Pattern of Microbial Degradation of Estrone and Triclosan Mixture and Its Effect on Soil Bacterial Community

Pattern of Microbial Degradation of Estrone and Triclosan Mixture and its Effect on Soil Bacterial Community

Ezinne Adabaram Osuji, B.S., M.B.A.

A Thesis

Biological Science

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE

Approved

Dr. Deborah L. Carr Chair of Committee

Dr. John Zak

Dr. Todd Anderson

Mark Sheridan Dean of the Graduate School

December, 2016

ACKNOWLEDGEMENTS

The journey to writing my thesis would not have been possible without the help of some people who are worth mentioning. My sincere and deepest appreciation goes to my

Advisor, Dr. Deborah Carr for giving me the opportunity to be a part of her team of graduate students, for believing in me, for being patient with me, always ready to share her wealth of knowledge with me all through the duration of the M.S program in Texas

Tech University and while working on my thesis and for helping me improve not only as a student but as a biologist. I also appreciate the members of my committee; Dr. John Zak and Dr. Todd Anderson for their help, support, advice and research guidance.

I am grateful to Dr. Rao Kottapalli and Dr. Pratibha Kottapalli of the Center for

Biotechnology and Genomics, Texas Tech University, Lubbock Texas for their help and patience during the metagenomics sequencing and analysis of the data. The completion of this project would not have been possible without the assistance and willingness to help of all the other members of the D. Carr lab; Meijun Dong, Anisha Navlekar, Bigyan

Rimal, and Jordan Brown for their time, help and inputs during the course of my research work both in the field and in the lab, especially Meijun Dong who was always willing to help with most part of the statistical analysis. I am also grateful to Texas Tech University

Association of Biologist (TTUAB) for funding part of this research work.

My deepest gratitude goes to my Parents, Dr. Sydney and Mrs Pauline Osuji,

Brother, Chukwudi Osuji, Sister-in-law, Faith Osuji and Niece, Nwadiuto Osuji, for their support all through this program, for their unrelenting believe in me and encouragement even during difficult times and showing me that I can achieve anything in life once I am ii

Texas Tech University, Ezinne Adabaram Osuji, December 2016 focused at it. I cannot thank my brother enough because without him I might not have been able to attend Graduate school. To my extended family here in Lubbock, in the

United States and in Nigeria, thank you for all the encouragement all through this journey. Lastly, my sincere thanksgiving and worship goes to God Almighty for giving me the strength to go through this journey hale and hearty. Thank you Lord for your love towards me.

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Texas Tech University, Ezinne Adabaram Osuji, December 2016

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... ii ABSTRACT ...... vi LIST OF TABLES ...... viii LIST OF FIGURES ...... ix LIST OF ABBREVIATIONS ...... x I. INTRODUCTION ...... 1 Pharmaceutical and Personal Care Products (PPCPs) in the Environment .... 1 Impacts of Estrone in the Environment ...... 2 Impacts of Triclosan in the Environment ...... 4 Soil Microbial Community ...... 5 Research Goal ...... 6 Hypotheses of the Study ...... 7 Specific Aims and Objectives ...... 8 II. MATERIALS AND METHODS ...... 9 Description of Site ...... 9 Soil Collection ...... 10 Experimental Design ...... 10 Test Chemicals ...... 11 Substrate Utilization profiling (Biolog Ecoplate) ...... 11 Statistical Data Analysis for Biolog Ecoplate ...... 13 High Performance Liquid Chromatography (HPLC) ...... 14 Statistical Analysis for HPLC ...... 15 Metagenomics Analyses ...... 15 DNA extraction ...... 16 Library Preparation and 16S rRNA gene sequencing ...... 16 Bioinformatics Data Analysis ...... 18

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Sequencing Data Processing ...... 18 Statistical Analysis of Sequencing data ...... 19 III. RESULTS AND DISCUSSION ...... 20 Substrate Utilization Profiling analysis ...... 20 Degradation of Test analytes ...... 22 16S Metagenomic analysis result ...... 24 IV. CONCLUSION ...... 31 Future Studies ...... 32 LITERATURE CITED ...... 33 FIGURES AND TABLES ...... 43

Texas Tech University, Ezinne Adabaram Osuji, December 2016

ABSTRACT

Pharmaceutical and personal care products (PPCPs) associated with land farming of municipal wastewater effluent may potentially, persist in the soil and alter soil microbial community processes. Estrone (E1) and the anti-microbial agent, Triclosan, were examined for their potential to persist and disrupt soil microbial community function.

Soil with 7 decades-long exposure to these chemicals (conditioned soil) and naive soil, which has not been previously exposed (unconditioned soil), was spiked with estrone, triclosan, or a 1:1 mixture of estrone: triclosan, and incubated for 90 days in the dark at

27°C. Control samples consisting of unspiked conditioned and unconditioned soil were included in the analysis. The community level physiological profile was examined using

BIOLOG® EcoPlates™ for the ability of their microflora to utilize ecologically relevant carbon sources. There was a significant increase in substrate activity and substrate richness in all treatments. Principal component analysis (PCA) of the data showed the microbial community utilized different carbon substrates by day 90 whereas they had exhibited similar substrate utilization at day 0. Microbial degradation rates were compared over the 90 days incubation period using high performance liquid chromatography (HPLC). Estrone and Triclosan showed the same pattern of biological degradation in both conditioned and unconditioned soils. Half-lives were determined to range between 5.9-6.8 days for the estrone treatments and 24.1-26.9 days in the triclosan treatments. The rate of degradation of the estrone:triclosan mixture was the same as the individual compound. 16S metagenome analysis of the conditioned day 0 control soil and the conditioned day 90 control, E1, triclosan and the binary mixture of estrone and

Texas Tech University, Ezinne Adabaram Osuji, December 2016 triclosan was done. The result showed that there was a decrease in species diversity between the control at day 0 and the other treatments at day 90, establishment of unique

OTUs in each treatment group at day 90 and Bacillus sp. being the most dominant bacterium specie in all the day 90 treatments.

