FACTORS TO CONSIDER IN THE INDOOR ENVIRONMENT WHEN EVALUATING THE RISK OF HUMAN HEALTH AT THE ENVIRONMENTAL DETECTION LIMIT By Amanda Blair Herzog A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Environmental Engineering–Doctor of Philosophy 2014 ABSTRACT FACTORS TO CONSIDER IN THE INDOOR ENVIRONMENT WHEN EVALUATING THE RISK OF HUMAN HEALTH AT THE ENVIRONMENTAL DETECTION LIMIT By Amanda Blair Herzog Quantitative analysis of the limit of detection, sample recovery efficiency (SRE) and characterization of the microbial community are necessary parameters for accurate exposure assessment as part of the quantitative microbial risk assessment framework for indoor environments. To control and remediate an indoor environment from an outbreak, accidental or intentional release of pathogens can be a challenging task. Without understanding the situation in quantitative terms, determination of a site as safe or “clean” especially when a sample result is negative will be unachievable. The negative result may not establish zero risk and can be due to variability in the sampling or detection methods. The release of Bacillus anthracis in 2001, the numerous outbreaks from the food industry, hospital settings, and on university campuses have highlighted the lack of quantitative information. A review of the literature for the limits of detection of methods detecting B. anthracis provided a distribution to quantify the variability in the instrument limit of detection; however there were only a few articles on the environmental limit of detection. An exponential dose response model estimated the risk at the dose equal to the environmental limit of detection to determine the probability of death as high as 0.52. The SRE of bacteriophage P22 was evaluated at the environmental limit of detection and was most affected by sampling time, fomite surface area, wetting agent and relative humidity. After samples dried on the fomite (20 min), less than 3% was able to be recovered even though the bacteriophage P22 was still active on the fomite. Genetically characterizing the bacterial communities on touched and untouched fomites resulted in two unique bacterial communities. Touched fomites were more diverse and had a high presence of fecal indicators which demonstrated potential reservoirs for pathogens. This research will improve exposure assessment by indicating the risk and limitations at the environmental limit of detection, enhancement of sampling strategies and the role fomites have in the transmission of infectious diseases. Copyright by AMANDA BLAIR HERZOG 2014 To my family, I will always be grateful for your unconditional love and support. v ACKNOWLEDGEMENTS I would like to express my gratitude and appreciation to all of those who have contributed, assisted, supported, and encouraged my graduate research. I would especially like to thank Dr. Syed Hashsham for the opportunity and his guidance that he provided to me from the time I started as an undergraduate laboratory intern to the completion of my Ph.D. degree. This research would not have been possible without the support by the Center for Advancing Microbial Risk Assessment funded by the U.S. Environmental Protection Agency Science to Achieve Results (STAR) program and U.S. Department of Homeland Security University Programs. I would also like to thank my Ph.D. guidance committee; Dr. Joan Rose, Dr. Thomas Voice, and Dr. Irene Xagoraraki. I very much appreciated their time, patience, expertise and their willingness to make me a better scientist. I would also like to take this opportunity to recognize all of the people I have met at Michigan State University through my service and teaching activities, you have all given me an enriched graduate experience. Finally, to all of the hard working teachers who have taught me over the years, from Mrs. Montooth to the present; thank you for inspiring me to pursue this vocation. I would like to express my deepest love and thankfulness to my family and friends. Everything I have accomplished has been a result of your unwavering love, support, encouragement, and belief in me. Specifically to my parents, Doug and Dolores Herzog, who have always been there for me in numerous ways, from my greatest accomplishments to my most challenging moments. Words cannot describe how much I love you. It should not be a surprise, that as my first teachers, I have chosen this path from their passion for science, education and helping others. Lastly, to my patient husband, Jason Grossbart, I love you so very much for the innumerable sacrifices you have made throughout this process. Thank you for allowing me to follow my dreams; I am very lucky to pass through life by your side. vi TABLE OF CONTENTS LIST OF TABLES .........................................................................................................................ix LIST OF FIGURES ........................................................................................................................x Chapter 1: Introduction ...................................................................................................................1 1.1 Exposure assessment .....................................................................................................1 1.2 The indoor environment ................................................................................................3 1.3 Indoor infectious disease transmission .........................................................................5 1.4 Dissertation outline and objectives ...............................................................................7 REFERENCES ...................................................................................................................9 Chapter 2: Implications of limits of detection of various methods for Bacillus anthracis in computing risk to human health ................................................................................................11 2.1 Abstract .......................................................................................................................11 2.2 Introduction .................................................................................................................12 2.3 Methods and approach ................................................................................................14 2.3.1 Instrument limit of detection ........................................................................15 2.3.2 Environmental limit of detection .................................................................16 2.3.3 Quantifying limits of risk estimates .............................................................22 2.4 Results and discussion ................................................................................................24 2.4.1 Instrument limit of detection ........................................................................24 2.4.2 Environmental limit of detection .................................................................27 2.4.2.1 Soil ................................................................................................27 2.4.2.2 Air .................................................................................................28 2.4.2.3 Fomites ..........................................................................................29 2.4.2.4 Water .............................................................................................31 2.4.3 Quantifying limits of risk estimates .............................................................32 2.5 Conclusion ..................................................................................................................36 APPENDIX .......................................................................................................................38 REFERENCES .................................................................................................................41 Chapter 3: Evaluation of sample recovery efficiency of bacteriophage P22 on fomites ..............50 3.1 Abstract .......................................................................................................................50 3.2 Introduction .................................................................................................................51 3.3 Materials and methods ................................................................................................57 3.3.1 Bacteriophage P22: preparation, application and sample recovery .............57 3.3.2 Single agar layer method to separate bacteriophage P22 survival from recovery ......................................................................................................59 3.3.3 Relative humidity and TSB wetting agent ...................................................60 3.3.4 Percent sample recovery efficiency computations and statistical analysis .....................................................................................................................61 3.4 Results .........................................................................................................................63 3.4.1 SRE of bacteriophage P22 from various fomites .........................................63 vii 3.4.2 SRE versus decay for bacteriophage P22 ....................................................68 3.4.3 Impact of wetting agent at varying relative humidity ..................................68 3.5 Discussion ...................................................................................................................71 3.6 Conclusions .................................................................................................................75 APPENDIX .......................................................................................................................76 REFERENCES .................................................................................................................86 Chapter 4: Genetic characterization of microorganisms on highly touched and untouched fomites ........................................................................................................................................................91 4.1 Abstract .......................................................................................................................91 4.2 Introduction .................................................................................................................92 4.3 Materials and methods ................................................................................................99 4.3.1 Fomite sample collection .............................................................................99 4.3.2 DNA extraction, PCR and 454 sequencing ................................................100 4.3.3 Data analysis ..............................................................................................101 4.4 Results .......................................................................................................................102 4.5 Discussion .................................................................................................................108 4.6 Conclusions ...............................................................................................................111 APPENDIX .....................................................................................................................113 REFERENCES ...............................................................................................................134 Chapter 5: Conclusions ..............................................................................................................139 REFERENCES ...............................................................................................................145 viii LIST OF TABLES Table 2.1 Parameters for the environmental limit of detection in soil ..........................................17 Table 2.2 Parameters for the environmental limit of detection on fomites ..................................20 Table 2.3 Risk estimates using instrument limit of detection and environmental limit of detection scenarios ........................................................................................................................................34 Table 3.1 Parameters from survival and SRE studies evaluating viruses applied to fomites .......53 Table A2.1 Comparison of Julian et al. 2011 to this manuscript .................................................77 Table A2.2 Fit statistic, type III test of fixed effects and parameter estimates for 100 cm2 at 0 min ........................................................................................................................................................78 Table A2.3 %SRE for fomites 100 cm2 at 0 and 20 min ..............................................................79 Table A2.4 Fit statistic, type III test of fixed effects and parameter estimates for 100 cm2 at 20 min ................................................................................................................................................80 Table A2.5 Fit statistic, type III test of fixed effects and parameter estimates for 1000 cm2 at 0 min ................................................................................................................................................81 Table A2.6 %SRE for fomites 100 cm2 at 0 and 20 min ..............................................................82 Table A2.7 Fit statistic, type III test of fixed effects and parameter estimates for 1000 cm2 at 20 min ................................................................................................................................................83 Table A2.8 Fit statistic, type III test of fixed effects and parameter estimates for the impact of TSB wetting at varying relative humidity .....................................................................................84 Table A2.9 %SRE for no wetting vs. wetting at varying RH .......................................................85 Table 4.1 Summary of studies analyzing the bacterial communities on fomites in the indoor environment using pyrosequencing ..............................................................................................95 Table A3.1 Summary of all samples sequenced .........................................................................114 Table A3.2 Sample description for those sequenced on the 454 GS FLX System .....................115 Table A3.3 Sample description for those sequenced on the 454 GS Junior System ..................119 ix LIST OF FIGURES Figure 1.1 Various sources and transmission routes in the indoor environment that can effect microbial diversity ..........................................................................................................................4 Figure 1.2 Environmental infection transmission model (8) ..........................................................6 Figure 2.1 Distribution of the instrument limit of detection for various methods ........................26 Figure A1.1 General schematic of the efficiency of sample processing and the effect on the environmental limit of detection ...................................................................................................39 Figure A1.2 Flow diagrams to illustrate the sample processing ...................................................40 Figure 3.1 Experimental SRE of bacteriophage P22 from fomites acrylic, galvanized steel, and laminate .........................................................................................................................................66 Figure 3.2 Loss of bacteriophage P22 due to decay versus the loss due to sample recovery and decay .............................................................................................................................................67 Figure 3.3 The experimental impact of RH and TSB wetting agent on the SREs of bacteriophage P22 after drying (20min) on 100 cm2 fomite surface area ............................................................70 Figure 4.1 Relative abundance of bacterial communities for each sample on (a) touched fomites and (b) untouched fomites at the phylum classification level .....................................................104 Figure 4.2 PCoA of the (a) un-weighted and (b) weighted UniFrac distances between sample exposures .....................................................................................................................................105 Figure 4.3 Phylogenetic diversity (PD whole tree per 400 sequences) of fomite exposures touched and untouched ...............................................................................................................106 Figure 4.4 Characterization of organisms on (a) touched and (b) untouched fomites at the lowest classification available ................................................................................................................107 Figure A3.1 PCoA of the weighted UniFrac distances between sample date and time ..............120 Figure A3.2 PCoA of the weighted UniFrac distances between sample locations .....................121 Figure A3.3 PCoA of the weighted UniFrac distances between dormitory rooms ....................122 Figure A3.4 PCoA of the weighted UniFrac distances between fomite materials .....................123 Figure A3.5 PCoA of the weighted UniFrac distances between fomite types ............................124 x Figure A3.6 Relative abundance of bacterial communities for each sample on touched fomites (A) and untouched fomites (B) at the phylum classification level (sample sequenced on the 454 GS Junior System) ......................................................................................................................125 Figure A3.7 PCoA of the (a) weighted and (b) unweighted UniFrac distances between sample exposures (sample sequenced on the 454 GS Junior System) ....................................................126 Figure A3.8 PCoA of the weighted UniFrac distances between sample date and time (sample sequenced on the 454 GS Junior System) ...................................................................................127 Figure A3.9 PCoA of the weighted UniFrac distances between sample locations (sample sequenced on the 454 GS Junior System) ...................................................................................128 Figure A3.10 PCoA of the weighted UniFrac distances between dormitory rooms (sample sequenced on the 454 GS Junior System) ...................................................................................129 Figure A3.11 PCoA of the weighted UniFrac distances between fomite materials (sample sequenced on the 454 GS Junior System) ...................................................................................130 Figure A3.12 PCoA of the weighted UniFrac distances between fomite types (sample sequenced on the 454 GS Junior System) ....................................................................................................131 Figure A3.13 Phylogenetic diversity (PD whole tree per 96 sequences) of fomite exposures touched and untouched (sample sequenced on the 454 GS Junior System) ...............................132 Figure A3.14 Characterization of organisms on (a) touched and (b) untouched fomites at the lowest classification available (sample sequenced on the 454 GS Junior System) ....................133 xi Chapter 1: Introduction 1.1 Exposure assessment Quantitative microbial risk assessment (QMRA) is a framework and approach that is used to address the spread of microorganisms through the environment (disease outbreak, bioterrorism event, etc.) and to characterize the risk to human health. There are four steps to the QMRA framework which includes hazard identification, dose response, exposure assessment, and risk characterization (5). The focus of the work presented is on obtaining quantitative date for exposure assessments. The exposure assessment quantitatively determines the dose of the organism exposed to the population and the route, concentration and duration of the exposure. The exposure assessment relies on acceptable methods and models for recovery, detection, and quantification. Methods should address sensitivity, specificity, virulence, viability and fate and transport through the environment (5). To analyze a wide range of risk scenarios it is also important to have knowledge on the ecology of microorganisms in the environment (fomites, water, soil, etc.), microbial sources, inactivation rates, survival in the environment, resistance to environmental factors (temperature, relative humidity (RH), UV, etc.) and the movement through the environment (5). The limit of detection of any method is critical for assessing a negative result during environmental monitoring. While the instrument limit of detection can give quantitative information for ideal conditions, it is the environmental limit of detection that can assess real field scenarios. The environmental limit of detection also points out limitations in the methodologies. Chapter 2 explores the current literature knowledge on the instrument and 1 environmental detection limits for methods detecting Bacillus anthracis. The risk to human health is then calculated by using the limit of detection (instrument and environmental) as the dose of exposure. The results from this work will aid in the evaluation of the preparedness for bioterrorism events. Sample recovery efficiency (SRE) directly affects the environmental limit of detection. The SRE is especially important to know when analyzing samples at concentrations close to the limit of detection. For instance, to confirm the efficacy of the cleaning protocols during environmental monitoring after an outbreak or bioterrorism event. With the use of pyrosequencing the knowledge of the microbial community is not limited to cultural method capabilities, such as an underestimation of organism (2). In chapter 3, the SRE is evaluated at the environmental limit of detection (large fomite area at low sample concentrations). The results will indicate the parameters that can affect variations in the SRE. This will lead to implementing appropriate sampling and decontamination strategies. Defining the microbial ecology of any environment is significant for understanding the relationship between microorganisms, humans and the environment. Especially in the indoor environment, the microbial community analysis can demonstrate the effect human behavior and environmental factors on the fate and transport of microbial communities. Chapter 3 defines the bacterial communities in the indoor environment found on touched and untouched fomites. The results will provide information for infectious disease transmission models and improve fomite hygiene interventions. 