Figure 3.9 Neighbour-Joining analysis showing variation in bathroom bacterial community structure...... 73 Figure 3.10 Neighbour-Joining analysis showing variation in drinking water bacterial community structure ...... 74 Figure 3.11 Hierarchical clustering based on Bray-Curtis similarity matrix of OTUs at ≥ 97% identity of bacterial communities ...... 76 Figure 3.12 Rank-abundance plot of species (≥97% identity) present in four samples from different habitats ...... 81 Figure 3.13 Relative abundance of the five most abundant bacterial phyla found ...... 83

XVIII

List of Figures

Figure 3.14 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for dust communities ...... 86 Figure 3.15 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for kitchen surface communities ...... 87 Figure 3.16 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for bathroom surface communities ...... 88 Figure 3.17 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for drinking water communities ...... 89 Figure 3.18 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in dust communities ...... 93 Figure 3.19 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in kitchen surface communities ...... 94 Figure 3.20 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in bathroom surface communities ...... 95 Figure 3.21 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in drinking water communities ...... 96 Figure 3.22 Relative abundance of five dominant shared bacterial genera ...... 99 Figure 3.23 Non-metric multidimensional (nMDS) representation of the 146 bootstrap nMDS means, and 95% confidence ellipses of household bacterial communities ...... 104 Figure 4.1 Detection of antibiotic resistance genes in different household habitats...... 126 Figure 4.2 Proportion (%) of detection of twelve antibiotic resistance genes ...... 131 Figure 4.3 Relative prevalence (%) of antibiotic resistance genes ...... 132 Figure 4.4 Abundance of 16S rRNA genes ...... 134 Figure 4.5 Abundances of tetracycline resistance (tetM) genes ...... 137 Figure 4.6 Abundances of tetracycline resistance (tetB) genes ...... 138 Figure 4.7 Abundances of erythromycin resistance (ermB) genes ...... 139 XIX

List of Figures

Figure 4.8 Abundances of sulfonamide resistance (sulII) genes ...... 140 Figure 4.9 Abundances of chloramphenicol resistance (catII) genes ...... 141 Figure 4.10 Abundances of ampicillin resistance (ampC) genes ...... 142

Figure 4.11 Abundances of carbapenem resistance (blaNDM-1) genes ...... 143 Figure 4.12 Abundances of colistin resistance ( ...... 144 Figure 4.13 Comparison of the relative abundance (%) of eight antibiotic resistance genes 152 Figure 5.1 Box and whisker plots of Shannon-Weiner indexes for diversity of antibiotic resistance gene ...... 168 Figure 5.2 Box and whisker plots of Chao1 indices for diversity of antibiotic resistance gene ...... 169 Figure 5.3 Rank-abundance plot of sequence reads (50% identity) of different antibiotic resistance genes ...... 180

XX

Chapter 1 Introduction

CHAPTER 1 INTRODUCTION

1.1 The indoor microbiome and human health

The environment has a major influence on human health. In 2012, it was estimated that environmental factors were the cause of 12.6 million deaths worldwide representing 23% of all deaths (World Health Organization, 2016). Today, the most important environments for humans are built environments where people stay for the majority of their time (Awad and Farag, 1999). Human health and well-being are affected either directly or indirectly by the quality of built ecosystems. Potential exposure to microbes at home, at work, in transportation, and in many other places intensifies the risk of contracting infectious diseases. Over recent years, there has been a growing awareness that health and lifestyle are very much affected by the structure and design of built ecosystems (e.g. housing). Many housing factors are associated with mental and physical health impacts such as air quality, dampness, infestation, noise, lighting, housing location and design (Evans et al., 2003). It has also been observed that the quality of built ecosystems can impact upon health issues and compound existing health conditions of children, aged people and other sensitive groups (Marmot et al., 2010). For children, dampness and poor air quality have been linked with heightened asthmatic symptoms (Northridge et al., 2010).

The built environment is a complex ecosystem with a vast diversity of trillions of microorganisms (Corsi et al., 2012). The microbial communities that exist in the indoor ecosystems interact continuously with human life as direct sources of both beneficial microbes and pathogenic microbes (Kellogg and Griffin, 2006). These microorganisms can cause many infectious diseases such as Legionnaire’s disease (Al Masalma et al., 2010), measles (Yuan et al., 2007), some non-infectious diseases such as asthma and allergies (Hoppe et al., 2012; Taylor et al., 2011), tuberculosis (Hauschild et al., 2010) and bacterial and influenza (Fiore et al., 1998). It is believed that respiratory diseases are mostly spread in indoor air (Kowalski, 2006). Some of the examples of microorganisms that are carried through airborne routes are influenza virus (Blachere et al., 2009), Mycobacterium tuberculosis (Fennelly et al., 2004) and rhinovirus (responsible for common cold) (Myatt, 2004). Furthermore, it has been found that airborne transmission is implicated in the outbreak of

1

Chapter 1 Introduction gastroenteritis which is caused by Norwalk-like virus (NLV) (Marks et al., 2003). Airborne transmission has also been linked to nosocomial outbreaks of Staphylococcus aureus, including methicillin-resistant strains (MRSA) (Kumari et al., 1998), Acinetobacter spp. (Bernards et al., 1998) and (Uduman et al., 2002). Almost a century ago, issues relating to the safe indoor ecosystems and microbial effects on health due to the vulnerability to environmental exposure had already been debated. However, a surgeon named John Griscom in New York had already highlighted in 1850 the issue of ventilation deficiency in dormitories and bedrooms and it was associated with the outbreak of tuberculosis and other such diseases that are usually infectious in a crowded place (Sundell, 2004).

For over a century, researchers have been increasingly studying the risk of airborne outbreaks of infectious disease both inside buildings and in outdoor environments (Goldmann, 2000). Since the beginning of the twenty-first century we have witnessed numerous epidemics, notably of viral infections, such as the outbreak of Severe Acute Respiratory Syndrome (SARS) in the indoor environment of Hong Kong in 2003 (Lee et al., 2003), also the 2009 H1N1 influenza pandemic (Fraser et al., 2009), and the 2014 Middle East respiratory syndrome epidemic (Zumla et al., 2015). This focuses our attention towards the importance of maintaining a safe built ecosystem that contains fewer infectious or pathogenic microorganisms e.g. bacteria, viruses or fungi which have the potential to activate infection resulting in health effects upon the occupants.

1.2 The microbiome of the indoor environment

People spend most of their time indoors. Some studies estimate that urban residents in advanced countries spend as much as ninety percent of their lives inside (Custovic et al., 1994). People are born in hospitals, grow up in homes, work in offices or factories and in their old age they move to nursing homes. Concentrations of some microbiological contaminants are higher indoors than outdoors (Spengler and Sexton, 1983), so we breathe and come into contact with trillions of microorganisms. Microbiological agents in indoor ecosystems include bacteria, fungi and viruses (Nevalainen et al., 1991). Traditionally, public health concerns in the built environment has focused around broad topics of sanitation, fire and damage prevention, and

2

Chapter 1 Introduction safety (Jackson 2003). However, only recently have researchers started to investigate the microbiome of indoor spaces and its potential effects on human health (Corsi et al., 2012).

Several studies have been conducted examining the microbiome of various indoor sites including: aircraft (Osman et al., 2008), daycare centres and schools (Andersson et al., 1999; Liu, et al. 2000), kitchens (Flores et al., 2012), office spaces (Hewitt et al., 2012), restrooms (Flores et al., 2011), and homes (Täubel et al., 2009). As a result of these studies and others, we now know that human skin appears to be the main source of bacterial contamination of frequently touched surfaces, such as computer keyboards and computer mice (Fierer et al., 2010).

Bacteria associated with humans, particularly skin, were also the most abundant bacteria found in office buildings (Hewitt et al., 2012). Human skin, gut, and vagina-associated bacteria were found on many different surfaces of public restrooms (Flores et al., 2011). Bacteria commonly associated with soils were also recovered from office buildings and the floor of public restrooms. In addition to humans, pets are also sources for microbial communities in built ecosystems (Fujimura et al., 2010). In a study examining microbial communities in dust, Fujimura and colleagues found that homes with pets, particularly dogs, had significantly more diverse microbial dust communities, and hypothesize that this could be due to dogs being permitted indoors and outdoors. Thus, bacteria found indoors can be identified and linked to locations (i.e., outdoors) or sources (i.e., humans), from where those taxa are typically isolated.

These studies demonstrate potential sources of bacteria found in indoor ecosystems, and also indicate the importance of humans as both sources for microbial communities in built ecosystems, and as possible vectors of microbes into the built ecosystems. A source is defined as where microorganisms in the home originated, while a vector is defined as the method of transport for that organism. In the home there are a diversity of sources and vectors, and some locations play both roles. Some studies of indoor dust and air have shown a variation in microbial composition in indoor ecosystems through different seasons (Rintala et al., 2008; Moschandreas et al., 2003). In addition, Rintala and colleagues (2008) have found that microbial communities of different office buildings from the same city are different. Thus, environmental factors such as weather and location may affect the composition of microbial communities in indoor ecosystems.

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Chapter 1 Introduction

1.2.1 The microbiome of indoor dust

Andersson et al. (1999) regard household dust to be a reservoir of various microorganisms. We may find different amounts of several substances within settled dust. Such substances may include: animal and human hairs, flakes of skin, insect parts, substances from plants, fibrous glass, atmospheric dust, fibres, living or dead fungal and bacterial species, allergens and dust mites. The substances contained in household dust are dependent upon the building’s occupants in addition to its surroundings and location (Macher, 2001). Microbes which occur in indoor dust originate principally from a building’s occupants; such occupants also include pets. In addition, the indoor microbiome will be influenced by inputs from outdoor air as well as other outdoor substances which can be identified as entering the building (Rintala et al., 2012). It is regarded that dust which is settled on the floor is a more appropriate representation of airborne dust. Contrastingly, samples of dust gathered from floors and mattresses contain not only particulate matter which has its source in the actual occupants, but also airborne dust (Kelley & Gilbert, 2013). Consequently, by studying settled dust we can obtain an understanding of microbial communities found in both air and household dust.

The total concentration of microorganisms in dust can vary greatly from a few hundred to tens of millions of microbial cells per gram of dust. The total concentration of fungi in dust range from 104 to 105 CFUg-1 using culture dependent methods (Chew et al., 2003; Heinrich et al., 2003), while the fungal cell equivalent (CE) counts are reported between 3.9x105 and 1.4x109 CEg-1 in dust samples using qPCR assays (Bloom et al., 2009). The concentrations of total cultivable bacteria in dust samples are reported to contain up to 107 CFUg-1 (Rintala et al., 2004, Bouillard et al., 2005), while bacterial concentrations determined by qPCR in vacuum cleaner dust bag dust of homes are reported to be 7.2x108 CEg-1 (Karkkainen et al., 2010).

Historically, the dominant genera found in dust by culture-based methods were Micrococcus, Bacillus, Staphylococcus, Paenibacillus, and many Actinomycetes including Streptomycetes spp. (Awad and Farag, 1999). Some studies have used PCR-based methods to analyse 16S rRNA gene sequences to determine bacterial diversity in household dust. One of these studies has been done in the Russian and Finnish Karelia by Pakarinen et al., (2008), who were able to identify 94 different genera of dust borne bacteria. Furthermore, another study using a

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Chapter 1 Introduction quantitative PCR method has reported concentrations of bacteria in household dust with a mean value of 4.71×106 (median value, 2.36 ×106) bacterial cell equivalents g-1 of collected dust from vacuum cleaners (Veillette et al., 2013). The dominant phyla found in house dust were Firmicutes, (alpha, beta, gamma and delta classes), Actinobacteria, and Bacteroidetes (Pakarinen et al., 2008, Kärkkäinen et al., 2010, Veillette et al., 2013). Dust samples collected using vacuum cleaners can be found to contain bacteria as well as other microbes. Haysom and Sharp (2003) have concluded that the dust bags of vacuum cleaners are clearly able to act as reservoirs and possible disseminators of pathogenic bacteria around the house. Also, they have found that microbes including pathogens can stay viable in vacuum cleaner bags for a long period of up to two months (Haysom and Sharp, 2003).

1.2.2 The microbiome of indoor surfaces

Comprehending the microbial ecology and distribution in the household ecosystems is important to improve and maintain the health and safety of the house occupants. The assessment of microbial ecology and diversity in household ecosystems can help identify sources of contamination and improve cleaning procedures in houses. Different culture- dependent and culture-independent studies have investigated the microbial diversity on surfaces in the indoor ecosystem. The most dominant phyla that have been found on indoor surfaces are Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (Flores et al., 2013).

Hewitt and his colleagues by using a culture-independent method have identified 500 bacterial genera found on surfaces within office environments. The most abundant genera are those commonly found on human skin, nasal, oral or intestinal cavities (Hewitt et al., 2012). Moreover, a study conducted on surfaces in a fitness centre has found some pathogenic or potential pathogenic bacterial genera such as Klebsiella, Staphylococcus, Salmonella and Micrococcus (Mukherjee et al., 2014). Furthermore, some bacteria of clinical interest have been found on household surfaces such as methicillin-sensitive Staphylococcus aureus (MSSA), and MRSA, Pseudomonas and Enterobacteriaceae (Scott et al., 2009). A study by Flores et al. (2012) of kitchens also found an abundance of human-associated bacteria on kitchen surfaces. Interestingly, the abundance of bacteria commonly associated with food was

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Chapter 1 Introduction widespread but such bacteria were identified at much lower frequencies than those from human origins. Of all locations in the household ecosystem, bathrooms and kitchens are among the most heavily colonized by bacteria (Sinclair and Gerba, 2011).

Staphylococci have been repeatedly isolated from indoor surfaces (McManus and Kelley 2005; Hewitt et al., 2012; Mukherjee et al., 2014). In their molecular survey of surfaces inside airplanes, McManus and Kelley (2005) amplified rRNA sequences belonging to staphylococci from handles, floors, sinks and toilet seats, but they did not recover S. aureus sequences. Lavatory fomites may therefore be staphylococcal reservoirs. Transmission of staphylococci on lavatory fomites from lavatory users to others would appear unlikely if lavatory users wash their hands before leaving the lavatory (McManus and Kelley, 2005). However, Jeong et al. (2007) observed that only 63% of individuals using restrooms in Korea washed their hands (Jeong et al., 2007).

1.2.3 The drinking water microbiome

A vital requirement for public health is clean and safe drinking water because water is essential for life. The surface of the earth is approximately 70 % water; however, fresh water is only 3 % of this (Jarrett, 1995). Nevertheless, it is usual for cell concentrations to range from 103 to 105 cells mL-1; therefore, drinking water is not sterile (Proctor and Hammes, 2015). In order for drinking water to be safe, it is essential to control microbial growth. One method of attaining this is by adding disinfectants (Berry et al., 2006), whereas another is by generating an oligotrophic environment by reducing nutrients like phosphate or organic carbon in the distributed water (Szewzyk et al., 2000). Nevertheless, the possibility of regrowth by forming small organic molecules of carbon, which are easily available to bacteria, can be increased by an ozone disinfection process (Hammes et al., 2006).

The majority of pathogens are conveyed by drinking water, especially those which originate from faecal matter. It has been discovered that the following significant pathogenic bacteria can be the source of human intestinal disease which has a connection with drinking water: Salmonella typhi, ; Salmonella paratyphi-A, ; other Salmonella species, , enteric fever; , S. flexneri, and S. sonnei, bacillary

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Chapter 1 Introduction dysentery; , ; Leptospira spp., leptospirosis; , gastroenteritis; , ; Escherichia coli. (some strains), gastroenteritis; and , several infections. The potential for infection is dependent upon the host’s state of health and age in addition to the actual bacterial strains (Ashbolt, 2004). Every year, approximately 560,000 people in the United States are affected by water-related diseases, and also 7.1 million are affected by mild infections which causes about 12,000 deaths annually (Medema et al., 2003). Furthermore, water-related diseases caused 3.4 million death in developing countries according the World Health Organization (WHO) (Dhanasekar et al., 2017).

In water distribution systems, there is a frequent occurrence of biofilms where 95 % of the bacteria are found (Hu et al., 2005). It has been discovered that biofilms in drinking water contain a considerable variety of pathogens such as enteric viruses and protozoa which include Giardia lamblia and Cryptosporidium parvum. Furthermore, the following bacteria are included: , Pseudomonas aeruginosa, pneumophila, , Aeromonas spp., and Mycobacterium spp. (Szewzyk et al., 2000; Park et al., 2001; Parsek and Singh, 2003; Berry et al., 2006).

It has been demonstrated that recent developments in molecular-based detection, including 16S rRNA gene sequences analysis, has indicated a greater occurrence of pathogens within the distribution systems of drinking water than was previously accepted, although such an occurrence was at a comparatively low proportion. The presence of Helicobacter spp. has been apparent within drinking water distribution systems, and it has been demonstrated that it can survive within drinking water biofilms for a minimum period of five days (Park et al., 2001; Azevedo et al., 2003). Watson et al. (2004) discovered the presence of Helicobacter DNA in 42 % of biofilms and in 26 % of water samples by utilising PCR assays. In Budapest in Hungary, a taxon-specific analysis was conducted on water which was drawn from a drinking- water system in a hospital identified Pseudomonas aeruginosa and Legionella spp. This analysis also detected Acinetobacter lwoffii, Escherichia albertii, as well as Corynebacterium tuberculostrearicum (Felföldi et al., 2010). Detection systems which have recently undergone further development have concentrated upon other pathogens within drinking-water biofilms such as Mycobacterium spp. and Campylobacter (Lehtola et al., 2006a; Lehtola et al., 2006b).

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An enhanced comprehension of the impacts of water distribution and treatment upon the composition and framework of the microflora within drinking water has been developed by the utilisation of the 16S rRNA gene sequence analysis. Such analysis has indicated that Proteobacteria is the most dominant bacterial phylum found in chlorinated drinking water and the major classes are Alpha-, Beta-, and Gamma-proteobacteria (Kormas et al., 2010; Poitelon et al., 2010; Williams et al., 2004). The following most frequently occurring bacterial phyla detected in samples of tap water are Actinobacteria (9 %), Bacteroidetes (2 %) and Cyanobacteria (4 %) (Revetta et al., 2011). Furthermore, according to a study which Williams et al. (2004) undertook, it was discovered that members of the and Sphingomonadaceae families were present in both chloraminated and chlorinated systems (Williams et al., 2004). The same species were reported in chlorinated systems in another study conducted by Srinivasan et al. (2007). In addition, Bautista-de los Santos et al., (2016) have conducted a meta-analysis of 14 studies that were conducted in different countries. All these studies involve extraction of DNA from drinking water samples without a cultivation step using high-throughput DNA sequencing platform such as Illumina MiSeq or 454 pyrosequencing. This meta-analysis has shown that the dominant phyla found in all drinking water samples were Proteobacteria in particular and regardless of location of study and the absence/presence or type of disinfectants. However, this study has shown the effect of the type and the presence or absence of disinfectants in drinking water distribution systems on the relative abundance and occurrence of some bacterial genera. For instance, there was a higher occurrence of Mycobacterium and Pseudomonas OTUs in the presence of disinfectants while there was a higher occurrence of Legionella OTUs in the absence of disinfectants in the system (Bautista-de los Santos et al., 2016). An Australian study of drinking water distribution systems has shown that the bacterial community structures found in the source water samples collected from West Australia (WA) were different from those collected from South Australia (SA), with some taxa present in one system but undetectable in the other (Shaw et al., 2015). For instance, the bacterial order were found in the SA source water at a relative abundance of approximately 14% compared to less than 1% in WA source water. Likewise, the bacterial orders Rhodospirillales and Sphingomonadales were detected in the WA source water comprising approximately 7% and 6% of sequences, respectively compared to their proportions in the SA source water which were less than 1% (Shaw et al., 2015).

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Chapter 1 Introduction

1.3 The Effects of occupants on the indoor microbiomes

1.3.1 Human occupants

In modern societies, people are more likely to spend their time indoors (Lax et al., 2014). It is believed that each individual carries his/her own microbial “fingerprint”. After that, this “fingerprint” is transferred to the indoor environment via respiratory activity, skin surface contact and skin shedding (Gao et al., 2007; Tringe et al., 2008; Meadow et al., 2014). There are several ways through which an individual can create impacts upon the indoor microbial content. Firstly, humans carry and transfer organisms found in soil and on outdoor surfaces via their shoes, clothing and hair from outdoor to indoor ecosystems (Kelley and Gilbert, 2013). Secondly, the indoor microbiome will be affected through routine activities like cleaning, opening windows and using air ventilation systems by the inhabitants. Finally, the human microflora serves as a reservoir that can contribute to the indoor microbial content through body surfaces and body fluids (Adams et al., 2015).

Despite multiple parts of the human body having the ability to contribute to indoor microbiomes, the skin is considered to be the prevalent source of human microorganisms into these systems. Human skin is colonized by different bacteria (around 104 bacterial species) with skin undergoing constant renewal with associated shedding of skin flakes, thereby releasing skin-associated bacteria into the environment (De Sario et al., 2013). Horak et al. (1996) illustrated that mattress dust is dominated by Corynebacteria, Staphylococci and Gram- positive bacteria. Furthermore, they assumed that the skin of inhabitants is the key source of the Gram-positive bacteria in dust (Horak et al., 1996). As depicted by Lax et al. (2014), there is similarity between the microbial communities found in the feet, hands and noses of inhabitants and the communities in their homes. Fox et al. (2003) concluded that the presence of school students causes the rise of bacterial biochemical markers in the dust from a classroom. Moreover, the dust of nursing homes usually contains prevailing bacterial species such as Streptococcus, Corynebacterium, Propionibacteria and Staphyloccus (Rintala et al., 2012).

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Chapter 1 Introduction

1.3.2 Animal occupants

The indoor dust microbiome is also influenced by pets (Giovannangelo et al., 2007). Similar, to humans, pets also serve as vectors to bring in outdoor materials (water, faeces, soil and faeces). Apart from that, they also function as reservoirs that transport fungi and bacteria from the animal into the indoor ecosystem (such as microbiota related with saliva, dander and faeces) (Barberan et al., 2015). Fujimura et al. (2010) have implied that pet ownership has an association with some specific taxa in house dust communities. The authors observed a noticeable rise in bacterial richness and diversity in the dust of houses with pet dogs (337 taxa; and the most dominant phyla were Spirochaete, Bacteroidetes, Actinobacteria, Verrucomicrobia, Firmicutes and Proteobacteria). Nevertheless, and in contrast, no significant results were found with relation to bacterial richness and abundance within the dust from houses in which cats were present (Fujimura et al., 2010). They suggested that the study of the indoor microbiome is very much dependent on the animal owner’s behaviour or animal behaviour in respect to transport of microbes between outdoor and indoor environments.

1.4 Antibiotics

Compounds that are capable of inhibiting the growth of, and killing of, microorganisms are known as antimicrobial agents (Lee and Bishop, 2012). Antibiotics are natural, semi-synthetic or synthetic molecules that are either bacteriostatic agents which inhibit the growth of bacteria, or bactericidal agents which kill bacteria (Walsh, 2003). The word antibiotic originates from the Greek language, ‘anti’ which means against while ‘bios’ means life. It is important to highlight that the terms ‘antibiotics’, ‘antimicrobials’, ‘antifungals’ and ‘antivirals’ are not strictly identical, since ‘antibiotic’ was proposed in reference to naturally occurring compounds of microbial origin (Waksman, 1947). These terms, however, can be used interchangeably in some contexts (Lee and Bishop, 2012).

One of the major challenges that scientists have faced for hundreds of years is dealing with infectious disease. From the thirteenth century, heavy metals have been used to treat infectious diseases. However, they were not widely used because of their toxicities. (Shlaes, 2010). It was back in 1907, that the first substantial inroads were made in this field. Paul Ehrlich and his co-

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Chapter 1 Introduction workers came across an arsenic derivative that was an effective tool against syphilis (Thorburn, 1983). This arsenic derivative was known as 606 or Salvarsan. However, due to the wider toxicity of the non-specific arsenic derivative compound it was not used at a larger scale for treatment of bacterial infections. However, this discovery was enough to cement the place of Ehrlich as the founder of chemotherapy in the modern era, since his discovery was touted as the start of a new approach to treat infectious diseases (Rubin, 2007).

The most important achievement in the field of antibiotics was the discovery of penicillin made by Sir Alexander Fleming. Discovered in the year 1928, it is to this day regarded by many as the single most important discovery in the annals of medicine and world history (Lee and Bishop, 2012). From the late 1930s to the 1960s, which is known as “The Golden Era of Antibiotics”, nearly all of the classes of antibiotics still in use today were discovered (Figure 1.1).

Figure 1.1 Timeline showing the history of discovery of major classes of antimicrobial and antibiotics. Data from Lewis, 2013. Graphic by author.

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Chapter 1 Introduction

1.5 Major classes of antibiotics and mode of actions

Antimicrobials are classified in several ways, based on their chemical structure, biosynthetic origin, spectrum of activity, biological effect on bacteria and mode of action. Antibiotics that share the same or similar chemical structure will show similar antibacterial activity, toxicity, effectiveness and allergic potential. Furthermore, antibiotics can be either natural antibiotics that are isolated from living organisms (e.g. penicillin, streptomycin, gentamicin and bacitracin which are isolated from Penicillium chrysogenum, Streptomyces griseus, Micromonospora purpureochromogenes and Bacillus licheniformis, respectively) (Bbosa et al., 2014), or semi- synthetic antibiotics that are modified from antibiotics from various natural sources (e.g. β- lactam antibiotics), or synthetic antibiotics that are produced only through chemical synthesis (e.g. sulfonamides and quinolones).

Moreover, antibiotics have different spectrums of activity either broad or narrow spectrum. Broad-spectrum antibiotics are those that are effective against a broad range of bacteria (e.g. tetracyclines). On the other hand, narrow-spectrum antibiotics target only particular species of bacteria (e.g. polymixins and glycopeptides). There are some antibiotics that kill bacteria directly which are called bactericidal antibiotics (e.g. aminoglycosides), while there are other antibiotics that only stop or delay bacterial growth and replication and are called bacteriostatic antibiotics (e.g. tetracycline and sulfa drugs). Also, the antibacterial action of antibiotics can be used to categorize them into many major classes (Table 1.1). Generally, antibiotics can be divided into the following categories based on their cellular targets and functions: 1) inhibitors of cell wall biosynthesis. 2) inhibitors of cell membrane function. 3) inhibitors of protein biosynthesis. 4) inhibitors of nucleic acids biosynthesis. 5) inhibitors of fatty acid biosynthesis.

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Chapter 1 Introduction

Table 1.1 Major classes of antibiotics which are classified based on their chemical structure and mechanism of action with examples.

Classification based on mode of action Antibiotic Classes (based on Examples chemical structure)

Cell Wall Synthesis Inhibitors β-lactam antibiotics Penicillins • Penicillin G • Penicillins • Amoxicillin • Cephalosporins Cephalosporins • Carbapenems • Cefoxitin • Cefotaxime

Carbapenem • Imipenem

Cell Membrane Function Inhibitors Polypeptides • Bacitracin • polymyxins Protein Synthesis Inhibitors Macrolides • Erythromycin • Azithromycin • Clarithromycin

Tetracyclines • Minocycline • Doxycycline

Aminoglycosides • Streptomycin • Neomycins • kanamycins

Lincosamides • Clindamycin Streptogramins • Pristinamycin • Virginiamycin Miscellaneous • Chloramphenicol

• Vancomycin

• Rifampicin

Nucleic Acid Synthesis Inhibitors Quinolones: • Norfloxacin • Ciprofloxacin

Imidazoles • Metronidazole Sulfonamides • Co-trimoxazole Diaminopyrimidines • Trimethoprim

Fatty Acid Synthesis Inhibitor • Platencin • Platensimycin

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Chapter 1 Introduction

1.5.1 Inhibitors of cell wall biosynthesis

Whilst the cells of humans and animals do not have cell walls, this structure is critical for the life and survival of bacterial species. A drug that targets cell walls can therefore selectively kill or inhibit bacterial organisms.

The β-lactams and the glycopeptides are the most important groups of cell wall inhibitors. As there is a difference between the structure of bacterial cell walls and the membranes of eukaryotes, this makes the bacterial cell walls an obvious target of antibiotics that are selectively toxic. Penicillin, carbapenems, cephalosporin and monobactams are all part of one β-lactam group that target peptidoglycan in the cell wall. They work by binding to the Penicillin Binding Proteins (PBPs). These proteins play a fundamental role in the process of peptidoglycan synthesis in the bacterial cell wall (Macheboeuf et al., 2006). This results in the formation of fragile spheroplasts (in which the cell wall has been modified and partially or completely removed) in Gram-negative cells that can easily be ruptured or damaged. As far as the situation in Gram-positive cells where the peptidoglycan is much thicker, the mode of action is thought to be autolysis triggered by the release of lipoteichoic acid following the exposure to the antibiotic (Greenwood et al., 2007).

Another important group of antibiotics that targets the cell wall are the glycopeptides which includes vancomycin and teicoplanin. These antibiotics inhibit cell wall synthesis of bacteria by binding to acyl-D-alanyl-D-alanine. This binding inhibits the growth of the peptidoglycan cell wall by preventing the addition of new subunits (Bryan, 1988). In Gram-negative cells, these large glycopeptide molecules are excluded from the outer membrane preventing entry of the antibiotic to the cell leading to intrinsic resistance. Consequently, the action of glycopeptides is limited to Gram-positive organisms (Greenwood et al., 2007).

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Chapter 1 Introduction

1.5.2 Inhibitors of cell membrane function

These inhibitors include both antibacterial agents, e.g. daptomycin, bacitracin and polymyxins, and antifungal agents e.g. imidazoles and nystatins. The polymyxins include five groups of polypeptide compounds (A, B, C, D, and E) which are produced by some Bacillus spp. There are only two of these in clinical use: polymyxin B and polymyxin E (known as colistin) (Poudyal et al., 2008). The polymyxins work by acting as cationic detergents for bacterial cell membranes, causing leakage of cytoplasmic components. They are active against Gram- negative bacteria, including Pseudomonas aeruginosa (Levinson, 2008). Another example of this type of antibiotics is daptomycin which is a lipopeptide antibiotic used to treat methicillin- resistant MRSA infections. It binds to the cell membrane and lead to massive depolarization and inhibition of most cellular functions and death (Steenbergen et al., 2005).

1.5.3 Inhibitors of protein biosynthesis

The synthesis of proteins (including enzymes) is fundamental for the survival and multiplication of the bacterial cell. Many different antibiotics inhibit the synthesis of bacterial proteins via binding to the 30S and 50S ribosomal subunits and inhibiting translation and hence protein synthesis (Pestka, 1971, Ikegami et al., 1978) The key to the specificity of these drugs is based on the structural differences that exist between bacterial ribosomes and eukaryotic ribosomes (Nissen et al., 2000). There are a wide range of antibiotics that specifically inhibit bacterial protein biosynthesis.

The aminoglycosides are one of the antibiotic groups which inhibit the synthesis of bacterial protein formation. Streptomycins, neomycins, and kanamycins are the three major classes within the aminoglycosides group (Kotra et al., 2000). These drugs enter bacterial cells by an active transport that involves quinones which are not found in some bacteria such as streptococci, therefore excluding these bacteria from the spectrum of action (Bryan and Kwan, 1983). Streptomycins bind to the 30S ribosomal subunit while kanamycins and neomycins bind to both the 50S subunit and to the 30S subunit but at different sites to those for streptomycin (Campuzano et al., 1979, Moazed and Noller, 1987).

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Chapter 1 Introduction

Chloramphenicol is an antibiotic that has a broad-spectrum activity and is produced by chemical synthesis. Chloramphenicol inhibits protein synthesis by preventing peptide bond formation on 70S ribosome (Pongs et al., 1973, Traut and Monro, 1964). The relative importance of this drug is due in particular because it can penetrate cerebrospinal fluid and eukaryotic cells, making it one of the leading choices for drugs to be used in the treatment of meningitis and other intracellular bacterial infections such as that caused by Chlamydia. However, its potentially fatal side-effects, known as aplastic anemia, have restricted its large- scale use in the medical field (Fraunfelder et al., 1982, Maurin and Raoult, 2001).

Tetracyclines are broad-spectrum antibiotics which inhibit bacterial protein synthesis. The use of active transport is vital in bringing them inside the cell. On reaching the cytoplasm, they bind to the 30S subunits preventing the binding of aminoacyl tRNA and thereby inhibiting protein translation (Roberts, 1996).

The macrolides are antibiotics which are commonly used to treat intracellular bacterial pathogens and Gram-positive infections. The first of this kind to be discovered was Erythromycin; from then on, there have been several other important macrolides discovered such as clarithromycin and azithromycin (Omura, 2002). The known mechanisms of macrolides are enhancing dissociation of tRNA from the ribosome, inhibiting assembly of ribosome, inhibiting formation of peptide bond, and preventing elongation of polypeptide chains (Menninger and Otto, 1982).

Another class of antibiotics which inhibits bacterial protein synthesis are the streptogramins. Streptogramins work more commonly in Gram-positive bacteria because of the decreased permeability to these antibiotics of the outer membranes in Gram-negative bacteria (Cocito et al., 1997). The most commonly used streptogramins are: quinupristin/dalfopristin, pristinamycin and virginiamycin. These drugs inhibit protein synthesis by either binding to separate sites on the 50S subunit or causing the release of incomplete protein chains (Vannuffel and Cocito, 1996).

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Chapter 1 Introduction

1.5.4 Inhibitors of nucleic acid biosynthesis

RNA and DNA are essential for the replication of every living form, of which bacteria are included. Certain antibiotics like fluoroquinolones bind to the components which are involved in DNA or RNA synthesis. This interferes with the normal cellular procedures which eventually will compromise the survival and multiplication of bacteria. However, other drugs, namely antifolates, can suppress RNA and DNA synthesis since they repress folic acid production. Antifolates cause a folic acid deficiency which is contributory to folate-dependent enzymes as well as their function in building procedures and cellular production. The rapid division of cells, as well as their production and growth, is impeded by such folic acid deficiency (Zhao & Goldman, 2003).

The antimicrobials that are used to treat diseases such as anthrax and are part of the broad-spectrum class of antibiotics known as the quinolones. Other drugs that are considered a part of this class are norfloxacin, nalidixic acid, and ciprofloxacin (Acar and Goldstein, 1997). Quniolones work by targeting the DNA gyrase and toposiomerase IV allowing them to inhibit bacterial growth which is essential to rectify the workings of super-coiled DNA (Greenwood et al., 2007). While it is true that quinolones target both enzymes, DNA gyrase is the main target in Gram-negative organisms and topisomerase IV is the main target in Gram-positive organisms (Ruiz, 2003).

1.5.5 Inhibitors of metabolite synthesis

The diaminopyrimidines and the sulfonamides are useful drugs especially when used in combination, their efficacy increases further due to a synergistic effect. The target of these antibiotics is to disrupt the folic acid pathway, which is an essential step for bacteria to produce precursors important for DNA synthesis, by targeting important enzymes for folic acid production. Sulfonamides inhibits dihydropteroate synthase, while trimethophrim targets dihydrofolate reductase (Tenover, 2006). The use of sulfonamides is no longer common in the world of medicine today, partly as a result of resistance but mainly because of the introduction of new and safer antibiotics. However, a combined drug known as trimethoprim- sulfamethoxazole has been used rather sparingly in infections of urinary tract disease. Usually, in the medical field, sulfonamides are known to be analogs of the acid p-aminobenzoic. They

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Chapter 1 Introduction inhibit the process of folate synthesis in an early step. The final step of folate synthesis is inhibited by diaminopyrimidines. (Greenwood et al., 2007).

1.5.6 Inhibitors of fatty acid biosynthesis

Fatty acid synthesis is a promising novel antibiotic target. The fatty acid synthesis pathway differs between mammals and bacteria offering the opportunity of specific treatment (Wright and Reynolds, 2007). Two inhibitors of fatty acid synthesis have been discovered, platencin and platensimycin. They are non-toxic and natural antibiotics which are produced by Streptomyces platensis. Platencin and platensimycin, are active against antibiotic susceptible and resistant Gram-positive bacteria. Platensimycin selectively targets β-ketoacyl-acyl-carrier protein (ACP) synthase (KAS) I/II or FabB/F, whereas platencin dually inhibits both FabF and KAS III or FabH (Jayasuriya et al., 2007). Although these two natural products offer a solution to treat infections caused by antibiotic-resistant bacteria, the poor pharmacokinetics of these two antibiotics has prevented them from entering clinical trials (Martens and Demain, 2011). Nevertheless, an understanding of the structural elements and the antibiotic activity possibly will provide a foundation for the development of analogs of these antibiotics. Thus, the pharmacokinetic problem associated with platensimycin and platencin might be solved through chemical modification and the development of new compounds (Lu and You, 2010).

1.6 Antibiotic resistance

For many years, antibiotics have seemed to be winning the war against infectious disease. However, almost as soon as antibiotics were discovered, resistance to the new drugs was reported. In a short time after the introduction of penicillin, resistance was detected in Staphylococcus aureus and by 1970, most S. aureus isolates were resistant to penicillin (Chambers and DeLeo, 2009). In a similar manner, after the Second World War, Shigella infections were treated using sulfonamides in Japan; however, by the year 1952, only 20 % of isolates were susceptible to sulfonamides. Multiple-resistant Shigella strains have appeared after the Japanese switched to chloramphenicol, streptomycin and tetracycline to fight Shigella infections. (Falkow, 1975). After thirty years of the discovery of sulfonamides, they are considered ineffective drugs for treating (O'Brien, 1997). In the years following, antibiotic resistance has grown increasingly common and pathogenic strains that are

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Chapter 1 Introduction resistant to almost all antibiotics have now been found. For every decade to follow, bacteria resistant not only to single but multiple antibiotics have become more and more widespread (Tenover and Hughes, 1996). At the same time, fewer new antibiotics are being discovered, and it is becoming more and more apparent that a careful and prudent use of antibiotics is necessary in order to curtail the development of bacterial resistance (Levy, 2001).

1.7 Mechanisms of antibiotic resistance

1.7.1 Intrinsic (passive) antibiotic resistance

Resistance to antibiotics in bacteria can be either intrinsic or active. Intrinsic resistance is based on cellular characteristics already prevalent in a particular bacterial taxon. The primary reason for such a situation to occur is the inability of the antibiotic to interact with or reach a particular cellular target or the target may be absent. A good example would be the resistance of Mycoplasmas to β-lactam antibiotics, owing to the lack of peptidoglycan (which the β-lactams are known to act on). In the same way, glycopeptide antibiotics are prevented from reaching their targets in Gram-negative cells, since the outer membranes stop them from reaching their targets. The example of Pseudomonas aeruginosa shows a high level of resistance to many antibiotics stemming largely from the restricted permeability to antibiotics due to the presence of an outer membrane (Okamoto et al., 2001).

1.7.2 Active antibiotic resistance

There are many active antibiotic resistance mechanisms, which are summarized in Figure 1.2. (a) Modification of the target sites of antibiotics. An interaction between antibiotics and their target site is very specific so any alterations in the structure or composition of the target in bacteria due to genetic mutation can prevent the antibiotic from binding to a target (Giedraitienė et al., 2011). For example, genetic modifications resulting in changes to the active site of penicillin-binding proteins (PBPs) in Streptococcus pneumoniae can inhibit the binding of β-lactam antibiotics and provide multi-resistance to antibiotics within this class (Kosowska et al., 2004). Other examples include modifications in ribosome subunits which provide

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Chapter 1 Introduction resistance to tetracyclines, macrolides and aminoglycosides; modifications in lipopolysaccharide structure providing resistance to polymyxins; modifications in peptidoglycan subunit peptide chains conferring resistance to glycopeptides; modifications in DNA gyrase conferring resistance to fluoroquinolones; and single point mutations resulting in modifications to RNA polymerase provide resistance to rifampin (Lambert, 2005). (b) Inactivation or destruction of the antibiotic by bacterial intracellular enzymes. There are three major classes of enzymes that inactivate antibiotics namely, β-lactamases, chloramphenicol acetyltransferases and aminoglycoside-modifying enzymes (Giedraitienė et al., 2011). For β-lactams, bacterial resistance can involve the enzymatic hydrolysis of the β- lactam bond within the β-lactam ring of the antibiotic molecule. When the β-lactam bond is cleaved, the antibiotic loses its antibacterial activity. This mechanism is mediated by β- lactamases, which can hydrolyse nearly all β-lactams that have ester and amide bonds, such as penicillins and carbapenems. Inactivation of chloramphenicol commonly occurs through an enzyme called chloramphenicol transacetylase which acetylates hydroxyl groups of chloramphenicol. The acetylated chloramphenicol is then unable to bind to to target ribosomal 50S subunit (Tolmasky, 2000). For aminoglycoside antibiotics, these are neutralized by specific enzymes such as phosphoryl-transferases, nucleotidyl-transferases or adenylyl- transferases. These aminoglycoside-modifying enzymes decrease the affinity of the modified antibiotic for its target and thereby inhibits binding to the 30S ribosomal subunit (Strateva and Yordanov, 2009). (c) modification of metabolic pathway functions. Some bacteria develop an altered metabolic pathway that bypasses the reaction inhibited by the antibiotic. For example, sulfonamides act as a competitive inhibitor of the enzyme dihydropteroate synthetase. This enzyme uses p- aminobenzoic acid which is an important nucleic acid precursor for synthesizing the essential folic acid. However, some sulfonamide resistant bacteria will not require p-aminobenzoic acid for synthesis of folic acid (Bockstael, and Aerschot, 2009). Bacteria can also increase the synthesis of a competitive molecule to compete with an antibiotic and then inhibit its antibacterial activity. For example, the development of sulfonamide resistance in occurs by increasing the synthesis of p-aminobenzoic acid which competes with sulfonamide molecules and decreases binding of the antibiotic to its target dihydropteroate synthase, thereby resulting in sulfonamide resistance (Deck and Winston, 2012). (d) Prevention of accumulation of antibiotics into bacterial cells, through decreasing uptake or increasing active efflux of the antibiotic from the cell. Decreasing drug permeability is common

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Chapter 1 Introduction in Gram-negative bacteria and involve changes in outer membrane lipid composition, porin channel selectivity or porin channel concentrations. For example, the mechanism of carbapenem resistance in Pseudomonas aeruginosa is to reduce numbers of OprD porins, which are the main entry channel for carbapenems through the outer membrane of this bacterium (Ruiz, 2003). In addition, many Gram-positive and Gram-negative bacteria produce efflux pumps that actively transport the antibiotics out of the bacterial cell and lower the antibiotic concentration inside the cell. For example, resistance to β-lactams, tetracyclines, and fluoroquinolones commonly occurs through active efflux pump mechanisms (Poole, 2007).

Figure 1.2 Active antibiotic resistance mechanisms: a. Alterations or removal of antibiotic targets. b. Enzymatic inactivation or destruction of antibiotic. c. Modification of metabolic pathway either by target overproduction or enzymatic bypass. d. Decreased accumulation of antibiotics via reducing antibiotic uptake and/or removal of antibiotics from the cell using efflux pumps.

1.7.3 Acquisition of antibiotic resistance

Bacteria can become resistant to antibiotic via acquisition of antibiotic resistance genes from other strains and/or mutation of existing genes (Martinez & Baquero, 2000). We can say that

21

Chapter 1 Introduction acquired resistance differs both between strains and within strains of an identical species. Vertical gene transfer, following mutation at a mutability value in the range of 10-8, implies that in a single infection occurrence, antibiotic resistance would be developed by just one bacterial cell by mutation within each 108 bacteria cells (Köhler et al., 1997). A more usual and efficient means of acquiring resistance is the acquisition from other strains of new ARGs.

1.7.3.1 Horizontal gene transfer

Horizontal gene transfer (HGT) is a key driver for an increasing incidence of bacterial antibiotic resistance in addition to also driving multidrug resistance. Acquired resistance suggests a greater potential for developing antibiotic resistance within human pathogens by obtaining ARGs from bacteria which are non-pathogenic antibiotic resistant within the environment; therefore, it is more problematic than intrinsic resistance (Gyles & Boerlin, 2014).

Horizontal gene transfer plays a key role in the widespread dissemination of antibiotic resistance genes. Despite the widespread occurrence of HGT, the importance of HGT varies between different species as well as between microbes inhabiting specific environments (Osborn, 2006). The three mechanisms of HGT are transformation, transduction and conjugation, (Figure 1.3). Transformation involves the acquisition by recipient bacteria of fragments of DNA which are released from donor bacteria directly from the extracellular environment (Ochman et al., 2000). Transduction refers to the process whereby DNA fragments are transferred from one bacterium to another through a viral vector namely bacteriophage. This is done by replication of bacterial viruses that have the capability of packaging bacterial DNA fragments into their capsids that then inject these fragments into a new host cell (Ochman et al., 2000), where they are integrated into the chromosome by recombination. Conjugation is considered as the most important HGT exchange mechanism for antibiotic resistance gene dissemination (Mazel and Davies, 1999). Bacterial conjugation is mediated by plasmids and other conjugative elements such as conjugative transposons and involves physical contact and mating between the donor and the recipient cells. Conjugation is able to transfer plasmids or conjugative transposons from one cell to another, potentially moving large arrays of resistance genes for most classes of antibiotics between cells (Jain et al., 1999).

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Chapter 1 Introduction

Figure 1.3 Mechanisms of horizontal gene transfer. a. Transformation: uptake of naked DNA from the environment. b. Transduction: transfer of genes from a donor to a recipient bacterial cell by a bacteriophage. c. Conjugation: transfer of genes between cells that are in physical contact with one another.

1.7.3.2 Mobile genetic elements (MGEs)

Many of the antibiotic resistance genes are carried on mobile genetic elements (MGEs) such as plasmids, transposons and integrons which are involved in horizontal transfer of antibiotic resistance genes to members of the same bacterial species or from another genus or species (de la Cruz and Davies, 2000).