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LIST OF TABLES 1. Grouping of 31 carbon substrates in the Biolog Ecoplate into six different guilds. Adapted from Choi & Dobbs (1999) ...... 56

2. Definition of parameters used in the biolog ecoplate analysis...... 56

3. Mean utilization of carbon sources in all treatment groups at day 90 by optical densities (OD) read from the spectrophotometer after 168h of plate incubation ...... 57

4. Mean values of Shannon’s diversity (H), evenness (E) and substrate richness (R) based on 168-h incubation of day 0 for the different treatment groups...... 57

5. Mean values of Shannon’s diversity (H), evenness (E) and substrate richness (R) based on 168-h incubation of day 0 for the different treatment groups...... 58

6. Calculated half-lives (days) of estrone, triclosan and their mixture...... 59

7. Total unique species, species with relative abundance of at least 0.5% & 1.0%. Species contributing more than 10% and 50% to the total number of species ...... 59

8. Count of bacteria phylum in the analysis of OTUs with relative abundance greater than 0.5% ...... 59

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LIST OF FIGURES 1. Environmental Fate of PPCPs taken from Lubliner et al., (2010)...... 43

2. Different views of the Lubbock Land Application Site (LLAS). Located at 4602 County Rd 6700, Lubbock, TX 79403 ...... 44

3. Plot of Substrate activity of conditioned & unconditioned soil...... 45

4. Plot of Substrate richness of conditioned & unconditioned soil...... 46

5. Principal component analysis (PCA) of the conditioned and unconditioned soil at day 0 and day 90...... 47

6. Degradation of estrone in soil using exponential degradation...... 48

7. Degradation of triclosan in soil using exponential degradation...... 48

8. Killed controls plot for the estrone treatment and the estrone component of the binary mixture treatment at different time points ...... 49

9. Killed controls plot for the triclosan treatment and triclosan component of binary mixture treatment at different time points ...... 49

10. Plot showing the top 20 abundant OTUs in each treatment group with their mean relative abundances shown. (A) Day 0 control (B) Day 90 control (C) Day 90 Estrone treatment (D) Day 90 Triclosan treatment (E) Day 90 Estrone and Triclosan mixture treatment ...... 52

11. Unique OTU with relative abundances of at least 0.5% (≥0.5%) for a combination of all the treatnment groups at day 0 and day 90 ...... 53

12. The Emperor Principal Coordinate analysis (PCoA) plot using weighted UniFrac distances for all treatment groups...... 55

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LIST OF ABBREVIATIONS

µg/mL Microgram per milliliter

µL Microliter

µm Micrometer

0C Centigrade

ANOVA Analysis of Variance bp Basepair

C Control treatment cm Centimeter cm/h Centimeter per height cm/week Centimeter per week

E Estrone treatment

E/T Combination of estrone and triclosan

EX Conditioned Soil ft Feet g Grams g/cu cm Gram per cubic centimeter h Hours ha Hectare mL Milliliter mm Millimeter mM Micrometer nm Nanometer

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OTU Operational Taxonomic Unit

PCA Principal Component Analysis pM Picometer

PPCPs Pharmaceutical and Personal Care Products rRNA Ribosomal Ribonucleic Acid

S.E. Standard error

Sp. Specie

T Triclosan treatment

UX Unconditioned Soil

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CHAPTER I INTRODUCTION

Pharmaceutical and Personal Care Products (PPCPs) in the Environment

Pharmaceutical and personal care products (PPCPs) are emerging contaminants of worry in drinking water and aquatic ecosystems. They have only recently (Xia et al.,

2005; Gibbons et al., 2011; Liu et al., 2013) been detected in the environment at levels that may pose environmental and health risks. Though their risks are not fully known at this time, their ubiquitous detection in effluent fed freshwaters has the potential to cause serious environmental and human health repercussions in general in the near future

(USGS, 2015; Colon & Toor, 2016). As water supplies become more limited and water demand and environmental needs increase in the United States and many parts of the world, there is need to recycle water, especially for agricultural and industrial use.

(USEPA 1998). The use of recycled water for drinking is less common in the United

States because many people are uncomfortable with the thought of having to drink water from our toilets. Other countries like Singapore, Australia, and Namibia and some U.S. states like California, Virginia, and New Mexico are already using recycled water for drinking purpose. And in truth, every downstream drinking water source is made of discharged effluent whether treated or untreated. PPCPs in municipal water supplies and the level of effluent treatment is fast becoming a crucial human and environmental health issue because of water recycling as a result of shortage in water supply.

Pharmaceuticals and personal care products (PPCPs) consist of a diverse group of chemicals which include prescription and over-the-counter drugs, veterinary drugs, 1

Texas Tech University, Ezinne Adabaram Osuji, December 2016 diagnostic agents, nutritional supplements and other consumer products, such as fragrances, cosmetics, bug repellants, and sun-screen agents among others (Ellis, 2008;

Environmental Fact Sheet, 2010). The discharge of these chemicals into the environment has been ongoing since manufacture and usage of these products containing these chemicals began (Colon & Toor, 2016). Although PPCPs are generally found to occur at very low concentrations in the environment and there are no known short-term effects on humans however, long-term effects cannot be totally ruled out until there is more research. These compounds, if not removed from the water or the process of wastewater treatment, tend to accumulate over a long period at high concentration (Daughton, 2004).

Wastewater contains an assortment of emerging chemicals of which PPCPs are inclusive (Anderson et al., 2010; Li et al., 2013). Discharge from waste water treatment plants are the major source of PPCP pollution in the environment (Wintgens et al., 2005;

Snyder et al., 2003; Kimura et al., 2004), as a result of agricultural irrigation using wastewater (Kinney et al., 2006), or through sewage water disposal even after primary and secondary treatments (Anderson et al., 2010; Hutchins et al., 2007). Reports show that these pharmaceutical and personal care products are not fully removed during municipal wastewater treatments (Al-Rifai et al., 2007; Castiglioni et al., 2006). Irrigation with wastewater over time could result in these contaminants migrating down through the soil profile (Chefetz et al., 2008), which eventually contaminates groundwater (Avisar et al., 2009) and surface water (Pedersen et al., 2005) (Figure1).