2 1.2 The indoor environment An important environment to study the exposure to a microorganism(s) from a bioterrorism event or disease outbreak is the indoor environment. The data presented in the following chapters evaluate the parameters affecting limit of detection, SRE, and microbial communities in the indoor environment, specifically the role of fomites. However, the limit of detection was also reviewed for the air, soil, and water environments. The indoor environment is a complex ecosystem with a vast diversity of trillions of microorganisms (4, 6, 7). Humans spend a majority of their time indoors and have designed a closed environmental system to maintain static conditions (temperature and RH) to feel comfortable during seasonal variations (4, 6). In addition, human occupancy and movement, frequency of cleaning fomites and the use of mechanical ventilation systems instead of windows have controlled the microbial community structures of the indoor environment (3, 7). There are a variety of microbial sources (skin, pets, food), environmental factors (ventilation, shoes, body secretions) and dispersal vectors (pH, temperature, fomite material) that affect the microbial diversity (Figure 1.1) (6). Research has suggested that bacteria such as Staphyloccus epidermis can help protect against skin infections and bacteria associated with dogs can prevent allergies in children (3, 10). However there are pathogens such as Salmonella and Escherichia coli that have negative effects on human health. Also, horizontal gene transfer of antibiotic resistant genes of pathogenic bacteria on touched fomites may increase risk of drug resistant infections (12). There is still a great extent of knowledge to obtain on the dynamics of microbial ecology and the effects it has on human health (positive, negative, or neutral) in the indoor environment (2, 4, 6). 3 Season Fomite Cleaning/Hygiene Plants Soil Location/Climate Pets Outside Air Dust Ventilation Mechanical/Window Skin Oral Shoes Nasal Fomite Gut Human Use and Movement Body Secretions/Shedding Material Temperature pH RH UV Exposure Food Water Building Design and Arrangement Bioterrorism Release of a BAC Vagina Figure 1.1 Various sources and transmission routes in the indoor environment that can effect microbial diversity. Where orange boxes represent microbiome source, purple boxes are environmental factors and red boxes are dispersal vectors (6). 4 1.3 Indoor infectious disease transmission Nonporous fomites can be an important point for transmission of bacterial and viral disease especially for populated indoor environments (1, 11). Pathogens can survive on fomites for hours to months depending on the microorganism, concentration of the microorganism and the indoor environmental factors (1, 11). In general, exposure to live pathogens from the environment may be picked up by susceptible individuals from a fomite by touching (hand to mouth, eye or nose), direct contact from fomite to mouth or through the inhalation of resuspended microorganisms from contaminated fomites (Figure 1.2) (1, 8, 9). Then infected or infectious individuals deposit pathogens into the environment through shedding (saliva, blood, feces, etc.). Individuals (infected or not infected) touching fomites can transfer microorganisms and can inoculate and re-inoculate the fomite through its usage (6). Over time individuals recover, become completely immune or the infection was fatal. In addition, pathogens are eliminated from the environment through natural decay, decontamination processes, or removed through other environmental processes (Figure 1.2) (8). 5 Susceptible Individuals Infected and Infectious Individuals Pathogens Picked up Pathogens Eliminated From Environment Completely Immune Individuals Pathogens Deposited Live Pathogens in the Indoor Environment (Fomites) Figure 1.2 Environmental infection transmission model (8). 6 1.4 Dissertation outline and objectives The overall objective of these chapters is to gain quantitative data for enhancement of exposure assessments in the QMRA framework for assessing risk to human health. Chapter 2 – Implications of limit of detection of various methods for Bacillus anthracis in computing risk to human health. The instrument limit of detection and environmental limit of detection for all methods detecting B. anthracis are reviewed from the literature. Risk estimates are calculated at concentration equal to the instrument and environmental limit of detection. The literature review also identifies current limitations in the knowledge of detection methods for B. anthracis in environment matrices (air, soil, water and fomites). Chapter 3 – Evaluation of sample recovery efficiency of bacteriophage P22 on fomites. Bacteriophage P22 is applied onto fomites at concentrations near the limit of detection to quantify the SRE. The variability in SRE as a function of fomite type, fomite surface area, sampling time, application media, relative humidity and wetting agent is evaluated. Survival of bacteriophage P22 (24 hr) on fomites indicated the enhancement of the sampling method for increased efficiency in sampling at low concentrations dried on the fomite. Chapter 4 – Genetic characterization of microorganisms on highly touched and untouched fomites. To determine the microbial ecology in the indoor environment samples are collected from university dormitories from touched and untouched fomites and sequenced. Touched and untouched fomites provided unique bacterial communities. Dominate genera indicated interactions with humans and reservoirs of possible pathogen contamination. 7 In addition, determined the correlation between bacterial community with fomite type, room location and dormitory. Chapter 5 – Conclusions. Results from the dissertation are summarized. Contributions of the science to the field and the future direction of the work are discussed. 8 REFERENCES 9 REFERENCES 1. Boone, S. A., and C. P. Gerba. 2007. Significance of fomites in the spread of respiratory and enteric viral disease. Appl. Environ. Microbiol. 73:1687-1696. 2. Corsi, R. L., Kinney, K.A., Levin, H. 2012. Microbiomes of built environments: 2011 symposium highlights and workgroup recommendations. Indoor Air 22:171-172. 3. Dunn, R. R., N. Fierer, J. B. Henley, J. W. Leff, and H. L. Menninger. 2013. Home life: factors structuring the bacterial diversity found within and between homes. PLoS One 8. 4. Green, J. L. 2014. Can bioinformed design promote healthy indoor ecosystems? Indoor Air 24:113-115. 5. Haas, C. N., J. B. Rose, and C. P. Gerba. 1999. Quantitative microbial risk assessment. John Wiley, New York. 6. Kelley, S. T., and J. A. Gilbert. 2013. Studying the microbiology of the indoor environment. Genome Biol. 14. 7. Kembel, S. W., E. Jones, J. Kline, D. Northcutt, J. Stenson, A. M. Womack, B. J. M. Bohannan, G. Z. Brown, and J. L. Green. 2012. Architectural design influences the diversity and structure of the built environment microbiome. ISME J 6:1469-1479. 8. Li, S., J. N. S. Eisenberg, I. H. Spicknall, and J. S. Koopman. 2009. Dynamics and control of infections transmitted from person to person through the environment. Am. J. Epidemiol. 170:257-265. 9. Lopez, G. U., C. P. Gerba, A. H. Tamimi, M. Kitajima, S. L. Maxwell, and J. B. Rose. 2013. Transfer efficiency of bacteria and viruses from porous and nonporous fomites to fingers under different relative humidity conditions. Appl. Environ. Microbiol. 79:5728-5734. 10. Schommer, N. N., and R. L. Gallo. 2013. Structure and function of the human skin microbiome. Trends Microbiol. 21:660-668. 11. Sinclair, R., S. A. Boone, D. Greenberg, P. Keim, and C. P. Gerba. 2008. Persistence of category A select agents in the environment. Appl. Environ. Microbiol. 74:555-563. 12. Warnes, S. L., C. J. Highmore, and C. W. Keevil. 2012. Horizontal transfer of antibiotic resistance genes on abiotic touch surfaces: implications for public health. mBio. 3:10. 10 Chapter 2: Implications of limits of detection of various methods for Bacillus anthracis in computing risk to human health This chapter is adapted from our published work in Applied and Environmental Microbiology: Amanda B. Herzog, S. Devin McLennan, Alok K. Pandey, Charles P. Gerba, Charles N. Haas, Joan B. Rose, and Syed A. Hashsham. Implications of Limits of Detection of Various Methods for Bacillus anthracis in Computing Risk to Human Health. Applied and Environmental Microbiology. 2009. 75(19):6331-6339. DOI:10.1128/AEM.00288-09. 2.1 Abstract Used for decades for biological warfare, Bacillus anthracis (Category A agent) has proven to be highly stable and lethal. Quantitative risk assessment modeling requires descriptive statistics of the limit of detection to assist in defining exposure. Furthermore, sensitivities of various detection methods in environmental matrices are vital information for first responders. A literature review of peer reviewed journal articles related to methods for detection of B. anthracis was undertaken. Articles focused on the development or evaluation of various detection approaches such as polymerase chain reaction (PCR), real-time PCR, immunoassay, etc. Realtime PCR and PCR were the most sensitive methods for the detection of B. anthracis with a median instrument limit of detection of 430 and 440 cells/ml, respectively. There were very few peer reviewed articles on the detection methods for B. anthracis in the environment. The most sensitive limit of detection for the environmental samples were 0.1 CFU/g for soil using PCRELISA, 17 CFU/L for air using ELISA-biochip system, 1 CFU/L for water using cultivation, and 1 CFU/cm2 for stainless steel fomites using cultivation. An exponential dose response model for 11 the inhalation of B. anthracis, estimates of risk at concentrations equal to the environmental limit of detection determined the probability of death if untreated as high as 0.520. Though more data on the environmental limit of detection would improve the assumptions made for the risk assessment, this study’s quantification of the risk posed by current limitations in the knowledge of detection methods should be considered when employing those methods in environmental monitoring and clean up strategies. 2.2 Introduction According to the Centers for Disease Control (CDC), a category A agent is an organism that poses a risk to national security because it can be easily disseminated or transmitted from person to person, results in high mortality rates, has the potential for major public health impact, might cause public panic and social disruption, and requires special action for public health preparedness (23). Quantitative information on category A agents in environmental matrices (soil, air, fomite, water) are very limited (64). However, from the literature it has been concluded that B. anthracis are the most environmentally stable category A agent overall (64). After the release of B. anthracis through mail envelopes in 2001, assessment of the decontamination process revealed an important question: could the detection methods effectively determine if the environment is clean? An evaluation of the effectiveness of sampling methods at a U.S. postal facility in Washington D.C. contaminated with B. anthracis spores concluded that neither of the sampling methods used (HEPA vacuum or wipes) were sensitive enough to ensure that spores had been removed completely. In addition, the event exposed the necessity of 12 quantifying recovery and extraction efficiency during sample collection and processing to improve the method limit of detection (69, 63) In this literature review, the limit of detection of methods for B. anthracis is characterized as either an instrument limit detection or environmental limit of detection. An instrument limit of detection is generally evaluated with pure cultures. An environmental limit of detection is evaluated with cultures/cells spiked into an environmental matrix (soil, air fomites, water), which then undergoes various recovery and concentration procedures (i.e. filtration and extraction, or direct extraction) before detection (Figure A1.1 in the Appendix). Compared to instrument limit of detection, establishment of environmental limit of detection poses more challenges, including dilute target concentrations, environmental impurities, background inhibitors, organisms in a viable but not cultivable (VNBC) state, and overall processing efficiency. There are many steps in processing environmental samples prior to detection. At each process step, there can be a loss of the initial target organism and thus, each step has a recovery efficiency, which could be interpreted as a set number, distribution or range (Figure A1.1). Since recovery efficiency directly affects the limit of detection, improving recovery efficiency would result in a more sensitive detection method. In determining if an environmental site is “clean”, another component that should be evaluated is the quantification and characterization of the potential health risk. Quantitative microbial risk assessment (QMRA) is a method to assess the likelihood of infection based on specific exposures to hazardous pathogenic organisms. QMRA risk modeling has been used for water and food and 13 could be useful for management decisions during a disease outbreak or a bioterrorism attack (37). Environmental monitoring is used to inform the exposure assessment as well as the efficiency of disinfection. The limit of detection is a critical criterion for any method, which dictates the application and usefulness of demonstrating a “zero” during environmental monitoring. The limit of detection of a chosen analytical method is also an input variable for the QMRA model; a statistical distribution quantifying the variability in limit of detection is preferred for realistic modeling. The objectives of this study were to review, from the literature, the instrument limit of detection and the environmental limit of detection for methods to detect B. anthracis and to compare the estimated risk at the instrument limit of detection and the environmental limit of detection. Though the number of articles on B. anthracis was extensive there was a paucity of articles that specifically included environmental limit of detection. This information is essential for a QMRA of B. anthracis in the establishment of future environmental monitoring strategies and clean up goals. 2.3 Methods and approach Journal articles were searched on the ISI Web of Science database searching for B. anthracis and the following keywords; method, sensitivity, limit of detection, detection limit, limit, water, air, soil, fomite, surface, specificity, PCR sensor, environmental, rapid, assay, diagnostic, immunoassay, antibody, real time, real-time PCR, microfluidic, polymerase, quantitative, bioaerosol, aerosol, microarrays, biosensor, electrochemiluminiscence, Raman spectrometry, and mass spectrometry. Approximately 1700 references (and abstracts, when available) were 14 retrieved and were saved in an EndNote file. Though the search defaults were set for the years 1900 through 2007, the oldest article used to evaluate the limit of detection was published in 1994. Abstracts were manually screened for information on the detection of B. anthracis. Some studies used a surrogate for B. anthracis to determine the limit of detection. It is assumed that B. anthracis would behave as the surrogate and was included in this review. If the abstract pertained to a detection method then the full article was downloaded, saved in another database, and reviewed for quantitative data describing the limit of detection. The remaining references and abstracts that were not used in this literature review either did not indicate information about detection methods or the articles were not retrievable. At the end, 71 articles were retrieved and analyzed to obtain instrument limit of detection or environmental limit of detection. 2.3.1 Instrument limit of detection Instrument limit of detection was extracted from the articles describing a method that detected B. anthracis in a pure culture without spiking B. anthracis into an environmental matrix (soil, air, fomite, water). Raw data extracted were recorded in units of cells, spores, DNA, colony forming units (CFU), protective antigen, and genomic copies in volumes that ranged from liters to microliters. Articles that used units of protective antigens were not used in this literature review due to the unknown conversion factor for antigens to cells. All data were converted into standard units of cells per milliliter of reaction solution and the data by method were graphed and compared. 15 2.3.2 Environmental limit of detection In studies reporting environmental limit of detection, B. anthracis spores were spiked into the matrix, extracted, and detected using various detection methods. The articles that reported the environmental limit of detection of B. anthracis were categorized according to the matrix in which B. anthracis was detected (soil, air, fomite, water). Additional parameters extracted from the articles varied with the matrix (Figure A1.2). These included the following parameters (i) For soil, they included the amount of soil, sample concentration, extraction volume, volume of extracted sample added to the reaction, and total volume. In addition, the type of pretreatment or extraction method and soil type or location were noted (Table 2.1). (ii) For air, they included the sample volume, airflow rate, duration, sample concentration, extraction volume, volume of extracted sample added to the reaction and total volume. (iii) For fomites, they included the surface area, sample concentration, surface seeding method, extraction volume, and total volume. In some cases recovery efficiency and extraction efficiency were available and noted. In addition, the type of fomites, sampling method, extraction method and culturing method were noted (Table 2.2). (iv) For water, they included the sample volume, sample concentration, extraction volume, volume of extracted sample added to the reaction and total volume. In addition the condition of the water was noted. 16 Table 2.1 Parameters for the environmental limit of detection in soil. Method Soil (g) Sample conc. Pretreatment/ extraction method Easy DNA kit (Invitrogen) Easy DNA kit (Invitrogen) Time (hr) Difficulty level PCRELISA 100 1-100CFU/100 g 2.5 100 1-100CFU/100 g Nested PCR+2x culture 1 0,1,10,102,103 CFU/g Nested PCR+ culture 1 Nested PCR 1 0.1 PCR 1 Added to rxn (µl) 60 Total vol. (µl) 60 LOD (CFU/g soil) 2 Extract vol. (µl) 100 2.5 2 100 60 60 1.0 FastDNA SPIN kit 36 4 -- -- 25 1.0 0,1,10,102,103 CFU/g FastDNA SPIN kit 18 3 -- -- 25 1.0x102 Garden soil w/ 3% peat (26) 0,1,10,102,103 CFU/g 106 CFU/100 mg FastDNA SPIN kit Three freeze thaw cycles/glass beads and glassmilk Hot detergent/bead mill homogenization 2 2 -- -- 25 1.0x103 (26) 3.5 5 30 5 25 1.0x105 Garden soil w/ 3% peat Litter, meadow, cultivated, swamp and lawn 1 3 100 10 100 2.5x103 Anthony fine sandy loam from New Mexico agriculture fields (46) 2.5x103-2.5x107 CFU/g 17 0.1 Soil type/ location Non-suspicious sites Contaminated sites w/ organic compounds and tanning agents Garden soil w/ 3% peat Ref. (10) (10) (26) (65) Table 2.1 (cont’d). Method Soil (g) Sample conc. Pretreatment/ extraction method Time (hr) Difficulty level 1 103-107 CFU/g 0.75 1 103-107 CFU/g Real-time PCR 0.1 103-107 CFU/g Aqueous polymer twophase system Aqueous polymer twophase system Heat treatment w/ 1.22g/ml sucrose-0.5% TritonX-100 Multiplex PCR 0.1 103-107 CFU/g 0.1 103-107 CFU/g IM Added to rxn (µl) 20 Total vol. (µl) 40 LOD (CFU/g soil) 2 Extract vol. (µl) 100 5.6x103 Sand (2) 0.75 2 100 20 40 1.4x104 Garden (2) 0.75 3 1000 5 25 1.0x104 (62) Heat treatment w/ 1.22g/ml sucrose-0.5% TritonX-100 0.75 3 1000 1 25 1.0x105 Heat treated with sterilized water and 10% TritonX-100-PBS 1.5 3 1000 1 25 1.0x108 National Institute of HealthKorea National Institute of HealthKorea National Institute of HealthKorea 18 Soil type/ location Ref. (62) (62) Table 2.1 (cont’d). Method IM-ECL Biosensor assay Soil (g) Sample conc. Pretreatment/ extraction method IM separation twice and resuspend in PBS Time (hr) Difficulty level 0.001 0-106 CFU/assay 0.001 Added to rxn (µl) -- Total vol. (µl) -- LOD (CFU/g soil) Soil type/ location Ref. 3 Extract vol. (µl) -- 1.5 1.0x105 (18) 1.5 3 -- -- -- 1.0x106 IM separation twice and resuspend in PBS 1.5 3 -- -- -- 1.0x107 Washed with 1 ml PBST 0.25 1 30 5 25 3.2x108 Moist dark brown to black soil and dry light yellowish sandy soil from diverse military and agriculture fields Moist dark brown to black soil and dry light yellowish sandy soil from diverse military and agriculture fields Moist dark brown to black soil and dry light yellowish sandy soil from diverse military and agriculture fields Talc-based powder, cornstarch, confectioners’ sugar, baking soda, and B. thuringiensis based pesticide 0-106 CFU/assay IM separation twice and resuspend in PBS 0.001 0-106 CFU/assay 0.01 3.2x103-3.2x105 CFU/ml 19 (18) (18) (71) Table 2.2 Parameters for the environmental limit of detection on fomites. Surface area Sample conc. Surface seeding Sampling method Extraction method a Hodges et al. 2006 10 cm2 0.2-3,000 CFU/cm2 Inoculated 0.5 ml spore solution Macrofoam swab Extraction vol. Total vol. Recovery efficiency (%) Vortex in 5 ml PBST for 2 min at 10 s intervals 5 ml -31.7-49.1 Extraction efficiency (%) 93.4 Culture method Sheep blood agar Limit of detection 12 CFU/cm2 Rose et al. 2004 25 cm2 2x104 CFU/cm2 Inoculated 0.5 ml spore solution Cotton swab Macrofoam swab Vortex in 5 ml PBST for 2 min at 10 s intervals 5 ml 100 µl 41.7: Cotton swab 43.6: Macrofoam swab 93.9: Cotton Swab 93.4: Macrofoam swab Trypticase soy agar w/ 5% sheep blood 20 CFU/cm2 a Brown et al. 2007 25 cm2 100-100,00 CFU/cm2 Dry aerosol deposition Polyester-rayon blend