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Chapter 1 Introduction

Plasmids are extra-chromosomal DNA and are typically circular. Plasmids can replicate autonomously (Paulsen et al., 2003). When considering the role of the plasmids in the context of antibiotic resistances, they are capable of effective transfer, via both conjugation and transformation mechanisms, between bacteria closely or distantly related (Paulsen et al., 2003). Conjugative plasmids carry tra genes, which encode DNA transport, and mating pair formation. Plasmid conjugation can effectively happen at high frequencies and between distantly related bacterial species (Zechner et al., 2000). Plasmid-encoded antibiotic resistance has been reported for many classes of antibiotics, which includes β-lactams, aminoglycosides, macrolides, tetracyclines, phenicols, trimethoprim/sulfamethoxazole, and is attributed to being responsible for the multiple resistance phenomena (Cambray et al., 2010).

Transposons have been defined as a small piece of DNA which is capable of moving from one site to another within the genome or into another mobile genetic element (Roberts et al., 2008). Transposons are not capable of self-replication and thus for maintenance, they need to be integrated into conjugative plasmids (allowing intercellular transposition) or chromosomes (intracellular transposition) (Salyers et al., 1995). Transposons have a role to play in disseminating of antibiotic resistance genes, as they can frequently carry antibiotic resistance genes and have the capability of mobilizing (Paulsen et al., 2003).

Conjugative transposons (CTns) are a hybrid mobile genetic element, comprising plasmid conjugative transfer functions and bacteriophage related integrases allowing integration of the CTn into the chromosome. Conjugative transposons were first identified in Gram-positive bacteria (Franke and Clewell, 1981)and are now widely found in both Gram-positive and Gram-negative bacteria. Conjugative transposons often have a broad host range, and they possibly contribute importantly to the prevalence of ARGs in some bacterial genera, including pathogens. Furthermore, Conjugative transposons such as Tn916, Tn1545, Tn6003, CTnBST and CTnGERM1, are important determinants of antibiotic resistance, particularly in Gram- positive bacteria. The broad host range conjugative transposons such as Tn916 and Tn1545 carry tetM genes can allow for the transfer between bacteria belong to diverse taxa (Roberts and Mullany 2009). In addition, Conjugative transposons may facilitate co-transfer across a broad host-range. For example, tetM and ermB genes can be found on the same mobile genetic elements namely Tn1545 and Tn6003 within the Tn916 family (Cochetti et al., 2008).

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Chapter 1 Introduction

Integrons function as a gene capture and an expression system. Integrons incorporate Open Reading Frames (ORF) and are able to convert them into functional genes as gene cassettes which are downstream of an active promoter (Rowe-Magnus and Mazel, 1999). The structure of integrons are constituted of a functional platform which consist of a site-specific tyrosine recombinase (intI gene), a primary incorporation site (attI) and a strong promoter (Pc). The key component of an integron is the intI gene, which catalyzes the particular excision and incorporation of the integron into chromosomes or plasmids of discrete genes cassette (Partridge et al., 2009). The incorporation of genes cassette happens at a particular locus, known as the primary incorporation site (attI) (Hall et al., 1999). An expansive number of gene cassettes are promoter-less; nevertheless, the expression of these genes is driven from the Pc promoter (Partridge et al., 2009). Integrons are not themselves portable components, yet are carried on other mobile genetic elements (e.g., transposons and/or conjugative plasmids), enabling proficient transmission between intra- and interspecies (mobile integrons) (Recchia and Hall, 1995). Gene cassettes typically contains just a promoter-less single gene and a recombination site (attC) (a 59-base sequence) which is specifically recognized by the IntI protein (Cambray et al., 2010).

1.8 The clinical significance of antibiotic resistance

Antibiotics are used widely for the prevention and treatment of infectious diseases in human and veterinary medicine. The phenomenon of antibiotic resistance is a growing problem, worldwide. Bacteria have become increasingly resistant to commonly used antibiotics, and we are facing a growing resistance problem (Spellberg and Gilbert, 2014). According to the USA Center for Disease Control and Prevention (CDC), more than two million Americans are infected each year with resistant pathogens and 23,000 die as a result (Health and Services, 2013). In addition, a recent report estimated that 10 million deaths will be attributed to antibiotic resistance by 2050 (O’Neill et al., 2016). Before the introduction of antibiotics, the hospital pathogens of major concern were Staphylococcus aureus and Group A-streptococci (Zervos et al., 2003). Thereafter, the physicians started to face many different resistant bacteria that involve in infectious diseases in humans such as Enterococcus faecium, S. aureus, K. pneumoniae, A. baumannii, Pseudomonas aeruginosa and Enterobacter spp. which has been named as ESKAPE bacteria by the CDC because they increasingly escape the effects of

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Chapter 1 Introduction antibiotics (Pendleton et al., 2013). ESKAPE bacteria also gained further global attention by being listed by the WHO as priority antibiotic resistant bacteria (WHO, 2017).

Antibiotic resistant strains of S. aureus in hospitals grew from 14% in 1946 to more than 90% today (Greenwood et al., 2007). Furthermore, more than 80% of S. aureus strains are resistant to both ampicillin and penicillin worldwide (O'Brien, 1997). So, we now face a problem of multi-resistant Salmonella, Shigella, Campylobacter, Pseudomonas aeruginosa and Mycobacterium tuberculosis, penicillin-resistant pneumococci, vancomycin-resistant enterococcus (VRE), MRSA, vancomycin- resistant S. aureus (VRSA), carbapenem-resistant Enterobacteriaceae (CRE) and multi-resistant Acinetobacter baumannii (Centres for Disease Control and Prevention, 2013). Thus, infections that were readily cured by antibiotics in the past may today be difficult or impossible to treat. This threat has brought forward the concept of a “post-antimicrobial era”, in which some infections would no longer be susceptible to antibiotic therapy (Cohen, 1992). Since antibiotics are not only important for treating specific infections, but also have a profound impact on many other aspects of medicine, such as oncology and transplantation surgery, the “post-antimicrobial era” represents a worst-case scenario similar to the pre-antibiotic era, when morbidity and mortality associated with infectious diseases were high (Lohner and Staudegger, 2001).

1.9 Antibiotic resistance in the environment

It is now widely recognized that antibiotic resistance traits occur naturally in the environment and have evolved since long before antibiotics were introduced (pre-antibiotic era) into human medicine (Davies, 1994). The pre-antibiotic era is the period before the introduction of antibiotics which was from before the late 1930s onwards. In that time, there was no individual use or industrial production of antibiotics. However, in more ancient times, heavy metals were used to treat diseases, which probably partly contributed to the emergence of resistance in the environment before antibiotics by the long-standing co-selection pressure of metal contamination (Baker-Austin et al., 2006). The levels of antibiotic resistance during the pre- antibiotic era were extremely low in all bacterial populations (Houndt and Ochman, 2000). After the introduction of antibiotics, long exposure to high levels of these antibiotics may have provided selective pressure to develop new resistance mechanisms and to raise inherent intrinsic resistance (Mazel and Davies, 1999). It is not surprising that, antibiotic-producing

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Chapter 1 Introduction bacteria have resistance naturally to their products, since they possess genes encoding resistance to the compounds that they produce (Ogawara et al., 1999). For example, Streptomyces produce a number of bioactive natural products and at the same time it possesses self-resistance for these products (Malla et al., 2010). Furthermore, some studies of bacteria isolated before widespread use of antibiotics using lyophilized bacteria from 1946, illustrate that resistance genes can be found in non-antibiotic producing bacteria. For instance, four out of thirty E. coli strains (lyophilized from 1946) were resistant to eight tested antibiotics (Smith, 1967).

1.10 Sources of antibiotic resistance in the environment

Concerns about the increasing prevalence of antimicrobial resistance was initially constrained to pathogenic strains, which cause infections and diseases. However, antibiotic resistance in bacteria has been identified in every environment on earth, with environmental microorganisms harbouring antimicrobial resistance genes in areas free of human impacts (Baker-Austin et al., 2006). Resistant bacteria can be obtained from all environments, yet the greater part of the resistance is connected with anthropogenic impacts (Martínez, 2008). Resistant bacteria can be discharged into the environment, e.g. via wastewater sewage or agricultural runoff (Zhang et al., 2009). Physical transport of resistant bacteria can occur via wind and water as well as by biological vectors such as human, animals, birds and insects can cause widespread dissemination of antimicrobial resistance bacteria and genes throughout nature (Martínez, 2008).

The usage of antibiotics involves not only consumption by humans, but also by animals as they are added to their food in order to promote growth development as well as to treat diseases (McEwen & Fedorka-Cray, 2002). Global consumption of antibiotics in food animal production was estimated at 63,151 tons in 2010 and is expected to increase by 67%, to 105,596 tons by 2030 (Van Boeckel et al., 2015). In the United States antibiotic use in food animals is estimated to account for ∼80% of the annual antibiotic consumption (Food and Drug Administration, 2010). According to European Union (EU) joint commission of antimicrobial use, the human and animal usage of antibiotics in the EU is 3821 tons for human consumption and 8927 tons for animal consumption (European Medicines Agency, 2017). Import statistics in Australia from 1992-3 to 1996-7 reveal the following usage of imported antibiotics: animal

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Chapter 1 Introduction feed, 55.8 %; human consumption, 36.4 %; and veterinary application, 7.8 % (Joint Expert Technical Advisory Committee on Antibiotic Resistance, 1999). Levy claims that in the United States, 22.68 million kg of antibiotics were applied for agricultural use in 1998 (Levy, 2001). The application of antibiotics in animal feed has many positive impacts; for example, inhibition of potentially harmful gut bacteria resulting in higher growth rates and a reduced rate of mortality. This enabled farming to be more intensive, and, in the USA alone, it was estimated that $3.5 billion were saved in annual production costs (Schwarz et al., 2001). Nevertheless, this caused the selection of antibiotic-resistant organisms inside the gut of food animals. Consequently, such organisms infiltrate the human food chain through the environment by means of waste discharge or during the slaughtering process. A study from 7 European countries found a strong correlation between consumption levels for 8 classes of antibiotics and the prevalence of antibiotic resistant E. coli in some animals (Chantziaras et al., 2013).

1.10.1 The impact of agricultural uses of antibiotics

In the entire poultry production procedure, considerable use is made of antibiotics. An early study into the poultry production industry revealed that of the poultry raised in the United States, 80 % have been fed on antibiotics, although it is not easy to obtain statistics for the use of antibiotics (Schwarz et al., 2001). When antibiotics are used in poultry, this results in organisms which are resistant in the chickens, as well as through the entire environment of production. Strains of several organisms which are resistant have been isolated from such sources. These include: Aeromonas, Clostridium, Pseudomonas, Staphylococcus and Streptococcus. In live chickens, it has been demonstrated that faeces from broiler farms contain bacteria which are resistant to ampicillin, chloramphenicol, clindamycin, erythromycin, streptomycin, tetracycline and virginiamycin in the range of 2 % to 94.8 %. Erythromycin and Tetracycline resistance were the greatest, having frequencies of 94.8 and 89.7 % respectively (Yoshimura et al., 2000). A German study of broiler chicken carcass and turkey manure showed that isolates revealed a result of 100 % for erythromycin, 90 % for oxytetracycline, 50 % for ciprofloxacin, 20 % for chloramphenicol, 20 % for streptomycin, 5 % for vancomycin and 5 % for ampicillin (Werner et al., 2000).

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Chapter 1 Introduction

Furthermore, antibiotics were given to pigs for disease prevention or for the promotion of growth; for example, sulfonamides, tetracyclines and tylosin are the most frequently applied antibiotics (McEwen & Fedorka-Cray, 2002). However, a German study on the topic of pig manure samples reported an enterococcal resistance as 100 % for tetracycline, 100 % for erythromycin, 61.9 % for streptomycin, 28.6 % for ciprofloxacin and 4 % for vancomycin, (Werner et al., 2000).

Similarly, antibiotics are given through water and food in cattle production in order to prevent disease and to promote growth. It was estimated that in 1999, a total of 90 % of veal calves and 60 % of beef cattle had been given antibiotics (McEwen and Fedorka-Cray, 2002). Furthermore, dairy calves, which are accommodated within groups, are frequently given antibiotics which include: cephalosporin, erythromycin, penicillin and tetracycline (Dibner and Richards, 2005). Butaye et al. (2001) reported in 1998-1999 that 20 % of Enterococcus faecium isolated from ruminants showed resistance to ampicillin while 80 % were resistant to tetracycline.

1.10.2 The impact of human use of antibiotics

Human beings have a critical impact upon the antimicrobial resistance in nature; the same is true in agriculture. When wastewater containing antibiotics and also pollution from industrial sectors like drug manufacturing is released from residential buildings and hospitals, environmental resistance can by prompted by the human utilisation of antimicrobial agents (Larsson, 2014). It is possible for unabsorbed antimicrobials, as well as antimicrobial resistant bacteria emanating from the human gastrointestinal tract, to enter the environment through sewage. It has been detected that there is a growing presence of antibiotic resistance within hospital wastewater, having a greater proportion of antibiotics in comparison with residential wastewater (Reinthaler et al., 2003). It was revealed by one report that 80.5 % of faecal samples in healthy persons contained antibiotic resistant organisms (Reinthaler et al., 2003). High volumes of antibiotics and their antecedents can be found in industrial wastewater emanating from pharmaceutical factories that would create enormous selective pressures on bacterial communities via the wastewater treatment process. A study conducted in India in 2007 revealed exceptional pharmaceutical emissions from drug manufacturers (Larsson et al.,

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2007). Furthermore, it is likely that antibiotics may be present in wastewater from households and healthcare facilities since it is impossible for 80 % of the intake antibiotic to be absorbed; consequently, it is excreted in the same form (Aarestrup, 2000). In this case, the remaining antibiotics would be spread into aquatic ecosystems by means of effluent discharge (Baquero et al., 2008). However, antibiotics adsorbed by sludge as well as by antibiotic resistant genes, which microorganisms within sludge are carrying, may be distributed in soil by means of land application or sludge disposal (Aarestrup et al., 2001) and ultimately transmitted to humans. Moreover, wastewater treatment plants are significant reservoirs of resistance genes and antibiotic-resistant bacteria (Rizzo et al., 2013).

1.11 Antibiotic resistance in the indoor environment

1.11.1 Antibiotic resistance in indoor dust

Vacuum cleaners can represent an important reservoir for antibiotic resistance in the home environment. In a study in Queensland, Australia (Veillette et al., 2013), conducted using a molecular approach (endpoint PCR), to detect antibiotic resistance genes in vacuum dust, they found that collected dust samples were positive for antibiotic resistance genes such as ermB, tetA and tetC genes which encode erythromycin and tetracycline resistance, respectively. Another study that has been carried out in Canada, showed that resistance genes for three classes of antibiotics (aac(6’)-aph(2’’) (encoding aminoglycoside resistance), ermA (encoding macrolide–lincosamide–streptogramin B resistance), and mecA (encoding methicillin resistance) were detected by PCR in multiple dust samples from a hospital air filter. This study concluded that dust may serve as a reservoir of antibiotic resistance genes (Drudge et al., 2012).

1.11.2 Antibiotic resistance in indoor surfaces

Bacteria can be recovered from many different indoor surfaces, and some studies indicate a potential transmission of surface bacteria to human hands and subsequently onto other surfaces ( Zhao and Li, 2019; Reynolds et al., 2005). A range of multi-drug resistant microorganisms have been found to remain and persist on environmental surfaces for extended periods of time, 30

Chapter 1 Introduction causing increased likelihood of surface contamination, and an ongoing risk of transmission via hands. In such instances, the environment can lead to continuing outbreaks and thus carries a long-term risk of infection (Willmann et al., 2015).

Intensive care units have been identified to be significant sources of pathogenic Pseudomonas aeruginosa with 26% of wet and 6% of dry environments being colonised (Quick et al., 2014). This pathogen has been found to persist for up to 2.5 years on a wet surface and for up to 5 weeks on a dry surface (Kramer et al., 2006). Bures et al. (2000) obtained sterile swab samples from 10 computer keyboards and 8 pairs of faucet handles in a medical intensive care unit in Hawaii. The contamination rates of MRSA were 36% for keyboards and 36% for faucet handles. The authors noted that these rates were similar to rates reported by others for direct patient contact surfaces such as gowns (50%), blood pressure cuffs (32%) and bedside rails (30%) (Bures et al., 2000). Snitkin et al. (2012) documented an outbreak in hospital environments of carbapenem resistant Klebsiella pneumoniae in 18 patients, which resulted in 11 deaths (Snitkin et al., 2012).

1.11.3 Antibiotic resistance in drinking water

Water is an essential resource for humans and therefore it is most important to ensure that it meets quality and safety standards. Water can easily become contaminated with microorganisms and act as a vehicle for the dissemination of bacteria potentially including infectious bacteria and their associated antibiotic resistance genes. Antibiotic-resistant bacteria and antibiotic-resistant genes have been isolated from various water environments in many countries, with many organisms exhibiting multiple drug resistance (Armstrong et al., 1981, Garcia-Armisen et al., 2011, Girlich et al., 2011, Pruden et al., 2006, Storteboom et al., 2010, Walsh et al., 2011, Yang and Carlson, 2003).

Drinking water treatment processes do not always remove bacteria and antibiotic resistant genes from source waters (Armstrong et al., 1981, Xi et al., 2009, Dodd, 2012). Xi and his colleagues found that even when non-pathogenic heterotrophs were reduced 10,000-fold, there was only a 10-fold reduction in antibiotic resistant genes (Xi et al., 2009). Certain types of water treatment may actually increase the proportion of multiple antibiotic resistant bacteria by creating selective pressure that favours the resistant bacteria (Shrivastava et al., 2004). A

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Chapter 1 Introduction variety of antibiotic resistant bacteria have been found in treated drinking water (Gaur et al., 1992, Pruden et al., 2006, Ram et al., 2008, Storteboom et al., 2010, Peter et al., 2012). In the early 1990s, Gaur and his colleagues found that over 90% of thermotolerant (fecal) coliforms isolated from drinking water, which are sometimes chlorinated, in rural India had single (13.7%), double (48.6%), or multiple (31.4%) antibiotic resistances and many of these bacteria were able to transfer antibiotic resistance to an Escherichia coli recipient (Gaur et al., 1992). Antibiotic-resistant bacteria have been isolated via heterotrophic plate counts (HPC) from the source, finished, and tap waters in several USA cities. In some USA tap water samples, over 80% of the HPC bacteria were resistant to at least a single antibiotic (Xi et al., 2009). Walsh et al. (2011) analyzed 50 New Delhi tap water samples and found that 28% contained organisms that grew on media containing antibiotics, and that 4% of the samples had bacteria harboring the blaNDM-1 gene (which makes bacteria resistant to a broad range of beta-lactam antibiotics). The authors commented that this may be an underestimation of the prevalence of blaNDM-1 due to long sample holding times (Walsh et al., 2011). The bacteria which were isolated included both pathogenic and non-pathogenic bacteria and are commonly found in tap water throughout the world (Walsh et al., 2011). Moreover, the researchers were able to demonstrate the transfer of the resistance gene from many of the non-pathogenic heterotrophs isolated from waste seepage sites (e.g. water pools in streets or rivulets) to Escherichia coli and other pathogenic organisms (Walsh et al., 2011). Studies have also demonstrated that antibiotic resistant genes can be transferred from such non-pathogenic bacteria to pathogens in the human gut (Huddleston et al., 2014, Schjørring and Krogfelt, 2011, Shoemaker et al., 2001). The results from the Walsh study indicate a potential for transmission of the blaNDM-1 gene to large segments of the human population via bacteria that are not the target of conventional drinking water treatment, nor are they subject to water testing under current regulations (Walsh et al., 2011).

1.12 Molecular approaches for characterisation of the indoor microbiome and resistome

During the 1960s, the advancement of molecular techniques allowed for the discovery of new bacterial genera and species and also enabled the establishment of genetic relatedness between bacterial species which led to changes. For examples, some bacterial species and genera were reclassified (Almeida & Araujo, 2013). Nucleic acid-based techniques offer easy detection of particular microorganisms and they are not biased by the ability of microorganisms

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Chapter 1 Introduction to grow on laboratory media; therefore, they have considerable advantages over traditional culture-based methods. However, the principal limitation of molecular techniques is that they do not provide information regarding the viability of microorganisms in the sample while this information can be obtained by using traditional culture methods (Galvin et al., 2012). The polymerase chain reaction (PCR) enables a particular DNA sequence to be amplified, a process which produces millions of copies for later analysis. This is especially helpful for analysing environments having low numbers of targeted genes, or for locating those which are difficult to culture. Furthermore, molecular techniques are advantageous in that they are usually quicker than conventional culture; for instance, a real-time PCR would specify a particular gene inside an environmental sample. The amplified product could be quantified by this technique, normally in just a few hours, whereas it would take one or two days using bacterial culture (Espy et al., 2006). Previously, real-time PCR has been applied to quantify Acinetobacter baumannii on surfaces in hospitals (McConnell et al., 2012). Nevertheless, Otter et al. (2007) claim that a real-time PCR technique indicated a poor specificity for recognising MRSA on hospital surfaces in comparison with traditional culture. This is due to the existence of inhibiting substances such as humic acids within environmental samples (Otter et al., 2007).

1.12.1 16S Ribosomal RNA gene sequence analysis

Carl Woese identified, during the late 1970s and early 1980s, that ribosomal RNA (rRNA) genes are exceptional phylogenetic markers. This is because of their high information content, and their universal presence within all prokaryotes and eukaryotes (Fox et al., 1977; Woese et al., 1980). Norman Pace and his colleagues, who were encouraged by this outcome, prepared a technique for the rapid sequencing of 16S rRNA genes to categorise organisms phylogenetically (Lane et al., 1985). Pace’s sequencing methodology, which was expanded by the advancement of comprehensive PCR primers for the amplification of the 16S rRNA gene, allowed researchers to analyse microbial communities in effectively any environment without any culture bias. Sequence analysis of 16S rRNA genes is now among the principal instruments in ecology (Pace, 1997). These reveal a wide biodiversity of archaea and bacteria in several environments which include: crude oil (Yamane et al., 2008), food products (Delbès et al., 2007; Ottesen et al., 2009), oceans (Rusch et al., 2007), soils (Schloss & Handelsman, 2006), as well as the human gut (Dinsdale et al., 2008; Turnbaugh et al., 2009). Currently, analysis of 16S rRNA gene markers utilise high-throughput sequencing (HTS ) technologies; for example,

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Chapter 1 Introduction

Illumina and pyrosequencing platforms. These enable a more comprehensive sampling in order to observe members of the community which comprise miniscule fractions of the total community known as the rare biosphere which contains most of the species in the microbial community (Lynch & Neufeld, 2015). Since large computational infrastructures are currently functioning, it is a straightforward task to automate sequence analysis. The following webservers provide researchers with high-quality computing power as well as informative data analysis: Green Genes (DeSantis et al., 2006), Ribosomal Database Project (RDP) (Cole et al., 2005), GHAP pipeline (Greenfield, 2017), MG-RAST (Meyer et al., 2008), MEGAN v6 (Huson et al., 2016) and FunGene Pipeline of RDP server (Fish et al., 2013).

1.12.2 Quantitative real-time polymerase chain reaction technique

Quantitative real-time PCR (qPCR) has recently emerged as a rapid and sensitive quantitative DNA method. The abundance of a target gene is recognised and enumerated in real time or in the course of each cycle by means of fluorescence during the reaction procedures, in comparison with the PCR endpoint in which the result of the reaction is detected at the end (Derveaux et al., 2010). Two assays exist for the recognition of DNA in qPCR. The first of these is non-specific fluorescent dyes which intercalate with any DNA which is double stranded; for instance, SYBR Green (Orlando et al., 1998). The second assay involves the use of an additional DNA probe which is sequence-specific and comprise oligonucleotides which are have a fluorescent reporter label which enables recognition only when it is subsequent to the probe being hybridised together with its DNA target which is complementary; for instance, TaqMan (Hiratsuka et al., 1999). Fluorescence is detected and subsequently evaluated in the qPCR thermocycler. There are advantages and disadvantages to both detection techniques. TaqMan requires specific hybridization between the probe and target which increases specificity, however a different probe must be synthesized for each unique target sequence, rendering the TaqMan assay relatively more expensive (Papin et al., 2004). SYBR Green dye binds to any double-stranded DNA, thus specificity is highly dependent on the specificity of the primers as false positive signals may be generated if non-specific products are amplified (Kim et al., 2007). However, each amplification product will have its unique melting temperature and melting curve of the qPCR product, so the specificity of the assay can be determined (Bookout and Mangelsdorf, 2003).

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Chapter 1 Introduction

1.13 Aims and hypotheses of the project

The overall aim of this research was to investigate variation in the structure and diversity of bacterial communities and their associated antibiotic resistance genes within human residences.

The overall aim of this research was to investigate variation in the structure and diversity of bacterial communities and their associated antibiotic resistance genes within human residences. The specific aims and hypotheses for each research chapter are given below

Aim 1 (Chapter 3): • Determine the variation in composition and diversity of bacterial communities within and between different human residences utilizing culture-independent Illumina sequencing of 16S rRNA genes, addressing the following questions: ▪ Are bacterial communities found in human residences house-specific or habitat- specific? ▪ How do bacterial communities vary within and between different houses? ▪ What are the most abundant bacterial phyla and genera found in human residences? ▪ Which clinically important bacterial species are present therein? Hypotheses for Research Aim 1 (Chapter 3): o There is variation in composition and diversity of bacterial communities between houses and habitats. o Bacterial communities in human residences are habitat-specific. o The most dominant bacterial taxa in human residences are human-associated bacteria and include potential pathogens.

Research Aim 2 (Chapter 4): • Determine the prevalence, distribution and abundance of antibiotic resistance genes in human residences via PCR and real time quantitative PCR methods. This specifically addresses the following questions: ▪ Which antibiotic resistance genes are present in human residences? ▪ What are the most abundant antibiotic resistance genes?

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Chapter 1 Introduction

Hypotheses for Research Aim 2 (Chapter 4): o Human residences are a reservoir for different antibiotic resistance genes. o There is variation in the frequency of detection of antibiotic resistance genes within and between human residences. o The abundance of antibiotic resistance genes varies within and between human residences.

Research Aim 3 (Chapter 5): • Describe the structure and diversity of the antibiotic resistance gene pool within and between different human residences.

Hypothesis for Research Aim 3 (Chapter 5): o There is diversity within antibiotic resistance gene both within and between houses and habitats.

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Chapter 2 General Materials and Methods

CHAPTER 2 GENERAL MATERIALS AND METHODS

2.1 MATERIALS

2.1.1 Bacterial strains

Bacterial strains used in this study are shown in Table 2.1. All strains were obtained from the medical microbiology teaching laboratory culture collection at RMIT University.

Table 2.1 Bacterial strains used as positive controls for PCR amplification studies. Bacterial species RMIT I.D. Number Antibiotic resistance phenotype

Enterobacter cloacae 100/2-3 Sulfonamide

Chloramphenicol

Klebsiella sp. 180/- Carbapenem

Serratia marcescens (strain: T10) 342/1-7 Ampicillin

Staphylococcus saprophyticus 344/1-1 Colistin

Staphylococcus aureus 344/2-3 Erythromycin

Staphylococcus aureus 344/2-7 Methicillin

Staphylococcus aureus 344/2-10 Tetracycline

Staphylococcus aureus 344/2-16 Aminoglycoside

Enterococcus faecium 345/19-1 Vancomycin

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2.1.2 Residential samples

2.1.2.1 Sources of residential samples

Residential samples were collected from four types of habitats (n=3 per each habitat) from eleven houses located in the metropolitan area of Melbourne. Volunteers were recruited by word of mouth and through advertising posters (Appendix A3). A questionnaire was given to participants to provide meta-data on houses and their inhabitants. The research study has been approved by The RMIT University Human Research Ethics Committee HREC (Appendix A1) and the approval number was 49-15. A participant information sheet was provided and informed consent was obtained from all participants.

2.1.2.2 Residential questionnaire

Data were collected from occupants of 11 houses using a home characterization questionnaire (Appendix A2) with resulting shown in Table 2.2. This included the house location, type and inclusion of any outdoor space; participant-specific questions such as number of all family members, gender and occupation; antibiotics usage-specific questions, such as the use of antibiotics within the last thirty days or the use of antibiotics in the longer term. Additionally, occupants were able to provide information on flooring and walling-specific information such as the type of floor covering used in the living area and type of bathroom wall covering; animal- specific questions such as number, type, and health of all pets; and cleaning- specific questions such as frequency of cleaning and type of cleaning products used. Participants were asked if there is any purifier or humidifier in the house and what type of air-conditioning is used and finally whether occupants wear shoes inside the house.

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Table 2.2 Summary of questionnaire meta-data collected from residential participants.

Location Occupants Type of Outdoor Type of air Type of Pets Type of Wearing

dwelling space conditioning flooring cleaning shoes Adults Children products inside House 1 Preston 2 1 Apartment Balcony/ Split-system air Carpet No Conventional/ No terrace conditioner antimicrobial products

House 2 Melbourne 3 1 Apartment Balcony/ Split-system air Carpet No Conventional/ No (CBD) terrace conditioner antimicrobial products House 3 Mill Park 2 4 House Garden/ Central (ducted) Carpet No Conventional/ No yard air conditioning antimicrobial products

House 4 Kingsville 2 1 House Garden/ Central (ducted) Wooden Cat Eco-friendly Yes yard air conditioning cleaning products House 5 Mill Park 2 5 House Garden/ Central (ducted) Carpet No Conventional/ No yard air conditioning antimicrobial products

House 6 Fairfield 2 1 House Balcony/ Split-system air Wooden Dog Conventional/ Yes terrace conditioner antimicrobial (rarely) Garden/ products yard

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Location Occupants Type of Outdoor Type of air Type of Pets Type of Wearing

dwelling space conditioning flooring cleaning shoes products inside Adults Children House 7 Mill Park 3 3 House Garden/ Central (ducted) Carpet No Conventional/ Yes yard air conditioning antimicrobial products

House 8 Preston 2 0 Apartment Balcony/ Split-system air Carpet Lizard Conventional/ Yes terrace conditioner antimicrobial products House 9 Preston 2 2 House Garden/ Split-system air Carpet Cat Conventional/ No yard conditioner antimicrobial products

House 10 Glenhuntly 1 1 House Garden/ Split-system air Wooden Cat Conventional/ Yes yard conditioner antimicrobial products

House 11 Preston 1 1 House Garden/ Split-system air Wooden/Ca Dog Conventional/ Yes yard conditioner rpet antimicrobial products

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2.2 Methods

2.2.1 Methods for collecting samples from human residences

Handheld vacuum cleaners (Hoover 12v Wet & Dry Handivac, Hoover) with changeable and disposable single use filters (Hoover Handivac Filter Set, Hoover) were used to collect settled dust samples. Sterile 1 L glass bottles (Schott Duran, Germany) were used to collect water samples. Whirl-Pak Sterile Dry Sponge Probes (Nasco, USA) were used to collect surface samples. A 1 m2 plastic template was used to measure living area floors for collection of dust samples; a 25 cm2 plastic template was used to measure kitchen and bathroom surfaces for collecting surface samples; An ice box (Esky) was used to keep collected samples at 4 °C during transportation from the house to the laboratory; Non-powdered gloves (Ni-Tek Nitrile Gloves, Livingstone) were used to avoid contamination from the investigator’s hands; Ethanol 80% was used to wipe the plastic templates between each sample.

2.2.2 Description of collection and processing of samples from human residences

2.2.2.1 Residential dust sampling

The 1 m2 plastic template was placed onto the floor of the living area then the vacuum cleaners were used to collect dust from triplicate areas. After each sample, the plastic template was moved and wiped with 80% ethanol and it was randomly placed again on the floor in another area to collect the next dust sample. There was a clear area of at least approximately 10 cm between each sample. After collecting dust samples was completed, the vacuum cleaners were wrapped carefully with parafilm. Finally, the vacuum cleaners were taken to the laboratory and the dust was extracted from each vacuum cleaner and its filter inside the flow cabinet on the same day. The vacuum cleaners were opened in a microbiological safety cabinet, and the dust was extracted from the vacuum cleaners’ filters. The dust was then weighed, and 0.25 g of the dust was placed into a Power Soil Bead Tube which was stored in -80 °C freezer until DNA extraction was performed.

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2.2.2.2 Residential surface sampling

Firstly, the Sterile Dry Sponge Probe was opened inside a microbiological safety cabinet and 10 ml of sterile water was added to moisten the sponge. At each house and prior to sampling the 25 cm2 plastic template was sterilised with ethanol before use and between each sample. Surface samples from kitchen tops and from bathroom walls were collected using moistened Sterile Dry Sponge Probe. Three samples were obtained from kitchen worksurfaces using the 25 cm2 template. Also, three samples were taken from bathroom wall tiles. After collection of each sample was completed, the sponge was placed back into the plastic bag while the plastic probe that attached to the sponge was thrown away. The plastic bag with the sponge inside was securely folded three times and fastened and was stored on ice and during transport to the laboratory.

The six surface samples with one negative control sample were then processed in a microbiological safety cabinet. Firstly, the sample bags were opened and 90 ml of sterile water was added to each sample bag; each bag then contained 100 ml of sterile water and the sponge. Then sample bags were folded carefully and then placed in a stomacher (Interscience, France) for 2 minutes at 2 strokes per second at room temperature. The sample bag was then taken back to the microbiological safety cabinet and the sponge was squeezed firmly and the extracted liquid (approximately 100 ml) filtered through 0.22 µm sterile filters (Merck Millipore, Darmstadt, Germany) using a vacuum pump (ROCKER, Chemker-411, Taiwan) to collect biomass. Finally, the filters containing biomass were folded and placed into a Power Water Bead Tube (MO BIO Laboratories, Inc., Carlsbad, CA, USA) using sterile forceps. Bead tubes were stored at -80 °C until DNA extraction was performed.

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2.2.2.3 Drinking water sampling

Tap water from each house was collected using sterile 1 L glass bottles. Firstly, the tap was opened, and the water run through for approximately ten seconds then the first sample was taken. There were ten seconds wait between each sample. After completing sampling, each bottle was placed back into the ice until processing in the laboratory.

The tap water samples (1000 ml, n = 3) were filtered through 0.22 µm filters (Merck Millipore, Darmstadt, Germany) using a vacuum pump (ROCKER, Chemker-411, Taiwan). Lastly, the filters were folded and inserted into a Power Water Bead Tube (MO BIO Laboratories, Inc., Carlsbad, CA, USA) using sterile forceps. All the filtration work was done in a microbiological safety cabinet and Bead tubes were stored at -80 °C until DNA extraction was done.

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2.2.3 Oligonucleotides primers

The oligonucleotide primers used in this study are shown in Table 2.3.

Table 2.3 Oligonucleotides used in this study Target Primer Nucleotide sequence (5’ to 3’) * Amplicon size (bp) PCR Reference Gene name Annealing temperature (o C) 16S rRNA 515F AATGATACGGCGACCACCGAGATCTACAC ~300-350 50 Caporaso et al., TATGGTAATT GT GTGCCAGCMGCCGCGGTAA 2011

806R CAAGCAGAAGACGGCATACGAGAT AGTCAGTCAG CC GGACTACHVGGGTWTCTAAT

16S rRNA 341F CCTACGGGAGGCAGCAG 200 55 Muyzer et al., 1993 518R ATTACCGCGGCTGCTGG blaNDM-1 NDM1-F TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 621 52 Nordmann et GGTTTGGCGATCTGGTTTTC al., 2011

NDM1-R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACA G CGGAATGGCTCATCACGATC mecA mecA F AAAATCGATGGTAAAGGTTGGC 533 54 Murakami et al., 1282 1991 mecA R AGTTCTGCAGTACCGGATTTGC 1793 tet(B) tet(B)-F CCTTATCATGCCAGTCTTGC 774 51 Maynard et al., 2003 tet(B)-R GGAACATCTGTGGTATGGCG

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Target Primer Nucleotide sequence (5’ to 3’) * Amplicon size PCR Reference Gene name (bp) Annealing temperature (o C) tet(M) tetM-FW TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 171 49 Aminov et al., ACAGAAAGCTTATTATATAAC 2001

tetM-RW GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG TGGCGTGTCTATGATGTTCAC ampC ampC-F TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 550 52 Schwartz et al., TTCTATCAAMACTGGCARCC 2003

ampC-R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG CCYTTTTATGTACCCAYGA vanA VanA1 GGGAAAACGACAATTGC 732 54 Dutka-Malen et al., 1995 VanA2 GTACAATGCGGCCGTTA mcr-1 CLR5-F TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 309 52 Liu et al., 2016 CGGTCAGTCCGTTTGTTC

CLR5-R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG CTTGGTCGGTCTGTAGGG erm(B) ermB-1 TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 405 53 Gevers et al., CATTTAACGACGAAACTGGC 2003

ermB-2 GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG GGAACATCTGTGGTATGGCG aac(6’)-Ie- aac6-aph2F CAGAGCCTTGGGAAGATGAAG 348 53 Vakulenko et aph(2’’)- al., 2003 Ia aac6-aph2R CCTCGTGTAATTCATGTTCTGGC 45

Chapter 2 General Materials and Methods

Target Primer Nucleotide sequence (5’ to 3’) * Amplicon size PCR Reference Gene name (bp) Annealing temperature (o C) SulII Sul2-F TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 722 51 Maynard et al., CGGCATCGTCAACATAACC 2003

Sul2-R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG GTGTGCGGATGAAGTCAG

CatII CatII-F TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG 495 50 Vassort- CCTGGAACCGCAGAGAAC Bruneau, et al., 1996 CatII-R GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG CCTGCTGAAACTTTGCCA dfrA1 dhfrI-F AAGAATGGAGTTATCGGGAATG 391 50 Maynard et al., 2003 dhfrI-R GGGTAAAAACTGGCCTAAAATTG

F = forward primer; R = reverse primer; bp = base pair, * M=A or C, H=A or C or T, V=A or G or C, W=A or T, R= A or G, Y= C or T. Primer sequences show both the Illumina adapter and the sequence on the targeted gene; sequences in bold represent the sequence being targeted.

Theoretically, the annealing temperature (TA) is 5 °C below the calculated temperature of the primer melting point (Tm). Also, the melting point

(Tm) can be calculated using the following equation: Tm (°C) = 4 (no. of G + C) + 2 (no. of A + T) (Suggs et al., 1981).

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2.2.3.1 Preparation of primers

Stock primers were prepared according to the manufacturer’s recommendations (GeneWorks, Australia) by adding 1 ml of sterile distilled water (UltraPure™ DNase/RNase-Free Distilled Water, Thermo Fisher Scientific) and mixing gently to obtain different concentrations as detailed in Appendix A1. Primers were diluted to the required working concentrations 10 pmol μl-1 by using this formula: Vol (stock) x Conc (stock) = Vol (final) x Conc (final) Appendix A3.

2.2.4 Nucleic acid (bacterial DNA) extraction samples

DNA was extracted directly from each human residential sample (water, dust and surface) using two MoBio DNA extraction kits MoBio Power soil® DNA isolation kit #12888-50 (MO BIO Laboratories, Inc., Carlsbad, CA, USA) for dust samples; MoBio Power Water® DNA isolation kit # 14900-50-22 (MO BIO Laboratories, Inc., Carlsbad, CA, USA) for water and surface samples. The DNA extraction was processed according to the manufacturer’s supplied protocols. In the last step of DNA extraction, sterile elution buffers solution PW6 (10 mM Tris) for water and surface samples and solution C6 (10 mM Tris) for dust samples was added and the final volume of extracted DNA for each sample was 100 µl. The extracted DNA was stored at -80 °C for further investigation.

2.2.5 DNA extraction from bacterial colonies

DNA extraction from cultured bacterial cells was performed using a heat treatment method. Three to five fresh colonies were taken from a plate (using a sterile loop) and transferred to a microcentrifuge tube and mixed in 100 μl of sterile distilled water. The cells were mixed using a small vortex machine (Vortex-Genie 2, Scientific Industries) for 3-5 seconds and then heated using a Dry Block Heater (Ratek DBH10, Australia) at 95 oC for five minutes followed by centrifugation at 14,000 rpm for two minutes using a microcentrifuge (Microcentrifuge 5415D, Eppendorf, Hamburg, Germany) to pellet the cellular debris. The crude lysate containing DNA was transferred into a new microcentrifuge tube and stored at -20 oC for subsequent use.

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2.2.6 PCR amplification of extracted DNA

The Polymerase Chain Reaction (PCR) is used to amplify target DNA sequences. PCR is conducted via three steps: denaturation, which allows the separation of the two DNA strands (carried out at a high temperature, from 94 C to 96 C for 2-8 minutes initially and then for 30-120 seconds for subsequent cycles); the annealing of a specific oligonucleotide primer complementary to the target sequence (carried out for 30-120 seconds at temperatures between 49-54 C (Table 2.3) depending upon the sequence of the primers; and finally DNA extension carried out at 72 C (which is near to the optimum temperature for Taq DNA polymerase) for 1-2 minutes. These steps are repeated multiple times (25-35 cycles) resulting in exponential amplification of the target sequence. The final step of the PCR is generally a longer, single temperature step (10 minutes at 72 °C) to ensure that all PCR products are completely elongated (Lo and Chan, 2006).

PCR was used to amplify the 16S rRNA gene to be used for the analysis of bacterial community structure and composition. PCR was also performed for the detection of genes encoding antimicrobial resistance. The sets of primers used therein (Table 2.3) were selected according to those used in previous studies.

2.2.7 PCR amplification of the 16S rRNA gene

PCR amplifications were conducted in a 25-μl volume. Reactions were prepared using barcoded universal bacterial primer pair 515F (10 μM) as forward primer and 806R (10 μM) as reverse primer (Table 2.3). This primer set targets the V4 hypervariable region of bacterial 16S rRNA genes (Caporaso et al., 2011). They are recommended by the Earth Microbiome Project and have been designed to be used with the Illumina Platform (Earth Microbiome Project,2015).

The PCR mixture (25 μl) was comprised of 16.5 μl sterile water (UltraPure™ DNase/RNase-

Free Distilled Water, Thermo Fisher Scientific), 1 NH4 reaction buffer, 2.5 mM of magnesium chloride (MgCl2), 0.2 mM of dNTPs (Bioline Pty Ltd, Alexandria, NSW, Australia), 0.4 pmol/μl of the forward (F) primer and 0.4 pmol/μl of the reverse (R) primer (Geneworks, Hindmarsh, Australia) and 1.25 U of Taq DNA polymerase enzyme (Bioline Pty Ltd,

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Alexandria, NSW, Australia). For each sample, DNA template (2 μl) was added to the PCR master mix (23 μl). Alternatively, 2 μl of a positive control (Table 2.1) and 2 μl of a negative control (sdH2O) were added instead of environmental DNA. The PCR thermocycling conditions for 96-well thermocycler were as follows: an initial denaturing step of 94°C for three minutes and then 35 cycles (94°C, 45 seconds; 50°C, 60 seconds; 72°C, 90 seconds) followed by a final elongation step at 72 °C for ten minutes. All PCR amplifications were performed in a Bio-Rad C1000TM thermal cycler (Bio-Rad, Hercules, USA).

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2.2.8 PCR amplification of antibiotic resistance genes

For amplification of antibiotic resistance genes, 2 μl of environmental DNA sample, 2 μl of a positive control (Table 2.1) and 2 μl of a negative control (sdH2O) were used in PCR amplifications. A master mix was prepared that contained two sets of primer: 0.4 pmol/μl of the forward (F) and 0.4 pmol/μl of the reverse (R) sequences (Geneworks, Hindmarsh,

Australia); 10x NH4 reaction buffer; 2.5 mM of MgCl2; 0.2 mM of dNTPs, 1.25 units of Taq DNA polymerase enzyme (Bioline Pty Ltd, Alexandria, NSW, Australia) and made up to a total volume of 23 μl with sterile distilled water (UltraPure™ DNase/RNase-Free Distilled Water, Thermo Fisher Scientific).

PCR conditions were applied using different annealing temperatures for the different selected primers which were as described in previous studies (Table 2.3), as follows: preheating (initial) at 94 °C for three minutes, followed by 35 cycles of denaturation at 94 °C for one minute, annealing for one minute at the temperatures specified in Table 2.3 for each primer and elongation at 72 °C for one minute, with a final extension for ten minutes at 72 °C. All PCR amplifications were done using a Bio-Rad C1000TM thermal cycler (Bio-Rad C1000™ Thermal Cycler, Australia).

To avoid some issues with the amplification of the target gene such as weak or non-specific secondary bands after the PCR reaction which may hinder or even prevent further analysis of the PCR product, a gradient PCR technique was used, which allowed the experimental determination of an optimal annealing temperature. This was achieved by choosing a temperature range (e.g., 49.9, 50.6, 52, 54, 56.4, 58.5, 59.6 and 60.2 °C), and setting this across different rows of wells (at different annealing temperatures) in the thermocycler. For example, row 1 for 49.9 °C, row 2 for 50.6 °C, and row 3 for 52 °C (i.e., gradually raising the annealing temperature to amplify the target gene). With each sample, the PCR mix had the same concentrations for all of the reactants. Therefore, each reaction was performed at a different annealing temperature, with all other values being the same.

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2.2.9 Agarose gel electrophoresis

Tris-Acetate-EDTA Buffer 50X concentrated (TAE) buffer (MO BIO Laboratories, Carlsbad, CA, USA) was diluted using distilled water to 1X. The appropriate concentration of agarose gel in 1X TAE buffer (depending upon the expected amplicon size of DNA, e.g. to give a percentage of agarose (w/vol) of between 1-2%) was prepared and then heated and dissolved in a microwave oven on a low power setting (300W) for 90 seconds. Then the agarose was allowed to cool until hand hot with continued shaking to avoid solidification of the agarose. 5 μl of SYBR-SAFE DNA gel stain 10,000X concentrate in DMSO (Invitrogen, Carlsbad, CA, USA) was added to 100 ml agarose solution after the agarose had been dissolved for DNA or PCR products staining. The agarose containing the dye was mixed gently before being poured into the gel tray and then allowed to solidify inside the gel tray. The solid agarose gel was then placed inside the electrophoresis tank, which was filled with 1X TAE buffer to the limit point.