Impacts of Estrone in the Environment

Estrone is one of the naturally occurring estrogens in women and it is the main

Texas Tech University, Ezinne Adabaram Osuji, December 2016 circulating estrogen during menopause. Estrone, also known as E1 is secreted by the ovaries and adipose tissues and has a chemical name of 3-hydroxyestra-1,3,5(10)-triene-

17-one and the chemical formula C18H22O2. According to the national center for biotechnology information (2004), estrone is an odorless, solid crystalline powder, white in color. It has a melting point of 260.2 °C and a density of 1.23 g/cu cm at 250C (Lide,

D. R., 2008).

Natural and synthetic estrogens, of which estrone (E1) is the simplest form, are fast becoming contaminants of interest because of their ability to cause long-term effects to wildlife and human health, and also their ability to disrupt the endocrine system

(Norris et al., 2006; Carr et al., 2011). Estrogens, which are found in humans and animals, are female hormones that are crucial for maintaining the condition of the reproductive tissues, breasts, skin, and brain. (Zhang et al., 2016). They bind to the estrogen receptor and interfere with normal biological responses if they come up in more quantities than needed by the body (Zhang et al., 2016). Epidemiological studies have shown estrogens at high levels to be carcinogenic (Russo et al., 2006). Absorption of excess estrogens into the body causes serious outcomes including endometriosis, uterine fibroids, ovarian cancer, breast cancer in female groups, and testicular cancer, prostate cancer,and inferior quantity and quality of sperm in male groups (Gao et al., 2016; Li et al., 2015; Schug et al., 2011). Estrone at different concentrations affects fish fecundity and fertility in different species (Imai et al., 2007; Dammam et al., 2011; Nakamura et al.,

2015; Overtuf et al., 2015). Literature shows that many forms of the estrogens are rapidly oxidized to its simplest form: Estrone. Then estrone is decomposed as a result of

Texas Tech University, Ezinne Adabaram Osuji, December 2016 microbial activity (Termes et al., 1999, Suzuki & Maruyama, 2006). Many studies have examined endocrine-disrupting chemicals (EDC) and their removal from waste water treatment plants. Research has shown that the concentration of estrone and the breakdown product can be removed by bacteria in activated sludge systems

(Hermanowicz and Wozei, 2002; Andersen et al. 2003; Ying and Kookna 2005).

Regardless of this degradation potential, estrogens are still found in treated effluent (Carr et al 2011).

Impacts of Triclosan in the Environment

Triclosan, with a chemical name of 5-chloro-2-(2,4-dichlorophenoxy) phenol and a chemical formula of C12H7Cl3O2, is found in the environment as a result of antimicrobial product popularity (HCEC 2012, Huang et al., 2016). Although triclosan was recently banned by the US Food and Drug Administration (FDA) from soaps in the USA

(FDA, 2016), it is still allowed in many personal care and household products, such as cosmetics, creams, toothpaste, cleaning agents, clothing, kitchen utensils, and baby toys to name a few (Ying et al., 2007; Dhillon et al., 2015). Triclosan concentrations in such products are as high as 0.1% to 0.3% triclosan by weight (Singer et al., 2002: McBain et al., 2002). The widespread daily use of triclosan is likely attributed to its high antimicrobial efficiency and ease of processing into solutions and solids (Singer et al.,

2002). Although there is a ban on the use of triclosan in soap in the USA, it is still unregulated in many other products, thus leading to increasing triclosan concentrations in aquatic and terrestrial environments (Dhillon et al., 2015). Triclosan has biocidal action with multiple cytoplasmic and membrane targets when at high concentration (Russel

Texas Tech University, Ezinne Adabaram Osuji, December 2016

2004). At lower concentrations, it appears to be bacteriostatic in nature targeting bacteria by inhibiting fatty acid synthesis. Triclosan binds to bacterial enoyl-acyl carrier protein reductase (ENR) enzyme and thus increases the enzyme’s attraction to nicotinamide adenine dinucleotide (NAD+) which results in the formation of a stable complex. (Regos et al, 1974; Heath et al, 1999; Dann et al, 2011). The effect of triclosan in soils include increasing dehydrogenase activity (Ying et al., 2007a), and increases the rate of microbial respiration. (Waller & Kookana, 2009). One of the major worries is that the mode of action and where it targets in the microbes is analogous to antibiotics, and thus raises the concern that the overuse of triclosan may result in cross resistance to antibiotics and resulting in bacterial strains that are resistant to both triclosan and antibiotics (Dhillon et al., 2015).

Soil Microbial Community

Soil has one of the most convoluted microbial environments, considering species richness and the size of the microbial community (Kuppusamy et al., 2016). The microbial community in the soil environment is responsible for many activities relating to soil fertility, including decomposition, formation of soil aggregates, stabilized soil organic matter (Fernandez et al., 2016), and decontamination of the soil environment when it is contaminated with toxic substances through natural attenuation and bioremediation (Deutschbauer, 2006). The quick proliferation of the microbial community in the soil makes it easy to study the community interaction much more easily than in plants and animals (Garland 1997). This makes it easy for experimental operation within a short period of time (Frac et al., 2012). During degradation in the soil

Texas Tech University, Ezinne Adabaram Osuji, December 2016 environment, enzymes produced by microorganisms play a vital role in the other processes that take place in the soil and also responsible for the breakdown of complex organic compounds (Frac et al., 2011). A possible indicator of ecological stress in the soil may come from a change in the organic life as a result of environmental or human induced causes (Jezierska-Tys and Frąc, 2008; Carbonell et al., 2009). A major setback to our understanding of how the microbial communities found in the soil carry out bioremediation is as a result of most microorganisms not being cultivable. Literature is replete with the fact that just 1% of microbes found in nature have been cultured in the laboratory, meaning that about 99% have not been cultured or are uncultivable. By implication, microbial enumeration using cultivability on plates may not really be representative of the actual diversity in situ since some of the organisms which are non- cultivable may be viable but non-culturable, (VBNC). Alteration in the functionality of soil environment may be caused by changes in either community structure or physiological composition (Garland and Mills, 1994; Deng et al., 2011). Microorganisms play an important role in the biogeochemical processes in soil. To keep the ability of the soil microorganisms in decontaminating the environment, it is important to know the functional capabilities and metabolic characteristics of the microbial community.