gauze wipe Sonication and heat treatment 30 ml 1 ml 35: Stainless steel 29: Painted wall board 93 Brain heart infusion agar 90 CFU/cm2: Stainless steel 105 CFU/cm2:Painted wallboard Limit of detection not recorded in article and was calculated to be at the least 20 CFU/cm2 20 Table 2.2 (cont’d). Parameter Surface area Sample conc. Surface seeding Sampling method Extraction method Extraction vol. Brown et al. 2007 25 cm2 100-100,00 CFU/cm2 Dry aerosol deposition Rayon swab Sonication and heat treatment 10 ml Brown et al. 2007 100 cm2 100-100,00 CFU/cm2 Dry aerosol deposition Vacuum filter sock Sonication and heat treatment 30 ml Total vol. Recovery efficiency (%) 1 ml 41: Stainless steel 41: Painted wallboard Extraction efficiency (%) Culture method Limit of detection 76 Brain heart infusion agar 1 CFU/cm2: Stainless steel and Painted wallboard 1 ml 29: Stainless steel 25: Painted wallboard 28: Carpet 19: Concrete -Petrifilm aerobic plate count media 105 CFU/m2: Stainless steel 102 CFU/m2: Painted wallboard 105 CFU/m2: carpet 160 CFU/m2: concrete 21 Buttner et al. 2004 1 m2 105 CFU/ m2 Inoculated spore solution BiSKit-wet and dry Foam compression 3.3 ml: wet sampling 16.1 ml: dry sampling 1 ml 11.3: Wet sampling 18.4: Dry sampling -Trypticase soy agar 42±6 CFU/m2: wet sampling 100±10 CFU/m2: dry sampling 2.3.3 Quantifying limits of risk estimates Risk of mortality by inhalation of B. anthracis spores was estimated for concentrations corresponding to the instrument limit of detection and the environmental limit of detection in air. For each limit of detection, a distribution of risks was calculated by the Monte Carlo method using 100,000 replicates in Crystal Ball® 7.3.1 (Oracle, 2007). The number of replicates was chosen at the point where the 90% confidence interval was stable over a range from 1/10 to 10 times the number of replicates used. A recent evaluation of dose response data for B. anthracis spores through the inhalation exposure route found that the dose response relationship could be modeled by the exponential equation (4), P(d)=1-e-kd (2.1) where P(d) is the probability of death (when untreated) at dose d and k is the probability that one organism will survive to initiate the response (4). In this study, a k value generated from a pooled guinea pig and rhesus monkey data set was used. A distribution of 10,000 best-fit k values generated using bootstrap replicates of that data set was provided by Timothy Bartrand of Drexel University and fit to a gamma distribution. Dose was calculated as, d = Cair ⋅ R ⋅ t (2.2) where Cair is the number of spores per cubic meter of air (instrument limit of detection or environmental limit of detection), R is the breathing rate (m3/hr), and t is the duration of exposure (hr). When Cair was evaluated as a range of limits of detection it was modeled as a lognormal distribution; otherwise, it was evaluated as a point estimate. Breathing rate, R, was modeled as a Pareto distribution fit to the short term breathing rates of adults (18 years of age 22 and up) of both sexes from rest to moderate activity (72). Exposure time, t, was modeled as a uniform distribution from 1 min to 8 hr. Five risk scenarios were evaluated with this model using different values of Cair. For each risk scenario, either instrument limit of detection or environmental limit of detection, a sensitivity analysis was generated using Crystal Ball® 7.3.1 (Oracle; 2007). The median real-time PCR instrument limit of detection and the range of real-time PCR instrument limit of detection were two scenarios used to explore the effect of instrument limit of detection on risk. For the instrument limit of detection scenarios, it was assume that all B. anthracis spores in a cubic meter of air could be collected without any loss and concentrated into 1 ml of solution for analysis. Log transformed real-time PCR and PCR instrument limits of detection were checked for normality with a Lilliefors test and compared using analysis of variance (ANOVA). Then, the range of PCR instrument limits of detection were combined with the range of real-time PCR instrument limits of detection to increase the data in the distribution. There were three environmental limits of detection scenarios; Cair was set to the environmental limits of detection reported for B. anthracis detected in air. There were only two articles on the environmental limit of detection in air. Due to the lack of data on the environmental limit of detection, the two limits of detection were referred to as the lower and upper environmental limit of detection. These two risk scenarios were evaluated as point estimates. The last risk scenario assumed that the environmental limit of detection for air fit the same distributions as the lognormal instrument limit of detection, ranging from 17,000 to 50,000 CFU/m3 (this may not be the true range). 23 2.4 Results and discussion 2.4.1 Instrument limit of detection Out of 56 articles on the instrument limit of detection, 17 articles were on real-time PCR (6, 7, 11, 25, 27, 29, 41, 43, 47, 51, 53, 55, 58, 60, 62, 73, 74), 6 were on PCR (13, 33, 50, 59, 78), 10 were on biosensors (1, 3, 21, 22, 35, 38, 42, 54, 74, 75), 5 were on microarray/PCR (5, 19, 52, 66, 77), 6 were on immunoassay (31, 32, 34, 48, 67, 70), 3 were on electrochemiluminescence (17, 36, 79), 2 were on ELISA (12, 28), 3 were on Raman spectroscopy (39, 57, 80), and 4 were on mass spectrometry (8, 9, 30, 45) (Figure 2.1). Limits of detection ranged from 10 cells/ml (for real-time PCR) to 108 cells/ml (for mass spectrometry). Considering the median instrument limit of detection, real-time PCR and PCR were the most sensitive methods with the median instrument limits of detection of 430 and 440 cells/ml, respectively. It should be noted that there was one instrument limit of detection (4.29x106 cells/ml) that was not added to the distribution for real-time PCR because it was a multiplex assay and the other instrument limits of detection in the distribution were of a singleplex assay (44). The least sensitive methods were Raman spectroscopy and mass spectrometry, with median instrument limits of detection of approximately 1.0x107 and 8.0x107 cells/ml, respectively. The number of journal articles for real-time PCR and biosensor allowed limits of detection to be fit to a statistical distribution. When fewer articles were published, as was true for the other eight methods, assigning distributions were not possible. ECL, ELISA, Raman spectroscopy, and mass spectrometry (having less than 4 articles) were the methods with the least sensitive instrument limits of detection. With limited information on these methods, the median instrument limit of detection may not properly represent these detection methods’ capabilities for 24 detecting B. anthracis. For example, the instrument limit of detection for ECL had only three published articles with limits of detection ranging from 102 cells/ml to 106 cells/ml. For some emerging techniques, such as immuonmagnetic-ECL (IM-ECL) and aptamer-magnetic beadECL (AM-ECL), limits of detection differed by 4 orders of magnitude. While the instrument limit of detection gives insight to the instruments capabilities, when evaluating cleanup goals and assessing risk, the environmental limits of detection are needed to understand the challenges and capabilities for addressing the contamination. 25 Detection Limit (cells per mL of reaction solution) 109 108 107 106 105 104 103 102 101 100 n= a lRe eP tim 17 CR 2 4 10 5 6 3 3 r y ce R y R A ay so en IS op e tr PC c c /P C L ass en s s m s y o E o o n tro in e rra c tr Bi mu ec oa pe um p r l m s s i c I i n m M ass he ma c a M o R tr ec El 6 Method Figure 2.1 Distribution of the instrument limit of detection for various methods. On the box plot, the solid line represents the median result, and the dashed line represents the mean result. The box plot whiskers above and below the box indicate the 90th and 10th percentiles, respectively. The solid circles represent the outlying limits of detection, and n represents the number of journal articles available on each detection method for Bacillus anthracis. 26 2.4.2 Environmental limit of detection Out of 15 articles on the environmental limit of detection, 8 articles were on detection in soil (2, 10, 18, 26, 46, 62, 65, 71), 2 were on detection in air (49, 68), 4 were on detection on fomites (15, 20, 40, 61), and 1 was on detection in water (56). The results for the environmental limit of detection could not be reported as distributions due to the limited number of articles for each matrix. The two most predominant methods used for the environmental limit of detection were cultivation and PCR based methods. 2.4.2.1 Soil The environmental limit of detection of B. anthracis spiked into soil ranged from 0.1 (reported as 10 CFU/100g of soil) to 3.2x108 CFU/g of soil, with a median limit of detection of 1.2x104 CFU/g soil (Table 2.1). The median environmental limit of detection for soil should be used with caution, since there is a 9 orders of magnitude range due to the many approaches used to evaluate the environmental limit of detection. The approximate time for the extraction method (Table 2.1) was the time for one sample to be processed based on the information reported. If it was not an automated extraction procedure, then with the increase in samples there would be an increase in extraction process time. The difficulty level for the extraction process (1 easy to 5 difficult) was based on the number of steps in the procedure, the preparation time and the approximated time for the extraction (Table 2.1). The biosensor assay, the easiest extraction method, resulted in the poorest limit of detection (3.2x108 CFU/g of soil). The detection methods with the most sensitive limits of detection (PCR-ELISA, Nested PCR, and PCR) had extraction methods with difficulty levels ranging from 2 to 5 (Table 2.1). 27 The environmental limit of detection depended highly on the pretreatment/extraction process; for instance Ryu et al. 2003 (62) used multiplex PCR, and reported a difference of 3 orders of magnitude between heat treatment with 1.22 g/ml sucrose-0.5% TritonX-100 and heat treatment with sterilized water and 10% TritonX-100-phosphate buffered saline (PBS) (Table 2.1). Similar results were found for Bruno and Yu 1996 (18) when using IM-ECL as the detection method. Differences in the environmental limit of detection were also based on the location or the type of soil. Beyer et al. 1999 (10) reported that the PCR-ELISA method was more sensitive when using soils from non-suspicious locations compared to former tannery sites. While Agarwal et al. 2002 (2) reported that the immunofluorescence assay was more sensitive when spores were spiked into sand (103) rather than into garden soil (104). For the IM-ECL method, Bruno and Yu 1996 (18), reported differences due to different strains, Sterne (105) being more sensitive in the assay than Ames (106) and Vollum B1 (107). 2.4.2.2 Air There were only two studies on the evaluation of aerosolized B. anthracis spores collected by an air sampler and extracted for detection. The ELISA-biochip system coupled with a portable bioaerosol collection system collected aerosolized spores at an air sampling rate of 150 L/min for 2 min into 5 ml of phosphate buffered saline (PBS). The ELISA-biochip system consisted of an ELISA for antibody-based identification in combination with the biochip detection instrument. The environmental limit of detection of the ELISA-biochip system was 17 CFU/L. For the ELISA-biochip system, the efficiency of the air sampler was reported as approximately 50% but the distribution was not fully described (68). The anthrax smoke detector collected aerosolized 28 spores using a bioaerosol collection system at a rate of 15 L/min for 1 min onto a glass fiber filter tape. The detection of the spores using the lifetime-gated fluorimeter occurred after a thermal lysis and addition of TbCl3. The environmental limit of detection of the anthrax smoke detector was 50 CFU/L (49). 2.4.2.3 Fomites Spores were seeded on fomites (stainless steel, plastic, laminar, wood, glass, etc.), recovered, extracted, and detected by cultivation. The environmental limit of detection was evaluated from stainless steel fomites ranging in surface area from 10 cm2 to 1 m2 (Table 2.2). Brown et al. 2007 (14-16) also evaluated the environmental limit of detection on painted wallboard. In addition, the vacuum filter sock study tested porous fomites, carpet and concrete (14). The sampling methods evaluated in the articles were macrofoam swab, cotton swab, polysterrayon blend gauze wipe, rayon swab, vacuum filter sock and a biological sampling kit (BiSKit) (Table 2.2). Sampling methods such as cotton, macrofoam, polyester, and rayon swabs were all tested by Rose et al. 2004 (61). It was concluded that the cotton and macrofoam swabs produced the highest recovery when the swabs were premoistened as opposed to dry. Similarly, Buttner et al. 2004 (20) tested the BiSKit, cotton swab, and foam swab. The BiSKit was designed to do wet and dry sampling of large surfaces for bacteria, viruses, and toxins. BiSKit resulted in the highest recovery of the three methods. Using a wetting agent to recover spores from the surfaces enhanced the recovery and environmental limit of detection. Brown et al. 2007 (15, 16) used sterilized deionized water (except when using the vacuum filter sock), Buttner et al. 2004 (20) used potassium phosphate buffer with 0.05% Tween-20 (PBT), and the other two articles used 29 phosphate buffered saline with 0.04% Tween-80 (PBST). According to the CDC the recommended wetting agents were sterile water, a sterile saline solution, or a sterile phosphatebuffered solution (24). The detection method for all fomite studies was cultivation; however, a different agar was used in each study. The focus of Rose et al. 2004 (61) article was on achieving the best recovery, and did not determine an environmental limit of detection. From the information given in the article, the environmental limit of detection was calculated by using the initial suspension concentration, the surface area and the lowest recovery reported. The calculated environmental limit of detection was approximately 20 CFU/cm2. The recovery efficiencies for all the fomite studies ranged from 10 to 50%, and the extraction efficiency ranged from 75 to 99%. Recovery of B. anthracis spores from fomites depends on many parameters such as fomite type, sampling procedure and sampling processing for detection. The recovery efficiency from the sampling method was primarily the controlling factor in determining the limit of detection and secondarily the efficiency from the extraction method. Interestingly, in survival studies using cultivation as the detection method on fomites, surface characteristics, relative humidity, and temperature were the most important contributors to viability (64). It was not clear whether recovery and limit of detection changed with time in the environment, as this was difficult to differentiate from survival/degradation of the target. However, this distinction could be made by adding a marker along with the biological agent that does not degrade. For environmental monitoring, the separate time dependence of survival and 30 recovery will be critical to define in future studies. Only the articles from Brown et al. 2007 (1416), reported and maintained the relative humidity and temperature in the fomite studies at 30 ± 10% and 25 ± 2 oC, respectively. Determining and maintaining the relative humidity and temperature that are most optimal for viability may increase recovery efficiency. In addition, this information could be used at a contaminated site to inform first responders of the possible viability of remaining levels of the biological agent of concern. 2.4.2.4 Water The spores were spiked into a volume of water, filtered through a 0.2 to 0.45 µm pore size filter, extracted from the filter and then detected by various methods. The main challenge for detection of B. anthracis in water was the ability to concentrate the sample. If the sample was too dilute, then the number of B. anthracis cells per liter of water could fall below the environmental limit of detection. When the sample is concentrated, some loss of the initial cells is likely. There was only one article that evaluated the limit of detection of B. anthracis in water; the lack of articles could be due to this matrix being less likely a vehicle for transmission (64). Perez et al. 2005 (56), spiked B. anthracis spores into tap and source water in volumes ranging from 0.1 to 10 L. Sample concentrations were detected using sheep red blood cell agar plate, B. anthracis chromogenic agar plate (R&F Laboratories), PCR or nested PCR. Cultivation was used to determine the viability of the organisms in the sample, and PCR was used to confirm the identity of any suspect colonies. When using the cultivation approach for the source water samples (Chesapeake Bay and Patuxent River), overgrowth of non-targeted flora 31 occurred in all studies. PCR was only successful for testing source water when the sample concentrations were at least 26 CFU/ml. The environmental limit detection for tap water was reported as 10 CFU/10L using the cultivations methods, while for PCR based methods, the environmental limit of detection decreased to 534 CFU/L. Though the PCR based methods have a rapid detection time compared to the cultivation methods (more than 24 hr), in this case, PCR was less sensitive. Challenges, such as loss of initial cells, could occur when concentrating large sample volumes (i.e., 10 L) into 5 to 10 µl for the PCR reactions. 2.4.3 Quantifying limits of risk estimates Five risk scenarios using instrument limits of detection and environmental limits of detection for Cair were evaluated. Log transformed PCR and real-time PCR instrument limits of detection were normally distributed (Lilliefors test, p = 0.65 PCR, p = 0.78 real-time PCR) and were not significantly different (ANOVA, p = 0.94). Therefore, the PCR and real-time PCR instrument limit of detection distributions were combined to increase the data set for the real-time PCR instrument limit of detection. With the assumption of 100% recovery, the median risk when Cair equaled the median real-time PCR instrument limit of detection was 0.006. When Cair was modeled with a lognormal distribution of real-time PCR instrument limits of detection, the estimated risk was 0.0062. The median risk of death from the inhalation of the entire dose of B. anthracis at the environmental limit of detection in air was 0.22 at the lower reported environmental limit of detection and 0.52 at the upper environmental limit of detection. Assuming that the environmental limit of detection would have a similar distribution as the instrument limit of detection (lognormal) and ranged from 17,000 to 50,000 spores/m3, the median risk of death was 0.32 (Table 2.3). This assumption should be further evaluated with 32 environmental studies to confirm that the environmental limit of detection would have the same distribution as the instrumental limit of detection. These risk estimates assumed that 100% of the spore sample was inhalable. Risk estimates were also reported for the percentages of 66.5%, 10%, and 1% of spores in the sample that were inhalable or respirable (Table 2.3). Approximately 70% of inhaled air volume actually contacts alveoli in the lungs, allowing spores to enter the body (68). In addition, the 5th and 95th percentiles of each risk distribution were used to define a 90% confidence interval for each risk estimate (Table 2.3). 33 Table 2.3 Risk estimates using instrument limit of detection and environmental limit of detection scenarios. Risk scenario Analyzed limit of detection Percentile Real-time PCR, median instrument limit of detection 429 cells/ml 5th Median 95th 100 % 0.007 0.006 0.037 66.5 % 0.0047 0.0042 0.025 10 % 0.0007 0.00063 0.0038 1% 0.00007 0.000063 0.00038 Real-time PCR, instrument limits of detection 10-34,300 cells/ml 5th Median 95th 0.0001 0.0062 0.28 0.000067 0.0041 0.19 0.00001 0.00062 0.032 0.000001 0.000062 0.0032 Lower environmental limit of detection in air 17,000 CFU/m3 5th Median 95th 0.026 0.22 0.78 0.017 0.15 0.63 0.0026 0.025 0.14 0.00026 0.0025 0.015 Upper environmental limit of detection in air 50,000 CFU/m3 5th Median 95th 0.075 0.52 0.998 0.051 0.39 0.98 0.0078 0.071 0.46 0.00078 0.0073 0.06 Assumed environmental limits of detection in air 17,000-50,000 CFU/m3 5th Median 95th 0.03 0.32 0.94 0.02 0.23 0.85 0.003 0.038 0.25 0.00031 0.0038 0.028 34 Estimates of risk for percent of sample inhaled A sensitivity analysis of the risk model was generated by Crystal Ball® 7.3.1 (Oracle; 2007) for each of the five risk scenarios. For the real-time PCR instrument limit of detection lognormal distribution, the limit of detection (79.4%) was the most sensitive factor in determining risk, followed by the exposure time (11.7%) and breathing rates (8.4%). The dose response function parameter k (0.5%) had the least impact on the risk estimates. Similarly, for the assumed environmental limit of detection lognormal distribution, the analysis resulted in the exposure time (45%) being the most significant factor, followed by the limit of detection (27.5%), breathing rates (26.1%) and the k parameter (0.9%). The median real-time PCR instrument limit of detection and the two environmental limits of detection (lower and upper) scenarios resulted with the exposure time as the dominant factor in determining risk, followed by breathing rates, and the k parameter. Cair values in these scenarios are point estimates rather than a distribution; therefore, the limit of detection was not a measured parameter in the sensitivity analysis. Even assuming perfect sample collection and processing (no loss in initial concentration), the estimated risk at the instrument limit of detection was far above the commonly used 1:10,000 level. Environmental limits of detection increase due to the imperfect efficiency of sample collection and processing, increasing the risk at these higher detectable concentrations. These risk estimates show that, using current techniques reported in the literature, even allowing for all possible improvements in collection technology, any detectable B. anthracis constitutes an unacceptable risk. Moreover, these estimates define the lowest risk that could be determined from measurement, quantifying the risk that can exist even when no B. anthracis was detected. 