5 μl of PCR products were mixed with 2 μl of 5x DNA Loading Buffer Blue (Bioline Pty Ltd, Alexandria, NSW, Australia) then the mixture was loaded into the gel. Also, 7 μl of a 100 bp ladder (HyperLadder 100bp Plus, Bioline Pty Ltd, Alexandria, NSW, Australia) was loaded in the gel to determine the size of PCR products. The gels were run at 80 V for 50 minutes. DNA was visualized under UV at 302 nm using a Gel Doc XR Gel Documentation System (Bio-Rad, Hercules, CA, USA).

2.2.10 Construction of qPCR Standards

Endpoint PCR, as described above was used to amplify gene specific amplicons which were used as template DNA standards for qPCR. Amplicons were purified using a Geneclean Turbo™ Kit (MP Biomedicals, LLC., Australia) and quantified fluorometrically using a NanoDrop Lite spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The ratio of absorbance at 260 nm and 280 nm was used to evaluate the purity of DNA. The ratio of all -1 DNA samples was between 1.8–2 which is suitable for qPCR analysis. Serial dilutions (10 to 10-8) of purified PCR products of the gene of interest were used for the generation of the standard curve. Standards were generated in triplicate for each dilution.

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2.2.11 Quantitative Polymerase Chain Reaction

qPCR was performed after DNA extraction and normalization were performed on all samples. A triplicate of a positive control (Table 2.1) and a duplicate of a negative control, contained 1μl of molecular grade water in place of DNA, were included for each qPCR. First, a master mix was prepared using the SensiFAST SYBR No-ROX Mix (Bioline Pty Ltd, Alexandria, NSW, Australia) and as recommended by the supplier and as shown in Table 2.5. Then, the Rotor-Gene Q System (QIAGEN, Hilden, Germany) was used to detect and quantify 16S rRNA genes and antibiotic resistance genes. qPCR cycling conditions were used as described in Shahsavari et al., (2016). Briefly, an initial denaturation step at 95 °C (5 min) followed by 40 cycles of 95 °C denaturation (10 s), annealing at a different temperature for each primer set as according to Table 2 (30 s), 72°C extension (30 s), 80 °C primer dimer removal and signal acquisition (10 s). Due to the formation of primer dimers, it is recommended that signal acquisition should be carried out at 78–80 °C. In this temperature range, primer dimers will be removed. Melting curves were generated for further data analysis to look for the formation of non-specific products.

Table 2.5 qPCR master mix components. Reagent Volume Final concentration

2x SensiFAST SYBR No-ROX Mix 10 L 1x

10 M forward primer 0.8 L 400 nM

10 M reverse primer 0.8 L 400 nM

Template DNA 1 L < 20 ng/l

PCR-grade water 7.4 L N/A

20 L Final volume

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2.2.12 Target amplicon library preparation and DNA sequencing

PCR product amplicons for 16S rRNA gene (148 samples) and antibiotic resistance genes (96 samples) were purified using AMPure XP beads and multiplexed in the indexing PCR using the Illumina Nextera® XT index primers by following the Illumina® Nextera® DNA Library Prep Reference Guide (Illumina, San Diego, CA, USA). The DNA from the library was quantified using a Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). Confirmation of the indexing PCR differed from the Illumina® protocol where a Bioanalyzer (Agilent, Santa Clara, USA) was recommended. Instead, a subset of 5 random amplicons were selected from each plate and run on a 1.5 % agarose gel from amplicons before and after the indexing PCR. The samples containing indexed amplicons were normalised and pooled to a final concentration of 4 nM and loaded together with 15% PhiX and then sequenced on an Illumina® MiSeq instrument using Nextera® XT chemistry (Illumina, San Diego, CA, USA) in the School of Science, RMIT University.

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2.2.13 Sequence reads processing

The Greenfield Hybrid Analysis Pipeline (GHAP) (Greenfield, 2015) (version 1.0) was used to classify reads and generate classified operational taxonomic units (OTUs) tables. Initially split reads were demultiplexed then merged by paired reads. After paired reads were combined a read-length histogram was created to determine where the reads should be trimmed, reads were trimmed at 250 bp. The GHAP amplicon pipeline utilises the free version of USearch which is a sequence analysis tool which produces search and clustering algorithms as well as quality controls procedures such as identifying and removing chimeric sequences (Edgar, 2017). 16S rRNA gene sequences were compared to the reference sequences on the Ribosomal Database Project (RDP).

2.2.14 Data analysis

Data generated by the Illumina MiSeq were initially analysed using the Illumina BaseSpace application, 16S Metagenomics software. Principal Component Analysis (PCA) plots and Hierarchical Clustering Dendrograms for bacterial communities were generated for all samples together as well as for each residential habitat separately. In addition, the rRNA gene sequence reads were processed and annotated through the GHAP pipeline (Greenfield, 2017), the sequence data were then analysed using three analysis pipelines using software packages PRIMER v7 (Clarke and Gorley, 2015) and MEGAN v6 (Huson et al., 2016) as well as the R packages Vegan (Oksanen et al., 2018) and iNEXT (Hsieh et al., 2016) in R-Studio. After an initial square-root transformation of the OTU tables, PRIMER v7 was used to perform Analysis of Similarities (ANOSIM), Similarity Percentages (SIMPER), Permutational Multivariate Analysis of Variance (PERMANOVA) and non-Metric Multidimensional Scaling (nMDS) plots. MEGAN v6 was used to construct Neighbor-Joining trees and bubble plots showing the relative abundance of known taxa within the communities (species with ≥97 % I.D). The R package, vegan, was used to calculate the Shannon and Simpson diversity, Pielou evenness, nMDS plots and rarefaction curves using the observed OTU datasets, and the R package, iNEXT, was used to calculate Chao1 species richness.

The antibiotic resistance gene sequence reads were processed through the Functional Gene (FunGene) Pipeline and Repository (FunGene, http://fungene.cme.msu.edu/FunGenePipeline/)

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Chapter 2 General Materials and Methods on the RDP server (Fish et al., 2013). Primer sequences were trimmed, amplicon sequences with low quality were removed, and remaining sequences were translated into their amino acid sequence using the tool FrameBot of the FunGene Pipeline (Fish et al., 2013). All subsequent analyses were carried out on amino acid sequences. Amino acid sequences were aligned and clipped at alignment reference positions from 60 aa to 80 aa with a 0.4 identity cutoff. OTUs were classified, and rarefaction curves were constructed based on the distance matrices of amino acid sequences at 0.05, 0.2 and 0.5 similarity (95%, 80% and 50% similarity) using the tools mcClust and rarefaction of the FunGene Pipeline (Fish et al., 2013). Richness estimates and diversity indices were determined based on the clustering data (Shannon, 1997; Chao & Bunge, 2002). Representative sequences at 0.4 similarity (60% similarity) were selected for the following phylogenetic analysis, where only the most five abundant clusters were assigned. All sequencing reads were then compared against the reference proteins (RefSeq protein) at the National Centre for Biotechnology Information NCBI GenBank using Blastp (protein-protein BLAST) using the E-value cut-off 10-5 (best hit used).

2.2.15 Statistical analysis

All statistical analysis and graphical representations were conducted using Excel 2016 (Microsoft Corporation), the IBM-SPSS Statistics (version 23) and GraphPad Prism (version 7.03) statistical programs. The t-test is used to compare whether there are significant differences between the two independent variables. Analysis of one-way variance (ANOVA) is performed when comparing multiple variables to detect any significant differences between the means of more than two independent groups. Differences between variables were considered statistically significant when the P value is < 0.05.

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS

CHAPTER 3 VARIATION IN BACTERIAL COMMUNITY STRUCTURE AND DIVERSITY IN HUMAN RESIDENCES USING 16S RIBOSOMAL RNA GENE SEQUENCING

3.1 Introduction

With increasing urbanisation, individuals tend to spend most of their time indoors. Some studies have estimated that individuals spend at least 90% of their time indoors (Hoppe & Martinac 1998). Humans are born in a hospital, are raised in either homes or apartments, go to day cares and schools, work in office buildings and then may move into old age homes or retirement villages. In these internal environments, humans are surrounded by a variety of living organisms, such as bacteria, fungi, plants and arthropods (Dunn et al., 2013). The impact of these organisms on our overall well-being and health is understudied. This is especially the case for bacteria and fungi that reside within our homes. These taxa can either have a positive or adverse effect on human health (Flores et al., 2011, Hewitt et al., 2013).

Until very recently little has been known about the microbial communities present in homes, and how their community structure, diversity and composition vary within a home, or between different households within the same location (Kembel et al., 2012a). However, this has become a growing topic of interest (Dunn et al., 2013, Lax et al., 2014, Meadow et al., 2014a, Meadow et al., 2014b). Studies have established that microbial community composition can impact upon human health (Hanski 2012, Ege et al., 2011). However, it remains unclear what factors within the residential environment affect the community structure and diversity of the indoor microbiome.

Understanding the processes that structure microbial communities in built environments is an important aim, since we spend the majority of our time indoors and probably exchange many of our microorganisms with various indoor habitats. Recently, researchers have begun to recognize the importance of characterizing these human-associated habitats. There are many research studies that have sought to determine the biodiversity, ecology, and public health implications of microbial communities found in built environments. Research studies to date 56

Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS have included assessments of hospitals (Bokulich et al., 2013, Brooks et al., 2014, Gaüzère et al., 2014, Oberauner et al., 2013, Hewitt et al., 2013), residences (Nonnenmann et al., 2012, Dunn et al., 2013, Jeon et al., 2013, Adams et al., 2013, Adams et al., 2014, Lax et al., 2014, Wilkins et al., 2016), kitchens (Flores et al., 2013), university classrooms and office buildings (Meadow et al., 2014, Hospodsky et al., 2012, Hewitt et al., 2012), daycare centres (Lee et al., 2007), public restrooms (Flores et al., 2011, Gibbons et al., 2015), athletic facilities (Wood et al., 2015), museums (Gaüzère et al., 2014) and metropolitan subways (Robertson et al., 2013, Leung et al., 2014, Afshinnekoo et al., 2015, Hsu et al., 2016).

It has long been known that under laboratory conditions, the majority of the microorganisms present in microbial communities are unculturable (Staley and Konopka, 1985, Rappé & Giovannoni 2003). Thus, any culture-dependent method used to grow bacteria may not determine the true extent of bacterial diversity existing in environmental samples (Riesenfeld et al., 2004). Since the late 1980S, a second method, which is a culture-independent method, has provided much insight into bacterial communities and their diversity (Jacobsen et al., 2005). Currently, there are two widely used molecular techniques for analysing microbial communities through genetic analysis diversity. These are 16S ribosomal RNA gene sequence analysis and metagenomics. Culture-independent techniques like metagenomic sequencing and 16S rRNA gene analysis give a less biased perspective on the microbial composition of environmental samples. This is because the DNA is extracted directly from the environmental sample and the bias associated with selective culture is removed (Tringe et al., 2008). Culture- independent techniques allow the communities of unculturable and dead and dormant microbial cells and spores to become accessible for phylogenetic analysis (Schloss et al., 2005). Several molecular-based techniques have been used for the identification of bacteria and also of fungi present within household samples.

Many culture-dependent and independent studies have investigated the microbial diversity in human residences. The majority of bacterial species found in indoor environments belong to only four bacterial phyla: Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (Flores et al., 2013). Some studies sought to identify the bacterial diversity in household dust. The dominant genera found in house dust by culture-methods were Micrococci, Bacillus, Staphylococcus, Paenibacillus, and many Actinomycetes including Streptomycetes spp. (Awad and Farag, 1999). Furthermore, culture-independent studies found that the dominant phyla

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS found in house dust were Firmicutes, Proteobacteria (Alpha, Beta, Gamma and Delta classes), Actinobacteria, and Bacteroidetes (Pakarinen et al., 2008, Kärkkäinen et al., 2010, Veillette et al., 2013). Other studies focused on household surfaces. By using culture-dependent methods, researchers have found some bacteria of clinical interest on household surfaces such as species of MSSA, and MRSA, Pseudomonas and Enterobacteriaceae (Scott et al., 2009), while culture-independent molecular studies of microbes on home surfaces were able to identify not only potential pathogens but also many hundreds or even thousands of other taxa that can be found on household surfaces (Flores et al., 2012, Dunn et al., 2013). Home tap water is another habitat which is considered as a potential reservoir and source for different microorganisms (Samie et al., 2012). Some studies have identified many bacterial species including opportunistic pathogens in tap water such as Aeromonas and Pseudomonas spp., some of these bacteria are resistant to several classes of antibiotics (Pruden et al., 2006, Ram et al., 2008, Xi et al., 2009, Storteboom et al., 2010, Peter et al., 2012, Mulamattathil et al., 2014).

There is still a gap in knowledge with regards to the variation in the structure, composition and diversity of bacterial communities in the human residences. Most prior studies were conducted mainly in North America and Europe; however, a gap still exists in regard to national studies that investigate bacterial communities found in other countries, including Australia which can then be compared to global data. Furthermore, the overall structure of bacterial communities across different locations and type of samples within individual houses and between different houses, is yet to be understood. Dust, surface and tap water samples have been studied separately in many previous studies. However, in this current research study, these samples have been studied in combination with a comprehensive investigation of the bacterial diversity in human residences. This has been done using HTS of the 16S rRNA gene using Illumina sequencing. In the current study I hypothesized that 1) there is variation in composition and diversity of bacterial communities between houses and habitats. 2) bacterial communities in human residences are habitat-specific. 3) the most dominant bacterial taxa in human residences are human-associated bacteria and include potential pathogens. The main aim of this research study is to determine the variation in the structure, composition and diversity of bacterial communities within and between different houses and habitats, utilizing culture-independent sequencing of the 16S rRNA genes. This aim seeks to determine whether bacterial communities found in human residences are house-specific or habitat-specific. Secondly, the research investigates how bacterial diversity varies within and between different houses and habitats. Thirdly, the research aims to identify the most abundant bacterial taxa 58

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(phyla, class, family and genera) found in human residences. Finally, the fourth aim is to determine which clinically important bacterial species are present in human residences. To address these aims, a total of 132 samples (33 dust, 33 kitchen surfaces, 33 bathroom surfaces and 33 drinking water samples) were obtained from eleven houses located in the metropolitan area of Melbourne and DNA sequencing of PCR-amplified 16S rRNA genes from these samples was used to assess variation in the structure, composition and diversity of their bacterial communities.

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3.2 Results

3.2.1 Amplification of 16S rRNA genes using the Polymerase Chain Reaction

To study the variation in the structure, composition and diversity of bacterial communities in the human residence, a total of 132 samples of four different habitats (drinking water, dust and kitchen and bathroom surfaces) were obtained from eleven houses. 16S rRNA genes from different household samples were successfully amplified by PCR from DNA extracted from different habitats, prior to sequencing (Figure 3.1). For description of collection and processing of samples from household ecosystem see section 2.2.

Figure 3.1 Agarose gel electrophoresis of PCR amplified 16S rRNA genes from household samples (House 1). D: Dust samples; DW: Drinking water samples; SB: Bathroom surface samples; SK: Kitchen surface samples; S-: Negative control for surface samples (unused sampling sponge); C-: Negative control (Nuclease free water); First and Last Lanes: 100 bp DNA ladder.

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3.2.2 Quality of Illumina Miseq DNA sequence data

16S rRNA gene sequencing was conducted, and quality control statistics of this sequencing run are shown in Table 3.1. A total of 11.38 Gb of DNA sequences was obtained for this run with an average Q30 score of 72.39% and the lower limit of cluster density was at 654.3 k/mm2. The run was loaded with 15% PhiX control DNA, of which 8.20% was aligned. The total error rate was 2.01% for reads from each end of the amplicon. In this run, a total of 18,531,667 reads passed filter which was 94.18% of total DNA sequence reads. The sequence run had a mean number of reads that passed filter per sample of 72,275 ± 1906, with a highest number of reads that passed filter of 160,330. Four samples which had fewer than 11,000 reads that passed filter were repeated in another run. These samples were H10SK1, H10SB3, H1SK3 and WKC.

Table 3.1 Quality statistics of Illumina MiSeq amplicon sequencing run of 16S rRNA genes.

Total DNA Reads Mean* Reads % ≥Q302 Yield Aligned to Error sequence reads passed passed filter (Gbp) PhiX (%)3 rate (%)4 filter1 per sample

19,676,055 18,531,667 72275 ± 1906 72.39 11.38 Gbp 8.20 2.01

*16S rRNA gene data set (n=148). 1 A quality filter of the Illumina MiSeq filters out unreliable sequences. 2 The average percentage of bases greater than Q30 which is a quality score predicts the probability of a wrong base call (1 in 1,000). 3 Positive control DNA. 4 The calculated error rate of the reads that aligned to PhiX.

The Greenfield Hybrid Analysis Pipeline (GHAP) (Greenfield, 2015) was used to process the raw DNA sequences. The GHAP pipeline clusters sequences with ≥ 97% sequence identity to each other and allocates sequences to operational taxonomic units (OTUs). These OTUs were then compared to the Ribosomal Database Project (RDP) (Cole et al., 2008) to allocate sequences to bacterial taxa with ≥ 97% sequence identity. Two rarefaction curves were generated for OTUs (Figure 3.2) and species (Figure 3.3) using R version 3.5.0 (RStudio Team, 2015). The results of these curves show that the rate of discovering new OTUs and species (≥ 97% identity) approached saturation.

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Figure 3.2 Rarefaction curves of 16S rRNA gene sequences assigned to OTUs at ≥ 97% identity.

Figure 3.3 Rarefaction curves for 16S rRNA gene sequences with ≥ 97% identity to sequences from known species in the RDP taxonomic database.

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3.2.3 Structure and diversity of bacterial communities in human residences

3.2.3.1 Alpha diversity, richness and evenness of bacterial communities in human residences

Taxon diversity, richness and evenness were studied for bacterial communities found in dust, kitchen and bathroom surfaces and drinking water samples. Numbers of observed OTUs and species, and Chaol richness estimates for OTUs and species are shown in Table 3.2. In addition, Shannon-Weiner and Simpson diversity and Pielou evenness for OTUs are shown in Figure 3.4, and for each individual household habitat in Table 3.3. Among the different bacterial communities obtained from the human residences across the 11 houses, the highest number of OTUs was observed in the dust communities (mean of 2096 ± 168), followed by kitchen (mean of 504 ± 84) and bathroom (mean of 289 ± 51) communities. While the lowest number of OTUs was observed in drinking water communities (mean of 239 ± 20). Furthermore, the number of species observed in dust communities (867 ± 38) was two, four and ten times greater than the number of observed species in the kitchen (314 ± 43), bathroom (199 ± 34) and drinking water communities (73 ± 7), respectively (Table 3.2).

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Table 3.2 Estimates of observed OTUs, Chaol richness for OTUs, observed species, Chaol richness for species, by habitat type and site of the samples.

Chao1 Habitat Observed Chao1 OTU Observed Species Type Site OTU Richness Species Richness House 1 1078 ± 140 1341 ± 210 595 ± 55 721 ± 75 House 2 1820 ± 293 2289 ± 287 831 ± 71 1030 ± 69 House 3 2244 ± 118 2760 ± 161 938 ± 24 1091 ± 41 House 4 2035 ± 175 2365 ± 165 890 ± 48 1011 ± 50 House 5 2442 ± 47 2912 ± 39 959 ± 21 1106 ± 13

House 6 1545 ± 164 1912 ± 232 674 ± 25 804 ± 58

Dust House 7 2673 ± 33 3043 ± 11 1008 ± 16 1153 ± 27 House 8 3167 ± 362 3735 ± 277 1092 ± 35 1290 ± 23 House 9 1947 ± 219 2434 ± 229 880 ± 62 1043 ± 61 House 10 1731 ± 140 2127 ± 171 719 ± 16 885 ± 23 House 11 2378 ± 160 2888 ± 146 955 ± 47 1136 ± 51 Mean 2096 ± 168 2528 ± 175 867 ± 38 1025 ± 44

House 1 465 ± 64 522 ± 68 300 ± 25 329 ± 26

House 2 587 ± 170 743 ± 160 349 ± 78 435 ± 65

House 3 553 ± 20 753 ± 53 345 ± 9 458 ± 18

House 4 320 ± 34 359 ± 47 237 ± 26 265 ± 32

House 5 397 ± 132 514 ± 166 229 ± 65 288 ± 88

House 6 368 ± 99 511 ± 152 249 ± 56 336 ± 81

House 7 561 ± 83 728 ± 105 366 ± 49 448 ± 56

Kitchen Surfaces Kitchen House 8 486 ± 44 626 ± 32 320 ± 26 400 ± 14

House 9 359 ± 118 521 ± 155 227 ± 62 328 ± 79

House 10 779 ± 63 995 ± 92 438 ± 26 546 ± 32

House 11 670 ± 95 793 ± 125 394 ± 50 448 ± 62

Mean 504 ± 84 642 ± 105 314 ± 43 389 ± 50

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Chao1 Habitat Observed Chao1 OTU Observed Species Type Site OTU Richness Species Richness House 1 249 ± 45 277 ± 41 182 ± 36 195 ± 34

House 2 223 ± 40 269 ± 38 163 ± 25 195 ± 18

House 3 246 ± 46 305 ± 64 194 ± 28 243 ± 45

House 4 194 ± 37 262 ± 56 138 ± 29 185 ± 46

House 5 321 ± 35 378 ± 46 215 ± 19 250 ± 22

House 6 225 ± 52 287 ± 53 156 ± 33 192 ± 32

House 7 406 ± 128 531 ± 161 267 ± 86 347 ± 114

House 8 255 ± 54 338 ± 56 175 ± 40 221 ± 40 Bathroom Surfaces Bathroom House 9 313 ± 36 374 ± 53 225 ± 22 254 ± 28

House 10 456 ± 22 551 ± 31 282 ± 18 335 ± 23

House 11 289 ± 68 371 ± 59 189 ± 42 239 ± 33

Mean 289 ± 51 358 ± 60 199 ± 34 241 ± 40

House 1 320 ± 29 384 ± 23 88 ± 13 123 ± 13

House 2 188 ± 10 240 ± 16 49 ± 5 83 ± 23

House 3 153 ± 9 187 ± 12 55 ± 3 79 ± 7

House 4 145 ± 13 174 ± 5 58 ± 4 80 ± 11

House 5 356 ± 40 406 ± 35 99 ± 10 127 ± 8

Water House 6 312 ± 8 355 ± 3 88 ± 6 106 ± 8

House 7 138 ± 4 160 ± 5 64 ± 7 87 ± 1

Drinking House 8 290 ± 37 361 ± 34 87 ± 11 126 ± 17

House 9 146 ± 6 181 ± 17 61 ± 1 93 ± 24

House 10 404 ± 41 477 ± 37 94 ± 11 134 ± 10

House 11 179 ± 28 237 ± 38 58 ± 5 94 ± 12

Mean 239 ± 20 287 ± 21 73 ± 7 103 ± 12

OTUs defined at ≥ 97% identities. Variances are the standard error (SE) of the mean for individual houses (n=3) and for habitats (n=33).

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A)

*

B) *

C)

Figure 3.4 Box and whisker plots of diversity indexes for household bacterial communities (dust, kitchen and bathroom surfaces and drinking water) n = 33 for each. (A) Shannon-Weiner index (greater Shannon index indicates higher diversity of the bacterial community. (B) Simpson diversity index (higher Simpson index indicates higher diversity of the bacterial community). (C) Pielou’s evenness (higher Pielou J indicates greater evenness). Boxes represent the interquartile range (IQR) between the first and third quartiles and the line inside boxes describes the median. Whiskers represent the lowest and highest values. Asterisks indicates significant difference between surface samples (p < 0.05).

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Table 3.3 Estimated overall Shannon-Wiener diversity index, Simpson diversity index and Pielou’s Evenness by type of habitat and site of the samples.

Habitat Type Diversity Site Index DUST Kitchen Bathroom Drinking Surfaces Surfaces Water House 1 4.23 ± 0.08 3.77 ± 0.07 2.97 ± 0.12 2.23 ± 0.13

House 2 4.72 ± 0.19 2.88 ± 0.36 2.22 ± 0.17 2.73 ± 0.09 House 3 4.86 ± 0.12 3.26 ± 0.12 2.46 ± 0.85 1.97 ± 0.03 House 4 5.40 ± 0.13 3.20 ± 0.13 2.01 ± 0.28 1.77 ± 0.12 House 5 5.39 ± 0.19 2.94 ± 0.40 3.10 ± 0.02 3.59 ± 0.16 House 6 3.94 ± 0.86 2.44 ± 0.17 1.95 ± 0.06 2.49 ± 0.06

House 7 5.77 ± 0.05 3.52 ± 0.29 2.8± 0.76 1.28 ± 0.04 Wiener Diversity Wiener - House 8 5.92 ± 0.59 2.88 ± 0.18 2.34 ± 0.45 2.27 ± 0.19 House 9 4.34 ± 0.22 1.95 ± 0.38 3.10 ± 0.57 1.76 ± 0.10 House 10 4.55 ± 0.42 3.60 ± 0.38 3.20 ± 0.13 1.64 ± 0.28

Shannon House 11 5.53 ± 0.18 3.43 ± 0.35 2.48 ± 0.37 2.32 ± 0.06

Mean 4.97 ± 0.28 3.08 ± 0.26 2.60 ± 0.34 2.19 ± 0.11 House 1 0.96 ± 0.003 0.93 ± 0.01 0.87 ± 0.02 0.73 ± 0.05 House 2 0.96 ± 0.0005 0.86 ± 0.02 0.79 ± 0.04 0.87 ± 0.02

House 3 0.97 ± 0.004 0.89 ± 0.02 0.70 ± 0.23 0.77 ± 0.01

House 4 0.98 ± 0.01 0.89 ± 0.01 0.77 ± 0.06 0.69 ± 0.01 House 5 0.97 ± 0.01 0.88 ± 0.04 0.92 ± 0.004 0.95 ± 0.01 House 6 0.88 ± 0.09 0.84 ± 0.02 0.70 ± 0.05 0.81 ± 0.01 House 7 0.99 ± 0.002 0.92 ± 0.02 0.77 ± 0.15 0.48 ± 0.02 House 8 0.98 ± 0.01 0.86 ± 0.02 0.75 ± 0.09 0.80 ± 0.03

House 9 0.90 ± 0.03 0.70 ± 0.12 0.83 ± 0.10 0.70 ± 0.02 SimpsonDiversity House 10 0.91 ± 0.04 0.92 ± 0.02 0.90 ± 0.02 0.49 ± 0.08

House 11 0.98 ± 0.004 0.88 ± 0.04 0.83 ± 0.03 0.82 ± 0.02

Mean 0.95 ± 0.02 0.87 ± 0.03 0.80 ± 0.07 0.74 ± 0.02 House 1 0.61 ± 0.001 0.62 ± 0.02 0.54 ± 0.01 0.39 ± 0.03 House 2 0.63 ± 0.01 0.45 ± 0.04 0.41 ± 0.02 0.52 ± 0.02

House 3 0.63 ± 0.01 0.52 ± 0.02 0.44 ± 0.15 0.39 ± 0.01

House 4 0.71 ± 0.01 0.56 ± 0.03 0.39 ± 0.06 0.36 ± 0.02 House 5 0.69 ± 0.02 0.50 ± 0.04 0.54 ± 0.01 0.61 ± 0.02

House 6 0.54 ± 0.11 0.42 ± 0.01 0.37 ± 0.02 0.43 ± 0.01

House 7 0.73 ± 0.01 0.56 ± 0.03 0.46 ± 0.11 0.26 ± 0.01 House 8 0.73 ± 0.06 0.47 ± 0.02 0.42 ± 0.07 0.40 ± 0.02

Pielou'sEvenness House 9 0.58 ± 0.04 0.33 ± 0.05 0.54 ± 0.10 0.35 ± 0.02 House 10 0.61 ± 0.06 0.54 ± 0.05 0.52 ± 0.02 0.27 ± 0.04 House 11 0.71 ± 0.02 0.53 ± 0.06 0.44 ± 0.05 0.45 ± 0.02 Mean 0.65 ± 0.03 0.50 ± 0.03 0.46 ± 0.05 0.40 ± 0.02

Variances are the standard error (SE) of the mean for individual houses (n=3) and for habitats (n=33).

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The bacterial communities in dust had higher Shannon-Wiener diversity (4.97 ± 0.28) than the kitchen (3.08 ± 0.26), bathroom (2.60 ± 0.34) and drinking water communities (2.19 ± 0.11). Similarly, the Simpson diversity of the dust communities (0.95 ± 0.02) was greater than in kitchen (0.87 ± 0.03), bathroom (0.80 ± 0.07) and drinking water (0.74 ± 0.02) communities. The dust community had a higher Pielou evenness (0.65 ± 0.03) when compared to kitchen, bathroom and drinking water communities (0.50 ± 0.03, 0.46 ± 0.05 and 0.40 ± 0.02), respectively (Figure 3.4). Comparison of surface samples showed that indices of diversity and evenness of the kitchen communities were higher than those of the bathroom communities. In addition, Shannon-Wiener and Simpson diversities of the kitchen communities were significantly higher than in the bathroom (t-test, p = 0.03 and t-test, p = 0.03), while there was no significant difference in evenness (t-test, p = 0.22) between kitchen and bathroom surface samples. Other samples cannot be compared as they are of different sample type.

The number of observed OTUs and species and the diversity indexes (Shannon-Weiner and Simpson diversity and Pielou evenness) were found to vary between houses (Tables 3.2 and 3.3). In dust communities, houses 7 and 8 had the highest number of identified OTUs and Shannon-Wiener diversity index (2673 ± 33 and 5.77 ± 0.05 and 3167 ± 362 and 5.92 ± 0.59, respectively). Whereas the lowest number of observed OTUs and Shannon-Wiener diversity index in dust were in houses 1 and 6 (1078 ± 140 and 4.23 ± 0.08 and 1545 ± 164 and 3.94 ± 0.86, respectively). In the kitchen surface communities, the highest number of OTUs identified were found in house 10 (779 ± 63) and house 11 (670 ± 95) and higher Shannon-Wiener diversity was observed in house 1 (3.77 ± 0.07) and house 10 (3.60 ± 0.38). While houses 4 and 9 showed the lowest number of identified OTUs (320 ± 34 and 359 ± 118 respectively), and lowest Shannon-Wiener diversity was observed in houses 6 and 9 (2.44 ± 0.17 and 1.95 ± 0.38 respectively). In the bathroom communities, more OTUs were observed in house 7 (406 ± 128) and house 10 (456 ± 22) and higher Shannon-Wiener diversity were found in houses 9 and 10 (3.10 ± 0.57 and 3.20 ± 0.13 respectively). Whilst the lowest number of observed OTUs were in houses 2 and 4 (223 ± 40 and 194 ± 37 respectively) and Shannon-Wiener diversity index was found to be lowest in houses 4 (2.01 ± 0.28) and house 6 (1.95 ± 0.06). In the drinking water communities, houses 5 and 10 had the highest identified OTUs (356 ± 40 and 404 ± 41 respectively) and houses 5 and 6 had the highest Shannon-Wiener diversity index (3.59 ± 0.16 and 2.49 ± 0.06 respectively). However, the lowest number of identified OTUs and Shannon- Wiener diversity index were found in house 7 (138 ± 4 and 1.28 ± 0.04), Tables 3.2 and 3.3.

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3.2.3.2 Bacterial community similarity in human residences

A Principal component analysis (PCA) plot showing variation in bacterial community structure based on 16S rRNA gene DNA sequences from different habitats was generated (Figure 3.5). In addition, a non-metric multidimensional scaling (nMDS) representation based on Bray- Curtis similarity indices was conducted based on the relative abundance of OTUs from each habitat (Figure 3.6). Both PCA and nMDS plots illustrate strong clustering with type of sample rather than with the house of origin of samples (Figures 3.5 and 3.6). Also, from the PCA plot and nMDS, it can be seen that drinking water community and dust community showed greater clustering when compared to kitchen and bathroom surfaces communities which showed greater variability (Figures 3.5 and 3.6).

Neighbour-joining analyses based on a Bray-Curtis dissimilarity matrix was conducted for each habitat sample type using bacterial species data. In the dust samples obtained from eleven houses, the bacterial communities in six houses (1, 2, 3, 7, 9 and 11) were clustered with respect to their house of origin. Conversely, bacterial communities in the other five houses (4, 5, 6, 8 and 10) showed greater variability (Figure 3.7). In the kitchen surface samples, the bacterial communities in seven houses (1, 3, 4, 6, 8, 10 and 11) were clustered with respect to the house from where the samples collected. Although the communities in house 11 also clustered with that from one sample from house 2. Whereas samples from the other four houses (2, 5, 7 and 9) showed greater variability with one sample from each house being divergent (Figure 3.8). For the bathroom samples, only four houses (2, 4, 10 and 11) showed clustering of bacterial communities with respect to house origin, whilst the majority of houses (1, 3, 5, 6, 7, 8 and 9) had more variable bacterial communities (Figure 3.9). Conversely in drinking water samples, the bacterial communities in the majority of houses (1, 2, 3, 5, 6, 7, 9 and 10) were clustered with respect to their house of origin, while only three houses (4, 8 and 11) showed non house- specific clustering (Figure 3.10).

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Figure 3.5 Principal Component Analysis (PCA) based on similarity between bacterial communities at genus level from different type of samples found in human residences. A) Drinking Water communities; B) Dust communities; C) Kitchen Surface communities; D) Bathroom Surface communities. PCA plot produced using the 16S Metagenomics (Illumina, version 1.0.1) app.

Figure 3.6 Non-metric multidimensional (nMDS) representation of the household bacterial communities (11 houses and 3 replicates per habitat) based on Bray-Curtis similarity. Dust community (D), Kitchen Surface community (SK), Bathroom Surface community (SB) and Drinking Water community (DW). nMDS plot was generated using the PRIMER7 program.

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Figure 3.7 Neighbour-Joining analysis showing variation in dust bacterial community structure across different household ecosystems based on a Bray-Curtis dissimilarity matrix using 16S rRNA gene sequence data. Each colour (3 samples each) represents a different house (H), as numbered. Scale bar indicates a distance of 0.1.

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Figure 3.8 Neighbour-Joining analysis showing variation in kitchen bacterial community structure across different household ecosystems based on a Bray-Curtis dissimilarity matrix using 16S rRNA gene sequence data. Each colour (3 samples each) represents a different house (H), as numbered. Scale bar indicates a distance of 0.1.

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Figure 3.9 Neighbour-Joining analysis showing variation in bathroom bacterial community structure across different household ecosystems based on a Bray-Curtis dissimilarity matrix using 16S rRNA gene sequence data. Each colour (3 samples each) represents a different house (H) , as numbered. Scale bar indicates a distance of 0.1.

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Figure 3.10 Neighbour-Joining analysis showing variation in drinking water bacterial community structure across different household ecosystems based on a Bray-Curtis dissimilarity matrix using 16S rRNA gene sequence data. Each colour (3 samples each) represents a different house (H), as numbered. Scale bar indicates a distance of 0.1.

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3.2.3.3 Potential outliers

Four samples were considered as potential outliers when compared to other samples from the same type and household ecosystem. These samples were H6D2 from dust samples, H7SK2 from kitchen samples, H7SB2 from bathroom samples and H4DW1 from drinking water samples (Figure 3.11). However, the sequence datasets of these potential outliers had high numbers of reads that passed filter mean of 96%, and also a number of OTUs and a total number of reads assigned to OTUs that ranged from 6977 to 27552 which were consistent with other samples from the same habitat (Table 3.4). As the potential outliers showed consistency with other similar samples, they were retained for further analysis.

Table 3.4 Sequence run data of different type of samples from household ecosystems which were considered potential outliers.

Potential Number of Reads % of Reads Number of Total Number Outlier Passed Filter Passed OTUs of Reads Filter Assigned to OTUs H6D2 87,558 94.7 358 14681

H7SK2 49,560 96.2 412 7394

H7SB2 84,853 96.8 651 6977

H4DW1 109,698 96.5 136 27552

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a. b.

c. d.

Figure 3.11 Hierarchical clustering based on Bray-Curtis similarity matrix of OTUs at ≥ 97% identity of bacterial communities in household ecosystems from different habitats a) dust samples b) kitchen surface samples c) bathroom surface samples and d) drinking water samples.

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3.2.3.4 Variation in bacterial community structure by sampling type

Analysis of Similarities (ANOSIM) was performed using different type of samples obtained from different human residences (Table 3.5) to compare the similarity or dissimilarity between bacterial communities in the four habitats (dust, kitchen, bathroom and drinking water) in the human residence. ANOSIM provides the P value (i.e., significance levels) and a R value (i.e., the strength of the factors on the samples). The R value varies between 0 and 1, where a R value close to 1 indicates high separation between levels of the tested factor, while a R value close to 0 indicates no separation between levels of the tested factor.

The overall results of ANOSIM showed that the bacterial communities in the four different human residences differed significantly (P = 0.001) and strongly (R = 0.931) between habitats (Table 3.5). The drinking water community showed the greatest variation (R = 1, P = 0.001) from the bacterial communities in the other habitats. Conversely, the kitchen and bathroom surface communities showed the lowest R-statistic values (R = 0.733) when compared to other combinations i.e. dust vs kitchen, dust vs bathroom and drinking water vs any other habitats (R = 0.838, R= 0.98 and R= 1, respectively) (Table 3.5).

Permutational multivariate analysis of variance (PERMANOVA) was also performed on the Bray-Curtis similarity matrix to examine the hypothesis that the bacterial communities differed according to their habitats. The results of PERMANOVA again showed a significant difference between each of the different habitats (P < 0.005).

Table 3.5 ANOSIM of a Bray-Curtis similarity matrix based on OTUs at ≥ 97% identity of bacterial communities from different type of samples. 999 permutations were performed. P- value = 0.001. Habitats R Statistic Significance Level (P-value) Dust, Kitchen 0.838 0.001 Dust, Bathroom 0.98 0.001 Dust, Drinking Water 1 0.001 Kitchen, Bathroom 0.733 0.001 Kitchen, Drinking Water 1 0.001 Bathroom, Drinking Water 1 0.001 Overall 0.931 0.001

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3.2.3.5 SIMPER analysis of differences in bacterial community structure based on Bray-Curtis similarity indices in household communities by site and type of sample

Similarity percentage (SIMPER) analysis of the Bray-Curtis similarity index was performed to determine the percentage of similarity and dissimilarity between different habitats and houses. SIMPER analysis was conducted for all different habitats (dust, kitchen, bathroom and drinking water) (Table 3.6) and for each habitat across eleven houses (Table 3.7). The results of SIMPER showed that the overall average similarity of bacterial communities in the different habitats were 49.22%, 37.29%, 37.12% and 41.9% for dust, kitchen, bathroom and drinking water, respectively. When comparing each habitat to another, the highest average dissimilarity was between dust and drinking water communities 95.35%, followed by 93.57% between kitchen and drinking water communities. While the lowest average dissimilarity was 72.15% between dust and kitchen communities and 72.37% between kitchen and bathroom communities (Table 3.6). The average similarity of bacterial communities across different houses and habitats was highest in house 6 for kitchen (70.16%) and drinking water (80.37%) communities, in house 7 for dust community (69.72%) and in house 10 for bathroom community (66.92%). Whereas the lowest average similarities were 25.02% in house 6, 37.93% in house 2, 26.48% in house 7 and 40.97% in house 4 for dust, kitchen, bathroom and drinking water communities, respectively.

In addition, the statistical test of one-way analysis of variance (One-Way ANOVA) for SIMPER percentages was conducted between all houses and considering all habitats. The results of ANOVA tests showed that there was a significant difference between the bacterial communities between different habitats (P = 0.0004). However, there was no significant difference in similarity between the bacterial communities between any of the houses with each other (P > 0.05), when all habitats within a house were considered together for each house.

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Table 3.6 SIMPER analysis (%) of dissimilarity matrix based on OTUs at ≥ 97% identity of bacterial communities between different habitats: dust, kitchen, bathroom and drinking water.

Habitats Average dissimilarity (%) Dust & Kitchen 72.15 Dust & Bathroom 81.25 Kitchen & Bathroom 72.37 Dust & Drinking Water 95.35 Kitchen & Drinking Water 93.57 Bathroom & Drinking Water 89.8

Table 3.7 SIMPER analysis (%) based on a Bray-Curtis similarity matrix based on OTUs at ≥ 97% identity of bacterial communities of household ecosystems, comparing different habitats across eleven houses.

Habitats Site D SK SB DW House 1 63.03 65.81 47.36 71.01 House 2 57.9 37.93 53.18 72.16 House 3 67.71 69.73 38.87 77.84 House 4 52.71 61.31 39.43 40.97 House 5 58.8 50.69 53.21 69.59 House 6 25.02 70.16 56.2 80.37 House 7 69.72 41.9 26.48 74.55 House 8 55.25 55.04 44.14 70.41 House 9 59.81 49.04 42.73 66.35 House 10 48.7 47.61 66.92 73.1 House 11 65.9 50.98 55.07 69.97

Dust (D), kitchen (SK), bathroom (SB) and drinking water (DW).

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3.2.4 Taxonomic composition of household microbiome communities

3.2.4.1 Rare bacterial biosphere

The rare biosphere in a bacterial community is a large and possibly predominant part of the diversity of this community which is represented by many small populations. The number of species identified in the dust, kitchen, bathroom and drinking water communities were 867 ± 38, 314 ± 43, 199 ± 34 and 73 ± 7, respectively (Table 3.2). Figure 3.12 illustrates the number of DNA sequence reads per species for dust, kitchen, bathroom and drinking water communities from samples H9D2, H10SK3, H11SB2 and H3DW1 respectively, which are used as examples for each environment. As shown in the curves, only a few species were dominant in each community while the majority of species are present within the rare biosphere with a relative abundance of < 0.1%. In the sample H9D2, for a dust community 55% of the number of sequence reads were assigned to only five species. In the sample H10SK3, for kitchen community over 65% of the number of sequence reads were assigned to only five species. In the sample H11SB2, for the bathroom community, the five most abundant species represented more than 75% of the total number of sequence reads. For the drinking water community, 77% of the number of sequence reads in the sample H3DW1 were assigned to only three species (Figure 3.12).

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A. B. 16000 14000

14000 12000 12000 10000 10000 8000 8000 6000 6000 4000

4000 per species per species (≥97%)

2000 per species (≥97%) 2000 NumberDNA of sequnce reads

0 NumberDNA of sequnce reads 0 Species (≥97%) Species (≥97%)

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12000 14000 12000 10000 10000 8000 8000 6000 6000

4000 per species per species (≥97%) per species per species (≥97%) 4000

2000 2000

NumberDNA of sequnce reads NumberDNA of sequnce reads 0 0 Species (≥97%) Species (≥97%)

Figure 3.12 Rank-abundance plot of species (≥97% identity) present in four samples from different habitats: A) dust community (sample: H9D2), B) kitchen community (sample: H10SK3), C) bathroom community (sample: H11SB2) and D) drinking water community (sample: H3DW1).

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3.2.4.2 Taxonomic composition of household bacterial communities

3.2.4.2.1 Phylum composition of household bacterial communities

Proteobacteria was the most abundant phylum found in all human residences. The other four most abundant phyla in human residences were Firmicutes, Actinobacteria, Cyanobacteria and Bacteroidetes (Figure 3.13). In the dust community, a total of 29 bacterial phyla were identified in house dust samples across all houses. The five predominant bacterial phyla were Proteobacteria (38.1%), Firmicutes (19.8%), Cyanobacteria (19.4%), Actinobacteria (13.4%) and Bacteroidetes (6.5%), (Figure 3.13). For the kitchen community, a total of 27 bacterial phyla were found in kitchen surface samples obtained from all houses. The dominant bacterial phyla with the overwhelming majority of sequences (~ 99.5% of all sequences) belonged to only five bacterial phyla: Proteobacteria (62.5%), Firmicutes (15.7%), Actinobacteria (9.5%), Bacteroidetes (8.3%) and Cyanobacteria (3.5%) (Figure 3.13). In the bathroom community, a total of 27 bacterial phyla were identified in bathroom surface samples across all houses. The predominant bacterial phyla with the majority of sequences (> 99% of all sequences) belonged to only five bacterial phyla: Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes and Cyanobacteria with mean relative abundance of 66%, 21.7%, 10.3%, 1.3% and 0.2%, respectively (Figure 3.13). For the drinking water community, a total of 26 bacterial phyla were detected in all drinking water samples. The predominant bacterial phyla were Proteobacteria (92.3%), Bacteroidetes (3.4%), Cyanobacteria (1.7%) and Firmicutes (0.8%) (Figure 3.13).

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Figure 3.13 Relative abundance of the five most abundant bacterial phyla found in dust, kitchen surface, bathroom surface and drinking water samples across houses. Sum of other phyla is shown as ‘Others’.

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3.2.4.2.2 Class and family composition of household bacterial communities

In the household bacterial communities, the most prevalent classes shared between all habitats were alphaproteobacteria and . However, the relative abundance of these classes varied amongst habitats. For example, alphaproteobacteria and gammaproteobacteria had relative abundances, respectively of 75.11% ± 7.19% and 3.95% ± 0.67% in drinking water communities, of 14.11% ± 3.98% and 46.61% ± 5.56% in kitchen communities, of 37.27% ± 4.83% and 27.44% ± 4.03% in bathroom communities and of 11.26% ± 0.59% and 18.49% ± 3.92% in dust communities (data not shown). In addition, Bacilli and Actinobacteria were among the most abundant classes in dust (17.24% ± 2.5% and 13.54% ± 1.36%), kitchen (15.33% ± 2.74% and 9.57% ± 1.99%) and bathroom (8.57% ± 2.3% and 21.69% ± 2.87%) communities, respectively. While the classes Bacilli and Actinobacteria were present in drinking water at very low relative abundances (0.37 ± 0.06 and 0.38 ± 0.07), respectively (data not shown).

In the bacterial communities of these human residences, the total number of bacterial families detected were 244, 184, 163 and 166 families in the dust, kitchen, bathroom and drinking water communities, respectively. Comparison of the relative abundances of the 10 most abundant families from dust, kitchen, bathroom and drinking water communities are shown in Figure 3.14, Figure 3.15, Figure 3.16 and Figure 3.17, respectively.