Research Goal

So far in the literature, information regarding the impact of PPCPs in secondary effluent once they get to the soil environment are few. The long-term and short-term effect of PPCPs need to be studied and understood if treated effluent is to be used as the principal source for irrigation in agricultural practices or in general water recycling.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Information as regards the health risks of active PPCPs found in the environment remain inadequate. Although there are no known immediate effects on humans, the effects at the long run cannot be ruled out until there is more research. The impact of these metabolites and their transformation products resulting from the parent compound is not yet fully known.

This study examines the rates and effects on the soil bacterial community of a mixture comprising an antimicrobial and a simple estrogen known to be degraded by microbial processes. PPCPs occur as a complex mixture rather than as a single compound whenever they are present in the environment (Brian et al., 2007). Few studies exist that actually characterize the effect of pharmaceutical and personal care products mixtures

(Cleuvers, 2004; Brian et al., 2007; Galus et al., 2013; Runnalls et al., 2015). The method of studying just the single compound does not adequately account for the capacity of these chemicals to act as a combination (Brian et al., 2007) because every compound has a different mode of action (Runnalls et al., 2015).

Hypotheses of the Study i). Soil bacterial community can effectively transform or degrade the estrone and triclosan mixture in soil despite the anti-microbial properties of triclosan. ii) The estrone and triclosan mixture in soil is degraded by soil bacterial communities at the same rate as the individual compounds. iii) The presence of estrone will not affect the degradation rate of triclosan and vice versa.

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Specific Aims and Objectives

Aim 1. – To determine the rate of degradation of estrone and triclosan mixture in soil.

Objective – To achieve this aim, we measured the microbial degradation of these compound mixtures in the soil after exposure using High Performance Liquid chromatography (HPLC).

Aim 2. – To determine how soil microbial community reacts to the stress of estrone and triclosan in the environment.

Objective – To achieve this, we analyzed the microbial community response to the environmental stress using Biolog-Ecoplate to measure utilization of ecologically relevant carbon sources.

Aim 3. - To determine the effect on microbial community composition due to exposure and during the degradation process.

Objective – To achieve this, we directly assessed the genetic content of the soil microbial community and compared 16S metagenomics data sets from pre- to post degradation.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

CHAPTER II MATERIALS AND METHODS Description of Site

The site of interest for this study was the Lubbock land application site (LLAS) which serves as a good site due to exposure to decades of waste water treatment effluent and in turn exposure of the microbial community in the soil environment to PPCPs

(Figure 2). This site which is part of the Lubbock waste water treatment plant (LWWTP) has been used for municipal waste water treatment for decades. The site encompasses over 6000 acres east of the city of Lubbock. The primary PPCP exposure is through center pivot irrigation of effluent which irrigates 31 plots and the area of those plots ranges from 7.7 to 76.9 ha. (Fedler, 2000). The LWWTP currently applies over 13 million gallons of effluent from the southeast water reclamation plant daily to this site to irrigate crops through the 31 center pivot sprinkler systems. The soil from the LLAS as described by Carr et al. (2011) classified it as Friona loam of the Estacado series which is a slightly alkaline brown clay loam. It has a weak granular structure with permeability of

1.5-5.0 cm/h. Soil samples were taken from the conditioned site (- soil sample which are receiving effluent from an irrigation pivot) and the unconditioned site (– soil sample from the same site but has not received any effluent). The two soil sample sites have similar features and has been summarized in Carr et al., (2011). The conditioned soil had a reduced cation exchange capacity compared to the unconditioned soil, which can be attributed to the higher nitrogen levels in the effluent (Carr et al., 2011). The soil previously exposed to the effluent have less soluble salts contents than those that were not previously exposed likely a result of the salts being leached through regular irrigation 9

Texas Tech University, Ezinne Adabaram Osuji, December 2016 of secondary treated effluent at a rate of 3-4 cm/week (Carr et al., 2011).

Soil Collection

Soil samples were collected from the topsoil 5 – 15 cm deep from three different locations within the irrigation area, representing the conditioned soil or outside the irrigation area, representing the unconditioned soil in February 2015 at the Lubbock land application site. The soil samples were composited, air-dried and coarse sieved (4.0mm) to achieve a degree of homogeneity while maintaining micro aggregates and stored dark at room temperature until ready to use. The water capacity of the soil which was described by Tan et al (1986) as the mass of water taken up by a known amount of dry- weight soil after letting to stand for 24 hours of saturation, was measured and the water holding capacity was determined to be 44% of field capacity for the unconditioned soil and 48% for the conditioned soil. A portion of this soil sample served as the abiotic control in that it was autoclaved for 1 hour at 24-hour intervals for 3 days to ensure that the microbial community in the soil was destroyed. The soil was moistened with water prior to re-autoclaving.

Experimental Design

Ten grams of soil was added to 40 mL clear glass vials and spiked with 0.5 mL of a 50 µg/mL micro-pollutant standard in acetonitrile (ACN). Sterile water (0.5 mL) was added to bring the soil to 40% water holding capacity. Triplicate samples were incubated in the dark at 27oC under aerobic condition. Killed controls were also monitored under aerobic conditions in triplicate for each of the chemical exposures. Nine time points were subsampled during a 90-day incubation period (days- 0, 1, 3, 7, 14, -28, 42, 60 and 90). 10

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The moisture levels were maintained at regular intervals throughout the incubation period and sterile water was added whenever necessary to maintain the soil moisture at 40% of water capacity.

Test Chemicals

All compounds used for this study were Standard grade (>99%) They were obtained from Sigma Aldrich (St Louis, MO, USA) which includes Estrone (EC 53-16-7) and Triclosan (EC3380-34-5). Spiking solutions were made up in 100% acetonitrile

(ACN) at 50 µg/mL. Relevant Chemical properties, physical properties and sorption/desorption properties of the test compound is summarized in

Karnjanapiboonwong et al. (2010).

Substrate Utilization profiling (Biolog Ecoplate)

Microbial communities provide valuable information about changes that occur in the environment. Microorganisms are found in nearly all environments and are characteristically the first organisms to respond to perturbations in the environment which includes both physical and chemical. Changes in microbial communities physiologic profiles are often a first pointer to the changing health of the broader ecological community. Several methods have been used to study bacteria community assembly in the soil environment one of which includes the community level physiological profile which examines the ability of bacterial community to use different molecules as the lone source of carbon for metabolic activities and produce a useful pattern (Correa et al., 2007). Specifically, this method relies on measurements of the utilization of different ecologically relevant carbon sources by microorganisms in the soil 11

Texas Tech University, Ezinne Adabaram Osuji, December 2016 samples. The Biolog EcoplateTM was created specifically for community analysis and microbial ecological studies (Insam, 1997). The diversity of the microbial community and use of extensive carbon sources is necessary in determining the functionality (Deng et al., 2011) in the soil environment. In order to get the metabolic format of microbial community structure and functional diversity, substrate utilization pattern is a generally used means (Deng et al., 2011; Staddon et al., 1998; Garland et al., 1994).

Community level physiological profiles were measured using the Biolog Ecoplate

BIOLOG® EcoPlates™ (Biolog inc., 3938 Trustway Hayward, CA 94545, USA). The biolog technique assesses the ability of a bacterial community to utilize or catabolize 31 different ecological relevant carbon substrates and also to evaluate microbial communities (Zak et al., 1994; Gomez et al., 2006). The rate of utilization of the carbon source in each plate was determined by the reduction of tetrazolium violet redox dye which changed from colorless to purple when the microbial community utilize the substrate. The 96-well plate consists of three replicates of each of the31 carbon substrates and a water blank which serves as the control. The biolog ecoplate experiment was performed using the procedure from Zak et al., (1994). 10g dry weight of soil was blended at high speed using 0.2% water agar to ensure that the soil particles are homogenously dispersed. Standard dilution of the sample was done to a final dilution of

10-4 and 100 µL aliquots of the final dilution was inoculated into each of the 96 wells.

The plates were placed in a plastic bag to prevent evaporation of the sample from the wells and incubated at 25oC and color development read on the spectrophotometer at

590nm every 24 hours for 7days.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

The data was analyzed for substrate activity (SA), and substrate richness (SR)

(Table 2) as defined by Sobek & Zak, (2003), principal component analysis of the multivariate dataset grouping the carbon source into six guilds (Choi & Dobbs, 1999)

(Table 1) comparing microbial samples based on the difference in sole carbon source utilization pattern which defines how the samples are dissimilar as described by Garland

& Mills (1991) was performed using the day 0 and day 90 data.

Diversity in the utilization of substrate in each treatment group was deduced using the

Shannon-weaver index (H). This index takes into account both the specie numbers and abundance (Muniz et al., 2014). The Shannon Index depicts the capability of the microbial community to degrade little or less variety of carbon source. It compares the catabolic potential amongst treatment groups. A high index shows that the microbial community is able to degrade more substrates or with similar efficiency (Muniz et al.,

2014). The formula used for the calculation of this index is shown in Table 3.

Statistical Data Analysis for Biolog Ecoplate

All the obtained data at different time points were analyzed using statistical software R version 3.3.1 (R Development core team 2004). Plate reading at 168h of incubation for the Biolog Ecoplate analysis was used to calculate Substrate richness, substrate activity, Shannon-Weaver diversity indices. This is because it was the shortest incubation time that allowed the best determination among treatments. Data comparisons were done using one-way ANOVA at a 5% confidence level (p≤0.005) followed by

Tukey’s multiple comparison test to assess significant difference.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

High Performance Liquid Chromatography (HPLC)

High performance or High Pressure Liquid Chromatography (HPLC) is one of the most widely used analytical tools for the separation of different components from a compound or the mixture of the compounds. It is widely used for qualitative and quantitative analysis of unknown mixtures as well as separation of compounds from mixture for further analysis. HPLC is a type of column chromatography in which sample mixtures are moved in a solvents referred to as the mobile phase which has varying polarities through a column with different packing materials hereby referred to as the stationary phase. Variations between sample retention times referred to as time it takes to remove the samples from the column (Blum, 2014) will be subject to the interface between the stationary phase, solvents used and the sample being analyzed. As a result of different polarities in the analytes, the samples interact between the two phases at different rates as the sample passes through the column. Sample being analyzed with the lowest amount of connection with the stationary phase or the most amount of connection with the mobile phase will exit the column faster.

For the determination of the degradation rates we measured the amount of analytes remaining at each incubation period, the soil was extracted with 10mL of HPLC grade acetonitrile and filtered with a syringe filter with pore size of 0.2µm. The filtered extracts were stored in sealed 2mL amber glass vials frozen until it was analyzed by

HPLC with UV detection. Stock solutions were prepared for the two chemicals and serial dilutions were prepared for analytical standards using HPLC grade acetonitrile to achieve a range of expected soil concentrations. Standards and soil concentrations were deduced

Texas Tech University, Ezinne Adabaram Osuji, December 2016 by reversed phase HPLC (Agilent 1100 series) with variable wavelength detector furnished with Chemstation analytical software, and a C-18 column (Econosphere, 250 x

4.6mm, 5µm particle size, Deerfield, IL, USA). The sample injection volume was 20µL, run time was 8 minutes for both analytes. Acetonitrile: water mobile phase was used for both the triclosan and estrone at a ratio of 80:20 at a variable wavelength of 200nm.

Retrieval of spiked solution from the soil was 94% for Triclosan and 78% for Estrone

(E1).

Statistical Analysis for HPLC

Data generated from the HPLC run was analyzed using Microsoft Excel 2016.

Exponential regression analysis to determine the rate of degradation at different time points for different treatments. Data comparisons were done using one-way ANOVA at a

5% confidence level (p≤0.005) followed by Tukey’s multiple comparison test to assess significant differences. Killed control plots for different treatment groups was done using

Excel 2016. Degradation plots were done using statistical software R version 3.3.1 (R

Development core team 2004).

Metagenomics Analyses

Metagenomics involves a series of connected genomic technologies and bioinformatics tools to directly examine the genome content of entire communities of organisms (Thomas et al., 2012; Wooley et al., 2010). It has appeared as a powerful tool that can be used to analyze microbial communities irrespective of the ability of member organisms found in the community to be cultured in the laboratory. Since the soil environment contains the highest amount of microbial diversity (Roesch et al., 2007) and 15

Texas Tech University, Ezinne Adabaram Osuji, December 2016 currently less than 1% of this microbial diversity is cultivable by traditional methods, hence the need for metagenomic approach. This approach involves the extraction and analysis of soil DNA and thus helps improve our assessment of the soil environment. The technique gives an idea of Who is there? Who is not there? What functional genes are there? One of the limitations of the metagenomics approach is that it gives information on the totality of the microbes in that environment but it does not tell who is actively involved in the transformation or biodegradation process.

DNA extraction

Total soil microbial community DNA was extracted from the soil samples using

0.25g of soil from each treatment. For this extraction, MoBio PowerSoil® DNA Isolation

Kit (MO BIO Laboratories, Inc, Carlsbad, CA 92010) was used and the manufacturer’s instructions for the extraction of low biomass soil was followed with the following changes: (1) From the powerBead tubes 200µL of bead solution was removed and replaced with 200µL of Phenol:Chloroform:Isoamylalcohol pH 7.0-8.0 (PCI). The extracted DNA was quantified using NanoDropH spectrophotometer (Nanodrop

Technologies, Wilmington, DE). The extracted DNA was stored at -20oC until library preparation and metagenomics sequencing were done.

Library Preparation and 16S rRNA gene sequencing

Library Preparation and sequencing was performed by the Center for

Biotechnology and Genomics, Texas Tech University, Lubbock Texas using the Illumina

16S-metagenomics library prep protocol. The sequencing strategy used was the dual paired-end sequencing on MiSeq. The preparation of the samples for high throughput

Texas Tech University, Ezinne Adabaram Osuji, December 2016 sequencing involved using a two-step PCR approach which is according to the instruction supplied by Illumina. The amplification of the variable region V3 and V4 of bacterial rRNA gene was done using universal bacterial primer set 341F (5′-

CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′) containing Illumina adaptors as illustrated in Klindworth et al (2013). 1.5% agarose gel was used to verify the PCR products. Nextera XT index kit v2 (Illumina) was used to do the index PCR which attached a unique index for each sample to the illumina sequencing adapter on each end. Following this was the cleaning up of the amplicon product using

AMPure XP beads (Beckman Coulter Inc.) in accordance to the manufacturer’s instructions and eluted in 50 µL of 10mM Tris buffer (pH 8.5). Quantification of the cleanup product in triplicate was done using the Qubit 2.0 fluorometer using dsDNA HS

Assay kit (Invitrogen, Carlsbad, CA, USA). Concentration of the DNA in nM was determined for each sample based on the average of triplicates of concentration (ng/µL) and average library size of 615 bp. Normalization of the libraries was done by diluting to

4 nM using 10 mM Tris (at pH 8.5) and pooled by aliquoting 5µL each of 10 nM diluted library. The library pool with unique indices was run on an Agilent 2200 tape station

(Agilent Technologies, Santa Clara, CA, USA) using D1000 Screen Tape (Agilent

Technologies, Santa Clara, CA, USA) according to the manufacturer’s guidelines to get the final size of library pool and then determination of the final library concentration by fluorometric quantification using Qubit (Invitrogen). Sequencing was done using MiSeq

Reagent Kit v3 (600 cycle kit) (Illumina) and the cartridge and reagents were handled according to manufacturer’s instructions. The library was denatured using 0.2 N NaOH and diluted to 4.5 pM using pre-chilled HT1 buffer solution. Also, PhiX control libraries 17

Texas Tech University, Ezinne Adabaram Osuji, December 2016 were denatured and diluted to 4.5 pM. PhiX library was spiked in at 10% to increase diversity. In order to ensure efficient template loading on the Miseq flow cell, the combined library was heat denatured in a heat block at 96oC for 2 minutes after which the tube is inverted 1-2 times and immediately placed in ice-water bath for 5 minutes which is subsequently loaded into the MiSeq reagent cartridge. A mean cluster density of

1100K /mm2 was obtained for the library and 72.8% of clusters passed the quality filter.

The forward and reverse adapters were trimmed, samples were demultiplexed, and fastq.gz files were generated utilizing MiSeq reporter software (MSR) from Illumina. All the sequences for data analysis were uploaded into the NCBI-Bio Project data archive.

Bioinformatics Data Analysis

Sequencing Data Processing

Sequencing reads in fastq format were obtained by unzipping the fastq.gz files by gunzip command on linux server. Processing of the demultiplexed samples was done using quantitative insights into microbial ecology (QIIME, version 1.8.0) pipeline developed at CBG (Singh et al. 2016, in press). Assembling of the paired end reads of each sample was done using fastq-join through join_paired_ends.py script. This was followed by quality trimming the assembled sequences to remove poor quality reads using split_libraries_fastq.py script with default parameters (Bokulich et al. 2013) which included: truncation of any reads that has three consecutive low quality base calls, and trimming of sequence to last high quality score position which was defined by q equals to three. Sequences that were not at least 75% of the expected amplicon length together with ambiguous base calls were removed from the data pool. The 16S rRNA gene sequence

Texas Tech University, Ezinne Adabaram Osuji, December 2016 were clustered based on 97% similarity of the reads and OTUs (operational taxonomic units) were recognized against the subset Greengenes database (http://greengenes.lbl.gov) using UCLUST algorithm (Edgar 2010). Alignment of the sequences on PyNast aligner

(Caporaso et al. 2010b) and allocating representative sequence from each OUT to taxonomy against Greengenes 16S rRNA reference sequences (gg_97_otus_aug_13) was done. Removal of singleton from data pool was performed before taxonomy was assigned and further analysis. Texas Tech University high computational resource, Hrothgar was used to accomplish this computational data analysis.

Statistical Analysis of Sequencing data

QIIME was used to calculate Pair-wise distances between microbial communities based on phylogenic relatedness of whole communities which was calculated using

UniFrac method (beta diversity between samples) (Lozupone et al., 2005) and comparison of the Day 0 Sample with the Day 90 treatment groups to ascertain if there were similarities and differences of bacterial communities between the treatment groups.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

CHAPTER III RESULTS AND DISCUSSION

Substrate Utilization Profiling analysis

Substrate activity (SA) (Figure 3) and substrate richness (SR) (Figure 4) showed that contaminants inhibited carbon utilization at day 0 and then stimulated carbon utilization at day 90. There was a significant increase (p<0.05) in SA and SR between the

Day 0 and Day 90 sample of the conditioned and unconditioned soils except for the unconditioned soil with the mixture of estrone and triclosan. A comparison of the day 90 control to the other treatments showed a significant increase in both substrate SA and SR.

At Day 0, there was little to no SA or SR for both the conditioned and unconditioned soil samples. This could be as a result of the effect of the estrogen and triclosan on the soil microbial community which either inhibited, stimulated the growth of a new community

(Zhang et al., 2014) or outright killed the microbial community in the soil sample. By

Day 90, there was a significant increase in SA and SR for either soil samples which could be a result of formation of a new microbial community, or the development of microbial community resistance to the effect of the contaminants. It is possible that the contaminants actually stimulated microbial activity of specific microbes with the ability to degrade and use those contaminants as their carbon sources by regulating protein enzymes.

Principal component analysis (PCA) of the data from the 168 hour reading for the day 0 and Day 90 samples corrected with the control was analyzed and grouped into six carbon guilds - carbohydrates, amines, carboxylic acids, phenolic compounds, amino 20

Texas Tech University, Ezinne Adabaram Osuji, December 2016 acids, polymers (Choi & Dobbs, 1999). The PCA shows that at day 0 (Figure 5A), there was no difference among soil microbial communities in their use of sole carbon source.

There is a wide overlap in carbon substrate utilization pattern by all treatments at day 0 while at Day 90, (Figure 5B), the PCA showed distinct carbon substrate guilds being utilized by all treatments. At day 0, PC1 accounted for 45.2% of the variance in the data set while PC2 accounted for 26.6% while at day 90 PC1 accounted for 53.8% variance while PC2 accounted for 23.1% variance. The microbial community composition was clearly distinguished by PC1 and PC2. It shows that there were distinct qualitative differences among soil microbial community in their use of sole carbon source and in terms of their functional characteristics based on contaminant exposure. Our data shows that at day 90, there was a compositional and structural change in the bacterial community to adjust to the effect of the estrone and triclosan in the environment. To our understanding, this is the first study to have looked at community physiology profile of soil microbial community involved in degrading soil contaminated with estrone and triclosan mixture using Biolog Ecoplates.

Table 3 shows how these compounds were utilized in each of the guild of carbon substrate by the microbial community for each different treatment groups using the

Biolog-Eco plates. The response pattern showed that carbon source utilization of carbohydrates was higher in the conditioned soil treatment of the control, triclosan and the unconditioned soil treatment of triclosan. Carbon source utilization for amino acids was higher in the conditioned soil treatment of estrone, binary mixture of estrone & triclosan, unconditioned soil treatment of control, and estrone. Carbon source utilization

Texas Tech University, Ezinne Adabaram Osuji, December 2016 of carboxylic acid was higher in the unconditioned soil treatment of the binary mixture of estrone & triclosan. Each of the bacterial communities in each treatment groups appeared to have observably different metabolic activities as revealed by the principal component analysis which could be due to the bacterial types and the interaction between the bacterial communities. Ordinarily, we could have expected the triclosan to use more of the phenolic compounds as a result of the structure of triclosan which has functional groups of phenols and ethers but this was not the case in this research. The bacterial community in the triclosan treatment rather increased the use of carbohydrates, amino acids and carboxylic acids and decreased the use of phenolic compounds as their carbon source. This could be possibly as a result of the bacterial communities seen in the different groups.

At day 0, all treatment groups significantly altered microbial community catabolic activity. Shannon-Weaver diversity and evenness was significantly low for all treatment groups (Table 4). At day 90, the diversity index and evenness increased tremendously

(Table 5). The control group showed the highest substrate richness index but a lower evenness while the treatment groups showed a lower richness but a higher evenness than the control group except for the conditioned soil that had the mixture of estrone and triclosan.

Degradation of Test analytes

Triclosan and Estrone were degraded in the soil under aerobic condition (Figure 6

& 7) which concurred with previous studies of Carr et al (2011). Half-life was calculated from best fit regression equations (Table 6). Estrone for the conditioned soil sample was

Texas Tech University, Ezinne Adabaram Osuji, December 2016

6.8 days, for the unconditioned soil sample was 6.1 days. Calculated half-life for the

Estrone in combination with Triclosan for the conditioned soil was 6.5 days while for the unexposed soil was 5.9 days. The calculated half-life of estrone was higher than that reported from Carr et al., (2011) (0.6-1.1 days) and may be due to seasonal differences related to bulk soil collection. Xu et al., (2009) showed that the concentration at which estrone was spiked into the soil did not have any effect on the rate of degradation, rather degradation was influenced by temperature and microbial biomass (Chen et al., 2015).

Calculated half-life for Triclosan for the conditioned soil sample was 26.7 days, for the unconditioned soil sample was 25 days. Calculated half-life for the Estrone in combination with Triclosan for the conditioned soil was 26.9 days and 24.1 days for the unexposed soil (Table 6) which is higher than that reported from Carr et al. (2011) (5.9-

8.9 days), Ying et al., (2007b) (18 days). Kookana et al. (2011) observed the half-life in clay loam to be 18 days. There was no significant difference in the degradation rate for each treatment to another (p<0.001). There was no significant loss of compound over time in the samples that contained autoclaved soil. (Figure 8 & 9). This further goes to buttress the point that loss of the compounds was a result of biological processes versus physical/chemical processes in the soil environment and also in accordance with the findings of Zhang et al. (2014).

This study, shows that the estrone and Triclosan mixture in the soil is degraded by soil microbial communities at the same rate as the individual compound, although other research including our lab (Carr, 2009; Svenningsen et al., 2011) showed that triclosan inhibited the degradation of other compounds. This may be as a result of a seasonal soil

Texas Tech University, Ezinne Adabaram Osuji, December 2016 condition when the soil was collected which could influence the structure of the microbial community present and which affects the degradation of the analytes or there may be a dynamic interaction between triclosan and estrone such as estrone suppressing the effect of triclosan as a result of soil temperature and conditions. Research dating back to 1935 shows estrogens has antimicrobial effects in vitro to microorganisms which is inclusive of gram-positive bacteria, molds, yeasts and protozoa (Frazier et al., 1935;

Brownlee et al., 1943) although another study by Faulkner (1943) found that this antimicrobial action involved only synthetic estrogens and not estrone. Past exposure of these soils to the chemicals does not appear to be a significant influence in the ability of soil microbial community in degrading these chemicals as there was no significant difference between the conditioned soil and the unconditioned soil in terms of half-life of the analytes examined. Previous studies by Carr et al., (2011) also showed that there was no significant difference between the soil that was previously exposed to the contaminants and naïve soil from the same location.

16S Metagenomic analysis result

The soil treatment which the DNA content was extracted were the conditioned soils since there was no significant difference between the conditioned and unconditioned soil includes; Day 0 and Day 90 treatments for the control, estrone, triclosan and estrone- triclosan mixture were extracted for whole community DNA.

The Emperor Principal Coordinate analysis (PCoA) plot using weighted UniFrac distances is shown (Figure 12 A-D). UniFrac is a beta-diversity measure (Luzopone et al.,

2011) used to measure the phylogenic distance between microbial communities for

Texas Tech University, Ezinne Adabaram Osuji, December 2016 phylogenetic evidence about the operational taxonomic units (OTUs) is known.

(Lozupone & Knight, 2005; Fukuyama et al., 2012). The weighted UniFrac takes into cognizance differences in abundance of taxa between samples. The PCoA plots show a comparison between the Day 0 control group with the other four treatments. Figure 12A,

PC1 and PC2 accounted for 86.96% & 3.48% of the total variation respectively and the first three components together accounted for 93.40% of the total variation. Figure 12B,

PC1 & PC2 accounted for 88.63% & 3.07% of the total variation respectively while the all three components accounted for 93.93% of the total variation. Figure 12C, PC1 &

PC2 accounted for 84.90% & 3.50% of the total variation respectively while the all three components accounted for 91.41% of the total variation. Figure 12D, PC1 & PC2 accounted for 79.73% & 4.74% of the total variation respectively while all three components accounted for 88.15% of the total variation. In all of the PCoA plots, there was a distinctive difference in clustering between the day 0 control group and the day 90 control, triclosan and the binary mixture treatment which shows the grouping into diversity within each replicate. This result suggest that the microbial community seen in the control treatment is different from that seen in the four treatment groups even as there is a community compositional change through time as seen in the controls.

The day 0 control group had 674 unique identities (Table 7), which is higher compared to the other treatment groups. The mixture treatment had the second highest.

The other four treatment groups had relatively the same amount of total unique identities.

Day 0 control group had 28 operational taxonomic units (OTUs) with abundance of at least 1%. The mixture treatment had 23, triclosan treatment had 22 while the estrone

Texas Tech University, Ezinne Adabaram Osuji, December 2016 treatment had 17. The control treatment at day 0 had no OTU representing more than

10% of the total but the control treatment at day 90 and other treatment groups had. 19

OTUs contributed more than 50% to the total number of taxonomic units. The mixture treatment had 12 while the other treatments had lower numbers. The control treatment had more OTU with abundance of at least 0.5% as compared to the other treatment groups. (Figure 11). The result shows that there was a decrease in species diversity between the control at day 0 and the other treatments at day 90. At day 90, the control treatment group showed an establishment of some unique OTUs that were previously not found at day 0. In all the other treatment groups at day 90, obviously an OTU was more dominant than others were. Since the metagenome study does not differentiate the DNA from active or non-active microbial species, transcriptome analysis will need to be done to determine if this dominant specie is effectively altering the soil characteristics to enable for the degradation or if this dominant specie is involved in appropriate gene expression for the degradation process.

At day 90, the genus Bacillus was the most dominant bacterium in the estrone, triclosan, control and mixture treatment but in the day 0 control treatment, other taxa in the order Acidimicrobiales was the most prominent. (Figure 10A-E). Some OTUs including those from Chloracidobacteria, Solibacterales, Nocardioidaceae,

Acidiomicrobiales, Adhaeribacter, Chitinophagaceae, Flabobacterium, Pontibacter,

Sphingobacteriales, Caldlineaceae, Chloroflexi, Skermanella, Steroidobacter and three from phylum Gemmatimonadetes were found only in the control treatment at day 0 of which many of them were not seen in the day 90 control treatment. At day 90, some new

Texas Tech University, Ezinne Adabaram Osuji, December 2016

OTUs which includes those from Bacillales, Arthrospira, Luteimonas, Mycoplana,

Rhamlibacter, Rhizobiaceae, Xylophilus and Verrumicrobiaceae were observed in the control treatment. This could be attributed to the time the soil sample was collected which at that time had more microbial activity as compared to when the soil was being used and also the condition of incubation (temperature, pH, oxygen etc) which either stimulated the growth of some new taxonomic units or lead to their attenuation. The estrone treatment at day 90 experienced the growth of some taxa that includes

Solwaraspora, Streptomycetaceae, Ammoniphilus, Brevibacillus, Comamonadaceae,

Enterobacteriaceae, Phyllobacteriaceae, Ralstonia, Sphingomonas and Akkermansia. The triclosan treatment at day 90 also had one new OTU from the genus Janthinobacterium which contributed about 9.1% of the relative abundance of the microbial community found in this treatment. The mixture treatment had some new OTUs which includes

Acidimicrobiales, Intrasporaniaceae, Rhodococcus, Thermomicrobia, Clostridium,

Alcaligenaceae, Myxococcales, Rhodospirollales, and Pedosphaerales. Many of the

OTUs seen in the control were not found in the microbial community of the treatment groups at day 90. Count of bacteria phylum in the analysis of OTUs with relative abundance greater than 0.5% (Table 8) showed a reduction in the number of

Acidobacteria, Chloroflexi, Gemmatimonadetes, Proteobacteria, Firmicutes and the

Bacteroides. The number of OTU in Actinobacteria, Planctomycetes and

Verrucomicrobia remained relatively same across all treatment groups.

Microorganisms which includes bacteria constantly develops and utilizes a lot of channel (Kolvenbach et al., 2014) to get to their source of nutrients or energy and also to

Texas Tech University, Ezinne Adabaram Osuji, December 2016 purify them, microbial degradation seems to be a technique to adequately remove contaminants (Writer et al., 2012; Yu et al., 2013; Zhang et al., 2016) which includes

PPCPs from the environment. Some microbial species has been shown to degrade estrones which includes bacillus sp. in activated sludge (Jian et al., 2010), