The Factors That Influence Prokaryotic and Eukaryotic Community Structure in Indoor Dust

The Factors that Influence Prokaryotic and Eukaryotic Community Structure in Indoor Dust

Sepideh Pakpour

B.Sc. Shahid Beheshti University, Iran, 2006 M.Sc., McGill University, Canada, 2010

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY in

THE COLLEGE OF GRADUATE STUDIES

(Biology)

THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan)

December 2015 © Sepideh Pakpour, 2015

Abstract

Buildings are complex ecological systems that support a high diversity of prokaryotic and eukaryotic communities. Since many of these organisms interact with humans and their activities, and adding the considerable amount of time modern humans spend indoors, it is critical to understand the taxonomic composition of indoor biota as well as the ecological processes and environmental factors that may influence their diversity and composition.

In this thesis, I report on studies where I employed a Next-Generation DNA Sequencing technique, monitored 668 environmental factors and occupants’ activities, conducted advanced multivariate statistical analyses, and demonstrated that community composition of different organisms in indoor environments, including Fungi, Bacteria, and Animals may be influenced by different building characteristics, furnishings, type of occupants as well as their activities. In addition, this is the first study reporting the presence of Archaea in household dust as a common part of the indoor microbiota; however, the archaeal abundance in indoor environment was considerably lower than Bacteria, perhaps because of less available sources contributing to indoor Archaea or more environmental filtering preventing archaeal establishment in indoor environments. This also can be interpreted that the indoor archaeal assemblages are probably allochthonous for the most part (passive entrants of archaeal traces from different sources), in contrast, to those of the Bacteria which are a mixture of allochthonous and autochthonous (live and active inhabitants of dust). Finally, in a separate study with samples collected over a 20-year period, I found that the total airborne fungal spore count in outdoor air would likely increase significantly in future years as a result of climate change, indicating a likely rise in indoor fungal spore abundance as well.

Overall, this study as one of the first to look for members of all domains of life in a single cohort study, has advanced our understanding of biological components of residential houses and illustrated how community composition of different organisms in the indoor environment may be influenced by different building characteristics, furnishing, and its occupants. I believe such comprehension of indoor ecology can help researchers design intervention studies to provide public health policy decision makers with new tools to improve the built environment.

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Preface

This research was conducted at the University of British Columbia, Okanagan campus, under supervision of Dr. John Klironomos. The results of this research have been submitted in peer-reviewed journal articles and conference proceedings.

A version of Chapter 1 and Chapter 2 will be submitted as a review journal article (S.

Pakpour and J. Klironomos “A synopsis of indoor environment ecology”). This work was wholly drafted by S. Pakpour with editorial comments by Dr. John Klironomos. In addition, a version of some sections of the literature review in Chapter 2 has been published as a book chapter entitled “Micro-fungi in indoor environments: what is known and what is not”, and coauthors of this work are C. Yang, J. Klironomos, and D.W. Li. This In-Press work was partially drafted by S. Pakpour with editorial comments by Drs. J. Klironomos and D.W. Li.

Chapter 3 – 6 are collaborative research projects between UBC and research teams of the

Canadian Healthy Infant Longitudinal Development (CHILD) study. Dust samples for this research were collected by the research teams of the miniCHILD study in Vancouver, BC,

Canada, and sent to S. Pakpour and J. Klironomos by T. Konya in Dr. J. Scott laboratory. A version of Chapter 3 has been submitted as a journal article (S. Pakpour, J. A. Scott, S. E.

Turvey, J. R. Brook, T. K. Takaro, M. R. Sears, and J. Klironomos “The Relationship between Indoor Environmental Characteristics and Fungal Community Structure”, submitted to PLoS ONE). For this work, I conducted all the molecular experiments, bioinformatics and statistical analyses, and prepared the manuscript for publication. A preliminary version of this work was accepted as a conference presentation (S. Pakpour, J.

Scott, S. Turvey, and J. Klirlonomos (2013) “The environmental determinants of fungal

iv community structure in indoor dust”, The Canadian Society for Ecology and Evolution

(CSEE), Kelowna, BC, Canada, May 2013).

A version of Chapter 4 will be submitted as a journal article (S. Pakpour, J. A. Scott, S. E.

Turvey, J. R. Brook, T. K. Takaro, M. R. Sears, and J. Klironomos “Influence of Occupants and Their Activities on Bacterial Communities in Residential Houses”). I conducted all the molecular analyses, bioinformatics and statistical analyses, and prepared the manuscript for publication.

A version of Chapter 5 has been submitted as a journal article (S. Pakpour, J. A. Scott, S. E.

Turvey, J. R. Brook, T. K. Takaro, M. R. Sears, and J. Klironomos “The relationship between housing characteristics and animal community composition in the dust of indoor environments”, submitted to Indoor Air). I conducted all the molecular analyses, bioinformatics and statistical analyses, and prepared the manuscript for publication.

A version of Chapter 6 has been submitted as a journal article (S. Pakpour, J. A. Scott, S. E.

Turvey, J. R. Brook, T. K. Takaro, M. R. Sears, and J. Klironomos “Archaea in the indoor environment and their relationship with housing characteristics”, submitted to FEMS

Microbiology Ecology). I conducted all the molecular analyses, bioinformatics and statistical analyses, and prepared the manuscript for publication.

A version of chapter 7 has been published in a journal article (S. Pakpour, D. W. Li, and , J.

Klironomos (2014) “Relationships of fungal spore concentrations in the air and meteorological factors”, Fungal Ecology, 13: 130-134.), and accepted as a conference presentation and published as an extended abstract (S. Pakpour, D.W. Li, and J. Klironomos

(2014) “Climatic drivers of airborne fungal spore concentrations in two North American

v cities”, Indoor Air, Hong Kong, July 2014). For this study, I conducted the data analysis of test data and wrote the manuscript and the extended abstract.

Table of Contents

Abstract ...... ii

Preface ...... iv

Table of Contents ...... vii

List of Tables ...... xiii

List of Figures ...... xv

Acknowledgments ...... xxiii

Dedication ...... xxv

Chapter 1: Introduction ...... 1

1.1 Objectives...... 3

1.2 Outline of the Thesis ...... 4

Chapter 2: Literature Review...... 6

2.1 Fungi and the Indoor Environment ...... 7

2.1.1 Fungal growth on construction and finishing materials ...... 9

2.1.2 Fungal products: Mycotoxins ...... 14

2.1.3 The indoor mycobiome and health problems ...... 19

2.2 Bacteria and the Indoor Environment ...... 23

2.2.1 The indoor bacterial biota and health problems ...... 25

2.3 Animals and the Indoor Environment ...... 27 vii

2.3.1 Dust Mites ...... 27

2.3.2 Cockroaches ...... 28

2.4 Climate Change and the Indoor Environment ...... 30

Chapter 3: The Relationship between Indoor Environmental Characteristics and Fungal

Community Structure ...... 35

3.1 Overview ...... 35

3.2 Background ...... 36

3.3 Materials and Methods ...... 38

3.3.1 Study design and sample collection ...... 38

3.3.2 DNA extraction ...... 40

3.3.3 PCR amplification and sequencing ...... 41

3.3.4 Analysis of pyrosequencing data ...... 43

3.3.5 Statistical Analyses ...... 44

3.4 Results and Discussion ...... 46

3.4.1 Taxonomic composition of indoor fungal communities ...... 46

3.4.2 Diversity and community composition structure ...... 49

3.4.3 Trends of individual components of indoor fungal community ...... 55

3.5 Summary ...... 71

Chapter 4: Influence of Occupants and Their Activities on Indoor Bacterial

Communities ...... 72

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4.1 Overview ...... 72

4.2 Background ...... 73

4.3 Materials and Methods ...... 74

4.3.1 Study design and sample collection ...... 74

4.3.2 DNA extraction and PCR Amplification ...... 74

4.3.3 Analysis of pyrosequencing data ...... 76

4.3.4 Statistical analyses ...... 77

4.3.5 Trends of individual components of the indoor bacterial community ...... 79

4.4 Results ...... 79

4.4.1 Diversity and community composition structure ...... 80

4.4.2 Trends of individual components of the indoor bacterial community ...... 90

4.4.3 Bedroom ...... 90

4.4.4 The most used room ...... 96

4.5 Discussion ...... 105

4.5.1 Type and density of occupants ...... 106

4.5.2 Building characteristics ...... 108

4.5.3 Furniture and occupant activities ...... 108

4.6 Summary ...... 109

Chapter 5: The Relationship between Housing Characteristics and Animal Assemblages in the Dust of Indoor Environments ...... 111

5.1 Overview ...... 111

5.2 Background ...... 112

5.3 Materials and Methods ...... 113

5.3.1 Study design and sample collection ...... 113

5.3.2 DNA extraction and PCR amplification ...... 113

5.3.3 Analysis of pyrosequencing data ...... 115

5.3.4 Statistical analyses ...... 116

5.3.5 Trends of individual components of the indoor animal biota ...... 118

5.4 Results ...... 118

5.4.1 Taxonomic composition of indoor animal communities ...... 118

5.4.2 Diversity and community composition ...... 119

5.4.3 Trends of individual components of indoor animal biota ...... 127

5.5 Discussion ...... 140

5.6 Summary ...... 143

Chapter 6: Archaea in the Indoor Environment and their Relationship with Housing

Characteristics ...... 145

6.1 Overview ...... 145

6.2 Background ...... 146

6.3 Material and Methods ...... 147

6.3.1 Sample Collection ...... 147

6.3.2 DNA extraction and quantitative PCR analyses ...... 147

6.3.3 Collection of environmental variables and statistical analyses ...... 149

6.4 Results ...... 150

6.4.1 Presence of Archaea in indoor environments and their total abundance

compared to bacteria ...... 150

6.5 Discussion ...... 156

6.6 Summary ...... 158

Chapter 7: Relationships of Fungal Spore Concentrations in the Air and Meteorological

Factors ...... 160

7.1 Overview ...... 160

7.2 Background ...... 161

7.3 Material and Methods ...... 162

7.4 Results and Discussion ...... 163

Chapter 8: Concluding Remarks ...... 170

8.1 Strengths and limitations of the dissertation research ...... 172

8.2 Future directions ...... 173

Bibliography ...... 175

Appendices ...... 230

Appendix A Supplementary graph for indoor fungal OTUs ...... 230

Appendix B Supplementary table for indoor fungal OTUs ...... 231

Appendix C Supplementary graph for indoor bacterial OTUs ...... 238

Appendix D Supplementary graph for indoor animal OTUs ...... 239

Appendix E Supplementary table for airborne fungal spore concentrations ...... 240

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List of Tables

Table 2.1 A comparison of common analytical methods for mycotoxin detection...... 21

Table 3.1 Sub-sample of 668 collected environmental factors...... 41

Table 3.2 The 25-most abundant fungal OTUs in dust samples of bedrooms versus the

most used rooms...... 52

Table 3.3 Environmental and behavioural factors and their significance levels that best

explain the indoor dust-borne fungal communities in bedrooms...... 53

Table 3.4 Environmental and behavioural factors and their significance level that best

explains the indoor dust-borne fungal communities in the most used rooms..... 54

Table 4.1 The 20-most abundant bacterial OTUs in dust samples of bedrooms versus

the most used rooms...... 83

Table 4.2 Environmental and behavioural factors that best explain the indoor dust-

borne bacterial richness in bedrooms and the most used rooms...... 84

Table 4.3 Environmental and behavioural factors that best explain the indoor dust-

borne bacterial communities in bedrooms...... 88

Table 4.4 Environmental and behavioural factors that best explain the indoor dust-

borne bacterial communities in the most used rooms...... 89

Table 5.1 The 20-most abundant animal OTUs in dust samples of bedrooms...... 121

Table 5.2 The 20-most abundant animal OTUs in dust samples of the most used

rooms...... 122

Table 5.3 Environmental and behavioural factors and their significance levels that best

explain the indoor animal community in bedrooms...... 125

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Table 5.4 Environmental factors and their significance levels that best explain the

indoor animal community in the most used rooms...... 126

Table 6.1 Environmental and behavioural factors that best explain variation of the total

archaeal abundance in bedrooms...... 152

Table 6.2 Environmental and behavioural factors that best explain variation of the total

archaeal abundance in the most used rooms...... 153

Table 7.1 Regression results used for the selection of the significant time interval for

environmental variables ...... 164

Table B.1 Taxonomy of indoor dust fungal OTUs ...... 231

Table E.1 Airborn fungal spore concentrations between the period of 1992 – 2011

measured in the first week of each September in New York City and

Toronto...... 240

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List of Figures

Figure 1.1 The Schematic diagram of thesis outline ...... 5

Figure 3.1 Statistical analysis platform used in this study...... 46

Figure 3.2 List of fungal classes detected in dust samples along with their OTU

abundances...... 48

Figure 3.3 Alpha diversity of fungal OTUs in dust samples of bedrooms versus the

most used rooms; (A) rarefaction curves of theoretical total OTU richness

by the Chao richness estimators; (B) observed richness of fungal OTUs

across sample locations...... 50

Figure 3.4 Correlation and significance of important environmental factors for the

most used room (from Table 3.4) on the beta diversity of detected fungal

classes. Only those with Rho or R > 0.1 and p < 0.05 are noted...... 56

Figure 3.5 Shade plot showing the affected OTUs by the use of garden spray or weed

killers. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit. Taxonomy of OTUs is shown in Appendix B...... 57

Figure 3.6 Shade plot showing the affected OTUs by the use of air conditioner.

Colour boxes illustrate the magnitude of each individual OTU under each

sample unit. Taxonomy of OTUs is shown in Appendix B...... 58

Figure 3.7 Correlation and significance of important environmental factors (from

Table 3.3) on beta diversity of fungi in bedrooms. Only those with Rho or

R > 0.1 and p < 0.05 are noted...... 61

Figure 3.8 Shade plot showing the affected OTUs by the number of adults in the

house. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit. Taxonomy of OTUs is shown in Appendix B...... 62

Figure 3.9 Shade plot showing the affected OTUs by the presence of moths in the

house. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit. Taxonomy of OTUs is shown in Appendix B...... 64

Figure 3.10 Shade plot showing the affected OTUs by the use of liquid or solid air

fresheners. Colour boxes illustrate the magnitude of each individual

OTU’s abundance under each sample unit. Taxonomy of OTUs is shown

in Appendix B...... 65

Figure 3.11 Shade plot showing the affected OTUs by the presence of plastic or vinyl

furniture. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit. Taxonomy of OTUs is shown in Appendix B...... 66

Figure 3.12 (A) Two dimensional PCoA ordination plot showing fungal communities

of dust based on type of vacuum used in the houses. (B) Shade plot

showing the affected OTUs by the type of vacuum. Colour boxes illustrate

the magnitude of each individual OTU under each sample unit. Taxonomy

of OTUs is shown in Appendix B...... 67

Figure 3.13 Shade plot showing the affected OTUs by the use of scented candles.

Colour boxes illustrate the magnitude of each individual OTU under each

sample unit. Taxonomy of OTUs is shown in Appendix B...... 69

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Figure 3.14 Shade plot showing the affected OTUs by the use of unscented candles.

Colour boxes illustrate the magnitude of each individual OTU under each

sample unit. Taxonomy of OTUs is shown in Appendix B...... 70

Figure 4.1 List of bacterial phyla detected in dust samples along with their OTU

abundances...... 81

Figure 4.2 Alpha diversity of bacterial OTUs in dust samples of bedrooms versus the

most used rooms; (A) rarefaction curves of theoretical total OTU richness

by the Chao richness estimators; (B) observed richness of bacterial OTUs

across sample locations...... 82

Figure 4.3 Variation of alpha diversity of bacterial OTUs versus potentially effective

environmental factors for bedroom (A) length of occupancy; (B) number

of rooms in the house; (C) cleanliness of basement; (D) evidence of leak

in the house; and (E) presence of short-hair cats...... 85

Figure 4.4 Variation of alpha diversity of bacterial OTUs versus potentially effective

environmental factors for the most used rooms (A) presence of electronic

devices; (B) multi-surface cleaners...... 86

Figure 4.5 Shade plot showing the affected OTUs in bedrooms by the presence of

humidifiers and vaporizers. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 92

Figure 4.6 Shade plot showing the affected OTUs in bedrooms by the use of wet

cloths for cleaning. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 93

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Figure 4.7 Shade plot showing the affected OTUs in bedrooms by the geographical

location of houses. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 94

Figure 4.8 Shade plot showing the affected OTUs in bedrooms by the use of glass

cleaners. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit...... 95

Figure 4.9 Shade plot showing the affected OTUs in the bedrooms by the presence of

long-hair dogs. Colour boxes illustrate the magnitude of each individual

OTU under each sample unit...... 97

Figure 4.10 Shade plot showing the affected OTUs in bedrooms by the use of scented

laundry detergents. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 98

Figure 4.11 Shade plot showing the affected OTUs in bedrooms by the frequency of

house cleaning per month. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 99

Figure 4.12 Shade plot showing the affected OTUs in the most used rooms by the use

of multi-surface cleaners. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 100

Figure 4.13 Shade plot showing the affected OTUs in the most used rooms by the

presence of carpets. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 103

xviii

Figure 4.14 Shade plot showing the affected OTUs in the most used rooms by the

number of house visitors per day. Colour boxes illustrate the magnitude of

each individual OTU under each sample unit...... 104

Figure 5.1 List of animal phyla and classes detected in dust samples along with their

OTU abundances...... 120

Figure 5.2 Alpha diversity of animal OTUs in dust samples of bedrooms versus the

most used rooms; (A) rarefaction curves of theoretical total OTU richness

by the Chao richness estimators; (B) observed species richness and

Shannon-Weiner index of animal OTUs across sample locations...... 123

Figure 5.3 Shade plot showing the affected OTUs by the evidence of leakage inside

the most used, dining, or family rooms. Colour boxes illustrate the

magnitude of each individual OTU under each sample unit...... 128

Figure 5.4 Shade plot showing the affected OTUs in bedrooms by the bedroom area

(sq. m). Colour boxes illustrate the magnitude of each individual OTU

under each sample unit...... 129

Figure 5.5 Shade plot showing the affected OTUs in bedrooms by the use of scented

candles. Colour boxes illustrate the magnitude of each individual OTU

under each sample unit...... 130

Figure 5.6 Shade plot showing the affected OTUs in the most used rooms by the use

of plug-in deodorizers. Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 132

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Figure 5.7 Shade plot showing the affected OTUs in the most used rooms by the use

of air conditioner. Colour boxes illustrate the magnitude of each individual

OTU under each sample unit...... 133

Figure 5.8 Shade plot showing the affected OTUs in the most used rooms by the

presence of mouse in the house. Colour boxes illustrate the magnitude of

each individual OTU under each sample unit...... 134

Figure 5.9 Shade plot showing the affected OTUs in the most used rooms by the

presence of body of water in neighborhood. Colour boxes illustrate the

magnitude of each individual OTU under each sample unit...... 135

Figure 5.10 Two dimensional PCoA ordination plot showing animal communities of

dust according to the frequency of bathroom fan use in houses (A); Shade

plot showing affected OTUs in the most used rooms by the frequency of

bathroom fan usage (B). Colour boxes illustrate the magnitude of each

individual OTU under each sample unit...... 137

Figure 5.11 Shade plot showing the affected OTUs in the most used rooms by the use

of chemical sprays for cleaning. Colour boxes illustrate the magnitude of

each individual OTU under each sample unit...... 138

Figure 5.12 Shade plot showing the affected OTUs in the most used rooms by the

presence of plastic or vinyl furniture. Colour boxes illustrate the

magnitude of each individual OTU under each sample unit...... 139

Figure 6.1 Total abundance of (A) Archaea and (B) Bacteria in bedrooms and the

most used rooms...... 151

Figure 6.2 The relationship between use of electric dryer vented outdoors and total

archaeal abundance in bedrooms...... 154

Figure 6.3 The relationship between the presence of an upgraded plumbing system

and total archaeal abundance in the most used rooms...... 154

Figure 6.4 The relationship between hanging wet clothes inside the house and total

archaeal abundance in the most used rooms...... 155

Figure 6.5 The relationship between use of a broom for cleaning and total archaeal

abundance in the most used rooms...... 155

Figure 6.6 The relationship between use of liquid or solid air fresheners and total

archaeal abundance in the most used rooms...... 156

Figure 7.1 Variation of total spore concentration with (A) July – August average

precipitation, and (B) average temperature in Toronto and New York from

1992 to 2011...... 165

Figure 7.2 Mean distribution of the main spore types present in the atmosphere of

Toronto from 1992 to 2011 (A), and their corresponding trends of changes

with the level of precipitation: (B) Alternaria (AL); (C)

Aspergillus/Penicillium (AP); (D) Ascospores (AS); (E) Basidiospores

(BA); (F) Baspora (BA); (G) Cladosporium (CL); (H) Curvularia (CU);

(I) Epicoccum (EP); (J) Fusarium (FU); (K) Pithomyces (PI); (L) Other

(OT)...... 167

Figure 7.3 Mean distribution of the main spore types present in the atmosphere of

New York from 1992 to 2011 (A), and their corresponding trends of

changes with the level of precipitation: (B) Alternaria (AL); (C)

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Aspergillus/Penicillium (AP); (D) Ascospores (AS); (E) Basidiospores

(BA); (F) Baspora (BA); (G) Cladosporium (CL); (H) Curvularia (CU);

(I) Epicoccum (EP); (J) Fusarium (FU); (K) Pithomyces (PI); (L) Other

(OT)...... 168

Figure A.1 A rank “frequency” distribution of indoor dust fungal OTUs...... 230

Figure C.1 A rank “frequency” distribution of indoor dust bacterial OTUs...... 238

Figure D.1 A rank “frequency” distribution of indoor dust animal OTUs ...... 239

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Acknowledgments

Although this dissertation has only my name on its cover page, the essential and gracious support of many individuals have contributed to its production. In my opinion, the most influential person during my Ph.D. study has been my supervisor, Dr. John Klironomos.

John’s passion, guidance, and discipline have been indispensable to my growth as a scientist and as a person over these past four years. Your insightful comments and constructive criticisms at different stages of my research are greatly appreciated. I am grateful to you for holding me to a high research standard. Your patience and support, both academically and emotionally, helped me through my Ph.D. thesis. I hope that one day I would become as good an advisor to my students as you have been to me and there is no doubt that I will be always your student.

I also highly acknowledge the study families and research teams of the Canadian Healthy

Infant Longitudinal Development (CHILD) study, more specifically Dr. James Scott and Dr.

Stuart Turvey for giving me access to the unique dust samples collected as part of the mini

CHILD study. I am grateful to my advisory committee, Dr. Louise Nelson, Dr. Daniel

Durall, Dr. Andis Klegris and Dr. Rehan Sadiq, as well as Dr. Miranda Hart who all provided me with valuable insights in our meetings and discussions. I would also extend a heartfelt thanks to Dr. Joyce Boon for her support when times were tough. My sincere thanks also go to Geet Hans for her generous help and contribution in the research presented in chapter 6. I would also like to thank David Kadish, a great friend who was always willing to help and give his best suggestions when I had the most difficult time with the Python programming language.

xxiii

I am grateful to all my friends and colleagues, Kristin Aleklett, Cameron Egan, Jennifer

Forsythe, Monika Gorzelak, Taylor Holland, Negin Kazemian, Bill Kokkoris, Hongguang

Liu (Tony), Brian Ohsowski, Antreas Pogiatzis, Alicia Tymstra, Mieke van der Heyde, and

Eric Vukicevich. The group has been a source of friendship as well as advice and collaboration. I also thank Sareh Karami and Mehdi Ghahramani for being wonderful friends and a great source of inspiration. I cherish their wisdom, and appreciate their support over the past years.

I gratefully acknowledge the funding sources that made my Ph.D. work possible. I was funded by the Fonds de recherche du Québec – Nature et technologies (FQRNT), Doctoral

Research Scholarships (B2) for my first 3 years and was honored to be a University Graduate

Fellow for 4 years. My work was also supported by the Natural Sciences and Engineering

Research Council of Canada (NSERC).

Lastly, I would like to thank my family for all their love and encouragement. For my great parents who raised me with a love of science and supported me in all my pursuits. They have taught me about hard work and self-respect, about persistence and about how to be independent. And most of all for my loving, supportive, encouraging, and patient husband

Abbas whose faithful support during every single stage of this Ph.D. is so appreciated. Thank you with all my heart and soul. Your unconditional love and unwavering support carries me through always.

Sepideh Pakpour University of British Columbia July 2015

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Dedication

To the one, and the only one I love the most …

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1 Chapter 1: Introduction

Homo sapiens sapiens, the only surviving species of the human genus (Homo) first appeared

about 195 000 years ago in Africa [1], and marched steadily from the primitive cave

inhabitants to the complex modern humans who found the glory in high-rises. In this

progress narrative, the perception of home has evolved from being a shelter providing the

basic needs of humans and protecting them from the hostile outdoor environment to

dwellings that provide amenities and comforts far beyond the basic needs of a shelter.

Through this progress, humans have confined themselves more and more in physical spaces

until now where people in developed countries spend up to 90% of their time indoors [2],

the “finite potential well” of modern human, known as the built environment (i.e., the finite

potential well is a concept borrowed from quantum mechanics, describing a particle free to

move in a small space surrounded by solid barriers with the probability to be found outside

the box [3]).

The indoor built environment is a complex ecosystem, the quality of which may be affected

by its macro-environment, design and building infrastructure, as well as furnishings and

occupants’ activities [2]. For many years, architects and engineers worked together to design

and make the perfect built environment in which thermal comfort is controlled seasonally in

cold and warm climates, and odors and health threatening materials are eliminated using

naturally and mechanically ventilated systems. When energy conservation became a major

issue facing the 20th century’s societies, thermal insulation and air tightness in buildings

increased [4], which in turn separated the indoor environment more from outdoors, and

ultimately limited the occupants’ contact with nature and biodiversity. Consequently indoor

1 air has been turned into an important exposure medium of many indoor emitted health threatening substances and its quality has become an important safety concern for modern building developers [5, 6]. Among different substances in indoor environments, exposure to bioaerosols (i.e., aerosols or particulate matter of microbial, plant or animal origin [7]) has been a research focus in the last few decades, mainly because of the emerging evidence of associations between exposures to biological agents in built environments with a wide range of consequences for occupants. For example, early life exposure of children to clean environments (low indoor dust Bacteria and Fungi) has been shown to be associated with increased risk for developing asthma [8, 9]. However, the opposite has also been shown - exposure to biological agents is associated with a wide range of adverse health effects, including contagious infectious diseases, acute toxic effects, allergies, respiratory problems and cancer [7, 10, 11].

A better understanding of the composition of indoor microbiota as well as what factors associate with their abundance and diversity may help architects, engineers, scientists, and public health leaders, make more informed decisions on built environment characteristics, which can in turn result in healthier habitats for humans. To date, several studies have focused on organisms present indoors as part of bioaerosols, and investigated their composition and diversity as well as environmental parameters affecting them [12-24]; however most of these studies largely focused on Bacteria [12, 15, 25-28] and to a lesser degree on Fungi [14, 16, 20, 22, 29, 30], whereas other organisms such as Archaea and

Animals in the indoor environments have not been studied in any great depth.

Moreover, dwellings are vulnerable to extreme outdoor weather events (heat waves, excessive precipitations or drought), and increased pollution [2]. Also, the outdoor

2 environment is one of the main sources of indoor bioaerosols, entering through windows, ventilation systems or being brought inside by occupants (human, pets, plants) [13, 16].

Hence, comprehending how outdoor bioaerosols may respond to changes in climate may partly determine how indoor bioaerosols will be affected in future, and accordingly help architects and engineers as they design new buildings. This is another subject that had been scarcely explored in the past (to be addressed in objective # 3 below).