The two most abundant families identified in human residences, were Rivulariaceae (17.83% ± 3.33%) and Enterobacteriaceae (7.15% ± 2.42%) in the dust community (Figure 3.14), (25.51% ± 5.56%) and Enterobacteriaceae (11.38% ± 4%) in the kitchen community (Figure 3.15), Moraxellaceae (23.28% ± 4.03%) and Sphingomonadaceae (20.22% ± 4.83%) in the bathroom community (Figure 3.16) and Xanthobacteraceae (31.88% ± 6.23%) and Sphingomonadaceae (24.08% ± 3.51%) in the drinking water community (Figure 3.17).

In addition, amongst the ten most abundant families, there were two families Sphingomonadaceae and Pseudomonadaceae which were identified in all habitats. Nevertheless, the relative abundance of these families differed between habitats. For instance,

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS the relative abundance of Sphingomonadaceae were 3.94% ± 0.59%, 5.29% ± 1.68%, 20.22% ± 4.83% and 24.08% ± 3.51% in dust, kitchen, bathroom and drinking water communities, respectively (Figures 3.12 - 3.15). While the relative abundance of Pseudomonadaceae in dust, kitchen, bathroom and drinking water communities were 3.77% ± 0.87%, 5.21% ± 1.67%, 2.07% ± 0.97% and 1.63% ± 0.64%, respectively (Figures 3.12 - 3.15). Another three families Moraxellaceae, Streptococcaceae, and Staphylococcaceae were all identified among the most prevalent families in the dust, kitchen and bathroom communities but not in the drinking water community. Moraxellaceae had greater relative abundances in kitchen and bathroom communities (25.51% ± 5.56% and 23.28% ± 4.03%, respectively) compared to those in dust communities (4.19% ± 1.49%). Streptococcaceae were more prevalent in dust and kitchen communities (7.09% ± 1.74% and 6.57% ± 2.74%, respectively) compared to those in bathroom communities (1.87% ± 0.58%). The relative abundances of Staphylococcaceae in dust and bathroom communities (4.92% ± 0.76% and 5.46% ± 2.30%, respectively) were higher than those in kitchen communities (2.12% ± 0.62%).

Within the ten most abundant families within each habitat, there were many shared families between habitats. Also, there were a number of these families which were unique to one habitat. These unique families within particular habitats were Oxalobacteraceae (2.78% ± 0.5%) and Lactobacillaceae (2.15% ± 0.81%) in the dust communities (Figure 3.14), Flavobacteriaceae (6.99% ± 2.01%) and Caulobacteraceae (4.06 % ± 2.31%) in the kitchen communities (Figure 3.15), Methylobacteriaceae (10.53% ± 2.24%) and Intrasporangiaceae (4.22% ± 1.98%) in the bathroom communities (Figure 3.16) and Xanthobacteraceae (31.88% ± 6.23%), Bradyrhizobiaceae (9.84% ± 3.19%), Hyphomicrobiaceae (4.83% ± 0.88%), Polyangiaceae (3.13% ± 0.92%), Comamonadaceae (1.92% ± 1.19%), Thermaceae (1.90% ± 1.89%) and Chitinophagaceae (1.80% ± 0.94%) in the drinking water communities (Figure 3.17).

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Figure 3.14 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for dust communities (D) across eleven houses (H).

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Figure 3.15 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for kitchen surface communities (SK) across eleven houses (H).

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Figure 3.16 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for bathroom surface communities (SB) across eleven houses (H).

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Figure 3.17 Heatmap showing a representation of the relative abundance (%) of the ten most dominant classes/families with others (total of less dominant bacteria) for drinking water communities (DW) across eleven houses (H).

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3.2.4.2.3 Genus composition of household bacterial communities

Comparison of the taxonomic profiles for the 20 most abundant genera from the bacterial communities in dust (Figure 3.18), kitchen (Figure 3.19), bathroom (Figure 3.20) and drinking water (Figure 3.21) samples showed that the structure and composition of the bacterial communities were firstly associated with habitat type and secondly with respect to sampling sites (houses).

The most abundant genera identified in human residences, were Calothrix (18.38% ± 2.94%) in the dust community, Acinetobacter (19.94% ± 2.87%) in the kitchen community, Enhydrobacter (21.27% ± 4%) in the bathroom community and Xanthobacter (27.03% ± 2.92%) in the drinking water community. Among the 20 most abundant genera, the dust, kitchen, bathroom and drinking water communities each had 6, 4, 10 and 18 unique genera, respectively. The 6 unique genera detected only in the dust community were: Janthinobacterium, Pedobacter, Bacteroides, Thermogemmatispora, Chroococcidiopsis, and Neisseria. The 4 unique genera detected only in the kitchen community were: Chryseobacterium, Serratia, Arthrospira and Agrobacterium. The 10 unique genera detected only in the bathroom community were: Janibacter, Paracoccus, Rhodococcus, Dermacoccus, Novosphingobium, Varibaculum, , Peptoniphilus, Mycobacterium, and Roseomonas. The 18 unique genera detected only in the drinking water community were: Xanthobacter, Bradyrhizobium, Ancylobacter, Hyphomicrobium, Hydrogenophilus, Chondromyces, Thermus, Bosea, Halothiobacillus, Leptolyngbya, Chitinophaga, Azoarcus, Cellvibrio, Bdellovibrio, Tepidimonas, Methylosinus, Rhodothermus, and Limnobacter. In the household habitats, there was only one genus shared between all habitats which was Sphingomonas. The relative abundance of this genus varied between habitats. For instance, the relative abundances of Sphingomonas were 2.80 ± 0.30% in the dust, 2.95% ± 0.73% in kitchen, 18.64% ± 3.22% in the bathroom and 24.28% ± 2.37% in drinking water communities. Another six shared genera were all present in the bacterial communities in three habitats (dust, kitchen, and bathroom). These genera were: Staphylococcus, Streptococcus, Pseudomonas, Corynebacterium, Acinetobacter and Methylobacterium. Streptococcus had a higher relative abundance of 5.01% ± 0.84% in dust and 5.37% ± 1.61% in kitchen communities compared to a relative abundance of 1.63% ± 0.47% in the bathroom community. Conversely, the relative

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS abundance of Methylobacterium was greater (11.57% ± 1.82%) in the bathroom community than in dust (0.80% ± 0.09) and on kitchen surface (1.23% ± 0.70%) communities.

In the dust communities, the frequency of detection and the relative abundance of bacterial genera differed between houses (Figure 3.18). For instance, Calothrix were identified across all houses with relative abundances that ranged from 7.26% ± 1.22% in house 3 and 8.56% ± 1.53% in house 7 up to 35.07% ± 0.72% in house 1 and 40.43% ± 9.21% in house 6. In addition, house 3 had the highest relative abundance of Acinetobacter (18.63% ± 4.45%) and Pseudomonas (9.56% ± 1.36%). Furthermore, the genus Bacillus was only detected in house 2 (7.52% ± 2.62%) and house 8 (1.20% ± 0.61%). Moreover, the genus Lactococcus was detected in five houses (3,4,5,7 and 10), and with a highest relative abundance of 19.20% ± 9.93% in house 10 (Figure 3.18).

In the kitchen communities (Figure 3.19), the relative abundance and the frequency of detection of bacterial genera varied from house to another. For example, Acinetobacter was detected in all houses with the highest relative abundance of 58.19% ± 11.49% in house 9 and the lowest relative abundance of 4.91% ± 3.89% in house 10. Furthermore, house 6 had the highest relative abundances of 21.11% ± 0.28%, 15.99% ± 2.61% and 5.05 ± 1.14% of Enhydrobacter, Kocuria, and Agrobacterium, respectively. Also, house 3 had the highest relative abundance of Serratia (16.64% ± 5.35%) and Erwinia (13.82% ± 2.36%). Some genera such as Bacillus and Lactococcus were detected in only two houses with highest relative abundances of 11.87% ± 4.28% in house 2 and 18.73% ± 3.80% in house 10, respectively (Figure 3.19).

In the bathroom communities (Figure 3.20), variation in the frequency of detection and the relative abundance of bacterial genera between houses were observed. For instance, Enhydrobacter, Sphingomonas, and Methylobacterium were the most frequent and also the most abundant genera detected across all houses. The highest relative abundances of these genera were 44.47% ± 6.29%, 61.01% ± 10.09% and 30.46% ± 1.91% in houses 6, 2 and 10, respectively, while the lowest relative abundances were 2.14% ± 0.78% in house 1, 4.65% ± 3.50% in house 4 and 0.16% ± 0.03% in house 4. In addition, house 4 had highest relative abundances of Kocuria, Paracoccus, Dermacoccus and Micrococcus of 21.96% ± 16.53%, 17.26% ± 7.39%, 12.60% ± 7.15% and 11.49% ±10.82%, respectively. Moreover, Varibaculum were detected in only one house with a relative abundance of 5.17% ± 4.49% in house 9 (Figure 3.20).

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In the drinking water communities (Figure 3.21), differences in the relative abundance and the frequency of detection of bacterial genera were seen. For example, Xanthobacter and Sphingomonas were the most frequent and abundant genera detected in all houses. Houses 10 and 6 had the highest relative abundances of Xanthobacter and Sphingomonas of 56.82% ± 6.27% and 45.18% ± 1.20%, respectively, whilst houses 5 and 7 had the lowest relative abundances of these genera of 5.78% ± 1.85% and 6.99% ± 0.71%, respectively. There were also other bacterial genera which were identified in only one house such as Thermus, Azoarcus, Cellvibrio, Tepidimonas, Rhodothermus and Limnobacter with relative abundances of 21.26% ± 10.6%, 9.38% ± 0.42%, 8.77% ± 0.34%, 0.26% ± 0.13%, 4.25% ±0.10% and 4.84% ± 2.14% in houses 11, 5, 6, 4, 1 and 5, respectively. Furthermore, house 5 had highest relative abundances of four bacterial genera Chondromyces, Leptolyngbya, Azoarcus and Limnobacter of 11.80% ± 4.29%, 8.01% ± 2.76%, 9.38% ± 0.42 and 4.84% ± 2.14%, respectively (Figure 3.21).

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Calothrix Staphylococcus Streptococcus Erwinia Pseudomonas Corynebacterium Acinetobacter Sphingomonas Lactococcus Janthinobacterium Lactobacillus Pedobacter Bacillus Hymenobacter Bacteroides Enterobacter Thermogemmatispora Chroococcidiopsis Methylobacterium Neisseria

House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11

Figure 3.18 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in dust communities across eleven houses.

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Acinetobacter Enhydrobacter Chryseobacterium Pseudomonas Streptococcus Calothrix Sphingomonas Serratia Erwinia Enterobacter Arthrospira Kocuria Staphylococcus Micrococcus Lactobacillus Corynebacterium Bacillus Lactococcus Agrobacterium Methylobacterium

House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11

Figure 3.19 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in kitchen surface communities across eleven houses.

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Enhydrobacter Sphingomonas Methylobacterium Staphylococcus Corynebacterium Kocuria Janibacter Paracoccus Pseudomonas Streptococcus Rhodococcus Dermacoccus Micrococcus Acinetobacter Novosphingobium Varibaculum Moraxella Peptoniphilus Mycobacterium Roseomonas

House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11

Figure 3.20 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in bathroom surface communities across eleven houses.

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Xanthobacter Sphingomonas Bradyrhizobium Ancylobacter Hyphomicrobium Hydrogenophilus Chondromyces Thermus Bosea Halothiobacillus Leptolyngbya Chitinophaga Azoarcus Cellvibrio Bdellovibrio Tepidimonas Hymenobacter Methylosinus Rhodothermus Limnobacter

House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11

Figure 3.21 Bubble chart showing the proportions (relative abundance) of the 20 most abundant bacterial genera (based on 16S rRNA gene analysis) in drinking water communities across eleven houses.

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3.2.4.2.4 Variation of the composition of bacterial communities between human residences

Comparison of the composition of the household bacterial communities showed that although overall there were shared bacterial genera between houses; these bacterial genera were not the most abundant genera in some other houses (Figure 3.22).

In the household dust, the five most abundant bacterial genera that were shared in all houses included Calothrix, Staphylococcus, Streptococcus, Erwinia and Pseudomonas (Figure 3.22 a). However, there were other bacterial genera that were dominant in one house but not dominant in other houses. For example, the five genera with the highest relative abundance in house 10 were Lactococcus, Calothrix, Corynebacterium, Staphylococcus and Sphingomonas. This house shared only two bacterial genera (Calothrix and Staphylococcus) among the five most abundant genera within other houses. The genus Lactococcus was the most abundant genus identified in house 10 with a relative abundance of 19.2%, while it was not predominant in other houses (Figure 3.22 b).

In the household surfaces, the five most abundant bacterial genera found on household surfaces were Acinetobacter, Enhydrobacter, Pseudomonas, Streptococcus and Calothrix on kitchen surfaces (Figure 3.22 c), and Sphingomonas, Methylobacterium, Enhydrobacter, Staphylococcus and Corynebacterium on bathroom surfaces (Figure 3.22 e). However, there were some other bacterial genera that were found to be dominant in only some houses. For example, the five most dominant bacterial genera found on kitchen surfaces in house 4 were Chryseobacterium, Arthrospira, Micrococcus, Acinetobacter and Paracoccus which constitute approximately half of the bacterial 16S rRNA gene sequences identified in this house (Figure 3.22 d). Also, the five most dominant bacterial genera found on bathroom surfaces in house 4 were Kocuria, Paracoccus, Dermacoccus, Janibacter and Micrococcus which constitute ~ 75% of bacterial sequences identified in house 4 (Figure 3.22 f). Among the five dominant bacterial genera found in house 4 only one genus (Acinetobacter) in kitchen communities was found in other houses, while there were no shared genera among the five dominant bacterial genera in bathroom communities of house 4. These results show that the composition of bacterial communities found inside house 4 differed markedly from those in the other houses.

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According to the information obtained from the participants in the participant survey (Table 2.4), house 4 was the only house using eco-friendly cleaning products in this study.

The five bacterial genera with the highest relative abundance identified in drinking water communities were Xanthobacter, Sphingomonas, Bradyrhizobium, Ancylobacter and Hyphomicrobium (Figure 3.22 g). These were the five most abundant genera found in all drinking water samples across 10 out of 11 houses. However, the five most abundant genera identified in house 5 were Chondromyces, Azoarcus, Leptolyngbya, Bradyrhizobium and Hyphomicrobium (Figure 3.22 h). This house shared only two bacterial genera (Bradyrhizobium and Hyphomicrobium) among the five genera with the highest relative abundances within other houses. The three most dominant bacterial genera (Chondromyces, Azoarcus and Leptolyngbya) identified in house 5 which constitute approximately one-third of total bacterial genera, were not present among the shared dominant bacterial genera in drinking water communities from the other 10 houses.

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100% House 10 30 80% 25 60% 20

40% 15 10 20% 5

0% 0 Relative (%) Abundance Relative Relative (%) Abundance Relative 1 2 3 4 5 6 7 8 9 10 11 HOUSES Calothrix Streptococcus Staphylococcus Erwinia Pseudomonas Other c) d)

100% HOUSE 4 25 80% 20 60% 15

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20% 5 Relative (%) Abundance Relative

0% 0 Relative (%) Abundance Relative 1 2 3 4 5 6 7 8 9 10 11 HOUSES Acinetobacter Enhydrobacter Pseudomonas Streptococcus Calothrix Other e) f) HOUSE 4 100% 30

80% 25 20 60% 15 40% 10

20% 5 Relative (%) Abundance Relative 0%

Relative (%) Abundance Relative 0 1 2 3 4 5 6 7 8 9 10 11 HOUSES Sphingomonas Methylobacterium Enhydrobacter Staphylococcus Corynebacterium Other g) h)

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20% 5

0%

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1 2 3 4 5 6 7 8 9 10 11 (%) Abundance Relative HOUSES Xanthobacter Sphingomonas Bradyrhizobium Ancylobacter Hyphomicrobium Other Figure 3.22 Relative abundance of five dominant shared bacterial genera found in (a) dust, (c) kitchen, (e) bathroom and (g) drinking water samples across eleven houses. Relative abundance of five dominant bacterial genera identified in (b) dust of house 10, on (d) kitchen and (f) bathroom surfaces in house 4 and (h) drinking water from house 5. Each multi-colored stack bar graph represents the relative abundance of the five most abundant bacterial genera in each house. Error bars show ± SE (n = 3).

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3.2.5 Potential pathogens

A comprehensive list of names of potentially pathogenic bacteria (BG Chemie, 1995), was used to identify potential pathogens in the dust, kitchen bathroom and drinking water samples obtained from human residences. There were 65 potential pathogenic species detected across all of the human residence communities, 62 species within dust communities, 55 species within kitchen communities, 51 species within bathroom communities and 35 species within drinking water communities (Table 3.8).

Potentially pathogenic species identified with their highest relative abundances in individual houses in the household ecosystems were Acinetobacter ursingii (33.5%), Acinetobacter calcoaceticus (31.8%), Streptococcus vestibularis (31.2%), Streptococcus salivarius (31.1%), Acinetobacter septicus (23.3%), Brevundimonas vesicularis (22.1%), Chryseobacterium hominis (21.5%), Kocuria rhizophila (21.2%), Corynebacterium tuberculostearicum (18.3%) and Acinetobacter baumannii (17.4%) (Table 3.8). In addition, the most frequently detected potential pathogens among all 132 samples were Acinetobacter ursingii (131), Streptococcus tigurinus (128), Neisseria mucosa (113), Streptococcus vestibularis (111), Enterobacter amnigenus (109), Brevundimonas vesicularis (108), Kocuria rhizophila (108), Acinetobacter johnsonii (107), Corynebacterium tuberculostearicum (106), Acinetobacter septicus (105), Streptococcus salivarius (101), Staphylococcus aureus (100) and Staphylococcus caprae (100). Furthermore, the four potentially pathogenic species with highest number of DNA sequence reads in all samples were Acinetobacter ursingii, Acinetobacter septicus, Streptococcus salivarius and Streptococcus vestibularis with 207,060, 186,602, 122,538 and 114,322 reads, respectively (Table 3.8).

The frequency of detection of potentially pathogenic species varied between habitats. From the total of 65 potential pathogens identified, the highest frequency of detection (62 species) was within dust communities, followed by 55 species within kitchen communities, 51 species within bathroom communities and the lowest frequency of detected 35 species was within drinking water communities (Table 3.8).

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS Table 3.8 Prevalence and proportion of potentially pathogenic bacteria in human residence communities. Data show the number of samples in which potential pathogens were present, total number of sequences reads per pathogen, the highest relative abundance (%) of potential pathogen present in an individual habitat and the household habitats (a. dust, b. kitchen, c. bathroom and d. drinking water) where the potential pathogen is detected.

Species No. of samples Total Reads in Highest Presence in all present all samples relative habitats abundance (%)* Acinetobacter baumannii 96 47,713 17.4 (b) a,b,c,d Acinetobacter calcoaceticus 98 88,475 31.8 (b) a,b,c,d Acinetobacter johnsonii 107 55,192 7.3 (a) a,b,c,d Acinetobacter lwoffii 86 29,488 9.5 (a) a,b,c,d Acinetobacter septicus 105 186,602 23.3 (b) a,b,c,d Acinetobacter ursingii 131 207,060 33.5 (b) a,b,c Brevundimonas vesicularis 108 77,722 22.1 (b) a,b,c,d Capnocytophaga cynodegmi 16 487 0.2 (a) a,b,c Capnocytophaga gingivalis 26 138 0.02 (a) a,b,c Capnocytophaga sputigena 33 211 0.03 (a) a,b,c Chryseobacterium hominis 97 86,395 21.5 (b) a,b,c,d Clostridium chauvoei 28 1602 1.0 (a) a,b,c Clostridium difficile 26 77 0.01 (a) a,b,c Clostridium magnum 6 11 0.004 (c) a,c Clostridium perfringens 35 1,448 0.7 (a) a,b,c Corynebacterium afermentans 97 26,514 3.4 (c) a,b,c,d Corynebacterium amycolatum 12 23 0.003 (d) a,b,c Corynebacterium diphtheriae 1 9 0.007 (a) a Corynebacterium jeikeium 91 12,593 2.3 (c) a,b,c,d Corynebacterium minutissimum 35 240 0.1 (c) a,b,c Corynebacterium 37 1,848 0.8 (a) a,b,c pseudodiphtheriticum Corynebacterium 46 324 0.1 (a) a,b,c pseudotuberculosis Corynebacterium 106 69,761 18.3 (a) a,b,c,d tuberculostearicum Corynebacterium urealyticum 28 1,023 0.7 (a) a,b,c Corynebacterium xerosis 66 917 0.3 (a) a,b,c,d Diplorickettsia massiliensis 44 5,646 3.1 (a) a,b,c Enterobacter amnigenus 109 41,076 8.7 (b) a,b,c,d Finegoldia magna 95 12,888 3.7 (c) a,b,c,d Klebsiella pneumoniae 28 79 0.01 (b) a,b,c Kocuria rhizophila 108 95,423 21.2 (c) a,b,c,d

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Species No. of samples Total Reads in Highest Presence in all presence all samples relative habitats abundance (%)* Legionella anisa 7 51 0.1 (d) a,d Legionella cincinnatiensis 31 213 0.1 (d) a,b,c,d Legionella maceachernii 16 707 0.5 (d) a,d 16 45 0.01 (d) d Legionella sainthelensi 16 270 0.2 (d) a,b,d Legionella shakespearei 62 1,097 1.1 (d) a,b,c,d Legionella taurinensis 18 633 0.3 (d) a,c,d Mycobacterium ulcerans 5 22 0.01 (d) b,d Mycoplasma falconis 6 16 0.01 (a) a,b,c Mycoplasma putrefaciens 1 50 0.03 (a) a Neisseria cinerea 80 7,183 1.3 (a) a,b,c,d Neisseria mucosa 113 13,283 7.4 (a) a,b,c,d 5 264 0.2 (d) b,d Propionibacterium acnes 22 942 0.2 (c) a,b,c Pseudomonas aeruginosa 31 228 0.1 (a) a,b,c,d Pseudomonas alcaligenes 32 248 0.1 (a) a,b,c,d Pseudomonas mendocina 1 3 0.002 (a) a Rickettsia bellii 12 530 0.4 (a) a,b,d 1 2 0.001 (a) a 15 96 0.03 (d) a,b,d 27 1,656 0.9 (a) a,b,c 65 12,876 4.9 (b) a,b,c Serratia marcescens 29 374 0.1 (a) a,b,c Sphingomonas paucimobilis 27 106 0.02 (c) a,b,c,d Staphylococcus aureus 100 21,835 7.2 (c) a,b,c Staphylococcus caprae 100 10,039 3.1 (c) a,b,c Staphylococcus hyicus 8 17 0.003 (a) a Staphylococcus lugdunensis 24 56 0.01 (c) a,b,c Staphylococcus pasteuri 15 29 0.01 (c) a,b,c Staphylococcus saprophyticus 25 259 0.1 (a) a,b Staphylococcus sciuri 84 7,386 1.9 (b) a,b,c,d Stenotrophomonas maltophilia 77 13,414 2.0 (b) a,b,c,d Streptococcus salivarius 101 122,538 31.1 (b) a,b,c,d Streptococcus tigurinus 128 39,695 5.9 (a) a,b,c Streptococcus vestibularis 111 114,322 31.2 (b) a,b,c Wautersiella falsenii 75 25,224 13.6 (b) a,b,c,d * Letter indicates habitat in which highest relative abundance was observed.

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3.2.6 Negative controls

In this research, 14 negative controls, which were extraction blank PCR amplicons, (see section 2.2.5) were analysed by DNA sequencing. Before conducting DNA sequencing, PCR was performed on both household samples and on negative control samples. All negative controls had shown no bands with PCR. After attaching Illumina barcoded primers to the amplicons, the concentrations of PCR products for environmental 16S rRNA gene samples and for negative controls were quantified using a Qubit fluorometer. The mean concentrations of 16S rRNA gene samples were 47.2 ng µl-1 ± 11.7 ng µl-1 of DNA for drinking water, 43.5 ng µl-1 ± 5.3 ng µl-1 of DNA for dust, 39.4 ng µl-1 ±8.8 ng µl-1 of DNA for bathroom and 38.9 ng µl-1 ± 7.8 ng µl-1 of DNA for kitchen samples. Whereas the mean concentrations after PCR for negative controls were 3.6 ng µl-1 ± 0.2 ng µl-1 of DNA for dust negative controls, 3 ng µl-1 ± 0.5 ng µl-1 of DNA for surface negative controls and 1.5 ng µl-1 ± 0.5 ng µl-1 of DNA for drinking water negative controls. Note that PCR products from negative controls could not be visualised by agarose gel electrophoresis (see section 3.2.1).

The possible influence of contaminant sequences on the interpretation of the sequencing results was examined. From the Qubit results, it was clear that the concentrations of negative controls were much lower than the concentrations of 16S rRNA gene samples. However, the concentration of all PCR products from samples and negative controls were normalised before the pooling process which increased the relative proportion of DNA from negative controls in the sequencing run. Non-metric multidimensional scaling (nMDS) based on Bray-Curtis similarity indices showed that bacterial communities from negative controls were clearly and significantly distinct from bacterial communities of dust, kitchen, bathroom and drinking water samples (Figure 3.23). Consequently, the effect of contaminant sequences from negative controls on the interpretation of the sequence data is considered to be negligible.

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A) B)

C) D)

Figure 3.23 Non-metric multidimensional (nMDS) representation of the 146 bootstrap nMDS means, and 95% confidence ellipses of household bacterial communities derived from Bray-Curtis similarity indices. A) Dust community and negative controls, B) Kitchen Surface community and negative controls, C) Bathroom Surface community and negative controls and D) Drinking Water community and negative controls.

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3.3 Discussion

This research has investigated variation in the structure, diversity and composition of bacterial communities within and between different habitats (dust, surfaces and drinking water) in indoor human residences. Bacterial diversity differed markedly between the four habitats. In total, 244, 184, 163 and 166 bacterial families were identified in dust, kitchen, bathroom and drinking water communities, respectively. Overall, bacterial communities in these human residences were found to be habitat-specific rather than house-specific, with ANOSIM and PERMANOVA showing that the bacterial communities in the four household habitats varied significantly and strongly (ANOSIM R = 0.931, P = 0.001) between habitats (Table 3.5) and with Principal component analysis (PCA) plot and non-metric multidimensional scaling (nMDS) analyses showing strong clustering due to types of samples rather than the house of origin (Figures 3.5 and 3.6). This finding was consistent with results of previous studies which concluded that sampling location (habitat) was the most important factor affecting the structure of bacterial communities within kitchens and bathrooms (Flores et al., 2013; Gibbons et al., 2015). Flores et al. (2013) found that different sampling locations (habitat) harboured significantly different bacterial communities, whilst Gibbons et al. (2015) concluded that microbial communities were clustered with respect to sampling surface (habitat) (Flores et al. 2013; Gibbons et al., 2015).

Drinking water bacterial communities across the 11 houses were shown to have the greatest variation (ANOSIM R = 1, P = 0.001) from those bacterial communities in the other three habitat types (dust, kitchen and bathroom surfaces). This result likely reflects that the drinking water is a source of household bacteria rather than a sink (Falkinham et al., 2008; Ichijo et al., 2014). Moreover, the bacterial community in drinking water is affected strongly by external factors such as drinking water treatment (Shaw et al., 2015) and change in community structure during passage along the water distribution system (Berry et al., 2006; Poitelon et al., 2010; Hwang et al., 2012), which explains the unique structure of bacterial community in this habitat. Conversely, SIMPER analysis showed that the lowest dissimilarity (71.15%) between the different habitats was between dust and kitchen surfaces (Table 3.6); this may be partly because all kitchens sampled in this study were directly adjacent to the living area from where the dust samples were collected from (Adams et al., 2014).

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Neighbour-joining analyses within each habitat (dust, kitchen and bathroom surface and drinking water) showed samples clustering with respect to their house of origin (Figures 3.7, 3.8, 3.9, and 3.10). Similarly, Flores et al. (2013) found that bacterial communities from different locations within individual kitchen were more similar to each other when compared to bacterial communities from different kitchens. Considerable variation was seen in the bacterial communities between kitchens from different homes with an average similarity (SIMPER) of 37.29%. Similar findings of variation in microbial communities from kitchen surfaces have been illustrated by some previous studies. Dunn et al. (2013) collected samples from different locations within the house including kitchen counter, kitchen cutting board, a refrigerator, toilet seat, pillowcase, television screen, the surface of exterior and interior doors. They reported variation of bacterial communities within homes across different locations and found that the bacterial communities obtained from the kitchens were highly variable across the homes. In addition, Flores et al. (2013) sampled different surfaces within residential kitchens such as countertops, sinks, stoves. They similarly described variation between homes for a specific location (kitchen) and concluded that bacterial communities from different kitchens were more variable than communities from within the same kitchen. This might be due to the fact that the occupants of each house have unique skin microbiomes (Fierer et al., 2010), consume different foods (Wu et al., 2011) and have different cleaning practices and surface disinfection routines which could affect bacterial communities. Moreover, each kitchen has different design, surface materials and environmental conditions such as temperature and moisture. These factors could also affect the structure of bacterial communities found on kitchen surfaces (Dunn et al., 2013).

In the current study, multiple diversity and similarity metrics, namely Chaol richness estimates, Shannon-Weiner and Simpson diversity and Pielou evenness, have been generated. Although each index assesses different measures of biodiversity and community composition, the trends identified were consistent across indices. The bacterial diversity found in each of the eleven houses was high with average OTUs numbers of 2096 ± 168, 504 ± 84, 289 ± 51 and 239 ± 20 in dust, kitchen, bathroom and drinking water samples, respectively (Table 3.2). The numbers of OTUs varied markedly between habitats. For example, the average diversity of some of the communities, such as those found in house dust, was several times greater than that of other habitats. The bacterial communities in dust had the highest Shannon-Wiener diversity (4.97 ± 0.28), Simpson diversity (0.95 ± 0.02) and Pielou evenness (0.65 ± 0.03) when compared to

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS other habitats (Table 3.3 and Figure 3.4). The enormous diversity found in house dust could be due to the fact that settled dust is considered to be a more appropriate representation of indoor and outdoor airborne dust (Hospodsky et al., 2012). In addition, dust samples collected from floors contain both airborne dust as well as bacteria that originated from the occupants’ bodies (such as skin flakes or hair) or bacteria tracked into the house via shoes or clothing (Kelley & Gilbert 2013; Lax et al., 2014). Drinking water communities were the least diverse when compared to other habitats in this study with Shannon-Wiener diversity of 2.19 ± 0.11, Simpson diversity of 0.74 ± 0.02 and Pielou evenness of 0.40 ± 0.02 and average OTUs numbers of 239 ± 20 (Table 3.3 and Figure 3.4). The number of OTUs found in this study (239 ± 20) was substantially (three time) higher than OTUs numbers of 85 ± 60 identified previously in chlorinated water samples from different countries (Bautista-de los Santos et al., 2016). Several factors have been identified that may impact on diversity of bacterial community in disinfected samples such as origin of study, type of source water and disinfection type (Bautista-de los Santos et al., 2016). Conversely, the number of OTUs found in drinking water in the current study was much lower than OTUs numbers of 1328 ± 485 identified by Roeselers and colleagues in drinking water samples from domestic household taps from Netherlands (Roeselers et al., 2015). They also stated that the high diversity and richness observed in that study may be due to the absence of disinfectants in Dutch drinking water distribution systems. The significant difference in observed OTUs between the current study and Dutch study can be explained by other studies who have suggested that chlorinated drinking water systems are less species-rich than non-chlorinated systems (Pinto et al., 2012; Lautenschlager et al., 2013).

A total of 26 bacterial phyla were detected across all drinking water samples. The most dominant bacterial phyla found were Proteobacteria (92.3%), Actinobacteria (13.4%), Bacteroidetes (3.4%), Cyanobacteria (1.7%) and Firmicutes (0.8%) (Figure 3.13). Similarly, Holiger et al. (2014) detected the same taxa that have been identified in the current study but with different relative abundances. They observed that the most abundant bacterial phyla found in drinking water from public taps were Proteobacteria (35%), Cyanobacteria (29%), Actinobacteria (24%), Firmicutes (6%), and Bacteroidetes (3.4%) (Holiger et al., 2014). Proteobacteria was the most frequent and abundant phylum as similarly reported in many previous studies on drinking water (Williams et al., 2004; Berry et al., 2006; Kormas et al., 2010; Poitelon et al., 2010; Holiger et al., 2014; Proctor et al., 2015; Hull et al., 2017). The dominant phyla identified by William et al., (2004) were Proteobacteria, Cyanobacteria and

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Firmicutes in chlorinated distribution system water. In addition, Pinto et al., (2012) identified a total of 13 bacterial phyla (compared to 26 phyla in the current study) in chlorinated drinking water and the dominant phyla were Proteobacteria, Bacteroidetes, Actinobacteria, Nitrospira, Planctomycetes and Acidobacteria. Also, Roeselers et al., (2015) observed phyla such as Proteobacteria, Bacteroidetes, Actinobacteria, Nitrospira, Planctomycetes and Acidobacteria, Chloroflexi, Elusimicrobia, Chlamydiae, Firmicutes, and Verrucomicrobia in unchlorinated treated drinking water. Nevertheless, there were some dominant bacterial phyla shared between this study and previous studies (Pinto et al., 2012; Roeselers et al., 2015) such as Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes, there were also some other phyla such as Nitrospira, Planctomycetes and Acidobacteria reported as dominant phyla in the previous studies but which were not dominant in our study. In addition, Bautista-de los Santos et al. (2016) have conducted a meta-analysis of 14 studies that were conducted in different countries. All these studies used HTS of DNA extracted from drinking water samples. This meta-analysis concluded that Proteobacteria was the most dominant phyla found in all drinking water samples regardless of location of study and absence/presence or type of disinfectants (Bautista-de los Santos et al., 2016). Proteobacteria are very diverse, and include different classes including alphaproteobacteria which were the most dominant class detected in this study among drinking water communities with a mean relative abundance of 75.11% ± 7.19%. Similarly, Bautista-de los Santos et al., (2016) reported Alphaproteobacteria (32%) in high proportions in drinking water samples across all locations. In contrast, the class Gammaproteobacteria has been found to dominate bacterial community (75%) in treated water in South Australia (SA) (Shaw et al., 2015), while Betaproteobacteria (40%) was found to be the most dominant class in drinking water distribution systems in the U.S. (Pinto et al., 2012).

In the current study, the most dominant families identified, belonged to the class Alphaproteobacteria namely, Xanthobacteraceae (31.88 ± 6.23), Sphingomonadaceae (24.08 ± 3.51), Bradyrhizobiaceae (9.84 ± 3.19) and Hyphomicrobiaceae (4.83 ± 0.88) (Figure 3.17). In addition, the five major shared bacterial genera identified in drinking water communities included Xanthobacter, Sphingomonas, Bradyrhizobium, Ancylobacter and Hyphomicrobium (Figures 3.21 and 3.22 g). Xanthobacter are found within many different habitats, including water, sediments of lakes and seas and wet meadow soil (Wiegel 2006; Garrity et al., 2005; Doronina and Trotsenko 2003). The widespread distribution of Xanthobacter may be due to their physiology, with members of the genus able to grow under chemoheterotrophic and

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS lithoautotrophic conditions (Wiegel 2006). Most species of the genus Xanthobacter use organic acids, alcohols and carbohydrates, as carbon and energy sources (Loginova et al., 1982; Doronina et al., 1984). Sphingomonas isolates have previously been found in municipal drinking water distribution systems in Finland and Sweden (Koskinen et al., 2000). In addition, Sphingomonadales were found in high proportions (8%) in treated water samples from South Australia. (Shaw et al., 2015). Members of the Sphingomonadaceae family were reported to be found in drinking water from both chlorinated and chloraminated systems (Williams et al., 2004). Sphingomonadales are known to contain chlorine-resistant species (Shaw et al., 2014; Sun et al., 2013) and they are also found in drinking water samples containing high concentrations of monochloramine (Shaw et al., 2015) potentially explaining their high abundance in treated water samples. Members of Hyphomicrobiaceae which belongs to the dominant phylum Proteobacteria, were detected in both chloraminated and chlorinated water in U.S.A (Williams et al. 2004).

The composition and abundances of bacterial genera in drinking water varied between houses. For example, house 5 shared only two bacterial genera (Bradyrhizobium and Hyphomicrobium) which were among the five genera with the highest relative abundance within other houses. While the other three most dominant bacterial genera identified in house 5 were Chondromyces, Azoarcus and Leptolyngbya which constituted one-third of the total bacterial community (Figure 3.22 h). Furthermore, Houses 10 had the highest relative abundances of Xanthobacter of 56.82% ± 6.27%, whilst houses 5 showed the lowest relative abundances of this genus of 5.78% ± 1.85%. Variation in the composition and the relative abundance of bacterial communities in drinking water was detected in this study as well as in some previous studies (Kersters et al., 2006; Proctor and Hammes, 2015; Shaw et al., 2015). Shaw et al., (2015) showed that the bacterial community structures found in the drinking water distribution system samples collected from West Australia (WA) (8 samples) were different from those collected from South Australia (SA) (10 samples), with some taxa present in one system but undetectable in the other. This variation could be explained by the fact that there are variety of factors affect bacterial communities in water samples, such as surface material (Yu et al., 2010), source water (Eichler et al., 2006), temperature and season (Henne et al., 2013, Ling et al., 2016), nutrients (Szewzyk et al., 2000; Escobar et al., 2001), disinfectants (Eichler et al., 2006), hydraulic regimes (Douterelo et al., 2016, Douterelo et al., 2013), and stagnation time (Lautenschlager et al., 2010).

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In house dust, the five predominant bacterial phyla were Proteobacteria (38.1%), Firmicutes (19.8%), Cyanobacteria (19.4%), Actinobacteria (13.4%) and Bacteroidetes (6.5%). Hence, the bacterial communities of house dust in this study were dominated by Gram-negative bacterial phyla such as Proteobacteria, Cyanobacteria and Bacteroidetes rather than Gram- positive bacterial phyla such as Firmicutes and Actinobacteria (Figure 3.13). In contrast, some previous studies have indicated that the house dust bacterial community is dominated by Gram- positive bacterial species primarily Actinobacteria and Firmicutes with lower proportions of Gram-negative bacterial species although Proteobacteria was still the most prevalent individual phylum (Pakarinen et al., 2008; Rintala et al., 2008; Täubel et al., 2009; Konya et al., 2014; Adams et al., 2014; Veillette et al., 2013). Rintala and colleagues found that the bacterial community in indoor dust from office rooms in Finland was dominated by Gram- positive species (approximately 60% of the OTUs) while 40% of the OTUs were of Gram- negative origin (Rintala et al., 2008). In another study, Gram-positive sequences represented the majority of bacteria in floor dust from homes in Russian Karelia and accounted for approximately 45% of bacterial sequences (Pakarinen et al., 2008). Firmicutes (include many human and pet-associated bacteria) were identified as the phylum which was relatively more abundant in house dust as well as in many different indoor ecosystems (Ley et al., 2006). Many Proteobacteria and Actinobacteria are common in soil (Doughari et al., 2009; Ghai et al., 2011). The predominance of Proteobacteria and Actinobacteria present in the house microbiome, suggests frequent tracking in of soil bacteria (by pets and/or occupants) through open doors and wind dispersal of soil bacteria through open doors and/or windows (Rintala et al., 2012). Prior estimates are that about 20–85% of indoor soils (dust) come from outdoors (Hunt et al. 2006).

House dust was found to be the most diverse habitat in this study. In the current study, the most abundant bacterial genera that were detected in this habitat include Calothrix, Staphylococcus, Streptococcus, Erwinia, Pseudomonas, Corynebacterium, Acinetobacter, Sphingomonas, Lactococcus, Janthinobacterium and Lactobacillus (Figure 3.18). Similarly, many previous studies showed that the most common genera in dust were Corynebacterium, Peptostreptococcus, Lactobacillus, Lactococcus, Staphylococcus, Propionibacterium, Streptococcus, Sphingomonas, Janthinobacterium, Stenotrophomonas, Pseudomonas, and Neisseria. which are most commonly associated with gut and skin bacterial communities (Pakarinen et al., 2008; Rintala et al., 2008; Täubel et al., 2009; Noris et al., 2011; Konya et

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS al., 2014; Adams et al., 2014). These genera are mainly human-derived bacteria (Lax et al., 2014) as well as soil bacteria which were perhaps tracked into the houses from outdoor environments (Rintala et al., 2012). In contrast, Calothrix, Erwinia and Acinetobacter were identified among the genera with highest abundances in the current study in the household dust, which not have been reported as dominant genera in previous studies.

In the current study, we observed variation in the relative abundance of bacterial taxa in household dust. There were some bacterial genera that were dominant in one house but not dominant in other houses. For example, the five most abundant bacterial genera that were shared in all houses were Calothrix, Staphylococcus, Streptococcus, Erwinia and Pseudomonas (Figure 3.22 a). However, house 10 shared only two bacterial genera among the five genera (Calothrix and Staphylococcus) with highest abundances to those in other houses, while the genus Lactococcus was the most abundant genus identified in this house (19.2%), but not in other houses (Figure 3.22 b). This might be due to the fact that the house dust microbiome has been found to be influenced by several external factors include: such as human activities (Lax et al., 2014), pets (Ownby et al., 2013), cleaning methods and habits (Smedje and Norba¨ck , 2001; Cheong and Neumeister-Kemp, 2005), ventilation systems (Chen and Zhao, 2011), type of building (Wu et al., 2005), geographical location (Kärkkäinen et al., 2010) and seasonal variation (Kaarakainen et al., 2008).

In this study, the presence of pets (e.g., cats and dogs), did not influence the relative abundance of the house dust microbiome (Tables 2.4, 3.2 and 3.3). The similarity index (SIMPER analysis) based on OTUs at ≥ 97% identity of the bacterial communities was similar between houses with and without pets (Tables 2.4 and 3.7). Many previous studies investigated the impact of the presence of pets on the household dust microbiome (Chew et al., 2003; Giovannangelo et al., 2007; Ownby et al., 2013; Fujimura et al., 2010). Fujimura and colleagues (2010) have observed significant differences in relative abundance of different taxa between households with dogs and households with no pets and they identified 337 taxa (including Proteobacteria, Actinobacteria, Firmicutes, Verrucomicrobia, Bacteroidetes and Spirochaetes) which significantly increased in abundance in houses in which dogs were present. Conversely, they showed that the presence of cats within a home did not influence the house dust microbiome (Fujimura et al., 2010). However, our study could not distinguish the

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS influence that different pet types (i.e., dogs or cats) may have on the house dust microbiome, due to an insufficient number of houses with pets being sampled, to permit statistical analysis.

The results from the current study suggested occupants are major sources of the bacteria within communities identified in household dust and surfaces. Similarly, many studies concluded that occupants are a main source of bacteria present within a household ecosystem (Noss et al., 2008; Tringe et al., 2008; Adams et al., 2014; Lax et al., 2014; Hospodsky et al., 2012; Noris et al., 2011; Dunn et al., 2013). Lax and colleagues suggested that there is similarity between the microbial communities found in the feet, hands and noses of inhabitants and the communities present in samples in their homes (2014; Lax et al., 2014). In addition, a study conducted by Hospodsky et al., (2012) hypothesised that direct shedding of bacteria on human skin of occupancy strongly influences the character and concentration of bacteria present within indoor air and floor dust. They showed that direct shedding of desquamated skin cells from human occupancy resulted in a considerable increase of bacterial numbers (Hospodsky et al., 2012). In the current study, a possible influence of the number of occupants on the numbers of OUTs was observed in household dust. Similarly, other studies have described that buildings containing greater numbers of occupants (e.g., such as in schools), tend to have a higher bacterial burden (Noris et al., 2011; Kembel et al., 2014). Additionally, another study has shown that the bacterial composition of indoor air tends to resemble that of outdoor air in well- ventilated spaces, irrespective of occupancy (Meadow et al., 2014). The frequency of cleaning was another possible factor observed in this study to influence the structure and diversity of microbial communities in household dust. In the current study, house 8 which had the lowest frequency of cleaning of house dust showed the highest number of observed OTUs (3167 ± 362), species numbers (1092 ± 35) and Shannon-Wiener diversity (5.92 ± 0.59), (Tables 3.2 and 3.3). Sordillo et al. (2010) stated that less frequent cleaning (vacuuming and dusting) may explain higher levels of bacteria in the house. They found that cleaning the bedroom at least once a week was linked to lower levels of Gram-negative bacteria. In addition, Wu et al. (2012) who collected dust samples from mattresses found that daily vacuum cleaning of mattresses was associated with a significant reduction of bacterial endotoxin in the dust. Furthermore, Dunn et al., (2013) found that regularly cleaned surfaces harboured lower bacterial diversity when compared to infrequently cleaned surfaces.

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Kitchen surfaces host remarkably diverse bacterial communities that have also previously reported (Flores et al., 2013). Our data illustrate that the dominant phyla in kitchen surfaces in the 11 houses were Proteobacteria (62.5%), Firmicutes (15.7%), Actinobacteria (9.5%) and Bacteroidetes (8.3%), Cyanobacteria (3.5%) (Figure 3.13). Within these dominant phyla, the most abundant families were Moraxellaceae (25.51%), Enterobacteriaceae (11.38%), Flavobacteriaceae (6.99%), Streptococcaceae (6.57%), Sphingomonadaceae (5.29%), Pseudomonadaceae (5.21%) and Micrococcaceae (4.84%) (Figure 3.15). Previous studies have also identified some of these as the dominant bacterial families on kitchen surfaces (Dunn et al., 2013; Flores et al., 2013). The most abundant bacterial families identified on residential kitchens by Flores et al. (2013) were Moraxellaceae (14%), Streptococcaceae (10%), Micrococcaceae (6%) and Flavobacteriaceae (4%). All of these bacterial families have been detected in our study (as mentioned above) but with different relative abundances. In addition, Streptococcaceae, Corynebacteriaceae, Lactobacillaceae and (which include Moraxellaceae) were identified on kitchen surfaces (Dunn et al., 2013). In the 11 houses in this current study, kitchen surfaces were, in particular, dominated by human- and food-associated bacteria as has been previously suggested in other studies (Cole et al., 2003; Dunn et al., 2013; Flores et al., 2013; Scott et al., 2009). To determine the relative importance of different sources of bacteria on kitchen surfaces, we identified indicator genera from soil (Acinetobacter and Pseudomonas), from human body (Enhydrobacter, Chryseobacterium, Staphylococcus, Streptococcus and Corynebacterium), from food (Lactobacillus, Lactococcus and Bacillus) and from tap water samples (Sphingomonadaceae, Methylobacterium). In addition, the most abundant genus detected on kitchen surface in this study was Acinetobacter (19.94% ± 2.87%). Many Acinetobacter spp. are known to survive on surfaces for extended periods of time. It has been successfully cultivated from a variety of dry and wet surfaces (e.g. stainless steel, ceramic, rubber) up to two weeks after inoculation (Kramer et al., 2006; Santo et al., 2010; Wendt et al., 1997). Thus, the results of this study show that the major bacterial taxa found on kitchen surfaces are similar to those found in other indoor environments, and many of the dominant taxa appear to be able to persist on surfaces for extended periods of time.