35 Finding significant risk at B. anthracis limits of detection suggests that direct measurement will rarely be adequate for declaring a contaminated site “clean” and alternative approaches (e.g., extrapolating from demonstrated log reductions) are needed. For fomites, soil, and water, further work is needed regarding the probability of infection by ingestion and contact before one can adequately address limits of detection and risk estimates. Direct measurement could, at best, reveal a catastrophic failure of decontamination. With respect to preventative monitoring, these estimates showed that significant risk was posed by undetectable concentrations of B. anthracis spores. This means that a low concentration B. anthracis release would be more likely to be detected by the symptoms in exposed humans rather than by current sampling technology. Where there was danger or suspicion of a B. anthracis release, close monitoring of human health would be needed in addition to environmental sampling in order to ensure timely medical treatment. Health monitoring alone may be preferred where resources are limited. The risk assessment approach presented here could be further improved if an experimental probability distribution of the estimated dose was available. However, such a probability distribution was not available even for the most common matrix (soil). To obtain such a distribution, a large number (e.g., 30) of different true doses must be spiked in the environmental matrix of interest, and the sample processed through an entire protocol. This time-consuming process has not yet been reported. 2.5 Conclusion Instrument and environmental limits of detection are necessary for QMRA when evaluating exposure to human pathogens in a contaminated environment. Due to the lack of pertinent data 36 on the detection of B. anthracis, the environmental limit of detection could not be represented as a distribution. These distributions were necessary in estimating the risk at the environmental limit of detection. Even so, it was clear that environmental samples may be expected to have broad distributions due to the many challenges in sample processing that affect the limit of detection. More environmental limit of detection studies need to be conducted in order to produce distributions similar to those of the instrument limit of detection. This would improve the risk assessment and improve the applicability of the information in regard to survival and clean up goals, providing valuable information for first responders. 37 APPENDIX 38 Figure A1.1 General schematic of the efficiency of sample processing and the effect on the environmental limit of detection tection. αη is an example of the average recovery efficiency along with the potential normal distribution. 39 Soil Type (a) Number of Spores Spiked Amount of Soil (100g-1mg) •Anthony fine sandy loam •Cultivated soil •Garden soil •Garden soil w/ 3% peat • Lawn • Litter •Meadow •Sand •Swamp (b) Number of Spores Spiked Air Sampler •Flow rate: 15-150 L/min Extraction Method Pretreatment/Extraction Method Soil Location •Aqueous polymer 2-phase system •Easy DNA Kit •FastDNA SPIN Kit •Cultivation in TSB •2x cultivation in TSB •Hot Detergent/beadmill Homogenization •Heat Treatment w/ 1.22g/mL sucrose/ 0.5% TritonX-100 •Heat Treatment w/ sterilized water and 10% TritonX-100/PBS •3 Freeze thaw cycles/ Glass beads and Glassmilk •IM separation twice and resuspend in PBS •Agriculture fields •Contaminated sites w/ organic compounds and tanning agents •National Institute of Health-Korea •Non-suspicious sites •Military fields •PBS wash •Thermal lysis Detection Method •ELISA-biochip system •Lifetime-gated fluorimeter (ASD) Detection Method •Biosensor Assay •IM-Electrochemiluminiscence •Immunofluorescence Assay •PCR •PCR-ELISA •Multiplex PCR •Nested PCR •Real-Time PCR (d) Number of Spores Spiked Surface Seeding • Dry aerosol • Inoculated spore solution Fomite Area (10cm2-1m2) Fomite Type • Stainless steel • Painted wallboard Sampling Method (c) • Biological Sampling Kit (BiSKit) • Cotton swab • Macrofoam swab • Polyester swab • Polyester-rayon blend gauze wipe • Rayon swab Water Type • Tap • Source Number of Spores Spiked Volume of Water (0.1-10L) Extraction Method • 1,2,3 RAPID DNA extraction kit • Qiagen DNeasy Tissue kit • No extraction-filter placed on SRBC plate Water Location • Patuxent River, MD • Chesapeake Bay, MD • Beltsvile, MD • Phoenix, AZ Extraction Method • Sonication and heat treatment • Compressed foam • Vortex in 5mL PBST for 2mins at 10s intervals Detection Method • Cultivation – Brain Heart Infusion Agar – Sheep Blood Agar – Trypticase Soy Agar – Trypticase Soy Agar w/ 5% Sheep Blood Detection Method • Cultivation Assay • PCR • Nested PCR Figure A1.2 Flow diagrams to illustrate the sample processing. 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Pandey, David Reyes-Gastelum, Charles P. Gerba, Joan B. Rose, and Syed A. Hashsham. Evaluation of Sample Recovery Efficiency of Bacteriophage P22 on Fomites. Applied and Environmental Microbiology. 2012. 78(22):7915-7922. DOI:10.1128/AEM.01370-12. 3.1 Abstract Fomites are known to play a role in the transmission of pathogens. Quantitative analysis of the parameters that affect sample recovery efficiency (SRE) at the limit of detection of viruses on fomites will aid in improving quantitative microbial risk assessment (QMRA) and infection control. The variability in SRE as a function of fomite type, fomite surface area, sampling time, application media, relative humidity (RH), and wetting agent was evaluated. To quantify the SRE, bacteriophage P22 was applied onto the fomites at average surface densities of 0.4 ± 0.2 and 4 ± 2 PFU/cm2. Surface areas 100 and 1000 cm2 of nonporous fomites found in indoor environments (acrylic, galvanized steel, and laminate) were evaluated with pre-moistened antistatic wipes. The parameters with the most effect on the SRE were sampling time, fomite surface area, wetting agent, and RH. At time zero (initial application of bacteriophage P22), the SRE for 1000 cm2 fomite surface area was, on average, 40% lower than that for the 100 cm2 fomite surface area. For both fomite surface areas, application media trypticase soy broth (TSB) and/or the laminate fomite predominantly resulted in a higher SRE. After the applied samples dried on the fomites (20 min), the average SRE was less than 3%. A TSB wetting agent applied 50 on the fomite improved the SRE for all samples at 20 min. In addition, RH greater than 28% generally resulted in a higher SRE than RH less than 28%. Parameters impacting SRE at the limit of detection have the potential to enhance sampling strategies and data collection for QMRA models. 3.2 Introduction Nonporous fomites (inanimate or nonliving objects) can be an important vehicle in the transmission of viral disease, especially for populated indoor environments, such as schools, daycare centers, nursing homes, hospitals, food preparation settings, or any civil infrastructure (4-6, 25, 32). Human exposure can be through touching and transfer of pathogens present on the fomite to the hands and then to the mouth, nasopharynx, and eyes (5, 24). Exposure can also be from the inhalation of re-aerosolized organisms from contaminated fomites (5, 25). Controlling and remediating an indoor environment from an outbreak resulting from an accidental or intentional release of viruses can be challenging tasks (1, 25). To declare an indoor environment “clean” after decontaminating it, quantification of the loss due to sample recovery that is specific to the method(s) used is essential for verifying the efficacy of the decontamination (16). Quantitative analyses of the parameters that affect SRE from fomites are vital for implementing efficient sampling and detection methods (16). Infection transmission models that include the environmental dynamics (environmental conditions, human behavior, survival characteristics of the agent in the environment, etc.) can be used to make decisions on interventions for preventing viral outbreaks (19). Without a quantitative assessment of the abundance of such agents in the environment, generic intervention recommendations could be 51 ineffective (4, 35). Survival and SRE studies with viruses have generally been conducted on fomites at surface densities of 102 PFU/cm2 or higher by applying virus stocks in volumes ranging from 5 to 500 µl on fomite areas ranging from 0.38 to 32 cm2 (Table 3.1). The use of higher initial titers is known to extend the viral survival rate on fomites (5). Under these optimal conditions, results may represent the upper limits of SRE. The surface densities may also be lower than what has been studied so far and pose significant risk (Table 3.1). However, parameters affecting survival at very low surface densities are less well studied. To our knowledge, only two survival studies (3, 7) and two SRE studies (17, 36) have been conducted at surface densities ranging from 0.02 to 50 PFU or at the 50% tissue culture infective dose (TCID50)/cm2 (Table 3.1 and Table A2.1 in the Appendix). These factors may have a significant effect on quantifying the risk to human health after decontamination. The objective of this study was to evaluate the parameters that affect SRE of bacteriophage P22, a surrogate for DNA viruses (20, 30), at concentrations close to the limit of detection. Bacteriophage P22 was chosen because it is a surrogate for DNA viruses such as adenovirus (13, 30), it meets many of the desired characteristics of a surrogate (20, 30, 33), and it has been used successfully by our group in environmental release and recovery studies (20, 30). We evaluated the variability of SRE from the parameters, such as fomite type, fomite surface area, sampling time, application media, wetting agent, and RH. The results presented here have implications for sampling strategies and subsequent microbial risk assessment at low concentrations. 52 Table 3.1 Parameters from survival and SRE studies evaluating viruses applied to fomites. Organism Survival studies Alphaviruses Ebola virus Lassa virus Astrovirus Sample conc. 1.5x1074.5x1010 PFU/ml 1x105-5x105 PFU 3.1x1066.3x106 TCID50/mlb 1x109 PFU/ml Avian metapneumovirus Avian influenza Bacteriophage P22 Calicivirus 107 TCID50/ml Coronavirus 104-105 MPNc Coronavirus 107 PFU/ml Feline calicivirus 109 PFU/ml, Norwalk virus 106 RTPCRU/ml Hepatitis A virus 10-fold Polio virus dilution Fomite area (cm2) App. vol. (µl) Surface density 0.25 NRa 1,3 20, 50 1 10 3.4x104-6.3x104 TCID50/cm2 10 10 1 1 6.4x106 PFU/cm2, 7.6x107 PFU/cm2, 5.6x107 PFU/cm2 3.3x104-1.6x105 PFU/cm2 Application medium RH (%) Temp. (oC) Survival SRE Ref. NR 30-40 20, 25 6-14 d (25) PBS or 20% FS NR 90±5 4, 20 10-90 d (2) NR NR 1-6 d (38) 107 PFU/cm2 NR 50 25 859 hr (20) 20 10 2.0x105 TCID50/cm2 104-105 MPN/cm2 NR Cell culture medium NR 4-72 hr 4, 20, 40 0.25-28 d (10) (8) 0.79 25 10 NR 1.3x105 PFU/cm2 4x107 PFU/cm2, 4x102 RT-PCRU/cm2 PBS 20% FS in PBS NR 20±3, 50 ±3, 80 ±3 55, 70 75–88 21 22±2 (34) (11) 0.79 10 N/A 10% FS in saline 25±5, 55±5, 80±5, 95±5 5, 20, 35 4-96 hr 4-12 hr a. NR-not reported b. TCID50-median tissue culture infective dose c. MPN-most probable number d. N/A-not applicable, not able to calculate surface density from information reported 53 3-6 hr 7d (21) Table 3.1 (cont’d). Organism Sample conc. Hepatitis A virus NR Human rotavirus Enteric adenovirus Poliovirus Rotavirus 103–105 PFU/ml Poliovirus Bacteriophage f2 Influenza A virus 2x108 PFU/ml Influenza A virus 106 TCID50/ml Influenza A virus 1.5x108 TCID50/ml Influenza A and B 103–104 virus TCID50/0.1 ml Fomite area (cm2) 1 App. vol. (µl) 20, 50, 100 250 Surface density Application medium RH (%) Temp. (oC) N/A PBS or FS 50±5, 85±5, 90±5 4, 20 Misted N/A NR NR 1 20 10 N/A 104 TCID50/cm2 2 10 7.07– 19.63 100 7.5x105 TCID50/cm2 50-1.4x103 TCID50/cm2 Distilled water, distilled water with 10% FS NR Eagle minimal essential medium with 25mM HEPES and Earle’s salts 1% BSA 54 NR Survival SRE Ref. NR 30-60 d, 30-60 d, 5-60 d, 5-60 d 0.75-1.5 hr (1) (18) 50-60 30-50 22±2 21-28 6-24 hr 2 hr-17 d (22) (37) 23-24 17-21 4-9 hr (14) 35-40, 55-56 27.8-28.3, 26.7-28.9 24-48 hr (3) Table 3.1 (cont’d). Organism Sample conc. Parainfluenza 1.5x100, 1.5x103, 1.5x104 TCID50/ml Rhinovirus 107 PFU/ml Zaire Ebola virus 1x106 Lake Victoria TCID50/ml Marburg virus Fomite area (cm2) 32 App. vol. (µl) 500 Surface density Application medium 0.02–2.0x102 TCID50/cm2 0.79 10 1.3x105 PFU/cm2 0.38 20 5.2x104 TCID50/cm2 Minimal essential medium with Earle’s salts Tryptose phosphate broth, bovine mucin, human nasal discharge Guinea pig sera, tissue culture media 55 RH (%) Temp. (oC) Survival SRE Ref. NR 22 6-10 hr (7) 20±5, 50±5, 80±5 20±1 2-25 hr (27) 55±5 4, 22 14-50 d (23) Table 3.1 (cont’d). Organism SRE studies Bacteriophage MS2 Sample conc. 1x106 PFU/ml Feline calicivirus 7.0x105-1.3x106 TCID50/100 µl Rotavirus 103-105 PFU/ml Poliovirus Bacteriophage f2 Norovirus 2.0x107 RTRotavirus PCRU/ml 2.0x105 RTPCRU/ml Rhinovirus 107 PFU/ml Fomite area (cm2) App. vol. (µl) Surface density 25 5 3.7 PFU/cm2 25.8, 929, 5,290 250 20 26-104 TCID50/cm2 Misted N/A 10 100 0.79 10 2.0x103, 2.0x104 RTPCRU/cm2 2.0x101, 2.0x102 RTPCRU/cm2 1.3x105 PFU/cm2 56 Survival SRE Ref. Application medium RH (%) Temp. (oC) 50% solution of TSB and dilution buffer (5 mM NaH2PO4 and 10 mM NaCl) 10% FS in PBS 45-60 20-22 7-40% (17) NR NR 3-71% (36) Distilled water, distilled water with 10% FS 10% PBS NR NR 16.8±6%, 42.3±1.9%, 10.6±5.7% (18) NR NR (28) Tryptose phosphate broth, bovine mucin, human nasal discharge 50±5 22 10.3±13.051.9±38.5% 5.4±1.557.7±25.9% 40.3-98.4% (27) 3.3 Materials and methods 3.3.1 Bacteriophage P22: preparation, application and sample recovery Bacteriophage P22, which infects the bacterial host Salmonella enterica serovar Typhimurium LT2 (ATCC 19585), was provided by Charles P. Gerba (University of Arizona). Bacteriophage P22 is a double stranded DNA (dsDNA) icosahedral-shaped virus with a short tail (52 to 60 nm in size) and belongs to the family Podoviridae (30). To prepare bacteriophage P22, 1 ml of bacteriophage P22 stock was added to 25 ml of the bacterial host, S. Typhimurium, at log phase in TSB (Difco, Becton Dickinson and Company, Sparks, MD). After a 24 hr incubation at 37 oC, 0.1 ml of lysozyme and 0.75 ml of EDTA were added to the solution and centrifuged at 2390 x g for 10 min. The supernatant was filtered through a 0.45 µm filter (Millipore) to remove the bacterial cells and debris (30). Bacteriophage P22 was then diluted in suspensions of phosphate buffered saline Tween-80 (Fisher Scientific, NJ) (PBST), TSB or sterile distilled water. The fomites, simulating an indoor environment, included acrylic (Optix; Plaskolite Inc., Columbus, OH), galvanized steel (type 28 GA galvanized; MD Building Products, Oklahoma City, OK) and laminate (type 350, no. 60 mate finish; Wilsonart International Inc., Temple, Texas) with surface areas of 100 and 1000 cm2. The fomites and testing area were disinfected with 70% ethanol, rinsed with sterile distilled water, and dried. Bacteriophage P22 was applied in PBST, TSB or water on the fomite in a grid formation comprising of fifty 1 µl droplets. The average amount of bacteriophage P22 applied to the fomite was 433.1 ± 194.5 PFU, approximately 8.66 PFU/droplet, with average surface densities of 4.3 ± 1.9 PFU/cm2 for the 100 cm2 fomite and 0.4 ± 0.2 PFU/cm2 for 1000 cm2 fomite. The recovery materials, pre-moistened Fellowes screen cleaning wipes (no. 99703; Fellowes, Itasca, IL), are generally used to remove 57 dirt, dust and finger prints from office equipment and are antistatic, non-toxic and alcohol free. The pre-moistened wipes are made of crepe fabric (crepe material is treated as a trade secret by Fellowes) and wetted by the manufacture with water and detergent (propylene glycol ethers). The pre-moistened wipes were cut into 48 cm2 pieces using sterilized scissors and stored in sterile Whirl-Pak bags at room temperature during the experiment, lasting no more than 12 hr. Fresh pieces were cut and used each day. The sampling was done by moving the pre-moistened wipes over the entire fomite twice (in perpendicular directions to each other). Two samples were taken, one immediately after the initial application (referred to as 0 min) and another after the samples were visibly dry (which was 20 min). The control experiments conducted with bacteriophage P22 suspensions to determine if the moistening agent had an effect on the viability of the virus indicated that, on average, 95% (range, 80 to 125%) of bacteriophage P22 could be recovered with inoculation directly onto the wipe and dissolution with PBST. Very high recovery rates were also seen at time zero on fomites with no drying. After sampling, the recovery material was placed into a 50 ml tube containing 5 ml of PBST and vortexed for 30 s. Bacteriophage P22 was assayed using a double agar layer method (39). The sample containing bacteriophage P22 (1 ml) was added to 2.5 ml of melted 1% agar overlay (1 g bacto agar/100 ml TSB) (Bacto agar; Difco, Becton, Dickinson and Company, Sparks, MD) with 0.3 ml of S. Typhimurium in the log phase. The solution was rolled by hand for mixing and immediately dispensed evenly onto 1.5% Trypticase soy agar (TSA) (Difco, Becton, Dickinson and Company, Sparks, MD) plates. After the overlay agar solidified, the plates were incubated at 37 oC for 24 hr; the number of PFU was then counted. A total of 324 plates were used in the conduction of the SRE experiments. These experiments included 3 fomite types, 2 sampling 58 times, 3 application media, and 2 fomite surface areas. Each SRE measurement was made in triplicate and repeated on three different days. Because the PFUs recovered were already very low, dilution of samples was not necessary. For each sample recovery experiment, positive control experiments were conducted in triplicate. Fifty 1 µl droplets of bacteriophage P22 inoculated in 950 µl of PBST (same as extraction solution) were dispensed into a 1.5 ml microcentrifuge tube. The 1 ml bacteriophage P22 control was dispensed as described above. 3.3.2 Single agar layer method to separate bacteriophage P22 survival from recovery When evaluating the survival of bacteriophage P22 on fomites, fifty 5 µl droplets containing an estimated 3.96 PFU/droplet suspended either in TSB or water were applied on polystyrene Petri dish surface (100 by 15 mm) in a grid formation. An average of 198 ± 65 PFU was applied to each plate, with an average surface density of 2.5 ± 0.9 PFU/cm2. For this experiment, the time of first sampling (other than the initial at time zero) was changed to 1 hr instead of 20 min, because the 5 µl droplets took longer to visibly dry on the petri dish. The samples were evaluated at 0, 1, 2, 4, 8, 12, and 24 hr by implementing a single agar layer method. This method allowed us to evaluate the PFU remaining but eliminated the need to recover them from a surface, because bacteriophage P22 was directly applied on the petri dish surface. The assay consisted of dispensing 3 ml of melted 1% agar overlay (1 g Bacto agar/100 ml TSB) with 0.5 ml of S. Typhimurium in the log phase and 2 ml of TSB onto the petri dish surface where bacteriophage P22 was applied. After the overlay agar solidified, the plates were incubated at 37 o C for 24 hr, at which point the number of PFU was then counted. The experiment was conducted twice using six replicates per time point spanning 7 sampling time points and 2 application media (thus using a total of 168 plates). For each survival experiment, a positive 59 control experiment, which consisted of fifty 5 µl droplets of bacteriophage P22 inoculated in 750 µl of PBST in 1.5 ml microcentrifuge tubes, was also included in triplicate. One millilter of this positive control was plated as described above. 3.3.3 Relative humidity and TSB wetting agent The RH and temperature in the laboratory were measured before each experiment with a digital RH and temperature meter (VWR Scientific Products). The average temperature of the laboratory during these experiments was 20.8 ± 0.23 oC (mean ± standard deviation). The RH ranges of 9 to 23% and 28 to 32% were the natural RHs of the laboratory during the winter and summer months, respectively (Figure 3). For RH range of 55 to 58%, a small laboratory space (14 ft by 7 ft by 9 ft) was equipped with a humidifier (Bionaire, Milford, MA). Previous studies support that use of a wetting agent applied to the recovery material (wipe or swab) to enhance the SRE (9, 17, 18, 28). In a preliminary experiment, PBST and TSB were compared as wetting agents applied on the fomite surface to evaluate their effects on SRE enhancement at 20 min. There was no statistical differences between the SREs when PBST or TSB as wetting agent was applied on the fomite (p=0.232, n=27, Student’s t-test, data not shown). Hence, in further SRE experiments, a TSB wetting agent was used (this step is referred to as TSB wetting). Using a disposable spreader, 200 µl of TSB was applied and uniformly distributed over 100 cm2 fomite surface area. The recovery material sampled both the disposable spreader and the fomite. The recovery materials were processed as described above. This experiment used a total of 162 plates consisting of 2 wetting conditions, 1 fomite surface area, 1 sampling time, 3 fomites, 3 application mediums, and 3 RHs. Each measurement was 60 made in triplicate. A positive control was also conducted in triplicate as described previously for the SRE experiments. 3.3.4 Percent sample recovery efficiency computations and statistical analysis Percent sample recovery efficiency was calculated as, %SRE = N assay (D ) N control × 100 (3.1) where %SRE s the sample recovery efficiency from the fomite, Nassay is the number of PFU counted on the agar plate from sampling the fomite, D is the dilution factor (the total extraction volume divided by the volume of sample assayed), and Ncontrol is the number of units on the agar plate from the control experiment. The data (%SRE) had considerable differences in variance, especially between 0 min and 20 min. Due to this, the data were transformed by adding 1 (to account for the zero values) and converted to a log scale. After analyzing the residuals, it was determined that the normality assumption of the residual did not fit the equation; therefore, the residuals were fitted under the assumption of a gamma distribution. Two equations for the transformed outcome were used to study the relationships between fomite type, application media, RH, and wetting condition. Log (%SRE +1) = β0 + β1X1 + β2X2 + β3X1X2 + e 61 (3.2) For equation 3.2, log (%SRE +1) is the log transformed SRE, X1 is an independent variable that denotes the fomite type so X1 is a nominal variable with no numerical value (acrylic, laminate, and galvanized steel as categories) for which laminate was taken as the reference category in the analyses, X2 is an independent variable that denotes the application media so X2 is a nominal variable (PBST, TSB, and water as categories) for which water was taken as the reference category in the analyses, X1X2 is the interaction between fomite type and application media, and e is the error term. The intercept β0 represents the average value of the reference group, in this case, is the average value of the log of the reference categories laminate fomite and water medium. The terms β1, β2, and β3 are the regression coefficients known as the effect for the corresponding independent variable X1, X2, and X1X2, respectively. Log (%SRE +1) = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X1X2 + β6X2X3 + β7X1X3 + e (3.3) In equation 3.3, log(%SRE +1) is the log transformed SRE, X1 and X2 are defined as in equation (3.2), X3 is an independent variable that denotes RH range so X3 is a nominal variable (9 to 23%, 28 to 32%, and 55 to 58% as categories) for which 55 to 58% was taken as the reference category in the analyses, X4 is an independent variable that denotes the use of TSB wetting for the sample collection so X4 is a nominal variable (no wetting and TSB wetting as categories) for which TSB wetting was taken as the reference category in the analyses, and e is the error term. As previously explained, the intercept β0 represents the average value of the log of the reference categories laminate fomite, water medium, 55 to 58% RH, and TSB wetting. The interaction terms are X1X2 (fomite type and application medium), X2X3 (application medium and RH), and 62 X1X3 (fomite type and RH). The regression coefficients (β1-7) are known as the effect for the corresponding independent variable X1-4, X1X2, X2X3, and X1X3, respectively. Because the independent variables used were nominal, dummy variables were used to compare the different categories to the corresponding reference categories. The dummy variable described the set of experimental conditions consisting of fomite type, application media, surface area, sampling time, RH, and wetting condition as single entity and evaluate the SRE for each set to the next by treating two such sets as reference (laminate and water). A regression was run using SAS 9.2 with the GLIMMIX procedure to evaluate the equations. The data was analyzed to evaluate the type III test of fixed effects (emanating from the factors being investigated) to determine the significance of each of the parameters specified in the model statement (26). Analyses of the model were performed on wetting condition, fomite surface area and sampling time groups. The patterns in the experimental data indicated differences to explore certain effects. This limited the error rates and avoided cancellation of significant effects. 3.4 Results 3.4.1 SRE of bacteriophage P22 from various fomites For 100 cm2 fomite surface area and the three application media (PBST, TSB, or water), the average SRE for the experimental data at 0 min were 46 ± 6.9% (SRE ± standard deviation) for acrylic, 70 ± 7.7% for galvanized steel, and 92 ± 6.4% for laminate (Figure 3.1a). The type III test of fixed effects (equation 3.2) for 100 cm2 fomite surface area at 0 min was significant for fomite type (p<0.0001), application medium (p<0.0001) and the interaction between fomite type and application medium (p=0.0128) (Table A2.2). Based on equation 3.2, laminate yielded the 63 highest SRE, while acrylic gave the lowest SRE regardless of which application media was used. However, use of TSB did result in a higher SRE than did the other media. PBST and TSB performed similarly on acrylic, while PBST and water performed similarly on laminate (Table A2.3). At 20 min, the average SRE for acrylic, galvanized steel and laminate were all less than 1 ± 0.9% for all application media (Figure 3.1a). The type III test of fixed effects for 100 cm2 fomite surface area at 20 min was significant for fomite type (p=0.0047) and application media (p<0.0001) but not significant (p=0.3589) for the interaction between fomite type and application media (Table A2.4). For these conditions, the application media significantly affected the SRE, and a higher SRE was observed from application media TSB. Similar to 0 min, laminate resulted with a higher SRE, while acrylic resulted in a lower SRE. Similar results were observed on acrylic and galvanized steel when applied in PBST medium (Table A2.3). Considering all application media for 1000 cm2 fomite surface area, the average SRE for the experimental data at 0 min were 21 ± 6.9% for acrylic, 26 ± 3.1% for galvanized steel, and 42 ± 19.2% for laminate (Figure 3.1b). The type III test of fixed effects for 1000 cm2 fomite surface area at 0 min was significant for fomite type (p<0.0001), application medium (p=0.0037) and the interaction between fomite type and application medium (p=0.0998) (Table A2.5). The laminate fomite yielded the highest SRE, while acrylic fomite gave the lowest SRE irrespective of the application medium. The use of TSB resulted in higher SRE, while PBST and water had statistically equivalent SREs (Table A2.6). At 20 min, the average SRE for the 1000 cm2 fomite surface area were 2 ± 1.4% or less for all surfaces and application media (Figure 3.1b). The type III test of fixed effects for 1000 cm2 fomite surface area at 20 min was significant for fomite type (p<0.0001) and application medium (p=0.0053) but not significant for the interaction between 64 fomite type and application medium (p=0.3720) (Table A2.7). The laminate fomite had the highest SRE, while acrylic and galvanized steel had lower and comparable SREs. The use of TSB and water as application media resulted in a higher SRE than the use of PBST (Table A2.6). 65 Acrylic Galvanized Steel Laminate (a) 120 100 SRE (%) 80 100 cm2 0 min 20 min 60 40 20 0 PBST TSB Water PBST TSB Water Application Medium (b) 1000 cm2 120 0 min 20 min SRE (%) 80 60 40 Acrylic Galvanized Steel Laminate 100 20 0 PBST TSB Water PBST TSB Water Application Medium Figure 3.1 Experimental SRE of bacteriophage P22 from fomites acrylic, galvanized steel, and laminate. Bacteriophage P22 was applied in media PBST, TSB and water to fomites surface areas of (a) 100 cm2 and (b) 1000 cm2. A pre-moistened wipe recovered bacteriophage P22 at the initial application time (0 min) and after drying (20 min). Each bar represents the average of nine plates, and the error bars represent their standard deviation. 66 No Loss 100 Survival or SRE (%) 80 60 Loss due to decay (single agar layer method) Loss due to sample recovery and decay (double agar layer method) 40 20 0 0.1 1 10 100 Log Time (hr) Figure 3.2 Loss of bacteriophage P22 due to decay versus the loss due to sample recovery and decay. The survival of bacteriophage P22 are signified by circles (●, applied in TSB; ○, applied in water), and each point represents the mean and standard deviation of 12 plates. SREs from 100 cm2 fomite surface area are signified with triangles (▼, applied in TSB; ∆ applied in water), and the SREs from 1000 cm2 fomite surface area are signified by squares (■, applied in TSB; □, applied in water). Each point of the SRE data represents the mean and standard deviation of 9 plates. 67 3.4.2 SRE versus decay for bacteriophage P22 The method employed to determine SREs included the loss due to decay. To separate this loss from the SRE, bacteriophage P22 was directly applied onto a petri dish (using TSB and water), and decay was quantified as described in the material and methods section using the single agar layer method. The decay rates for bacteriophage P22 were 7.97x10-2 hr-1 when applied in TSB and 6.81x10-2 hr-1when applied in water. After 1 hr, when the 5 µl droplets were visibly dry on the petri dish, the majority of the applied bacteriophage P22 was still infective (89.4 ± 6.7% in TSB and 87 ± 7.9% in water). These SREs were substantially higher than the SREs detectable at 20 min by employing the double agar layer method, which was 0% in water and 0.62 ± 1.3% in TSB for the 100 cm2 acrylic fomite and 0.76 ± 1.6% in water and 0.69 ± 1.5% in TSB for the 1000 cm2 acrylic fomite. Even at 24 hr, 2 to 5% of bacteriophage P22 was detectable using the single agar method (Figure 3.2). These results indicate that low or zero SRE may not always indicate an absence of the target, because SREs also include loss due to sample recovery. 3.4.3 Impact of wetting agent at varying relative humidity From the described experiment, it was clear that significant portion of the bacteriophage P22 was still active on the fomite at 20 min and the recovery material was unable to recover the dried sample. To enhance recovery, TSB was applied to the fomite as a wetting agent for SREs at 20 min (Figure 3.3). Each point on the distribution represents the experimental data for each fomite type, application medium, RH and TSB wetting combination. The type III test of fixed effects using equation 3.3 was significant for application medium (p<0.0001), RH (p<0.0001), the interaction between fomite type and application medium (p=0.0001), the interaction between fomite type and RH (p=0.0048) and the interaction between application medium and RH 68 (p<0.0001). It was not significant for fomite type (p=0.7634). The results of using the TSB wetting step were significantly different from those when it was not used (p<0.0001) (Table A2.8). The TSB wetting step improved the mean SRE for all cases. For both TSB wetting and no TSB wetting, bacteriophage P22 applied in TSB medium resulted in a higher SRE than when applied in the PBST and water. The exception to this was the acrylic and galvanized steel, where water gave higher SRE at 55 to 58% RH range (Table A2.9). Overall, regardless of wetting agent, higher average SREs were primarily observed at RH of 28 to 32% and 55 to 58%. The SRE values for both these humidity ranges were not statistically different from each other. The mean predicted SRE was predominantly lower for RH range of 9 to 23% than for those at the other two ranges. When water was used as the application medium, the highest average SRE was always obtained for 55 to 58% RH range and the lowest in the 9 to 23% RH range (Table A2.9). The effects of RH on SREs with the other application media (TSB and PBST) were less obvious than those with water. 69 40 40 (a) 9-23% rH 40 (b) 28-32% RH (c) 55-58% RH Laminate + TSB Acrylic + TSB 30 30 Laminate + TSB 20 30 Laminate + TSB 20 20 Acrylic + TSB SRE (%) Acrylic + TSB 10 10 0 No Wetting TSB Wetting 10 0 0 No Wetting TSB W etting No Wetting TSB Wetting Figure 3.3 The experimental impact of RH and TSB wetting agent on the SREs of bacteriophage P22 after drying (20min) on 100 cm2 fomite surface area. Bacteriophage P22 was sampled with pre-moistened wipes at RH ranges of (a) 9 to 23%, (b) 28 to 32%, and (c) 55 to 58%. Each dot on the distribution represents the SRE from a single fomite (acrylic, galvanized steel, and laminate) and application medium (PBST, TSB, and water) combination. Those with the highest SREs are labeled. The solid line in the box plot represents the median SREs, and the dashed lines represent the mean SREs. The box plot whiskers above and below the box indicate the 90th and 10th percentiles, respectively. 70 3.5 Discussion Once decontamination has been conducted on an indoor site due to a viral outbreak or bioterrorism event, environmental monitoring and quantitative microbial risk assessment modeling will help determine the risk to human health and if the indoor site can be declared “clean” (15, 16). When monitoring fomites for viruses near the limit of detection, the results from the linear regression equation suggest that the sampling priority should be for 100 cm2 laminate fomite. At both sampling times (0 min and 20 min) and both fomite surface areas evaluated (100 cm2 and 1000 cm2), laminate resulted in a higher SRE than those resulting from the other fomites under the same conditions. An increase in the fomite surface area from 100 to 1000 cm2 decreased the average SRE at 0 min by approximately 25% for acrylic, 40% for galvanized steel, and 50% for laminate (Figure 3.1). A lower SRE for the larger surface area was expected, because the surface density was also lower. Previous studies suggest that one method may not fit all scenarios, and in sampling for larger fomite surface areas the use of alternative recovery material may be more appropriate (12). Wipe methods are generally used for fomite surface areas of 10 to 25 cm2, but it is unknown what influence fomite surface area may have on the SRE (12). Low surface densities will require sampling of larger surface areas. Given that the SRE at 1000 cm2 area was lower than the SRE at 100 cm2 area and SRE includes decay, sampling at low surface densities must be carried out with caution. In general, the application medium TSB produced higher SREs than PBST and water. TSB is an organic medium used for the growth of bacteria and may have properties that were more stabilizing for the bacteriophage P22 on the fomite than on other media. It has been suggested that suspension in more complex media may affect resistance to desiccation (29). Most of the 71 SRE studies reported earlier used organic media to suspend viral particles before applying to the fomites (Table 3.1). The higher SREs in the TSB application medium suggest that the application medium may also influence the SRE, especially at low surface densities. The most dramatic reduction in the average SRE of bacteriophage P22 from the fomite was with time (0 min vs. 20 min). Initially, inactivation of bacteriophage P22 could be the main reason for this loss in SRE when the sample was dry on the fomite (20 min). Most of the rapid inactivation occurs during the period of desiccation when bacteriophage P22 becomes less stable on the fomites than in a liquid medium (20). In addition, the concentration that was applied to the fomite was rather low and close to the limit of detection of the plaque assay. Viral survival rates increase with increases in concentration, which can stabilize the virus against environmental stressors (5). On average, less than 3% of bacteriophage P22 was recoverable after 20 min on the fomite. The SREs reported at 20 min varied widely from 3 to 98.4% (Table 3.1). Each of the studies had a different experimental approach for determining the SREs from fomites which may account for the broad range of SREs reported. Keswick et al. 1983 (19), using cotton swabs, recovered rotavirus, poliovirus and bacteriophage f2 immediately after applying the samples to the fomite. Similarly, cotton swabs were used to recover norovirus and rotavirus dried for 15 min on the fomite by Scherer et al. 2009 (28). Taku et al. 2002 (36) evaluated three methods, moistened cotton swabs or nylon filter, fomite contact with elution buffer and aspiration, and scraping with aspiration, to recover feline calicivirus dried for 15 min on the fomite. The recovery materials antistatic cloth, cotton swab and polyester swab were evaluated by sampling bacteriophage MS2 dried for 45 min on the fomite (17). Sattar et al. 1987 (27), analyzed the SREs of human rhinovirus 14 dried on 1 cm diameter disks for 1 hr and then eluted the virus by 72 submerging the disk in 1 ml of tryptose phosphate broth and sonicating. It is evident that the number of parameters influencing the SREs is rather large, posing a challenge for simple comparison. A positive sample result indicates surface contamination and potential risk of exposure. However, a negative result does not entirely ensure the absence of infectious agents and the absence of the potential risk of exposure (28). Following the same protocol, Masago et al. 2008 (20) found bacteriophage P22 to survive for 36 hr on 10 cm2 fomites (aluminum, ceramic, glass, plastic, stainless steel, and laminate) when applied at a surface density of approximately 107 PFU/cm2 (Table 3.1). The decay rate of bacteriophage P22, reported in Masago et al. 2008 (20), for the plastic fomite was 5.2x10-3 hr-1. When eliminating the recovery method by applying the bacteriophage P22 (surface density 2.5 ± 0.9 PFU/cm2) directly onto the petri dish (plastic fomite), 2 to 5% of bacteriophage P22 could be detected at 24 hr. The decay rates for bacteriophage P22 on petri dish were estimated to be 7.97x10-2 hr-1 when applied in TSB and 6.81x10-2 hr-1 when applied in water. The differences between the decay rates in Masago et al 2008 (20) and this study were most likely due to the sample concentrations, since higher initial titers have shown to extend survival on fomites (5). As seen in Figure 3.2, at 1 hr (5 µl droplets were visibly dry) an average of 88.2 ± 7.3% of the applied bacteriophage P22 was still active. The majority of the loss (40 to 60%) occurred between hours 1 and 2. Compared to this, the average SRE from the 100 cm2 and 1000 cm2 acrylic surfaces at 20 min (1 µl droplets visibly dry) was less than 1% (Figure 3.2). The survival of organisms on fomites is known to be agent specific and ranges from 0.75 hr for rotavirus to 90 days for astrovirus (Table 3.1). Temperature, RH, fomite surface area, and sample concentration are all known to affect survival (5, 32, 40). 73 Knowledge of the organisms’ response to environmental stress on the fomite is important in determining the appropriate detection methods and employing clean up strategies. The results of the experiment designed to separate the decay from sample recovery (Figure 3.2) revealed that for surface densities of 0.4 to 4 PFU/cm2, SREs were low due to poor efficiency of the recovery method rather than decay. The TSB wetting step improved the SREs for all cases at 20 min (Table A2.9). The SRE results doubled in the majority of the cases, especially when the application medium was TSB. However, this TSB wetting step, the combination of the scraping from the disposable spreader and the application of the TSB wetting solution applied onto the fomite, may have physically dislodged the viral particles, resulting with a higher SRE than without the TSB wetting step (36). It can also be speculated that the bacteriophage P22 may adhere strongly to the fomite surface after drying or may attach to an imperfection on the fomite so that the sampling material cannot desorb the virus off the fomite. Surface roughness has been shown to influence adhesion and cell retention to fomites, which can affect recovery (31, 38). In this study, surface roughness was not measured. The addition of the TSB wetting step demonstrates the potential to further desorb viruses from the fomite and improve SRE. The RH and temperature are crucial parameters for viral survival on fomites (Table 3.1) (4, 5, 32). Higher SREs were observed for bacteriophage P22 at RH ranges of 28 to 32% and 55 to 58% regardless of the use of a wetting agent (Figure 3.3). At RH range of 9 to 23%, the lowest SREs were obtained (Table A2.9). The combination of application medium and RH may also play a significant role in SRE. Bacteriophage P22 applied in water consistently had the highest SREs at 55 to 58% and the lowest SREs at 9 to 23%. However, for the RH ranges evaluated, its 74 effect was not as obvious for the other application media. The interaction between RH and application media may be a useful parameter in implementing sampling strategies. 3.6 Conclusions In summary, efficient sample recovery and detection methods are essential in determining the exposure of humans to viruses and the resulting risk in a contaminated indoor environment. The SREs of bacteriophage P22 from fomites at concentrations near the limit of detection were influenced most by time of sampling, fomite surface area, the use of a wetting agent and RH. The observations made here using bacteriophage P22 as a surrogate highlight some of the factors that must be considered when sampling for very low surface densities of threat agents. Understanding the contributions of decay and recovery in the overall measured SREs under various conditions and the parameters affecting them will assist in implementing appropriate sampling methods and decontamination strategies. 75 APPENDIX 76 Table A2.1 Comparison of Julian et al. 2011 to this manuscript. Parameter Organism Surface density Julian et al. 2011 Bacteriophage MS2 3.7 ± 0.13 PFU/cm2 Fomites PVC, Type 304 Stainless Steel Fomite area Application vol. 25 cm2 5 µl Application medium Fomite seeding 50% solution of TSB and dilution buffer (5 mM NaH2PO4 and 10 mM NaCl) In the center of the fomite Sampling time Relative humidity 45 min (dry) 45 to 60% Sampling material/implements Cotton swab, polyester swab, premoistened antistatic wipe Eluents (applied to sampling material) Ringer’s solution, saline solution, viral transport media, and 1mM sodium hydroxide solution N/A Fomite wetting agent (applied to fomite) Extraction medium Detection method Ringer’s solution, saline solution, viral transport media, and 1 mM sodium hydroxide solution Culture and qPCR 77 This manuscript Bacteriophage P22 4.3 ± 1.9 PFU/cm2 for 100 cm2, 0.4 ± 0.2 PFU/cm2 for 1000 cm2 Acrylic, galvanized steel, laminate 100 cm2, 1000 cm2 Fifty 1 µl droplets PBST, TSB and sterile distilled water Fifty 1 µl droplets places in grid formation on the fomite 0 min (initial) and 20 min (dry) 9 to 23%, 28 to 32% and 55 to 58% Pre-moistened antistatic wipe N/A TSB (PBST evaluated in preliminary experiments) PBST Culture Table A2.2 Fit statistic, type III test of fixed effects and parameter estimates for 100 cm2 at 0 min. 100 cm2 at 0 min Fit statistics -2 Log likelihood AIC BIC 627.83 647.83 671.77 Type III test of fixed effects Num DFa Effect Fomite 2 Media 2 Fomite*Media 4 Parameter estimates Effect Intercept Fomite (Laminate)d Acrylic G. Steel Media (Water) PBST TSB Acrylic*PBST Acrylic*TSB G. Steel*PBST G. Steel*TSB a. b. c. d. Den DFb 72 72 72 F-value 106.56 19.67 3.42 p-value <0.0001 <0.0001 0.0128 Estimate 4.4785 SEc 0.05883 DF 72 t-value 76.12 p-value <0.0001 -0.9253 -0.3935 0.0832 0.0832 72 72 -11.12 -4.73 <0.0001 <0.0001 -0.0041 0.1271 0.4053 0.2884 0.1775 0.2227 0.0832 0.0832 0.1177 0.1177 0.1177 0.1177 72 72 72 72 72 72 -0.05 1.53 3.44 2.45 1.51 1.89 0.9608 0.1309 0.0010 0.0167 0.1359 0.0624 Num DF-numerator degrees of freedom Den DF-denominator degrees of freedom SE-standard error (Laminate or Water)-reference category 78 Table A2.3 %SRE for fomites 100 cm2 at 0 and 20 min. Estimates reported from log transformed data. Mean and 95% confidence interval (CI) reported in %SRE. 100 cm2 at 0 min Media Fomite Mean PBST Acrylic 51.16 45.39 57.66 PBST G. Steel 69.69 61.87 78.49 PBST Laminate 86.74 77.03 97.66 TSB Acrylic 51.92 46.06 58.50 TSB G. Steel 83.33 74.00 93.83 TSB Laminate 99.04 87.97 100.00 Water Acrylic 33.93 30.06 38.27 Water G. Steel 58.44 51.86 65.83 Laminate 87.10 77.35 98.06 Water 95% CI 2 100 cm at 20 min PBST Acrylic 0.22 0.00 0.77 PBST G. Steel 0.26 0.00 0.83 PBST Laminate 0.35 0.00 0.96 TSB Acrylic 0.62 0.11 1.35 TSB G. Steel 1.79 0.92 3.06 TSB Laminate 2.86 1.66 4.62 Water Acrylic 0.00 0.00 0.45 Water G. Steel 0.40 0.00 1.04 Water Laminate 0.80 0.23 1.61 79 Table A2.4 Fit statistic, type III test of fixed effects and parameter estimates for 100 cm2 at 20 min. 100 cm2 at 20 min Fit statistics -2 Log likelihood AIC BIC 199.63 219.63 243.58 Type III test of fixed effects Num DFa Effect Fomite 2 Media 2 Fomite*Media 4 Parameter estimates Effect Intercept Fomite (Laminate)d Acrylic G. Steel Media (Water) PBST TSB Acrylic*PBST Acrylic*TSB G. Steel*PBST G. Steel*TSB a. b. c. d. Den DFb 72 72 72 F-value 5.77 13.08 1.11 p-value 0.0047 <0.0001 0.3589 Estimate 0.5853 SEc 0.1880 DF 72 t-value 3.11 p-value 0.0027 -0.5853 -0.2488 0.2658 0.2658 72 72 -2.20 -0.94 0.0309 0.3524 -0.2852 0.7662 0.4841 -0.2852 0.1781 -0.07744 0.2658 0.2658 0.3760 0.3760 0.3760 0.3760 72 72 72 72 72 72 -1.07 2.88 1.29 -0.76 0.47 -0.21 0.2869 0.0052 0.2020 0.4506 0.6371 0.8374 Num DF-numerator degrees of freedom Den DF-denominator degrees of freedom SE-standard error (Laminate or Water)-reference category 80 Table A2.5 Fit statistic, type III test of fixed effects and parameter estimates for 1000 cm2 at 0 min. 1000 cm2 at 0 min Fit statistics -2 Log likelihood AIC BIC 593.08 613.08 637.03 Type III test of fixed effects Num DFa Effect Fomite 2 Media 2 Fomite*Media 4 Parameter estimates Effect Estimate Intercept 3.5090 Fomite (Laminate)d Acrylic -0.5045 G. Steel -0.1526 Media (Water) PBST 0.06866 TSB 0.5963 Acrylic*PBST 0.0129 Acrylic*TSB -0.4139 G. Steel*PBST -0.2064 G. Steel*TSB -0.5511 a. b. c. d. Den DFb 72 72 72 F-value 25.57 6.07 2.03 p-value <0.0001 0.0037 0.0998 SEc 0.1106 DF 72 t-value 31.72 p-value <0.0001 0.1564 0.1564 72 72 -3.22 -0.98 0.0019 0.3327 0.1564 0.1564 0.2212 0.2212 0.2212 0.2212 72 72 72 72 72 72 0.44 3.81 0.06 -1.87 -0.93 -2.49 0.6621 0.0003 0.9537 0.0654 0.3541 0.0150 Num DF-numerator degrees of freedom Den DF-denominator degrees of freedom SE-standard error (Laminate or Water)-reference category 81 Table A2.6 %SRE for fomites 100 cm2 at 0 and 20 min. Estimates reported from log transformed data. Mean and 95% confidence interval (CI) reported in %SRE. 1000 cm2 at 0 min Media Fomite PBST Acrylic PBST G. Steel PBST Laminate TSB Acrylic TSB G. Steel TSB Laminate Water Acrylic Water G. Steel Water Laminate 2 1000 cm at 20 min PBST Acrylic PBST G. Steel PBST Laminate TSB Acrylic TSB G. Steel TSB Laminate Water Acrylic Water G. Steel Water Laminate Mean 20.89 24.00 34.79 23.21 29.01 59.66 19.18 27.69 32.41 0.00 0.00 1.52 0.76 0.53 3.79 0.69 0.55 1.42 95% CI 16.56 26.29 19.05 30.16 27.71 43.62 18.42 29.19 23.07 36.42 47.66 74.62 15.18 24.15 22.01 34.76 25.80 40.66 0.00 0.00 0.70 0.18 0.03 2.22 0.14 0.05 0.63 0.49 0.49 2.75 1.61 1.27 6.11 1.51 1.31 2.60 82 Table A2.7 Fit statistic, type III test of fixed effects and parameter estimates for 1000 cm2 at 20 min. 1000 cm2 at 20 min Fit statistics -2 Log likelihood AIC BIC 220.86 240.86 264.81 Type III test of fixed effects Num DFa Effect Fomite 2 Media 2 Fomite*Media 4 Parameter estimates Effect Intercept Fomite (Laminate)d Acrylic G. Steel Media (Water) PBST TSB Acrylic*PBST Acrylic*TSB G. Steel*PBST G. Steel*TSB a. b. c. d. Den DFb 72 72 72 F-value 16.36 5.64 1.08 p-value <0.0001 0.0053 0.3720 Estimate 0.8847 SEc 0.1984 DF 72 t-value 4.46 p-value <0.0001 -0.3606 -0.4436 0.2806 0.2806 72 72 -1.29 -1.58 0.2028 0.1183 0.04088 0.6814 -0.5649 -0.6426 -0.4820 -0.6980 0.2806 0.2806 0.3968 0.3968 0.3968 0.3968 72 72 72 72 72 72 0.15 2.43 -1.42 -1.62 -1.21 -1.76 0.8846 0.0177 0.1589 0.1097 0.2285 0.0829 Num DF-numerator degrees of freedom Den DF-denominator degrees of freedom SE-standard error (Laminate or Water)-reference category 83 Table A2.8 Fit statistic, type III test of fixed effects and parameter estimates for the impact of TSB wetting at varying relative humidity. 100 cm2 at 20 min – Impact of TSB wetting at varying relative humidity Fit statistics -2 Log likelihood AIC BIC 750.05 792.05 856.89 Type III test of fixed effects Num DFa Effect Fomite 2 Media 2 RH 2 Wetting 1 Fomite*Media 4 Fomite*RH 4 Media*RH 4 Parameter estimates Effect Intercept Fomite (Laminate)d Acrylic G. Steel Media (Water) PBST TSB RH (55-58%) 9-23% 28-32% Wetting (TSB wetting) No TSB wetting Acrylic*PBST Acrylic*TSB G. Steel*PBST G. Steel*TSB Acrylic*9 – 23% Acrylic*28 – 32% G. Steel*9 – 23% G. Steel*28 – 32% PBST*9 – 23% PBST*28 – 32% TSB*9 – 23% TSB*28 – 32% a. b. c. d. Den DFb 142 142 142 142 142 142 142 F-value 0.27 64.58 11.19 69.43 6.14 3.91 7.33 p-value 0.7634 <0.0001 <0.0001 <0.0001 0.0001 0.0048 <0.0001 Estimate 2.3953 SEc 0.2441 DF 142 t-value 9.81 p-value <0.0001 0.6651 0.3896 0.2960 0.3071 142 142 2.25 1.27 0.0262 0.2067 -1.2233 0.3315 0.2972 0.3062 142 142 -4.12 1.08 <0.0001 0.2807 -1.7154 -0.3712 0.3009 0.3006 142 142 -5.70 -1.23 <0.0001 0.2190 -0.9655 -1.2437 -0.9918 -0.03479 -0.7465 0.6897 -0.3686 -0.2752 -0.3207 1.3659 0.8506 1.6817 0.9008 0.1159 0.3303 0.3302 0.3300 0.3318 0.3319 0.3308 0.3320 0.3311 0.3305 0.3298 0.3333 0.3316 142 142 142 142 142 142 142 142 142 142 142 142 142 -8.33 -3.77 -3.00 -0.11 -2.25 2.08 -1.11 -0.83 -0.97 4.13 2.58 5.05 2.72 <0.0001 0.0002 0.0032 0.9162 0.0260 0.0395 0.2670 0.4085 0.3344 <0.0001 0.0109 <0.0001 0.0074 Num DF-numerator degrees of freedom Den DF-denominator degrees of freedom SE-standard error (Laminate, Water, 55 to 58%, TSB wetting)-reference category 84 Table A2.9 %SRE for no wetting vs. wetting at varying RH. Estimates reported from log transformed data. Mean and 95% confidence interval (CI) reported in %SRE. Bacteriophage P22 was sampled at RH ranges of 9 to 23%, 28 to 32%, and 55 to 58%. 100 cm2 at 20 min Fomite Media RH Acrylic PBST 9 – 23% Acrylic PBST 28 – 32% Acrylic PBST 55 – 58% G. Steel PBST 9 – 23% G. Steel PBST 28 – 32% G. Steel PBST 55 – 58% Laminate PBST 9 – 23% Laminate PBST 28 – 32% Laminate PBST 55 – 58% Acrylic TSB 9 – 23% Acrylic TSB 28 – 32% Acrylic TSB 55 – 58% G. Steel TSB 9 – 23% G. Steel TSB 28 – 32% G. Steel TSB 55 – 58% TSB 9 – 23% Laminate TSB 28 – 32% Laminate Laminate TSB 55 – 58% Acrylic Water 9 – 23% Acrylic Water 28 – 32% Acrylic Water 55 – 58% G. Steel Water 9 – 23% G. Steel Water 28 – 32% G. Steel Water 55 – 58% Laminate Water 9 – 23% Laminate Water 28 – 32% 55 – 58% Laminate Water Mean 0.00 0.00 0.00 0.00 1.05 0.75 0.00 0.99 0.23 7.09 3.93 3.20 1.99 4.02 3.07 4.63 8.88 4.82 1.91 2.88 7.13 0.00 2.09 5.16 0.00 1.88 3.18 No Wetting 95% CI 0.00 0.54 0.00 0.27 0.00 0.11 0.00 0.52 0.26 2.35 0.05 1.94 0.00 0.43 0.21 2.25 0.00 0.99 3.93 12.28 2.07 6.92 1.53 5.95 0.79 4.00 2.01 7.38 1.60 5.39 2.46 8.15 5.03 15.20 2.46 8.79 0.76 3.82 1.37 5.33 4.16 11.80 0.00 0.33 0.91 3.99 2.68 9.33 0.00 0.22 0.76 3.71 1.59 5.74 85 Mean 1.54 1.02 0.81 1.46 4.40 3.60 1.28 4.21 2.23 20.25 11.95 10.03 6.85 12.18 9.70 13.78 24.96 14.28 6.65 9.18 20.34 1.21 7.11 15.20 0.97 6.57 9.97 Wetting 95% CI 0.57 3.13 0.22 2.35 0.09 2.00 0.51 3.02 2.39 7.58 1.81 6.55 0.37 2.78 2.23 7.41 1.03 4.14 12.25 33.07 7.12 19.66 5.54 17.57 3.71 12.11 6.96 20.85 5.85 15.71 8.35 22.37 15.12 40.81 8.24 24.27 3.57 11.79 5.25 15.58 12.15 33.62 0.36 2.61 4.02 12.09 8.59 26.35 0.20 2.25 3.64 11.36 5.77 16.77 REFERENCES 86 REFERENCES 1. 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U.S. Environmental Protection Agency. 2001. Method 1602: male-specific (F+) and somatic coliphage in water by single agar layer (SAL) procedure. EPA 821-R-01-029. U.S. Environmental Protection Agency, Washington, DC. 40. Vasickova, P., I. Pavlik, M. Verani, and A. Carducci. 2010. Issues concerning survival of viruses on surfaces. Food Environ. Virol. 2:24-34. 90 Chapter 4: Genetic characterization of microorganisms on highly touched and untouched fomites 4.1 Abstract In the indoor environment, an important route of transmission of bacterial and viral disease is through the interaction with fomites. Touched fomites are mostly influenced by the interactions with individuals while untouched fomites are influenced by air movement. Understanding the bacterial communities on the fomites in an indoor environment may affect disease transmission models and quantitative microbial risk assessments. Therefore, in this study, the bacterial communities on highly touched and untouched fomites in a university setting were analyzed. Samples from touched and untouched fomites were collected from the common lounge, computer room, and cafeteria of six dormitories at the University of Michigan. Non-porous fomites of plastic, metal, and wood (e.g., computer mouse, door knob, and window sill, respectively) with surface areas ranging approximately 10 to 100 cm2 were sampled using premoistened wipes. DNA was exacted from the samples and analyzed using 454 GS FLX Roche sequencing of the 16S rRNA genes. Results from 69 samples shows that the majority of the sequences found on both touched and untouched fomites were from three core phyla. Proteobacteria, Actinobacteria and Firmicutes represented an average of 81.4% and 83.8% of the bacteria community on touched and untouched fomites, respectively. The bacterial communities on touched and untouched fomites were statistically different. The bacterial communities on touched fomites were more diverse and had more fecal related bacteria present compared to untouched fomites. There were no correlations observed between sample date/time, locations, dormitory rooms, fomite materials or fomite types. The knowledge of the bacterial communities 91 on touched and untouched fomites can further explain the exposure pathway for fomites in the indoor environment. 4.2 Introduction In 2010, the United States had 20.3 million people enrolled in higher education and of these approximately 2.5 million people lived in college residence halls (6, 43). In addition to personal living quarters, many of these residence halls offer shared amenities such as common lounges, computer rooms, study/meeting rooms, gyms, cafeterias, classrooms and offices. College students are generally at a higher risk of infectious diseases due to a shared living space, closer contact with other residents, variable hygiene habits and likeliness to engage in risk behavior (8, 30). Interactions with bacterial contaminated fomites in a setting where the population is working, eating and sleeping could lead to an added risk, especially when there is variable cleaning or infection control practices (3, 31). Recent outbreaks on university campuses have included meningitis, seasonal influenza, gastroenteritis, measles, mumps, Streptococcus, pertussis and Methicillin-resistant Staphylococcus aureus (MRSA) (8, 20, 30, 31). Fomites can be a vehicle in the transmission for both enteric and respiratory pathogens in the indoor environment (2, 16, 26). Pathogens can survive on fomites for hours to months depending on the environmental conditions, such as type and concentration of the microorganism, temperature, relative humidity, and UV exposure (14, 16, 26, 41). The microbial diversity in the indoor environment is comprised of microorganisms from outdoor habitats (i.e., soil, water, and air) being transported indoors by humans, pets, animals or ventilation systems; microorganisms originating on indoor sources (i.e., fomites or water); and humans or pets 92 shedding of body secretions (i.e., blood, feces, urine, saliva and nasal fluid) (2, 21, 23). Touched fomites (e.g., study table, door knob, or computer mouse) are mostly influenced by the direct interactions with humans. Untouched fomites (e.g., top of a cabinet or bookshelf) are more influenced by air movement and less direct interaction with humans. Differences between the bacterial communities from touched and untouched fomites may be due to the cleaning frequencies, ventilation rates, and the continual inoculation and re-inoculation of fomites that are frequently touched (9, 21, 26). There are many studies that have shown pathogen contamination on fomites and the opportunity for transmission in daycare centers, schools, nursing homes, office buildings, residential homes, public areas, hospitals, and food preparation settings (16, 26). There have also been a few studies that sampled fomites in the university setting. These studies focused on detecting one organism (i.e., MRSA, S. aureus or Stenotrophomonas maltophilia) and the total coliforms or total heterotrophic bacteria on fomites in bathrooms, kitchens and computer rooms (3, 8, 31, 38). When trying to better understand the diversity of the bacteria on fomites there are limitations to those methods, which include diluted target concentrations, environmental impurities, background inhibitors, and organisms in a viable but not cultivable state (15). High-throughput sequencing of the 16S rRNA gene allows us to examine the microbial communities on fomites to begin understanding the interactions between microorganisms, humans, and the indoor environment (13). When evaluating bacterial communities on hospital fomites, Oberauner et al. 2013 (34), was only able to detect 2.5% of the total bacterial diversity using standard cultivation when in comparison to the 16S rRNA pyrosequencing technique. 93 The pyrosequencing approach has provided insight into the diversity of bacterial communities on fomites in offices (17), hospitals (18, 34, 35), and residential households (9, 12, 19). In all of these studies, there was a core set of phyla present on the indoor fomites, which consisted of Proteobacteria, Firmicutes, Actinobacteria and in most cases Bacteroidetes. Distinct differences in the bacterial community structure were observed in comparison of different locations, rooms, and types of fomites (Table 4.1) (9, 12, 17-19, 34, 35). To our knowledge, only two studies have evaluated the bacterial communities on indoor university fomites (13, 23). Flores et al. 2011 (13) sampled public restroom surfaces and was able to cluster the fomite communities into three general groups: fomites associated with toilets, restroom floors, and routinely touched fomites (i.e., door handle, faucet knobs, and soap dispenser). Kembel et al.2014 (23) sampled fomites in restrooms, offices, and classrooms. The bacterial communities were most influenced by architectural design characteristics, building arrangement, human use and movement, and ventilation sources (Table 4.1) (23). 94 Table 4.1 Summary of studies analyzing the bacterial communities on fomites in the indoor environment using pyrosequencing. Location Jeon et al. (19) N/A Flores et al.(12) Boulder, CO Dunn et al.(9) Raleigh-Durham, NC Category Room Household Kitchen, bathroom Household Kitchen Fomite Refrigerator, toilet Date N/A Counter top, faucet, sink, cabinet, microwave, refrigerator, freezer, oven, stove, wall, garbage can, floor September 2011 Household Kitchen, bathroom, bedroom, living room, outside Cutting board, kitchen counter, refrigerator, toilet seat, pillowcase, door handle, tv screen, door trim (interior, exterior) Autumn 2011 Sampling method Sequencing method Area No. sample Easy swab kit Sterile cotton swab 454 GS Junior System Occupancy Hewitt et al.(17) New York, NY; San Francisco, CA; Tucson, AZ Office Hospital Neonatal intensive care units (NICU) Chair, phone, computer mouse, keyboard, desktop Baby bedside, door button, incubator, pyxus, sink, weigh cart N/A January 2009 (NICU1), February 2009 (NICU2) Dual-tipped sterile BBL culture swabs 454 GS FLX System Illumina HiSeq2000 Dual-tipped sterile BBL culture swabs Illumina HiSeq or MiSeq Dual-tipped sterile BBL culture swabs 454 GS FLX System 25cm2 2/10 houses N/A 82/4 houses N/A 9/40 houses 4-5 ppl./house N/A Ppl., children, cats, dogs 13 cm2 5/90 offices (3 buildings) Half offices inhabited by men or women 95 Hewitt et al.(18) San Diego, CA 12 cm2 13/NICU1, 17/NICU2 (2 hospitals) N/A Table 4.1 (cont’d). Jeon et al. (19) Untouched, touched Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria Skin, gut Flores et al.(12) Untouched, touched Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria Skin, food, water Dunn et al.(9) Untouched, touched Proteobacteria, Firmicutes, Actinobacteria Hewitt et al.(17) Touched Proteobacteria, Firmicutes, Actinobacteria Hewitt et al.(18) Untouched, touched N/A Skin, oral cavity, gut, leaves, soil Skin, nasal cavity, oral cavity, gut, soil Influence community N/A Communities were different between kitchens. Sample area had different communities (ie moist vs dry). Communities were correlated with location, especially NY and CA compared to AZ suggesting an effect to climate. No influence community Communities from refrigerator and toilet were similar. Communities on fomite in the same kitchen were similar. Fomite hygiene Assumption: more DNA was present in refrigerator vegetable drawer than toilets probably due to cleaning frequency. Assumption: higher diversity was found on floors, exhaust fans, and freezer doors probably due to infrequent cleaning. Communities were grouped into depositional environments, kitchen associated, and frequently touched surfaces. Communities were different between kitchens. Presence of dogs attributed mostly to variation in communities. Significant distinction between exterior and interior microbial communities. Occupants, presence of cats, presence of children, use of pesticides, presence of carpet, and the presence of allergies. Assumption: surfaces that are regularly cleaned have lower levels of diversity than surfaces that are cleaned infrequently. Gut, oral cavity, skin, urine, vagina, outdoor air, soil Communities were significantly different between buildings. Exposure Core phyla Source 96 Offices inhabited by men or women. Different fomites. NICU samples clustered with other fomites in offices, healthcare centers and restrooms. N/A N/A Table 4.1 (cont’d). Location Category Room Fomite Date Poza et al.(35) Coruna, Spain Hospital ICU and entrance hall Oberauner et al.(34) Graz, Austria Hospital ICU Kembel et al.(23) Eugene, OR University Lillis Hall: classroom, office, restroom Fomites above head level Flores et al.(13) Boulder, CO University Restroom Computer screen, monitor, drawer, medical device, keyboard, door handle, refrigerator, microwave June 2009 (3 consecutive days) Sterile lint Floor, medical device, workspace, bandage trolley, keyboard N/A June 22-24, 2012 November 2010 BiSKit, nylon flocked swab Sterile cotton swab N/A 10/12 bathrooms (6 male, 6 female in 2 buildings) N/A Sampling method Sequencing method Area No. sample 454 GS FLX System 454 GS FLX System Shop-Vac 9.4L Hang Up vacuum. Illumina MiSeq N/A N/A Occupancy N/A 1 m2, 25 cm2 24/ICU (5 floor, 11 devices, 8 workplace) N/A 2 m2 155 fomites (4 floors in Lillis Hall) Low and high occupancy 97 Door handle, faucet handle, soap dispenser, toilet seat, toilet flush handle, floor 454 GS Junior System Table 4.1 (cont’d). Exposure Core phyla Poza et al.(35) Touched Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria Oberauner et al.(34) Touched Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Acidobacteria, Cyanobacteria, Nitrospira Skin, gut, water, soil Kembel et al.(23) Untouched Proteobacteria, Firmicutes Deinococci Flores et al.(13) Touched Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria Human, soil, plants Architectural design characteristics, building arrangement, human use and movement, and ventilation caused largest influence on communities. High occupant space (classrooms) and restrooms were associated with human microbiome. Low occupant space (offices) was associated with outdoor environments. N/A Skin, gut, urine, vagina, oral, water, soil Communities were grouped into those found on toilet surfaces, restroom floors, and surfaces routinely touched with hands. Toilet flush handle had similar communities as the floor. Source Skin, oral, gut, water Influence community ICU and entrance hall bacterial communities were different. Entrance hall was more diverse than ICU. Floor bacterial communities formed clusters distinct from medical devices. No influence community Fomite hygiene N/A Communities from medical devices were similar to workplaces. N/A Sampling was done at 8AM before routine cleaning. 98 N/A Male and female restrooms were not statistically significant N/A The microbial community structure for indoor environments and their impact on human health remains relatively unknown (7, 13, 17, 23, 37). Our hypothesis is that touched fomites, which have direct human interaction, will have a more diverse bacterial community and will have more genera associated with the human microbiome than the untouched fomites. Understanding the indoor environment, especially since humans spend approximately 90% of their lives indoors, will assist in determining the affect (good, bad or neutral) microorganisms have on human health (21, 22). Defining the bacterial community on touched and untouched fomites may indicate patterns and insight on the exposure routes in the indoor environment (25). This will lead to effective cleaning and infection control practices (21, 25). 4.3 Materials and methods 4.3.1 Fomite sample collection Non-porous fomites (plastic, metal, and wood) were sampled in the common lounge, computer room and cafeteria from 6 dormitories at the University of Michigan. Samples were collected in the afternoon from the dormitories; East Quadrangle (EA) on February 19, 2007, Bursley (BU) on February 20, 2007, Stockwell (ST) on February 21, 2007, Couzen (CO) on March 6, 2007, Betsy Barbour-Helen Newberry (BN) on March 7, 2007, and Alice Lloyd (AL) on March 8, 2007. Fomite surface areas ranging from 10 to 100 cm2 were sampled with pre-moistened Fellowes screen cleaning wipes (no. 99715; Fellowes, Itasca, IL), as described in Herzog et al. 2012 (16). Five touched and five untouched fomites were randomly collected in each location for a total of 180 samples. After sampling the fomite, the pre-moistened wipe was placed into a 50 ml tube containing 10 ml of phosphate buffered saline Tween-80 (PBST). The samples were extracted from the wipe by vortexing the tube for 1 min. The sample solution and wipe were 99 poured into a 60 ml syringe. The sample solution was pressed into the Amicon ultra-15 centrifugal filters (UFC910024, Millipore, Billerica, MA) for concentration. The original 50 ml tube was rinsed with 5 ml PBST and vortexed for 30 s. The rinsate was then added to the Amicon tube and centrifuged for 1 min at 3,000 x g. Concentrated samples were pipetted into a 1.5 ml eppendorf tube and stored in a -80 oC freezer. 4.3.2 DNA extraction, PCR and 454 sequencing. DNA was extracted from all of the samples using the QIAampDNA mini kit (Qiagen, Valencia, CA). DNA was quantified using a Qubit fluorometer (Invitrogen, Grand Island, NY). Extracted DNA concentrations ranged from 0.066 to 3.74 ng/µg. PCR amplification was performed in triplicate on all samples. Samples were amplified using the Fast Start High Fidelity PCR system (Roche, Indianapolis, IN). PCR reactions were carried out in a total volume of 20 µl which included 1 µl of sample DNA (approximately 10 ng/µl), 0.4 µl of 10 mM dNTP, 3 µl of MgCl2, 2 µl of 10x buffer, 0.3 µl of BSA, 0.3 µl of Taq DNA polymerase, 2 µl of Fusion Tag primer, 2 µl of 10mM reverse primer and 9 µl of dH2O. Thirty cycles of PCR amplification were performed and included an initial denaturing step at 95 oC for 45 s, an annealing step at 57 oC for 45 s, and an extension step at 72 oC for 1 min. Prior to the first amplification cycle there was a denaturing step at 95 oC for 3 min. At the end of the 30 amplification cycles there was a final 72 o C extension for 4 min. PCR amplicon sizes were determined with 1% agarose gel (1xTAE) in three separate wells per samples. Gel bands within 270 to 300 bps were excised and DNA extraction was done using Qiagen gel extraction kit (Qiagen, Valencia, CA). For samples with a negative PCR results, a total of three separate attempts were made for amplification. Final eluted samples were purified using Qiagen PCR purification kit (Qiagen, Valencia, CA). Purified 100 samples were combined to final concentration of 0.5 ng/µl in a volume of 10 µl (per plate). Sixty-nine samples were given to the Research Technology Support Facility (RTSF) at Michigan State University for sequencing on the 454 GSFLX Titanium Sequencer. In addition, the Robert Britton Lab at Michigan State University ran 24 samples on the 454 GS Junior System. Detailed lists of the sequenced samples are found with the supplemental material in the appendix (Tables A3.1-A3.3). 4.3.3 Data analysis Analysis of the sequences was through QIIME, software for a wide range of 16S rRNA microbial community analyses (5). Quality controls were maintained by using default parameters for high quality sequences (>200 bp in length, quality score >25, and exact match to barcode and primer) and clustered into OTUs at 97% sequence identity (13, 17). UCLUST generated the high quality clusters (10). Representative sequences for each OTU was aligned against Greengenes core dataset using PyNAST, used to efficiently align thousands of 16S rRNA genes (4). Taxonomy was assigned with the RDP-classifier (42). UniFrac analysis was used for the principal coordinated analysis (PCoA) to determine differences of the microbial communities (28). Phylogenetic diversity (PD) was calculated by the sum of the branches of the phylogenetic tree leading to sequences in a sample (11, 22). For PD, a cutoff of 400 sequences was made for an equal comparison of samples. Samples CO.CL.2, ST.CP.6, EA.CF.4, EA.CP.7, and BU.CL.1 only had 10 sequences and were not included in the PD comparison. For the samples sequenced on the 454 GS Junior System, the cutoff was 96 sequences; therefore, EA.CP.2 was not included in this analysis. The Student’s t-test was used to determine the statistical difference between the diversity on touched and untouched fomites. 101 4.4 Results Touched and untouched fomites had a core set of phyla which included Proteobacteria, Actinobacteria and Firmicutes. The relative abundance of the core set of phyla represented an average of 81.4% and 83.8% of the bacterial community on touched and untouched fomites, respectively (Figure 4.1). However, the compositions of the community structure were different between touched and untouched fomites. The average relative abundance of Proteobacteria, Actinobacteria, Thermi, and Cynobacteria were higher on untouched fomites. For touched fomites Firmicutes, Bacteroidetes, Deferribacteres, and Tenericutes had a higher relative abundance compared to untouched fomites. It should be noted that the phylum Deferribacteres represents only one genus (Mucispirillum) and the average relative abundance on touched fomites was 3%. From this analysis there seems to be no patterns with the bacterial community and the fomite material (Figure 4.1). The PCoA of the un-weighted and weighted UniFrac distances between sample exposures resulted in a clear clustering of touched and untouched fomites (Figure 4.2). When three of the fomites were sampled, it was assumed that they represented untouched fomites but the community structure fit with the touched fomites, depicted as green squares in figure 4.2. There were also fomites that were assumed to be touched at the time of sampling but the community analysis appeared to be untouched (Figure 4.2). PCoA plots were also analyzed to distinguish if there were any correlations between sample date/time, locations, dormitory rooms, fomite materials, and fomite types. There were no correlations with any of those parameters (Figures A3.1-A3.5). The PD of the bacterial communities from both fomite exposures were significantly 102 different (Student’s t-test p=0.0005) (Figure 4.3). Touched fomites had a higher bacterial diversity than untouched fomites. A general examination of the compositions of the genera (or lowest classification available) on touched and untouched fomites were compared, with a cutoff at an average relative abundance of 0.5% or higher. The total relative abundance of the genera were 79.6% and 74.9% for touched and untouched fomites, respectively (Figure 4.4). The main sources of the genera found on touched fomites were from the animal gut (50.6%), skin and oral (18.8%), plants (3.4%), water and soil (3.1%), and extremophiles (3.0%) (Figure 4.4a). On untouched fomites the sources of the genera were from skin and oral (39.8%), water and soil (14.4%), extremophiles (12.3%), plants (7.16%), and the animal gut (2.6%) (Figure 4.4b). Similar relative abundance, PCoA and PD results were observed from samples sequenced on the 454 GS Junior System and presented in the appendix (Figures A3.6-A3.14). 103 AL.CF.8 ST.CF.1 BU.CL.4 Unclassified;Other WS3 WPS-2 Aquificae OP3 GN02 Chlamydiae TM6 WYO Fibrobacteres Chlorobi BRC1 NKB19 Lentisphaerae Armatimonadetes Other Verrucomicrobia TM7 Synergistetes Thermi Tenericutes Spirochaetes Planctomycetes OP11 OD1 Nitrospirae Gemmatimonadetes Fusobacteria Deferribacteres Chloroflexi Bacteroidetes Cyanobacteria Firmicutes Actinobacteria Acidobacteria Proteobacteria Tile Glass ST.CL.6 ST.CP.3 EA.CF.2 EA.CL.3 Metal EA.CL.10 EA.CL.2 ST.CF.3 BN.CP.9 EA.CP.10 BN.CL.5 Plastic CO.CP.8 ST.CF.5 BN.CL.2 ST.CP.9 ST.CL.9 ST.CP.6 EA.CP.8 EA.CP.4 EA.CP.2 CO.CF.1 CO.CL.1 CO.CP.3 BU.CP.7 BU.CL.1 BU.CL.10 ST.CL.4 Wood BN.CP.7 BN.CL.6 Plastic AL.CL.1 (a) Wood Wood EA.CL.9 EA.CL.8 ST.CL.7 BU.CL.7 EA.CL.5 EA.CL.4 CO.CL.2 BN.CP.8 BN.CP.5 ST.CF.6 AL.CL.4 ST.CF.2 ST.CL.8 CO.CL.9 CO.CL.5 BU.CP.5 BU.CL.9 BU.CL.6 BN.CF.3 BN.CL.4 AL.CF.5 ST.CL.1 ST.CP.10 EA.CF.10 EA.CF.8 EA.CF.4 BU.CP.3 Metal Unclassified;Other WS3 WPS-2 Aquificae OP3 GN02 Chlamydiae TM6 WYO Fibrobacteres Chlorobi BRC1 NKB19 Lentisphaerae Armatimonadetes Other Verrucomicrobia TM7 Synergistetes Thermi Tenericutes Spirochaetes Planctomycetes OP11 OD1 Nitrospirae Gemmatimonadetes Fusobacteria Deferribacteres Chloroflexi Bacteroidetes Cyanobacteria Firmicutes Actinobacteria Acidobacteria Proteobacteria Tile Glass Cement Plastic EA.CP.7 CO.CF.9 BU.CF.4 BU.CF.10 BU.CF.3 BU.CL.3 BU.CP.9 AL.CF.1 AL.CL.5 AL.CP.7 (b) Figure 4.1 Relative abundance of bacterial communities for each sample on (a) touched fomites and (b) untouched fomites at the phylum classification level. The fomite material for each sample is labeled with plastic, metal, wood, tile, glass or cement. 104 (a) (b) Figure 4.2 PCoA of the (a) un un-weighted and (b) weighted UniFrac distances between sample exposures. Where ▲ are touched fomites, ■ are assumed to be from untouched fomites but community appears to be from touched fomites, ● are untouched fomites, and ► are assumed to be from touched fomites but community appears to be from untouched fomites. 105 Phylogenetic Diversity (PD Whole Tree per 400 sequences) 35 30 25 20 15 10 5 0 Touched Untouched Fomite Exposure Figure 4.3 Phylogenetic diversity (PD whole tree per 400 sequences) of fomite exposures touched and untouched. The solid and dotted lines represent the median result and mean result, respectively. The solid circles represent the outlying samples. The box plot whiskers above and below the box indicate the 90th and 10th percentiles, respectively. Touched and untouched samples are significantly different (p=0.0005). 106 5 Skin/Oral Other 15 0 Taxon 107 15 10 5 Plant 25 Animal Gut 30 Water/Soil Extremophile Other 20 Relative Abundance (%) Extremophile Water/Soil Plant 10 f_Enterobacteriaceae;Other o_Clostridiales;f_Lachnospiraceae f_Veillonellaceae;g_Veillonella 15 f_Propionibacteriaceae;g_Propionibacterium f_Streptococcaceae;g_Streptococcus f_Pasteurellaceae;g_Haemophilus f_Clostridiaceae;g_Anaerococcus o_Rickettsiales;f_Mitochondria f_Staphylococcaceae;Other f_Neisseriaceae;g_Neisseria f_Lactobacillaceae;g_Lactobacillus f_Clostridiaceae;g_Peptoniphilus f_Clostridiaceae;g_Finegoldia 16 c_Chloroplast;o_Streptophyta f_Streptococcaceae;g_Lactococcus 17 f_Moraxellaceae;g_Acinetobacter f_Pseudomonadaceae;Other f_Pseudomonadaceae;g_Pseudomonas f_Corynebacteriaceae;g_Corynebacterium f_Xanthomonadaceae;Other f_Rhodobacteraceae;g_Paracoccus f_Acetobacteraceae;Other f_Xanthomonadaceae;g_Stenotrophomonas 18 f_Thermaceae;g_Meiothermus f_Deinococcaceae;g_Deinococcus f_Bacillaceae;g_Geobacillus f_Bacillaceae;g_Anoxybacillus f_Moraxellaceae;g_Psychrobacter 19 o_Gemellales;f_Gemellaceae f_Lachnospiraceae f_Ruminococcaceae;g_Oscillospira o_Bacteroidales;f_S24-7 c_Clostridia;o_Clostridiales f_Deferribacteraceae;g_Mucispirillum f_Lachnospiraceae;Other f_Alcaligenaceae;g_Sutterella f_Ruminococcaceae c_Mollicutes;o_RF39 f_Rikenellaceae f_Ruminococcaceae;Other f_Ruminococcaceae;g_Ruminococcus f_Bacteroidaceae;g_Bacteroides c_Clostridia;Other;Other;Other 15 f_Propionibacteriaceae;g_Propionibacterium f_Streptococcaceae;g_Streptococcus f_Lactobacillaceae o_Rickettsiales;f_Mitochondria f_Pasteurellaceae;g_Haemophilus f_Staphylococcaceae;Other f_Clostridiaceae;g_Anaerococcus f_Lactobacillaceae;g_Lactobacillus 24 c_Chloroplast;o_Streptophyta f_Streptococcaceae;g_Lactococcus 27 f_Pseudomonadaceae;g_Pseudomonas f_Pseudomonadaceae;Other f_Corynebacteriaceae;g_Corynebacterium f_Moraxellaceae;g_Acinetobacter 32 f_Deinococcaceae;g_Deinococcus f_Bacillaceae;g_Geobacillus f_Thermaceae;g_Meiothermus 36 k_Bacteria;Other;Other;Other;Other;Other Relative Abundance (%) (a) (b) 30 Animal Gut 25 20 Skin/Oral 0 Taxon Figure 4.4 Characterization of organisms on (a) touched and (b) untouched fomites at the lowest classification available. Where the taxonomic rank is represented by c for class, o for order, f for family, and g for genus. The total relative abundance illustrated is 79.6% and 74.9% for touched and untouched fomites, respectively. 4.5 Discussion Similar to the literature, the touched and untouched fomites had a common core set of phyla which included Proteobacteria, Actinobacteria and Firmicutes (9, 12, 17-19, 34, 35). The touched fomites had an additional dominate phyla of Bacteroidetes and Deferribacteres with an average relative abundances of 6.3% and 3.0%, respectively. Bacteroidetes was also observed as a dominate phylum in studies that analyzed touched fomites (Table 4.1). Additional dominate phyla for untouched fomites were Thermi (6.8%) and Cynobacteria (6.2%). By observing the bacterial community structure at the phylum level there seems to be a distinct difference between the two fomite exposures (Figure 4.1). Kembel et al. 2014 (23) discovered that human use and movement can influence the bacterial community structure. Each of the rooms in the dormitories that were sampled would have had a high occupancy for more than 14 hr per d. Most of the bacterial exposure of touched fomites comes from the direct interaction between humans and fomites while untouched fomite exposures are mainly influenced by air movement. Therefore, touched fomites would have a different community structure than untouched fomites. The PCoA showed a distinct clustering of bacterial communities on touched and untouched fomites (Figure 4.2). While the majority of the samples were collected and identified as either touched or untouched, there were some samples that were assumed to have a certain exposure but the community analysis proved to be the opposite. For instance, a wood sofa hand restEA.CL.4, was assumed to be a touched fomite but the bacterial community appeared to be from an untouched fomite. All of the samples were collected at random and there were no observation in human behavior prior to sampling. Therefore, these samples could either be untouched fomites or it may be possible that these fomites were touched more frequently and the 108 microorganisms were transferred to human hands at a rate not equal to the microorganisms being deposited onto the fomites. Transfer efficiencies from non-porous fomites to fingers for Escherichia coli, S. aureus, Bacillus thuringiensis, MS2 coliphage, and poliovirus ranged from <0.04 to 57% at low relative humidity and 12.8 to 79.5% at high relative humidity (26). In a real-world scenario, the transfer efficiencies may be even higher than the reported ranges because unwashed hands have resulted in greater fomite to finger microbial transfer efficiency than washed hands (26). In addition, information regarding the fomite hygiene, such as how often the fomites were cleaned and which fomites were chosen, were not available. The use of disinfectant cleaning wipes can physically remove microorganism as well as chemically degrade the remaining microorganisms left on the fomite. The use of disinfectant cleaning wipes resulted in a range of 2.5 to 5 log10 reductions for E. coli, S. aureus, B. thuringiensis and poliovirus on nonporous fomites (27). Both limitations could explain why some fomites did not fit within the initial labeling. The PCoA analysis between dormitory halls, rooms, fomite material and fomite type did not have any clear correlations on the bacterial communities (Figure A3.2-A3.5). Other studies have varied results on the correlation between different buildings, rooms, and fomites (Table 4.1). Many of the variations in the results could be due to the indoor environmental conditions. Natural environmental conditions such as seasonal variations and climate, relative humidity, temperature, and UV exposure are known to effect microbial community structure and survival (14, 16, 17, 21, 37). In addition, mechanical conditions can have an effect on bacterial communities. Frankel et al. 2012 (14) observed indoor bacterial concentration became more diluted with an increase in the air exchange rate. Similarly, Kembel et al. 2012 (22) found that 109 human pathogens were higher in rooms with lower flow rates and that mechanically ventilated rooms were less diverse than those ventilated with windows. Other important factors influencing bacterial communities were fomite hygiene, architectural design, building arrangements, and human use and movement (12, 23). Though all of these parameters effect the survival of bacterial communities, many of these parameters were rarely reported. Further investigation will be needed to better understand the interaction between the bacterial community structure and the influences of natural environmental conditions, mechanical conditions and human behavior. The bacterial communities were more diverse on touched fomites than on untouched fomites (Figure 4.3). Frequently touched fomites are a reservoir for the transmission on pathogens, bacteria are picked up and/or deposit through hands direct contact with the fomites (25, 26). Higher bacterial diversity on touched fomites supports the idea that touched fomites have direct interactions with humans in comparison to untouched fomites. With the high occupancy of the university dormitories and the students’ variable hygiene habits this could be a potential for disease outbreaks. On touched fomites the majority of the community was of bacteria found in the human gut while the majority on untouched fomites was from bacteria found on skin and the oral cavity (Figure 4.4). The Lachnospiraceae family are a dominate group found in the gut community of humans and animals (29, 33, 36). Lachnospiraceae, especially concurrently with Bacteroidales, has been studied as an indicator for human fecal contamination in water (29, 32, 33). On touched fomites, the average relative abundance of Lachnospiraceae was 25.8% and Bacteroidales was 4.2% (Figure 4.4a). Lachnospiraceae was also present on untouched fomites but at a much lower 110 average relative abundance, 0.65%. To our knowledge, Deferribacteres Mucispirillum has only one species M. schaedleri studied in the literature (1, 24, 39, 40). M. schaedleri is found in the mammalian gastrointestinal tract (39). Mucispirillum was present on touched fomites at average relative abundance of 3%. Propionibacterium and Streptococcus were skin related genera and possible pathogens that were dominate on both touched and untouched fomites (Figure 4.4b) (18, 34). Propionibacterium had an average relative abundance of 6.4% and 12.6% for touched and untouched fomites, respectively. Streptococcus had an average relative abundance of 3.2% and 8.7% for touched and untouched fomites, respectively. All of these possible pathogen genera were found in every dormitory and room (common lounge, computer room and cafeteria). The limitation of the data collected on touched untouched fomites is the ability for quantification of the pathogens present at the species classification level to be able to determine the exposure concentration and potential risks. However, this data is able to identify possible exposure pathways and hazards. It was evident that for the indoor environment, exposure to potential pathogens occurs on touched fomites more commonly. 4.6 Conclusions By characterizing the bacterial communities on touched an untouched fomites in dormitory halls helped to further understand the interactions between humans, bacteria and fomites. Humans clearly impacted touched fomites more than untouched fomites, which was evident by the higher diversity on touched fomites. Touch fomites also indicated an abundance of fecal indicators and genera associated with the human microbiome which appeared in every dormitory and room. Additional studies analyzing the various parameters (e.g., relative humidity, temperature, air flow rate, etc.) that effect microbial community structure would improve the understanding of 111 the indoor environment. The defined bacterial communities on touched and untouched fomites presented will give insight on probable exposure routes for effective fomite hygiene interventions to improve risks to human health. 112 APPENDIX 113 Table A3.1 Summary of all samples sequenced. Housing Students Number of positive samples (JR samples) Alice Lloyd (AL) Co-ed 500 7 (2) Couzen (CO) Co-ed 525 8 (7) Stockwell (ST) Female 400 15 (2) Betsy Barbour & Helen Newberry (BN) Female 230 9 (2) Bursley (BU) Co-ed 1270 14 (3) East Quadrangle (EA) Co-ed 860 16 (7) Dormitory Room Common lounge (CL) 30 (6) Computer lab (CP) 21 (7) Cafeteria (CF) 18 (11) Exposure Touched 32 (9) Untouched 37 (15) Fomite Material Plastic 30 (10) Metal 17 (7) Wood 15 (5) Tile 2 (1) Glass 2 (0) Cement 1 (0) 114 Table A3.2 Sample description for those sequenced on the 454 GS FLX System. Sample ID Fomite Fomite material Exposure Dormitory Room Common lounge Common lounge Common lounge Common lounge Cafeteria Common lounge Computer lab Common lounge Cafeteria EA-CL-4 Sofa hand rest Wood U2a East Quade EA-CL-5 Chair hand rest Wood U2 East Quad EA-CL-8 Cabinet top Glass Ub East Quad EA-CL-10 Bookshelf Wood Tc East Quad EA-CF-2 BU-CL-4 Chair Study table Wood Tile T T East Quad Bursley EA-CP-4 Computer Mouse Top fire alarm Plastic T East Quad Plastic T2d Stockwell Plastic T Couzen BN-CL-2 Microwave button Faucet Metal T Betsyf BN-CL-5 Lock Metal T Betsy AL-CF-1 Microwave Plastic U2 BU-CP-3 Doorknob Metal U2 Alice Lloyd Bursley EA-CP-7 Computer stand Plastic U East Quad BN-CL-6 TV control Plastic T Betsy CO-CL-9 Bin top Metal U Couzen ST-CF-2 Salad bar utensil Doorknob Metal U2 Stockwell Metal U2 Bursley ST-CL-9 CO-CF-1 BU-CL-6 Final conc. (ng/µl) 25.80 Primer no. 4.14 66 1.87 67 2.48 68 1.87 3.11 70 71 9.30 72 2.14 73 4.17 74 Common lounge Common lounge Cafeteria 25.50 75 22.60 77 7.17 79 Computer lab Computer lab Common lounge Common lounge Cafeteria 33.50 80 7.00 81 2.38 82 4.40 83 6.10 84 Common lounge 8.10 85 65 a. U2-are samples that were assumed to be from touched fomites but the community appears to be from untouched fomites. b. U-untouched fomites c. T-touched fomites d. T2-are samples that were assumed to be from untouched fomites but the community appears to be from touched fomites. e. East Quad-East Quadrangle f. Betsy-Betsy Barbour/Helen Newberry g. N/R-not recorded 115 Table A3.2 (cont’d). Sample ID Fomite Fomite material Exposure Dormitory Room Common lounge Computer lab Common lounge Common lounge BU-CL-10 M card reader Plastic T2 Bursley BU-CP-7 Plastic T Bursley ST-CL-4 Top of computer Windowsill Wood T Stockwell EA-CL-2 Study table Wood T East Quad EA-CP-2 Keyboardspace key Top of telephone Computer hard drive Tissue box Vending machine Button Microwave buttons Ice cream dispenser Study table Plastic T East Quad Plastic T Stockwell Plastic T East Quad Plastic Plastic U T Plastic U2 Metal U2 Wood U2 Salad bar sneeze guard Doorknob Glass T Metal U2 East Quad Alice Lloyd Alice Lloyd Alice Lloyd Alice Lloyd Alice Lloyd Betsy Plastic U Bursley Metal U2 Couzen BU-CF-4 CO-CP-3 Switch knob top Water fountain button Dining table Study table Plastic Plastic U2 T Bursley Couzen CO-CP-8 Top of scanner Metal T Couzen CO-CF-9 Top of fire alarm Plastic U Couzen ST-CP-9 EA-CP-8 EA-CF-8 AL-CL-1 AL-CL-5 AL-CF-5 AL-CL-4 AL-CF-8 BN-CL-4 BU-CP-9 CO-CL-5 116 Final conc. (ng/µl) 30.10 Primer no. 7.90 87 3.09 88 11.00 89 Computer lab Computer lab Computer lab Cafeteria Common lounge Common lounge Cafeteria 16.80 90 1.52 92 4.50 93 29.00 7.70 95 96 18.50 97 19.60 99 Common lounge Cafeteria 3.95 100 3.18 101 12.30 102 1.64 103 12.90 104 18.80 10.90 105 65 1.10 66 6.82 67 Common lounge Computer lab Common lounge Cafeteria Computer lab Computer lab Cafeteria 86 Table A3.2 (cont’d). Sample ID ST-CL-8 Fomite Fomite material Exposure Dormitory Room Metal U Stockwell Wood Cement T U Stockwell East Quad Common lounge Cafeteria Common lounge Common lounge Cafeteria Cafeteria ST-CF-1 EA-CL-9 Top of waste bin Dining table Windowsill ST-CL-6 Study table Wood T Stockwell EA-CF-10 ST-CF-6 Plastic Metal U U East Quad Stockwell Wood U Stockwell Plastic U Stockwell Plastic U Bursley Plastic U2 Bursley Plastic T Couzen Wood U Betsy Wood T2 Betsy BN-CF-3 ST-CL-1 Food cover Top of ice container Top of fire place Top of air conditioner Cover for ice container ATM machine button TV remote control Computer table (backside) Wall close to door Milk dispenser Water fountain Metal Plastic U2 U2 Betsy Stockwell ST-CP-3 Study table Wood T Stockwell EA-CL-3 Front desk Wood T East Quad ST-CP-6 Keyboard-enter and space bar Plastic T Stockwell ST-CL-7 ST-CP-10 BU-CF-10 BU-CL-3 CO-CL-1 BN-CP-8 BN-CP-9 117 Common lounge Computer lab Cafeteria Common lounge Common lounge Computer lab Computer lab Cafeteria Common lounge Computer lab Common lounge Computer lab Final conc. (ng/µl) 9.82 Primer no. 2.52 10.61 70 71 3.45 72 9.04 2.81 73 74 2.09 75 2.47 77 11.40 79 1.93 80 6.20 81 4.84 83 1.77 84 3.47 5.45 85 86 3.60 89 13.70 90 7.91 91 68 Table A3.2 (cont’d). Sample ID Fomite Fomite material Exposure Dormitory Room Computer lab Cafeteria Cafeteria Computer lab Cafeteria EA-CP-10 Doorknob Metal T East Quad ST-CF-3 ST-CF-5 BU-CL-1 Salad bar deck N/Rg Public phone Metal Plastic Plastic T T T Stockwell Stockwell East Quad EA-CF-4 Handle of rice cooker Study table Plastic U2 East Quad Wood U2 Couzen Metal U Bursley BU-CL-7 Top of heater board Top of shelf Tile U Bursley BU-CP-5 Stapler U2 Bursley BN-CP-5 Study table Metal & plastic Wood U2 Betsy BN-CP-7 Top of printer Plastic T Betsy BU-CF-3 Salad bar spoon Top of printer Plastic U2 Bursley Common lounge Common lounge Common lounge Computer lab Computer lab Computer lab Cafeteria Plastic U2 Alice Lloyd Computer lab CO-CL-2 BU-CL-9 AL-CP-7 118 Final conc. (ng/µl) 5.80 Primer no. 1.52 5.76 6.67 93 94 95 20.80 96 1.36 97 3.05 99 10.70 100 8.66 101 3.70 102 5.20 103 2.60 104 2.40 105 92 Table A3.3 Sample description for those sequenced on the 454 GS Junior System. Sample ID BN-CL-8 BU-CF-7 AL-CL-2 AL-CP-3 CO-CP-9 CO-CF-2 BN-CF-5 Fomite Fomite material Exposure Dormitory Room Water fountain handle Doorknob Soda fountain button Counter workshop Top of shelf Plastic U2 Betsy Metal Plastic T U2 Plastic U2 Wood U Bursley Alice Lloyd Alice Lloyd Couzen Wood Plastic U2 U2 Couzen Betsy Common lounge Cafeteria Common lounge Computer lab Computer lab Cafeteria Cafeteria Final conc. (ng/µl) 1.31 Primer No. 4.77 0.97 67 70 1.80 71 3.60 73 4.30 3.40 75 77 65 Wood Tile Metal U U U Couzen Couzen Stockwell Cafeteria Cafeteria Cafeteria 7.60 7.30 4.15 79 81 86 EA-CL-6 Dining table Salad dressing utensil Windowsill Floor Top of microwave TV top Plastic T2 East Quad 9.15 91 EA-CP-5 Study table Plastic T East Quad 27.50 92 EA-CF-9 CO-CF-5 EA-CF-5 BN-CP-6 Fire alarm Doorknob Dining table Computer screen Microwave Bin top Plastic Metal Wood Metal T2 U2 T U2 East Quad Couzen East Quad Betsy 1.55 8.71 9.40 3.60 94 95 96 102 Plastic Metal U2 U Couzen Couzen 4.17 4.40 74 83 Metal T Stockwell Common lounge Computer lab Cafeteria Cafeteria Cafeteria Computer lab Cafeteria Common lounge Cafeteria 6.10 84 Metal U2 Bursley 8.10 85 Top of computer Study table Plastic U2 Bursley 7.90 87 Wood T East Quad 11.00 89 Keyboardspace key Computer hard drive Plastic T East Quad 16.80 90 Plastic T East Quad 4.50 93 CO-CF-8 CO-CF-10 ST-CF-7 CO-CF-1* CO-CL-9* ST-CF-2* BU-CL-6* BU-CP-7* EA-CL-2* EA-CP-2* EA-CP-8* Salad bar utensil Doorknob *Samples that were also run on the 454 GS FLX System 119 Common lounge Computer lab Common lounge Computer lab Computer lab Figure A3.1 PCoA of the weighted UniFrac distances between sample date and time. Where the dates and sample time are represented by ▼for for 2/21/07 at 1:05 to 2:45P, ◄ for 3/8/07 at 1:45 to 3:10P, ● for 3/7/07 at 12:30 to 2:15P, ▲ for 2/19/07 at 1:30 to 4:00P, ► for 2/20/07 at 3:50 to 5:18P, and ■ for 3/6/07 at 1:20 to 3:05P. 120 Figure A3.2 PCoA of the weighted UniFrac distances between sample locations. Where the dormitories are represented by ▼ for Stockwell, ► for Alice Lloyd, ■ for Betsy Barbour and Helen Newberry, ▲ for East Quadrangle, ◄ for Bursley, and ● for Couzen. 121 Figure A3.3 PCoA of the weighted UniFrac distances between dormitory rooms. Where ● are the common lounges, ■ are the computer labs, and ▲ are the cafeterias where samples are collected. 122 Figure A3.4 PCoA of the weighted UniFrac distances between fomite materials. Where ▼are wood, ▲ are plastic, ■ are metal, ► are glass, ● are cement, and ◄ are tile. 123 Figure A3.5 PCoA of the weighted UniFrac distances between fomite types. 124 (a) EA.CF.9 EA.CL.6 EA.CP.2 EA.CP.5 EA.CP.8 BU.CF.7 Metal BU.CP.7 Plastic EA.CF.5 EA.CL.2 GN02 TM6 WYO Fibrobacteres Chlorobi BRC1 NKB19 Lentisphaerae Armatimonadetes Other Verrucomicrobia TM7 Synergistetes Thermi Tenericutes Spirochaetes Planctomycetes OP11 OD1 Nitrospirae Gemmatimonadetes Fusobacteria Deferribacteres Chloroflexi Bacteroidetes Cyanobacteria Firmicutes Actinobacteria Acidobacteria Proteobacteria Wood (b) Plastic Metal Wood Tile GN02 TM6 WYO Fibrobacteres Chlorobi BRC1 NKB19 Lentisphaerae Armatimonadetes Other Verrucomicrobia TM7 Synergistetes Thermi Tenericutes Spirochaetes Planctomycetes OP11 OD1 Nitrospirae Gemmatimonadetes Fusobacteria Deferribacteres Chloroflexi Bacteroidetes Cyanobacteria Firmicutes Actinobacteria Acidobacteria Proteobacteria Figure A3.6 Relative abundance of bacterial communities for each sample on touched fomites (A) and untouched fomites (B) at the phylum classification level (sample sequenced on the 454 GS Junior System). The fomite material for each sample is labeled with plastic, metal, wood, or tile. 125 (b) (a) Figure A3.7 PCoA of the (a) weighted and (b) unweighted UniFrac distances between sample exposures (sample sequenced on the 454 GS Junior System). Where ▲ are touched fomites, ■ are assumed to be untouched fomites but community appears to be touched fomites, ● are untouched fomites,, and ► are touched fomites. 126 Figure A3.8 PCoA of the weighted UniFrac distances between sample date and time (sample sequenced on the 454 GS Junior System). Where the dates and sample time are represented by ▼for for 2/21/07 at 1:05 to 2:45P, ◄ for 3/8/07 at 1:45 to 3:10P, ● for 3/7/07 at 12:30 to 2:15P, ▲ for 2/19/07 at 1:30 to 4:00P, ► for 2/20/07 at 3:50 to 5:18P, and ■ for 3/6/07 at 1:20 to 3:05P. 127 Figure A3.9 PCoA of the weighted UniFrac distances between sample locations (sample sequenced on the 454 GS Junior System). Where the dormitories are represented by ▼ for Stockwell, ► for Alice Lloyd, ● for Betsy Barbour and Helen Newberry, ▲ for East Quadrangle, ◄ for Bursley, and ■ for Couzen. 128 Figure A3.10 PCoA of the weighted UniFrac distances between dormitory rooms (sample sequenced on the 454 GS Junior System). Where ● are the common lounges, ■ are the computer labs, and ▲ are the cafeterias where samples are collected. 129 Figure A3.11 PCoA of the weighted UniFrac distances between fomite materials (sample sequenced on the 454 GS Junior System). Where ● are wood, ► are plastic, ▲ are metal, and ■ are tile. 130 Figure A3.12 PCoA of the weighted UniFrac distances between fomite types (sample sequenced on the 454 GS Junior System). 131 Phylogenetic Diversity (PD Whole Tree per 96 sequences) 12 10 8 6 4 2 0 Touched Untouched Fomite Exposure Figure A3.13 Phylogenetic diversity (PD whole tree per 96 sequences) of fomite exposures touched and untouched (sample sequenced on the 454 GS Junior System). The solid and dotted lines represent the median and mean, respectively. The solid circles represent the outlying samples. The box plot whiskers above and below the box indicate the 90th and 10th percentiles, respectively. Touched and untouched samples are significantly different (p=0.005), using the student t-test. 132 5 Extremophile 15 10 Skin/Oral 0 Relative Abundance (%) Water/Soil Plant 15 10 5 133 Water/Soil Extremophile Plant 25 Animal Gut 30 Other 20 f_Enterobacteriaceae;Other f_Lachnospiraceae 15 f_Propionibacteriaceae;g_Propionibacterium f_Streptococcaceae;g_Streptococcus o_Rickettsiales;f_mitochondria f_Pasteurellaceae;g_Haemophilus f_Neisseriaceae;g_Neisseria f_Staphylococcaceae;Other f_Lactobacillaceae;g_Lactobacillus f_Clostridiaceae;g_Anaerococcus 16 c_Chloroplast;o_Streptophyta f_Streptococcaceae;g_Lactococcus 17 f_Pseudomonadaceae;Other f_Pseudomonadaceae;g_Pseudomonas f_Moraxellaceae;g_Acinetobacter f_Oxalobacteraceae;Other f_Methylobacteriaceae;g_Methylobacterium f_Sphingomonadaceae;g_Sphingomonas f_Xanthomonadaceae;Other f_Comamonadaceae;g_Acidovorax f_Methylobacteriaceae f_Corynebacteriaceae;g_Corynebacterium 18 f_Bacillaceae;g_Anoxybacillus f_Deinococcaceae;g_Deinococcus f_Bacillaceae;g_Geobacillus 19 f_Gemellaceae f_Lachnospiraceae f_Ruminococcaceae;g_Oscillospira o_Bacteroidales;f_S24-7 c_Clostridia;o_Clostridiales f_Deferribacteraceae;g_Mucispirillum f_Alcaligenaceae;g_Sutterella f_Lachnospiraceae;Other o_Clostridiales;f_Ruminococcaceae f_Ruminococcaceae;Other o_Bacteroidales;f_Rikenellaceae c_Mollicutes;o_RF39 f_Bacteroidaceae;g_Bacteroides f_Ruminococcaceae;g_Ruminococcus 15 f_Propionibacteriaceae;g_Propionibacterium o_Lactobacillales;f_Lactobacillaceae f_Pasteurellaceae;g_Haemophilus f_Streptococcaceae;g_Streptococcus f_Clostridiaceae;g_Anaerococcus f_Staphylococcaceae;Other o_Rickettsiales;f_mitochondria f_Neisseriaceae;g_Neisseria o_Clostridiales;Other;Other 16 c_Chloroplast;o_Streptophyta f_Streptococcaceae;g_Lactococcus 17 f_Pseudomonadaceae;Other f_Moraxellaceae;g_Acinetobacter 18 c_Thermomicrobia;o_JG30-KF-CM45 f_Deinococcaceae;g_Deinococcus Relative Abundance (%) (a) (b) 30 Animal Gut 25 20 Skin/Oral 0 Taxon Taxon Figure A3.14 Characterization of organisms on (a) touched and (b) untouched fomites at the lowest classification available (sample sequenced on the 454 GS Junior System). 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Comparison of the cecal microbiota of domestic and wild turkeys. Microb. Ecol. 56:322-331. 41. Sinclair, R., S. A. Boone, D. Greenberg, P. Keim, and C. P. Gerba. 2008. Persistence of category A select agents in the environment. Appl. Environ. Microbiol. 74:555-563. 42. Wang, Q., G. M. Garrity, J. M. Tiedje, and J. R. Cole. 2007. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73:5261-5267. 43. Williams, J., De Vos B., Russell D. 2013. 2010 Census group quarters enumeration assessment report. Decennial Statistical Studies Divsion 138 Chapter 5: Conclusions The QMRA framework is a widely accepted formal process for estimating human health risks. QMRA has been a useful approach to a variety of scenarios in, but not limited to, public health, emergency response, environmental control measures, and decontamination efficacy. QMRA addresses probabilities of disease using mathematical models and integrated data sets that characterize microbial pathogen exposures through the environment. This framework has proven to be more sensitive than conventional epidemiological approaches for estimating human health risks (5, 2). One of the critical steps to a QMRA is the exposure assessment. The exposure assessment identifies and determines the exposed population, the exposure pathway, environmental fate and transport, concentration, frequency, length of time of exposure and estimates the dose (or distribution) for an exposure (5, 2). The exposure assessment presents the most variability and uncertainty in the risk assessment due to the vast and dynamic nature of this data. Variability in the measured exposure data can be caused by differences in location, activity, human behavior, environmental conditions and the environmental matrix. This variation in the data will result in differences in exposure to a microbial pathogen (5, 2). Complete data sets and quantitative information for the exposure assessment are often not available, either lacking in information or non-existent. By simplifying assumptions to compensate for the lack of data may result in uncertainty about the exposure estimates. Availability and quality of data and information can reduce the amount of uncertainty. For better risk management decisions the variability and uncertainty in the exposure assessment should be characterized or limited (5, 2). 139 The overall objective of the presented work was to address knowledge gaps and concerns regarding microbial pathogens (bioterrorism and infectious disease) associated with public health risks at concentrations at the limit of detection in the indoor environment. The results collected in these three chapters can be applied for the enhancement of the exposure assessments for the indoor environment by addressing the data variability and uncertainty. The objectives of this work were addressed by reviewing the instrument and environmental limits of detection, calculating the risk estimates at the instrument and environmental limits of detection, evaluating the SRE variability to environmental factors at the limit of detection, and characterizing the bacterial communities on touched and untouched fomites. In the exposure assessment, the limit of detection of any method plays a critical role in determining the method capabilities and assists in defining the exposure concentration. The environmental limit of detection is important because it takes into consideration the many steps to processing samples in an environmental matrix prior to detection. Whereas, the instrument limit of detection is evaluated with pure cultures and represents ideal conditions. A decontamination efficacy risk scenario for the indoor environment may have substantial variability and uncertainty due to working with microbial concentrations at or near the limit of detection. Data assessment issues (i.e., quantitatively validating the methods and sampling procedures) were revealed during the decontamination process from the released B. anthracis spores in 2011 (3). Therefore, in chapter 2, a literature review was conducted on the instrument and 140 environmental limit of detection for methods detecting B. anthracis. The instrument limit of detection from 9 different methods ranged from 10 cells/ml to 108 cells/ml. Instrument limits of detection for real-time PCR and PCR were the most sensitive with median instrument limits of detection of 430 and 440 cells/ml, respectively. There were only 15 studies (out of the 71 articles on method limits of detection for B. anthracis) that reported the environmental limit of detection. Of those, there were only 4 articles on the detection of B. anthracis from fomites, even though; fomites are a critical exposure matrix in the transmission of pathogens in the indoor environment (1, 4). The most sensitive environmental limits of detection were 0.1 CFU/g soil, 17 CFU/L air, 1 CFU/L water, and 1 CFU/cm2 fomite. The potential risk to human health was then calculated using the limits of detection as the exposure concentration and assumptions based on the inhalation route in an indoor environment. There were enough articles published on the instrument limit of detection for PCR and real-time PCR for there to be a distribution of values for the exposure concentration parameter. However, the environmental limit of detection could not be evaluated as a distribution due to the lack of information. More environmental limit of detection studies should be conducted in order to further define the environmental exposure. Or to obtain this distribution experimentally evaluate the pathogen in an environmental matrix and processed through to detection. The median risk estimate at the instrument limit of detection and the environmental limit of detection were 0.0062 and 0.52, respectively. The sensitivity analysis of the risk models indicated that the limit of detection model parameter had the most influence in determining the risk. Since SRE directly affects the limit of detection, 141 characterizing the SRE would result in a more sensitive and less variable detection method. Therefore, in chapter 3, the focus was to quantitatively evaluate the parameters that affect SRE from fomites (a relevant environmental matrix in the indoor environment) at the environmental limit of detection (fomite surface areas 100 and 1000 cm2 at low concentrations 0.4 and 4 PFU/cm2). Similarly to B. anthracis, viral pathogens pose a potential risks to human health in the indoor environment but may have more persistence issues on fomites. In chapter 3 bacteriophage P22, a virus surrogate, was chosen over the robustness of B. anthracis spores to evaluate the variability in SRE as a function of fomite type, fomite surface area, sampling time, application media, relative humidity and wetting agent. Experimentally, the parameters that had the most effect on the SRE were sampling time, fomite surface area, wetting agent, and relative humidity. Sampling time affected the SRE the most, within 20 min less than 3% of bacteriophage P22 was recoverable. Fomite surface area of 100 cm2 resulted in a higher SRE than 1000 cm2 fomite surface area. The low (<3%) recovery of bacteriophage P22 from fomites at 20 min prompted the comparison of the loss due to recovery versus inactivation. These results indicated that bacteriophage P22 was active (approximately 90% of sample concentration) on the fomite at 20 min and remained detectable (2 to 5%) 24 hr later. To enhance the standard recovery method for dry samples, a TSB wetting agent was applied with a disposable spreader to the fomite. This added step improved the SRE for all samples at 20 min. Relative humidity and temperature are critical parameters for virus survival on fomites. The effect of relative humidity on SRE was 142 investigated and it was observed that the SRE was higher when relative humidity was greater than 28%. Chapter 3 highlighted some of the parameters that must be considered when recovering viruses on fomites near the environmental limit of detection. In addition, it revealed that there was still substantial risk to human health after samples had dried onto the fomite. The survival and SRE are known to be organism-specific. Future work should focus on to quantitatively evaluating the parameters that affect SRE from fomites of select model organisms for pathogenic gramnegative, gram-positive and spore-forming bacteria. To further define the indoor exposure pathway, samples were collected in university dormitories to determine the bacterial communities on fomites for an in situ case scenario. The use of cultivation method was necessary in chapter 3 to be able to evaluate recovery and survival of active bacteriophage P22 on fomites. However, in chapter 4, the use of high-throughput sequencing of the 16s rRNA gene provided the ability to examine bacterial communities at a greater depth than culture-dependent methods, which underestimates the bacterial community structure. The bacterial community structure on touched and untouched fomites were defined, as well, as any effect from the dormitory halls, rooms, fomite material, and fomite type had on the community. Three core phyla (Proteobacteria, Actinobacteria, and Firmicutes) were present on touched and untouched fomites at different relative abundances. Touched fomites had a more diverse bacterial community compared to untouched fomites. 143 Fecal related bacteria (such as Lachnospiraceae and Mucispirillum) were at a higher relative abundance on touch fomites than untouched fomites. There were no correlations of bacterial community structure and the parameters of dormitory halls, rooms, fomite material, and fomite type. The work presented in chapter 4 did not quantify the amount of bacteria present or employ the improved recovery method, but instead examined the potential hazards on touched fomites (where exposure interaction between fomite, pathogen and human take place) compared to untouched fomites. The knowledge of bacterial communities on touched and untouched fomites is further evidence of the role fomites have on the transmission of infectious diseases in the indoor environment. Additional studies should focus on the possible effects of the parameters such as human behavior, fomite hygiene protocols, sampling time and environmental conditions (temperature, relative humidity, etc.) on the bacterial community. While the use of high- throughput sequencing of the 16s rRNA gene provided multiple indicators, a limitation to this technique was the inability to identify the microorganism at the species level. The next step of this research would be to develop and use molecular techniques based these results from fomites samples in the indoor environment to identify specific pathogen that are responsible for the potential risk to human health. The results from these chapters aimed to define parameter for the exposure assessment of microbial hazards at the environmental limit of detection for the indoor environment. The impact of these results is the establishment of environmental monitoring strategies and clean up goals. 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