1.1 Objectives

This thesis is part of the miniCHILD study - a preliminary cohort of 54 families in the Lower

Mainland area (BC, Canada) recruited to assist in the optimization and validation of data collection tools for the Canadian Healthy Infant Longitudinal Development (CHILD) study

(www.canadianchildstudy.ca). Given the above gaps in knowledge, the specific objectives of the thesis were defined as:

Objective # 1 To investigate the biological profiles of household dust (Fungi, Bacteria, and

Animal) as well as the environmental factors that may influence their diversity and composition.

Objective # 2 To determine if Archaea are regular components of household dust and if so, then to better understand how building characteristics and occupant’s activities would influence the variation of archaeal abundances in indoor spaces.

Objective # 3 To investigate how long-term inter-annual changes in climate may affect the abundance of fungal propagules in airborne dust.

1.2 Outline of the Thesis

As visualized in Figure 1.1, Chapter 2 will provide a detailed literature review of past and current research on indoor biota, as well as the effect of climate on the quality of indoor environment. The first objective of this thesis will be covered in Chapter 3, Chapter 4, and

Chapter 5, focusing on Fungi, Bacteria, and Animals, respectively. Chapter 6 will cover the second objective, focusing on archaeal detection in indoor dust and Chapter 7 is related to the third objective which investigates the effect of climate variation on airborne fungal spores. Finally conclusions and prospects for future research will be discussed in Chapter 8.

Figure 1.1 The Schematic diagram of thesis outline

2 Chapter 2: Literature Review

The indoor environment encompasses a complex mixture of viable and dead organisms as

well as allergens, small fragments, volatile microbial organic compounds and mycotoxins

[5, 17]. These particulate matters of microbial, plant or animal origin are known as

bioaerosols or organic dust [7, 17]. As part of indoor bioaerosols, built environments

support a high diversity of microbial life including human pathogens and commensals.

Since people spend most of their daily lives indoors, their exposure to microbes is mainly

limited to the current species in such environments. Epidemiological studies have

demonstrated an increase in the incidence of allergic symptoms, a decrease in lung

function, and emergency department visits for asthma in children [7, 11, 31, 32].

Although microorganisms play a key role in exacerbation, if not development, of such

health problems, the knowledge is still limited on how building attributes influence the

composition of microbial communities and associated health problems. In addition to

Fungi and Bacteria, indoor bioaerosols comprise largely ignored categories of organisms,

both prokaryotes (e.g., Archaea) and eukaryotes (e.g., protozoans, mites, insects, etc.).

Detection and health consequences of indoor protozoans, unicellular algae, and viruses

have been reviewed recently [17], but no literature exists on the detection of Archaea in

residential houses. Investigations on the diversity of the Animal community in indoor

environments are also very scarce, despite the existence of a body of literature on indoor

allergenic animal particulates (e.g., pets’ dander and dried saliva, fragments of

cockroaches, fecal pellets of dust mites, etc.) and their health consequences [32]. Here in

the following sections of this chapter, first a general review of past and current research

on Fungi, Bacteria, and Animals in the indoor environment will be presented. A review

on the effect of environmental variables on the community composition of each biota has

been discussed in more detail in the related chapters 3 – 6. The last section of this chapter

will provide a summary of the existing literature on the effect of climate change on the

quality of the indoor environment, as related to chapter 7.

2.1 Fungi and the Indoor Environment

The world of microorganisms was discovered by Antonie van Leeuwenhoek (1632 –

1723), who made microscopic observations of microbial cells (protozoa, algae, yeasts,

and bacteria ) [33]. Almost two hundred years later, in the 18th century when it was

widely believed that microorganisms could ascend instinctively from no living matter and

the process of fermentation was a vague and strange phenomenon, Louis Pasteur (1822 –

1895) demonstrated that airborne microbes are responsible for lactic acid fermentation and that a microscopic fungus can transform wine into vinegar [33-36]. Later, Pierre

Miquel (1983 – 1922), the father of aerobiology, sampled particles of a cubic meter of air on the surface of a sticky slide using a preliminary volumetric air sampling technique

[37], and for the first time measured airborne fungal spores and demonstrated constant presence of fungi in the atmosphere [36-38]. However, further investigations on fungal spores in the air were largely ignored by Miquel and other researchers, until 1924 when

Cadham [39] for the first time illustrated that spores of the cereal rust fungi can cause asthma, thus reviving an interest in studying airborne fungal spores [40]; albeit fungi were mentioned as early as 1698 as being associated with respiratory problems (more specifically asthma), and the first report of severe asthma attack as a response to mould exposure was documented in 1726 [41, 42].

Fungi are among the most common indoor microbiota [4]. The interest in indoor fungi

dates back to research conducted in Scandinavia in 1948 and England and Holland in the

1950s [43-46]. The energy crises of the 1970s encouraged the construction of better

insulated and more air-tight buildings [47], which resulted in a higher number of

dwellings suffering from dampness, mould growth, and lower air quality inside buildings.

As a result, many studies were conducted to investigate the consequences of the aforementioned changes, e.g., investigations by David Strachan conducted in Scotland

[48], Harvard Six Cities studies group [49], and Health Canada [50], all demonstrating associations between the presence of home dampness and mould with the prevalence of lower respiratory problems. Thereafter, the modern age of mould in damp dwellings and associated diseases began to be revealed, and numerous research efforts and governmental authorities advised that “exposure to fungal propagules and products generates a wide range of adverse responses”, and that significant fungal growth in indoor environments must not be tolerated regardless of its role to the indoor airborne spore-load [36, 42].

Under normal situation and absence of fungal contamination, indoor fungi encompass three main groups: (i) dry spore fungal taxa inhabiting the surface of plant leaves known as phylloplane taxa, which are mostly saprotrophs and are key agents of structural decay; however some may also be biotrophic to necrotropic parasites. Most basidiomycetes

(e.g., rusts, smuts, agarics, etc.) and several species belonging to anamorph genera such as Alternaria, Cladosporium and Epicoccum, and lichenized ascomycetes are examples of phylloplane taxa that are abundant outdoors especially during the growing season and may enter into indoor spaces [36]. The second group belongs to the indigenous soil

inhabiting taxa or saprotrophic and necrotrophic fungal species in the soil. This group,

known as pedosphere fungi (e.g., Acremonium, Fusarium, Trichoderma, Penicillium,

etc.), mostly produce wet spores and enter indoor spaces passively, disperse from the

outdoor environment via air currents or occupants (for example on footwear) or directly

through the mechanical ventilation systems [36]. Finally, the third group of indoor fungi

is skin related dermatoplane taxa such as Malassezia yeasts (Exobasidiomycetidae),

which have been detected in indoor dust using DNA sequencing techniques [9, 14, 36].

Fungi may also be classified based on their growth abilities on a particular material and

response to changes in water activity as follows: i) storage moulds are primary colonizers

that can grow at aw less than 0.8 (low moisture level in material) such as P.

brevicompactum, A. versicolor (at 12°C) and less frequently A. fumigatus, A. niger, A.

sydowii, A. ustus, several Eurotium species, P. brevicompactum, P. commune, P.

corylophilum, P. palitans, Paecilomyces variotii, and Wallemia sebi ; ii) secondary

colonizers that require a minimum aw between 0.8 and 0.9 (intermediate moisture level in

material) including species of Alternaria, Cladosporium, Phoma, Ulocladium, and A. versicolor (at 25°C); and finally iii) water damage moulds as tertiary colonizers which require aw of at least 0.9 (high moisture level in material) including various toxic fungi such as Chaetomium globosum, Memnoniella echinata, Stachybotrys chartarum, and

species of Trichoderma [51, 52].

2.1.1 Fungal growth on construction and finishing materials

Concern for mould growth in buildings is not a new problem restricted to the last 25

years [7, 53-57]; however, the number of buildings with mould problems has increased

9 during this time mainly because of poor planning and accelerated building processes and more complex building constructions [58]. For example, one quarter of social housing

(almost 14 million units) of all member states of the European Union are suffering from dampness and mould [59]. The proportion of buildings with fungal growth in the United

Kingdom is estimated at 30 – 45% [60], the Netherlands at 20 – 25% [60], the USA at

40% [49], and 30% in Canada [50].

Numerous microscopic examinations have shown that indoor fungal growth is restricted to the surface of building materials, textiles, or other matrices as substrates [4]. In addition, fungal colonization of building materials from different countries varies considerably because of the differences in climate, materials, different isolation procedures and difficulties in identifying the isolates at the species level especially in

Penicillium, Aspergillus and Cladosporium genera. For example, Black Aspergilli

(Aspergillus section Nigri) were recently studied in indoor environments in six countries:

Algeria, Croatia, Hungary, the Netherlands, Thailand and Turkey [61]. The highest species diversity (seven species) was observed in indoor samples from Thailand, while the lowest (two species) was found in Algeria. A. niger, A. tubingensis, A. luchuensis, and

A. welwitschiae were identified in all three temperate European countries, while A. tubingensis, A. luchuensis, and A. welwitschiae were also detected in Turkey, but not A. niger [61].

Most natural and manmade compounds, in particular construction and finishing materials that contain natural organic polymers including starch, cellulose, hemicelluloses, pectin and lignin are susceptible to fungal growth [6, 42]. Inorganic compounds may also become mouldy because of the absorption of dust which is a good medium for different

fungal species [62, 63]. In addition, the distribution of moisture within any material in the building is not uniform, and hence different parts of a structural element may support the growth of different fungi. For example, the hydrophilic mould Stachybotrys chartarum

was detected in the extremely wet surface of a boundary wall, while Aspergillus versicolor and various penicillia (xerophilic species) were more abundant on the drier

margins of the affected area [52]. All species in the indoor environment have their own

individual growth requirements and identification of the detected fungi at the species

level may be a good indication of the condition of the building [4]. In the following sub-

sections, fungal growth on a few most common building materials will be discussed.

2.1.1.1 Wood

Wood is highly susceptible to colonization by fungi such as Cladosporium, Penicillium

and Aspergillus [64]. Wood drying, specifically kiln drying, causes a higher amount of nitrogen and low molecular carbohydrates on the wood surface, which makes it more susceptible to mould growth [65, 66]. Other studies illustrated that some of the engineered wood products such as oriented strand boards (OSB), plywood, and medium- density fiberboard (MDF) are more susceptible to growth of Aspergillus, Trichoderma

and Penicillium than solid wood, particleboard [67], acylated wood [68], and wood

polyethylene composites [69].

2.1.1.2 Wallpaper and Plasters

Both, paper and glue, are very good media for many indoor fungi because they hold

organic debris which can then become a food source for mould growth [52, 70]. Synthetic

polymers such as synthetic rubber and plastic can also be degraded by different species of

11 fungi such as Aspergillus niger, Aspergillus flavus, Aureobasidium pullulans,

Chaetomium spp., Penicillium funiculosum, Penicillium luteum and Trichodema spp.

[51]. Recently, prefabricated gypsum plaster paper boards (drywall) are commonly being used in new buildings. The boards are highly susceptible to mould growth mainly by the cellulytic S. chartarum because of the paper used to reinforce the material. In addition, the gypsum itself can support fungal growth because of its nutrient content and additives that make it more hygroscopic at lower humidity levels [71-73].

2.1.1.3 Plastic and Glass

Fungi can grow on polyethylene and polyvinyl chloride (PVC) as they can degrade most plasticizers including commonly used organic acid esters such as dioctyl phthalate (DOP) and dioctyl adipate (DOA) [74]. Glass-reinforced plastic (GRP), popularly known as fiberglass, and fiberglass ceiling tiles which contain 10% ureaphenol-formaldehyde resin are other susceptible materials that can support fungal growth, especially A. versicolor and Penicillium spp. [75].

2.1.1.4 Paint

Water-based or solvent-based paints may also support fungal growth; however, it is not clear whether moulds found on the surface are using the paint components or taking nutrients from dirt also found on the surface [76]. In general, paints can either enhance or decrease the susceptibility of a given base material to fungal growth. For example, paints prevent the growth of Aureobasidium pullulans, while Penicillium and Aspergillus species can grow rapidly on paint [77].

2.1.1.5 Dust

Continuous elutriation of organic and inorganic airborne particles, originating from

various indoor and outdoor sources, forms dust [24]. Dust composition varies depending

on a given building type and use, and major particle sources [78, 79]. The main

constituents of house dust are fibrous organic materials (e.g., textile fibres, hairs and shed

epithelial debris) [80]. These organic substrates act as a rich source of nutrients that

enable numerous organisms such as animals (arthropods, and to some extent larger

animals such as rodents, etc.), bacteria and fungi to grow on the surface of building

materials such as steel, glass, brick, concrete or stone [80, 81]. In addition to being a

source of nutrients for microbes, hygroscopic fibers as the main components of stable

dust absorb atmospheric moisture and consequently provide it to the organisms contained

within dust [82].

The main components of the dust microbiome are viable and non-viable fungal conidia,

spores and spore clumps, fragments of spores, hyphae, sclerotia, lichen soredia, fruiting

bodies, bacterial cells, endospores, and fragmented cells [83]. The microbes in dust can

be i) autochthonous, ii) allochthonous, or iii) a combination of the two [80]. Active

fungal growth and propagation are the key factors shaping the autochthonous

populations. In contrast, allochthonous populations (pseudopopulations) are formed by

passive accumulation of particles from the surrounding environments. As an example of

the latter passive case, indoor microbial assemblages may be shaped and even enriched

by house hold vacuum cleaners or HVAC systems without an appropriate high efficiency

particulate air (HEPA) filtration, because of ineffective removal of small spores and

fragments which may pass through the filters and return into the indoor air [84, 85].

Through a case study in [86], fungal concentrations in dust from Finnish schools and residences were investigated using both a quantitative polymerase chain reaction (qPCR) and a culture method. Total microbial concentrations detected by the qPCR were at least two orders of magnitude greater than the culture method in both schools and homes. In another study, Amend et al. [16] analyzed the dust metagenome of 72 buildings in six continents using 454 GS FLX titanium technology. Dothideomycetes including species of

Cladosporium, Alternaria and Epicoccum were detected; however, they did not detect

any xerophilic species such as Wallemia and Eurotium [16]. Their results demonstrated

that considerably higher and taxonomically wider microbial diversity can be detected

from dust by culture-independent methods compared to traditional cultivation.

Nevertheless, the understanding of indoor microbial composition both quantitatively and

qualitatively is not yet consistent in the literature and more investigations are required to

characterize the main constituents of indoor dust.

2.1.2 Fungal products: Mycotoxins

Fungi produce a large number of secondary metabolites, including plant growth

regulators (e.g., gibberellins), pharmaceutically valuable compounds (e.g., penicillin,

statins), pigments (e.g., carotenoids), and toxins [87]. Different toxic metabolites

produced by fungi, based on their concentration and targets, are categorized into: i)

products that are mainly toxic to bacteria known as antibiotics; ii) fungal metabolites

known as phytotoxins that are toxic to plants; iii) mycotoxins that are by-products of

microfungi, mostly non-volatile, low-molecular weight compounds (below 1 500 Da),

which cause toxic responses at low doses in vertebrates, and iv) other low molecular

weight metabolites that are toxic at high concentration, such as ethanol (vapor-phase

14 chemicals) [77, 88]. Namely, mycotoxins have caused major epidemics such as i) ergotism, which was responsible for the death of hundreds of thousands of people in

Europe because of the ingestion of the alkaloids produced by the Claviceps purpurea; ii) alimentary toxic aleukia (ATA), which killed at least 100 000 Russian people between

1942 and 1948; iii) stachybotryotoxicosis, which caused the death of tens of thousands of horses in the USSR in the 1930s; and iv) aflatoxicosis, which was responsible for the death of more than 100 000 turkey spoults in the UK in 1960 because of the consumption of mould-contaminated peanut meal [89, 90].

Numerous factors may affect mycotoxin production, such as water activity, temperature, gas composition, the presence of chemical preservatives and microbial interactions [91].

There is a traditional view that mycotoxin production occurs in the idiophase when fungal growth is halted, usually because of the nutrient exhaustion [92]; however, there is evidence depicting mycotoxins production during the fungal active growth, even when there is no competition for nutrients [93]. Other studies showed that mycotoxin production was stimulated by the initiation of stress, particularly drought stress, when conditions were marginal for growth [93]. For instance, Aspergillus carbonarius grows

o o optimally at 30 – 35 C and 0.99 – 0.98 water activity (aw) [94]. In contrast, at 15 – 20 C and 0.95 aw, instead of growth, Aspergillus carbonarius produces ochratoxin A (OTA) which is an important mycotoxin with nephrotoxicity, hepatotoxicity, and immunotoxicity effects on animals, including humans [95, 96].

Individual mycotoxigenic species may be capable of producing a variety of other mycotoxins. The profile of production may also vary as a result of interactions with other species or because of growing under different conditions, such as different nutrient types

and water activity [77, 88]. Moreover, they may have diverse patterns of distributing such

metabolites into the mycelium, spores and substrate. For example, under optimum

temperature and water activity conditions (20oC, 0.98 – 0.95 aw), 60% of the OTA

produced by A. ochraceus was found in the biomass, and about 20% was in the spores and the medium; while under optimum conditions, 60% of OTA produced by A. carbonarius isolates was in the spores, 30% was in the mycelial biomass and 20% in the medium [93].

In the following subsections, two important fungal mycotoxins including

Sterigmatocystin and Trichothecenes will be reviewed, because of their detection in indoor environments by earlier studies (cited below).

2.1.2.1 Sterigmatocystin

Sterigmatocystin is among the most toxic, mutagenic, and carcinogenic natural products, but considerably lower than aflatoxins [97]. Sterigmatocystin has been shown to affect the human body mainly in liver and kidney [98], and based on the International Agency for Research on Cancer (IARC), this group of mycotoxins has been classified as a class

2B carcinogen [99].

Strigmatocystin is either an intermediate product in the aflatoxin biosynthetic pathway or the end product in different ascomycetes. These fungal polyketides are produced by twenty different species of fungi such as A.versicolor, and A. nidulans [97]. In addition,

among Aspergillus species, A. ochraceoroseus is the only known fungus that can produce

both aflatoxin and sterigmatocystin, simultaneously [100]. Aspergillus versicolor is one of the most frequently occurring species identified in damp indoor environments, as a

result of its ability to grow on very nutrient-poor materials and low aw such as dust,

concrete, and plaster [77]. A high amount of sterigmatocystin was found on artificially

inoculated building materials, especially on wallpaper. In addition, more sterigmatocystin

was found in the non-sporulating mycelium in contrast to conidia [71, 101]. Furthermore,

in a study of carpet dust from damp indoor environments, 98% of A. versicolor isolates

were found to be toxigenic in vitro [98].

2.1.2.2 Trichothecenes

Trichothecene mycotoxins are produced by different genera including Fusarium,

Myrothecium, Spicellum, Stachybotrys, Cephalosporium, Trichoderma, and

Trichothecium. Trichothecene-producing fungi are responsible for the spoilage of cereal

crops and various fruits; however among these genera, Stachybotrys has been

demonstrated as a significant indoor environment contaminant which is associated with

damp building-related illnesses [62].

Trechothecenes are biologically active compounds that inhibit eukaryotic protein

synthesis, particularly by obstructing peptide bond formation at the peptidyl transferase

center of the 60S ribosomal subunit at different stages of polypeptide chain formation

including initiation, elongation, and less frequently termination [102-104]. In addition,

they can inhibit DNA, RNA synthesis, cell division, membrane structure and mitochondrial protein synthesis [105].

Ingestion of trichothecene mycotoxins has been associated with animal diseases such as necrotic lesions, lowered platelet and white blood cell counts, nervous system disorders, and death [73, 106]; moreover, trichothecene mycotoxin ingestion can cause various

human illnesses including chronic fatigue, neurological disorders, impaired lung function in infants and sick building syndrome [19, 107-109].

Simple and/or macrocyclic trichothecene mycotoxins produced by Stachybotrys chartarum, Stachybotrys echinata and Stachybotrys chlorohalonata, are commonly found

indoors. For example, Stachybotrys chartarum can produce six toxic macrocyclic and

four non-macrocyclic trichothecenes, known as Roridin E, Satratoxin H, Verrucarin J,

Satratoxin F, Satratoxin G, Verrucarin B, Trichoverrol A and B, and Trichoverrin A and

B. Among these trichothecenes, satratoxin G was commonly found on water damaged

building materials, while trichoverrol A and B or verrucarin B and J were detected most

often in natural samples [71, 110]. In general, the trichothecene-producing species can

easily grow on water-damaged building materials (e.g., gypsum board, wallpaper and

insulation) and cellulose-based building materials such as acoustical ceiling tiles [72, 73].

Residents of trichothecene-contaminated houses may suffer from numerous illnesses including cold and flu, sore throats, diarrhea, headaches, fatigue, dermatitis, intermittent focal alopecia and generalized malaise [107].

Detection of mycotoxins requires sensitive and reliable methods that can (i) fulfill international regulatory limits and (ii) be suitable for monitoring programs. There is no single extraction, clean-up, and detection strategy that can be used for all mycotoxins because of variation in their physicochemical properties such as different polarities, different UV absorption and fluorescent spectra, different ionic nature, and different level of contamination. In addition, the environmental matrix that mycotoxins have been extracted from should also be considered before selection of the appropriate detection

18 method. To date, numerous analytical methods for the major mycotoxins have been developed [111, 112], a summary of which is shown in Table 2.1.

2.1.3 The indoor mycobiome and health problems

Several respiratory and non-respiratory diseases and symptoms have been associated with exposures to indoor fungi, as detailed in the review by Yang et al [113]. In general, numerous studies have demonstrated that people living and working in damp or mouldy buildings have an increased risk of airways infections, respiratory diseases and symptoms

[11, 53-56]. The potential health problems related to mouldy or damp buildings can be categorized into three major groups: i) general symptoms including excessive fatigue, memory and concentration problems, nausea, and a poorly performing immune system; ii) mucosal symptoms such as blocked nose, itching eyes, skin burning sensation, hoarseness, and recurrent upper airway infections mainly sinusitis; iii) and finally lung symptoms such as wheezing, cough, bronchitis, asthma, and pulmonary hemosiderosis in infants [72, 107]. The causal agents related to dampness were presumed to be dust mite or fungal allergens [113]. In addition, the rate of opportunistic infections caused by a variety of fungal species which had been formerly considered benign, has increased in recent years because of the growing proportion of immunocompromised and chronically ill people [114].

Asthma is a serious chronic inflammatory problem that can be generated by a variety of allergens, such as pollen, animal dander, dust mites, air pollutants and moulds [4, 49, 50,

113, 114]. Muddari and Fisk in 2007 [115] estimated that 21% of current US asthma cases (i.e. 4.6 million cases) are attributed to dampness and mold exposure, and positive

associations between home dampness and asthma and related symptoms have been detected by several studies [116-123]. The latest review by Kanchongkittiphon et al. [32] concluded that there is “sufficient evidence of a causal association between dampness or dampness-related agents and exacerbation of asthma in children, and of an association in adults”, not restricted to individuals with specific sensitization to fungi or dust mites [32].

Contrary to this, another study in Sweden has shown no associations between culturable fungi, (1-3, 1-6)-β-d-glucan, and ergosterol concentrations in collected dust samples with asthma, rhinitis, or eczema diagnoses in children [124]; nevertheless the validly of this study was questioned by Rylander [125].

As described above, epidemiological studies have found consistent associations between built environment mycobiota and respiratory or allergic health effects in infants, children, and adults [6, 114]; however, the causality, causative agents and illness mechanisms are poorly understood. For example, associations between indoor dampness or mould and respiratory or allergic health effects [6, 114] have been illustrated only when visible dampness or water damage, visible mold, or mold odor were qualitatively assessed.

However, when similar quantitative studies were used for measurements of fungi or other microbiologic exposures such as counts of culturable airborne fungi, less consistent associations with health effects have been reported [11].

Table 2.1 A comparison of common analytical methods for mycotoxin detection.

This could be because of deficiencies in the traditional culture-based techniques [82, 115-

117] that were used to investigate the occurrence and prevalence of microbes in indoor environments. These techniques have numerous limitations which have resulted in an imprecise and inaccurate understanding of indoor microbial composition both quantitatively and qualitatively [118]. The key problems relate to selectivity and low resolution of such methods. For example, traditional plate cultivation may only detect certain viable organisms that grow and produce characteristic morphological structures in laboratory conditions, but not those that are unculturable, dormant or dead; yet the latter can represent major exposure hazards [83]. In contrast, direct microscopy and microbial biomass measurements via biochemical proxies (e.g., ergosterol, β-D-glucans, etc.) may also reveal unculturable organisms, but not those that are dormant or dead. The limited capacity to distinguish between fungal taxa is another limitation of such methods [119].

The potential health-effects of microbes can be species- or strain-specific and independent of cell viability [120].

Health problems associated with mycotoxins depend on the type of mycotoxin, the duration and dose of exposure, age, health and nutritional status of the individual affected

[121]. In general, these toxins are low molecular weight natural products which have acute, chronic, mutagenic or teratogenic toxicity. Acute toxicity of mycotoxins could cause the degeneration of liver or kidney function or interfere with protein synthesis. In addition some are neurotoxins, which may cause sustained trembling in animals (at low concentrations) and brain damage or death at higher doses. Chronic toxicity is the result of low dose mycotoxin ingestion over a long time period which can result in cancer or tumour induction. Finally, some toxins interfere with DNA replication, and thus can

22 produce mutagenic or teratogenic effects [4, 91]. Although exposure to mycotoxigenic molds has been recognized as a significant health risk since 20 years ago [122], the health effects from mycotoxins carried in airborne fungal structures remain under debate.

2.2 Bacteria and the Indoor Environment

Bacteria have exclusively occupied several natural and man-made ecosystems such as air, soil, buildings, cars, or the human intestinal tract, skin or pulmonary system. Bacterial succession and ubiquity in different environments have been dependent on three major properties including their small size for easy dispersal by air and water, their metabolic versatility and flexibility, and their ability to tolerate extreme conditions [33].

For many years, air had been believed to convey diseases to humans, animal and crops, and this belief was evidenced when air particles including bacteria for the first time were observed 334 years ago by the Dutch merchant Antonie van Leeuwenhoek (1632 – 1723)

[33]. Almost 200 years later, Robert Koch (1843 – 1910) for the first time evidenced the pathogenicity of bacteria causing anthrax, tuberculosis, typhoid fever, and diphtheria, which was a starting point for several subsequent investigations over a 25-year span, which led to the discovery of bacterial agents of the main diseases in human and animals

[33, 40].

Before invention of PCR based methods, surveys of airborne bacteria were much less common compared to fungi, because of the challenges in identification of bacteria using phenotypic characteristics and the existence of many unculturable bacteria [123]. One of the first studies on detection of bacteria in indoor environments using culture based methods dates back to research conducted 120 years ago by Carnelley et al. [124]. They

23 showed that the concentration of viable airborne bacteria were higher in naturally ventilated than in mechanically ventilated schools. It has also been demonstrated that the concentration of culturable airborne bacteria in indoor environments has a positive correlation with occupation density as well as the number of visitors; the ratio of bacteria to fungi was elevated in overcrowded houses, and human-shed Staphylococcus spp. counts were positively correlated with the number of visitors and visiting hours [124,

125]. Detection of airborne bacteria in indoor environments using culture-based methods demonstrated that indoor bacteria are predominantly Gram-positive [17, 125]. An increase in culturable Gram negative bacteria is an indicator of excess moisture which permits bacterial proliferation within indoor spaces [17].

Via development of molecular techniques which also detect non-culturable microorganisms in the environment, the study of indoor bacteria has advanced significantly [8, 15, 26-28, 83, 126, 127]. Many investigations have measured specific groups of bacteria such as Gram-positive or negative bacteria in indoor environments, illustrating that Gram-negative bacteria are as abundant as Gram positive bacteria in indoor environments with no dampness problems [15, 27, 128]. More recently, investigations have been conducted on the built environment microbiome and bacterial diversity in indoor spaces [15, 27, 128, 129]. These attempts using advanced sequencing techniques confirmed that occupants are one of the main sources of indoor bacteria by shedding millions of cells hourly into their local environment [83, 126, 129-131]. Hence, each household has a simplified and less diverse bacterial community comprising the bacterial signature of its own occupants [131]. However, there has been a debate on the relative importance of different sources for indoor bacteria, and some have suggested that

24 occupant density is the main driver in larger, multi-use buildings; and the outdoor environment in smaller buildings [27, 28]. In addition, it has been demonstrated that outdoor air and occupant body sites other than skin are the main sources of airborne bacterial communities in indoor environments, while skin is the main source of bacteria inhabiting indoor surfaces [132].