In the kitchen surface communities, the relative abundance and the frequency of detection of bacterial genera varied from house to another. For instance, Acinetobacter were detected in all houses with a highest relative abundance of 58.19% ± 11.49% in house 9 and a lowest relative abundance of 4.91% ± 3.89% in house 10. Moreover, some genera such as Bacillus and

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Lactococcus were detected in only two houses with highest relative abundances of 11.87% ± 4.28% in house 2 and 18.73% ± 3.80% in house 10, respectively (Figure 3.19). In addition, among the five dominant bacterial genera found in house 4 which constituted approximately 50% of total bacteria identified in this house, only one genus (Acinetobacter) in kitchen communities was shared with the communities in the other 10 houses. This might be due to different building materials and environmental conditions e.g., painted surfaces (Odokuma et al., 2013), cleaning chemicals and frequency (Røssvoll et al., 2015), light intensity (Maclean et al., 2014), temperature (McEldowney and Fletcher 1988), relative humidity (Dannemiller et al., 2017) and carbon sources (Eiler et al., 2003) which can cause various selective pressures for microorganisms if varied over wide ranges, which can result in differential survival and persistence rates (Adams et al., 2016).

A total of 27 phyla were observed on bathroom surfaces across 11 houses with the most abundant phyla belonging to only three bacterial phyla; Proteobacteria, Actinobacteria and Firmicutes with mean relative abundance of 66%, 21.7% and 10.3%, respectively (Figure 3.13). Previous cultivation-dependent and –independent studies have also frequently identified these as the dominant phyla in a variety of indoor environments (Kelley et al., 2004; McManus et al., 2005; Lee et al., 2007; Rintala et al., 2008). In addition, Flores et al. (2011) showed that the most abundant phyla (from a total of 19 phyla) identified in public restrooms were Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria. Within these dominant phyla, taxa typically associated with either human skin (e.g. Moraxellaceae (23.28% ± 4.03%), Staphylococcaceae (5.46 ± 2.30), Corynebacteriaceae (5.12 ± 2.31) Streptococcaceae (1.87 ± 0.58) or with water (e.g., Sphingomonadaceae (20.22% ± 4.83%), Methylobacteriaceae (10.53 ± 2.24)) were abundant on all bathroom surfaces. (Figure 3.16). The most abundant bacterial genera found on bathroom surfaces were mainly human-related genera confirming previous findings (Gibbons et al., 2015; Flores et al., 2011; Sinclair et al., 2011). The predominant bacterial genera found in all bathroom surface samples include Sphingomonas, Methylobacterium, Enhydrobacter, Staphylococcus, and Corynebacterium (Figure 3.20). The most abundant genus identified on bathroom surfaces in this study was Enhydrobacter with average relative abundance of 21.27% ± 4%. In the current study, variations in the frequency of detection and the relative abundance of bacterial genera between houses were observed. For example, Enhydrobacter was the most frequent and abundant genus detected across all houses. The highest relative abundance of this genus was 44.47% ± 6.29% in house 6, whereas the

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS lowest relative abundance was 2.14% ± 0.78% in house 1. Furthermore, the genus Kocuria (22%) was found to be dominant in one house. This house was the only house using eco- friendly cleaning products which may be associated with the abundance of this genus on the bathroom surface. Interestingly, Mycobacterium was detected on bathroom surfaces. This genus includes pathogens known to cause serious diseases in human. This genus was found in showerheads in two previous studies (Feazel et al., 2009; Thomson et al., 2013) and we have found it on the shower tiles in some houses. People interact with microbes through inhalation of aerosols while taking showers where the presence of such bacteria can pose health risks for patients at risk.

Despite the significant variations between habitats within the same house, there were common bacterial genera found across different habitats. For example, among the 20 most abundant bacterial genera there were 13 genera (Calothrix, Staphylococcus, Streptococcus, Erwinia, Pseudomonas, Corynebacterium, Acinetobacter, Sphingomonas, Lactococcus, Lactobacillus, Bacillus, Enterobacter and Methylobacterium) which were shared between both dust and kitchen surface communities (Figures 3.18 and 3.19). This is possibly due to most kitchens being open to the living areas from where dust was collected. Furthermore, the genus Sphingomonas was the only abundant genus that was detected across all habitats. The genus Sphingomonas is widely distributed in nature and has previously been isolated from a variety of environmental habitats such as air (Kim et al., 2014), soil (Mueller et al., 1997; Yoon et al., 2008), plants (Takeuchi et al., 1995; Zhu et al., 2015), river water (Tabata et al., 1999), spring water (Sheu et al., 2015) and drinking water (Koskinen et al., 2000; Gauthier et al., 1999) house dust (Barberan et al., 2015) kitchen (Flore et al., 2013) and shower curtains (Kelley et al., 2004). The widespread distribution of Sphingomonas can be explained by their ability to survive under harsh environmental conditions such as low temperatures, low nutrient concentrations and in toxic chemical environments (Bowman et al., 1997; Laskin and White 1999).

In the present study, we identified the presence of 65 pathogenic or potentially pathogenic bacterial species in dust, kitchen bathroom and drinking water samples across 11 human residences. In the present study, we identified the presence of several Staphylococcus spp. (S. aureus, S. caprae, S. hyicus, S. lugdunensis, S. pasteuri, S. saprophyticus and S. sciuri) in dust

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Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS and surface samples. Among them, pathogenic S. saprophyticus, commonly present in the human urogenital and gastrointestinal tract, in food products such as cheese, meat, and vegetables, and in the environment, and which has been associated with urinary tract infections (UTI), particularly in young women (Gillespie et al., 2009; Raz et al., 2005). Human behaviours such as outdoor swimming, sexual intercourse, and work in meat production may be associated with UTI caused by S. saprophyticus (Hedman et al., 1991). S. saprophyticus in this study were detected in 25 dust and bathroom surface samples with highest relative abundance at 0.1% in dust. One of the most infamous antibiotic resistant strains among staphylococci, MRSA, has been known to transmit from people to people by skin contact, fomite to people contact, or through touching of contaminated surfaces (Eady and Cove 2003; Miller et al., 2008). The transmission of S. aureus has been reported from public venues such as gymnasia, playgrounds, beaches, schools, day-care centres, and athletic facilities (David et al., 2008; Montgomery et al., 2010; Newsome et al., 2009; Rackham et al., 2010; Stanforth et al., 2010; Ryan et al., 2011). Moreover, MRSA was also identified in indoor environments such as kitchen and bathroom surfaces (Scott et al., 2009). Interestingly, S. aureus can survive on inanimate surfaces for a long time (Desai et al., 2011; Huang et al., 2006). The human infection related to community-associated MRSA is distributed widely throughout the world (Bures et al., 2000) Moreover, S. aureus is implicated to skin and soft-tissue infections (Hersh et al., 2008). S. aureus in this study was found in 100 samples from dust, kitchen and bathroom surfaces with highest relative abundance at 7.2% in a bathroom surface sample. The presence of S. aureus in this study is a potential public health concern. However, the high prevalence of Staphylococcus spp. in these samples is not surprising as most of these species are part of the human normal flora. In addition, food-borne pathogenic bacteria such as Salmonella enterica associated with cattle and poultry (Scallan et al., 2011), have been detected in this study in 65 dust, kitchen and bathroom surface and drinking water samples with highest relative abundance at 4.9% in a kitchen surface sample. Another pathogenic bacterium, Klebsiella pneumoniae associated with urinary tract infections (Lin et al., 2014) and bacteremic liver abscess (Wang et al., 1998), was identified in our study in 28 dust and surface samples but with low relative abundance (0.01%). The presence of these bacteria may also be a public health concern. 80% of Salmonella and Campylobacter infections are acquired in the home in European countries as England, Wales and the Netherlands (Kagan et al., 2002). Furthermore, the genus Legionella which causes Legionnaires’ disease, was associated with Australia’s largest outbreak of Legionnaires’ disease (Greig et al., 2004). In the this study some Legionella spp. (L. anisa, L. cincinnatiensis, L. maceachernii, L. pneumophila, L. sainthelensi, L. shakespearei and L. 116

Chapter 3 Variation in Bacterial Community Structure and Diversity Using NGS taurinensis) have been detected in drinking water samples. Legionella pneumophilia is an environmental bacterium capable of causing a range of adverse health effects from severe Legionnaires’ disease to moderate influenza-like symptoms in humans. Legionella pneumophilia has been detected in 16 drinking water samples in this study albeit with a low relative abundance of 0.1%. A recent survey also identified detectable levels of Legionella pneumophila in about 40% of homes sampled (Donohue et al., 2014). Furthermore, there are some dust-borne potential or opportunistic pathogens which are previously reported from indoor environments (Andersson et al., 1999) which were also identified in the current study including Acinetobacter, Pseudomonas, Micrococcus, Staphylococcus, Klebsiella, and Bacillus. In the current study, we detected some potential pathogenic Acinetobacter spp. (A. baumannii, A. calcoaceticus, A. johnsonii, A. lwoffii, A. septicus and A. ursingii) in all habitats. The highest relative abundances of the species A. baumannii (17.4%), A. calcoaceticus (31.8%), A. septicus (23.3%) and A. ursingii (33.5%) were found on kitchen surfaces, while the highest relative abundances of the species A. johnsonii (7.3%) and A. lwoffii (9.5%) were detected in dust samples. Members of the genus Acinetobacter are considered as a leading cause of hospital-acquired infections. In particular, Acinetobacter baumannii is an important opportunistic pathogen in hospitals which is rapidly becoming resistant to commonly prescribed antibiotics (Rajamohan et al., 2009). A. baumannii accounts for approximately 80% of all reported Acinetobacter infections (Camp and Tatum, 2010). In addition, A. baumannii has been isolated from different environmental locations (Byrne-Bailey et al., 2009; Girlich et al., 2010; Choi et al., 2012). In this study as mentioned above, A. baumannii has been found in a high relative abundance on kitchen surfaces which poses a major public health concern. The source of this bacterial species could be from food brought to the kitchen or through frequent contact between human skin and kitchen surfaces. In a previous study A. baumannii was isolated from vegetables collected in supermarkets, greengrocers, and private gardens (Berlau et al., 1999). Furthermore, a recent study conducted in South Korea reported the presence of A. baumannii on inanimate surfaces that are often in contact with humans (Choi et al., 2012). Moreover, a study performed in New York City reported that 10.4% of community residents carried A. baumannii on their hands (Zeana et al., 2003).

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A number of conclusions can be drawn from this high-throughput study. First, the structure, diversity and composition of bacterial communities vary within and between different habitats in the human residences. In addition, the bacterial communities in the human residences were habitat-specific rather than house-specific. However, within each habitat (dust, kitchen and bathroom surface and drinking water), samples clustered with respect to their house of origin. Moreover, the bacterial communities in dust had the highest diversity, while drinking water communities were the least diverse. The numbers of OTUs varied markedly between habitats. Furthermore, drinking water bacterial communities differed significantly from those bacterial communities in the other three habitat types. The source of bacteria in human residences are mainly from human-derived and soil bacteria in dust, from soil, human skin, food and tap water in kitchen surface samples, and in bathroom surface samples bacteria were mainly associated with those from either human skin or from water. In addition, in the current study, pathogenic or potentially pathogenic bacteria have been detected in dust, kitchen bathroom and drinking water samples which represent a major public health risk in our homes. The public health concern could increase if those bacteria identified in this study poses antibiotic resistance genes. This research question will be addressed in the next chapter.

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CHAPTER 4 MOLECULAR IDENTIFICATION AND QUANTIFICATION OF ANTIBIOTIC RESISTANCE GENES IN HUMAN RESIDENCES

4.1 Introduction

The discovery of antibiotics is one of the 20th century’s most significant achievements in public health (Gandara et al., 2006). However, pathogens increasingly become resistant to the medicines to which they were previously vulnerable (Ventola, 2015). Bacteria have developed antibiotic resistance as a consequence of misuse and overuse of antibiotics in both human and veterinary situations (Martínez, 2008; Cantas et al., 2013). In the majority of instances, antibiotic resistance does not originate in human pathogens; rather, it is believed that the issues of antibiotic resistance that present in the clinical settings originate from bacteria within the environment (Walsh, 2013). Antibiotic resistance occurs in bacteria through mutation because of selection pressure caused by the overuse and misuse of antibiotics. It also can be acquired from other bacteria through HGT (see section 1.7.3).

Many different classes of antibiotic resistance genes have previously been detected in diverse environmental communities, and these genes are similar, or identical, to those found in pathogenic bacteria (Canton, 2009; Boulund et al., 2012; Segawa et al., 2013; D’Costa et al., 2011; Mukherjee et al., 2014). Communities of both commensal and environmental bacteria have been shown to possess a wide variety of antibiotic-resistant genes, many of which have not yet been seen in clinical situations (Forsberg et al., 2012; Wichmann et al., 2014). Forsberg et al. (2012) found various human pathogens and multidrug-resistant soil bacteria to contain identical antibiotic resistance genes which were present as gene cassettes conferring resistance to five classes of antibiotics (Forsberg et al., 2012). This presents strong evidence for the transfer of resistance genes between bacteria in the natural environment and pathogenic bacteria. Moreover, antibiotic exposure in these natural communities could be a selective pressure that leads to a greater abundance and variety of antibiotic resistance genes (Gillings and Stokes, 2012). For example, sub-inhibitory concentrations of quinolones in water resulted in the marine bacteria, Shewanella, developing not only quinolone resistance but also subsequently transmitting this resistance via horizontal gene transfer to Enterobacteriaceae

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(Nordmann and Poirel, 2005). It is probable that the environment has an enduring role as the reservoir of resistance genes and these will continue to be transferred to pathogenic species of bacteria (Allen et al., 2010).

The direct relationship between antimicrobial resistance in natural and clinical environments has been firmly established; soils and water serve as a reservoir for resistant bacteria, capable of transferring resistance genes to other bacteria (Wellington et al., 2013). As many antibiotics used in clinical settings were originally derived from soil microorganisms with antimicrobial properties, soil microbiota continue to be of particular significance (Dantas and Sommer, 2014). Further research into environmental bacteria is warranted as the effect of antibiotic usage has implications for the environment. Patients excrete antibiotics that migrate from sewage systems into the environment, to become a selective pressure for resistance genes in environmental bacteria (Reinthaler et al., 2003). This situation is compounded by the ubiquitous use of various antimicrobials in agriculture and animal feeds applying further selective pressure causing even more strains to become resistant to antibiotics (Kummerer, 2004). Bacteria with resistance genes, that may be inherent or acquired, are then transferred by direct contact or through the food chain to humans (Wellington et al., 2013). Aquatic ecosystems are also antibiotic resistance-gene reservoirs (Garcia-Armisen et al., 2011, Girlich et al., 2011, Pruden et al., 2006, Storteboom et al., 2010, Walsh et al., 2011, Xi et al., 2009, Dodd, 2012), as demonstrated by the study conducted by Stoll et al. (2012) in which the genes conferring resistance to β-lactams (ampC), chloramphenicol (catII), macrolides (ermB), sulfonamides (sulI & sulII) and trimethoprim (dfrA1) were found to be widely distributed in surface water isolates in both Germany and Australia. Genes encoding antibiotic resistance have been identified in numerous animal and agricultural environments (Heuer et al, 2011).

Bacteria can acquire resistance genes from sources other than natural environments. The trend of urbanisation in which an increasing proportion of the human population spends the bulk of their lives indoors, presents greater opportunities for commensal and pathogenic bacteria to exchange resistance genes within that environment. Studies of antibiotic resistance in the indoor environment have historically focused on pathogenic species (Boyce 2007; Dietze et al. 2001; Goebes et al., 2011; Haiduven, 2009), such as MRSA and norovirus, which have been found to be present on hard surfaces, such as floors (Coughenour et al. 2011; Dancer 2009). Hospitals are not exempt from the problems with robust pathogenic bacteria such as MRSA

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Chapter 4 Molecular Identification and Quantification of ARGs and VRE being identified on surfaces (Lemmen et al., 2004; Yazgi et al. 2009). According to Gao et al. (2018), , Arcobacter butzleri, Bacillus cereus, Corynebacterium minutissimum, Escherichia coli, Staphylococcus saprophyticus, Pseudomonas aeruginosa and Streptococcus pneumoniae are the microorganisms most frequently found in air samples collected from hospitals (Gao et al., 2018). However, to understand the full extent of the abundance and diversity of resistance genes requires in depth exploration of the bacterial communities present in these environments (Bengtsson-Palme and Larsson, 2015). Although the general human population may be most concerned by antibiotic resistance in microbes that cause disease, the phenomenon is ubiquitous in the environment. The environmental microbiome is regarded as the natural reservoir of genes that confer antibiotic resistance and this reservoir is more diverse than previously anticipated (Lin et al, 2015).

The prevalence of antibiotic resistance genes and bacteria found in indoor environments has been the subject of a number of prior culture-dependant and culture-independent studies. In one such study, Messi et al. (2015) isolated 280 bacterial strains, representing different genera, from air in a hospital operating theatre; of these, only 14 (5%) of the strains were found to be sensitive to all of the antibiotics tested. Meanwhile the other 266 strains were resistant to three (13%), four (14%), five (9%) and six (10%) antibiotics (Messi et al., 2015). The presence of multiple antibiotic-resistant bacteria in air samples highlights the potential for the nosocomial transfer of resistance genes to pathogenic bacteria, posing conceivable risks to patients. These results echo those of Gilbert et al. (2010) who collected isolates bearing the antibiotic resistance genes ermX, ermF and tetG from hospital air samples (Gilbert et al., 2010). Gao et al. (2018) also found that samples of hospital air in China yielded species bearing the antibiotic resistance genes, mecA and blaCTX-M (Gao et al., 2018). Dust within the hospitals is regarded as a reservoir of antibiotic resistance genes (Drudge et al., 2012). In a culture-independent study, Drudge et al., (2012) identified genes conferring resistance against aminoglycosides (aac(6’) and aph(2”)), macrolide–lincosamide–streptogramin B (ermA) and methicillin A (mecA) within numerous dust samples from a hospital air filter in Canada; the authors of the study deduced that the dust could be acting as reservoir for antibiotic resistance genes (Drudge et al., 2012).

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In terms of considering the reservoirs of microorganisms present in household environments, researchers have primarily focused on pathogenic and allergy aspects, as opposed to the potential for antibiotic resistance. For instance, Scott et al. (2009) obtained samples of MSSA, and MRSA, Pseudomonas and Enterobacteriaceae, which are of clinical interest, from household surfaces (Scott et al., 2009). The majority of studies investigating human residences as a source of antibiotic resistance have studied isolates sampled from air. Gandara et al. (2006) collected bioaerosol samples from different homes and identified bacteria resistant to penicillin, ampicillin and cefaclor (Gandara et al., 2006). According to Lis et al. (2009) at 40%, the prevalence of airborne methicillin-resistant (MR) strains in homes of people who had contact with a hospital environment was higher than the reference homes (in which residents had no contact with hospital for last two years), of which only 12% had MR strains present. A high frequency (33%-100%) of the mecA gene was present in the MR strains collected (Lis et al., 2009). In addition to household air, household dust was also sampled for the presence of antibiotic resistance genes. In a study conducted by Veillette et al. (2013), in Queensland, Australia, using PCR to detect antibiotic resistance genes in vacuum dust, they found that household dust samples were positive for antibiotic resistance genes such as ermB (encoding erythromycin resistance) and tetA and tetC genes (encoding tetracycline resistance) (Veillette et al., 2013).

There is still a knowledge gap regarding the prevalence and diversity of the antibiotic resistance gene pool in the human residence. So, this will be the first Australian study to assess antibiotic resistance genes found in human residences, to the best of my knowledge, across different type of household samples (dust, surface and drinking water samples) within and between different houses. This study aims to address this knowledge gap by conducting a comprehensive assessment of the antibiotic resistome within human residences across different habitat types and between different locations (houses). The findings of this research will contribute towards global efforts to improve understanding of the ecology of antibiotic resistance within the built environment.

The main aim of this study was to determine the prevalence, distribution and abundance of the antibiotic resistance gene pool within and between different houses via a culture-independent approach using PCR and qPCR. This study aims to answer the following questions, firstly, what type of resistance genes can be found in human residences? Secondly, which are the most common and abundant antibiotic resistance genes present? Thirdly, how do these vary within 122

Chapter 4 Molecular Identification and Quantification of ARGs and between houses and different household habitats? The prevalence and distribution of antibiotic resistance genes within and between different houses was determined using endpoint PCR. Subsequently, the abundance of these antibiotic resistance genes in human residences was determined via quantitative PCR. Twelve antibiotic resistance genes which, cover nine different classes of antibiotics, were chosen to assess the antibiotic resistance in human residences using different type of samples (dust, surface and water samples). The antibiotic resistance genes used in this study were tetM and tetB, ermB, ampC, vanA, mecA, aac(6′)-Ie- aph(2"), sul2, catII, dfrA1, mcr-1 and blaNDM-1 encoding resistance to tetracycline, erythromycin, ampicillin, vancomycin, methicillin, aminoglycoside, sulfonamide, chloramphenicol, trimethoprim, colistin and carbapenem, respectively. These genes were chosen as the focus of this study for three reasons. Firstly, because these resistance genes have a high prevalence in the environment. Secondly, they were chosen on the basis of the taxonomic composition within the bacterial communities within these houses (see chapter 3). Thirdly, these resistance genes were chosen as they confer resistance to major classes of antibiotics and including several clinically important antibiotics.

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4.2 Results

To assess the prevalence of antibiotic resistance genes in human residences, three different type of samples (33 drinking water, 33 dust and 33 kitchen surfaces and 33 bathroom surfaces) were collected from houses in Melbourne, Victoria. DNA was extracted from these samples (Chapter 3). The presence and abundance of 12 antibiotic resistance genes were screened and quantified using molecular methods (PCR and qPCR).

4.2.1 Detection of antibiotic resistance genes in human residences using the polymerase chain reaction

Overall, the frequency of detection of the 12 antibiotic resistance genes within the four different indoor habitats varied markedly both between individual genes and between habitats (Figure 4.1; Tables 4.1, 4.2, 4.3 and 4.4). The frequency of detection of tetracycline resistance gene tetM was 100% in dust, kitchen and bathroom samples while this gene was not detected in any water samples. The other tetracycline resistance gene tetB was detected by PCR from 7 (21%) kitchen surface samples (Table 4.2) and from 12 (36%) bathroom surface samples (Tables 4.3) and with low frequencies in drinking water 3 (9%) (Table 4.4). While the tetB genes were not detected by PCR in any of the dust samples (Table 4.1). In addition, the frequency of detection of macrolide resistance gene ermB was high in dust and bathroom samples 27 (79%) and 23 (70%), respectively. However, the ermB genes were detected in 18 (55%) kitchen surface samples and in only 6 (18%) of the drinking water samples (Table 4.4). β-lactam resistance genes ampC and blaNDM-1 were both detected on bathroom surfaces in relatively high frequencies: 24 (73%) and 15 (45%) samples, respectively. However, The ampC genes were detected with low frequencies in both drinking water and dust: 6 (18%) and 3 (9%) samples, respectively (Tables 4.1 and 4.4), while the blaNDM-1 genes were detected with low frequencies in both drinking water and kitchen surface: 6 (18%) and 3 (9%) samples, respectively (Tables 4.2 and 4.4). Furthermore, the sulfonamide resistance (sulII) gene was detected in 9 (27%) dust samples (Table 4.1) and in 17 (52%) and 19 (58%) sample for kitchen and bathroom surface, respectively (Table 4.2 and 4.3). While, the chloramphenicol resistance (CatII) gene was detected in 13 (39%) dust samples and 10 (30%) and 16 (48%) samples for kitchen and bathroom surfaces, respectively (Table 4.2 and 4.3). Both resistance genes were detected in

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Chapter 4 Molecular Identification and Quantification of ARGs only 6 (18%) of the drinking water samples (Table 4.4). Polypeptide resistance mcr-1was detected in high frequency in both dust and drinking water samples: (13) 39% and (12) 36%, respectively, while colistin resistance (mcr-1) genes were detected at lower frequencies: 5 (15%) and 3 (9%) samples in kitchen and bathroom surfaces, respectively (Tables 4.2 and 4.3). Methicillin (mecA), aminoglycoside (aac(6)-aph(2)), trimethoprim (dfrA1) and vancomycin (vanA) resistance genes were not detected by PCR from any of the dust, kitchen, bathroom and drinking water samples collected from the 11 houses (data not shown; Tables 4.1-4.4).

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a) tetM Gene b) ermB Gene c) ampC Gene d) blaNDM-1 Gene 80 80 60 100 70 70 50 80 60 60 40 50 50 60

40 40 30

detected (%) detected (%) detected (%) detected 40 (%) detected 30 30 20 20 20 20 10

10 10

Proportion of samples in which gene gene which in samples of Proportion gene which in samples of Proportion gene which in samples of Proportion Proportion of samples in which gene gene which in samples of Proportion 0 0 0 0 D K B DW D K B DW D K B DW D K B DW Habitats Habitats Habitats Habitats e) mcr-1 Gene f) sulII Gene g) catII Gene h) tetB Gene 60 60 60 60

50 50 50 50

40 40 40 40

30 30 30 30

detected (%) detected

detected (%) detected detected (%) detected 20 (%) detected 20 20 20

10 10 10 10

Proportion of samples in which gene gene which in samples of Proportion

Proportion of samples in which gene gene which in samples of Proportion

Proportion of samples in which gene gene which in samples of Proportion Proportion of samples in which gene gene which in samples of Proportion 0 0 0 0 D K B DW D K B DW D K B DW D K B DW Habitats Habitats Habitats Habitats Figure 4.1 Detection of antibiotic resistance genes in different household habitats. The percentage of samples of each habitat from which each gene was detected by PCR amplification of a) tetM b) ermB c) ampC d) blaNDM-1 e) mcr-1 f) sulII g) catII h) tetB. The aac(6)-aph(2), dfrA1, vanA and mecA genes were not detected by PCR assays (data not shown). Habitats: D= Dust, K= Kitchen Surface, B= Bathroom Surface, DW= Drinking water.

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Table 4.1 Detection of antibiotic resistance genes by endpoint PCR in house dust samples. Resistance House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11 Genes

D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

blaNDM-1 - - - + + + + + + - - - + + + + + + ------+ + + ------

mecA ------

tet(M) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ampC ------+ + + ------

vanA ------

mcr-1 + + + + + + + + + + - - + + + ------

tet(B) ------

erm(B) - - - + + + + + + + - + + + + + + + + + + + + + + + + - - - + + +

aac(6)------aph(2) sulII ------+ + + ------+ + + ------+ + +

catII - - - + + + + + + - - + + + + ------+ + + - - -

dfrA1 ------

(+) Antibiotic resistance gene detected by PCR amplification, (-) Antibiotic resistance gene not detected by PCR amplification.

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Table 4.2 Detection of antibiotic resistance genes by endpoint PCR in kitchen surface samples. Resistance House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11 Genes

K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K K 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

blaNDM-1 ------+ + + ------

mecA ------

tet(M) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ampC - - - + + + ------+ + + - - - + + + + + + + + + ------

vanA ------

mcr-1 - - - + + + + + ------

tet(B) ------+ + + + + - - - - + - + - - -

erm(B) ------+ + + + + + + + + - - - + + + + + + + + +

aac(6)------aph(2) sulII - + - + + + + + + + - - + + + ------+ + + + + + ------

catII - - + ------+ + + + + + ------+ + + ------

dfrA1 ------

(+) Antibiotic resistance gene detected by PCR amplification, (-) Antibiotic resistance gene not detected by PCR amplification.

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Table 4.3 Detection of antibiotic resistance genes by endpoint PCR in bathroom surface samples. Resistance House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11 Genes B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

blaNDM-1 - - - + + + + + + ------+ + + + + + + + + ------

mecA ------

tet(M) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ampC - - - + + + + + + ------+ + + + + + + + + + + + + + + + + +

vanA ------

mcr-1 - - - + + + ------

tet(B) ------+ + + + + + ------+ + + + + +

erm(B) - - - + + + + + + - - - + + + + - + + + + + + + + + + + + + - - -

aac(6)------aph(2) sulII + ------+ + + + + + + + + + + + - - - + + + + + +

catII - + - + + + - - - - - + + + + + - + + + + + + + ------

dfrA1 ------

(+) Antibiotic resistance gene detected by PCR amplification, (-) Antibiotic resistance gene not detected by PCR amplification.

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Table 4.4 Detection of antibiotic resistance genes by endpoint PCR in drinking water samples. Resistance House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11 Genes DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW DW 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

blaNDM-1 ------+ + + ------+ + + ------

mecA ------

tet(M) ------

ampC ------+ + + - - - + + + ------

vanA ------

mcr-1 ------+ + + - - - + + + + + + + - + ------+ - -

tet(B) ------+ + + ------

erm(B) ------+ + + - - - + + + ------

aac(6)------aph(2) sulII ------+ + + + + + ------

catII ------+ + + - - - + + + ------

dfrA1 ------

(+) Antibiotic resistance gene detected by PCR amplification, (-) Antibiotic resistance gene not detected by PCR amplification.

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4.2.1.1 Comparison of the prevalence of detected antibiotic resistance genes in different habitats

The prevalence of antibiotic resistance genes that were detected in human residences differed between habitats. The highest proportion of antibiotic resistance genes were detected on bathroom surfaces (37%) followed by dust (28%) then kitchen surfaces (27%). The lowest proportion of antibiotic resistance genes was detected in drinking water (11%) (Figure 4.2).

40

35

30

25

20

15

10

5 Proportion of samples in which Proportion of samples in which genes detected(%)

0 D SK SB DW Habitats

Figure 4.2 Proportion (%) of detection of twelve antibiotic resistance genes collected within human residences. The percentage of samples of each habitat (33 samples per habitat) from which all antibiotic resistance genes were detected by PCR amplification is shown. Habitats: D= Dust, SK= Kitchen Surface, SB= Bathroom Surface, DW= Drinking water.

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4.2.1.2 Comparison of the prevalence of detected antibiotic resistance genes across different human residences

50

45

40

35

30

25

20

genes) were (%) detected were genes) 15

10

5 Overall proportion of samples in which antibiotic resistance genes (8 genes resistance antibiotic which in samples of proportion Overall 0 House 1 House 2 House 3 House 4 House 5 House 6 House 7 House 8 House 9 House 10 House 11 Houses

Figure 4.3 Relative prevalence (%) of antibiotic resistance genes between different houses. The percentage of samples of each house from which all antibiotic resistance genes across all habitats were detected by PCR amplification is shown.

The prevalence of antibiotic resistance genes detected in human residences varied across the 11 different houses. The highest proportion of antibiotic resistance genes were detected in houses 3, 5 and 7 (35%, 42% and 35% respectively) while the lowest proportion of antibiotic resistance genes were detected in houses 1 and 4 (11% and 10% respectively) (Figure 4.3).

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4.2.2 Quantification of antibiotic resistance genes in human residences using quantitative PCR

From the twelve antibiotic resistance genes targeted in this research study, only eight resistance genes (tetM, ermB, ampC, blaNDM-1, mcr-1, sulII, catII and tetB) were detected in different houses and habitats by PCR (section 4.2.1). The relative abundance of these eight genes and also of bacterial 16S rRNA genes between houses and between and within different habitats was then investigated using quantitative PCR (qPCR).

4.2.2.1 Quantification of 16S rRNA genes across different habitats

In the household dust, the abundance of 16S rRNA genes across all houses ranged from 2.21x109 (in house 1) to 2.65x1010 g-1 dust (in house 2) with ANOVA showing significant variation across all houses (р < 0.05) (Figure 4.4 a). However, in drinking water samples there was no significant difference overall in the number of 16S rRNA genes across all houses (р = 0.56) (Table 4.5) with lower abundances (7.59x103 to 4.29x105 ml-1 drinking water) in houses 9 and 6 respectively (Figure 4.4 d). The overall abundance of 16S rRNA genes did not vary significantly in either kitchen (р = 0.44) or bathroom (р = 0.34) surfaces across houses (Table 4.5). Higher 16S rRNA gene numbers were found on bathroom surfaces 2.69x104 cm-2 bathroom surface (in houses 1) to 1.40 x107 cm-2 bathroom surface (in house 6), than on kitchen surfaces 3.38x103 cm-2 kitchen surface (in house 4), to 8.70x107 cm-2 kitchen surface (in houses 5) (Figure 4.4 b and c).

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Figure 4.4 Abundance of 16S rRNA genes across different houses and habitats. a) Mean numbers of 16S rRNA genes g-1 dust b) Mean numbers of 16S rRNA genes cm-2 kitchen surface c) Mean numbers of 16S rRNA genes cm-2 bathroom surface d) Mean numbers of 16S rRNA genes ml-1 drinking water. Error bars show ± SE (n=3). 134

Chapter 4 Molecular Identification and Quantification of ARGs

Table 4.5 Comparisons of P-values from one-way ANOVA test for abundance of 16S rRNA and antibiotic resistance genes between houses within four different habitats.

Residential 16S tetM tetB ermB sulII catII ampC NDM-1 mcr-1 Habitats rRNA

Dust ARG 0.4083 0.0072 0.0582 0.1726 0.6592 0.0769

ARG/16S 0.0260 0.1334 0.0256 0.0749 0.5539 0.5046 0.1377 rRNA

Kitchen ARG 0.3245 0.6158 0.2909 0.0902 0.4805 0.4310 0.2384 Surfaces

ARG/16S 0.4363 0.0032 0.1758 0.4586 0.0043 0.2023 0.0928 0.1688 rRNA

Bathroom ARG 0.2802 0.00003 0.7376 0.3747 0.3866 0.2619 0.4559 Surfaces

ARG/16S 0.3428 0.00004 0.0364 0.0001 0.0031 0.0355 0.000003 0.0059 rRNA

Drinking ARG 0.0021 0.5252 0.0083 0.0025 0.2024 0.00004 Water

ARG/16S 0.5590 0.3552 0.2587 0.0630 0.0402 0.1036 0.0938 rRNA

The red numbers indicate for a significant variation (р < 0.05).

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4.2.2.2 Quantification of the abundance of antibiotic resistance genes in residential habitats

The abundance of antibiotic resistance genes was quantified by qPCR and normalized against mass or volume of environmental samples used for DNA extraction (Figure 4.5 - 4.12 a,c,e,g) or against the abundance of 16S rRNA genes in each sample (Figure 4.5 - 4.12 b,d,f,h).

House-to-house comparisons revealed considerable variation in the overall abundance of some antibiotic resistance genes between houses (Table 4.5). For example, there was considerable variation in the overall abundance of tetB genes between houses in both kitchen (р < 0.05) and bathroom (р < 0.05) surface samples (Table 4.5). In addtion, there was significant variation in the overall abundance of ermB genes between houses in dust (р < 0.05) and drinking water (р < 0.05) samples (Table 4.5). Furtherore, there was significant variation in the overall abundance of catII, ampC, mcr-1 genes between houses in drinking water (р < 0.05) samples (Table 4.5).

In contrast, there was no significant variation in the overall abundance of tetM genes in dust (р = 0.41), kitchen (р = 0.32) or bathroom (р = 0.28) surface samples across all houses (Table

4.5). Also, the overall abundance of sulII and blaNDM-1 genes showed no significant variation in dust (р = 0.06) and (р = 0.66), drinking water (р = 0.52) and (р = 0.20), and bathroom surface (р = 0.37) and (р = 0.46) samples, respectively (Table 4.5).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b) 8.0E-02 16 7.0E-02 6.0E-02 8 5.0E-02

/16S /16S rRNA genes 4.0E-02 4

tetM 3.0E-02 of Dust of 2 2.0E-02

Ratio Ratio of 1.0E-02 1 0.0E+00 Log10 number of per of g genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d) 7.0E-02 16 6.0E-02

8 5.0E-02

of surface of 4.0E-02

2 /16S /16S rRNA genes

4 3.0E-02 tetM 2.0E-02 2

Ratio Ratio of 1.0E-02 Log10 number of of gene Log10 number copies per cm copies 1 0.0E+00 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f) 3.0E-01 16 2.5E-01 8 2.0E-01 1.5E-01 4 /16S rRNA genes

tetM 1.0E-01 of of surface

2 2

5.0E-02 cm 1 Ratio of 0.0E+00 1 2 3 4 5 6 7 8 9 10 11 Log10 number of per of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.5 Abundances of tetracycline resistance (tetM) genes between houses in dust (a,b), on kitchen surfaces (c,d) and bathroom surfaces (e,f). Absolute gene numbers (panels a,c,e) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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3 5.0E-04

4.0E-04 2

3.0E-04 /16S /16S rRNA genes

2.0E-04 tetB

of of surface 1 2

1.0E-04

cm Ratio Ratio of 0 0.0E+00 Log10 number of per of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

8 1.5E-03

4

1.0E-03

/16S /16S rRNA genes tetB of of surface 2

2 5.0E-04

cm Ratio Ratio of

1 0.0E+00 Log10 number of per of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f)

0.25 8.0E-05 7.0E-05 0.2 6.0E-05 0.15 5.0E-05

4.0E-05 /16S rRNA genes rRNA /16S

0.1 3.0E-05 tetB

ml Water of ml 0.05 2.0E-05 1.0E-05

0 of Ratio 0.0E+00 Log10 number of per of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.6 Abundances of tetracycline resistance (tetB) genes between houses on kitchen surfaces (a,b), on bathroom surfaces (c,d) and in drinking water (e,f). Absolute gene numbers (panels a,c,e) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b) 1.2E-02 16 1.0E-02 8 8.0E-03 4

6.0E-03 /16S /16S rRNA genes

2 4.0E-03 ermB

1 2.0E-03 per g g Dust perof 1 2 3 4 5 6 7 8 9 10 11 Ratio of 0.0E+00 Houses Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 Houses c) d) 1.4E-02 16 1.2E-02 8 1.0E-02 8.0E-03

4 /16S rRNA genes

6.0E-03

of surface of 2 2 ermB 4.0E-03

2.0E-03 per cm per

1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f)

8.00 1.2E-01 1.0E-01 4.00 8.0E-02

6.0E-02

/16S /16S rRNA genes of surface of

2 2.00 4.0E-02 ermB 2.0E-02

per cm per 1.00 0.0E+00 Ratio Ratio of Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h) 6 1.2E-02 1.2E-02 4 1.1E-02

1.1E-02 /16S /16S rRNA genes

2 1.0E-02 ermB

9.5E-03 per ml of ml Water per

0 Ratio of 9.0E-03 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.7 Abundances of erythromycin resistance (ermB) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b)

5.0E-03 16 4.0E-03 8 3.0E-03

4 /16S rRNA genes 2.0E-03

2 sulII 1.0E-03 per g g Dust perof

1 Ratio of 0.0E+00

Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

8 4.0E-02 3.0E-02 4

2.0E-02

of surface of /16S /16S rRNA genes

2 2

sulII 1.0E-02

per cm per 1 0.0E+00 Ratio Ratio of Log10 number of genes of genes number Log10 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f) 6.0E-02 16 5.0E-02 8 4.0E-02

3.0E-02 /16S /16S rRNA genes

of surface of 4

2 2.0E-02 sulII

2 1.0E-02 per cm per

1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h) 1.4E-02 1.2E-02 4 1.0E-02 8.0E-03

/16S /16S rRNA genes 6.0E-03 2

sulII 4.0E-03 2.0E-03 1

Ratio Ratio of 0.0E+00

Log10 number of of gene Log10 number Water per of ml copies 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.8 Abundances of sulfonamide resistance (sulII) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b)

16 8.0E-04

8 6.0E-04

4 4.0E-04 /16S /16S rRNA genes

2 catII 2.0E-04 per g g Dust perof 1

Ratio Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

2.5E-04 8 2.0E-04

4 1.5E-04 /16S /16S rRNA genes of surface of 1.0E-04

2 2 catII 5.0E-05

per cm per 1

Ratio Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f) 4.0E-04 8 3.0E-04 4

2.0E-04

/16S /16S rRNA genes

of surface of 2

2 catII

1.0E-04 per cm per

1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h) 6.0E-04 3.1 2.6 5.0E-04 2.1 4.0E-04

1.6 3.0E-04 /16S /16S rRNA genes

1.1 catII 2.0E-04

0.6 1.0E-04 per ml of ml Water per

0.1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.9 Abundances of chloramphenicol resistance (catII) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b)

8 2.0E-02

1.5E-02 4

1.0E-02 /16S /16S rRNA genes

2 per g g Dust perof ampC 5.0E-03

Log10 number of of genes Log10 number 1

Ratio Ratio of 0.0E+00 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

2.5E-02 16 2.0E-02 8 1.5E-02

4 /16S rRNA genes

of surface of 1.0E-02 2 2 ampC 5.0E-03

per cm per 0.0E+00 1 Ratio of Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f) 2.5E-01 16 2.0E-01 8 1.5E-01

4 /16S /16S rRNA genes

of surface of 1.0E-01 2

2 ampC 5.0E-02

per cm per 1

Ratio Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h)

1.0E+00 10 8.0E-01 8 6.0E-01

6 /16S /16S rRNA genes

4 4.0E-01 ampC 2 2.0E-01

Ratio Ratio of 0.0E+00 copies per ml Water per of ml copies Log10 number of of gene Log10 number 0 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.10 Abundances of ampicillin resistance (ampC) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b)

1.4E-03 16 1.2E-03

8 /16S rRNA 1.0E-03 1 - 8.0E-04

4 genes 6.0E-04 2 blaNDM 4.0E-04

per g g Dust perof 2.0E-04 1 Ratio of 0.0E+00

Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

2.5E-04 8

2.0E-04 /16S /16S rRNA

4 1

- 1.5E-04 genes

of surface of 1.0E-04

2 2 blaNDM 5.0E-05

per cm per 1 Ratio Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f)

1.5E-03

8 /16S /16S rRNA

1 1.0E-03

4 -

genes

of surface of 2

2 blaNDM 5.0E-04 per cm per

1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h) 4 1.4E-02 1.2E-02

1.0E-02

/16S /16S rRNA 1 - 8.0E-03 2 genes 6.0E-03 blaNDM 4.0E-03

per ml of ml perWater 2.0E-03 Ratio Ratio of 0.0E+00 Log10 number of genes of genes number Log10 1 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses

Figure 4.11 Abundances of carbapenem resistance (blaNDM-1) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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Chapter 4 Molecular Identification and Quantification of ARGs a) b) 6.0E-03 16 5.0E-03 8 4.0E-03

4 /16S rRNA genes 3.0E-03

1 - 2.0E-03

2 MCR

1.0E-03 per g g Dust perof

1 0.0E+00 Ratio Ratio of

Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses c) d)

8 1.4E-03 1.2E-03 1.0E-03 4

8.0E-04

/16S /16S rRNA genes 1

- 6.0E-04 of surface of

2 2 4.0E-04 MCR

2.0E-04 per cm per

1 Ratio of 0.0E+00 Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses e) f)

6.0E-03 4 5.0E-03 4.0E-03

/16S /16S rRNA genes 3.0E-03

1 of surface of 2 - 2 2.0E-03 MCR 1.0E-03

per cm per 1 0.0E+00 Ratio Ratio of Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses g) h) 8.0E-03

4 6.0E-03

4.0E-03

/16S /16S rRNA genes 1

2 -

2.0E-03 MCR

per ml of ml perWater 1 0.0E+00 Ratio Ratio of Log10 number of of genes Log10 number 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Houses Houses Figure 4.12 Abundances of colistin resistance (mcr-1) genes between houses in dust (a,b), on kitchen surfaces (c,d), on bathroom surfaces (e,f) and in drinking water (g,h). Absolute gene numbers (panels a,c,e,g) and relative abundance, whereby gene numbers were normalised to 16S rRNA genes (panels b,d,f,h) are shown. Normalisation is used to account for differences in bacterial 16S rRNA gene abundances between samples. Error bars show ± SE (n=3).

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4.2.2.3 Quantification of different antibiotic resistance genes in residential dust

In household dust, the highest numbers of tetM genes were in house 8 (7.73 × 108 g-1 dust) with lowest numbers in house 1 (5.64 × 106 g-1 dust), as summarized in Figure 4.5a. When tetM gene numbers were normalized to 16S-rRNA gene numbers, the relative abundance (ratio) of tetM genes was found to be highest in house 6 (4.42 × 10−2), and lowest in house 10 (2.39 × 10-3) (Figure 4.5 b). For ermB genes, the highest numbers were in house 2 (2.23 × 108 g-1 dust) while the lowest numbers were found in house 6 (1.64 × 106 g-1 dust), as shown in Figure 4.7a. When ermB gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) was observed between houses (Table 4.5). The ratio of ermB genes to 16S-rRNA genes was highest in house 8 (9.35 × 10-3), and lowest in house 6 (1.24× 10-4), as shown in Figure 4.7b. For sulII genes, the highest numbers were found in house 3 (8.46 × 107 g-1 dust) with lowest numbers in house 11 (3.77 × 106 g-1 dust), as shown in Figure 4.8a. When sulII gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.07) was found between houses (Table 4.5). The ratio of sulII genes to 16S-rRNA genes was highest in house 3 (3.93 × 10−3) and lowest in house 6 (4.61 × 10-4), as shown in Figure 4.8b. For catII genes, the highest numbers were found in house 3 (1.13 × 107 g-1 dust), while lowest gene numbers were found in house 10 (1.02 × 106 g-1 dust), as shown in Figure 4.9a. When catII gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.55) was observed between houses (Table 4.5). The ratio of catII genes to 16S-rRNA genes was highest in house 3 (5.27 × 10-4), and lowest in house 10 (2.33 × 10-4), as shown in Figure 4.9b. For ampC genes, this gene was found in one house only, namely, house 6 (2.12 × 108 g-1 dust), as shown in Figure 4.10a. The ratio of ampC genes to 16S-rRNA genes in house 6 was 1.61 × -2 7 10 (Figure 4.10b). For blaNDM-1 genes, the highest numbers were found in house 3 (2.08 × 10 -1 6 -1 g dust) and lowest in house 9 (2.24 × 10 g dust), as shown in Figure 4.11a. When blaNDM-1 gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.50) was observed between houses (Table 4.5). The ratio of blaNDM-1 genes to 16S-rRNA genes was highest in house 6 (1.13 × 10−3) and lowest in house 9 (2.10 × 10-4), as shown in Figure 4.11b. For mcr-1 genes, the highest numbers were found in house 2 (1.09 × 108 g-1 dust) and the lowest numbers were found in house 1 (3.74 × 106 g-1 dust), as shown in Figure 4.12a. When mcr-1 gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.14) was observed between houses (Table 4.5). The ratio of mcr-1 genes to 16S-rRNA genes was highest in house 5 (4.72 × 10−3) and lowest in house 1 (1.69 × 10-3), as shown in Figure 4.12b.

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4.2.2.4 Quantification of different antibiotic resistance genes in residential surfaces

In household surfaces, the highest numbers of tetM in kitchen surface samples were in house 5 (2.85 × 105 cm-2 kitchen surface) and in bathroom surface samples in house 6 (1.92 × 105 cm-2 bathroom surface). The lowest numbers of tetM in kitchen surface samples were in house 4 (5.64 × 106 cm-2 kitchen surface), and in bathroom surface samples house 1 had the lowest numbers of tetM genes (3.41 × 102 cm-2 bathroom surface) as shown in Figure 4.5c and e. When tetM gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) in tetM gene numbers was seen in both kitchen and bathroom surface samples (Table 4.5). The ratio of tetM genes to 16S-rRNA genes was highest on kitchen surfaces in house 2 (5.69 × 10−2), and on bathroom surfaces in house 3 (2.46 × 10-1). Whilst the lowest ratio of tetM genes to 16S-rRNA genes was in kitchen surface samples in house 5 (3.27 × 10-3), and in bathroom surface samples in house 7 (6.31 × 10-4) as shown in Figure 4.5d and f. For tetB genes, the highest numbers in kitchen surface samples were in house 8 (3.21 × 101 cm-2 kitchen surface) and in bathroom surface samples in house 6 (175. × 104 cm-2 bathroom surface). The lowest numbers of tetB in kitchen surface samples were in house 7 (9.43 cm-2 kitchen surface), whilst in bathroom surface samples, house 10 had the lowest numbers of the tetB gene (2.89 × 101 cm-2 bathroom surface) as shown in Figure 4.6a and c. When tetB gene numbers were normalized to 16S-rRNA gene numbers, the ratio of tetB genes to 16S-rRNA genes was highest on kitchen surfaces in house 10 (3.15 × 10−4), and on bathroom surfaces in house 6 (1.25 × 10- 3). While the lowest ratio of tetB genes to 16S-rRNA genes in kitchen surface samples was in house 7 (8.89 × 10−6), and in bathroom surface samples in house 10 (3.50 × 10-5), as shown in Figure 4.6b and d. There was significant variation in the relative abundace of tetB genes in both kitchen (р < 0.05) and bathroom (р < 0.05) surface samples across houses (Table 4.5). For erythromycin resistance, the highest numbers of ermB genes in kitchen surface samples were in house 5 (5.06 × 104 cm-2 kitchen surface) and in bathroom surface samples in house 3 (4.34 × 104 cm-2 bathroom surface). The lowest numbers of ermB in kitchen surface samples were 3.58 × 101 cm-2 kitchen surface from house 11, and in bathroom surface samples, house 9 had the lowest numbers of the ermB gene (1.52 × 102 cm-2 bathroom surface) as shown in Figure 4.7c and e. When ermB gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) was seen on bathroom surfaces between houses (Table 4.5). The ratio of ermB genes to 16S-rRNA genes was highest on kitchen surfaces in house 6 (1.01 × 10−2), and on bathroom surfaces in house 3 (1.13 × 10-1). While the lowest ratio of ermB genes to 16S- rRNA genes in kitchen surface samples was in house 5 (5.81 × 10-4), and in bathroom surface

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Chapter 4 Molecular Identification and Quantification of ARGs samples in house 6 (1.62 × 10-4), as shown in Figure 4.7d and f. For sulphonamide resistance, the highest numbers of sulII genes in kitchen surface samples were found in house 5 (1.39 × 104 cm-2 kitchen surface) and in bathroom surface samples in house 6 (4.77 × 105 cm-2 bathroom surface). The lowest numbers of sulII genes in kitchen surface samples were in house 8 (8.09 × 101 cm-2 kitchen surface), and in bathroom surface samples, house 7 had the lowest numbers of the sulII gene (4.67 × 102 cm-2 bathroom surface) as shown in Figure 4.8c and e. When sulII gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) was observed in kitchen and bathroom surface samples (Table 4.5). The ratio of sulII genes to 16S-rRNA genes was highest on kitchen surfaces in house 3 (2.92 × 10−2), and on bathroom surfaces in house 11 (4.75 × 10-2). While the lowest ratio of sulII genes to 16S-rRNA genes in kitchen surface samples was in house 5 (1.60 × 10-4), and in bathroom surface samples in house 7 (3.58 × 10-5), as shown in Figure 4.8d and f. For chloramphenicol resistance, the highest numbers of catII genes in kitchen surface samples were found in house 5 (2.41 × 102 cm-2 kitchen surface) and in bathroom surface samples in house 6 (3.58 × 103 cm-2 bathroom surface). The lowest numbers of catII genes in kitchen surface samples was in house 9 (7.18 × 101 cm-2 kitchen surface), and in bathroom surface samples, house 5 had the lowest numbers of the catII gene (6.92 × 101 cm-2 bathroom surface) as shown in Figure 4.9c and e. When catII gene numbers were normalized to 16S-rRNA gene numbers, significant variation was observed in bathroom surface samples (р < 0.05) while there was no significant variations (р = 0.20) in the relative abundance of kitchen samples (Table 4.5). The ratio of catII genes to 16S-rRNA genes was highest on kitchen surfaces in house 6 (1.68 × 10-4) and on bathroom surfaces in house 2 (3.32 × 10-4). While the lowest ratio of catII genes to 16S-rRNA genes in kitchen surface samples was found in house 5 (2.77 × 10-6) and in bathroom surface samples in house 7 (4.05 × 10-5), as shown in Figure 4.9d and f. For ampicillin resistance, the highest numbers of ampC genes in kitchen surface samples were found in house 5 (1.67 × 106 cm-2 kitchen surface) and in bathroom surface samples in house 7 (2.55 × 106 cm-2 bathroom surface). The lowest numbers of ampC genes in kitchen surface samples were in house 7 (1.80 × 103 cm-2 kitchen surface), and in bathroom surface samples, house 9 had the lowest numbers of the ampC gene (1.10 × 103 cm-2 bathroom surface) as illustrated in Figure 4.10c and e. When ampC gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.09) was observed in the relative abundance in kitchen samples, while there was significant variation in bathroom surface samples (р < 0.05) (Table 4.5). The ratio of ampC genes to 16S- rRNA genes was highest on kitchen surfaces in house 9 (1.99 × 10−2), and on bathroom surfaces in house 7 (1.96 × 10-1) while the lowest ratio of ampC genes to 16S-rRNA genes in kitchen

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Chapter 4 Molecular Identification and Quantification of ARGs surface samples was in house 7 (1.70 × 10-3) and in bathroom surface samples in house 6 (6.17 -3 × 10 ), as illustrated in Figure 4.10d and f. For carbapenem resistance, the blaNDM-1 gene was found in only one kitchen surface sample from house 7 (2.41 × 102 cm-2 kitchen surface), as shown in Figure 4.11c. The ratio of blaNDM-1 genes to 16S-rRNA genes in house 7 was 2.28 × -4 10 (Figure 4.11 d). The highest numbers of blaNDM-1 in bathroom surface samples were found 3 -2 in house 6 (1.79 × 10 cm bathroom surface). The lowest numbers of blaNDM-1 in bathroom surface samples was in house 8 (9.36 × 101 cm-2 bathroom surface), as shown in Figure 4.11e.

When blaNDM-1 gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) was observed between houses (Table 4.5). The ratio of blaNDM-1 genes to 16S-rRNA genes was highest on bathroom surfaces in house 3 (1.37 × 10-3), whilst the lowest ratio of blaNDM-1 genes to 16S-rRNA genes in bathroom surface samples was in house 8 (7.32× 10-6), as shown in Figure 4.11f. For colistin resistance, the mcr-1 genes were found in two kitchen surface samples from houses 2 and 3. The highest numbers of mcr-1 in kitchen surface samples were found in house 2 (6.58 × 102 cm-2 kitchen surface) and the lowest numbers of mcr-1 in kitchen surface samples were in house 3 (2.42 × 102 cm-2 kitchen surface), as shown in Figure 4.12c. When mcr-1 gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.17) was observed between houses (Table 4.5). The ratio of mcr-1 genes to 16S-rRNA genes was highest in house 2 (1.09 × 10-3), and lowest in house 3 (7.03 × 10-4), as shown in Figure 4.12d. Regarding bathroom surfaces, the mcr-1 gene was found in only one bathroom surface sample from house 2 (7.35 × 103 cm-2 bathroom surface), as shown in Figure 4.11e. The ratio of mcr-1 genes to 16S-rRNA genes in house 2 was (5.82 × 10-3), as shown in Figure 4.12f.

4.2.2.5 Quantification of different antibiotic resistance genes in drinking water

In drinking water, the tetracycline resistance (tetB) genes was found in one house only, namely house 3 (1.18 gene ml-1 drinking water), as shown in Figure 4.6e. The ratio of tetB genes to 16S-rRNA genes in house 3 was (7.35 × 10-5) as shown in Figure 4.6f. In addition, the erythromycin resistance (ermB) gene was found in only two houses (5 and 7). The highest numbers of ermB were found in house 5 (1.73 × 103 ml-1 water), and the lowest numbers were in house 7 (8.40 × 101 ml-1 water), as shown in Figure 4.7g. Inversely, when ermB gene were numbers normalized to 16S-rRNA gene numbers, it was observed that the ratio of ermB genes to 16S-rRNA genes was slightly higher in house 7 (1.11 × 10−2) than in house 5 (1.01 × 10-2),

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Chapter 4 Molecular Identification and Quantification of ARGs as shown in Figure 4.7h. Furthermore, the sulfonamide resistance (sulII) gene was found only in two houses (5 and 6). The highest numbers of sulII were found in house 6 (2.82× 102 ml-1 water), and the lowest numbers were in house 5 (1.10 × 102 ml-1 water), as shown in Figure 4.8g. When sulII gene numbers were normalized by 16S-rRNA gene numbers, the ratio of sulII genes to 16S-rRNA genes was highest in house 6 (7.89 × 10-3), and lowest in house 5 (6.45 × 10-4), as shown in Figure 4.8h. Similarly, the chloramphenicol resistance (catII) gene was found in only two houses (3 and 5). The highest numbers of catII genes was found in house 5 (7.13 × 101 ml-1 water), and the lowest numbers in house 3 (1.84 ml-1 water) (Figure 4.9g). When catII gene numbers were normalized by 16S-rRNA gene numbers, it was shown no significant variations (р = 0.063) was observed between houses (Table 4.5). The ratio of catII genes to 16S-rRNA genes was highest in house 5 (4.18 × 10-4) and lowest in house 3 (1.15 × 10-4) (Figure 4.9h). Moreover, the ampicillin resistance (ampC) gene was found only in two houses (5 and 7). The highest numbers of ampC genes was found in house 5 (9.27 × 104 ml-1 water), and the lowest numbers in house 7 (1.44 × 102 ml-1 water), as shown in Figure 4.10g. When ampC gene numbers were normalized to 16S-rRNA gene numbers, significant variation (р < 0.05) was observed between houses (Table 4.5). The ratio of ampC genes to 16S-rRNA genes was highest in house 5 (5.44 × 10-1) and lowest in house 7 (1.90 × 10-2), as illustrated in Figure

4.10h. Likewise, the carbapenem resistance (blaNDM-1) genes were found in only two houses (3 1 -1 and 7). The highest numbers of blaNDM-1 were found in house 7 (6.93 × 10 ml water) and the lowest numbers were in house 3 (2.67 × 101 ml-1 water), as shown in Figure 4.11g. When blaNDM-1 gene numbers were normalized by 16S-rRNA gene numbers, no significant variation

(р = 0.10) was observed between houses (Table 4.5). The ratio of blaNDM-1 genes to 16S-rRNA genes was highest in house 7 (9.13 × 10-3), and lowest in house 3 (1.66 × 10-3), as shown in Figure 4.11h. Furthermore, the highest numbers of mcr-1 genes detected in drinking water was found in house 5 (1.05 × 103 ml-1 water) and lowest in house 3 (1.66 × 101 ml-1 water), as shown in Figure 4.12g. When mcr-1 gene numbers were normalized to 16S-rRNA gene numbers, no significant variation (р = 0.09) was observed between houses (Table 4.5). The ratio of mcr-1 genes to 16S-rRNA genes was highest in house 7 (6.23 × 10−3), and lowest in house 3 (1.04 × 10-3), as shown in Figure 4.12h.

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4.2.2.6 Comparison of the abundance of antibiotic resistance genes within and between different houses and habitats

The frequency and relative abundance of different antibiotic resistance genes varied between habitats. The highest frequency of antibiotic resistance genes was detected on bathroom surfaces (37%), followed by dust (28%), kitchen surfaces (27%) and the lowest frequency of antibiotic resistance genes was detected in drinking water (11%), (see also Figure 4.2). Similarly, the highest relative abundance of antibiotic resistance genes was in bathroom surface samples followed by dust, kitchen surface and drinking water samples (Figure 4.13). The bathroom surfaces were the most diverse and abundant habitat for resistance genes. Antibiotic resistance genes with the highest relative abundance on bathroom surfaces were tetM, ampC and ermB with relative abundances of 24%, 19% and 11%, respectively. While the blaNDM-1 and catII genes had the relative lowest abundance of 0.001% and 0.004% of the abundance of 16S rRNA genes, respectively, as illustrated in Figure 4.13c. The highest and lowest relative abundance of antibiotic resistance genes found in dust was similar to those found on bathroom surfaces but with lower proportions.

Antibiotic resistance genes with the highest relative abundance in dust were tetM, ampC and ermB with relative abundance of 4.42%, 1.61% and 0.93% of the abundance of 16S rRNA genes, respectively. Whereas the catII and blaNDM-1 genes had the lowest relative abundance in dust with relative abundance of 0.02% for both genes (Figure 4.13a). On kitchen surfaces, the tetM gene (5.69%) was also the most abundant gene followed by sulII (2.92%) and ampC (1.99%). Whilst the tetB (0.0009%) and catII (0.0003%) genes had the lowest relative abundance of genes found on kitchen surfaces (Figure 4.13b). Antibiotic resistance genes were detected at the lowest frequency and also abundance in drinking water. Surprisingly, the ampC gene was found in drinking water with a very high relative abundance (54.36%) compared to other habitats. Whereas the resistance genes found in drinking water with the lowest relative abundance were the tetB and catII genes with relative abundances of 0.01% for both genes (Figure 4.13d).

The frequency of detection and relative abundance of different antibiotic resistance genes varied within and between habitats. The tetM gene was detected in high frequency in dust,

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Chapter 4 Molecular Identification and Quantification of ARGs bathroom surfaces and kitchen surfaces with different relative abundances. The highest relative abundance of tetM genes were found on bathroom surfaces (24.60%) followed by kitchen surfaces (5.69%) and dust (4.42%), as shown in Figure 4.13 a,b,c. In addition, the abundance of tetM genes showed a significant variation (р < 0.05) between houses in both kitchen and bathroom surface samples (Table 4.5). The ermB gene was detected in all habitats but the relative abundance of this gene varied within and between habitats. The highest relative abundance of ermB genes was found on bathroom surfaces (11.26%) followed by drinking water (1.11%), kitchen surfaces (1.01%) and dust (0.093%), as shown in Figure 4.12. The abundance of ermB genes revealed a significant variation (р < 0.05) between houses in both dust and bathroom surface samples (Table 4.5). The ampC gene was detected at low frequencies in dust and drinking water while it was detected with a high frequency in both kitchen and bathroom surface habitats. However, the relative abundance of ampC genes varied greatly between habitats. The highest relative abundance of ampC genes were found in drinking water from house 5 (54.36%), followed by bathroom surfaces where there was a high relative abundance of ampC genes in three houses (4,7 and 11) with relative abundances of 14.29%, 19.56% and 17.04% respectively (Figure 4.13 c,d). Conversely, the relative abundance of ampC genes in kitchen surfaces (1.99%) and dust (1.61%) was low (Figure 4.13 a,b). Furthermore, the abundance of ampC genes revealed a significant variation (р < 0.05) between bathroom surface samples (Table 4.5). The sulII gene was found on kitchen and bathroom surfaces with high relative abundance of 2.92% and 4.75% respectively (Figure 4.13 b,c). This gene also showed a significant variation (р < 0.05) between houses in both kitchen and bathroom surface samples (Table 4.5). The mcr-1 gene was detected at a high frequency in drinking water and dust samples but with low relative abundance of 0.62% and 0.47% respectively (Figure 4.13 a,d).

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4.3 Discussion

The emergence and widespread distribution of antibiotic resistance genes in environmental and pathogenic bacteria has increased the risks for human health as these bacteria can be transmitted to humans either through direct or indirect contact (Iversen et al., 2004). An enhanced understanding of the distribution, diversity and abundance of antibiotic resistance genes in the human residences is important to manage the spread of antibiotic resistant bacteria and to better evaluate the associated health risks. In this research study, twelve antibiotic resistance genes were targeted to investigate the antibiotic resistance gene pool in different household samples.

Results from this research study revealed the presence of clinically relevant antibiotic resistance genes in the human residence for many different antibiotic classes. In the current research study, seven antibiotic resistance genes (tetM, ermB, ampC, sul2, catII, mcr-1 and blaNDM-1) were detected in the household dust. These genes were detected in 28% of the total number of dust samples. The most common resistance genes detected in household dust were tetM and ermB genes with high frequencies of 100% and 79%, respectively. Similarly, the presence of tetracycline and erythromycin resistance genes has been reported in other studies in bacterial communities from many indoor (or also natural) environments (Veillette et al., 2013, Drudge et al., 2012, Hartmann et al., 2016, Aminov et al., 2001, Stoll et al., 2012, Kobayashi et al., 2007 and Gilbert et al., 2010). For example, Drudge et al., (2012), has detected the ermA gene which encodes macrolide–lincosamide–streptogramin B resistance in dust samples from a hospital air filter (Drudge et al., 2012). Also, Hartmann et al., (2016) has identified antibiotic resistance genes such as tetW, ermB and ermX in non-residential indoor dust samples (Hartmann et al., 2016). More relevantly, antibiotic resistance genes have been detected in household dust samples in Australia by Veillette et al., (2013), who found that collected dust samples were positive for antibiotic resistance genes such as ermB, tetA and tetC genes which encode erythromycin and tetracycline resistance, respectively (Veillette et al., 2013). The high frequency of occurrence of tetM genes is most likely due to that tetracycline is one of the antibiotics that is heavily used for human therapy as well as for veterinary and agricultural purposes (Kresken et al., 2009). Similarly, macrolides (erythromycin, tylosin, roxithromycin and oleandomycin) are one of the most commonly used antibiotic groups in

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Chapter 4 Molecular Identification and Quantification of ARGs human medicine in Australia, with approximately 650 tonnes imported into Australia from 1992 to 2003 (TGA, 2003).

The antibiotic resistance genes tetM and ermB were not only detected with high frequencies in this study but they also were found in high abundances in household dust samples. The average gene copy numbers were tetM (2.95 × 108 g-1 dust) and ermB (7.30 × 107 g-1 dust). The abundance of tetM genes has not previously been reported in household dust. However, the abundance of ermB gene quantified in the household dust in this study were higher than that previously found in urban dust (Zhou et al., 2018). Furthermore, results of this study were compared to studies conducted on soil samples and the average gene copy numbers were tetM (4.53 107 g-1 soil) (Wu et al., 2010) and ermB (9.89 105 g-1 soil) (Li, 2016). From this comparison, it can be observed that the resistance of tetracycline and erythromycin is found in household dust can be higher than their abundances in outdoor soil. This could be because of concentrations of some microorganisms (which may contain antibiotic resistant bacteria) are higher indoors than outdoors (Spengler and Sexton, 1983).

Surprisingly, the blaNDM-1 and mcr-1 genes which confer resistance to last-resort antibiotics such as carbapenems and colistin, were detected in household dust samples, with a frequency of 45% and 39%, respectively. To the best of our knowledge, this is the first study to have determined the occurrence and abundance of the carbapenem resistance blaNDM-1 gene and colistin resistance mcr-1 gene in household dust. The detection and prevalence of the blaNDM-1 and mcr-1 genes represents an important aspect of this research study. Many reports have warned of the worldwide spread of these genes, with the widespread spread of antibiotic resistant bacteria in many countries around the world. The blaNDM-1 gene was first detected in India in 2008, and by 2013, more than 40 countries were affected (Johnson and Woodford, 2013). Similarly, the mcr-1 gene was first detected in China in 2015 and since then it has been identified in many countries from different continents (Wang et al., 2018). The issue of the spread of these antibiotic resistance genes all over the world requires the cooperation of global authorities to control the spread by imposing and applying the necessary policies, regulations and procedures (Nordmann et al., 2011).

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There are several factors which may play a part in the global spread of these resistance genes.

Firstly, the presence of the blaNDM-1 and mcr-1 genes on a conjugative plasmid, where it can easily spread to other bacteria through horizontal gene transfer (Majewski et al., 2012). Secondly, the higher percentage of these resistance genes in countries like India and China, where tourism to and immigration from these countries helps global spread. Since 2010, China and India have continued to provide the highest number of permanent migrants in Australia (Phillips and Simon-Davies, 2017). One study has reported the presence of bacteria carrying the mcr-1 gene which was isolated from patients in Australia (Ellem et al., 2017). The detection of mcr-1 gene in Australia may present the warning signs of the further spread of this gene, which need to be contained and controlled. Furthermore, some studies have reported the predominance of blaNDM-1 in the U.K. and they have referred this to the historical relationship between the U.K. and India (Grundmann et al., 2010). These factors may show that currently the world is again approaching the conditions found in the pre-antibiotic era (Carlet et al 2012).

In the current study, the antibiotic resistance genes mecA and aac(6’)-aph(2”) were not detected in dust samples. However, some previous studies have reported the presence of these genes in indoor dust samples from hospital air filters (Drudge et al., 2012) and houses (Lis et al., 2009). This could be explained through the differences in the dust contents, as the household dust is significantly influenced by the geographic location and occupants of the house.

Rises in urbanization have been known to affect water quality and have also been found to be linked positively with the occurrence of antibiotic resistance genes (McKinney et al., 2010). Antibiotic resistant bacteria and antibiotic resistant genes have been detected from various aquatic ecosystems (Pruden et al., 2006, Xi et al., 2009, Storteboom et al., 2010, Walsh et al., 2011, Garcia-Armisen et al., 2011, Girlich et al., 2011), including drinking water (Schwartz et al., 2003, Cernat et al., 2007, Xi et al., 2009, Faria et al., 2009, Luo et al., 2012; Zhou et al., 2011, Shi et al., 2013, Fernando 2016, Lu et al., 2018) in many countries. Results from the current study have confirmed the presence of antibiotic resistance genes in drinking water for different antibiotic classes. However, the frequency of occurrence and the overall relative abundance of antibiotic resistance genes in drinking water samples was lower than those in dust and surface samples. These results are consistent with the results in Chapter 3 of this research project, where bacterial communities in drinking water were the least diverse when compared to dust and surface habitats (see section 3.2.3.1). Seven antibiotic resistance genes

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(tetB, ermB, ampC, sul2, catII, mcr-1 and blaNDM-1) across eleven houses were detected in the drinking water. These genes were detected in 45 (11%) of the total number of drinking water samples. Antibiotic resistance genes that confer resistance to erythromycin, ampicillin, sulfonamide, chloramphenicol, colistin and carbapenem were the most commonly found resistance genes in drinking water. The occurrence of erythromycin resistant bacteria and resistance genes have been reported previously in treated water (Faria et al., 2009; Shi et al.,

2013). Further studies have detected resistance genes such tetA, tetG, blaTEM-1, ampC, ermA and ermB in drinking water (Schwartz et al., 2003; Xi et al., 2009; Shi et al., 2013). In another study Fernando et al. detected antibiotic resistance genes i.e. ampC, tet(A), mecA, β-lactamase genes and carbapenemase in drinking water sources (Fernando et al. 2016). In studies from Chian, resistance of tetracycline (tetA and tetG) and sulfonamide (sulI) were detected in drinking water sources (Zhou et al., 2011; Luo et al., 2012). In addition, previous studies have revealed the occurrence of aminoglycoside resistance genes (aphA2) in drinking water (Shi et al., 2013; Cernat et al., 2007). Furthermore, vancomycin resistance genes (vanA) and β-lactam resistance genes (ampC) were detected in drinking water in Germany (Schwartz et al., 2003).

By comparing the results of current study with the results of previous studies, there are some antibiotic resistances that have been frequently reported in many studies including ours such as erythromycin, β-lactam, tetracycline and sulfonamide resistance. Conversely, other antibiotic resistances were detected in previous studies such as vancomycin, aminoglycoside, methicillin but these resistances were not found in the current study. This could be explained through the differences in the antibiotic usage patterns and the bacterial communities between these studies. The occurrence and abundance of antibiotic resistance genes in the aquatic ecosystems vary depending on antibiotic usage patterns in different regions (Jiang et al., 2013). In addition, the correlation between antibiotic resistance genes and the composition of the bacterial taxa found in drinking water has been previously demonstrated (Lu et al., 2018).

There are few studies that have determined the abundance of antibiotic resistance genes in drinking water (Shi et al., 2013; Fernando et al. 2016). In the current study, the most abundant resistance gene was ampC which encodes β-lactam resistance. This gene was found in drinking water with a very high relative abundance (54.4%) which means more than half of the bacterial communities in drinking water samples harbor ampC genes. Infections in both humans and animals are commonly treated by β-lactams, which resulted in the emergence of resistance by

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β-lactamases like ampC (Voolaid et al., 2013). β-lactam resistance (ampC) has been detected in both wastewater and drinking water (Schwartz et al., 2003). The ampC gene also was detected from different household samples (Fernando et al. 2016).

The next most frequently detected abundant resistances were erythromycin (ermB) and sulfonamide (sulII) resistances. Sulfonamides are commonly used to treat humans and animal infections, rather than animal growth promoters (Pei et al., 2006) and resistance to these antibiotics is widespread (Huovinen et al., 1995). Although the prescription of sulfonamide for human treatment was reduced significantly in the UK, there was no corresponding change in the high frequency of sulfonamide resistance (Enne et al., 2008), while this could be because of the continued use of sulfonamides in agriculture (Barker-Reid et al., 2010). The gene sulI was found at a significantly high abundance in drinking water sources in China, which may because of an exposure to a huge residual amount of sulfonamide over long periods (Zhou et al., 2011; Luo et al., 2012).

Unexpectedly, the carbapenem resistance blaNDM-1 genes and colistin resistance mcr-1 genes were detected in drinking water samples in this study, with high frequency and abundance. To the author’s knowledge, this is the first culture-independent study to have detected and quantified the blaNDM-1 and mcr-1 genes in drinking water in Australia. These two genes are considered as the last resort of antibiotics for the treatment of infections caused by resistant bacteria, therefore, detection of blaNDM-1 and mcr-1 genes in drinking water samples is a matter of grave concern. An additional cause for worry is that most of the resistance genes detected in this study are typically found on plasmids and can potentially be transferred to other susceptible bacteria including pathogenic bacteria.

The origin of these resistance genes in drinking water is unknown. It is unclear in which cases it results from environmental contamination. A major limitation to answer this question is related with the fact that most of the drinking water bacteria are of environmental origin and poorly or not at all characterized in terms of antibiotic resistance genes (Vaz-Moreir et al., 2014). Schwartz et al. (2003) cultivated bacteria from drinking water biofilms and tested for the occurrence of the resistance genes vanA and ampC. These genes were amplified from genomic DNA using PCR, while Enterococci and Enterobacteriaceae, which originally carry

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Chapter 4 Molecular Identification and Quantification of ARGs these genes, were not identified. The evidence suggests that, these specific resistance genes are present in uncultivable bacteria (Schwartz et al., 2003).

Several studies have shown that antibiotic resistant bacteria may be more prevalent in tap water than in the source water (Xi et al., 2009; Gomez-Alvarez et al., 2012; Narciso-da-Rocha et al., 2013). This effect may be due to the selective effect of the disinfection processes. Other studies do not support the conclusion that the antibiotic resistance detected in tap water originates from the water source (Vaz-Moreira et al., 2011b; 2012; Narciso-da-Rocha et al., 2013).

Among water treatment processes, chlorine is believed to enrich the proportion of antibiotic resistant bacteria and the abundance of antibiotic resistance genes because of the co-selection of resistance bacteria (Guo et al., 2014; Bai et al., 2015; Jia et al., 2015; Xu et al., 2016). This result was supported by Xi et al., (2009) demonstrating that chlorination could evidently concentrate various antibiotic resistance genes in drinking water. Even short-term chlorination could affect bacterial community structure and select for the antibiotic resistance in drinking water (Huang et al., 2011; Shi et al., 2013). Furthermore, replication of plasmids in bacterial cells was promoted by extracellular stress (Wegrzyn and Wegrzyn, 2002), therefore chlorination might increase the copy number of plasmids in the cells of surviving bacteria, resulting in the higher relative abundance of antibiotic resistance genes in treated water (Shi et al., 2013).

The presence of antibiotic resistance genes on household surfaces for different antibiotic classes has been confirmed by the results from this research study. There are many previous studies using culture-dependent methods isolated antibiotic resistance bacteria from different household surfaces including kitchen and bathroom surfaces (Scott et al., 2009, Roberts et al., 2011, Uhlemann et al., 2011, Medrano‐Félix et al., 2011, Kilonzo-Nthenge et al., 2012, Marshall et al., 2012, Martins et al., 2013, Wolde and Bacha, 2017). However, culture- independent studies reporting the prevalence and abundance of antibiotic resistance genes on household surfaces are scarce.

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In the current research study, eight antibiotic resistance genes (tetM, tetB, ermB, ampC, sulII, catII, mcr-1 and blaNDM-1) across eleven houses were detected on kitchen and bathroom surfaces. These genes were detected in 108 (27%) and 145 (37%) (Figure 4.2) of the total number of kitchen and bathroom surface samples, respectively. Antibiotic resistance genes tetM, ermB, ampC and sulII that confer resistance to tetracycline, erythromycin, ampicillin and sulfonamide, respectively, were the most commonly found resistance genes on both kitchen and bathroom surfaces. The occurrence of tetracycline and ampicillin resistant bacteria and resistance genes have been reported previously in both kitchens and bathroom surfaces. For example, Cronobacter sakazakii was recovered from domestic kitchens and found to be resistance for penicillin, tetracycline, ciprofloxacin and nalidixic acid (Kilonzo-Nthenge et al., 2012). In addition, Salmonella isolates from kitchen sponges were found to be resistant to ampicillin, tetracycline, nalidixic acid, kanamycin and chloramphenicol (Wolde and Bacha, 2017). Furthermore, different bacterial isolates obtained from kitchens and bathroom showed resistance for tetracycline, gentamicin, ciprofloxacin, ampicillin, cephalothin and kanamycin, and the highest proportions of resistance occurred for ampicillin (Marshall et al., 2012).

Moreover, Martins et al., detected antibiotic resistance genes (ampC, blaOXA, blaSHV, aadA, gyrA, parC and tetB) in E. coli isolates obtained from refrigerator door samples using PCR (Martins et al., 2013). However, these earlier studies were limited to only resistant bacteria that are able to grow in culture, while in the current study environmental DNA was extracted directly from samples to target antibiotic resistance genes present in bacterial communities in these samples.

The highest number of antibiotic resistance genes 145 (37%) detected in this study were in bathroom surface samples. The first possible explanation for this finding is that bathroom walls where the samples obtained from, are less frequently cleaned. The second possible explanation is the large use of antimicrobial chemical compounds such as Triclosan, Triclocarban, and Methyl-, Ethyl-, Propyl-, and Butyl-paraben, Diethanolamine (DEA), Propylene Glycol, Sodium Lauryl and Dioxane which are frequently used in the bathroom in cosmetics and personal-care products including shampoos, soaps, oral rinses, toothpastes and deodorants, may select for antimicrobial resistance. There is considerable evidence that resistant bacteria including Enterobacter gergoviae, Pseudomonas aeruginosa, Bacillus, Micrococcus and Staphylococcus can be found in consumer products protected with preservatives such as

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Triclosan, Parabens and Phenoxyethanol (Suller and Russel 2000, Davin-Regli et al., 2012 and Ferrarese et al., 2003).

Of particular note was that, the lowest frequency of occurrence of antibiotic resistance genes (10%), in this study, was detected in house 4 which is the only house use eco-friendly cleaning products. Although the impact of antimicrobial cleaning products on the antibiotic resistance is controversial there are some studies showed that antimicrobial products could select for resistance genes in the environment (Block and Furman 2002; Middleton and Salierno 2013; Drury et al., 2013; Hartmann et al., 2016). For example, a strong link has been found between the concentration of triclosan, triclocarban, and methyl-, ethyl-, propyl-, and butylparaben and the relative abundance of antibiotic resistance genes conferring resistance to tetracycline, erythromycin and beta–lactam antibiotics in indoor environments (Hartmann et al., 2016). The influence of the exposure to antimicrobial cleaning products on the occurrence of antibiotic resistance in household ecosystems merit further attention.

Interestingly, a potential correlation has been observed between the number of occupants and the frequency of occurrence of antibiotic resistance genes. The highest number of occupants in this study were found in houses 3, 5 and 7 which have the highest total numbers of antibiotic resistance genes (50, 60 and 50), respectively. Similar relationships have been previously observed between the concentration of indoor bacteria and human occupancy (Hospodsky et al., 2012).

In conclusion, the occurrence and abundance of some clinically relevant antibiotic resistance genes have been determined in the human residence using culture-independent methods (PCR and qPCR). In addition, the abundance and the frequency of detection of antibiotic resistance genes varied between different habitats and the houses. The most commonly detected antibiotic resistance genes were tetM and ermB genes. Furthermore, critically important antibiotic resistance genes such as blaNDM-1 and mcr-1 genes were detected in the human residences. The most abundant resistance gene in drinking water was ampC which encodes β-lactam resistance. Moreover, the highest number of antibiotic resistance genes were detected in bathroom surface samples. Additionally, the low occurrence of antibiotic resistance genes in one house may be linked to the absence of antimicrobial cleaning products in this house. A possible correlation was observed between the number of occupants and the number of detected antibiotic

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Chapter 4 Molecular Identification and Quantification of ARGs resistance genes. Future work (albeit unfortunately beyond the scope of this current research) should seek to investigate these potential drivers impacting upon the presence and abundance of antibiotic resistance in the household environment. Finally, the presence of antibiotic resistance genes in human residences is a public health concern especially if these genes were linked to or originated from potential pathogenic taxa. This research topic will be determined in the next chapter.

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CHAPTER 5 DIVERSITY AND TAXONOMIC ORIGIN OF CLINICALLY RELEVANT ANTIBIOTIC RESISTANCE GENES IN HUMAN RESIDENCES

5.1 Introduction

The emergence of a serious risk to environmental and public health is now recognised in light of accumulating evidence of the presence of antibiotic resistant non-pathogenic and pathogenic bacteria (LaPara et al., 2011; Munir and Xagoraraki 2011). The genetic factors that enable a bacterium to tolerate increased concentrations of antibiotic are termed antibiotic resistance genes (ARGs ) (Bengtsson-Palme et al., 2017). Recent research studies showed that through horizontal gene transfer (HGT), ARGs can transfer from environmental bacteria to clinical pathogens (Smillie et al., 2011; Forsberg et al., 2012;). This highlights the clinically significant role of bacteria in the natural environment in creating a possible source of pathogenic ARGs via transfer from a natural resistome (Forsberg et al., 2012). Hence, the extensive investigation and comprehensive understanding of the abundance and diversity of ARGs in different habitats is necessary for managerial decision-making to control and manage the emergence and the prevalence of antibiotic resistance.

Antibiotic resistance genes have so far not been considered within public health surveillance systems around the world, e.g. the first Australian report on antimicrobial use and resistance in human health (Australian Commission on Safety and Quality in Health Care (ACSQHC), 2017) or the European Antimicrobial Resistance Surveillance Network (EARS-Net) (European Centre for Disease Prevention and Control (ECDC), 2018), which have tended to concentrate on antibiotic resistance isolates in public health laboratories and on the clinical use of antibiotics. A primary factor contributing to this omission is the absence of a fast, reliable and universally applicable analytical methodology for broad-spectrum ARG identification and quantification in samples from the environment. Detection and identification of ARGs have been obtained via the frequent application of a range of molecular technologies, including DNA microarray techniques, the polymerase chain reaction (PCR) and quantitative PCR (qPCR), to evaluate the presence and abundance of ARGs in the environment. Nevertheless, the

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Chapter 5 Diversity and Taxonomic Origin of Clinically Relevant ARGs amplification-based methods of PCR and qPCR possess a number of shortcomings, such as amplification bias, false-negatives due to inhibition in PCR, and low-throughput (Yang et al., 2013).

The application of metagenomics in biological research has been recently expanded due to the rapid development of HTS (Thomas et al., 2012). Moreover, the range of metagenomic analysis on samples from the environment has been significantly enhanced by next-generation sequencing (NGS) (Mardis 2008). The NGS technique has been proven effective for detection and identification of new genes or gene modifications that result in new virulence or antibiotic resistance (La Scola et al. 2008, Crofts et al., 2017). HTS is regarded as a promising technique for the analysis of ARGs and diversity of other functional genes in samples from the environment, having already been successfully employed to reveal the incidence of mobile genetic elements and ARGs in a range of environments (Monier et al., 2011, Zhang et al., 2011). The amount of NGS data available has increased exponentially in response to a decrease in the cost per nucleotide of DNA sequencing which rapidly outpaces the rate suggested by Moore’s law (Sboner et al., 2011). For example, during the initial six months of the 1000 genomes project (a global research effort to study human genetic variation), more NGS sequences were acquired than had been collected during more than twenty years of the National Centre for Biotechnology (NCBI) Genbank database (Pennisi, 2011). By exerting increasing demands upon computational resources for data analysis, this torrent of HTS data replaced the sequencing cost as the limiting factor for metagenomic analysis. For instance, while Illumina NextSeq 500 can produce between 30 and 120 Gb of microbiological metagenomic data in as little as 30 h, an analysis of these data for the identification of actual microbial functions could take months. Alongside the hard-to-quantify cost of human resources and time spent on metagenomic analysis, the cost of computational resources for handling the resulting torrent of data is also increasing (Sboner et al., 2011).

Many researchers have employed a range of HTS techniques to examine the diversity and prevalence of ARGs in different environments. For example, Fitzpatrick and Walsh (2016) were able to describe the relative abundance and distribution of ARGs across a broad range of biomes generated by using various sequencing platforms on datasets submitted to the open- source MG-RAST metagenomic analysis application. They compared 432 environmental shotgun sequencing datasets with significantly variable sequencing depths, including animal,

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Chapter 5 Diversity and Taxonomic Origin of Clinically Relevant ARGs human, insect, water, plant and soil metagenomes to find that the human gut microbiomes contained the widest variety of AR genes (n = 78), followed by the animal gut (n = 42). Among the environmental samples, the highest diversity (n= 24) was found within hospital wastewater treatment plant metagenomes (Fitzpatrick and Walsh, 2016).

A metagenomic approach was also used by Li et al. (2015) to study the co-occurrence and wide-spectrum profiles of ARGs in various environments. Their 50 samples included one from wastewater biofilm, three from sediments, three from soils, 12 faecal samples (chicken, human and pig), 13 water samples (drinking water, river water, sewage, swine wastewater and treated wastewater) and 18 sludge samples (anaerobic digestion sludge and activated sludge). This enabled them to identify 18 ARG types containing 260 subtypes (Li et al., 2015). Similarly, Urbaniak et al. (2018) employed Ion AmpliSeq to evaluate ARG prevalence in 24 surface samples acquired from the International Space Station (ISS). They identified as many as 65 ARGs demonstrating resistance to 23 types of antibiotic, 9 of which were also detected during whole genome sequencing (WGS) of selected isolate cultures. These 9 ARGs were bla1, bla- OKP, blaZ, fos, norA, oqxA, oqxB, qacB and tet38 (Urbaniak et al., 2018).

Sediment samples from 18 estuaries in China were examined by Zhu et al. (2017) to identify copious and varied ARGs with over 200 subtypes (Zhu et al., 2017). Other research indicated the presence of the sul1 and blaSHA genes in airborne samples from Californian town and city parks (Echeverria-Palencia et al., 2017). Functional metagenomics was employed by Torres- Cortes et al. (2011) to recognise 11 previously unknown antibiotic resistance genes in libraries produced from agricultural soil samples from Spain. Four of these provided resistance to trimethoprim; three gave resistance to ampicillin, two to chloramphenicol and two to gentamicin (Torres-Cortes et al., 2011). A metagenomic library was constructed from soil bacterial communities and screened by Donato et al. (2010) to identify 13 clones that provided antibiotic resistance to one or more of the aminoglycosides, β-lactams or tetracyclines (Donato et al., 2010).

A small number of studies have also employed the HTS approach to examine the occurrence and variety of ARGs in indoor environments. The shotgun metagenomics method was used by Hartmann et al. (2016) and identified 17 antibiotic resistance gene families within various

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Chapter 5 Diversity and Taxonomic Origin of Clinically Relevant ARGs built-up environments such as houses, offices, a metropolitan transit system, a hospital and a pier. The three predominant antibiotic resistance genes were identified as tet(W), blaSRT-1, and erm(B), conferring resistance to the antibiotics tetracycline, beta-lactamase and macrolide, respectively (Hartmann et al. (2016)). Another study has been done by Ling et al. (2013) who detected tetracycline resistance genes of tetX and tetW in aerosol samples obtained from human-occupied indoor environments (two clinics and a homeless shelter) in Colorado (Ling et al., 2013). Although significant research efforts have concentrated on using HTS techniques to study the incidence and predominance of different ARGs in various environments, there remains only limited information regarding the diversity of ARG within indoor environments. Hence, little is yet known about the role of the indoor environment as an ARG reservoir.

The main aim of this study was to determine the diversity of clinically relevant antibiotic resistance genes within and between different human residences through a culture-independent approach using next generation sequencing (NGS). From antibiotic resistance genes that were detected by PCR in previous chapter, four genes were chosen to be sequenced. The antibiotic resistance genes sequenced in this study were tetM, ermB, ampC and sulII encoding resistance to tetracycline, erythromycin, ampicillin and sulfonamide, respectively. This study aims to answer the following questions, firstly, whether there is diversity within each gene and does this vary between different habitats and houses. Secondly, this research sought to identify the taxa in which these dominant gene sequence types were likely to originate.

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5.2 Results

5.2.1 Quality of Illumina Miseq sequence data

Next generation sequencing was conducted using PCR amplicons (see Chapter 4) for four different antibiotic resistance genes: tetM, ermB, sulII and ampC. Quality control statistics of this sequencing run are shown in Appendix A5. A total of 6.13 Gb of amplicon sequence data was obtained for this run with an average Q30 score of 67.33%. The run was loaded with 15% PhiX control DNA, of which 9.79% was aligned. The total error rate was 2.82% for reads from each end of the amplicon. In this run, a total of 19,976,014 reads passed filter which was 96.48% of total sequence reads; 84.7% of sequence reads that passed filter were identified.

5.2.2 Alpha diversity and richness of antibiotic resistance genes in human residences

5.2.2.1 Diversity and richness of antibiotic resistance genes in different habitats

The diversity and richness of antibiotic resistance genes (tetM, ermB, sulII and ampC) detected in different habitats (dust, kitchen and bathroom surfaces and drinking water) were studied across 11 houses using HTS. Sequence data were analysed using the FunGene Pipeline (section 2.2.13). This analysis involves removal of chimera, translation of DNA to polypeptide (protein) sequences and comparison to curated datasets of protein sequences for each antibiotic resistance gene determinant. Numbers of clusters were defined at 95%, 80%, and 50% similarity cutoff for protein sequences, and their corresponding Chaol richness, confidence upper and lower limits and Shannon-Wiener diversity index (H’) were determined for sequence reads of tetM, ermB, sulII and ampC genes. These data are shown for each individual household habitat in Tables 5.1., 5.2, 5.3 and 5.4. In addition, overall Chaol richness and Shannon-Weiner diversity for each resistance gene are shown in Figure 5.1 and Figure 5.2, respectively.

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Comparisons of Shannon-Wiener diversity indexes with similarity cutoff of 95% (Figure 5.1) and of Chao1 estimated richness indices at the similarity cutoff level of 50% (Figure 5.2) for translated tetM, ermB, sulII and ampC genes sequences were performed. Kitchen surface samples had the highest Chao1 estimated richness and Shannon-Wiener diversity for both translated tetM and ampC genes sequences, while the highest Chao1 estimated richness and Shannon-Wiener diversity for translated ermB and sulII genes sequences were found in dust and bathroom surface samples, respectively. Conversely, the dust samples had the lowest Chao1 estimated richness and Shannon-Wiener diversity for translated tetM and sulII genes sequences, while the lowest Chao1estimated richness for translated ermB and ampC genes sequences were in drinking water and bathroom surface samples, respectively (Figures 5.1 and 5.2). In addition, there was no significant difference between kitchen and bathroom surface samples in Shannon-Wiener diversity and Chao1estimated richness for TetM (t-test, p = 0.9 and t-test, p = 0.2), ErmB (t-test, p = 0.8 and t-test, p = 0.8) and SulII (t-test, p = 0.6 and t-test, p = 0.5) genes, respectively. For AmpC, a t-test could not be performed as there was only one bathroom surface sample. Other samples could not be compared as they were from different sample types.

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Figure 5.1 Box and whisker plots of Shannon-Weiner indexes for diversity of antibiotic resistance gene products following translation of gene sequences to polypeptides using the FunGene Pipeline (Version 9.6) detected in different household samples (dust, kitchen and bathroom surfaces and drinking water). Boxes represent the interquartile range (IQR) between the first and third quartiles and the line inside boxes describes the median. Whiskers represent the lowest and highest values. The cutoff of percent similarity for translated gene (i.e. protein) sequences was 95%. Number of samples (houses): for TetM (dust: 10, kitchen: 11, bathroom: 11); for ErmB (dust: 9, kitchen: 2, bathroom: 3, drinking water: 1); for SulII (dust: 3, kitchen: 5, bathroom: 5); for AmpC (kitchen: 4, bathroom: 1).

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Figure 5.2 Box and whisker plots of Chao1 indices for diversity of antibiotic resistance gene products following translation of gene sequences to polypeptides using the FunGene Pipeline (Version 9.6) detected in different household samples (dust, kitchen and bathroom surfaces and drinking water). Boxes represent the interquartile range (IQR) between the first and third quartiles and the line inside boxes describes the median. Whiskers represent the lowest and highest values. The cutoff of percent similarity for translated gene (i.e. protein) sequences was 50%. Number of samples (houses): for TetM (dust: 10, kitchen: 11, bathroom: 11); for ErmB (dust: 9, kitchen: 2, bathroom: 3, drinking water: 1); for SulII (dust: 3, kitchen: 5, bathroom: 5); for AmpC (kitchen: 4, bathroom: 1).

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5.2.2.2 Diversity and richness of translated tetM gene sequences in different habitats

The kitchen surface samples showed the highest number of clusters (mean of 2092 ± 248) at the similarity cutoff level of 95% for translated tetM gene sequences. While the lowest number of clusters was observed in dust samples (mean of 1933 ± 223). Furthermore, the highest Chao1 estimated richness for translated tetM gene sequences with similarity cutoff of 50% was found in kitchen surface samples (mean of 83 ± 15), followed by bathroom surface (mean of 72 ± 10) and dust (mean of 71 ± 13) samples. In addition, the kitchen surface samples had the highest Shannon-Wiener diversity (3.14 ± 0.13) for translated tetM gene sequences at the similarity cutoff level of 95% compared to the bathroom (3.13 ± 0.16) and dust samples (2.92 ± 0.22) (Table 5.1).

The number of observed clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated tetM gene sequences were found to vary between houses (Table 5.1). In dust samples, house 2 had the highest number of clusters and Chaol estimated richness for translated tetM gene sequences with similarity cutoff of 95% (2450 and 7946, respectively), and house 6 had the highest Shannon-Wiener diversity index (3.58), whereas the lowest number of observed clusters (1476) and Chao1 estimated richness (4699) were in house 8, and the lowest Shannon-Wiener diversity index (2.38) was in house 4. For kitchen surface samples, the highest number of clusters was in house 2 (2517) while house 10 had the highest Chao1 estimated richness (8770) and house 4 had the highest Shannon-Wiener diversity index (3.45). Conversely, the lowest number of observed clusters (1115) and Chao1 estimated richness (1915) were in house 7, and the lowest Shannon-Wiener diversity index (2.77) was in house 8. In bathroom samples, house 3 had the highest number of clusters (2366) and Chaol estimated richness (8178), and house 6 had the highest Shannon-Wiener diversity index (3.71). House 1 had the lowest number of detected clusters (1136) and Chao1 estimated richness (2135), and house 4 had the lowest Shannon-Wiener diversity index (2.84) (Table 5.1).

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Table 5.1 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of tetM gene translated polypeptide sequences identified in different habitats and houses.

Similarity Shannon Habitats Houses cut-offs Clusters Chao 1 LCI95 UCI95 (H') E House 1 95% 2226 7074 6324 7962 3.09 0.40 80% 545 1806 1462 2280 1.16 0.18 50% 92 123 105 165 0.12 0.03 House 2 95% 2450 7946 7099 8946 3.31 0.42 80% 546 1819 1474 2293 1.38 0.22 50% 78 89 82 110 1.06 0.24 House 3 95% 2244 7306 6496 8271 3.12 0.40 80% 463 1594 1256 2077 1.35 0.22 50% 66 89 74 134 0.55 0.13 House 4 95% 1490 4808 4171 5596 2.38 0.33 80% 356 1249 956 1686 0.57 0.10 50% 54 65 58 88 0.05 0.01 House 6 95% 2327 6924 6219 7757 3.58 0.46

80% 473 1563 1242 2018 1.57 0.26

50% 55 63 57 82 0.21 0.05 95% 1767 6243 5422 7250 2.65 0.35 Dust House 7 80% 432 1654 1272 2209 1.14 0.19 50% 65 72 67 87 0.43 0.10 House 8 95% 1476 4699 4093 5446 2.44 0.33 80% 355 1203 928 1611 1.12 0.19 50% 47 51 48 64 0.13 0.03 House 9 95% 1597 4793 4215 5500 2.63 0.36 80% 357 1321 1001 1800 1.38 0.23 50% 44 47 45 60 0.72 0.19 House 10 95% 2180 7040 6244 7991 3.10 0.40 80% 473 1286 1061 1597 1.61 0.26 50% 57 62 58 76 0.42 0.10 House 11 95% 1571 4481 3938 5147 2.84 0.39 80% 318 969 749 1301 0.92 0.16 50% 40 51 43 81 0.37 0.10 House 1 95% 1503 4584 3979 5336 3.21 0.44 80% 313 1344 967 1937 1.05 0.18 50% 41 43 41 52 0.07 0.02 House 2 95% 2517 8073 7239 9055 3.20 0.41 80% 600 2009 1638 2513 1.46 0.23

50% 81 90 84 109 0.36 0.08 House 3 95% 2390 7560 6765 8500 3.22 0.41 80% 532 1720 1390 2175 1.26 0.20 50% 70 89 77 124 0.47 0.11 House 4 95% 2348 6877 6190 7688 3.45 0.44 80% 538 1530 1267 1888 1.56 0.25

50% 85 106 93 138 0.77 0.17 Kitchen Surface Kitchen House 5 95% 2089 6853 6060 7805 2.84 0.37 80% 477 1453 1174 1843 1.41 0.23 50% 73 80 75 95 1.03 0.24 House 6 95% 2106 5486 4936 6142 3.35 0.44 80% 405 1342 1045 1777 1.65 0.27 50% 57 64 59 80 0.91 0.22

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Similarity Shannon Habitats Houses cut-offs Clusters Chao 1 LCI95 UCI95 (H') E House 7 95% 1115 1915 1724 2167 3.14 0.45 80% 109 162 132 229 1.97 0.42 50% 15 36 20 101 0.71 0.26

House 8 95% 2077 7428 6555 8472 2.77 0.36

80% 550 1978 1582 2526 1.09 0.17 50% 82 105 91 142 0.08 0.02 House 9 95% 2041 7046 6226 8027 2.85 0.37 80% 492 1594 1281 2031 1.27 0.21 50% 66 94 76 141 0.33 0.08 House 10 95% 2365 8770 7715 10033 3.35 0.43

Kitchen Surface Kitchen 80% 534 1572 1293 1954 1.64 0.26 50% 69 81 73 106 0.82 0.19 House 11 95% 2458 7147 6457 7957 3.20 0.41 80% 576 1871 1526 2343 1.13 0.18 50% 80 125 97 196 0.12 0.03 House 1 95% 1136 2135 1906 2432 3.23 0.46 80% 131 332 228 548 1.54 0.32 50% 20 34 23 87 0.20 0.07 House 2 95% 1856 5738 5060 6559 3.24 0.43 80% 405 1248 991 1619 1.42 0.24 50% 53 60 55 77 0.17 0.04 House 3 95% 2366 8178 7248 9284 3.52 0.45 80% 510 1940 1520 2534 1.99 0.32 50% 71 77 73 92 0.73 0.17 House 4 95% 1960 6760 5951 7732 2.84 0.38 80% 463 1537 1226 1974 1.15 0.19 50% 66 85 73 119 0.11 0.03

House 5 95% 2152 7425 6566 8450 2.94 0.38 80% 521 1485 1222 1848 1.42 0.23 50% 74 97 83 134 0.22 0.05 House 6 95% 2156 6137 5481 6921 3.71 0.48 80% 468 1197 994 1478 2.15 0.35 50% 56 64 58 85 1.07 0.27 House 7 95% 2069 5835 5230 6556 2.91 0.38 80% 471 1296 1065 1618 1.41 0.23 Bathroom Surface Bathroom 50% 52 61 54 88 0.15 0.04 House 8 95% 2300 7401 6619 8325 3.12 0.40 80% 517 1457 1197 1816 1.27 0.20 50% 61 70 64 89 0.37 0.09 House 9 95% 2355 7421 6645 8337 3.07 0.40 80% 558 2055 1639 2632 1.28 0.20 50% 76 95 83 129 0.34 0.08 House 10 95% 2121 6871 6096 7796 3.03 0.40 80% 506 1707 1365 2186 1.51 0.24 50% 67 80 71 106 0.39 0.09 House 11 95% 2059 6659 5905 7561 2.90 0.38 80% 478 1700 1337 2217 1.47 0.24 50% 63 69 65 84 0.39 0.09 Note: Chao1= Chao1 richness estimates, LCI95= confidence lower limits for chao1, UCI95= confidence upper limits for chao1, H'= Shannon’s diversity and E= Simpson’s evenness.

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5.2.2.3 Diversity and richness of translated ermB gene sequences in different habitats

For translated ermB gene sequences, the highest number of clusters at the similarity cutoff level of 95% was observed in the dust samples (mean of 2226 ± 612), followed by bathroom surface samples (mean of 859 ± 640) and kitchen surface samples (mean of 804 ± 552). While the lowest number of clusters was observed in a drinking water sample (45). In addition, the highest Chao1 estimated richness of ermB gene sequences with similarity cutoff of 50% was found in dust samples (mean of 36 ± 12), followed by bathroom surface (mean of 26 ± 7), kitchen surface (mean of 22 ± 2) and drinking water (12) samples. Furthermore, dust samples had higher Shannon-Wiener diversity (5.12 ± 0.41) for translated ermB gene sequences at the similarity cutoff level of 95% when compared to the bathroom surface (4.38 ± 0.27), kitchen surface (3.87±0.83) and drinking water samples (3.56) (Table 5.2).

Identified clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated ermB gene sequences varied between houses (Table 5.2). In dust samples, house 3 had the highest number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated ermB gene sequences with similarity cutoff of 95% (4463, 9836 and 6.77, respectively). Whereas the lowest number of observed clusters, Chao1 estimated richness and Shannon-Wiener diversity index (1045, 3475 and 4.11, respectively) was in house 6. For kitchen surface samples, the highest number of identified clusters, Chao1 estimated richness and Shannon-Wiener diversity index (1480, 4675 and 4.88, respectively) was in house 10, whilst the lowest number of identified clusters and Chao1 estimated richness and Shannon- Wiener diversity index (128, 583 and 2.85, respectively) was in house 5. In bathroom samples, house 5 had the highest number of clusters (1642), Chaol estimated richness (5133) and Shannon-Wiener diversity index (4.71), whilst house 8 had the lowest number of detected clusters, Chao1 estimated richness, and Shannon-Wiener diversity index (75, 2631 and 4.04, respectively). Only one drinking water sample from house 5 was sequenced for ermB gene and the number of clusters, Chaol estimated richness and Shannon-Wiener diversity index of this sample were 45, 991 and 3.56, respectively (Table 5.2).

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Table 5.2 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of ermB gene translated polypeptide sequences identified in different habitats and houses.

Similarity Shannon Houses Habitats cut-offs Clusters Chao 1 LCI95 UCI95 (H') E House 2 95% 2331 6648 5991 7423 4.92 0.63 80% 193 488 365 700 1.28 0.24 50% 12 14 12 28 0.03 0.01 House 3 95% 4463 9836 9041 10705 6.77 0.80 80% 439 1086 1031 1147 4.81 0.59 50% 53 77 70 86 0.81 0.19 House 4 95% 2965 8991 8156 9960 5.27 0.66 80% 325 1006 775 1355 1.71 0.30 50% 44 54 47 79 0.64 0.17 House 5 95% 2806 7722 7030 8527 5.38 0.68 80% 227 536 417 730 1.40 0.26 50% 24 33 26 64 0.24 0.07 House 6 95% 1045 3475 2933 4173 4.11 0.59 80% 109 409 255 727 0.55 0.12 Dust 50% 17 24 18 51 0.05 0.02 House 7 95% 1490 4430 3875 5114 4.71 0.65 80% 141 341 251 505 1.46 0.30 50% 25 55 33 137 0.64 0.20 House 8 95% 1422 3766 3323 4314 4.97 0.69 80% 117 310 215 496 1.44 0.30 50% 15 29 18 82 0.04 0.02 House 9 95% 1933 5386 4799 6094 5.12 0.68 80% 165 469 336 707 1.38 0.27 50% 19 23 20 38 0.03 0.01 House 11 95% 1575 4550 4002 5223 4.85 0.66 80% 120 255 191 376 0.90 0.19 50% 13 15 13 27 0.60 0.24 House 5 95% 128 583 349 1065 2.85 0.59 80% 24 119 52 345 0.23 0.07 50% 10 25 13 78 0.15 0.07

House 10 95% 1480 4675 4066 5428 4.88 0.67 Surface Kitchen Kitchen 80% 138 300 227 433 1.04 0.21 50% 14 19 15 43 0.03 0.01 House 5 95% 1642 5133 4495 5913 4.71 0.64 80% 131 320 232 484 0.49 0.10 50% 16 17 16 24 0.04 0.01 House 8 95% 75 2631 1690 4119 4.04 0.94

Surface 80% 59 430 203 1018 3.27 0.80 Bathroom Bathroom 50% 20 35 24 84 2.13 0.71

95% 45 991 572 1742 3.56 0.94 House 5 80% 37 136 73 307 3.20 0.89

Water 50% 10 12 10 26 1.76 0.76

Drinking Note: Chao1= Chao1 richness estimates, LCI95= confidence lower limits for chao1, UCI95= confidence upper limits for chao1, H'= Shannon’s diversity and E= Simpson’s evenness.

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5.2.2.4 Diversity and richness of translated sulII gene sequences in different habitats

The bathroom surface samples showed the highest number of clusters (mean of 1693 ± 513) at the similarity cutoff level of 95% for translated sulII gene sequences, whilst dust samples showed the lowest number of clusters (mean of 622 ± 335). Furthermore, the highest Chao1 estimated richness of sulII gene sequences with similarity cutoff of 50% was found in bathroom surface samples (mean of 39 ± 17), followed by kitchen surface (mean of 21 ± 10) and dust (mean of 10 ± 4) samples. In addition, the bathroom surface samples had higher Shannon- Wiener diversity (5.34 ± 0.55) of sulII gene sequences at the similarity cutoff level of 95% compared to the kitchen surface (5.21 ± 0.57) and dust samples (4.51 ± 0.18).

Variation between houses was found in the number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated sulII gene sequences (Table 5.3). In dust samples, house 3 had the highest number of clusters, Chaol estimated richness and Shannon- Wiener diversity index for translated sulII gene sequences with similarity cutoff of 95% (1224, 4036 and 4.82, respectively), whereas the lowest number of observed clusters (67) and Shannon-Wiener diversity index (4.20) were in house 6, and the lowest Chao1 estimated richness (1941) was in house 11. For kitchen surfaces the highest number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated sulII gene sequences with similarity cutoff of 95% (2453, 11284 and 6.90, respectively) were found in house 9, while the lowest number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated sulII gene sequences (539, 1484 and 4.35, respectively) was in house 5. In bathroom surface samples, house 6 had the highest number of clusters (2507), Chaol estimated richness (9481) and Shannon-Wiener diversity index (6.21), whilst house 7 had the lowest number of observed clusters (242) and Chao1 estimated richness (898), and Shannon-Wiener diversity index (3.85) (Table 5.3).

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Table 5.3 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of SulII gene translated polypeptide sequences identified in different habitats and houses. Similarity Shannon Habitats Houses Clusters Chao 1 LCI95 UCI95 E cut-offs (H') House 3 95% 1224 4036 3474 4740 4.82 0.68 80% 94 179 134 272 1.01 0.22 50% 6 6 6 21 0.02 0.01 House 6 95% 67 2278 1441 3624 4.20 1 80% 49 254 125 602 3.71 0.95 Dust 50% 4 5 4 12 0.29 0.21 House 11 95% 576 1941 1561 2468 4.50 0.71 80% 58 115 82 194 1.19 0.29 50% 11 19 12 54 0.06 0.03 House 2 95% 977 3351 2816 4041 4.72 0.69 80% 86 200 141 321 0.64 0.14 50% 11 13 11 27 0.04 0.02

House 3 95% 2019 5590 5039 6242 5.20 0.68

80% 152 311 240 439 0.97 0.19 50% 12 26 15 79 0.02 0.01 House 5 95% 539 1484 1226 1838 4.35 0.69 80% 40 70 50 128 0.51 0.14 50% 5 8 5 29 0.04 0.02 House 7 95% 1012 3793 3171 4596 4.87 0.70

Kitchen Surface Kitchen 80% 79 204 134 364 0.50 0.12 50% 8 9 8 16 0.02 0.01 House 9 95% 2453 11284 7660 16638 6.90 1 80% 306 913 853 980 5.11 0.86 50% 33 50 44 59 1.10 0.44 House 5 95% 1564 4747 4188 5425 5.10 0.69 80% 102 297 196 507 1.08 0.23 50% 11 19 12 54 0.03 0.01

House 6 95% 2507 9481 7273 12367 6.21 1 80% 393 1099 1038 1167 5.30 0.84 50% 43 72 63 85 1.39 0.36 House 7 95% 242 898 645 1312 3.85 0.70 80% 30 107 54 277 0.78 0.23 50% 11 39 18 116 0.10 0.04 House 10 95% 2284 9070 6828 12057 6.12 1 80% 361 977 919 1041 5.23 0.77 Bathroom Surface Bathroom 50% 40.2 59 53 68 1.94 0.33 House 11 95% 1868 5803 5156 6579 5.42 0.72 80% 130 285 210 427 1.38 0.28 50% 7 8 7 15 0.02 0.01 Note: Chao1= Chao1 richness estimates, LCI95= confidence lower limits for chao1, UCI95= confidence upper limits for chao1, H'= Shannon’s diversity and E= Simpson’s evenness.

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5.2.2.5 Diversity and richness of translated ampC gene sequences in different habitats

For translated ampC gene sequences, higher number of clusters at the similarity cutoff level of 95% were observed in the kitchen samples (mean of 2165 ± 820) than in the sole bathroom surface sample (10). Similarly, Chao1 estimated richness of translated ampC gene sequences at a similarity cutoff of 50% was higher in kitchen surface samples (mean of 18 ± 5), than in the bathroom surface sample (7), and the kitchen surface samples had higher Shannon-Wiener diversity (5.59 ± 0.26) translated ampC gene sequences at the similarity cutoff level of 95% when compared to the bathroom surface sample (2.01).

There was variation between houses in the number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated ampC gene sequences (Table 5.4). In kitchen surface samples, house 9 had the highest number of clusters, Chaol estimated richness and Shannon-Wiener diversity index for translated ampC gene sequences with similarity cutoff of 95% (4234, 12962 and 6.19, respectively), whereas the lowest number of observed clusters, Chao1 estimated richness and Shannon-Wiener diversity index (1019, 3602 and 5.09, respectively) was in house 8. For ampC gene, only one bathroom surface sample from house 7 was sequenced and the number of clusters, Chaol estimated richness and Shannon-Wiener diversity index of this sample was 10, 24 and 2.01, respectively (Table 5.4).

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Table 5.4 Number of clusters at 95%, 80% and 50% similarity, Chao1, LCI95, UCI95, H' and E for amplicon libraries of ampC gene translated polypeptide sequences identified in different habitats and houses.

Similarity Shannon Habitats Houses Clusters Chao 1 LCI95 UCI95 E cut-offs (H')

95% 1840 6037 5338 6875 5.59 0.74

House 2 80% 181 383 299 528 1.55 0.30

50% 10 15 11 42 0.70 0.30

95% 1567 4837 4259 5539 5.48 0.75

House 5 80% 149 294 231 405 2.22 0.44

50% 14 25 16 68 1.09 0.41

95% 1019 3602 3029 4338 5.09 0.73

Kitchen Surface Surface Kitchen House 8 80% 86 222 146 393 1.02 0.23

50% 6 8 6 21 0.06 0.03

95% 4234 12962 11994 14050 6.19 0.74

House 9 80% 371 826 681 1038 2.03 0.34

50% 17 25 18 60 1.08 0.38

95% 10 24 13 77 2.01 0.87

House 7 80% 7 10 7 30 1.67 0.86

Surface Bathroom Bathroom 50% 4 7 4 28 0.69 0.50 Note: Chao1= Chao1 richness estimates, LCI95= confidence lower limits for chao1, UCI95= confidence upper limits for chao1, H'= Shannon’s diversity and E= Simpson’s evenness.

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5.2.3 The rare biosphere

The rare biosphere of detected antibiotic resistance genes is a large and dominant part of the diversity of these resistance genes which is represented by many clusters each with low numbers of sequences. Figure 5.3 shows the number of translated gene sequence reads per cluster for antibiotic resistance genes: tetM gene from dust sample (tet9D), ermB gene from dust sample (erm5D), sulII gene from bathroom surface sample (sul5B) and ampC gene from kitchen sample (amp2K), which are used as examples for each resistance gene. As shown in the rank abundance plots (Figure 5.3) only a few clusters were dominant in each sample while the majority of clusters are present within the rare biosphere with a relative abundance of < 0.5%. In sample tet9D for the tetM gene, 95.5% of the number of sequence reads were assigned to only three clusters (sequence types). In sample erm5D for the ermB gene, 99.4% of the number of sequence reads were assigned to only three clusters (sequence types). In sample sul5B for the sulII gene, the three most abundant clusters represented 99.6% of the total number of sequence reads. For the ampC gene, 99.1% of the number of sequence reads in the sample amp2K were assigned to only three clusters (sequence types) (Figure 5.3).

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A. B. 7000 16000 6000 14000 5000 12000 10000 4000 8000 3000 6000 2000 4000

1000 2000

sequence sequence reads percluster

Number Number translatedof gene sequence sequence reads percluster

0 Number translatedof gene 0

1 6

16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

11 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 101 Clusters Clusters

C. D. 7000 4000 6000 3500 5000 3000 2500 4000 2000 3000 1500 2000 1000

1000 500

sequence sequence reads percluster sequence reads percluster Number Number translatedof gene Number Number translatedof gene 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Clusters Clusters

Figure 5.3 Rank-abundance plot of sequence reads (50% identity) of different antibiotic resistance genes present in four samples from different habitats: A) translated tetM gene from dust sample (sample: tet9D), B) translated ermB gene from kitchen surface sample (sample: erm5D), C) translated sulII gene from bathroom surface sample (sample: Sul5B) and D) translated ampC gene from kitchen surface sample (sample: amp2K).

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5.2.4 Taxonomic diversity of antibiotic resistance gene sequences in human residences

5.2.4.1 Taxonomic diversity of translated tetM gene sequences in different habitats

BLASTP analysis of the translated tetM gene sequences showed the dominance of TetM sequences related to those from the Firmicutes. The obtained TetM sequences were related to those from different families (Streptococcaceae, Staphylococcaceae, Lactobacillaceae, Clostridiaceae, Lachnospiraceae and Enterococcaceae) which belong to Clostridia and Bacilli classes. In addition, TetM sequences related to those in Proteobacteria were identified from the family Enterobacteriaceae, and also Campylobacteraceae which belong to Gammaproteobacteria and classes, respectively. TetM sequences from dust and from kitchen and bathroom samples in the houses showed high similarity (range 98- 100%) to TetM sequences from Streptococcus spp., Staphylococcus spp., Lactobacillus salivarius, Clostridium colicanis, Anaerostipes hadrus, Enterococcus faecalis, Enterococcus faecium, Escherichia coli, Salmonella enterica, Clostridioides difficile and Campylobacter ureolyticus species (Tables 5.5, 5.6 and 5.7).

Although there was no significant variation detected in the taxonomic affiliation of translated tetM genes between habitats, there was some variation observed in the taxonomic affiliation and their similarity between houses. For example, sequences of some species such as Streptococcus spp., Staphylococcus spp., Escherichia coli and Enterococcus faecium were detected across all houses but in house 6, in dust and kitchen samples, at a lower similarity (98%) when compared to those in all other houses (100% identity) (see Tables 5.5 and 5.6). In addition, there were TetM sequences related to those from species such as Campylobacter ureolyticus detected in dust and kitchen surface samples from three houses and to sequences from Salmonella enterica which were observed in dust, kitchen and bathroom surface samples from 6 houses (Tables 5.5, 5.6 and 5.7).

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Table 5.5 Closest related TetM protein sequences in the RefSeq protein database identified by BlastP to translated tetM gene sequences (using the FunGene Pipeline) in dust samples from different houses. DUST Samples House1 House 2 House 3 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Enterococcus faecium 100% Enterococcus faecium 100% Streptococcus suis 100% Streptococcus suis 100% Streptococcus suis 100% Streptococcus mitis 100% Streptococcus mitis 100% Clostridium colicanis 100% Escherichia coli 100% Escherichia coli 100% Enterococcus faecalis 100% Staphylococcus pseudintermedius 100% Staphylococcus pseudintermedius 100% Streptococcus mitis 100% Staphylococcus aureus 100% Staphylococcus aureus 100% Staphylococcus aureus 100% Streptococcus pyogenes 100% Lactobacillus salivarius 100% Streptococcus pseudopneumoniae 100% Clostridioides difficile 100% Enterococcus faecalis 100% Lactobacillus salivarius 100% Enterococcus faecalis 100% Escherichia coli 100% House 4 House 5 House 6 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Campylobacter ureolyticus 98% Enterococcus faecium 100% Enterococcus faecium 100% Anaerostipes hadrus 98% Streptococcus agalactiae 100% Staphylococcus pseudintermedius 100% Clostridium colicanis 98% Streptococcus mitis 100% Streptococcus mitis 100% Escherichia coli 98% Salmonella enterica 100% Lactobacillus salivarius 100% Staphylococcus pseudintermedius 98% Staphylococcus aureus 100% Anaerostipes hadrus 100% Staphylococcus aureus 98% Streptococcus suis 100% Streptococcus equinus 100% Streptococcus agalactiae 98% Escherichia coli 100% Streptococcus suis 100% Streptococcus suis 98% Streptococcus oralis 100% Staphylococcus aureus 100% Streptococcus oralis 98% Clostridium colicanis 100% Clostridium colicanis 100% Enterococcus faecalis 98% Staphylococcus pseudintermedius 100% Escherichia coli 100% Enterococcus faecium 98% Enterococcus faecalis 100% Enterococcus faecalis 100% Streptococcus pneumoniae 98% Lactobacillus salivarius 98%

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DUST Samples House 7 House 8 House 9 Organism Identity Organism Identity Organism Identity Clostridium colicanis 100% Streptococcus suis 100% Streptococcus pneumoniae 100% Escherichia coli 100% Streptococcus oralis 100% Streptococcus equinus 100% Staphylococcus pseudintermedius 100% Streptococcus mitis 100% Streptococcus gordonii 100% Staphylococcus aureus 100% Streptococcus pneumoniae 100% Escherichia coli 100% Streptococcus agalactiae 100% Clostridium colicanis 100% Enterococcus faecium 100% Streptococcus suis 100% Escherichia coli 100% Clostridium colicanis 100% Streptococcus oralis 100% Lactobacillus salivarius 100% Lactobacillus salivarius 100% Streptococcus mitis 100% Staphylococcus pseudintermedius 100% Anaerostipes hadrus 100% Enterococcus faecium 100% Staphylococcus aureus 100% Staphylococcus pseudintermedius 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Staphylococcus aureus 100% Lactobacillus salivarius 100% Enterococcus faecalis 100% Streptococcus agalactiae 100% Enterococcus faecalis 100% Streptococcus agalactiae 100% Enterococcus faecalis 100% House 10 House 11 Organism Identity Organism Identity Aerococcaceae bacterium 100% Lactobacillus salivarius 100% Escherichia coli 100% Anaerostipes hadrus 100% Lactobacillus salivarius 100% Escherichia coli 100% Clostridium colicanis 100% Enterococcus faecium 100% Staphylococcus pseudintermedius 100% Clostridium colicanis 100% Staphylococcus aureus 100% Staphylococcus pseudintermedius 100% Streptococcus pneumoniae 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Streptococcus pneumoniae 100% Streptococcus mitis 100% Streptococcus suis 100% Enterococcus faecium 100% Streptococcus mitis 100% Streptococcus equinus 100% Enterococcus faecalis 100%

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Table 5.6 Closest related TetM protein sequences in the RefSeq protein database identified by BlastP to translated tetM gene sequences (using the FunGene Pipeline) in kitchen surface samples from different houses. Kitchen Surface Samples House 1 House 2 House 3 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Enterococcus faecium 100% Enterococcus faecium 100% Streptococcus suis 100% Streptococcus suis 100% Streptococcus suis 100% Streptococcus mitis 100% Streptococcus mitis 100% Staphylococcus pseudintermedius 100% Escherichia coli 100% Escherichia coli 100% Clostridium colicanis 100% Staphylococcus pseudintermedius 100% Staphylococcus pseudintermedius 100% Streptococcus pyogenes 100% Staphylococcus aureus 100% Staphylococcus aureus 100% Lactobacillus salivarius 100% Streptococcus pyogenes 100% Streptococcus pyogenes 100% Streptococcus agalactiae 100% Streptococcus agalactiae 100% Streptococcus oralis 100% Enterococcus faecalis 100% Streptococcus oralis 100% Streptococcus agalactiae 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Escherichia coli 100% House 4 House 5 House 6 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Enterococcus faecium 100% Campylobacter ureolyticus 98% Enterococcus faecium 100% Streptococcus pneumoniae 100% Anaerostipes hadrus 98% Staphylococcus pseudintermedius 100% Streptococcus mitis 100% Clostridium colicanis 98% Staphylococcus aureus 100% Streptococcus suis 100% Escherichia coli 98% Streptococcus agalactiae 100% Streptococcus oralis 100% Staphylococcus pseudintermedius 98% Streptococcus suis 100% Staphylococcus pseudintermedius 100% Staphylococcus aureus 98% Clostridium colicanis 100% Staphylococcus aureus 100% Streptococcus agalactiae 98% Escherichia coli 100% Anaerostipes hadrus 100% Streptococcus suis 98%

Streptococcus oralis 100% Clostridium colicanis 100% Streptococcus equinus 98% Streptococcus suis 100% Escherichia coli 100% Enterococcus faecalis 98% Lactobacillus salivarius 100% Lactobacillus salivarius 100% Enterococcus faecium 98% Enterococcus faecalis 100% Streptococcus salivarius 100% Streptococcus pneumoniae 98% Salmonella enterica 100% Enterococcus faecalis 100% Lactobacillus salivarius 98% 184

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Kitchen Surface Samples House 7 House 8 House 9 Organism Identity Organism Identity Organism Identity Streptococcus suis 100% Streptococcus suis 100% Lactobacillus salivarius 100% Streptococcus oralis 100% Streptococcus oralis 100% Anaerostipes hadrus 100% Streptococcus mitis 100% Streptococcus mitis 100% Escherichia coli 100% Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Streptococcus pyogenes 100% Clostridium colicanis 100% Clostridium colicanis 100% Clostridium colicanis 100% Escherichia coli 100% Staphylococcus pseudintermedius 100% Escherichia coli 100% Lactobacillus salivarius 100% Staphylococcus aureus 100% Lactobacillus salivarius 100% Staphylococcus pseudintermedius 100% Streptococcus pneumoniae 100% Campylobacter ureolyticus 100% Staphylococcus aureus 100% Streptococcus agalactiae 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Streptococcus suis 100% Staphylococcus aureus 100% Anaerostipes hadrus 100% Streptococcus oralis 100% Enterococcus faecium 100% Streptococcus agalactiae 100% Streptococcus mitis 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Enterococcus faecalis 100% House 10 House 11 Organism Identity Organism Identity Escherichia coli 100% Lactobacillus salivarius 100% Enterococcus faecium 100% Anaerostipes hadrus 100% Staphylococcus pseudintermedius 100% Escherichia coli 100% Staphylococcus aureus 100% Enterococcus faecium 100% Streptococcus pneumoniae 100% Clostridium colicanis 100% Streptococcus agalactiae 100% Streptococcus agalactiae 100% Streptococcus suis 100% Streptococcus suis 100% Streptococcus oralis 100% Streptococcus mitis 100% Streptococcus mitis 100% Streptococcus pneumoniae 100% Streptococcus gordonii 100% Staphylococcus pseudintermedius 100% Streptococcus equinus 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Enterococcus faecalis 100%

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Table 5.7 Closest related TetM protein sequences in the RefSeq protein database identified by BlastP to translated tetM gene sequences (using the FunGene Pipeline) in bathroom surface samples from different houses. Bathroom Surface Samples House1 House 2 House 3 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Enterococcus faecium 100% Enterococcus faecium 100% Streptococcus suis 100% Streptococcus suis 100% Staphylococcus pseudintermedius 100% Streptococcus mitis 100% Streptococcus mitis 100% Staphylococcus aureus 100% Escherichia coli 100% Escherichia coli 100% Salmonella enterica 100% Staphylococcus pseudintermedius 100% Staphylococcus pseudintermedius 100% Streptococcus agalactiae 100% Staphylococcus aureus 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Streptococcus pyogenes 100% Streptococcus pyogenes 100% Clostridium colicanis 100% Streptococcus agalactiae 100% Streptococcus oralis 100% Escherichia coli 100% Streptococcus oralis 100% Streptococcus agalactiae 100% Streptococcus oralis 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Streptococcus suis 100% House 4 House 5 House 6 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Streptococcus pneumoniae 100% Streptococcus gordonii 100% Enterococcus faecium 100% Enterococcus faecium 100% Streptococcus mitis 100% Staphylococcus pseudintermedius 100% Staphylococcus pseudintermedius 100% Streptococcus suis 100% Staphylococcus aureus 100% Staphylococcus aureus 100% Streptococcus equinus 100% Streptococcus agalactiae 100% Streptococcus agalactiae 100% Streptococcus pneumoniae 100% Streptococcus suis 100% Streptococcus suis 100% Staphylococcus pseudintermedius 100% Clostridium colicanis 100% Clostridium colicanis 100% Staphylococcus aureus 100% Escherichia coli 100% Escherichia coli 100% Enterococcus faecium 100% Streptococcus gordonii 100% Streptococcus oralis 100% Escherichia coli 100% Enterococcus faecalis 100% Streptococcus mitis 100% Lactobacillus iners 100% Lactobacillus salivarius 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Streptococcus mitis 100% Salmonella enterica 100%

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Bathroom Surface Samples House 7 House 8 House 9 Organism Identity Organism Identity Organism Identity Escherichia coli 100% Staphylococcus pseudintermedius 100% Salmonella enterica 100% Staphylococcus pseudintermedius 100% Staphylococcus aureus 100% Escherichia coli 100% Staphylococcus aureus 100% Streptococcus suis 100% Lactobacillus salivarius 100% Streptococcus agalactiae 100% Streptococcus mitis 100% Streptococcus suis 100% Streptococcus suis 100% Enterococcus faecium 100% Enterococcus faecalis 100% Streptococcus mitis 100% Streptococcus pneumoniae 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Streptococcus gordonii 100% Staphylococcus aureus 100% Streptococcus pneumoniae 100% Streptococcus equinus 100% Streptococcus pneumoniae 100% Streptococcus gordonii 100% Escherichia coli 100% Enterococcus faecium 100% Streptococcus equinus 100% Enterococcus faecalis 100% Streptococcus pseudopneumoniae 100% Enterococcus faecalis 100% Streptococcus equinus 100% House 10 House 11 Organism Identity Organism Identity Salmonella enterica 100% Anaerostipes hadrus 100% Escherichia coli 100% Escherichia coli 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Staphylococcus aureus 100% Clostridium colicanis 100% Streptococcus pneumoniae 100% Streptococcus suis 100% Enterococcus faecium 100% Streptococcus oralis 100% Clostridium colicanis 100% Enterococcus faecalis 100% Streptococcus suis 100% Streptococcus gordonii 100% Streptococcus oralis 100% Staphylococcus pseudintermedius 100% Streptococcus agalactiae 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Streptococcus pneumoniae 100%

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5.2.4.2 Taxonomic diversity of translated ermB gene sequences in different habitats

The analysis of the translated ermB gene sequences using BLASTP showed the dominance of ErmB sequences related to those from the Firmicutes and Proteobacteria (Tables 5.8, 5.9 and 5.10). ErmB sequences related to those in Firmicutes were identified in different families (Streptococcaceae, Staphylococcaceae, Enterococcaceae, Peptostreptococcaceae, Clostridiaceae, Lachnospiraceae and Lactobacillaceae) which belong to the Bacilli and Clostridia classes. Additionally, ErmB sequences related to those in Proteobacteria were identified in Enterobacteriaceae, Moraxellaceae and Campylobacteraceae families which belong to Gammaproteobacteria and Epsilonproteobacteria classes. ErmB sequences from the dust and kitchen and bathroom samples in the houses showed high similarity (range 98-100%) to the ErmB sequences of Streptococcus spp., Staphylococcus spp., Enterococcus spp., Clostridioides difficile, Faecalibacterium prausnitzii, Roseburia intestinalis, Blautia hydrogenotrophica, Lactobacillus spp., Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii and Campylobacter coli species.

There was some variation in the taxonomic affiliation and their similarity between houses, while no significant variation was observed in the taxonomic affiliation of translated ermB genes between habitats. For example, sequences of some species such as Streptococcus spp., Staphylococcus spp., Enterococcus spp., Clostridioides difficile, Klebsiella pneumoniae, Escherichia coli and Acinetobacter baumannii were identified in the majority of houses but with different similarity from 98% to 100%, see Tables 5.8, 5.9 and 5.10. Furthermore, there were ErmB sequences related to those from species such as Campylobacter coli detected in five houses from dust (houses 3,5,8 and 9) and kitchen surface (house 5) samples. Also, translated ermB sequences from species such as Roseburia intestinalis and Blautia hydrogenotrophica were observed in only one house each from dust (house 3) and kitchen surface (house 10) samples, respectively (Tables 5.8, 5.9 and 5.10).

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Table 5.8 Closest related ErmB protein sequences in the RefSeq protein database identified by BlastP to translated ermB gene sequences (using the FunGene Pipeline) in dust samples from different houses. DUST Samples House 2 House 3 House 4 Organism Identity Organism Identity Organism Identity Staphylococcus aureus 100% Campylobacter coli 100% Staphylococcus hyicus 100% Enterococcus faecalis 100% Enterococcus faecium 100% Enterococcus faecium 100% Enterococcus faecium 100% Escherichia coli 100% Staphylococcus pseudintermedius 100% Acinetobacter baumannii 100% Clostridioides difficile 100% Staphylococcus aureus 100% Streptococcus pneumoniae 100% Staphylococcus pseudintermedius 100% Streptococcus pneumoniae 100% Streptococcus suis 100% Staphylococcus aureus 100% Clostridioides difficile 100% Lactobacillus paralimentarius 100% Streptococcus pneumoniae 100% Enterococcus faecalis 100% Lactobacillus reuteri 100% Streptococcus suis 100% Lactobacillus amylovorus 100% Faecalibacterium prausnitzii 100% Enterococcus hirae 100% Escherichia coli 100% Escherichia coli 100% Clostridium perfringens 100% Streptococcus lutetiensis 100% Clostridioides difficile 100% Streptococcus agalactiae 100% Enterococcus hirae 100% Enterococcus gallinarum 100% Roseburia intestinalis 100% Streptococcus suis 100% Klebsiella pneumoniae 99% Klebsiella pneumoniae 100% Streptococcus anginosus 100% Acinetobacter baumannii 100% Klebsiella pneumoniae 99% Lactobacillus johnsonii 100% Enterococcus faecalis 100% Lactobacillus amylovorus 99%

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DUST Samples House 5 House 6 House 7 Organism Identity Organism Identity Organism Identity Staphylococcus aureus 100% Enterococcus gallinarum 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Enterococcus faecium 100% Butyricimonas faecihominis 100% Acinetobacter baumannii 100% Lactobacillus amylovorus 100% Enterococcus faecalis 100% Streptococcus pneumoniae 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Enterococcus gallinarum 100% Streptococcus pneumoniae 100% Clostridioides difficile 100% Streptococcus agalactiae 100% Streptococcus suis 100% Escherichia coli 100% Campylobacter coli 100% Klebsiella pneumoniae 99% Acinetobacter baumannii 100% Escherichia coli 100% Streptococcus pyogenes 99% Streptococcus suis 99% Faecalibacterium prausnitzii 99% Staphylococcus aureus 98% Streptococcus pneumoniae 99% Klebsiella pneumoniae 99% Escherichia coli 98% Klebsiella pneumoniae 99% Clostridioides difficile 99% Acinetobacter baumannii 98% Streptococcus lutetiensis 99% Lactobacillus paralimentarius 99% Clostridioides difficile 98% Streptococcus anginosus 99% House 8 House 9 House 11 Organism Identity Organism Identity Organism Identity Streptococcus suis 100% Streptococcus suis 100% Staphylococcus aureus 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Staphylococcus pseudintermedius 100% Enterococcus faecium 100% Staphylococcus aureus 100% Enterococcus faecium 100% Staphylococcus aureus 100% Escherichia coli 100% Clostridioides difficile 99% Staphylococcus pseudintermedius 100% Campylobacter coli 100% Streptococcus pneumoniae 99% Clostridioides difficile 100% Clostridioides difficile 100% Acinetobacter baumannii 99% Klebsiella pneumoniae 100% Klebsiella pneumoniae 100% Streptococcus agalactiae 99% Escherichia coli 100% Bacteroides cellulosilyticus 99% Enterococcus gallinarum 99% Lactobacillales 100% Enterococcus faecium 99% Escherichia coli 99% Campylobacter coli 99% Staphylococcus pseudintermedius 99% Streptococcus pyogenes 99% Acinetobacter baumannii 99% Acinetobacter baumannii 99% Enterococcus faecalis 99% Streptococcus pneumoniae 99% Streptococcus pneumoniae 99% Klebsiella pneumoniae 98%

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Table 5.9 Closest related ErmB protein sequences in the RefSeq protein database identified by BlastP to translated ermB gene sequences (using the FunGene Pipeline) in kitchen surface samples from different houses. Kitchen Surface Samples House 5 House 10 Organism Identity Organism Identity Staphylococcus aureus 100% Streptococcus pneumoniae 100% Enterococcus faecalis 100% Streptococcus agalactiae 100% Enterococcus faecium 100% Streptococcus suis 100% Acinetobacter baumannii 100% Enterococcus faecalis 100% Streptococcus pneumoniae 100% Enterococcus faecium 100% Streptococcus suis 100% Blautia hydrogenotrophica 100% Lactobacillus johnsonii 100% Lactobacillus paralimentarius 100% Escherichia coli 100% Lactobacillus amylovorus 100% Klebsiella pneumoniae 99% Lactobacillus reuteri 100% Clostridioides difficile 99% Faecalibacterium prausnitzii 100% Campylobacter coli 98% Staphylococcus aureus 99% Staphylococcus pseudintermedius 99% Klebsiella pneumoniae 99%

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Table 5.10 Closest related ErmB protein sequences in the RefSeq protein database identified by BlastP to translated ermB gene sequences (using the FunGene Pipeline) in bathroom surface samples from different houses. Bathroom Surface Samples House 2 House 5 House 8 Organism Identity Organism Identity Organism Identity Streptococcus pneumoniae 100% Staphylococcus aureus 100% Enterococcus faecium 100% Enterococcus faecalis 100% Enterococcus faecalis 100% Staphylococcus aureus 100% Enterococcus faecium 100% Enterococcus faecium 100% Staphylococcus pseudintermedius 100% Streptococcus suis 100% Acinetobacter baumannii 100% Enterococcus faecalis 99% Lactobacillus paralimentarius 100% Streptococcus pneumoniae 100% Acinetobacter baumannii 99% Staphylococcus pseudintermedius 100% Enterococcus gallinarum 100% Streptococcus pneumoniae 99% Lactobacillus reuteri 100% Faecalibacterium prausnitzii 100% Clostridioides difficile 99% Staphylococcus aureus 100% Lactobacillus paralimentarius 99% Streptococcus suis 99% Faecalibacterium prausnitzii 100% Lactobacillus reuteri 99% Streptococcus pyogenes 99% Streptococcus dysgalactiae 100% Escherichia coli 99% Staphylococcus hyicus 99% Lactobacillus amylovorus 100% Streptococcus suis 99% Escherichia coli 99% Streptococcus pyogenes 99% Streptococcus dysgalactiae 99% Enterococcus gallinarum 99% Enterococcus gallinarum 99% Klebsiella pneumoniae 99% Klebsiella pneumoniae 98% Klebsiella pneumoniae 99% Streptococcus agalactiae 99%

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5.2.4.3 Taxonomic diversity of translated sulII gene sequences in different habitats

The dominance of SulII sequences related to those from the Proteobacteria was identified by BLASTP search for the translated sulII gene sequences. The obtained SulII sequences were related to those from different families (Enterobacteriaceae, Moraxellaceae, , and Shewanellaceae) within the class Gammaproteobacteria. SulII sequences from the dust and kitchen and bathroom samples in the houses showed high similarity (range 94-100%) to the SulII sequences of , Klebsiella pneumoniae, Escherichia coli, Salmonella enterica, , Acinetobacter baumannii, Moraxella bovoculi, Vibrio cholerae, , , Glaesserella parasuis, Bibersteinia trehalose, Mannheimia varigena and Shewanella halifaxensis species (Tables 5.11, 512 and 5.13).

The taxonomic affiliation of translated sulII genes revealed no significant variation between habitats, while there was some variation detected between houses. For instance, SullII sequences related to those from some species such as Shigella sonnei, Klebsiella pneumoniae, Escherichia coli and Acinetobacter baumannii were detected across all houses but with different similarity: 94% in dust samples from house 6 compared to 100% in most houses (see Tables 5.11, 512 and 5.13). In addition, there were SulII sequences related to those of some species that were detected in only one or two houses such as Aliivibrio salmonicida which was detected in only one kitchen surface sample from house 3, and which was detected in kitchen and bathroom surface samples in houses 3 and 5, respectively (Tables 512 and 5.13).

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Table 5.11 Closest related SulII protein sequences in the RefSeq protein database identified by BlastP to translated sullII gene sequences (using the FunGene Pipeline) in dust samples from different houses.

DUST Samples House 3 House 6 House 11 Organism Identity Organism Identity Organism Identity Shigella sonnei 100% Klebsiella pneumoniae 96% Shigella sonnei 100% Vibrio cholerae 100% Salmonella enterica 96% Escherichia coli 100% Acinetobacter baumannii 100% Citrobacter freundii 94% Klebsiella pneumoniae 100% Shewanella halifaxensis 100% Shigella sonnei 94% Salmonella enterica 100% Photobacterium damselae 100% Acinetobacter baumannii 94% Acinetobacter baumannii 100% Moraxella bovoculi 100% Enterobacter cloacae 94% Moraxella bovoculi 100% Escherichia coli 100% Escherichia coli 94% Citrobacter freundii 100% Klebsiella pneumoniae 100% porcitonsillarum 94% Pasteurella multocida 100% Salmonella enterica 100% Pasteurella multocida 94% Vibrio cholerae 100% Bibersteinia trehalosi 100% 94% Enterobacter cloacae 100% Citrobacter freundii 100% Raoultella ornithinolytica 94% Shewanella halifaxensis 100% Actinobacillus pleuropneumoniae 100%

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Table 5.12 Closest related SulII protein sequences in the RefSeq protein database identified by BlastP to translated sulII gene sequences (using the FunGene Pipeline) in kitchen surface samples from different houses. Kitchen Surface Samples House 2 House 3 House 5 Organism Identity Organism Identity Organism Identity Shigella sonnei 100% Shigella sonnei 100% Shigella sonnei 99% Klebsiella pneumoniae 100% Klebsiella pneumoniae 100% Klebsiella pneumoniae 99% Escherichia coli 100% Escherichia coli 100% Escherichia coli 99% Salmonella enterica 100% Acinetobacter baumannii 100% Salmonella enterica 99% Bibersteinia trehalosi 100% Moraxella bovoculi 100% Vibrio cholerae 99% Glaesserella parasuis 100% Proteus mirabilis 100% Acinetobacter baumannii 99% Acinetobacter baumannii 100% Shewanella halifaxensis 100% Moraxella bovoculi 99% Moraxella bovoculi 100% Enterobacter cloacae 100% Pasteurella multocida 99% Citrobacter freundii 100% Pasteurella multocida 100% Mannheimia varigena 99% Vibrio cholerae 100% Mannheimia varigena 100% Citrobacter freundii 99% Pasteurella multocida 100% Salmonella enterica 100% Enterobacter cloacae 99% Mannheimia varigena 100% Aliivibrio salmonicida 100% Shewanella halifaxensis 99% Vibrio cholerae 100% House 7 House 9 Organism Identity Organism Identity Escherichia coli 99% Salmonella enterica 96% Klebsiella pneumoniae 99% Escherichia coli 96% Enterobacter cloacae 99% Acinetobacter baumannii 96% Salmonella enterica 99% Vibrio cholerae 96% Acinetobacter baumannii 99% Klebsiella pneumoniae 95% Moraxella bovoculi 99% Shigella sonnei 95% Vibrio cholerae 99% Citrobacter freundii 95% Citrobacter freundii 99% Vibrio parahaemolyticus 95% Shigella sonnei 99% Enterobacter cloacae 95% Glaesserella parasuis 99% Bibersteinia trehalosi 95% Pasteurella multocida 99% Photobacterium damselae 95% Shewanella halifaxensis 99% Raoultella ornithinolytica 95% 195

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Table 5.13 Closest related SulII protein sequences in the RefSeq protein database identified by BlastP to translated sulII gene sequences (using the FunGene Pipeline) in bathroom surface samples from different houses. Bathroom Surface Samples House 5 House 6 House 7 Organism Identity Organism Identity Organism Identity Shigella sonnei 100% Vibrio parahaemolyticus 98% Klebsiella pneumoniae 100% Klebsiella pneumoniae 100% Shigella sonnei 98% Escherichia coli 100% Escherichia coli 100% Klebsiella pneumoniae 98% Acinetobacter baumannii 100% Acinetobacter baumannii 100% Escherichia coli 98% Enterobacter cloacae 100% Moraxella bovoculi 100% Salmonella enterica 98% Moraxella bovoculi 100% Proteus mirabilis 100% Acinetobacter baumannii 98% Vibrio cholerae 100% Enterobacter cloacae 100% Citrobacter freundii 98% Citrobacter freundii 100% Vibrio cholerae 100% Raoultella ornithinolytica 98% Shigella sonnei 100% Pasteurella multocida 100% Enterobacter cloacae 98% Glaesserella parasuis 100% Mannheimia varigena 100% Pasteurella multocida 98% Pasteurella multocida 100% Glaesserella parasuis 100% Actinobacillus porcitonsillarum 98% Salmonella enterica 100% Citrobacter freundii 100% Bibersteinia trehalosi 98% Shewanella halifaxensis 100% House 10 House 11 Organism Identity Organism Identity Salmonella enterica 99% Escherichia coli 100% Klebsiella pneumoniae 99% Shigella sonnei 100% Escherichia coli 99% Salmonella enterica 100% Shigella sonnei 99% Acinetobacter baumannii 100% Acinetobacter baumannii 99% Vibrio cholerae 100% Vibrio parahaemolyticus 99% Klebsiella pneumoniae 100% Pasteurella multocida 99% Citrobacter freundii 100% Enterobacter cloacae 99% Pasteurella multocida 100% Bibersteinia trehalosi 99% Moraxella bovoculi 100%

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5.2.4.4 Taxonomic diversity of translated ampC gene sequences in different habitats

AmpC sequences related to those from the Proteobacteria were shown to be dominant via BlastP analysis. These AmpC sequences were related to those from the family Enterobacteriaceae within the class Gammaproteobacteria. AmpC sequences from the houses showed a similarity to RefSeq AmpC protein sequences ranging from 94% to 100% in kitchen surface samples and from 65% to 93% in bathroom surface samples to the AmpC sequences of Enterobacter spp., Pantoea agglomerans, Klebsiella pneumoniae, Escherichia coli, Serratia marcescens, Citrobacter freundii and Salmonella enterica species (Tables 5.14 and 5.15).

There was some variation detected in the taxonomic affiliation of translated ampC genes between habitats. For example, AmpC sequences related to those from the species Serratia marcescens were identified in all kitchen surface samples, while translated ampC genes from this species were not detected in bathroom surface samples. In addition, there was some variation observed in the taxonomic affiliation and their similarity between houses. For example, ampC gene sequences related to those from some species such as Enterobacter spp., Klebsiella pneumoniae, Escherichia coli, Pantoea agglomerans and Citrobacter freundii were detected across all houses but with varying similarity ranging from 65% to 100% (Tables 5.14 and 5.15). Furthermore, translated ampC sequences were identified in a bathroom surface sample from house 7 that were related to those from Citrobacter spp. and Salmonella enterica (Tables 5.14 and 5.15).

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Table 5.14 Closest related AmpC protein sequences in the RefSeq protein database identified by BlastP to translated ampC gene sequences (using the FunGene Pipeline) in kitchen surface samples from different houses. Kitchen Surface Samples House 2 House 5 Organism Identity Organism Identity Enterobacter kobei 100% Enterobacter cloacae 100% Enterobacter cloacae 100% Klebsiella pneumoniae 100% Pantoea agglomerans 100% Enterobacter asburiae 100% Klebsiella pneumoniae 93% Escherichia coli 99% Escherichia coli 92% Citrobacter freundii 99% Serratia marcescens 92% Serratia marcescens 99% Enterobacter asburiae 92% Enterobacter roggenkampii 98% Enterobacter bugandensis 92% Enterobacter bugandensis 98% Enterobacter roggenkampii 92% Citrobacter freundii 92% House 8 House 9 Organism Identity Organism Identity Enterobacter kobei 99% Enterobacter cloacae 100% Enterobacter cloacae 97% Enterobacter kobei 100% Pantoea agglomerans 97% Pantoea agglomerans 98% Klebsiella pneumoniae 94% Klebsiella pneumoniae 93% Enterobacter asburiae 94% Serratia marcescens 92% Escherichia coli 94% Citrobacter freundii 92% Enterobacter roggenkampii 94% Enterobacter asburiae 92% Enterobacter bugandensis 94% Enterobacter roggenkampii 92% Serratia marcescens 94% Enterobacter bugandensis 92% Citrobacter freundii 94% Escherichia coli 91%

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Table 5.15 Closest related AmpC protein sequences in the RefSeq protein database identified by BlastP to translated ampC gene sequences (using the FunGene Pipeline) in bathroom surface samples from different houses.

Bathroom Surface Samples House 7 House 8 Organism Identity Organism Identity Citrobacter freundii 93% Enterobacter cloacae 72% Citrobacter werkmanii 93% Enterobacter kobei 71% Citrobacter braakii 92% Pantoea agglomerans 71% Salmonella enterica 91% Enterobacter bugandensis 67% Escherichia coli 91% Klebsiella pneumoniae 67% Klebsiella pneumoniae 91% Enterobacter mori 66% Enterobacter cloacae 80% Enterobacter asburiae 66% Enterobacter kobei 80% Escherichia coli 65% Pantoea agglomerans 80% Enterobacter roggenkampii 65%

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5.3 Discussion

This study has investigated the diversity of antibiotic resistance genes (tetM, ermB, sulII and ampC genes) within and between different habitats (dust, surfaces and drinking water) in human residences using targeted next generation sequencing. There are no HTS studies published on the diversity of PCR-targeted antibiotic resistance genes in human residences preceding this study. Consequently, the results of this study are discussed and compared to related studies that used other methods.

In this study, the highest diversity of translated tetM and ampC genes which confer resistance to tetracycline and ampicillin respectively, were found in kitchen surface samples with average Shannon-Wiener diversity index of (3.14 ± 0.13) for translated tetM gene sequences and (5.59 ± 0.26) for translated ampC gene sequences. Resistance to tetracycline and ampicillin has been previously reported in many different food items such as lettuce, carrots and tomatoes (Allydice-Francis and Brown, 2012) cilantro and arugula (Blau et al., 2018), ready-to-eat salad (Hölzel et al., 2018), raw milk cheese (Rodriguez‐Alonso et al., 2009), commercial fish and seafood (Ryu et al., 2012) and commercial meat products (Yücel et al., 2005; Lerma et al., 2014). The raw food items brought into the kitchen are likely to be a potential source of antibiotic resistance genes in kitchens. In addition, kitchen sponges are found to be another potential source of antibiotic resistance including tetracycline and ampicillin in the kitchen (Wolde and Bacha, 2017). This could explain the high diversity of tetM and ampC genes found in kitchen surface samples.

The ermB gene encodes resistance to macrolide antibiotics, which have long been used to treat Gram-positive and certain Gram-negative pathogens infecting humans and animals (Pyorala et al., 2014). In this study, the highest diversity of translated ermB gene sequences was found in dust samples with a Shannon-Wiener diversity index of (5.12 ± 0.41). Most bacteria found in indoor dust are from human-derived bacteria, particularly skin bacteria (Täubel et al., 2009; Rintala et al., 2012). Hartmann et al., (2016) identified many antibiotic resistance genes conferring resistance to erythromycin such as ermB, ermX, erm33, ermA and ermC, in the indoor dust microbiome in a variety of non-residential indoor environments using shotgun metagenomics. Choi, et al., (2018) investigated the diversity of erm genes in many different environmental metagenomes and concluded that significantly more erm genes are present in human-impacted environments (human feces, animal-associated, water and soil) than in natural

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Chapter 5 Diversity and Taxonomic Origin of Clinically Relevant ARGs environments. Their results also showed that erm genes are rare in environments with minimal human impact (Choi, et al., 2018). This could explain the high diversity of the ermB gene found in house dust samples.

The highest diversity of translated sulII genes that confer resistance to sulfonamide were detected in bathroom surface samples with an average Shannon-Wiener diversity index of 5.34 ± 0.55. Sulfonamide resistance has been detected previously in drinking water samples in different countries (Adesoji et al., 2017; Wu et al., 2018; Hu et al., 2018; Destiani and Templeton, 2019). In addition, both sulI and sulII genes were the most frequently detected genes in surface water samples from Germany and Australia (Stoll et al., 2012). Furthermore, the sulI gene has been previously detected in Greater Melbourne albeit within river water in the Werribee River (Barker-Reid et al., 2010).

Translated tetM gene sequences showed 100% identity to those that have been found within a diverse group of bacterial families within the Firmicutes such as Streptococcaceae, Staphylococcaceae, Lactobacillaceae, Clostridiaceae, Lachnospiraceae and Enterococcaceae. Those translated tetM gene sequences were similarly also identical to those present in families such as Enterobacteriaceae and Campylobacteraceae, within the Proteobacteria. It is likely that tetM genes have been transferred by horizontal gene transfer from the Firmicutes into the Proteobacteria. This potentially means the tetM genes in these indoor habitats may be present in either Firmicutes and/or Proteobacteria. The tetM gene is widely dispersed among various Gram-positive organisms, but it has only rarely been documented in Gram-negative bacteria (Poyart et al., 1995, Roberts, 1996), with the presence of the tetM gene in E. coli first reported in 2004 (Bryan et al., 2004). The presence of tetM gene in E. coli is most likely due to genetic transfer from Enterococcus, a common carrier of tetM (DeFlaun and Levy, 1989). Evidence for this possibility is provided by the studies of Poyart et al. (1995), who demonstrated the in vitro transfer of Tn916 from E. faecalis to E. coli. For example, the broad host range conjugative transposons Tn916 and Tn1545 carry tetM genes allowing transfer between diverse taxa (Roberts and Mullany 2009).

Translated ermB gene sequences were found mainly to be related to those from the Firmicutes with 100% identity to those from different bacterial families Streptococcaceae, Staphylococcaceae, Clostridiaceae, Lactobacillaceae, Lachnospiraceae, Peptostreptococcaceae and Enterococcaceae. In addition, translated ermB gene sequences

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Chapter 5 Diversity and Taxonomic Origin of Clinically Relevant ARGs were also found to have 100% identity to those in Enterobacteriaceae, Moraxellaceae and Campylobacteraceae families in the Proteobacteria. These erm genes were historically associated almost exclusively with Gram-positive bacteria, not with the Gram-negative species (Nishijima et al. 1999). Nevertheless, some studies have reported ermB and ermG genes in Gram-negative Bacteroides (Gupta et al., 2003; Shoemaker et al., 2001). The ermB and ermG genes have also been shown to be carried on the conjugative transposons CTnBST and CTnGERM1, respectively (Wang et al., 2003; Gupta et al., 2003). The presence of ermB and ermG genes in Bacteroides suggests that these genes have transferred to Bacteroides species from distantly related phylogenetic groups, such as Gram-positive bacteria. Interestingly, tetM and ermB genes can be found on the same mobile genetic elements namely Tn1545 and Tn6003 within the Tn916 family (Cochetti et al., 2008). Given the presence of tetM and ermB genes on Tn916-related transposons, this may facilitate co-transfer across a broad host-range.

Translated sulII gene sequences were found to be related exclusively to those within the class Gammaproteobacteria with identities ranging from 94% to 100% SulII protein sequences in bacterial species such as Shigella sonnei, Klebsiella pneumoniae, Escherichia coli, Salmonella enterica, Enterobacter cloacae, Acinetobacter baumannii, Vibrio cholerae, Citrobacter freundii and Pasteurella multocida (Tables 5.11, 512 and 5.13). Most of these species (Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Citrobacter freundii, Shigella spp. and Salmonella spp.) were reported previously to carry the sulII gene in clinical samples (Frank et al., 2007). The sulII gene is usually a plasmid-borne gene and located on plasmids such as RSF1010 and pVM11 (Guerry et al., 1974; Scholz et al., 1989). The plasmid RSF1010 is a broad host-range and mobilizable plasmid that confers resistance to streptomycin and sulfonamides, isolated originally from E. coli strain of a pig origin (Guerry et al., 1974). The transfer of the sulII gene from an E. coli strain of a pig origin to a sulfonamide-sensitive an E. coli strain of human origin was demonstrated (Sandvang et al., 1998). In addition, Sul plasmids have been found to be co-transferred with another plasmid, and the most frequently co- transferred resistances were to streptomycin, ampicillin and trimethoprim (Wu et al., 2010). Furthermore, Kehrenberg et al., (2003) reported a new type of antibiotic resistance gene cluster on plasmid pVM111 from Pasteurella multocida which comprised sul2, strA, strB, tetR and tetH (Kehrenberg et al., 2003). So, the potential for mobility of sul genes to be transferred or co-transferred with linked antibiotic resistance genes between different bacterial species identified in this study raises the possibilities of dissemination within the indoor microbiome and potential risk to public health.

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Translated ampC gene sequences were related to those found mainly in Enterobacteriaceae with identities ranging from 65% to 100% to AmpC sequences in bacterial species such as Enterobacter spp., Pantoea agglomerans, Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii and Serratia marcescens (Tables 5.14 and 5.15). The ampC gene is typically found in the chromosomes of several Enterobacteriaceae family members. However, there are some genes encoding AmpC β-lactamases that are carried on plasmids. The majority of plasmid-mediated ampC genes are found in organisms such as Klebsiella pneumoniae, Escherichia coli, and Salmonella enterica. Plasmid-mediated ampC genes are derived from the chromosomal ampC genes of several members of the family Enterobacteriaceae, including Enterobacter cloacae and Citrobacter freundii. Plasmid-borne ACT-type ampC genes were thought to originate in Enterobacter spp. (Bush, 2013) and have subsequently been discovered in plasmids isolated from K. pneumoniae (Ingti et al., 2017). Furthermore, plasmids carrying genes for AmpC β-lactamases often carry multiple other resistances including genes for resistance to aminoglycosides, chloramphenicol, quinolones, sulfonamide, tetracycline (Jacoby, 2009). In this study, multiple resistance genes have been identified that are related to those from bacteria such as E. coli and K. pneumoniae. This may suggest the presence of multi antibiotic resistance genes within individual members of the Enterobacteriaceae. In addition, the ampC genes, in this study, had low identities to previously characterised AmpC sequences in bathroom samples (65% to 72%) suggesting these AmpC sequences were novel variants of these resistance genes.

In conclusion, the diversity of antibiotic resistance genes varies within and between different habitats in the human residences. The highest diversity of translated tetM and ampC genes were in kitchen samples, of translated ermB gene in dust and of translated sulII gene in bathroom samples. In addition, antibiotic resistance genes were related to those from human-associated bacteria including those in pathogenic or potentially pathogenic bacteria. For the ampC gene, novel sequence types were identified. Furthermore, the relationship of these antibiotic resistance genes to those on mobile genetic elements including broad host range conjugative transposons suggests a strong likelihood for horizontal gene transfer, including co-transfer, of these antibiotic resistance genes in indoor habitats between both closely related and divergent bacterial taxa representing a potential threat to public health in human residences.

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Chapter 6 General Discussion

CHAPTER 6 General Discussion

Human health is affected either directly or indirectly by the quality of housing and including by biological factors such as indoor pollutants, infestations and mould growth (Evans et al., 2003). People spend the majority of their time indoors with increased exposure to a vast diversity of microorganisms within the indoor environments. The bacterial communities that exist in the indoor environment interact continuously with human life as direct sources of and sinks for both beneficial and pathogenic bacteria. These bacteria may also harbour antibiotic resistance genes. Humans, animals and different environments, including indoor environments have been found to be potential reservoirs of antibiotic resistance genes. The emergence and widespread distribution of antibiotic resistance genes in environmental and pathogenic bacteria intensifies the risks to human health as these bacteria can be transmitted to humans either through direct or indirect contact (Iversen et al., 2004). In order to better understand the health impacts of bacterial communities that are present around us and that share our homes, it is important to study these communities and their associated antibiotic resistances.

The overall aim of this research was to investigate variation in the structure and diversity of bacterial communities and their associated antibiotic resistance genes within human residences. There were three main aspects in this research study, firstly to investigate variation in the structure, composition and diversity of bacterial communities within and between different human residences; secondly, to determine the prevalence, distribution and abundance of antibiotic resistance genes in human residences; and thirdly, to describe the structure and diversity of the antibiotic resistance genes within and between different household environments. To address these aims, a total of 132 samples (including 33 dust, 33 kitchen surfaces, 33 bathroom surfaces and 33 drinking water samples) were obtained from eleven houses located in the metropolitan area of Melbourne and characterised using a suite of molecular approaches.

Research in Chapter 3 investigated the composition and diversity of bacterial communities in household dust, surfaces and drinking water and investigated the variation within and between these bacterial communities across different habitats and houses. This has been done using HTS of 16S rRNA genes amplified from DNA isolated from human residences. The bacterial

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Chapter 6 General Discussion communities in the household ecosystems varied significantly and strongly (ANOSIM R = 0.931, P = 0.001) between habitats and were found to be habitat-specific rather than house- specific. However, within each habitat (dust, kitchen and bathroom surface and drinking water), samples frequently also clustered with respect to their house of origin. Previous studies have concluded that sampling location (house of origin) was the most important factor affecting the structure of bacterial communities within kitchens and bathrooms (Flores et al., 2013; Dunn et al., 2013; Gibbons et al., 2015; Adams and Lymperopoulou, 2018). However, previous studies were not able to determine the variation between different type of samples, because they only used one type of sample (surface samples), compared to the current study which investigated four habitats including three type of samples (dust, surface and water).

The numbers of bacterial taxa (OTUs) varied significantly between habitats. The bacterial communities in dust had the highest diversity, while drinking water communities were the least diverse when compared to other habitats in this study. Furthermore, drinking water bacterial communities were shown to have the greatest variation (ANOSIM R = 1, P = 0.001) from those bacterial communities in the other habitat types (dust, kitchen and bathroom surfaces). This may be a consequence that the house dust is a sink for bacteria from many different sources such as indoor and outdoor air, the occupants’ bodies and shoes or clothing which bring bacteria from outdoor environments into the house (Hospodsky et al., 2012; Kelley & Gilbert 2013; Lax et al., 2014), whilst, the drinking water is also a potential source of household bacteria rather than a sink (Falkinham et al., 2008; Ichijo et al., 2014).

In drinking water, there were 26 bacterial phyla identified across all samples. The most dominant bacterial phylum found was Proteobacteria (92.3%) which has been previously identified in drinking water but often at a lower relative abundance (Holinger et al., 2014). The composition and abundances of bacterial genera in drinking water differed between houses. In house dust, the bacterial communities in this study were dominated by Gram-negative bacterial phyla such as Proteobacteria, Cyanobacteria and Bacteroidetes rather than Gram-positive bacterial phyla such as Firmicutes and Actinobacteria, which differs from the findings of previous studies (Pakarinen et al., 2008; Rintala et al., 2008; Täubel et al., 2009; Veillette et al., 2013; Konya et al., 2014; Adams et al., 2014), where house dust bacterial communities were dominated by Gram-positive bacterial phyla and with lower proportions of Gram-negative

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Chapter 6 General Discussion bacterial phyla. The most abundant bacterial genera found in house dust were mainly soil- and human-derived bacteria that are commonly associated with skin and gut microbiome. The composition and abundances of bacterial genera in house dust also varied between houses. In this study, the presence of pets (e.g., cats and dogs) could not be attributed to influence the relative abundance of the house dust microbiome, while other studies have reported a significant influence of pets (Giovannangelo et al., 2007; Ownby et al., 2013; Fujimura et al., 2010). In this current study, an insufficient number of houses with pets was sampled to enable statistical analysis. Nevertheless, these findings suggest that occupants are major sources of the bacteria within communities identified in household dust and surfaces. Furthermore, the structure and diversity of bacterial community in house dust may be influenced by the frequency of cleaning and number of occupants. In the present study, kitchen surfaces were mainly dominated by human- and food-associated bacteria. The frequency of detection and the relative abundance of bacterial genera differed from one house to another. The genus Acinetobacter which includes several opportunistic pathogens was detected in all kitchens in this study and at the highest relative abundance for any genera. In the bathroom surfaces, 27 bacterial phyla, dominated by Proteobacteria, Actinobacteria and Firmicutes, were identified compared to 19 phyla previously detected (Flores et al., 2011). Within these dominant phyla, taxa were identified that are typically associated with either human skin and/or with water. In this study, the most abundant genus identified on bathroom surfaces was Enhydrobacter. The genus Mycobacterium which includes pathogenic species was also detected on bathroom surfaces. Notwithstanding the significant variation between habitats within the same house, there were a number of shared bacterial genera between household habitats. For example, the genus Sphingomonas was the only genus which was in the ten most abundant genera that was detected across all habitats. Finally, in the current study, the presence of 65 pathogenic or potentially pathogenic bacterial species were identified both with different frequency of detection (0.8%-99.2%) and of their relative abundance (0.001-33.5%) (Table 3.8). For example, Acinetobacter lwoffii which causes gastritis was detected with a relative abundance of 9.5% in dust, Salmonella enterica which associates with food-borne illness was identified with a relative abundance of 4.9% in a kitchen surface sample, S. aureus which is implicated in skin and soft-tissue infections was found on bathroom surfaces with a relative abundance of 7.2%, and Legionella pneumophila which causes Legionnaires’ disease was detected with a relative abundance of 0.1% in drinking water. The finding of potential pathogens in household habitats across all houses may represent a public health risk in our homes.

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Research in Chapter 4 investigated the prevalence, distribution and abundance of the antibiotic resistance gene pool within and between different houses via a culture-independent approach using PCR and qPCR. Twelve antibiotic resistance genes which confer resistance to nine different classes of antibiotics were chosen to assess the antibiotic resistances in human residences using different type of samples (dust, surface and water samples). The antibiotic resistance genes used in this study were tetM and tetB, ermB, ampC, vanA, mecA, aac(6′)-Ie- aph(2"), sul2, catII, dfrA1, mcr-1 and blaNDM-1 encoding resistance to tetracycline, erythromycin, ampicillin, vancomycin, methicillin, aminoglycoside, sulfonamide, chloramphenicol, trimethoprim, colistin and carbapenem, respectively.

The presence and also the abundance of a number of clinically relevant antibiotic resistance genes namely: tetM, tetB, ermB, ampC, sul2, catII, mcr-1 and blaNDM-1, was determined in these household ecosystems. The tetM and ermB genes were the most commonly detected antibiotic resistance genes with high frequencies of detection in household ecosystems. In addition, the antibiotic resistance genes blaNDM-1 and mcr-1 genes encoding resistance to antibiotics of last resort were detected for the first time in human residences. The frequency of detection and the relative abundance of antibiotic resistance genes in drinking water samples were lower than those in dust and surface samples. The most commonly found antibiotic resistance genes in drinking water in this study were ermB, ampC, sul2, catII, blaNDM-1 and mcr-1. Furthermore, the most abundant resistance gene was ampC which was found in drinking water at a very high relative abundance (up to 54.4%, when ampC gene numbers were normalised to 16S rRNA gene numbers, see Figure 4.13). The most frequently detected antibiotic resistance genes on both kitchen and bathroom surfaces were tetM, ermB, ampC and sulII genes. Previous culture- dependent studies identified resistant bacteria for ampicillin, tetracycline and chloramphenicol in kitchens and bathrooms (Scott et al., 2009, Medrano‐Félix et al., 2011, Kilonzo-Nthenge et al., 2012, Wolde and Bacha, 2017), while in this study DNA extracted directly from samples and culture-independent methods were applied. The highest number of antibiotic resistance genes detected in this study were in bathroom surface samples. Furthermore, the low occurrence of antibiotic resistance genes in one house (house 4) may be linked to the absence of antimicrobial cleaning products in this house. Moreover, a possible link has been noticed between the number of occupants and the number of detected antibiotic resistance genes. In the current study, the overall relative abundance of antibiotic resistance genes may also be associated with the relative abundance of 16S rRNA genes. For example, the abundance of

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Chapter 6 General Discussion both 16S rRNA and tetM genes in kitchen surfaces were highest in house 5 and lowest in house 4, and similarly the abundance of both 16S rRNA and tetM genes in bathroom surfaces were highest in house 6 and lowest in house 1. The frequent detection and abundance of antibiotic resistance genes in multiple habitats within these households in this study demonstrates that antibiotic resistance is common within indoor microbiomes and may represent a public health concern.

Research in Chapter 5 investigated the diversity of clinically relevant antibiotic resistance genes namely: tetM, ermB, ampC and sulII genes, within and between different human residences through a culture-independent approach using next generation sequencing. The diversity of antibiotic resistance genes in this study was found to vary both within and between different habitats in the human residences. The highest diversity of translated tetM and ampC genes were found in kitchen surface samples. Whilst, the highest diversity of translated ermB and sulII genes sequences was detected in dust and bathroom surface samples, respectively. Translated tetM and ermB genes sequences showed 100% identity to those that have been found within a diverse group of bacterial families, mainly within the Firmicutes and some taxa within the Proteobacteria. Whereas, translated sulII gene sequences were found to be related exclusively to those within the class Gammaproteobacteria with identities between 98%-100% to previously characterised SulII sequences. Similarly, translated ampC gene sequences were exclusively related to those found mainly in Enterobacteriaceae with identities between 65%- 100% to previously characterised AmpC sequences. The translated ampC sequences, in bathroom samples had low identities (65% to 72%) to previously characterised AmpC sequences suggesting these AmpC sequences were novel variants of these resistance genes. Moreover, the relationship of the antibiotic resistance genes (tetM, ermB, sulII and ampC) to those carried on mobile genetic elements including broad host range conjugative transposons such as Tn916 and Tn1545 and on the conjugative plasmids RSF1010 and pVM111 suggests considerable potential for horizontal gene transfer, including co-transfer, of these antibiotic resistance genes within indoor habitats, between both closely related and divergent bacterial taxa representing a potential threat to public health in human residences.

Several factors were identified within the research that may have an influence on both bacterial diversity and the frequency and/or abundance of antibiotic resistance genes. In an earlier study, less frequent cleaning was associated with increased levels of home bacteria (Sordillo et al.,

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2010). Similarly, in this study, house 8 which had the lowest frequency of cleaning of house dust showed not only the highest observed OTUs and species numbers and Shannon-Wiener diversity 3167 ± 362, 1092 ± 35 and 5.92 ± 0.59, respectively, but also the highest abundance of antibiotic resistance genes; e.g. the high abundance of tetM gene (7.73E+08) in dust samples from this house. In addition, the number of occupants may have an influence on the diversity of bacterial communities and their associated antibiotic resistance. The dust samples from houses 3, 5 and 7 showed high observed OTUs, species numbers and Chaol richness and at the same time the highest occurrence of antibiotic resistance genes were also found in houses 3, 5 and 7. Furthermore, the absence of antibacterial cleaning products in house 4 may have an impact on both the diversity of bacterial communities and the frequency of antibiotic resistance genes (Drury et al., 2013). The surface samples from house 4 showed the lowest observed OTUs, species numbers and Chaol richness and concurrently the lowest occurrence of resistance genes was also found in this house.

In this research, the highest diversity within bacterial communities was detected in house dust (4.97 ± 0.28) followed by kitchen (3.08 ± 0.26) and then bathroom (2.60 ± 0.34) surfaces, whilst bathroom surfaces had the highest frequency of occurrence of antibiotic resistance genes (35%) compared to dust (28%), kitchen surfaces (27%) and (11%) drinking water. This may be explained by the heavy use of chemical cleaning products in bathrooms which may promote a co-selection for antibiotic resistance (section 4.3). In addition, the bacterial community in drinking water had the lowest diversity and also the lowest occurrence of antibiotic resistance genes.

In this research, a potential correlation was observed between the taxonomic compositions and antibiotic resistomes of the different household habitats. The bacterial phyla Proteobacteria and Firmicutes were among the three most abundant phyla identified in dust, kitchen and bathroom communities. Within the Firmicutes, the genera Staphylococcus and Streptococcus were among the five, ten and thirteen most abundant genera identified in dust, bathroom and kitchen communities, respectively. Members of these two genera are known to be resistant to both tetracycline and erythromycin. The most frequently detected antibiotic resistance genes in this study were tetM and ermB genes. In addition, translated tetM and ermB genes sequences showed 100% identity to those from some species of Staphylococcus (e.g. Staphylococcus aureus) and Streptococcus (e.g. Streptococcus pneumoniae). This may suggest that

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Staphylococcus spp. and Streptococcus spp. were a potential host for tetM and ermB genes. Within Proteobacteria, the genus Serratia, whose members are known to carry chromosomally-encoded beta-lactamase ampC genes, were among the eight most abundant genera detected on kitchen surfaces. Translated ampC gene sequences showed identity of 99% to AmpC sequences in Serratia marcescens. This demonstrates that some members of the genus Serratia were resistance to ampicillin. Another member of the Enterobacteriaceae, namely Enterobacter whose members are known to be resistant to ampicillin and sulfonamide, were among the ten and sixteen most abundant genera detected in kitchen and dust communities, respectively. Translated ampC and sulII genes sequences showed identity of 100% to those from some species of the genus Enterobacter such as Enterobacter cloacae. This may suggest that Enterobacter spp., identified in this study, harboured resistance to ampicillin and/or sulfonamide. Jia et al. (2015) observed a strong correlation between bacterial community shift and antibiotic resistance in drinking water. However, to the best of my knowledge, this is the first experimental study to assess the correlation between the diversity of bacterial community and antibiotic resistome in different household habitats via culture- independent molecular methods.

In the present study, many potential pathogenic bacterial species were identified at varying frequencies and abundances in household microbiomes. Some of these bacterial species were also detected with high identities (91%-100%) to sequences of antibiotic resistant bacteria in the NCBI database. For example, the bacterial genus Acinetobacter was the most abundant genus identified in kitchen surface communities and among the top seven and fourteen most abundant genera in dust and bathroom surfaces communities, respectively. Acinetobacter baumannii is a significant hospital pathogenic bacterium and has become rapidly resistant to commonly prescribed antibiotics (Rajamohan et al., 2009). The bacterial species found in this study were identical (100%) to sequences of Acinetobacter baumannii that are resistant to erythromycin (ermB) and sulfonamide (sulII). Other pathogenic bacterial species such as Salmonella enterica which is a food-borne pathogen and Klebsiella pneumoniae which is associated with urinary tract infections (Lin et al., 2014) and bacteremic liver abscess (Wang et al., 1998), were also identified in dust, kitchen and bathroom communities but were not among the most abundant genera. Furthermore, the results of translated antibiotic resistance genes sequences, in the current study, showed sequence identities ranging from 91%-100% to TetM, SulII and AmpC sequences in Salmonella enterica, and of 100% to ErmB, SulII and

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AmpC sequences in Klebsiella pneumoniae, in the NCBI database. The sequencing results of the bacterial diversity and the diversity of antibiotic resistance genes may suggest the presence of multi-antibiotic resistant bacteria including pathogens in the household ecosystem. The potential for horizontal gene transfer in bacteria within and between different phyla were observed in this study (Section 5.3). The presence of these potential pathogenic and multi- resistant bacteria represents a potential threat to human and public health in human residences.

For future research a number of further studies are proposed. It is suggested to increase the number of houses, number of samples in each location and sampling from other locations in the house such as bedrooms, hallway, gardens, courtyards and/or balconies. Further studies should improve metadata collection by obtaining additional data relating to the household environmental conditions such as the age, size and geolocation of the house, temperature, light intensity, relative humidity, presence of mould; as well as data relating to occupants such as the age, gender and ethnicity of occupants; presence of any health issues e.g. chronic disease and/or allergy; type of food consuming by occupants e.g. vegetarian, meat, dairy products... etc. This data will allow improved interpretation and understanding of molecular analyses and tracking of the source of microbes and antibiotic resistance genes present in the house.

Geographic location, occupancy, ventilation rates and types are important factors that influence indoor microbiomes. However, there are uncertainties within these environmental factors. For example, ventilation has been suggested to be a primary driver of microbial community composition in indoor environments acting as a source of microorganisms from outdoor air (Meadow et al., 2014). However, the exact influence of ventilation rates and types requires further study. Likewise, the roles of temperature, light intensity and relative humidity upon the structure and diversity of household microbiomes remain unclear. Further work is required to investigate the impact of these factors on the diversity, structure and composition of microbial communities and their associated antibiotic resistances in household ecosystems. Furthermore, future studies could include collection of skin, saliva, urine and feces samples from occupants and domestic animals residing within the homes to investigate the relationship and interaction between human-, animal- and household microbiomes. Also, this will allow investigation of the connections between household microbiomes and occupants’ health.

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In the present study, a possible effect of antibacterial cleaning products on the composition of bacterial communities and of antibiotic resistances was observed in the household ecosystem. However, the effect of antibacterial cleaning products on the antibiotic resistance level in indoor environments is a controversial research point. Aiello et al (2005) stated that the use of antibacterial cleaning products in homes did not affect the bacterial susceptibility to triclosan. Conversely, some studies found a link between the use of antibacterial cleaning products and an increase of antibiotic resistance (Braoudaki and Hilton 2004; Drury et al., 2013). Further research is required using a combination of culture-dependent and culture-independent molecular methods to extensively study the possible impacts of antimicrobial cleaning products with comparison to eco-friendly products on microbial diversity and on antimicrobial resistance in residential houses and particularly in bathroom surfaces where high levels of triclosan containing products (e.g. hand washing soap, toothpaste and deodorant) are used.

In the current study, the focus was on bacterial communities; however, future work should also consider studying the structure, diversity and abundance of fungal and viral communities which will also play an important role in impacting upon human health in the household ecosystem. Further work should apply metagenomic-based sequencing shotgun which would allow analysis of all of the taxa that are present in household ecosystems (bacteria, fungi and viruses), and also identify the most dominant metabolic pathways and antibiotic resistance genes that are present in each household sample. such as approach has recently been used to study bacterial communities and associated antibiotic resistance in dust (Hartmann et al., 2016) and also applied in a hospital setting (Lax and Gilbert 2015; Lax et al., 2017 ). The application of metagenomic studies will facilitate the comparison of the different antibiotic resistance genes from different bacterial communities and/or from novel bacterial isolates and will provide information which can lead to a better and more comprehensive understanding of the antibiotic resistome in household ecosystems. To accomplish this, further research may be required to assess and evaluate the combination of these two different technologies; the classical isolation of novel microbes through culture-dependent methods and culture-independent methods throughout advanced methodologies such as sequence- and function-based metagenomics.

Based on this work, the most important and realistic factors that should be tested in more depth in the future work are: a) the number and type of occupants. B) the frequency of cleaning and type of cleaning products (traditional chemical (e.g. bleach) versus eco-friendly products) used,

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Chapter 6 General Discussion c) the presence and mechanisms of ventilation (including air conditioning systems). A possible influence of these three factors on the structure and diversity of bacterial communities and of the antibiotic resistances genes in these human residences was noticed. However, the number of samples (for each variable) was not sufficient to allow for the determination of effects of these factors, within the current study.

In conclusion, the research in this thesis has shown that the bacterial communities in human residences were found to be habitat-specific rather than house-specific. Nevertheless, within each habitat, samples clustered additionally with respect to their house of origin. In addition, many clinically relevant antibiotic resistance genes including those encoding resistance to antibiotics of last resort were detected in human residences at high frequencies and abundances. Furthermore, the diversity of antibiotic resistance genes varies within and between different habitats in human residences. These antibiotic resistance genes were related to those from human-derived bacteria including those in potentially pathogenic bacteria. Multiple antibiotic resistance genes (ermB, sulII and ampC) related to those from clinically relevant pathogens such as E. coli and K. pneumoniae were identified. The link between these antibiotic resistance genes and those on mobile genetic elements including broad host range conjugative transposons indicates a strong likelihood of horizontal gene transfer, including co-transfer, of these antibiotic resistance genes in household habitats between both closely related and divergent bacteria. The accumulated strong evidences from this study supported that human residences are a reservoir for antibiotic resistance genes. Together, these findings demonstrate the indoor microbiome and antibiotic resistome will constitute an important driver to impact upon public health in human residences.

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Appendices

APPENDICES

1.1 Appendix A1

Volumes of primers and sterile distilled water used to prepare a concentration of 10 pmol μl-1 of primers in a final volume of 400 µl.

Oligo Name Concentration of Volume of Primer/400µl Volume of dH2O/400µl primer in nmol

NDM-Fm 241.8 16.5 µl 383.5 µl NDM-Rm 222.3 18 µl 382 µl mecA F 127.5 31.4 µl 368.6 µl 1282 mecA R 127.2 31.4 µl 368.6 µl 1793 VanA 1 137.3 29.1 µl 370.9 µl VanA 2 163.0 24.5 µl 375.5 µl AmpC-F 111.1 36 µl 364 µl AmpC-R 144.7 27.6 µl 372.4 µl CLR5-F 179.9 22.2 µl 377.8 µl CLR5-R 157.4 25.4 µl 374.6 µl TetM-FW 150.5 26.6 µl 373.4 µl TetM-RW 149.4 26.8 µl 373.2 µl tet(B)-F 419.3 9.5 µl 390.5 µl tet(B)-R 333.3 12 µl 388 µl ermB-1 312.7 12.8 µl 387.2 µl ermB-2 193 20.7 µl 379.3 µl aac6-aph2F 184.6 21.7 µl 378.3 µl aac6-aph2R 371.3 10.8 µl 389.2 µl Sul2-F 272.8 14.7 µl 385.3 µl Sul2-R 152.5 26.2 µl 373.8 µl CatII-F 273.3 14.6 µl 385.4 µl CatII-R 305 13.1 µl 386.9 µl dhfrI-F 202.2 19.8 µl 380.2 µl dhfrI-R 273.6 14.6 µl 385.4 µl

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Appendices

2.1 Appendix A2

257

Appendices

3.1 Appendix A3

Advertising posters on walls in different locations to recruit volunteers.

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Appendices

4.1 Appendix A4

Questionnaire:-

Project title: Prevalence and diversity of bacteria and clinically relevant antibiotic resistance genes in human residences.

Research team: Associate Professor PhD Student RMIT University RMIT University

Your participation in this questionnaire is extremely valuable and will help us in interpreting the outcomes of this research project. We would be most grateful if you could answer the following questions. Please tick the box(es) which

1. House location: ______2. Preferred contact details: ______3. How many people live in this house? Adults: Male: Female: Children: Male: Female: 4. What are occupations of the employed residents? (We are interested to know whether any occupant works in the health care sector) ……………….……………………………………………………………………. 5. What is the type of dwelling? House ☐ How many floors: …………………. Apartment ☐ Other (please state): ______6. Does the house include any outdoor space? Garden/ yard ☐ Balcony/ terrace ☐ Other (please state):______7. Are any of the residents (including pets) has been on antibiotic therapy within the past 30 days? Yes ☐ No ☐ 8. Are any of the residents is/was on long-term antibiotic therapy at any time during the year? Yes ☐ No ☐ 9. Do you use an air purifier, dehumidifier or humidifier in the house? Yes ☐ No ☐ If Yes state which type: ______10. What type of air conditioning do you have at home? Split-system air conditioner ☐ Central (ducted) air conditioning ☐ None ☐ Other (please state):______

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Appendices

11. What is the type of flooring used in the living area in your house (tick All that apply)? Carpet ☐ Wooden ☐ Ceramic Tile ☐ Vinyl ☐ Other (please state):______12. Do you have a tile in the bathroom wall? Yes ☐ No ☐ 13. Do you have any pets at home (please tick box that applies and state number of animals)? Cat ☐ ………… Dog ☐ ………… None ☐ Other (please state):______14. How often (approximately) do you clean and/or vacuum the floor of your living room? ………………………….……………………………………………………………………… 15. How often (approximately) do you clean kitchen worktop surfaces? ………………………….……………………………………………………………………… 16. How often (approximately) do you clean a tile in the bathroom wall? ………………………….……………………………………………………………………… 17. What types of cleaning products are being used in the house (tick All that apply)? Conventional/ antimicrobial products ☐ Eco-friendly cleaning products ☐ Other (please state):______18. Do you wear shoes inside the home (e.g. in living area)? Yes ☐ No ☐

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Appendices

5.1 Appendix A5

Quality statistics of Illumina MiSeq amplicon sequencing run of antibiotic resistance genes.

Total DNA Reads *Reads % ≥Q302 Yield Aligned to Error sequence reads passed Identified (%) (Gbp) PhiX (%)3 rate (%)4 filter1

20,703,980 19,976,014 84.7 67.33 6.13 Gb 9.79 2.82

*Antibiotic resistance genes data set (n=96). 1 A quality filter of the Illumina MiSeq filters out unreliable sequences. 2 The average percentage of bases greater than Q30 which is a quality score predicts the probability of a wrong base call (1 in 1,000). 3 Positive control DNA. 4 The calculated error rate of the reads that aligned to PhiX.

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