2.2.1 The indoor bacterial biota and health problems

Exposure to indoor bacteria has been shown to have bilateral health effects. Firstly, association of exposure to airborne bacteria with a number of symptoms and diseases has been noted in the literature [133, 134]. Buildings may act as a reservoir for infectious agents from where bacterial infections spread and transmit from occupant to occupant by airborne droplets [135]. Bacteria inhabiting buildings have been shown to be highly potent inducers of inflammatory and cytotoxic effects [136]. In the recent report by the

Institute of Medicine [2], Gram-negative bacteria have been suggested to be “special issues in climate-associated infectious-disease epidemiology.” This type of bacterium is well known to possess bioactive surface structures such as endotoxin as a major component of the outer membrane [136, 137]. Exposure to endotoxins in indoor spaces has been associated with several health problems such as wheezing in infants [138, 139], asthma in adults sensitive to dust mites [140, 141], humidifier fever [136], hypersensitivity pneumonitis [142], and lower and upper respiratory symptoms, fever and chills, and headaches [143]. One of the most well-known infections associated with the indoor environment is Legionnaire's disease, caused by inhalation of contaminated aerosols with the bacterium Legionella pneumophila [133, 134]. This bacterium is known as the second pathogenic agent after Streptococcus pneumonia [144, 145], and can thrive

25 in ambient to hot water systems and proliferate in biofilms formed in plumbing systems, warm water supplies, humidifiers or cooling towers [134]. The typical symptoms of

Legionnaire's disease as a life threatening pneumonia include headache, dry cough, chest pain, diarrhea, and altered mental status [133, 135], and despite the extensive research on the ecology and pathology of the causative bacterium, Legionnaire's disease is still a major cause of hospitalization and death each year [133, 145]. Other bacteria worth mentioning that cause infections associated with the indoor environment include anthrax caused by the Gram-positive bacterium Bacillus anthracis, tuberculosis caused by

Mycobacterium tuberculosis and nontuberculous mycobacterial infections [2].

Secondly, besides the negative impact of exposure to indoor bacteria, it has been demonstrated that exposure to a greater concentration of endotoxin and other bacterial wall components may protect children from atopic diseases [146]. In addition, studies have indicated that early life exposure of children to reduced indoor dust bacterial diversity is associated with a higher risk of developing asthma [8]. As our understanding of the human microbiome and its interaction with the built environment has expanded, the notion that microorganisms are all “bad” and need to be removed from the indoor environment has changed. There is now an appreciation that microbial biodiversity in indoor environments is essential for immune development and function [147, 148]. This suggests that increasing the diversity of indoor bacteria may be beneficial for long-term health, but to achieve higher diversity we need to manipulate the indoor environment

[148].

2.3 Animals and the Indoor Environment

Different types of animals are abundantly present in the indoor environment which are in constant interaction with each other as well as their environment. The prevalence of allergic diseases such as asthma, rhinitis, insect allergy, or eczema has increased dramatically over the last two decades along with industrialization and modernized lifestyles [149]. People spend most of their daily lives indoors where exposure to allergens mostly occurs and a body of recent literature supports a causal role of indoor allergens in the sensitization and development of atopic diseases [150, 151].

It has been suggested that the primary indoor allergens related to indoor fauna are those derived from arthropods, namely by two classes: Arachnida such as dust mites and

Insecta such as cockroaches, as well as cats and dogs [32, 152].

2.3.1 Dust Mites

Interest in house dust research dates back to the late 1600s [153], but it was not until

1921 that scientists began to investigate relationships between house dust and health problems such as allergic rhinitis and asthma. Namely, Kern in 1921 [154] for the first time illustrated the adverse cutaneous reactions of house dust in sensitive patients, and later Cook [155] and Coa [156] in 1922 proved that dust causes positive skin reactions in humans. For many years it was not known which of the many components of house dust is responsible for dust related health problems. This changed in 1964 when the contribution of mites in house dust allergies was first demonstrated by Voorhorst et al.

[157] and Oshima [158].

Among the 48 000 different described mite species [159], two are common inhabitants of houses worldwide: Dermatophagoides pteronyssinus and Dermatophagoides farina [152,

160, 161]. These species feed on protein-rich human skin scales and hence thrive in breeding sites where shed skin scales accumulate, such as carpets, sofas, easy chairs, and mattresses [152, 161, 162]. It has been demonstrated that type of breeding site has a significant impact on the abundance of mites as well as their allergens [161, 163]. For example, houses in the US have higher level of dust mites in carpets compared to mattresses and sofas [162]. In addition to the breeding site, seasonal fluctuations in the prevalence of live mites have been shown [164-166]; namely, highest prevalence of dust mites occurs during the humid summer months while the lowest levels occur during colder seasons when homes are drier [162, 164-166].

Dust mite allergens have been divided into seven distinct groups based on their immunological and physiochemical properties [42]. Major allergens of D. pteronyssinus are known as Der p 1 and Der p 2. Der f 1 is the major allergen of D. farina [32, 42].

These allergens are usually concentrated in mites’ fecal particles (mostly ≥ 10 μm in size) within indoor spaces, exposure to which has been shown to have a causal relationship with exacerbations of asthma, more specifically in children sensitized to dust mites [32].

2.3.2 Cockroaches

The existence of cockroaches on earth extends back to the Paleozoic era, approximately

400 million years ago, with little change in their appearance since then [167]. These insects are native to tropical Asia and can tolerate extreme environmental conditions

[168]. To date more than 3 500 species have been identified world-wide [169].

Cockroaches are considered one of the most important insect pests in urban communities.

They do best in warm and humid conditions offered by indoor spaces and may enter human dwellings through cracks, vents, pipes or be brought inside by furniture, food items, as well as through occupants themselves. Once inside a house, they travel quite easily and eat a wide variety of substances in indoor spaces, such as paper, wool clothing, sugar, cheese, bread, oil, lemons, ink, the dead bodies of other cockroaches, or their own cast skins [168].

Among different species of cockroaches, three, namely the German cockroach (Blattella germanica), American cockroach (Periplaneta americana), and Oriental cockroach

(Blatta orientalis) are the most common ones detected indoors [169]. Several studies have investigated relationships between indoor cockroaches and health problems in urban communities [170-173], and in 2000, the Institute of Medicine [174] reported that exposure to indoor cockroach allergens, which are mostly enzymes found in fecal particles as part of household dust, has a critical role in the occurrence of asthma among children in metropolitan areas [174]. More recently, Kanchongkittiphon et al. have provided an update to the 2000 Review by the Institute of Medicine, and they have concluded that there is a causal relationship between exposure to cockroach allergens and exacerbation of asthma in sensitized individuals to cockroaches; though this association has been shown mostly for sensitized adults and further evidence is needed to conclude the same for children [32].

2.4 Climate Change and the Indoor Environment

Several climate-control technologies need to operate properly in indoor environments to provide and maintain favorable amenities and conveniences for its inhabitants against the vagaries of weather and climate. However, despite all the attempts of architects, engineers and scientists, many buildings are not adequate shelters for their occupants, in that they are fragile artifacts under constant forces of outdoor conditions and indoor activities.

There is now strong evidence that climate change is an inevitable ongoing phenomenon.

It is expected that climate change will have consequences on the indoor environment

(indoor air quality, thermal comfort, acoustical quality, and visual or lighting quality).

Nevertheless, this issue had been given relatively little attention, until recently; the

Institute of Medicine (IOM) [2] for the first time provided a compelling report tackling the intersection of climate change, indoor environmental quality, and occupant health.

They have noted that climate change may influence the indoor environment through three main pathways [2, 175]:

(i) Alteration in outdoor environment: “Changes in the outdoor concentrations of

a pollutant due to alterations in atmospheric chemistry or other factors such

as atmospheric circulation will affect indoor concentrations.”

(ii) Climate change mitigation measures: “Mitigation measures to reduce energy

use in buildings could lead to systematically lower ventilation rates that would

cause higher concentrations and exposures to second hand smoke and other

indoor pollutants.”

(iii) Climate change adaptation measures: “Increased use of air conditioning, an

expected adaptation measure, could exacerbate emissions of greenhouse

gases and, if accompanied by reduced ventilation rates, increase the

concentrations of pollutants emitted from indoor sources.”

Global climate alteration seems to be unavoidable, because of the continued emissions of greenhouse gases into the atmosphere and the build-up of past emissions. Although the magnitude and time scale of this change may be indeterminate and still a subject of argument, the direction of the change is definite [176]. For example, as the climate gets warmer, longer and more severe heat waves are expected [177]. As a result, the periods of high temperature within indoor spaces will be elevated and consequently heat stress, hospitalization and mortality would increase [178], affecting mostly the elderly, young children, and immunocompromised individuals as well as people living in poor housing with no air conditioning system or those living in urban centers [178, 179]. This may result in higher heat-related mortality worldwide [180, 181]; e.g., 20% in Germany by

2055, several folds in Portugal by 2050 and 2080, three fold in Australian capital cities by

2100 and 10 fold increase (average of 2,379 deaths/year) by 2057 – 2059 in the Eastern

U.S. [178, 182].

The Intergovernmental Panel on Climate Change (IPCC) has also predicted a higher frequency and intensity of heavy precipitation, cyclones, and hurricanes as well as an increase in the global sea level which is expected to rise by 0.26 – 0.82 m by the end of the 21st century [183]. These would elevate the risk of water damage and flooding of buildings which eventually makes the indoor environment damper and a better

31 environment for microbial growth associated with respiratory problems and asthma [2,

178].

Among different air pollutants, ozone and particulate matter (PM) have been considered as the two main air quality indices of concern [184]. The deleterious effects of climate change on outdoor ozone and particulate matter concentrations have been widely investigated (see reviews by Jacob and Winner, 2009 [185]; Monks et al., 2009 [186];

Weaver et al., 2009 [187]), but only recently, have their attribution with indoor ozone and particulate matter concentrations been explored [2, 178, 188].

Regarding the ozone concentration, the IPCC indicated a positive correlation between temperature rise and the increase of ozone levels near urban areas [189], whereas modeling studies have predicted a 4.8 – 9.6 ppb increase in the outdoor ozone concentration in 50 US cities by 2050 [190], 0.43 ppb annual average increase in the southeast US by 2040 [191], a 2.3% (for 0 ppb threshold) and 27.3% (for a 40 ppb threshold) increase in mortality in Sydney Australia for 2051 – 2060 [192], and 300 additional deaths per year in the US caused by an increasing ozone concentration [193].

Despite some protection from exposure to outdoor ozone by the built environment, some the ozone along with other air pollutants may penetrate and persist into indoor spaces; in fact ozone level within indoor spaces has already been shown to be 30% –70% that of the outdoors [194]. Considering the time people spend indoors, exposure to such air pollutants is a great public health concern. Not only the exposure to ozone is associated with several health problems, but also the ozone reacts with indoor surfaces very quickly and generates some byproducts (e.g., formaldehyde, acrolein, etc.), exposure to which

32 can in turn lead to asthma, respiratory irritation, chronic obstructive pulmonary disease, hospitalizations, and mortality [195-198].

Particulate matter (PM) has been defined as “a widespread air pollutant, consisting of a mixture of solid and liquid particles suspended in the air”, and its composition is largely sulfate, nitrate, organic carbon, elemental carbon, inorganic ions, metals, soil dust, sea salt and bioaerosols [199]. Airborne particulate matter may be classified into four groups:

(i) inhalable or thoracic particles (PM10), (ii) thoracic coarse particles which are in the diameter range of 2.5 – 10 µm (PM10), (iii) fine particles which are in the diameter range of 0.1 – 2.5 µm (PM2.5), and (iv) ultrafine particles, those smaller than 0.1 µm in diameter

[188, 200]. A body of research studies has provided evidence for health problems caused by exposure to PM in the air, both the short term (hours, days) and long term (months, years). Three percent of cardiopulmonary and 5% of lung cancer deaths have been estimated to be associated with exposure to PM globally [199]. Among different particulate matter size categories, fine particles (PM2.5) play the most critical role in affecting human health as they are sufficiently small to (i) penetrate deeper into the lungs,

(ii) remain suspended for extended periods of time, (iii) enter more readily into indoor environments, (iv) travel over longer distances, and (v) have higher toxicity levels because of the adsorption of sulfates, nitrates, acids, metals, and particles with various chemicals onto their surfaces [200]. It has been demonstrated that long and short-term exposures to PM2.5 may lead to several health problems such as premature deaths (related to heart and lung diseases), hostile cardiovascular effects, increased hospitalizations and emergency department visits for heart attacks and strokes, development of chronic respiratory diseases and cancer [199].

The links between PM and climate change are more complex and less understood owing to the diversity of PM components, the intricate coupling of PM to the hydrological cycle, and various other compensating effects [201]. An example of existing un- addressing of the above link is that, as temperate and heat waves increase, wildfires are expected to occur more frequently, which may then lead to temporarily higher levels of airborne particles and gaseous air pollutants (e.g., carbon monoxide, nitrogen dioxide, formaldehyde, etc.) [178, 202]. Outdoor airborne particles and pollutants enter buildings through windows or ventilation systems, and hence when the concentration of the former increases, indoor air concentrations of pollutants also increase, most specifically in homes because they usually have no or low efficiency filtration systems [2, 178]. For example, it has been predicted that during wildfires, elevation in the concentration of particles with the size range of 0.25 – 5 µm in indoor environments will be 49% to 76% of the increases in outdoor air particles [203], exposure to which can lead to respiratory and cardiovascular health effects, hospitalization and mortality [31, 204-206]. The report by the Institute of Medicine [2] and the paper written by Nazaroff [188], as one of the members of the IOM committees, have included a comprehensive review on the consequences of climate change on different components of indoor particulate matter of outdoor origin such as sulfate, tailpipe emissions from vehicles, or windblown dust.

However, among the different components of bioaerosols as part of PM, pollen has been well studied, and the effect of climate change on other bioaerosols (e.g., Fungi, Bacteria, etc.) has been hardly addressed, let alone its associated effects on the indoor bioaerosols.

3 Chapter 3: The Relationship between Indoor Environmental Characteristics and Fungal Community Structure

3.1 Overview

Thousands of fungal species may enter and colonize indoor environments. Since many of

these fungi play important roles, and may directly interact with humans and their

activities, it is important to understand the taxonomic composition of indoor fungal

communities as well as the ecological processes and environmental factors that may

influence the diversity and composition of the indoor mycobiome. In this study,

conducted as a prelude to the national population based birth cohort known as “Canadian

Healthy Infant Longitudinal Development (CHILD)” study, I used high-throughput

sequencing of the fungal internal transcribed spacer 2 (ITS2) region in combination with

668 environmental factors to evaluate the relationships between house characteristics,

occupant activities, and fungal communities in indoor environments of houses located in

the city of Vancouver, BC, Canada. Our results showed that the fungal composition of

indoor environments is not a subsample of the outdoor fungal community. Indoor

communities are unique and many indoor factors, such as the type of room, type of

furniture, type of occupant, and human activities may significantly influence the indoor

mycota. Understanding such relationships may lead to improved indoor management

resulting in a healthier indoor environment and yield novel insights into the origins of

environmentally-mediated diseases.

3.2 Background

Fungi are very common in indoor environments. They can have positive and negative impacts on the quality of the indoor environment, and as such it is important that we understand the factors that may influence their abundance and community composition.

The observed patterns in the literature are conflicting. Children naturally exposed to a high diversity of fungi in indoor dust show reduced development of asthma later in life

[9]. However, exposure to indoor fungi has been associated with adverse effects on human health [6], such as excessive fatigue, memory and concentration problems, exacerbation of pre-existing asthma, allergic sensitization, respiratory tract infections, hypersensitivity pneumonitis (HP), organic dust toxic syndrome (ODTS) as well as cardiovascular disease and mortality [7, 11, 82, 207, 208].

With the use of traditional morphology-based methods as well as with nucleic acid sequencing technologies, researchers have characterized a vast number of indoor fungal species in indoor environments [14, 17, 21, 30, 83, 209]. More recently, the use of molecular high-throughput techniques has profoundly changed our understanding of fungal community composition, greatly expanding our knowledge of the fungal diversity within the spaces we live and work daily [13, 210]. However, the data arising from these methods report some inconsistencies. For example, although many studies observed that the dominant taxa in the indoor environment were mostly saprobic members of the Class

Dothideomycetes (Division Ascomycota) [16, 23, 211], a recent study found that

Agaricomycetes (Division Basidiomycota) were as frequent as Dothideomycetes [29].

Such variability may be the result of real differences (perhaps because of different environmental drivers) or alternatively it may result from bias introduced during sample

36 processing or by the particular analytical approach (e.g., sample preparation, DNA extraction, primers, sequencing platforms, bioinformatics, etc.) [212].

Apart from the composition of indoor fungal assemblages, patterns of indoor fungal community structure and the environmental parameters that shape them have been investigated only recently. As for any other community of organisms, a major determinant of community composition is the dispersal of species from different sources of fungal growth and subsequent environmental selection. Amend et al. [16] in a global study across continents suggested that type of building (e.g., homes versus offices) does not significantly influence indoor fungal community composition. They found that the indoor fungal community was most influenced by equatorial proximity, suggesting a potentially important role for local outdoor environments, variables such as rainfall, temperature, and vegetation [16]; however they did not report building conditions. In further support of these findings, more studies have been performed suggesting that the fungal community in indoor environments is a random subset of the outdoor fungal community and that building characteristics or human activities have little influence on indoor fungal assemblages [29, 30, 213]. In contrast, other studies showed that fungi may grow on indoor substrates [18], and as a result there may be an uncoupling of indoor and outdoor fungal assemblages. This is highly plausible, as most of the natural and manmade building materials that contain natural organic polymers are susceptible to fungal growth

[18, 214]. Inorganic indoor compounds may also become mouldy, especially in the presence of dust, which is a good source of fungal propagules [63, 215]. Moreover, the distribution of moisture within any material in the building is not uniform, and hence different parts of a structural element may support the growth of different fungi. For

37 example, the hydrophilic mould, Stachybotrys chartarum, was detected in extremely wet surfaces of an exterior wall, whereas Aspergillus versicolor and various penicillia

(xerophilic taxa) were more abundant on the drier margins of the affected area [18, 52].

I also suggest that even though the indoor mycobiome depends on dispersal from the outdoor mycobiome, up to now, most of the conducted studies have used ‘basic built environment’ metadata. There are several neglected parameters of residents’ activities and home characteristics which can potentially impact indoor fungal diversity.

Accordingly, considering the above inconsistencies and gaps in the reported literature the first general question of this study, by considering a broad number of environmental factors, is: how are building characteristics and occupants’ activities associated with the diversity and structure of community composition of the indoor mycobiome? And the second question is: which individual components of the indoor fungal community are altered because of the variation of environmental factors?

3.3 Materials and Methods

3.3.1 Study design and sample collection

This study examined biological profiles of dust collected from 54 homes located in the

Lower Mainland (BC, Canada), recruited to assist in optimization and validation of data collection tools for a national population-based birth cohort known as “Canadian Healthy

Infant Longitudinal Development (CHILD)” study (www.canadianchildstudy.ca).

Between May 2008 and May 2009, trained research assistants collected dust from the homes of families with newborn children using a clean, depyrogenated sterile custom- designed aluminum collection device attached to the end of a vacuum cleaner (Model

S3680, Sanitaire Canister Vac, Charlotte, NC, USA). The collection device held two nylon DUSTREAM filters (Indoor Biotechnologies Inc, Charlottesville, VA). Two dust samples were collected in each house when the subject child reached the age of three months (±15 days). The first sample was collected from the floor of the room where the subject child slept and the second sample was collected from the floor of the room occupied most often by the family. A standardized floor area was sampled (2 m2) and if insufficient dust was obtained, the sampling area was expanded. Research technicians visually observed the thimbles after vacuuming 2 m2; if the thimbles were less than half- full the technician continued vacuuming in a new area of that room until that amount was met. The exact vacuum area size was recorded for all samples taken. Samples were fractionated using a depyrogenated 100 Mesh sieve (~150 µm), and the fine fraction transferred to a depyrogenated sterile borosilicate glass vial with a Teflon-lined screw cap

(VWR 1 dram glass vial, West Chester, PA) and stored at -80°C until analysis. In addition, 668 housing characteristics were also monitored and recorded by trained environmental assessors using a standardized data collection tool and an administered occupant questionnaire which has been described in detail in recent publications [216,

217] (subset of collected factors is shown in Table 3.1). The data acquisition tools recorded the location, history, and physical characteristics of the unit, including the structural dimensions, construction details of the building envelope, furniture materials and finishes for interior designs, the occurrence of factors influencing moisture sources and air change, as well as the number, type and activities of occupants.

3.3.2 DNA extraction

Four commercial DNA extraction kits including SoilMaster™ DNA Extraction Kit

(Epicentre Biotechnologies, Madison, WI, USA), PowerSoil® DNA Isolation Kit

(MoBio Laboratories, Inc., Carlsbad, CA, USA); UltraClean™ Soil DNA Isolation Kit

(MoBio Laboratories, Inc., Carlsbad, CA, USA) and FastDNA® SPIN Kit for Soil (MP

Biomedicals, LLC, Solon, OH, USA) were compared for the extraction of DNA from dust samples under several quantitative DNA analysis criteria: yield of extraction, purity of extracted DNA (A260/280 and A260/230 ratios), degradation degree of DNA, absence of inhibitors, and easiness of PCR amplification. Using a systematic technique known as

Order Preference by Similarity to Ideal Solution (TOPSIS) as described before [218], the

FastDNA® SPIN Kit was defined as the best performing method for extracting DNA from dust samples (data not shown). After method selection, total DNA was extracted from 100 mg of fine dust. Extracted DNA samples were checked for integrity by agarose gel electrophoresis against a Lambda DNA HindIII-digested standard (New England

BioLabs, Ipswich, MA, USA) and their quantities were measured using the QuantiFluor® dsDNA System (Promega, Madison, WI, USA). We also determined the purity of extracted DNA from each sample by measuring its ratio of absorbance at 260 nm and 280 nm using the NanoVue Plus™ spectrophotometer (GE Healthcare, Buckinghamshire,

UK), before preserving at -20°C.

Table 3.1 Sub-sample of 668 collected environmental factors.

Building Design Characteristics Type and Density of Occupants Occupants' Activities

Age of ceiling Number of adults in the house Frequency of bathroom fan usage Age of floor Number of children in the house Frequency of house cleaning Age of house Number of plants Frequency of keeping the child Basement condition Number of visitors/day bedroom's window open Basement dampness Presence of long hair cat Frequency of keeping the living Basement foundation Presence of long hair dog room's window open Child room area (sq. m) Presence of short hair cat Hanging clothes inside the house Child room carpet area (sq. m) Presence of short hair dog Length of occupancy Child room wall cover Presence of plants Presence of stuff toys Child room window cover Presence of moth in house Type of vacuum Cleanliness of basement Presence of mouse in house Usage level of gas fireplace Condensation on bedroom Presence of pets Usage level of radiators windows in cooler weather Use of chemical spray and cloth Evidence of leak in the house Use of garden sprays/weed killers Furniture and Equipment Finished basement or added Use of mop Number of pieces of leather insulation Use of unscented or scented Number of pieces of metal Floor level candles Number of pieces of solid wood Furnace age Use of antibacterial hand cleaner Number of plastic/vinyl furniture Furnace condition Use of broom Number of press wood furniture Living room area (sq. m) Use of chemical sprays for cleaning Presence of air conditioning system Living room carpet area (sq. m) Use of disinfectant in baby’s room Presence of electronic devices Number of rooms in the house Use of disinfectants Presence of humidifier Presence of garage Use of feather duster Presence of stove fan in kitchen Type of garage Use of floor cleaners

Presence of swimming pool Use of glass cleaners Outdoor Related Presence of upgraded plumbing Use of liquid or solid air fresheners Geographic distance system Use of multi-surface cleaners Is the house within 100 meters of: Total volume of the house Use of oven cleaners Body of water Type of flooring Use of Plug-in deodorizers Type of foundation Factory Use of plumbing cleaners Farm Type of fuel in the house Use of scented laundry detergents Gas station Type of furnace's filter Use of spray air fresheners Major Highway/Artery Type of garage Use of toilet bowl cleaners Other source of pollution Type of house Use of vacuum Type of insulation Neighbor currently doing Use of wet cloth (water only) for renovations Type of lawn cleaning Type of wall covering Use of Swiffer wet jet

3.3.3 PCR amplification and sequencing

PCR amplifications were carried out in a C1000 Touch™ Thermal Cycler (BioRad,

Ontario, Canada) using 10 ng of DNA extracted from individual dust samples as template. The ITS2 region from all major phyla of fungi were PCR-amplified by using the primers fITS7 (5’ GTGARTCATCGAATCTTTG 3’) and ITS4 (5’

TCCTCCGCTTATTGATATGC 3’) [219]. The forward and reverse primers were modified to include a 10 bp tag specific to each sample to serve as a molecular identifier

(MID), a 4 bp TCAG key, and a 21 bp adapter for the GS FLX system. This set of primers produces shorter fragments and provides a less biased fungal community composition information by conserving the quantitative relationships between template genotypes in the amplicon pool, even for rare members [212, 219]. In addition, in comparison to ITS1, ITS2 normally has (i) less variation in length, (ii) lacks the problem of co-amplification of a 5′ SSU intron that is present in some taxa (iii) greater conservation of secondary structure, and (iv) better representation of taxonomic reference sequences in available classification databases [212, 220-222].

Each PCR mixture (25 µL) contained 2.5 µL of 10x PCR buffer supplied with the enzyme (600 mM Tris-SO4 (pH 8.9), 180 mM (NH4)2SO4), 200 µM dNTPs, 1.5 mM

MgSO4, 0.2 µM of each primer, 1 U Platinum® Taq DNA Polymerase High Fidelity

(Invitrogen, CA, USA), nuclease-free water (IDT, Coralville, IA, USA), and 2 µL extracted DNA (5 ng / µL). The PCR conditions for ITS2 region started with an initial

DNA denaturation (94°C for 5 min), followed by 25 cycles of 1 min at 94°C

(denaturing), 1 min at 63°C (annealing), and 1 sec at 72°C (extension), followed by a final extension of 7 min at 72°C. For each individual sample, PCR amplifications were repeated 5 times and included three negative controls consisting of all reagents in the reaction mixture without template DNA to assess external or cross-contamination.

Amplicon replicates for each sample were pooled and sent to the Prostate Centre

Microarray Facility (Vancouver, Canada) for library normalization, pooling, and clean-

42 up. Finally, library sequencing was completed on half of a 454 FLX Titanium pico-titer plate by the Prostate Centre Microarray Facility, Vancouver, Canada.

3.3.4 Analysis of pyrosequencing data

Raw pyrosequencing reads were initially passed through quality filters to reduce the overall error rate using the (QIIME) pipeline, version 1.8.0. Only those sequences with good proximal primer fidelity and a threshold quality score of 25 or more, a read length between 200 and 300 nucleotides and zero primer MID mismatches were retained for downstream analyses. From the total of 616 834 sequences, 339 534 sequences of fungal internal transcribed spacer 2 (ITS2) remained for downstream analyses across all sampling units with the mean of 4 105 sequence reads per sample. Chimeric sequences were removed using the UCHIME algorithm found in the USEARCH program [223], and then the remaining sequences were clustered into operational taxonomic units (OTUs) at a 97% sequence similarity cut-off via UCLUST. Representative sequences for each OTU were identified taxonomically by conducting the Basic Local Alignment Search Tool

(BLAST) against the UNITE database (version 6, released date 2014, 02, 09) [224].

Then, all singleton OTUs were excluded, and the subsequent OTU table was rarefied to

1000 sequences per sample, to ensure adequate sampling depth for the subsequent statistical comparisons between samples, which reduced the sample size to 77 (41 the most used rooms and 36 bedrooms).

3.3.5 Statistical Analyses

After sequence processing, all data were imported into PRIMER 7 [225] and

STATICTICA 12 [226] for multivariate and univariate statistical analyses, respectively, and data visualizations.

3.3.5.1 Taxonomic composition of indoor fungal communities

For the community composition, numbers of observed OTUs of each fungal class were pooled and the relative distribution of their abundances was tabulated.

3.3.5.2 Diversity and community composition structure

The theoretical OTU richness by the Chao richness estimator was plotted, based on 999 permutations, and Good’s coverage [227] was calculated to demonstrate whether or not samples were sequenced to saturation. Subsequently, the Wilcoxon Matched Pairs test was used to investigate the statistical difference between observed fungal richness (alpha diversity) in the most used rooms and bedrooms.

For the community composition analysis (beta diversity), preprocessing of data was required as follows. First the OTU table was standardized to the relative composition and subjected to a fourth root transformation to reduce the dominant contributions of abundant OTUs to Bray-Curtis dissimilarities, and also to avoid over-weighting infrequent and rare OTUs. Second, the environmental metadata were combinations of both numerical and categorical variables. To this end, numerical variables were individually log-transformed to justify the use of Euclidean distance as a measure of dissimilarity and to limit the distorting effects of outliers. Then both numerical and

44 categorical data were normalized, to ensure comparability of the scales, and inter- correlated variables were removed using the Pearson Correlation test.

After preprocessing, 5 main steps of multi- and univariate analyses were undertaken, as follows: In Step 1, a modified OTU table and the environmental data were used to investigate the correlation between bedroom and the most used room mycobiomes by using RELATE method (a comparative Mantel-type test of similarities to model structures). Then BEST (Bio-Env) routine, namely BVSTEP, was used to determine which environmental factors and residents’ activities best ‘explains’ the overall pattern of fungal community composition in both locations (Step 2). Subsequently, the significance of the BEST analysis result was validated through a permutational null distribution to ensure that the selected combinations of environmental variables were not obtained by chance. Subsequently, in Step 3, analysis of similarity (ANOSIM), for categorical variables, and RELATE correlations, for continuous variables, were used to identify the individual statistical significance levels (p-values) of screened factors by BEST.

3.3.5.3 Trends of individual components of indoor fungal community

Next, in Step 4, more detailed investigations were conducted to determine which fungal classes in the biota matrix are statistically affected by the significant environmental variables from Step 3 (using ANOSIM and RELATE). Finally, in Step 5, univariate data analyses, namely Mann-Whitney (for two level categorical factors), Kruskal-Wallis (for multi-level categorical variables), and Spearman Correlation tests (for continuous variables) were employed to understand which individual OTUs in each of the affected classes (Step 4) were significantly associated with the significant environmental variable

45 from Step 3. The above implementation steps have also been summarized schematically in Figure 3.1.

Figure 3.1 Statistical analysis platform used in this study.

3.4 Results and Discussion

3.4.1 Taxonomic composition of indoor fungal communities

Our data show an extremely diverse fungal community composition in floor dust samples dominated by moulds and yeasts (Figure 3.2). Members of the Ascomycota represent the majority of the total diversity in samples (58.7% of all fungal OTUs) followed by

Basidiomycota, encompassing 9.2% of the OTUs detected. Dominant fungal classes were

Dothideomycetes, Eurotiomycetes, Sordariomycetes, Leotiomycetes and

Tremellomycetes (Figure 3.2). The relative abundances of fungal classes in both the most used rooms and bedrooms were similar, except for members of the

Saccharomycetes and Agaricomycetes which were 1.5 times more abundant in bedrooms.

In addition, 31.2% of OTUs remained unidentified and could not be associated with a match in the UNITE database (Figure 3.2).

It is known that members of the Dothideomycetes and the Eurotiomycetes occur on the surface of leaves and/or as saprobes on all types of decaying materials. Hence, the fact that in our ITS2 sequencing results, these two classes were found as dominant fungal taxa in indoor environments is not surprising. These results are also consistent with other studies using culture-based methods [228] and molecular methods targeting the whole

ITS region [16, 83]. Other earlier studies targeting the ITS1 region showed that the number of detected OTUs of Agaricomycetes (Division Basidiomycota) is comparable to

Dothideomycetes [29]. In general, as addressed in section 2.3., it is believed that the ITS2 region would yield more accurate community composition data than ITS1 if only a single primer set is employed. Nonetheless, there is no doubt that employing multiple primer sets would enrich the representation of the taxonomic composition of fungal communities even further.

Figure 3.2 List of fungal classes detected in dust samples along with their OTU abundances.

3.4.2 Diversity and community composition structure

Chao richness estimators predicted near-asymptotic OTU accumulation curves at both sampling locations (Figure 3.3A). The Good’s coverage indicates that our sampling detected an average of 96.6% 4.6 (mean SD) of the total OTUs across all samples. In addition, both bedroom and the most used room fungal assemblages consisted predominantly of rare species, with only a few moderately abundant and a few highly abundant taxa (Appendix A). We obtained 2 108 fungal OTUs, varying from 135 to 241 in the most used rooms, and 120 to 285 in bedrooms. We did not detect a statistically significant difference (Wilcoxon Matched Pairs) between the numbers of observed OTUs

(alpha diversity) in bedrooms and the most used rooms (Figure 3.3B).

The four most abundant fungal OTUs in both room types received identical rankings and similar relative abundances, with the Cladosporium sp. F20 as the most common OTU across all samples, representing 13.9% of all sequences in bedrooms and 14.6% in the most used rooms (Table 3.2). However, as we move further to rare OTUs, the relative abundance values and hence the rankings of fungal OTUs differed between the two room types. For example, Candida parapsilosis was the 12th most abundant OTU in the most used room (rank 12) but ranked 39 in the bedroom. In some cases, OTUs were present in one room type but absent in the other (e.g., Stachybotrys sp. was found in 10% of the most used rooms but was not detected in bedrooms).

Figure 3.3 Alpha diversity of fungal OTUs in dust samples of bedrooms versus the most used rooms; (A) rarefaction curves of theoretical total OTU richness by the Chao richness estimators; (B) observed richness of fungal OTUs across sample locations.

When I compared fungal community structure (beta diversity) in bedrooms and the most used rooms (Step 1), assemblage patterns in these locations were dissimilar (RELATE,

Rho: –0.011, p = 0.53). This may indicate that there are different sources of fungi in these particular rooms (such as from outdoor air, occupants of the buildings including pets and plants, as well as microbial growth on moist construction materials), or alternatively there are spatial selective pressures filtering fungal taxa across sample locations. To give us some indication of what the driver of this difference is, I ran a BEST procedure (Step 2) to determine what subset of environmental factors can collectively explain most of the variance in the assemblage structures within each room type. I found that 17 of 668 collected environmental factors collectively explain 54% of variation of bedroom fungal community composition (Table 3.3), and 18 factors were identified to be responsible for almost 59 % of the variation in the most used room fungal assemblages (Table 3.4).

These factors included physical parameters (e.g., the number of adults in the house affecting both bedroom and the most used room biota or the number of rooms in the house affecting most used room fungal biota) as well as the behavioural characteristics of occupants such as burning unscented candles in most used rooms and scented candles in bedrooms. Among all these effective screened factors by the BEST routine, by assigning the significance level of 5%, the most significant individual factors (Step 3, ANOSIM for categorical variables and RELATE correlations for continuous variables) for bedrooms

(Table 3.3) are: the use of liquid/solid air fresheners (R = 0.303), the presence of moths

(R = 0.236), the use of a vacuum brush to clean dust (R = 0.228), the number of adults

(Rho = 0.225), the use of scented candles (R = 0.218), and the condensation on bedroom windows in cooler weather (R = 0.106).

Table 3.2 The 25-most abundant fungal OTUs in dust samples of bedrooms versus the most used rooms.

Bedroom The Most Used Room BLAST-assigned Relative BLAST-assigned Relative OTU rank OTU ID OTU ID taxonomy abundance (%) taxonomy abundance (%) 1 denovo1075 Cladosporium sp. F20 13.9 denovo1075 Cladosporium sp. F20 14.7 2 denovo1570 unidentified 10.0 denovo1570 unidentified 9.7 3 denovo3655 unidentified 6.6 denovo3655 unidentified 7.2 4 denovo1888 Leptosphaerulina chartarum 5.9 denovo1888 Leptosphaerulina chartarum 5.0 5 denovo7734 Pichia jadinii 3.6 denovo6272 unidentified 3.6 6 denovo6272 unidentified 3.1 denovo1415 Cladosporium sp. 2.7 7 denovo1415 Cladosporium sp. 3.0 denovo2992 unidentified 2.4 8 denovo2992 unidentified 2.6 denovo7734 Pichia jadinii 1.8 9 denovo5350 unidentified 1.0 denovo7234 unidentified 1.5 10 denovo6567 Epicoccum sp. TMS_2011 1.0 denovo1039 unidentified 1.2 11 denovo464 Botryotinia sp. 0.9 denovo464 Botryotinia sp. 1.1 12 denovo5307 unidentified 0.9 denovo6053 Candida parapsilosis 1.0 13 denovo5335 uncultured Sordariales 0.9 denovo3396 Capronia sp. 94006a 0.9 14 denovo1039 unidentified 0.8 denovo5350 unidentified 0.9 15 denovo7031 Exophiala eucalyptorum 0.8 denovo624 Alternaria sp. XZSBG_1 0.9 16 denovo7785 unidentified 0.7 denovo5335 Sordariales sp. 0.8 17 denovo3788 Exophiala sp. CPC_12172 0.7 denovo5194 unidentified 0.8 18 denovo5194 unidentified 0.7 denovo6873 Ulocladium sp. 0.8 19 denovo5381 unidentified 0.7 denovo7031 Exophiala eucalyptorum 0.8 20 denovo7434 fungal_endophyte 0.7 denovo7210 uncultured Ascomycota 0.8 21 denovo6873 Ulocladium sp. 0.7 denovo1166 Chaetothyriales sp. F1 0.7 22 denovo624 Alternaria sp. XZSBG_1 0.7 denovo5307 unidentified 0.7 23 denovo3673 unidentified 0.6 denovo6567 Epicoccum sp. TMS_2011 0.7 24 denovo3396 Capronia sp.94006a 0.6 denovo6948 unidentified 0.7 25 denovo3788 Exophiala sp. CPC_12172 0.7 denovo3788 Exophiala sp. CPC_12172 0.6

Table 3.3 Environmental and behavioural factors and their significance levels that best explain the indoor dust-borne fungal communities in bedrooms.

Bedroom Screened Factors by Best a Variation of Community Composition b (Multi-Factor Analysis) (Uni-Factor Analysis) Numeric p-value Rho Number of adults in the house 0.043 0.225 Number of pieces of plastic or vinyl -- -- Child room carpet area (sq. m) -- -- Categorical p-value R Basement foundation -- -- Upgraded windows -- -- Child room wall cover -- -- Presence of long hair dog -- -- How often windows of child sleeping bedroom are open? -- -- Use of Plug-in deodorizers -- -- Use of vacuum brush to clean dust 0.047 0.228 Use of liquid/solid air fresheners 0.032 0.303 Use of scented candles 0.046 0.218 Use of glass cleaners -- -- Type of Lawn -- -- Presence of moth in house 0.043 0.236 Condensation on bedroom windows in cooler weather -- -- Hanging Cloth inside the house -- -- a Multi-factor analyses: All factors are collectively responsible for 54.0 % of total variation of fungal community-composition. b Single-factor analyses: Only those with Rho or R > 0.1 and p < 0.05 are noted.

Table 3.4 Environmental and behavioural factors and their significance level that best explains the indoor dust-borne fungal communities in the most used rooms.

The Most used Room Screened Factors by Best a Variation of Community Composition b (Multi-Factor Analysis) (Uni-Factor Analysis) Numeric p-value Rho Geographic distance -- -- Number of rooms in the house 0.008 0.181 Number of adults in the house -- -- Number of adult visitors/day -- -- Categorical p-value R Type of Insulation in basement -- -- Furnace Age -- -- Furnace Condition -- -- Type of fuel in the house -- -- Usage level of radiators -- -- Usage level of gas fireplace -- -- Usage of air conditioner 0.046 0.226 Presence of plants -- -- Presence of plastic/vinyl furniture 0.045 0.131 Type of vacuum 0.013 0.180 Use of mop -- -- Use of unscented candles 0.050 0.154 Use of chemical spray and cloth -- -- Use of garden sprays/weed killers 0.001 0.656 a Multi-factor analyses: All factors are collectively responsible for 58.8 % of total variation of fungal community-composition. b Single-factor analyses: Only those with Rho or R > 0.1 and p < 0.05 are noted..

For the most used rooms, the list includes: the use of garden sprays/weed killers (R =

0.556), usage of air conditioner (R = 0.226), number of rooms in the house (Rho =

0.181), type of vacuum (standard vacuum versus vacuum with HEPA filter, R = 0.180), the use of unscented candles (R = 0.154), and the presence of plastic or vinyl furniture (R

= 0.131) (Table 3.4).

3.4.3 Trends of individual components of indoor fungal community

3.4.3.1 Variables linked to the outdoors

Our results, consistent with earlier studies [16, 21, 29, 30], support the idea that outdoor air is an important source for indoor fungi. For example, usage of herbicide to inhibit growth of unwanted plants in the yard had the highest correlation with indoor fungal community, more specifically increasing the frequency and abundance of saprobic and opportunistic fungal species in the most used rooms (Figure 3.4 and 3.5). Use of garden sprays/weed killers had the highest correlation with fungal OTUs belonging to the

Eurotiomycetes (ANOSIM, R = 0.615), and to a lesser degree was related to the

Leotiomycetes and Lecanoromycetes classes (Figure 3.4). Subsequently, univariate analyses (Mann-Whitney test, p = 0.0001 – 0.037) indicated that certain species such as

Lophodermium gamundiae (denovo8160) or Chaetothyriales sp. F1 (denovo1166) are less frequent when weed killers were used; whereas some others such as Exophiala dermatitidis (denovo4194) and Rhizoplaca aspidophora (denovo6598) were more prevalent where weed killers were used (Figure 3.5).

Figure 3.4 Correlation and significance of important environmental factors for the most used room (from Table 3.4) on the beta diversity of detected fungal classes. Only those with Rho or R > 0.1 and p < 0.05 are noted.

Figure 3.5 Shade plot showing the affected OTUs by the use of garden spray or weed killers. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Figure 3.6 Shade plot showing the affected OTUs by the use of air conditioner. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

This result is consistent with the use of herbicides that may interfere with the growth and reproduction of certain naturally occurring soil fungi, ultimately altering the complement of species that are dispersed into indoor environments. Interestingly, I find that the abundance of Exophiala dermatitidis (a human pathogen that causes phaeohyphomycosis), is positively correlated with the use of herbicides [229].

3.4.3.2 Fungal growth in moist areas

Air conditioning systems may also be a source of indoor fungi. In this study I found that homes with air condition systems harboured a high abundance of many fungal and yeast species in the most used rooms (Figure 3.4 and 3.6). This problem may arise when filtered fungal propagules accumulate in the air conditioner system along with dust and other contaminants. As the system cycles on and off, the air conditioner gets moist, cold, and warm which creates a dark incubator with optimal conditions for fungi to grow, sporulate, and disperse indoors.

3.4.3.3 Type and density of indoor occupants

Occupants of the house may be a source or dispersal vector for fungi by carrying spores and hyphal fragments on contaminated clothes, skin, and hair [21]. I found that the number of adults in the house was associated with the fungal community composition, especially in the bedroom by affecting the abundance of Dothideomycetes,

Eurotiomycetes, and Sordariomycetes (Figure 3.7). Generally OTUs belonging to these fungal Classes were either not correlated or positively correlated (Spearman Correlation,

Rho = 0.37 – 0.43, p < 0.05) with the number of adults in the house, except for Alternaria sp. (denovo624) which was negatively correlated (Rho = –0.38, p < 0.05) (Figure 3.8).

The number of occupants was also positively correlated with the presence of many plant pathogens such as members of the Diaporthales, Hypocreales, and Xylariales (Figure

3.8). I hypothesize that they were likely brought inside by humans, as was shown by an earlier study [213], which detected the presence of plant pathogens on the forehead of humans.

The presence and positive correlation of fungal species such as Cladosporium cladosporioides (denovo2585, p < 0.05, Rho = 0.38), which have been frequently detected in the human oral cavity [230] (Figure 3.8) indicates that humans may also be a source of fungal species in indoor environments. Emissions from respiratory activities such as sneezing, coughing, or even breathing may result in an increase in such fungi.

Our BEST multifactor analyses revealed (Table 3.3 & 3.4) that non-human occupants such as plants and long hair dogs are also among those factors collectively associated with the fungal community variation in the most used rooms and bedrooms, respectively; however, the individual effects of these factors were not uniformly significant.

Our result also demonstrated a significant correlation between the presence of moths in the house and saprotrophic and plant pathogenic fungal species, belonging to the

Dothideomycetes and Sordariomycetes (Figure 3.7 and 3.9). Sticky or slimy spores rely on dispersal vectors such as moths and other insects [231] which may transfer them into indoor spaces. For instance, Capnodium sp. also known as ‘sooty molds,’ grow on sugary excreta of insects and they may enter indoor environments along with insects [231].

Other moulds, such as Trichoderma (denovo7033), Acremonium (denovo751), and

Chaetomium (denovo7591) also utilize moth and other insects to enter buildings (Figure

3.9).

Figure 3.7 Correlation and significance of important environmental factors (from Table 3.3) on beta diversity of fungi in bedrooms. Only those with Rho or R > 0.1 and p < 0.05 are noted.

Figure 3.8 Shade plot showing the affected OTUs by the number of adults in the house. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Trichoderma and Acremonium produce vast quantities of small droplets at the apex of simple conidiophores, helping them connect to small insects moving through a mycelial mass [232, 233]; however, Chaetomium spp. use branched, hooked and coiled setae on their ascocarps [231].

3.4.3.4 Furniture materials and cleaning strategies

Finally, our results support the idea that furnishings and occupant activities may affect indoor fungal community composition by filtering certain fungal taxa [44, 60, 234, 235].

For example, I observed a correlation between the use of air fresheners and the diversity within the Eurotiomycetes, Leotiomycetes, and to a lesser degree Tremellomycetes

(Figure 3.7). Profoundly, the majority of individual OTUs in these fungal Classes were detected more frequently (Mann-Whitney test, p = 0.001 – 0.042) when liquid or solid air fresheners were used (Figure 3.10).

I also found that Candida albicans (denovo3400) and Candida parapsilosis

(denovo3065), opportunistic pathogens for immunocompromised people [236-238], were most frequently detected when plastic or vinyl furniture was present (Figure 3.11). This may be a result of fungal spores acquiring electrostatic charges in the air [239] and subsequently getting attracted to opposite charges on the surface of indoor furniture.

Indoor fungal assemblages may be shaped and even enriched by household vacuum cleaners. Previous studies demonstrated that vacuuming carpets without an exhaust filter may raise airborne fungal counts [60]. In this study, I detected a significant effect of vacuum type (standard filter vs HEPA filter) (Figure 3.12A, p = 0.041).

Figure 3.9 Shade plot showing the affected OTUs by the presence of moths in the house. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Figure 3.10 Shade plot showing the affected OTUs by the use of liquid or solid air fresheners. Colour boxes illustrate the magnitude of each individual OTU’s abundance under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Figure 3.11 Shade plot showing the affected OTUs by the presence of plastic or vinyl furniture. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Figure 3.12 (A) Two dimensional PCoA ordination plot showing fungal communities of dust based on type of vacuum used in the houses. (B) Shade plot showing the affected OTUs by the type of vacuum. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

The main advantage of HEPA filters is that they may remove a range of microorganisms

[240]. Interestingly I found a decrease in the spores and fragments of some fungal species such as Penicillium sp. BLE22 (denovo2780) and Cyphellophora laciniata (denovo5636) in the presence of high efficiency particulate air (HEPA) vacuum cleaners. However, they also enriched the indoor environment with other fungal species, such as Aspergillus fumigatus (denovo7026), Exophiala placitae (denovo2061) and Pilidium concavum

(denovo1530) (Figure 3.12B). It is not known why certain fungi increase in the presence of HEPA filters. Perhaps it is a result of not evaluating the efficiency and integrity of

HEPA filters at regular intervals to evaluate their performances.

Another interesting point of observation was the association between burning candles in the house with bedroom and the most used room fungal assemblages. For instance, when unscented candles were used in the house, Saccharomycetes were more abundant in the bedroom (Figure 3.7 and 3.13). Another example is an unusual and uncommon fungal species known as Erysiphe monascogera (denovo6574), originally detected on the surface of fruits or sepals of Styra. This species was positively correlated with burning unscented candles (Figure 3.14). I speculate the presence and abundance of these fungal species is related to the purity of the raw materials and additives used in candles. The relationship between emissions from burning candles and indoor harmful organic chemicals, volatile organic compounds (VOCs) and ultrafine particles has been demonstrated [241, 242]; however, our results denoted that candle combustion may also affect indoor fungal assemblages, yet its interaction with other type of combustion pollutant remains to be studied.

Figure 3.13 Shade plot showing the affected OTUs by the use of scented candles. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

Figure 3.14 Shade plot showing the affected OTUs by the use of unscented candles. Colour boxes illustrate the magnitude of each individual OTU under each sample unit. Taxonomy of OTUs is shown in Appendix B.

3.5 Summary

Overall, to the best of our knowledge, this is the first study that considers a broad number of environmental factors and occupants’ activities that may be associated with indoor fungal community composition and diversity. The data indicate that in addition to possible outdoor sources of fungal propagules, the indoor fungal community may be influenced by several indoor factors including room functions, furniture, cleaning, combustion and occupants. Clearly, the associations that I present here need further research to determine the causality between indoor environmental factors and fungal community composition or studies focused on determining the association of indoor fungi with health and disease. In the long run, such deeper comprehension of indoor fungal diversity may help public health policy decision makers, particularly for improving the quality of built environments.

4 Chapter 4: Influence of Occupants and Their Activities on Indoor Bacterial Communities

4.1 Overview

A better understanding of the factors driving indoor bacterial diversity and community

composition is needed so that we may better manage microbial biodiversity in the spaces

in which we live and work. In this study, using floor dust collected in 54 houses, I

employed high-throughput sequencing of the bacterial 16S rRNA gene, and measured

668 environmental factors to evaluate relationships of house characteristics, occupant

type and activities with bacteria in indoor environments. Based on the results, the

composition of bacterial communities in different types of rooms (bedrooms vs the most

used rooms) were significantly different, and the type of occupants (human, cat, and dog),

number of visitors per day, frequency and type of cleaning, furniture and equipment

within the houses were among the dominant drivers of bacterial diversity and their

community structure.

4.2 Background

Globally, more than half of the human population lives in urban areas, a proportion that is expected to grow and reach two thirds of the global human population (~ 6.3 billion people) by 2050 [243]. A fraction of this population will suffer from chronic inflammatory disorders [244]. One potential reason for such elevations could be related to human confinement in indoor spaces with limited contact with nature [244]. As the time in indoor spaces increases, human exposure to microbes become restricted to the species that are found in such environments [148]. For this reason, comprehending the microbial community composition of indoor environments as well as factors affecting their patterns is important, and has recently been the topic of a number of studies [4, 9, 13, 15-17, 27,

28, 83, 126, 245].

Among different types of microbial life that exists indoors, bacteria have received the most attention [9, 15, 26-28, 83, 126, 127], as a result of their ubiquity and their well- known beneficial and harmful effects (infections, allergies, toxic illness and asthma [7, 9-

11]) on human health and the human microbiome. Studies have demonstrated that occupants are one of the main sources of indoor bacteria by shedding millions of microbial cells hourly into their local environment [83, 126, 129-131]. Hence, each household has a simplified and less diverse microbial ecosystem comprising the microbial signature of its own occupants [131]. However, there is an ongoing debate on the relative importance of sources for indoor bacteria, and recent studies indicate that occupant density is the main driver of microbial diversity in larger, multi-use buildings and outdoors in smaller buildings [27, 28].

In addition, it has been demonstrated that early life exposure of children to low indoor dust bacterial diversity is associated with the development of asthma [9]. This suggests that increasing the diversity of indoor bacterial biota may be beneficial for long-term health consequences [148], but to do so the indoor environment would need to be manipulated to elevate exposure to a wider phylogenetic diversity of indoor bacteria, while also limiting pathogens and allergens [148]. For such manipulations, we need a broader understanding of what building characteristics and occupant activities may influence indoor bacterial community structure. To this end, in this exploratory study, as a first step I took into account a broad number of environmental factors and occupant activities to answer two general questions: First, what building characteristics and occupant activities can potentially influence the diversity and structure of community composition of indoor bacteria? Second, which groups of bacteria are specifically affected?

4.3 Materials and Methods

4.3.1 Study design and sample collection

As outlined in section 3.3.1.

4.3.2 DNA extraction and PCR Amplification

Total DNA was extracted from 100 mg coarse dust samples by using FastDNA® SPIN

Kit for Soil (MP Biomedicals, LLC, Solon, OH, USA), which was selected systematically using the Order Preference by Similarity to Ideal Solution (TOPSIS) method [218] as the most optimum extraction kit for dust samples. Subsequently, extracted DNA samples

74 were evaluated for integrity by agarose gel electrophoresis with Lambda DNA HindIII

Digest standards (New England BioLabs, Ipswich, MA, USA) and their quantities were measured using the QuantiFluor® dsDNA System (Promega, Madison, WI, USA). The purity of extracted DNA samples was evaluated by measuring each sample’s ratio of the optical density at 260 nm and 280 nm using the NanoVue Plus™ spectrophotometer (GE

Healthcare, Buckinghamshire, UK), before preserving at –20°C. For PCR amplification, I targeted the V5–V6 region of the bacterial 16S rRNA gene by employing a primer set that excludes the chloroplast DNA, 799F (5' AACMGGATTAGATACCCKG 3') and

1115R (5' AGGGTTGCGCTCGTTG 3') [246, 247]. In order to use this set of primers for

454 high-throughput sequencing technology, a 10 bp tag specific to each sample, a 4 bp

TCAG key, and a 21 bp adapter for the GS FLX system were added to the sequences of both primers sets.

Each PCR mixture (25 µL) contained 2.5 µL of 10x PCR buffer supplied with the enzyme (600 mM Tris-SO4 (pH 8.9), 180 mM (NH4)2SO4), 200 µM of dNTPs, 3 mM of

MgSo4, 0.2 µM of each primer, 1 U Platinum® Taq DNA Polymerase High Fidelity

(Invitrogen, CA, USA), nuclease-free water (IDT, Coralville, IA, USA), and 2 µL extracted DNA (5 ηg / µL). PCR amplifications were carried out in a C1000 Touch™

Thermal Cycler (BioRad, Ontario, Canada) and the PCR conditions were as follows:

95°C for 1 min, followed by 30 cycles of 95°C for1 min, 60°C for 1 min, and 72°C for 30 sec, and finally a final extension at 72°C for 5 min.

For each individual sample, PCR amplifications were repeated 5 times, along with three negative controls, consisting of all reagents in the reaction mixture without template

DNA, in order to assess the presence of external or cross-contamination of the PCR

75 reaction mixtures by DNA. Amplicon replicates for each samples were pooled and sent to the Prostate Centre Microarray Facility (Vancouver, Canada) for library normalization, pooling, and clean-up. Finally, library sequencing was completed on half of a 454 FLX

Titanium pico-titer plate by the Prostate Centre Microarray Facility, Vancouver, Canada.

4.3.3 Analysis of pyrosequencing data

Sequence data generated by the 454 genome sequencer were processed by the quantitative insights into microbial ecology (QIIME) pipeline, version 1.8.0.

Pyrosequencing of the DNA library resulted in 650 648 sequences. First, sequences with a minimum sequence quality score of 25 and zero primer MID mismatches were filtered, trimmed to 200 – 350 bp for this primer selection and assigned to different samples based on their barcodes. A total of 557 418 sequences remained for downstream analyses across all sampling units with the mean of 6 820 sequence reads per sample. Chimeric sequences were removed by using the UCHIME algorithm found in the USEARCH program [223], and then the remaining sequences were clustered into operational taxonomic units (OTUs) at a 97% sequence similarity via UCLUST. Representative sequences for each OTU were identified taxonomically the GreenGenes database (version

6, released date 2014, 02, 09) [248]. Then, all singleton OTUs were excluded, and the subsequent OTU table was rarefied to 1 700 sequences per sample, to ensure adequate sampling depth for the subsequent statistical comparisons between samples, which reduced the sample size to 79 (42 most used rooms and 37 bedrooms).

4.3.4 Statistical analyses

PRIMER 7 and STATICTICA 12 were used for the following multivariate and univariate data analyses [225, 226].

4.3.4.1 Taxonomic composition of indoor bacterial communities

For the community composition, numbers of observed OTUs at each bacterial phylum were pooled and the relative distribution of their abundances was visualized.

4.3.4.2 Diversity and community composition

In order to conduct alpha and beta diversity analyses, pre-processing of environmental data was required. Namely, the collected 668 environmental included both numerical and categorical variables and their scales were not comparable. Hence, numerical variables were individually log-transformed to justify the use of Euclidean distance as a dissimilarity measure and limit the distorting effects of outliers. Then, both numerical and categorical data were normalized to yield comparable scales. Finally, inter-correlated variables were removed using the Pearson Correlation test.

Alpha diversity

First, the theoretical OTU richness by the Chao richness estimator was plotted, based on

999 permutations, and Good’s coverage [227] was calculated to demonstrate whether or not samples were sequenced to saturation. First, Wilcoxon Matched Pairs test was used to investigate the statistical difference between observed bacterial richness (alpha diversity) in the most used rooms and bedrooms.

The BEST (Bio-Env) routine (namely BVSTEP) was used to determine which environmental factors and resident activities best ‘explain’ the overall pattern of alpha diversity (number of observed OTUs) in both room types. Then univariate data analyses, namely Mann-Whitney (for two level categorical factors), Kruskal-Wallis (for multi-level categorical variables), and Spearman Correlationtest (for continuous variables) were employed to explore which screened environmental variables would show some trends of associations with the number of observed OTUs across samples.

Beta diversity: Composition

For the community composition analysis (beta diversity), the QIIME pipeline’s resulting

OTU table (genus level) was first standardized to the relative composition and then moderately transformed by root square transformation before calculating the Bray–Curtis dissimilarities. Then the modified OTU table and the environmental data were used to investigate the correlation between bedroom and the most used room bacterial biota by using RELATE analysis (a comparative Mantel-type test of similarities to model structures). Then BEST (Bio-Env) routine, namely BVSTEP, was used to determine which environmental factors and resident activities best ‘explain’ the overall pattern of bacterial community composition in both room types. Subsequently, the significance of the BEST analysis result was validated through a permutational null distribution to ensure that the selected combinations of environmental variables were not obtained by chance.

Next, the analysis of similarity (ANOSIM), for categorical variables, and RELATE correlations, for continuous variables, were used to estimate the relative effect size of each screened factor by constructing R or Relate statistics. Then factors were ranked

78 based on their relative effect sizes, and those with R or Rho values larger than 0.1 and significance levels smaller than 0.05 were selected for further analysis as follows.

4.3.5 Trends of individual components of the indoor bacterial community

In this section, more in-depth investigations were conducted to explore which bacterial phylum (using ANOSIM and RELATE) and subsequently which individual OTU in the affected phylum (using Mann-Whitney, Kruskal-Wallis, and Spearman Correlation test) were mostly associated with the environmental variables. Finally, for phylum and OTUs passing the above step, shade plots were constructed to visualize their potential trends of associations with screened environmental variables.

4.4 Results

I identified 4 733 bacterial operational taxonomic units (OTUs, sequences binned at a

97% similarity cutoff) across samples, 3 722 in bedrooms and 4 223 in the most used rooms belonging to 20 phyla. Results revealed a wide variety of bacterial taxa in the dust samples, many of which were rare (Appendix C). The five most dominant phyla were

Firmicutes (28% of all OTUs), Proteobacteria (26.9%), Actinobacteria (26.5%),

Bacteroidetes (8.5%) and Deinococcus-Thermus (2.1%) (Figure 4.1). Staphylococcus sp., a common type of bacterium that resides on the skin and mucous membranes of humans, was classified as the most common OTU across all samples, representing 11.2% of all sequences in bedrooms and 9.1% in the most used rooms. In bedrooms, the second most abundant OTU was classified as Streptococcus sp. (7.7% of total sequences) followed by Propionibacterium sp. representing 5.3% of total sequences. Conversely,

Acinetobacter sp. and Streptococcus sp. ranked as the second and third most abundant

OTUs in the most used rooms, comprising 3.9% and 3.3% of total sequences, respectively (Table 4.1).

4.4.1 Diversity and community composition structure

4.4.1.1 Alpha diversity

I observed a wide range of OTU richness in both bedrooms and the most used rooms and the theoretical total OTU richness by the Chao richness estimators (Figure 4.2A) indicated that samples were sequenced to saturation with the Good’s coverage [227] of

90.5% 7.3 (mean SD) across samples. The number of observed OTUs varied from

161 to 606 in bedrooms and 183 to 563 in the most used rooms. However, no significant differences were found in the number of observed OTUs between bedrooms and the most used rooms (Wilcoxon Matched Pairs test, Figure 4.2B). Further analysis through BEST procedure illustrated that 48% and 42% variation of bacterial richness in bedrooms and the most used rooms may be explained by a small number of (17 and 18, respectively) the

668 environmental factors (Table 4.2). More specifically, results demonstrated that

“bedroom bacterial richness” was mostly associated with the length of occupancy

(Spearman Correlation, Rho = 0.37, p – value = 0.032), number of room in the house

(Spearman Correlation, Rho = 0.42, p – value = 0.021), cleanliness of basement (Mann-

Whitney test, p – value = 0.023), evidence of a water leak in the house (Mann-Whitney test, p – value = 0.006), and presence of a short hair cat (Mann-Whitney test, p – value =

0.006, Figure 4.3). For the most used room, bacterial richness was negatively associated with the presence of electronic devices (Mann-Whitney test, p – value = 0.05, Figure

4.4A) and positively associated with the usage of multi-surface cleaners (Mann-Whitney test, p – value = 0.006, Figure 4.4B).

Figure 4.1 List of bacterial phyla detected in dust samples along with their OTU abundances.

Figure 4.2 Alpha diversity of bacterial OTUs in dust samples of bedrooms versus the most used rooms; (A) rarefaction curves of theoretical total OTU richness by the Chao richness estimators; (B) observed richness of bacterial OTUs across sample locations.

Table 4.1 The 20-most abundant bacterial OTUs in dust samples of bedrooms versus the most used rooms.

Bedroom The Most Used Room Relative Relative OTU rank OTU ID BLAST-assigned taxonomy OTU ID BLAST-assigned taxonomy abundance (%) abundance (%) 1 denovo6427 Staphylococcus sp. 11.2 denovo6427 Staphylococcus sp. 9.1 2 denovo23916 Streptococcus sp. 7.7 denovo26032 Acinetobacter sp. 3.9 3 denovo27100 Propionibacterium sp. 5.3 denovo15228 Streptococcus sp. 3.3 4 denovo15228 Streptococcus sp. 3.5 denovo18723 Lactococcus sp. 3.3 5 denovo7727 Corynebacterium sp. 3.0 denovo23916 Streptococcus sp. 2.9 6 denovo26032 Acinetobacter sp. 2.3 denovo13041 Other 2.5 7 denovo18723 Lactococcus sp. 2.0 denovo7727 Corynebacterium sp. 1.9 8 denovo14860 Erwinia sp. 1.8 denovo27100 Propionibacterium sp. 1.8 9 denovo21651 Corynebacterium sp. 1.8 denovo8572 Exiguobacterium sp. 1.3 10 denovo16913 Corynebacterium sp. 1.6 denovo16913 Corynebacterium sp. 1.1 11 denovo20791 Lactobacillus sp. 1.5 denovo8099 Planomicrobium sp. 1.0 12 denovo13041 Other 1.5 denovo8872 Other 1.0 13 denovo26728 Lactobacillus sp. 1.4 denovo21651 Corynebacterium sp. 1.0 14 denovo26726 Bifidobacterium sp. 0.9 denovo11847 Granulicatella sp. 0.8 15 denovo11847 Granulicatella sp. 0.7 denovo20791 Lactobacillus sp. 0.7 16 denovo24537 Rothia sp. 0.7 denovo24326 Other 0.7 17 denovo19213 Bifidobacterium sp. 0.7 denovo21066 Acinetobacter sp. 0.7 18 denovo14454 Finegoldia sp. 0.6 denovo26728 Lactobacillus sp. 0.7 19 denovo16094 Haemophilus sp. 0.6 denovo8048 Micrococcus sp. 0.6 20 denovo18504 Kocuria sp. 0.6 denovo4938 Kocuria sp. 0.6

Table 4.2 Environmental and behavioural factors that best explain the indoor dust-borne bacterial richness in bedrooms and the most used rooms.

Screened Factors by Best for Bacterial Richness a Bedroom The Most Used Room Numeric Numeric Geographic distance Geographic distance Length of occupancy Number of pieces of leather furniture Number of pieces of pressed wood furniture Number of pieces of metal furniture Number of room in the house Number of room in the house Number of adult visitors/day Room Carpet area (sq. m) Categorical Categorical Cleanliness of Basement Evidence of leak in the house Evidence of leak in the house Presence of carpet Floor level Presence of electronics in the room (Computer/TV/Stereo) Glass cleaners Presence of humidifier or vaporizer How often windows of child sleeping bedroom are open? Roof Age Presence of short hair cat Use of antibacterial hand cleaners Presence of humidifier or vaporizer Use of disinfectants Use of antibacterial hand cleaners Use of glass cleaners Use of Electric dryer-vented outdoor Use of multi-surface cleaners Use of glass cleaners Use of plumbing cleaners Use of scented laundry detergents Use of scented laundry detergents Use of stuffed toys Use of swiffer wet jet Window cover Use of vacuum

a Multi-factor analyses: All factors are collectively responsible for 48% and 42% of total variation of bacterial richness in bedrooms and the most used rooms, respectively.

Figure 4.3 Variation of alpha diversity of bacterial OTUs versus potentially effective environmental factors for bedroom (A) length of occupancy; (B) number of rooms in the house; (C) cleanliness of basement; (D) evidence of leak in the house; and (E) presence of short-hair cats.

Figure 4.4 Variation of alpha diversity of bacterial OTUs versus potentially effective environmental factors for the most used rooms (A) presence of electronic devices; (B) multi-surface cleaners.

4.4.1.2 Beta diversity: composition

Bedrooms and the most used rooms differed significantly in bacterial community composition (Rho = 0.248, p = 0.061). Using the BEST procedure, I found 14 and 11 out of 668 environmental factors collectively accounting for 54.2% and 50.9% of the variation in the bacterial assemblage in bedrooms and the most used rooms, respectively

(Table 4.3 and Table 4.4).

The screened factors passing the selected cut-of criteria (R or Rho > 0.1 and p < 0.05) were considered for further analysis (Table 4.3). These factors (in the order of their relative effect size) for bedroom included: the presence of a humidifier or vaporizer

(ANOSIM, R = 0.357, p = 0.003), use of wet cloths (water only) for cleaning (ANOSIM,

R = 0.311, p = 0.004), geographic distance (longitude°, ANOSIM, Rho = 0.238, p =

0.040), use of glass cleaners (ANOSIM, R = 0.217, p = 0.010), presence of long-hair dogs (ANOSIM, R = 0.171, p = 0.050), use of scented laundry detergents (ANOSIM, R =

0.153, p = 0.024), and frequency of house cleaning (RELATE, Rho = 0.153, p = 0.048).

Selected screened factors for the most used rooms (Table 4.4) were ranked as follows: the use of multi-surface cleaners (ANOSIM, R = 0.304, p = 0.001), presence of carpet

(ANOSIM, R = 0.281, p = 0.009), and number of adult visitors per day (RELATE, Rho =

0.124, p = 0.029).

Table 4.3 Environmental and behavioural factors that best explain the indoor dust-borne bacterial communities in bedrooms.

Bedroom Screened Factors by Best a Variation of Community Composition Numeric p-value Rho Frequency of house cleaning 0.048 0.135 Geographic distance (longitude°) 0.040 0.238 Number of pieces of metal furniture -- -- Categorical p-value R Presence of humidifiers or vaporizers 0.003 0.357 Presence of long-hair dogs 0.050 0.171 Type of house -- -- Type of lawn -- -- Use of glass cleaners 0.010 0.217 Use of scented candles -- -- Use of scented laundry detergents 0.024 0.153 Use of stuffed toys -- -- Use of wet cloth (water only) for cleaning 0.004 0.311 Use of vacuum -- -- Window cover -- -- a Multi-factor analyses: All factors are collectively responsible for 54.2% of total variation of bacterial community-composition. Only factors with Rho or R > 0.1 and p < 0.05 are noted.

Table 4.4 Environmental and behavioural factors that best explain the indoor dust-borne bacterial communities in the most used rooms.

The Most Used Room Screened Factors by Best a Variation of Community Composition Numeric p-value Rho Geographic distance -- -- Number of adult visitors/day 0.029 0.124 Number of pieces of metal furniture -- -- Categorical p-value R Furnace age -- -- Presence of carpets 0.009 0.281 Presence of humidifiers or vaporizers -- -- Type of fuel for water supply -- -- Use of disinfectants -- -- Use of multi-surface cleaners 0.001 0.304 Use of scented laundry detergents -- -- Use of vacuum -- -- a Multi-factor analyses: All factors are collectively responsible for 50.9% of total variation of bacterial community-composition. Only factors with Rho or R > 0.1 and p < 0.05 are noted.

4.4.2 Trends of individual components of the indoor bacterial community

4.4.3 Bedroom

The presence of humidifiers and vaporizers was positively associated with the occurrence and abundance of several bacterial fimilies in bedrooms (Figure 4.5). More specifically, houses with humidifiers or vaporizers had higher prevalence of OTUs with 97% similarities with members of Enterobacteriaceae, Pasteurellaceae, and Moraxellaceae. For example, OTUs related to bacterial genera such as Finegoldia (Mann-Whitney test, p =

0.01), Peptoniphilus spp. (p = 0.006 – 0.034, a cause of bloodstream infections [249]),

Erwinia spp. (p = 0.003 – 0.045, plant pathogenic species [250, 251]),

Serratia marcescens (p = 0.045, a cause of nosocomial infections [252, 253]),

Haemophilus parainfluenzae (p = 0.003 – 0.045, potential pathogen in exacerbations of chronic obstructive lung disease [254]), Acinetobacter spp. (p = 0.045, aerosol exposure to which can provide a protective impact on the development of allergic diseases [8, 255,

256]), Moraxella spp. (p = 0.003 – 0.045, pathogenic species for opportunistic infection

[257]), and Pseudomonas spp. such as P. viridiflava (p = 0.012) which is pathogenic to plants [258]. In contrary, OTUs related to Enhydrobacter sp. (p = 0.025), and bacteria genera belonging Lachnospiraceae (p = 0.027 – 0.04) and Ruminococcaceae (p = 0.003 –

0.045) were less frequently detected in houses with humidifiers or vaporizers were present where negatively associated with the presence in houses (Figure 4.5).

Results also demonstrate that OTUs with 97% similarities with members of bacterial families including, Propionibacteriaceae, Bifidobacteriaceae, Clostridiaceae,

Gracilibacteraceae, as well as some members of Lachnospiraceae and Ruminococcaceae were less prevalent in houses where wet clothes were used for cleaning. However, this 90 factor had positive associations with OTUs representing Streptococcus spp. (Mann-

Whitney test, p = 0.013 – 0.04), Finegoldia sp. (p = 0.003), Peptoniphilus spp. (p = 0.012

– 0.021), and Faecalibacterium sp. (p = 0.028) (Figure 4.6).

The geographical location of houses also showed some associations with the indoor dust bacterial community and OTUs of bacterial genera belonging to Geodermatophilaceae and Mycobacteriaceae were detected more in houses located further west (Figure 4.7).

Use of glass cleaners was positively associated with the bacterial community of the bedroom, mostly affecting OTUs related to members of Actinomycetales and

Lactobacillales. For example, OTUs associated with Pimelobacter sp. were less frequent and abundant in houses where glass cleaners were used (Mann-Whitney test, p = 0.020), in contrast to several other genera such as Brevibacterium sp. (p = 0.043),

Pseudonocardia spp. (p = 0.034 – 0.039) or Streptococcus spp. (Mann-Whitney test, p =

0.009 – 0.024) which were more abundant (Figure 4.8). I also found that the presence of long-hair dogs in bedrooms was associated with the bacterial community composition, positively correlated with abundance of OTUs representing Corynebacterium spp.

(Mann-Whitney test, p = 0.001 – 0.039) and Mycobacterium sp. (p = 0.0005, Figure 4.9).

Figure 4.5 Shade plot showing the affected OTUs in bedrooms by the presence of humidifiers and vaporizers. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.6 Shade plot showing the affected OTUs in bedrooms by the use of wet cloths for cleaning. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.7 Shade plot showing the affected OTUs in bedrooms by the geographical location of houses. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.8 Shade plot showing the affected OTUs in bedrooms by the use of glass cleaners. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Finally, I found that the bedroom bacterial community may be affected by the use of scented laundry detergents and frequency of house cleaning. For example, OTUs associated with Truepera spp. (Mann-Whitney test, p = 0.0009 – 0.043) were less abundant in houses where scented laundry detergents were used; however, this factor had a positive association with the abundance of Propionibacterium spp. (p = 0.004 – 0.05,

Figure 4.10). Regarding the frequency of house cleaning, results demonstrated a potential positive correlation between this factor and abundance of OTUs representing

Corynebacterium spp. (Spearman Correlations, Rho = 0.34 – 0.35, p < 0.05) and

Atopobium sp. (Rho = 0.43). In contrast, Fusobacterium spp. (Rho = –0.40) and members of Dermabacteraceae (Rho = –0.36) were negatively correlated with this factor (Figure

4.11).

4.4.4 The most used room

OTUs of several bacterial genera listed in Figure 4.12 were more abundant in the most used rooms where multi-surface cleaners were used, such as Chryseobacterium. sp. (p =

0.045), Mycobacterium sp. (p = 0.002) Pseudonocardia spp. (p = 0.030 – 0.043,) and several members of Acetobacteraceae, Aurantimonadaceae, Bradyrhizobiaceae,

Caulobacteraceae, Geodermatophilaceae, Methylobacteriaceae, and Sphingomonadaceae.

In contrast, OTUs associated with several bacterial families including Streptococcaceae

(e.g., Planomicrobium sp. Planococcus sp.), Lactobacillaceae (Lactococcus spp.

Streptococcus spp. Lactobacillus spp.), Aerococcaceae (Aerococcus sp.), Pasteurellaceae

(e.g., Actinobacillus sp. Aggregatibacter sp.), Enterobacteriaceae (e.g., Shewanella spp. and Erwinia spp.), and Moraxellaceae (Acinetobacter spp.) were detected less frequently in the most used rooms where multi-surface cleaners were used (Figure 4.12).

Figure 4.9 Shade plot showing the affected OTUs in the bedrooms by the presence of long-hair dogs. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.10 Shade plot showing the affected OTUs in bedrooms by the use of scented laundry detergents. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.11 Shade plot showing the affected OTUs in bedrooms by the frequency of house cleaning per month. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

Figure 4.12 Shade plot showing the affected OTUs in the most used rooms by the use of multi-surface cleaners. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 4.12 Cont’d

101

Figure 4.12 Cont’d

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Figure 4.13 Shade plot showing the affected OTUs in the most used rooms by the presence of carpets. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 4.14 Shade plot showing the affected OTUs in the most used rooms by the number of house visitors per day. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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The presence of carpets was another significant factor influencing the bacterial community in the most used rooms. Univariate analyses on individual OTUs showed that most of significantly affected OTUs are more abundant when carpet is not present, except for Corynebacterium spp. (p = 0.026) Rothia dentocariosa (p = 0.026), Nocardioides sp.

Saccharopolyspora sp. (p = 0.026), and members of Oxalobacteraceae (p = 0.005 –

0.026) which were positively associated with this factor (Figure 4.13).

Lastly, the number of house visitors per day was significantly related with OTUs associated with members of the Actinomycetales and Deinococcales (Figure 4.14) in the most used rooms, some with negative correlation, e.g., Dietzia sp. (Spearman

Correlations, Rho = –0.36), and some with positive correlation such as Cellulomonas sp.

(Rho = 0.32). In addition, some members of Corynebacterium were positively correlated with this factor (Figure 4.14).

4.5 Discussion

The main goal of this study was to better understand what environmental variables help determine the diversity and composition of the bacterial biota in indoor environments.

Our results are in agreement with many other studies [83, 126, 129, 131], which reveal that indoor occupants and their activities are the dominant drivers of bacterial community structure of residential houses. However, consistent with some other studies [27, 28, 128] we cannot ignore the effect of the outdoor environment as a source of indoor bacteria, since the frequency of window opening in the room as well the geographical location of houses were among the screened factors summarized in Table 4.2 – 4.4 - collectively responsible for approximately 40% and 50% variation of total richness and community

105 composition of indoor bacterial biota, respectively. Recently, Barberán et al. [259] showed that bacterial communities of dust samples collected from the external surfaces of houses located across the United States do have geographic patterns. Here, I found that even one degree difference in the geographical location of houses affected bacterial community structure of the bedroom, and houses located further west had higher abundance of bacteria belonging to the Geodermatophilaceae and Mycobacteriaceae.

Clearly, indoor dust sampling over a wider range of longitudes would be expected to further elucidate the geographical distribution of indoor bacterial communities.

Kembel et al. [27] suggested that occupant density and activities are the main drivers of bacterial community structure, when larger, multi-use buildings are concerned; while dispersal from outdoors is believed to be more influential in smaller buildings with lower occupant density. Our results, to the contrary, demonstrated that the bacterial community of residential houses even with low number of occupants (2-10) may be significantly influenced by the type and density of occupants, building characteristics as well as furniture and cleaning strategies.

4.5.1 Type and density of occupants

Our results confirm that indoor bacteria are dominated by those associated with humans, such as Staphylococcus sp., Streptococcus sp., Propionibacterium sp., and Acinetobacter sp. [129, 210]. It is known that humans contribute considerably to indoor microbial diversity by shedding approximately 1.5 million skin cells an hour which yield approximately 15 million bacterial cells [210]. Similarly, Lax et al. [131] suggested that within only 24 h of occupancy, the microbial compositions of houses may be directly

106 attributed to the microbial signatures of occupants. Here I have additionally demonstrated that as the length of occupancy increases, the bacterial diversity (number of observed

OTUs) also increases. This indicates that the length of occupancy makes humans the predominant sources of indoor bacterial biota. Interestingly, among the two types of rooms studied within the sampled houses in this study, the number of visitors per day was significantly related to the bacterial community structure of the most used rooms.

In addition to possible human sources, I found that pets may also influence the indoor bacterial diversity. Earlier studies demonstrated that dog ownership was significantly associated with richness and alpha diversity of indoor bacterial biota, but did not detect any consistent association between cats and indoor bacterial diversity [260, 261]. Here, considering different categories of pets, I show that short-hair cats significantly increased bacterial richness in bedrooms (Figure 4.3E), largely by introduction of additional types of Proteobacteria; more specifically members of Bradyrhizobiaceae, Hyphomicrobiaceae,

Phyllobacteriaceae, Pasteurellaceae, and Xanthomonadaceae. On the other hand, the presence of long-hair dogs significantly altered the community composition of bedrooms, and houses with the presence of long-hair dogs had elevated abundance of bacterial species such as Corynebacterium spp. and Mycobacterium sp. (Figure 4.9), many of which are in the list of human pathogen hazard group [257] and under certain circumstances, e.g., skin lesions, pulmonary or immune dysfunctions and chronic diseases, may cause health complications [262]. In comparison with cats, it has been suggested that dogs contribute more to the indoor bacterial community because they are generally larger and spend more time transitioning between indoor and outdoor environments [260].

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4.5.2 Building characteristics

As previously reported in the literature [128], I also found that rooms with different functions encompass somewhat dissimilar bacterial communities, suggesting the presence of different sources in these particular rooms or some spatial selective pressures filtering bacterial taxa across sample locations. In addition to the type of room, I suggest that as the number of rooms increases, houses harbour a richer bacterial community since there is more space available for microbial colonization and accumulation. In addition, there is no doubt that when water leakage occurs, many bacterial taxa may migrate from outdoor to indoor spaces. In addition, when excess moisture is present (e.g., leakage occurrence or presence of humidifiers), indoor environments become more suitable for a wider range of bacterial taxa (e.g., bacteria with high water activity) to colonize which eventually increases bacterial diversity.

4.5.3 Furniture and occupant activities

Finally, our results support the fact that furniture materials and occupant activities may indeed influence the bacterial diversity and their community structures within the spaces we live and work. For example, I found that cleaner houses have richer (alpha diversity) and significantly different bacterial community structure (beta diversity); as the cleaning frequency increases, houses may harbor higher abundances of Corynebacterium spp.,

Atopobium sp., and Streptococcus spp.

In addition, different types of cleaning materials would impact the indoor bacterial community structure (Figure 4.6, 4.8, 4.12). I believe that, in general, cleaning strategies are anthropogenic disturbances occurring in indoor environments which alter the

108 physicochemical properties of surfaces and change the substrate availabilities. Every cleaning occurrence would remove some group of bacteria and provide vacant spaces for more opportunistic and resistant species to colonize. Supporting this hypothesis, for example, I demonstrated that as “multi-surface cleaners” were used, several numbers of bacterial taxa such as Chryseobacterium sp. (potential reservoirs for environmental infections, meningitis, bacteremia, pneumonia, etc. [263, 264]), Pseudonocardia spp., or

Mycobacterium sp. were increased; however some others such as Lactobacillus spp. and

Acinetobacter spp. were decreased. Exposures to the latter two bacterial species have been reported to provide protection to allergic diseases [8, 244, 255, 256, 265, 266], suggesting that the use of multipurpose cleaning materials in houses may stimulate the prevalence of asthma and allergic diseases by decreasing the chance of exposure of occupants to some beneficial bacteria.

4.6 Summary

Overall, by considering a broad number of environmental factors and occupant activities, this study provides interesting information on the indoor bacterial community and factors that could control their diversity in residential houses. However, the total contribution of all factors studied was approximately 50% of variation in the diversity and community composition of indoor bacterial biota, suggesting that there are other indoor and outdoor neglected factors, beyond the 668 monitored factors in this study, that need to be expanded on in future studies. Our results indicate that even in residential houses with low occupancy level, type and density of occupants, furniture materials, and cleaning strategies are the most important drivers of indoor bacterial diversity and community composition. Further ‘‘hypothesis-driven’’ studies are needed to assess the causality

109 between factors and such variation and their health consequences. In turn, such information may bring new insights towards modernizing regulations on building designs and type of activities occurring within indoor spaces to secure a wider phylogenetic diversity of beneficial indoor bacterial communities.

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5 Chapter 5: The Relationship between Housing Characteristics and Animal Assemblages in the Dust of Indoor Environments

5.1 Overview

Animal inquilines are unavoidable components of the indoor built environment,

interacting constantly with humans in their homes. Animals may be vectors of infectious

diseases, primary sources of indoor allergens, or dispersal vectors for microorganisms in

built environments. We need a better understanding of what animals are found indoors, as

well as the ecological processes and environmental variables that influence their

diversity. To this end, high-throughput sequencing of the cytochrome c oxidase 1 (CO1)

gene was employed in combination with the evaluation of 668 environmental factors to

describe the animal inquiline communities in house dust samples as well as to evaluate

relationships between physical housing characteristics, types of occupants and their

activities with animal community composition. Our results show that animal trace

composition varies significantly between rooms within homes, and with furnishing

materials, occupant activities, and cleaning strategies. In particular, rooms with higher

material heterogeneity and greater disturbance were associated with higher animal

biodiversity. Such observations may provide opportunities for better management of

indoor fauna by more informed choices of indoor environmental characteristics, which

may result in healthier indoor environments for human.

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5.2 Background

Built environments are complex ecosystems encompassing different forms of life, such as bacteria, protists, fungi, plants, and animals interacting with each other and with their environment. To date, a vast number of microorganisms has been reported from indoor environments, and more recently, studies have probed the ecological and evolutionary processes that regulate diversity and composition of indoor microbial life [15, 16, 20, 21,

27, 86, 118, 210, 215, 267]; however the diversity of animal inquilines in indoor environments and the environmental factors that affect them, have not been well studied.

Humans share the indoor environment with many faunal groups, including species that are sources of potent allergens that sensitize and induce IgE-mediated allergic reactions

[268]. For example, out of 1.5 million described animal species, 80% belong to the phylum Arthropoda [269, 270] and their presence in indoor environments may influence human health in a number of ways. Their impact ranges from benign nuisance to directly injurious through stinging or biting, emitting allergens or serving as disease vectors [271,

272]. In addition, studies have demonstrated that indoor allergens and other proteins produced by arthropods, such as dust mites and cockroaches, play an important role in chronic inflammatory diseases. For example, there is evidence that there is a causal relationship between exposures to dust mite allergens with exacerbations of asthma in children sensitized to dust mites. A similar relationship has been found between cockroach allergen exposure and exacerbations of asthma in sensitized individuals, mostly adults [32]. Reduction or elimination of the indoor allergen reservoirs and subsequent exposure to these inflammatory molecules are acknowledged to be important in the management of related health problems [273]. Arthropods may also act as vectors

112 of infectious disease by transmitting viruses, bacteria, protozoa and nematoda to humans

[271], or as dispersal vectors for other microorganisms; for example, house mites are dispersal vectors for many fungal species [80]. To this end, considering that animals are common in the indoor environment and associate intimately with humans, both directly as a primary source of indoor allergen and indirectly through dispersal of microorganisms as well as their psychological sequelae such as insomnia, anxiety, avoidance behaviors, and personal dysfunction [59, 274, 275], it is important to better understand the factors that determine patterns of indoor animal community composition. In this study, we used a high-throughput sequencing approach to analyze house dust samples collected from homes in the Vancouver area enrolled in the miniCHILD study – a birth cohort of 54 subjects recruited to optimize and validate data collection tools for the Canadian Healthy

Infant Longitudinal Development (CHILD) study. By analyzing these samples, we sought to address two general questions: First, what is the overall composition of animal biota in the indoor environment? Second, how do building characteristics and occupant activities influence diversity and structure of animal composition in the built environment?

5.3 Materials and Methods

5.3.1 Study design and sample collection

As outlined in section 3.3.1.

5.3.2 DNA extraction and PCR amplification

First total DNA was extracted from 100 mg of fine dust using a FastDNA® SPIN Kit for

Soil (MP Biomedicals, LLC, Solon, OH, USA), which was selected systematically using the Order Preference by Similarity to Ideal Solution (TOPSIS) method [218] as the most

113 optimum extraction kit for dust samples. Subsequently, extracted DNA samples were checked for integrity by agarose gel electrophoresis with Lambda DNA HindIII Digest standards (New England BioLabs, Ipswich, MA, USA) and their quantities were measured using the QuantiFluor® dsDNA System (Promega, Madison, WI, USA). The purity of extracted DNA samples was evaluated by measuring each sample’s ratio of the optical density at 260 nm and 280 nm using the NanoVue Plus™ spectrophotometer (GE

Healthcare, Buckinghamshire, UK), before preserving at -20°C. For PCR amplification, we targeted the 100-150 bp fragment of the cytochrome c oxidase 1 (CO1) gene for species-level identification of several animal groups by employing a mini-barcode universal primer set, Uni-MinibarF1 (5' TCCACTAATCACAARGATATTGGTAC 3') and Uni-MinibarR1 (5' GAAAATCATAATGAAGGCATGAGC 3'). These primers are designed to develop a species-specific sequence library of all eukaryotic lineages from environmental matrices by producing shorter amplicon regions which tend to increase the number reads compared to the 650 bp fragment of the cytochrome c oxidase 1 (CO1) gene [276]. In order to use this set of primers for 454 high-throughput sequencing technology, a 10 bp tag specific to each sample, a 4 bp TCAG key, and a 21 bp adapter for the GS FLX system were added to the sequences of both primers sets. DNAs from the

54 house dust samples were PCR amplified using PCR mixture (50 µL) as follows: 5 µL of 10x PCR buffer supplied with the enzyme (600 mM Tris-SO4 (pH 8.9), 180 mM

(NH4)2SO4), 100 µM of dNTPs, 1.5 mM of MgSO4, 0.08 µM of each primer, 1 U

Platinum® Taq DNA Polymerase High Fidelity (Invitrogen, CA, USA), molecular biology reagent grade water (Sigma-Aldrich, St.Louis, MO, USA), and 2 µL extracted

DNA (5 ng / µL). PCR amplifications were carried out in a C1000 Touch™ Thermal

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Cycler (BioRad, Ontario, Canada) using 10 ng of DNA extracted from individual dust samples as template; the PCR conditions were as follows: 95°C for 2 min, followed by 5 cycles of 95°C for 1 min, 46°C for 1 min, and 72°C for 30 sec, followed by 35 cycles of

95°C for 1 min, 53°C for 1 min, and 72°C for 30 sec, and finally a final extension at 72°C for 5 min. For each individual sample, PCR amplifications were repeated 5 times, along with three negative controls, consisting of all reagents in the reaction mixture without template DNA, in order to assess the presence of external or cross-contamination of the

PCR reaction mixtures by DNA. Amplicon replicates for each sample were pooled and sent to the Prostate Centre Microarray Facility (Vancouver, Canada) for library normalization, pooling, and clean-up. Library sequencing was completed on half of a 454

FLX Titanium pico-titer plate by the Prostate Centre Microarray Facility, Vancouver,

Canada.

5.3.3 Analysis of pyrosequencing data

From the total of 659 683 pyrosequencing sequences, 459 004 sequences remained after trimming, filtering and binning, resulting in a mean of 6 048 sequences for each sample.

Remaining sequences were clustered into operational taxonomic units (OTUs) at a 97 % sequence similarity cutoff via UCLUST, and representative sequences for each OTU

115 were identified taxonomically by conducting the Basic Local Alignment Search Tool

(BLAST) against the NCBI database. All sequences related to the other eukaryotes (e.g., fungi, algae, plants, etc.), sequences with no blast hits, as well as all singleton OTUs, were excluded. The subsequent OTU table was rarefied to 1 000 sequences per sample, to ensure adequate sampling depth for the subsequent statistical comparison between samples, which reduced the sample size to 41 of "the most used" rooms and 36 bedrooms.

Finally, to investigate changes in abundance at the species level, abundances of OTUs with the same taxonomic ID were pooled in each sample.

5.3.4 Statistical analyses

PRIMER 7 and STATICTICA 12 were employed for the following multivariate and univariate data analyses [225, 226].

5.3.4.1 Taxonomic composition of indoor animal communities

For the community composition, numbers of observed OTUs of each animal phylum were pooled and the relative distribution of their abundances was visualized.

5.3.4.2 Diversity and community composition

Alpha diversity

First, the theoretical OTU richness by the Chao richness estimator was plotted, based on

999 permutations, and OTU coverage was estimated using the conditional uncovered probability method to estimate the corresponding probability for each sample in the OTU table [277]. Subsequently, a Wilcoxon Matched Pairs test was used to investigate the

116 statistical difference in alpha diversity (species richness and Shannon-Weiner index) between the most used room and bedroom.

Beta diversity: Composition

For the community composition analysis (beta diversity), pre-processing of data was required. Namely, the resulting OTU table (at species level) was first standardized by relative composition and then subjected to root square transformation prior to the calculation of Bray–Curtis dissimilarities. The collected 668 environmental metadata included both numerical and categorical variables and hence their scales were not comparable. Numerical variables were log-transformed to permit the use of Euclidean distance as a dissimilarity measure and to limit the distorting effects of outliers. Both numerical and categorical data were then normalized in order to yield comparable scales; finally inter-correlated variables were identified using Pearson Correlation and removed.

After preprocessing, 5 main steps of multi- and uni-variate analyses were carried out as follows: in Step 1, both the modified OTU table and the environmental data were used to investigate the correlation between bedroom and the most used room animal biota by using RELATE method (a comparative Mantel-type test of similarities to model structures). Then BEST (Bio-Env) routine, namely BVSTEP, was used to determine which environmental factors and resident activities best ‘explains’ the overall pattern of animal community composition in both room types (Step 2). Subsequently, in Step 3, analysis of similarity (ANOSIM), for categorical variables, and RELATE method, for continuous variables, were used to identify the individual statistical significance levels

(p-values) of screened factors by BEST.

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5.3.5 Trends of individual components of the indoor animal biota

Next, in Step 4, a more detailed investigation was conducted to determine which animal phylum in the biota matrix is statistically affected by the significant environmental variables from Step 3 (using ANOSIM and RELATE). Finally, in Step 5, univariate data analyses, namely Mann-Whitney (for two level categorical factors), Kruskal-Wallis (for multi-level categorical variables), and Spearman Correlation (for continuous variables) tests were employed to understand which individual OTUs in each of the affected phyla

(Step 4) are significantly influenced by the environmental variable from Step 3.

5.4 Results

5.4.1 Taxonomic composition of indoor animal communities

Results revealed a wide variety of animal taxa in dust samples (Figure 5.1), in which arthropods represented the majority of the total diversity, covering 86% of total OTUs.

Two classes in this phylum were dominant, namely Arachnida encompassing 35% of total OTUs followed by the class of Insecta, covering 31% of OTUs (Figure 5.1). Most of the highly abundant OTUs belonged to this phylum (Table 5.1 and 5.2), such as

Dermatophagoides farinae as the most abundant species-level taxon (53% of total taxa in bedroom and 33% in the most used room), or D. pteronyssinus encompassing 13% of total taxa in both rooms (Table 5.1 and 5.2).

The phylum Chordata was identified as the second most abundant group in dust samples, followed by Rotifera, covering 4.5% and 4.4% of the total sequenced OTUs, respectively

(Figure 5.1); Bdelloidea was the only detected class in the latter phylum. In phylum

Chordata, five different classes were identified, amongst which members of the

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Mammalia were most abundant, followed by members of Actinopteri (Figure 5.1).

Although the phylum Nemetea (ribbon worms) was represented in only 1.7% of the observed OTUs, Cerebratulus longiceps belonging to this phylum was identified among the 20 most abundant OTUs in bedrooms and the most used rooms (Table 5.1 and 5.2).

5.4.2 Diversity and community composition

5.4.2.1 Alpha diversity

In total, we obtained 815 animal OTUs across samples, 686 in the most used rooms and

529 in bedrooms. The theoretical OTU richness plots showed asymptotic convergence at both locations (Figure 5.2A) and the conditional uncovered probability method indicated that the sequences covered an average of 88.2 % 4.5 (mean SD) of the total OTUs.

This indicated that there is greater diversity of animal traces in floor dust samples than our method was able to reveal. The steep rank-abundance plots (Appendix D) signify animal assemblages with high dominance in both the most used rooms and bedrooms; however, dominant species were more abundant in bedrooms than the most used rooms.

We also observed that the most used rooms contained richer animal communities than bedrooms, encompassing more rare species (Figure 5.2B, 5.2C and Appendix D).

Subsequently, using Wilcoxon Matched Pairs analyses we did not detect any significant difference of alpha diversity – both the number of observed OTUs and Shannon-Weiner index (Figure 5.2B and 5.2C).

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Figure 5.1 List of animal phyla and classes detected in dust samples along with their OTU abundances.

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Table 5.1 The 20-most abundant animal OTUs in dust samples of bedrooms.

Bedroom BLAST-Assigned Taxonomy Relative Abundance OTU Rank Family Genus Species (%) 1 Pyroglyphidae Dermatophagoides Dermatophagoides farinae 53.8 2 Pyroglyphidae Dermatophagoides Dermatophagoides pteronyssinus 12.8 3 Parathelphusidae Spiralothelphusa Spiralothelphusa parvula 12.2 4 Collembola Tomocerus Tomocerus similis 4.4 5 Scathophagidae NA Scathophagidae sp. BOLD:AAH0022 3.0 6 Cerebratulidae Cerebratulus Cerebratulus longiceps 2.6 7 Calliphoridae Pollenia Pollenia sp. BS-2012 2.0 8 Salmonidae Oncorhynchus Oncorhynchus nerka 1.4 9 Phalangiidae Phalangium Phalangium opilio 0.7 10 Paronellidae NA Paronellidae sp. DPCOL76388 0.7 11 Eupodidae NA Eupodidae sp. BOLD:AAZ2233 0.6 12 Katiannidae Sminthurinus Sminthurinus bimaculatus 0.5 13 Lepismatidae Thermobia Thermobia domestica 0.5 14 Noctuidae Lacinipolia Lacinipolia renigera 0.4 15 Pyroglyphidae Dermatophagoides Dermatophagoides sp. n. AD1210 0.3 16 Canidae Vulpes Vulpes vulpes 0.3 17 Coccinellidae Harmonia Harmonia axyridis 0.2 18 Chthamalidae Pseudoctomeris Pseudoctomeris sulcata 0.2 19 Collembola NA Katiannidae sp. DPCOL33322 0.2 20 NA NA Psocoptera sp. BOLD:AAN8448 0.2

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Table 5.2 The 20-most abundant animal OTUs in dust samples of the most used rooms.

Most Used Room BLAST-Assigned Taxonomy Relative Abundance (%) OTU Rank Family Genus Species 1 Pyroglyphidae Dermatophagoides Dermatophagoides farinae 32.9 2 Pyroglyphidae Dermatophagoides Dermatophagoides pteronyssinus 12.7 3 Collembola Tomocerus Tomocerus similis 7.1 4 Parathelphusidae Spiralothelphusa Spiralothelphusa parvula 3.4 5 Tineidae Tinea Tinea pellionella 3.2 6 Cerebratulidae Cerebratulus Cerebratulus longiceps 3.0 7 Katiannidae Sminthurinus Sminthurinus bimaculatus 2.7 8 NA NA Diptera sp. KMGHap_164 2.7 9 Pholcidae Pholcus Pholcus phalangioides 2.6 10 Eupodidae NA Eupodidae sp. BOLD:AAZ2233 2.3 11 Phalangiidae Phalangium Phalangium opilio 2.3 12 Salmonidae Oncorhynchus Oncorhynchus nerka 2.1 13 Lepismatidae Thermobia Thermobia domestica 2.5 14 Proctophyllodidae Proctophyllodes Proctophyllodes sp. AMUFM909 1.9 15 Paronellidae NA Paronellidae sp. DPCOL76388 0.9 16 Proctophyllodidae Monojoubertia Monojoubertia microphylla 0.9 17 Fanniidae Fannia Fannia armata 0.5 18 Katiannidae NA Katiannidae sp. DPCOL33322 0.5 19 Pyroglyphidae Dermatophagoides Dermatophagoides sp. n. AD1210 0.5 20 NA NA Psocoptera sp. BOLD:AAN8448 0.4

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Figure 5.2 Alpha diversity of animal OTUs in dust samples of bedrooms versus the most used rooms; (A) rarefaction curves of theoretical total OTU richness by the Chao richness estimators; (B) observed species richness and Shannon-Weiner index of animal OTUs across sample locations.

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5.4.2.2 Beta diversity: composition

Using RELATE analysis we found that animal community structure in bedrooms and the most used rooms were dissimilar and not related (Rho = –0.088, p = 0.72). Subsequently, the BEST procedure determined 9 and 23 out of 668 environmental factors collectively covering 61 % and 62 % of variation in the animal assemblage structure in bedrooms and the most used rooms, respectively (Table 5.3 and 5.4). When the screened factors by

BEST for bedrooms were analyzed individually (Table 5.3), three factors remained significant. Such factors included the evidence of water leakage in the most used room

(ANOSIM, R = 0.417, p = 0.011), area of the bedroom (RELATE, Rho = 0.167, p =

0.035) as well as burning scented candles (ANOSIM, R = 0.189, p = 0.033). When

BEST-screened factors for the most used rooms were considered individually (Table

5.4), seven factors were determined to be significant (p < 0.05). These included usage of plug-in deodorizers (ANOSIM, R = 0.382, p = 0.030), use of air conditioner (R = 0.301, p = 0.020), presence of mouse (R = 0.220, p = 0.050), presence of body of water in neighborhood (R = 0.187, p = 0.045), frequency of bathroom fan's usage (R = 0.175, p =

0.035), use of chemical sprays for cleaning (R = 0.167, p = 0.031), and the presence of plastic or vinyl furniture (R = 0.124, p = 0.050).

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Table 5.3 Environmental and behavioural factors and their significance levels that best explain the indoor animal community in bedrooms.

Bedroom Screened Factors by Best a Variation of Community Composition b (Multi-Factor Analysis) (Uni-Factor Analysis) Numeric p-value Rho Room area (sq. m) 0.035 0.267 Number of pieces of Metal -- -- Categorical p-value R Age of floor -- -- Evidence of leakage in the most used/dining/family 0.011 0.417 rooms Finished basement or added insulation -- -- Type of fuel in the house -- -- Use of disinfectant in baby’s room -- -- Use of dry cloth or paper towel for cleaning -- -- Use of scented candles 0.033 0.189 Use of toilet bowl cleaners -- -- a Multi-factor analyses: All factors are collectively responsible for 60.7 % of total variation of animal community-composition. b Single-factor analyses: Only factors with Rho or R > 0.1 and p-value < 0.05 are noted.

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Table 5.4 Environmental factors and their significance levels that best explain the indoor animal community in the most used rooms.

The Most Used Room Screened Factors by Best a (Multi-Factor Analysis) Variation of Community Composition b (Uni-Factor Analysis) Numeric p-value Rho Geographic distance -- -- Number of visitors/day -- -- Number of rooms in the house -- -- Number of pieces of solid woods -- -- Number of pieces of leather -- -- Categorical p-value R Basement Dampness -- -- Evidence of leakage in basement -- -- Evidence of leakage in bathroom -- -- Frequency of bathroom fan's usage 0.035 0.175 Presence of body of water in neighborhood 0.045 0.187 Frequency of keeping the most used room's window open -- -- Presence of plastic/vinyl furniture 0.050 0.124 Presence of mould in basement -- -- Presence of mouse in house 0.050 0.220 Type of wall covering -- -- Type of vacuum -- -- Usage level of gas fireplace -- -- Usage level of radiators -- -- Use of air conditioner 0.020 0.301 Use of chemical sprays for cleaning 0.031 0.167 Use of feather duster -- -- Use of plug-in deodorizers 0.030 0.382 Use of plumbing cleaners -- -- Use of spray air fresheners -- -- Use of swiffer wet jet -- -- a Multi-factor analyses: All factors are collectively responsible for 62.0 % of total variation of animal community-composition. b Single-factor analyses: Only factors with Rho or R > 0.1 and p-value < 0.05 are noted.

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5.4.3 Trends of individual components of indoor animal biota

5.4.3.1 Bedroom

Observed water leakage inside the most used, dining, or family rooms was the most important factor determining animal community composition (Beta diversity) in bedrooms, mainly by influencing arthropod composition (ANOSIM, R= 0.475, p =

0.010). More specifically, units with leakage had more species such as Coccinella septempunctata (known as the seven-spot ladybird; Mann-Whitney test, p = 0.001),

Contarinia maculipennis (known as the blossom midge, p = 0.001), and Ephippiomantis ophirensis (p = 0.033). In contrast, Dermatophagoides farinae (p = 0.0323) and

Entomobrya multifasciata (p = 0.027) decreased significantly where leakage was present

(Figure 5.3).

Arthropoda and Chordata communities were strongly related to room area (RELATE,

Rho = 0.157 and 0.173, p = 0.05 and 0.047, respectively). More specifically, species such as Tutelina similis (a spider that consistently feeds on ants; Spearman Correlations, Rho =

0.38, p < 0.05), Harmonia axyridi (the multicolored Asian lady beetle, Rho = 0.40, p <

0.05), and the Coho salmon (Oncorhynchus kisutch, Rho = 0.37, p < 0.05) were more frequent and abundant in “larger” bedrooms. In contrast, Allajulus groedensis had a significant negative correlation with this factor (Rho = - 0.38, p < 0.05) (Figure 5.4).

Burning scented candles in bedrooms was related to the Arthropoda community composition, mainly by increasing abundance of species such as Scutovertex sculptus

(Mann-Whitney test, p = 0.016) and Toxorhynchites edwardsi (p = 0.012), and

Ephippiomantis ophirensis (p = 0.016) (Figure 5.5).

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Figure 5.3 Shade plot showing the affected OTUs by the evidence of leakage inside the most used, dining, or family rooms. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.4 Shade plot showing the affected OTUs in bedrooms by the bedroom area (sq. m). Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.5 Shade plot showing the affected OTUs in bedrooms by the use of scented candles. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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5.4.3.2 The most used room

Similar to the bedroom factors, most of the significant factors in the most used rooms only affected the Arthropoda OTUs(R = 0.136 – 0.359. p = 0.028 – 0.05), except for the frequency of bathroom fan usage (R = 0.132, p = 0.035) and the presence of plastic or vinyl furniture (R = 0.143, p = 0.015) that also affected the Chordata OTUs.

Mann-Whitney analysis results revealed that when plug-in deodorizers were used, individual OTUs listed in Figure 5.6 were detected more frequently (p = 0.005 – 0.029).

In addition, air conditioning systems had a positive correlation with the abundance of species belonging to the classes Insecta and Maxillopoda such as Culex modestus (p =

0.0157), Bradysia difformis (p = 0.0150), Tineola bisselliella (clothing moth, p = 0.0132), and Chthamalus moro (p = 0.0110) (Figure 5.7).

The presence of mice was another significant factor influencing Arthropoda OTU diversity in the most used rooms. Univariate analyses on individual OTUs showed that most of significantly affected OTUs were more abundant when mice were noted, except for Anopheles argyritarsis and Chthamalus moro which were negatively correlated with this factor, p-values 0.009 and 0.002, respectively (Figure 5.8).

Presence of a nearby water body was also related to the animal community composition by increasing abundances of Micropsectra sp. ATNA53 (p = 0.0064) and Sarcophaga iota

(p = 0.0078) in the most used room (Figure 5.9).

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Figure 5.6 Shade plot showing the affected OTUs in the most used rooms by the use of plug-in deodorizers. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.7 Shade plot showing the affected OTUs in the most used rooms by the use of air conditioner. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.8 Shade plot showing the affected OTUs in the most used rooms by the presence of mouse in the house. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.9 Shade plot showing the affected OTUs in the most used rooms by the presence of body of water in neighborhood. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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The frequency of bathroom fan usage was also associated with the animal assemblage in the most used room. The largest difference was between homes where fans were “never” used and those that used fans “occasionally” (Figure 5.10A). Interestingly, many animal species were more abundant with fan use (Figure 5.10B).

The use of chemical sprays in the house was associated with elevated arthropod abundance, particularly Anopheles argyritarsis, Drosophila neomorpha and Xylena vetusta (p = 0.008 – 0.042, Figure 5.11).

Finally, the presence of plastic furniture in the house was associated with abundance in certain members of the Chordata (Figure 5.12). For example, Sockeye salmon

(Oncorhynchus nerka) was negatively related, whereas the spot-backed ant bird

(Hylophylax naevius) and the gray four-eyed opossum (Philander opossum) were positively related. Interestingly, we also encountered sequences of the banded numbfish

(Narcine westraliensis), which to date is only known from Western Australia [278]

(Figure 5.12).

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Figure 5.10 Two dimensional PCoA ordination plot showing animal communities of dust according to the frequency of bathroom fan use in houses (A); Shade plot showing affected OTUs in the most used rooms by the frequency of bathroom fan usage (B). Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.11 Shade plot showing the affected OTUs in the most used rooms by the use of chemical sprays for cleaning. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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Figure 5.12 Shade plot showing the affected OTUs in the most used rooms by the presence of plastic or vinyl furniture. Colour boxes illustrate the magnitude of each individual OTU under each sample unit.

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5.5 Discussion

The main goal of this study was to explore the composition of the animal community found in indoor environments by analyzing dust samples. We observed that bedrooms in

Lower Mainland (BC, Canada) have a more homogeneous animal community than the most used rooms, and are typically dominated by one animal species: Dermatophagoides farina (American house dust mite), which comprises greater than half of the total detected OTUs. In contrast, the most used rooms contained a more diverse animal community, with lower levels of Dermatophagoides farina and greater variety of rare species of Chordata, Annelida, and Rotifera.

Surprisingly, I detected members of the Chordata and Annelida 2 and 7 times more abundantly in the most used rooms than bedrooms. Similarity Bdelloid rotifers were 4.5 times more frequently encountered in the most used rooms than in bedrooms. It is known that Bdelloid rotifers mostly live in freshwater, marine and estuarine environments [279].

Their presence in indoor dust could be due to their ability to go dormant and form a desiccated state by using a strategy known as anhydrobiosis [280]. When dry, their metabolism is shut down and when exposed to moisture, they revive regardless of the time spent dry [280, 281].

It is generally recognized that diversity patterns and species coexistence in any ecosystem are driven by multiple and concurrent factors [282]. The animal inquilines in household dust can be divided into two ecological categories according to their origin: (i) autochthonous assemblages that are live and active inhabitants of dust (e.g.,

Dermatophagoides spp.), and (ii) passive entrants from other sources that are known as

140 allochthonous assemblages [80]. Here we detected indoor animal traces belonging to both of the above categories, for example Dermatophagoides spp., indigenous members of several homes worldwide (autochthonous), and Oncorhynchus spp. (a genus of fish in the family Salmonidae) as part of the allochthonous assemblages.

Our results show that the most used rooms have generally higher animal diversity. This may result from the presence of multiple “sources” contributing to enrichment of animal biota, such as outdoor air, occupants of the buildings including pets, plants and rodents, groceries brought inside, as well as growth and breeding of animal species within the built environment. For example, considering the fact that the outdoor environment is an important contributor to indoor fauna, we expect that differences in local ‘outdoor’ environment would drive the occurrence and abundance of certain ‘indoor’ OTUs.

Supporting this hypothesis, our data demonstrates that houses near a body of water differ in the qualitative composition of indoor animal traces. In fact, nearby water sources are associated with higher abundance of species in the genera Micropsectra (commonly found in many lotic and lentic freshwater habitats [283]) and Sarcophaga. It may be because when nearby water sources are present, abundances of such species would increase around the house and subsequently there would be greater chance of their entrance into the houses.

Another source of indoor fauna may be the growth and breeding of animals within the built environment. For instance, we found that use of air conditioners correlated with animal community composition in the most used rooms (Table 5.4). More specifically, units with air conditioning systems, had higher levels of species such as Culex modestus

(a vector of West Nile virus [284, 285] mostly distributed in Europe [286]), Bradysia

141 difformis (a common pest of greenhouse and forestry nurseries in Europe, South Africa, and China [287-289]), or webbing clothes moth (Tineola bisselliel) which prefer relatively high humidity [290]. We speculate that such species may enter houses through the vents of air conditioners or other sources, but in addition the moist environment may create a breeding habitat for such species.

We also show that the type of furnishing materials and their densities and cleaning strategies help to shape the composition of indoor animal traces (Tables 5.3 and 5.4).

Besides the higher human traffic and greater availability of sources, we suggest that the most used rooms have higher animal diversity than bedrooms because they normally have higher material heterogeneity (presence and variation of different types of furniture: solid wood, leather, plastic and vinyl) as well as higher levels of disturbance frequency and intensity (e.g., type of vacuuming or cleaning strategies). More specifically, material heterogeneity would collect more dust and provide more resources for different animal taxa to be established. In parallel, higher levels of disturbance would facilitate establishment of fast-growing and well-dispersed species, hence increasing diversity

[279, 291, 292]. Disturbance has been defined as "any relatively discrete and sudden event in time that disrupts the structure of ecosystems, communities, and populations, changing resources pools, substrate availability, or the physical environment" [293].

Considering this definition, occupants' activities and cleaning strategies may be considered a significant disturbance in indoor environments. For example, we demonstrated a significant correlation between the use of “plug-in deodorizers” and

“chemical sprays for cleaning purposes” with the animal community composition in the most used rooms. More specifically, homes where plug-in deodorizers and chemical

142 sprays were used had higher levels of arthropods such as moths, mosquitos, and other flies (Figure 5.6 and 5.11). I speculate that use of plug-in deodorizers and chemical sprays for cleaning would alter the physicochemical properties of the indoor environment and change the substrate availability. Subsequently intolerant species to such new conditions would be removed and fast growing and resistant species would replace them.

Also, the presence and abundance of these animal species can be related to the purity of the raw materials and additives used in plug-in deodorizers and chemical sprays.

Interestingly, we encountered sequences of the Anopheles argyritarsis (a potential vector of malaria [294, 295]), which were more abundant in houses where chemical cleaners were used. This is despite the fact that this species is not endemic to North America (has been only reported occasionally) and known not to live long enough to be a public health threat. One speculation may be that the DNA of these species is embedded in the raw materials and additives of chemical sprays, or alternatively the mosquitoes may have been accidentally transported from malarial regions (airplanes, luggage, shipping containers, clothes) and the chemical sprays have provide a suitable breeding habitat for such species.

5.6 Summary

To the best of our knowledge, very few studies have focused on providing information on animal composition in indoor environments [80, 296]. Yet there is still a lack of understanding of what ecological processes and environmental variables influence the indoor animal diversity. This research demonstrated that besides dust mites, which are identified as one of the most significant allergens in indoor environments [162], many other forms of animals which may be pathogenic, allergenic, or vectors of parasites,

143 bacteria, and fungi inhabit indoors. Our results suggest that type of room and activities within rooms may alter the composition of indoor animal traces. In this study, areas with higher material heterogeneity and disturbances (i.e., the most used rooms) tend to have higher sequence diversity and lower sequence numbers of dust mites, than the more homogeneous areas such as bedrooms. Other factors in bedrooms such as diurnal contribution of moisture to bedding during sleep [297], and bedroom activities [81] may also provide more preferable microenvironments for dust mites.

Considering the highly diverse assemblage of animal sequences in indoor environments and their intimate relationship with human life, both directly as a primary source of indoor allergen and indirectly through dispersing microorganism in built environments, these results may be used to form the basis of further ‘‘hypothesis-driven’’ studies assessing the causality and mechanisms governing better links between housing factors and indoor animal community variation or studies focused on determining the association of indoor animal fauna with health and disease. Better comprehension of indoor ecology and animal diversity can help researchers design intervention studies for improving the indoor built environment which may provide public health policy decision makers with tools to improve the built environment.

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6 Chapter 6: Archaea in the Indoor Environment and their Relationship with Housing Characteristics

6.1 Overview

Archaea are widespread and abundant in soils, oceans, or human and animal

gastrointestinal (GI) tracts. However, very little is known about their occurrence in the

indoor built environment and the factors that regulate their abundances. Here, we used a

quantitative PCR approach to survey if Archaea are a regular component of the indoor

microbiota and to determine their abundances compared to that of indoor bacteria. We

also measured a broad array of environmental factors to determine how total archaeal

abundance in indoor environments is related to building characteristics and occupants’

activities. Based on our results, total archaeal abundance ranged from 4.98×103 to

3.05×106 (16S rRNA gene copies / g dust) in bedrooms and 1.42×103 to 4×103 in the most

used rooms. Results also demonstrated that furniture, occupants, and activities can

potentially be associated with Archaeal abundance. This is the first report of detecting

archaea in household dust, and further study is needed to better understand the causes and

consequences of this group in indoor environments.

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6.2 Background

The Biology and ecology of the third domain of life, Archaea, have been studied much less when compared to the other domains including Bacteria, and Eukarya. Archaea are microorganisms discovered in the late 1970s [298]. For many years, scientists believed that Archaea were restricted to extreme environments, such as deep-sea hydrothermal vents, hypersaline waters, or strictly anoxic ecosystems [299]. However, development of culture-independent molecular techniques and high-throughput molecular sequencing approaches has transformed this belief by illustrating the presence of Archaea, often with high abundance and diversity, in terrestrial and aquatic environments [300-302], animal care facilities [303-305], deteriorated medieval wall paintings [306], as well as the human and animal microbiome such as gastrointestinal (GI) tracts [307-311] and human oral cavities [312]. However, the presence of Archaea in many other ecosystems has not been investigated and our understanding of their role in their habitat is limited.

One overlooked ecosystem is the indoor environment. There is significant ongoing research on better understanding the “built environment microbiome” [15], with a focus on characterizing microbial diversity as well as the environmental parameters that drive such patterns [12-17, 19-24]; however most of these studies considered bacteria [12, 15,

25-28] and to a lesser degree fungi [14, 16, 20, 22, 29, 30]. The potential presence of

Archaea in houses and environmental parameters affecting their abundance and diversity have not been investigated or detected to date, to the best of our knowledge.

We used culture-independent molecular approaches to study the archaeal assemblages in house dust from homes in the miniCHILD study - a preliminary cohort of 54 homes in

146 the Vancouver area recruited to assist in the optimization and validation of data collection tools for the Canadian Healthy Infant Longitudinal Development (CHILD) study. We sought to answer three general questions: (1) are Archaea regular components of built environment microbiomes? If yes, (2) what would their abundance magnitude be compared to indoor bacteria? And finally (3) how do building characteristics and occupants’ activities relate to the variation in archaeal abundances.

6.3 Material and Methods

6.3.1 Sample Collection

As outlined in section 3.3.1

6.3.2 DNA extraction and quantitative PCR analyses

Total DNA was extracted from 100 mg of collected coarse dust samples using a

FastDNA® SPIN Kit for Soil (MP Biomedicals, LLC, Solon, OH, USA), which was selected systematically by using the Order Preference by Similarity to Ideal Solution

(TOPSIS) method [218] as the most optimum extraction kit for dust samples.

Subsequently, extracted DNA samples were checked for integrity by agarose gel electrophoresis with Lambda DNA HindIII Digest standards (New England BioLabs,

Ipswich, MA, USA) and their quantities were measured using the QuantiFluor® dsDNA

System (Promega, Madison, WI, USA). The purity of extracted DNA samples was evaluated by measuring each sample’s ratio of the optical density at 260 nm and 280 nm using the NanoVue Plus™ spectrophotometer (GE Healthcare, Buckinghamshire, UK), before preserving them at –20 °C. Abundances of archaeal and bacterial small-subunit rRNA gene copy numbers were measured by quantitative PCR (qPCR) using A364aF (5’

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CGGGGYGCASCAGGCGCGAA 3’) and A934bR (5’ GTGCTCCCCCGCCAATTCCT

3’) primers for Archaea [313] and BACT1369F (5’ CGGTGAATACGTTCYCGG 3’) and PROK1492R (5’ GGWTACCTTGTTACGACTT 3’) for bacteria [314]. All PCR amplifications were carried out in a CFX96 Touch™ Real-Time PCR Detection System

(BioRad, Ontario, Canada) and each PCR reaction mixture (20 µL) contained 10 µL of

SsoFast™ EvaGreen® Supermix (Biorad, Hercules, CA), 1.5 µL of 1000 µg/ µL T4 gene

32 protein (Biolabs, Ipswich, MA), 0.4 µM of each primer, nuclease-free water (IDT,

Coralville, IA, USA), and 2 µL of extracted DNA (5 ng / µL). Thermal-cycling conditions for 16S Archaea were as follows: 95°C for 2 min, 40 cycles of 95°C for 30 s and 61.5°C for 30 s, 65°C for 5 s and 95°C for 50 sec. Thermal-cycling conditions for

16S bacteria were as follows: 95°C for 2 min, 40 cycles of 95°C for 30 s and 56°C for 30 s, 65°C for 5 s and 95°C for 50 s.

Standard curves were obtained using three replicates of 1:10 serial dilutions of linearized plasmids containing both cloned archaeal and bacterial 16S rRNA sequences, giving a concentration range from 10 to 106 copies/ µL. Amplification efficiencies of 92.2 – 94.7

% (R2 > 0.985) and 90.1 – 105.8 (R2 > 0.963) were observed for archaeal and bacteria standards, respectively. Finally, melting curve analyses at the end of all qPCR runs and agarose gel running of qPCR products were performed to check for amplification and specificity of the products.

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6.3.3 Collection of environmental variables and statistical analyses

We monitored and recorded 668 housing characteristics as well as building inhabitants’ activities by using standardized questionnaires and by direct on-site visits for the purpose of statistical analyses in the subsequent stages of the study, which has been described in detail in recent publications [216, 217] (subset of collected factors are shown in Table

3.1). The questionnaire was comprised of questions on the location, history, and characteristics of the unit, such as basic house dimensions, construction details of the building envelope, furniture materials and finishes for interior designs, the occurrence of factors which could influence moisture sources and air change as well as number, type and activities of the occupants.

All statistical analyses were performed in PRIMER 7 and STATICTICA 12 [225, 226].

Regarding the first two questions, the abundance of archaeal and bacterial genes in bedrooms versus the most used rooms were first plotted (log scale) to illuminate the indoor archaeal abundance relative to that of bacteria. Subsequently a Wilcoxon Matched

Pairs Test was used to investigate whether or not there is a significant statistical difference between total archaeal abundances in different types of rooms (Step 1). Then, for the third question, the BEST (Bio-Env) routine, namely BVSTEP, was used to determine which environmental factors and resident activities ‘collectively’ best explain the overall variation in archaeal total abundances in both room types (Step 2).

Subsequently, in Step 3, univariate data analyses, namely Mann-Whitney (for two level categorical factors), Kruskal-Wallis (for multi-level categorical variables), and Spearman

Correlation tests (for numerical variables) were employed to explore which individual

149 screened environmental variable from Step 2 would be relatively more associated with the variation of archaeal abundances.

6.4 Results

6.4.1 Presence of Archaea in indoor environments and their total abundance compared to bacteria

Our study demonstrated that Archaea are present in floor dust samples of houses.

Archaeal abundances varied between 4.98×103 to 3.05×106 (16S rRNA gene copies / g dust) in bedrooms and 1.42×103 to 4.21×106 in the most used room samples. However, these magnitudes were notably lower compared to the indoor bacterial abundances, between 4.35×107 to 9.07×1010 in bedrooms and 1.93×107 to 1.76×1010 in the most used rooms. When we compared the pairs of bedroom and the most used room samples taken from the same houses, a significant difference was detected between their archaeal abundances (Wilcoxon Matched Pairs Test, p = 0.04), with higher abundance occurring in the most used rooms (Figure 6.1A); however, no similar indication was found for indoor bacteria (Figure 6.1B).

Subsequently by using the BEST procedure, I determined that almost 55 % of variation of total magnitudes of indoor archaeal 16S rRNA gene copies can be explained by 15 and 21 out of 668 environmental factors in bedrooms and the most used rooms, respectively

(Table 6.1 and 6.2). When the screened factors by BEST for bedrooms were analyzed individually, only “use of electric dryer-vented outdoors” (Mann-Whitney U Test, p =

0.005) remained significant (Table 6.1), negatively affecting the total abundances of bedroom Archaea (Figure 6.2). The influence of this factor was also noted in the most

150 used room, albeit to a lesser degree (p = 0.06, Table 6.2). Independent of the influence of outdoor-vented electric dryers, there were other factors that influenced archaeal abundance in the most used room, including presence of upgraded plumbing system (p =

0.029), use of broom (p = 0.030), hanging wet clothes inside the house (p = 0.031), and use of liquid or solid air fresheners (p = 0.032). We found that the presence of an upgraded plumbing system (Figure 6.3) and hanging wet clothes inside the house

(Figure 6.4) were negatively associated with the total abundance of Archaea in the most used rooms. In contrast, use of broom, liquid or solid air fresheners positively affected the total abundance of Archaea in the most used rooms (Figure 6.5 and 6.6).

Figure 6.1 Total abundance of (A) Archaea and (B) Bacteria in bedrooms and the most used rooms.

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Table 6.1 Environmental and behavioural factors that best explain variation of the total archaeal abundance in bedrooms.

Bedroom Screened Factors by Best a Archaeal Total Abundance b (Multi-Factor Analysis) (Uni-Factor Analysis) Numeric p-value Rho Room area (sq. m)+1) ------Number of plastic or vinyl furniture ------Number of press wood furniture ------Number of leather furniture ------Categorical p-value NA Type of furnace's filter ------Do they take off shoes when enters the unit ------Is there a private child room ------Occurrence of condensation on windows in cooler --- weather --- Presence of humidifier ------Presence of long hair cat ------Presence of stuff toys ------Type of window's covering ------Use of Electric dryer-vented outdoor 0.005 --- Use of gas fire place ------Use of Swiffer wet jet ------a Multi-factor analyses: All factors are collectively responsible for 55.1% variation of total abundance of Archaea in bedrooms. b Single-factor analyses: Only significant factors are included.

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Table 6.2 Environmental and behavioural factors that best explain variation of the total archaeal abundance in the most used rooms.

The Most Used Room Screened Factors by Best a (Multi-Factor Analysis) Archaeal Total Abundance b (Uni-Factor Analysis) Numeric p-value Rho Number of plastic or vinyl furniture ------Categorical p-value NA Age of floor ------Basement condition ------Basement foundation ------Hanging wet clothes inside the house 0.031 --- Presence of air conditioning system ------Presence of Garage ------Presence of plants in home 0.061 --- Presence of plastic or vinyl covered furniture ------Presence of short hair cat ------Presence of stove fan in the kitchen ------Presence of swimming pool 0.056 --- Presence of upgraded plumbing system 0.029 --- Use of antibacterial hand cleaner ------Use of broom 0.030 --- Use of floor cleaners ------Use of Electric dryer-vented outdoor 0.062 --- Use of liquid or solid air fresheners 0.032 --- Use of oven cleaners ------Use of scented laundry detergents ------Use of vacuum ------a Multi-factor analyses: All factors are collectively responsible for 56.3% variation of total abundance of Archaea in bedrooms b Single-factor analyses: Only significant factors are included.

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Figure 6.2 The relationship between use of electric dryer vented outdoors and total archaeal abundance in bedrooms.

Figure 6.3 The relationship between the presence of an upgraded plumbing system and total archaeal abundance in the most used rooms.

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Figure 6.4 The relationship between hanging wet clothes inside the house and total archaeal abundance in the most used rooms.

Figure 6.5 The relationship between use of a broom for cleaning and total archaeal abundance in the most used rooms.

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Figure 6.6 The relationship between use of liquid or solid air fresheners and total archaeal abundance in the most used rooms.

6.5 Discussion

I demonstrated the presence of Archaea in house dust and the influence of selected indoor characteristics on archaeal abundance. These data add to the existing knowledge that

Archaea are not only present in extreme environments with physical limits for biological systems [298, 299] but also members of this domain of life are broadly distributed and abundant in many moderate environments [300, 304, 312, 315-319]; In our case, in houses where humans in developed countries spend the majority of their time and where most of the microbial exposures that they encounter occur [320]. When we compared total abundances of indoor Archaea with Bacteria, we consistently found greater

156 abundance of bacterial than archaeal 16S rRNA gene copies (between 100 to 10 000 times). Archaea comprise a significant proportion of microbial diversity in soil and pelagic ocean waters, with a ratio of Archaea : Bacteria around 1:10 [321, 322]. We observed a much smaller archaeal contribution in indoor environments with a ratio of

Archaea : Bacteria in the range around 0.02:10 in bedroom and 0.06:10 in the most used rooms. This could be because of less available sources contributing to indoor Archaea or more environmental filtering preventing archaeal establishment in indoor environments.

This also can be interpreted that the indoor archaeal assemblages are probably allochthonous for the most part (passive entrants of archaeal traces from different sources), in contrast to those of the bacteria which are a mixture of allochthonous and autochthonous (live and active inhabitants of dust).

Within houses, we found that Archaea are not equally distributed and the most used rooms had significantly higher total Archaea than bedrooms (Figure 6.1B), maybe because of the higher human traffic and a greater input of outdoor microbiota propagated indoors through open windows, on footwear or groceries brought inside. In addition, the most used rooms have normally higher material heterogeneity which may provide more resources for different type of archaeal taxa to be established.

Within each room type, total abundance of Archaea varied depending on different environmental factors. Use of electric dryer-vented outdoor was negatively correlated with total abundance of Archaea in bedrooms (Figure 6.2) and to a lesser degree in the most used rooms (Table 6.2). The reason for difference in the effectiveness of this factor between two rooms may be a result of electrical dryers located closer to bedrooms. Every time a laundry load is dried, some Archaea may be removed from the indoor environment

157 through exhaust fans, and hence the neighboring areas in the house would contain lower amount of Archaea. In addition, I found that houses where wet clothes were hung inside

(Figure 6.3), the total abundance of Archaea was lower. This could be because when clothes are hung indoors to dry (as opposed to outdoors), the indoor environment may have lower input of outdoor air and thus a lower input of airborne Archaea. It may be worth adding that such factors may somewhat be also related to alteration of indoor humidity levels. For example, the use of indoor driers and hanging wet clothes inside the house generally increase indoor relative humidity, and I observed in such cases the total archaeal abundance decreased.

Use of liquid or solid air fresheners inside the house can be another source of archaeal traces. Our result shows a positive correlation between this factor and the total abundance of Archaea. I speculate that the DNA of these species may be embedded in the raw materials and additives of air fresheners and hence distributed into the indoor environment upon freshener usage.

Finally, houses with old plumbing systems had higher levels of Archaea likely because this factor may act as a source of indoor Archaea by accumulation of archaeal biofilm

[323, 324] inside the plumbing system where biofilm-forming species can survive, release and disperse into the indoor environment to colonize new sites.

6.6 Summary

To the best of our knowledge, this is the first study to investigate archaeal abundance in household dust in relation to physical building characteristics and occupant activities.

These results may be used to form the basis of further manipulative studies assessing the

158 causality between factors and total abundance of indoor Archaea, diversity of the indoor archaeal community, as well as studies focusing on determining the association of the indoor archaeal community with human health and disease. Better understanding of indoor microbial diversity may provide useful insight into the role of environment as a determinant of health particularly in relation to non-infectious diseases in which inflammatory mediators are thought to be important.

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7 Chapter 7: Relationships of Fungal Spore Concentrations in the Air and Meteorological Factors

7.1 Overview

High concentrations of airborne fungal spores are associated with human health concerns.

Environmental variables, such as temperature and moisture, can influence growth and

reproduction in fungi and airborne spore concentrations can fluctuate seasonally.

However, we do not understand how long-term changes in climate affect fungal

abundance in the air. Here, airborne fungal spores were sampled annually (at peak

season, in September, over a 20-year period) in two North American cities (New York

and Toronto), and related fungal abundance to local variation in climate. I found that at

both locations, the total precipitation during the 2- month period prior to sampling (July-

August) was negatively related to observed fungal spore concentrations. Considering that

climate predictions for these two regions indicate an increase in drought events in

summer, I expect airborne fungal concentrations to increase in future years, potentially

exasperating human health concerns.

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7.2 Background

Terrestrial ecosystems will likely encounter noticeable changes in weather patterns including warmer temperatures and more precipitation, summertime droughts and extreme weather events such as tornados and storms in the next decades [325]. However, the effect of such changes on the formation and distribution of fungal particulates, a major concern for public health, has not been elucidated. Exposure to high concentrations of omnipresent fungal spores in the atmosphere can be associated with a wide range of infectious diseases, allergies, cardiovascular disease and mortality [7, 11, 82, 207, 326].

Exposure to specific species such as Aspergillus fumigatus, Coccidioides immitis, or

Cryptococcus neoformans can result in severe fungal diseases including Aspergillosis,

Coccidioidomycosis, and Cryptococcosis, respectively [326].

Sporulation, dispersion of spores and consequently their concentration are closely related to variations in meteorological conditions and is the result of complex interactions between biological and environmental factors [327]. Correlations between fungal spore abundance in the atmosphere and environmental factors have been shown previously.

However, most earlier studies have been conducted over a short time scale [328-331] ), with a focus on seasonal differences in spore abundance. Studies that used longer time intervals mostly focused on specific fungal taxa [331-334]. The goal of this research was to focus within a single season (late summer) and to sample over many years (20 years) to determine if spore abundance in the air correlates with inter-annual changes in temperature and precipitation.

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7.3 Material and Methods

As part of a long-term research project at Dr. John Klironomos’ laboratory, the total fungal spores had been collected from two locations (adjacent to City Hall in New York

City, U.S.A.; and adjacent to City Hall in Toronto, Canada). At both locations, sampling was performed annually (from 1992 – 2011), during the first week of each September.

Each yearly data point was an average from sampling over a 3 d period. On each day, six

10-min sub-samples were taken at 2-hr intervals with a Samplair-MK1 particle sampler

(Allergenco, 403-7834 Broadway, San Antonio, TX 78209, USA). The instrument collected 9 l of air min-1 and fungal spores and hyphal fragments were collected on a slide covered with Vaseline. The sampler was placed at a height of 1.8 m above the ground, located on a green space adjacent to City Hall. Slides were brought back to the laboratory for analysis using a compound microscope. The number of spores of different genera was identified (Alternaria, Aspergillus/Penicillium, Ascospores, Basidiospores, Bispora,

Cladosporium, Curvularia, Epicoccum, Fusarium, Pithomyces, and Other). Spores of

Aspergillus and Penicillium were pooled together as it was not possible to distinguish between them.

To determine how inter-annual variations in airborne spore concentrations were affected by inter-annual changes in climate, climate data (Temperature, Precipitation) were collected from the National Oceanic and Atmospheric Administration (NOAA) database.

I also recorded atmospheric [CO2], as it has been shown that this trace gas can directly affect fungal sporulation [335]. Variation in spore numbers was examined in relation to climatic variables (average Temperature, Precipitation, and CO2 concentration) at

162 different time intervals including the week of sampling (September 1st – 7th), 1 month before sampling (August), 2 months before sampling (July), average of the previous 2 months (July – August), the previous spring and summer, the previous winter, the previous fall, and the whole year. Using standard least square regression results, based on the lowest p-value and largest coefficient of determination (R2) value (Table 7.1), the mean temperature and the mean precipitation of the period July-August was found to have the highest effect on the variation of total spore concentrations in both cities. No relationship between CO2 concentration and total spore concentration was detected.

Furthermore, because of consistent relationships between precipitation and total spore numbers in the two cities, the relationship between this environmental factor and spore numbers of different taxonomic groups of fungi was studied using standard least square regression.

7.4 Results and Discussion

Total spore numbers over 20 yr varied between 114 800 – 236 700 spores/m-3 air in

Toronto and 60 200 – 204 800 spores/m-3 air in New York City (Appendix E). Although the variation in spore concentrations was related to different climatic variables, the strongest relationship was observed with one time interval (average temperature and precipitation in July – August). Changes in precipitation during this time interval had a significant negative effect on the total spore concentration in both cities. New York City had more precipitation as well as a larger range of total spore concentrations. However, in

Toronto I observed a sharper decrease of total spore concentration with increasing precipitation (Figure 7.1A).

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Table 7.1 Regression results used for the selection of the significant time interval for environmental variables

Average Temperature (oC) Average Precipitation (mm)

Time Interval Toronto New York Toronto New York

p-value r2 p-value r2 p-value r2 p-value r2

Week of Sampling 0.51 0.02 0.64 0.00 0.80 0.00 0.52 0.02 (September 1st-7th) Last August 0.12 0.13 0.00 0.36 0.07 0.17 0.07 0.17

Last July 0.19 0.09 0.05 0.20 0.00 0.37 0.00 0.51

Last July-August 0.35 0.15 0.00 0.39 0.00 0.61 0.00 0.58

Last May-August 0.14 0.11 0.02 0.26 0.14 0.11 0.00 0.65

Last January-April 0.17 0.10 0.26 0.06 0.94 0.00 0.04 0.20

Last September-December 0.10 0.16 0.86 0.00 0.74 0.01 0.00 0.46

Last Year 0.26 0.06 0.12 0.13 0.19 0.09 0.00 0.49

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Figure 7.1 Variation of total spore concentration with (A) July – August average precipitation, and (B) average temperature in Toronto and New York from 1992 to 2011.

165

Specific taxonomic groups of fungi followed similar patterns (Figure 7.2 and 7.3). In contrast to precipitation, observed patterns with temperature were less consistent. New

York City was generally warmer (July-August total average temperatures of 24.6◦C) and temperature had a positive effect on the concentration of spores; Toronto was cooler

(July-August total average temperatures of 21.4◦C) and no significant relationship between temperature and spore concentration was observed (Figure 7.1B).

Our results clearly demonstrate that annual variation in climate, particularly precipitation, and to a lesser degree temperature, can determine fungal concentrations in the air. The main observed pattern is that drier years resulted in higher fungal concentrations. This is a consistent result and part of a more general pattern in the literature. It is well known that fungi typically require moisture to grow and sporulate [231], many spores eventually become airborne [336], yet precipitation may “clean” the air by forcing fungal propagules back to the ground, or onto other surfaces [337, 338].

The results may also have climate change implications. Climate models predict drier and warmer summers in eastern North America [325]. Consequently, our results indicate that the total fungal spore count should increase significantly, in particular members of

Aspergillus, Penicillim, Cladosporium, Fusarium, Epicoccum and other ascomycetes.

There is a public health concern to such fungal responses since a constant exposure to high concentrations of total fungal spores or specific taxa can lead to severe allergic respiratory disorders, cardiovascular disease and mortality [11, 82, 339], specifically in individuals with a weakened immune system [339]. Next to allergic respiratory problems, the effect of invasive fungal infections on human health, which has been shown to escalate over the last few decades [339], is still not widely understood.

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Figure 7.2 Mean distribution of the main spore types present in the atmosphere of Toronto from 1992 to 2011 (A), and their corresponding trends of changes with the level of precipitation: (B) Alternaria (AL); (C) Aspergillus/Penicillium (AP); (D) Ascospores (AS); (E) Basidiospores (BA); (F) Baspora (BA); (G) Cladosporium (CL); (H) Curvularia (CU); (I) Epicoccum (EP); (J) Fusarium (FU); (K) Pithomyces (PI); (L) Other (OT).

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Figure 7.3 Mean distribution of the main spore types present in the atmosphere of New York from 1992 to 2011 (A), and their corresponding trends of changes with the level of precipitation: (B) Alternaria (AL); (C) Aspergillus/Penicillium (AP); (D) Ascospores (AS); (E) Basidiospores (BA); (F) Baspora (BA); (G) Cladosporium (CL); (H) Curvularia (CU); (I) Epicoccum (EP); (J) Fusarium (FU); (K) Pithomyces (PI); (L) Other (OT).

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Most public health agencies, such as World Health Organization (WHO), have no program on fungal infection and carry out little or no mycological investigations [339].

Finally, there is significant literature on the formation and accumulation of other air pollutants, ozone (O3) and particulate matter (PM), and how they are influenced by meteorological variables (e.g., temperature, precipitation, relative humidity, and wind)

[185, 186, 328]. However, determinants of fungal abundance and composition in the air, how they may interact with other pollutants, and the health consequences of long-term exposure are not understood.

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8 Chapter 8: Concluding Remarks

House dust is a layer of particulate matters originated indoors or brought inside on the

shoes and clothing of inhabitants and visitors, with pets, and along with the fresh air

through windows and ventilation systems. Floor dust can form stable mats because of its

fibrous nature, harboring enormously diverse organisms and protecting them from

desiccation. In fact, each bit of dust may be a microhistory of occupant’s life. Since many

of these organisms play important roles, and could directly interact with humans and their

activities, in this PhD study, I endeavored to better characterize the indoor biota and

provide some better comprehension of the factors that would potentially associate with

diversity and patterns of organisms in indoor spaces.

More specifically, floor dust samples were collected from 54 houses in the Lower

Mainland area (BC, Canada). When I analyzed these dust samples and sequenced the

fragments of fungal, bacterial, and animal DNA that they contained, I found that different

rooms formed distinct ecological niches and activities within houses possibly influenced

their diversity and patterns. Namely, results consistent with earlier studies supported the

hypothesis that outdoor air is an important source for indoor fungi. For example, usage of

herbicide to inhibit growth of unwanted plants in the yard had the highest correlation with

indoor fungal community; more specifically increasing the frequency and abundance of

saprobic and opportunistic fungal species. In addition to the outdoor source, indoor

fungal assemblages were influenced, though to a lesser degree, by density of occupants,

presence of pets as well as furniture materials and cleaning strategies. Bacterial

communities, on the other hand, were less associated with variables relating to the local

170 outdoor environment of homes, while the density of occupants, presence of pets, and cleaning strategies showed highest associations. For the animal community, the study indicated that besides dust mites, the indoor environment encompass many other forms of animals whose patterns may be affected by type of rooms as well as activities within rooms. More specifically, areas with higher material heterogeneity and disturbances (e.g., the most used rooms) tended to have higher diversity with lesser degree of dust mites, compared to more homogeneous areas such as bedrooms.

The presence of Archaea in indoor environment had been scarcely investigated in the past. Here, I showed that Archaea are present in household dust as a common part of the indoor microbiota, although at much less abundance level when compared to the indoor bacteria. I found that Archaea are not equally distributed within houses, and areas with higher material heterogeneity and greater input of outdoor microbiome tended to have higher abundance of Archaea. Results also added to the existing knowledge that Archaea are not only present in extreme environments with physical limits for biological systems, but also members of this domain of life can be broadly distributed and abundant in moderate environments (i.e., in our case houses where humans spend the majority of their time).

Finally, considering climate models which predict drier and warmer summers in eastern

North America, results of this study suggested that the total airborne fungal spore count in outdoor air could increase significantly, in particular members of Aspergillus,

Penicillim, Cladosporium, Fusarium, Epicoccum. On the other hand, as shown in the results of indoor fungal section, the outdoor environment is one of the main sources of indoor fungi, entering through windows, ventilation systems or brought inside by

171 occupants (human, pets, plants); i.e., many of the common indoor fungi had a likely outdoor origin. Consequently, I suggest the likelihood of an elevation of indoor fungal spore abundance in response to predicted changes in climate.

8.1 Strengths and limitations of the dissertation research

The indoor environment is a complex ecosystem and more recently many studies have attempted to better comprehend the biological component of this environment, mostly microbial community composition, and to bring light into processes that may affect microbial diversity.

This study was among the first to look for members of all domains of life in a single cohort study, including Archaea, Bacteria, as well as Animalia and Fungi in the domain of Eucarya. Another major strength of this research study was the inclusion of a broad range of building characteristics, indoor environmental conditions, and occupants’ activities (total of 668 factors) collected by standardized methods providing critical information on factors that may control patterns and diversity of biota in residential houses. I believe that such information in the long-term may help architects, engineers, scientists, and public health policy decision makers, to make more informed choices and develop best practices, which may in turn result in healthier habitats for human.

During this research, I employed a series of advanced multivariate and univariate statistical techniques not only to reveal the relationships between biotic and abiotic variables with the community composition of organisms of indoor dust, but also to illuminate which individual OTU in each group of studied organisms is associated with the variation of which environmental factors. Such information may help to manipulate

172 the indoor environment so that humans are exposed to a wider phylogenetic diversity of indoor biota, while removing the undesired organisms such as pathogenic and allergenic ones. This may be achieved by carefully controlling particular environmental factors.

Nonetheless, the study was limited by demonstrating only associations between factors and indoor biota. The causality between indoor environmental factors and community compositions of indoor biota is still poorly understood. Finally, one has to be careful in interpretating the results in this study, as it lacks longitudinal data and only characterizes floor dust samples collected in the City of Vancouver, BC, Canada.

8.2 Future directions

I believe that the results of this study have advanced our understanding of biological components of residential houses and illustrated how community composition of different organisms in the indoor environment may be influenced by different building characteristics, furnishing, type of occupants as well as their activities. However, the associations that I presented here need further research to determine (i) the causality between indoor environmental factors and community composition of indoor biota, and investigate associations between organisms in indoor environment with (ii) degradation of mechanical or thermal properties of building structures and (iii) specific health and disease concerns.

In addition, the total contributions of all factors studied were determinants of only a fraction of the total variation in diversity and community composition of indoor biota, suggesting that still there are several other indoor and outdoor neglected factors, beyond the 668 factors monitored in this study.

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Finally, humans share the indoor environment with thousands of micro and macro organisms, a complex ecosystem with patterns that are generally not well understood.

More research is needed that explores the direct and indirect interactions between different taxa which may eventually help to reveal non-random associations, deterministic processes and unanticipated relationships between community members.

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10 Appendices

Appendix A Supplementary graph for indoor fungal OTUs

Bedroom

Most Used Room

Figure A.1 A rank “frequency” distribution of indoor dust fungal OTUs.

230

Appendix B Supplementary table for indoor fungal OTUs

Table B.1 Taxonomy of indoor dust fungal OTUs

Blast-Assigned Taxonomy OTU # Phylum Class Orde r Family Genus Spe cies denovo79 Basidiomycota Tremellomycetes Filobasidiales Filobasidiaceae Cryptococcus Cryptococcus_dimennae denovo164 Ascomycota Sordariomycetes Sordariales Sordariaceae unidentified Sordariaceae_sp_C26_WL_2011 denovo208 Ascomycota Sordariomycetes Incertae_sedis Magnaporthaceaeunidentified uncultured Phialophora denovo235 Ascomycota Dothideomycetes Pleosporales Dothidotthiac Barriopsis Barriopsis_fusca denovo371 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified unidentified denovo416 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Embellisia Embellisia_sp_DAR_74619 denovo464 Ascomycota Leotiomycetes Leotiomycetes Sclerotiniaceae unidentified uncultured Botryotinia denovo530 Ascomycota Leotiomycetes Leotiomycetes Helotiaceae Rhizoscyphus Rhizoscyphus_ericae denovo624 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Alternaria Alternaria_sp_XZSBG_1 denovo750 Ascomycota Leotiomycetes Leotiomycetes Dermateaceae Pezicula Pezicula_sporulosa denovo751 Ascomycota Sordariomycetes Hypocreales Incertae_sedis Acremonium Acremonium_sp_SC121 denovo923 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Verrucaria Verrucaria_sp_A_Orange_16664 denovo965 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaPhialophora Phialophora_sp_MLB_Phi denovo965 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaPhialophora Phialophora_sp_MLB_Phi denovo1047 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Verrucaria Verrucaria_ditmarsica denovo1075 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Cladosporium_sp_F20 denovo1166 Ascomycota Eurotiomycetes Chaetothyriales unidentified unidentified Chaetothyriales_sp_F1 denovo1330 Ascomycota Lecanoromycetes Lecanorales Lecideaceae Hypocenomyce Hypocenomyce_scalaris denovo1415 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Cladosporium_sp_F21 denovo1422 Basidiomycota Tremellomycetes unidentified unidentified unidentified unidentified denovo1436 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales

231

Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Orde r Family Genus Spe cies denovo1530 Ascomycota Leotiomycetes Leotiomycetes Incertae_sedis Pilidium Pilidium_concavum denovo1533 Ascomycota Dothideomycetes Incertae_sedis Incertae_sedis Endococcus Endococcus_fusigera denovo1609 Ascomycota Eurotiomycetes Incertae_sedis Incertae_sedis Sarcinomyces Sarcinomyces_sp_SL_2011 denovo1614 Ascomycota Dothideomycetes Capnodiales Incertae_sedis VerrucocladospoVerrucocladosporium_dirinae denovo1858 Basidiomycota Tremellomycetes Tremellales unidentified unidentified uncultured Tremellales denovo1888 Ascomycota Dothideomycetes Pleosporales Pleosporaceae LeptosphaerulinaLeptosphaerulina_chartarum denovo1891 Ascomycota Sordariomycetes Xylariales unidentified unidentified uncultured Xylariales denovo1891 Ascomycota Sordariomycetes Xylariales Xylariaceae Xylaria Xylaria_curta denovo1930 Ascomycota Dothideomycetes Pleosporales Cucurbitariac PyrenochaetopsiPyrenochaetopsis_microspora denovo1981 Basidiomycota Tremellomycetes Tremellales Trichosporo Trichosporon Trichosporon_jirovecii denovo1982 Ascomycota Dothideomycetes Incertae_sedis Myxotrichac Oidiodendron Oidiodendron_griseum denovo2006 Ascomycota Eurotiomycetes Chaetothyriales unidentified unidentified Chaetothyriales_sp_F2 denovo2049 Ascomycota Leotiomycetes Leotiomycetes Sclerotiniaceae unidentified uncultured Botryotinia denovo2061 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_placitae denovo2101 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales denovo2119 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_crusticola denovo2305 Ascomycota Dothideomycetes Pleosporales Cucurbitariac Curreya Curreya_sp_OUCMBI101084 denovo2322 Ascomycota Saccharomycetes Saccharomycetales Pichiaceae Pichia Pichia_jadinii denovo2341 Basidiomycota Tremellomycetes unidentified unidentified unidentified unidentified denovo2373 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Cochliobolus Cochliobolus_sativus denovo2585 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Cladosporium_cladosporioides

232

Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Order Family Genus Species denovo2718 Ascomycota Lecanoromycetes Lecanorales Physciaceae Amandinea Amandinea_punctata denovo2780 Ascomycota Eurotiomycetes Eurotiales Trichocomaceae Penicillium Penicillium_sp_BLE22 denovo2811 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Verrucaria Verrucaria_elaeina denovo2824 Ascomycota Eurotiomycetes Incertae_sedis Incertae_sedis Sarcinomyces Sarcinomyces_sp_SL_2011 denovo2922 Ascomycota Sordariomycetes Hypocreales Bionectriaceae Bionectria Bionectria_ochroleuca denovo2997 Ascomycota Sordariomycetes Xylariales Hyponectriaceae Microdochium Microdochium_sp_B13 denovo3065 Ascomycota Saccharomycetes Saccharomycetales Incertae_sedis Candida Candida_parapsilosis denovo3142 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales denovo3166 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_eucalyptorum denovo3178 Ascomycota Eurotiomycetes Chaetothyriales Incertae_sedis Coniosporium Coniosporium_uncinatum denovo3245 Ascomycota Eurotiomycetes Chaetothyriales unidentified unidentified Chaetothyriales_sp_16708 denovo3247 Ascomycota Leotiomycetes Leotiomycetes Sclerotiniaceae unidentified uncultured Botryotinia denovo3270 Ascomycota Leotiomycetes Leotiomycetes Erysiphaceae Sawadaea Sawadaea_sp_MUMH2343 denovo3298 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Cladosporium_sp_F22 denovo3329 Ascomycota Leotiomycetes Leotiomycetes Rhytismataceae unidentified uncultured Rhytismataceae denovo3400 Ascomycota Saccharomycetes Saccharomycetales Incertae_sedis Candida Candida_albicans denovo3417 Ascomycota Sordariomycetes Phyllachorales Phyllachoraceae Colletotrichum Colletotrichum_coccodes denovo3463 Ascomycota Lecanoromycetes Lecanorales Teloschistaceae Caloplaca Caloplaca_atroflava denovo3549 Ascomycota Eurotiomycetes Chaetothyriales unidentified unidentified Chaetothyriales_sp_16708 denovo3604 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Verrucaria Verrucaria_ditmarsica denovo3702 Ascomycota Sordariomycetes Xylariales unidentified unidentified uncultured Xylariales

233

Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Order Family Genus Species denovo3727 Ascomycota Dothideomycetes Pleosporales Phaeosphaeria Ampelomyces Ampelomyces_sp_MosID19 denovo3773 Ascomycota Dothideomycetes Dothideales Dothideaceae Aureobasidium Aureobasidium_sp_E_000535660 denovo3775 Ascomycota Dothideomycetes Dothideales Dothideaceae Aureobasidium Aureobasidium_pullulans denovo3788 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_sp_CPC_12172 denovo3811 Ascomycota Eurotiomycetes Incertae_sedis Monascaceae Xeromyces Xeromyces_bisporus denovo3858 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales denovo3915 Ascomycota Eurotiomycetes Eurotiales Trichocomaceae Byssochlamys Byssochlamys_nivea denovo3933 Ascomycota Sordariomycetes Lulworthiales Lulworthiaceae Zalerion Zalerion_arboricola denovo3984 Ascomycota Dothideomycetes Dothideales Dothideaceae Dothidea Dothidea_sp_CanS_64 denovo3989 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_eucalyptorum denovo4017 Ascomycota Saccharomycetes Saccharomycetales Pichiaceae Pichia Pichia_jadinii denovo4097 Ascomycota Eurotiomycetes Onygenales Onygenaceae Auxarthron Auxarthron_umbrinum denovo4116 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Epicoccum Epicoccum_sp_OUCMBI101238 denovo4121 Ascomycota Leotiomycetes Leotiomycetes Incertae_sedis Scytalidium Scytalidium_sp_JZ_78 denovo4141 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Mycosphaerella Mycosphaerella_fragariae denovo4171 Ascomycota Leotiomycetes Leotiomycetes Erysiphaceae Erysiphe Erysiphe_elevata denovo4194 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_dermatitidis denovo4227 Ascomycota Eurotiomycetes Incertae_sedis Incertae_sedis Sarcinomyces Sarcinomyces_crustaceus denovo4255 Ascomycota Dothideomycetes Capnodiales Capnodiaceae Capnodium Capnodium_sp_OUCMBI101100 denovo4318 Ascomycota Dothideomycetes Pleosporales Incertae_sedis Phoma Phoma_exigua_var_exigua denovo4417 Ascomycota Leotiomycetes Leotiomycetes Sclerotiniaceae Sclerotinia Sclerotinia_trifoliorum

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Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Orde r Family Genus Spe cies denovo4554 Ascomycota Dothideomycetes Capnodiales Incertae_sedis Friedmanniomyc Friedmanniomyces_endolithicus denovo4633 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_salmonis denovo4637 Ascomycota Sordariomycetes Diaporthales Valsaceae Valsa Valsa_sordida denovo4662 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Leotiomycetes denovo4677 Ascomycota Eurotiomycetes Chaetothyriales Herpotrichiellaceaunidentified uncultured Cladophialophora denovo4737 Ascomycota Dothideomycetes Pleosporales Pleosporaceae LeptosphaerulinaLeptosphaerulina_chartarum denovo4739 Ascomycota Leotiomycetes Leotiomycetes Dermateaceae CryptosporiopsisCryptosporiopsis_sp_LZ_2012 denovo4760 Ascomycota Leotiomycetes Leotiomycetes Erysiphaceae Blumeria Blumeria_graminis denovo4920 Ascomycota Sordariomycetes Lulworthiales Lulworthiaceae Zalerion Zalerion_arboricola denovo4953 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified unidentified denovo5167 Ascomycota Dothideomycetes Pleosporales Corynesporasc Corynespora Corynespora_olivacea denovo5200 Ascomycota Sordariomycetes Hypocreales Nectriaceae Fusarium Fusarium_sporotrichioides denovo5201 Ascomycota Sordariomycetes Hypocreales Nectriaceae Gibberella Fusarium_lateritium denovo5406 Ascomycota Saccharomycetes Saccharomycetales SaccharomycetacDebaryomyces Debaryomyces_hansenii denovo5419 Ascomycota Lecanoromycetes Lecanorales Lecanoraceae Candelariella Candelariella_medians denovo5494 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Verrucaria Verrucaria_denudata denovo5530 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Thelebolales denovo5602 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales denovo5636 Ascomycota Eurotiomycetes Chaetothyriales Chaetothyriaceae Cyphellophora Cyphellophora_laciniata denovo5637 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaCapronia Capronia_sp_96005a

235

Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Order Family Genus Species denovo5659 Ascomycota Eurotiomycetes Chaetothyriales Incertae_sedis Coniosporium Coniosporium_sp_SL110414 denovo5679 Ascomycota Sordariomycetes Xylariales Incertae_sedis Phomatospora Dinemasporium_strigosum denovo5740 Ascomycota Eurotiomycetes Chaetothyriales Incertae_sedis Coniosporium Coniosporium_uncinatum denovo5845 Ascomycota Dothideomycetes Pleosporales Pleosporaceae unidentified uncultured Ulocladium denovo5968 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Cadophora denovo6061 Ascomycota Dothideomycetes Pleosporales Incertae_sedis Phoma Phoma_exigua_var_exigua denovo6089 Ascomycota Dothideomycetes Pleosporales Leptosphaeria Peyronellaea Peyronellaea_australis denovo6174 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Alternaria Alternaria_sp_XZSBG_2 denovo6239 Ascomycota Leotiomycetes Leotiomycetes Hyaloscyphaceaeunidentified uncultured Clathrosphaerina denovo6443 Ascomycota Dothideomycetes Incertae_sedis Incertae_sedis KirschsteiniothelKirschsteiniothelia_aethiops denovo6481 Ascomycota Eurotiomycetes Chaetothyriales Incertae_sedis Coniosporium Coniosporium_sp_MA4783 denovo6505 Basidiomycota Tremellomycetes Tremellales Tremellaceae Bullera Bullera_globispora denovo6546 Ascomycota Saccharomycetes Saccharomycetales Incertae_sedis Candida Candida_solani denovo6567 Ascomycota Dothideomycetes Pleosporales Pleosporaceae Epicoccum Epicoccum_sp_TMS_2011 denovo6574 Ascomycota Leotiomycetes Leotiomycetes Erysiphaceae Erysiphe Erysiphe_monascogera denovo6581 Ascomycota Dothideomycetes Pleosporales Phaeosphaeria PhaeosphaeriopsPhaeosphaeriopsis_sp_KH00317 denovo6598 Ascomycota Lecanoromycetes Lecanorales Lecanoraceae Rhizoplaca Rhizoplaca_aspidophora denovo6664 Ascomycota Sordariomycetes Sordariales Lasiosph Podospora Podospora_curvuloides denovo6668 Ascomycota Sordariomycetes Hypocreales Bionectriaceae Bionectria Bionectria_coronata denovo6771 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaPhaeococcomycePhaeococcomyces_chersonesos denovo6793 Ascomycota Sordariomycetes Xylariales Hyponectriaceae Microdochium Microdochium_sp_wb587 denovo6856 Ascomycota Leotiomycetes Leotiomycetes Erysiphaceae Podosphaera Podosphaera_xanthii denovo6986 Ascomycota Dothideomycetes Pleosporales unidentified unidentified Pleosporales_sp_UM98 denovo6993 Ascomycota Sordariomycetes Incertae_sedis Incertae_sedis Eucasphaeria Eucasphaeria_capensis denovo7026 Ascomycota Eurotiomycetes Eurotiales Trichocomaceae Aspergillus Aspergillus_fumigatus

236

Table B.1 (Cont’d)

Blast-Assigned Taxonomy OTU # Phylum Class Order Family Genus Species denovo7033 Ascomycota Sordariomycetes Hypocreales Hypocreaceae Trichoderma Trichoderma_asperellum denovo7062 Basidiomycota Tremellomycetes Incertae_sedis Incertae_sedis Holtermanniella Holtermanniella_festucosa denovo7168 Ascomycota Dothideomycetes Pleosporales Incertae_sedis Mycocentrospor Mycocentrospora_cantuariensis denovo7185 Ascomycota Eurotiomycetes Verrucariales Verrucariaceae Sporodictyon Sporodictyon_cruentum denovo7243 Ascomycota Eurotiomycetes Chaetothyriales HerpotrichiellaceaExophiala Exophiala_eucalyptorum denovo7452 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Dissoconium_proteae denovo748 Ascomycota Dothideomycetes Pleosporales Incertae_sedis Phoma Phoma_sp_NRRL_52421 denovo7482 Ascomycota Dothideomycetes Pleosporales Pleosporaceae LeptosphaerulinaLeptosphaerulina_chartarum denovo7556 Ascomycota Leotiomycetes Leotiomycetes unidentified unidentified uncultured Helotiales denovo7591 Ascomycota Sordariomycetes Sordariales Chaetomiaceae Chaetomium Chaetomium_globosum denovo7604 Ascomycota Dothideomycetes Pleosporales Pleosporaceae LeptosphaerulinaLeptosphaerulina_chartarum denovo7853 Ascomycota Sordariomycetes Diaporthales Diaporthaceae Diaporthe Diaporthe_phaseolorum denovo7853 Ascomycota Sordariomycetes Diaporthales Diaporthaceae Diaporthe Diaporthe_phaseolorum denovo7907 Ascomycota Dothideomycetes unidentified unidentified unidentified Dothideomycetes_sp_genotype_113 denovo8041 Basidiomycota Tremellomycetes Filobasidiales Filobasidiaceae Cryptococcus Cryptococcus_sp_HB_1222 denovo8099 Ascomycota Dothideomycetes Capnodiales Mycosphaerella Cladosporium Cladosporium_sp_F23 denovo8122 Basidiomycota Tremellomycetes unidentified unidentified unidentified unidentified denovo8160 Ascomycota Leotiomycetes Leotiomycetes Rhytismataceae Lophodermium Lophodermium_gamundiae denovo8189 Ascomycota Eurotiomycetes Chaetothyriales unidentified unidentified Chaetothyriales_sp_16709 denovo8277 Ascomycota Sordariomycetes Incertae_sedis Plectosphaerellac Verticillium Verticillium_cf_biguttatum_CBS_7773B denovo8310 Ascomycota Dothideomycetes Incertae_sedis Incertae_sedis Peltaster Peltaster_sp_ZalMj1_4 denovo8467 Ascomycota Dothideomycetes Incertae_sedis Myxotrichac unidentified unculturedMyxotrichaceae denovo8493 Ascomycota Sordariomycetes Hypocreales Nectriaceae Gibberella Fusarium_lateritium

237

Appendix C Supplementary graph for indoor bacterial OTUs

Bedroom

Most Used Room

Figure C.1 A rank “frequency” distribution of indoor dust bacterial OTUs.

238

Appendix D Supplementary graph for indoor animal OTUs

1 Bedroom

Most Used Room 0

-1

-2 Log Frequency

-3

-4 0 200 400 600 800 OTU Rank

Figure D.1 A rank “frequency” distribution of indoor dust animal OTUs

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Appendix E Supplementary table for airborne fungal spore concentrations

Table E.1 Airborne fungal spore concentrations between the period of 1992 – 2011 measured in the first week of each September in New York City and Toronto.

New York Spores/m3 air 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total Spores 141.9 167.8 136.9 203.4 111.8 78.2 189.2 196.5 89 204.8 139.6 116 81.8 162.1 86.9 72.3 151.5 60.2 186.9 78.9 Alternaria 0.9 0.5 0.4 0.8 0.8 0.8 0.4 0.8 0.7 0.8 1.1 1.1 0.1 1 0.8 0.4 0.3 0.5 0.4 0.3 Aspergillus/Penicillium 58.5 62.3 52.4 69.9 43.1 27.2 73.6 70 30.6 77.3 61.4 43 35.8 55.2 25.1 19.7 55.4 21.2 63.2 27.2 Ascospores 8 10.7 11.2 18.9 13.1 12.1 18.5 16.3 6.3 18.8 9.4 10.3 13 8.6 11.3 13 7.7 4.1 19.5 11.6 Basidiospores 0.3 0.6 0.5 0.3 0.7 0.7 0.2 0.2 0.3 0.7 0.6 0.2 0.4 0.6 0.3 0.3 0.4 0.1 0.7 0.7 Bispora 0.3 0 2.2 0.2 0 0.3 0.5 0 0.4 0.2 0 0.5 0.1 0 0 0.3 0 0.2 0.3 0.2 Cladosporium 30.4 44.6 32 52.8 23.5 8.5 39.2 49.7 28.2 44.8 24.5 19.1 4.8 35.1 11 10.7 22.6 9.2 52 13 Curvularia 0.3 0.4 0.4 0.5 0.4 0.1 0.2 0.3 0.2 0.2 0.1 0.3 0.2 0.2 0 0.1 0.1 0.2 0.5 0.2 Epicoccum 1.5 1.6 1.3 1.7 1.4 0.9 3.8 1.9 0.3 2.6 1.5 1.9 0.8 1.9 2 1.1 1.8 0.9 1.8 0.3 Fusarium 1.7 4.3 2.6 3.8 2.4 3 2.7 3.9 0.7 4.1 2.5 5.1 0.3 2.7 2.1 1 1.4 1.3 2.5 1.1 Pithomyces 0.9 0.3 0.6 0.4 0.3 0.1 0.4 0.5 0.8 2.8 0.9 0.8 0.2 0.3 0.6 0.2 0.1 0.2 0.9 0.5 Other 39.1 42.5 33.3 54.1 26.1 24.5 49.7 52.9 20.5 52.5 37.6 33.7 26.1 56.5 33.7 25.5 61.7 22.3 45.1 23.8

Toronto Spores/m3 air 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total Spores 134.9 168.8 181.6 122.4 176.4 174.7 236.7 211.4 234.4 229 225.9 171.2 114.8 162.2 167.7 218.2 127.4 131.8 164.6 228.5 Alternaria 0.4 1.3 1.3 0.6 0.8 1.6 0.8 1.7 1.5 0.3 1.5 1.3 0.3 0.8 0.2 0.6 1 0.8 0.7 1.5 Aspergillus/Penicillium 50.5 66.9 62.4 47.2 68.9 54.2 95.1 66.3 87.5 86.2 77.8 67.5 43.4 66.6 57.6 78.4 44.9 56.5 62 77.6 Ascospores 4.7 6.4 8.3 6.2 5.6 7.1 8.4 8.1 8.1 7.4 12.3 11.2 6.3 6.2 8.5 13.1 5 3.5 9.9 14.4 Basidiospores 0.2 0.3 0.1 0.1 0.2 0.4 0.3 0.3 0.2 0.1 0.1 0.4 0.1 0.4 0.1 0.2 0.3 0.2 0.1 0.2 Bispora 0.3 0.7 2.1 0 0.3 0.3 0.7 0.7 0.4 0.8 0.4 0.5 0 0.5 0.5 0.8 0.2 0.1 0.7 0.8 Cladosporium 22.4 30.3 42.5 19.6 34.2 44.1 51.5 49.7 58.6 58.2 57.8 30.9 16.4 29.4 32.3 51.4 24.7 14.3 36.1 58.5 Curvularia 0.1 0.2 0 0 0.5 0.5 0.2 0.4 0.2 0.4 2.4 0.5 0 0.5 0.3 0.2 0.1 0.2 0.2 0.2 Epicoccum 0.6 0.4 1 0.2 0.7 0.5 1 2.2 1.3 0.6 0.9 0.9 0.5 0.5 0.8 1 0.4 0.5 0.6 0.4 Fusarium 2.6 5.9 5.1 0.2 4.5 4.4 5.4 7.4 6.9 5.2 7 5.1 5.5 5 4.18.31.60.54.16.3 Pithomyces 0.8 1.5 1.4 0 1.4 1.1 0.9 0.7 1.3 0.3 0.9 0.3 0 0.6 1.7 1.4 0.8 3.1 1.2 1.6 Other 52.3 54.9 57.4 48.3 59.3 60.5 72.4 73.9 68.4 69.5 64.8 52.6 42.3 51.7 61.6 62.8 48.4 52.1 49 67

240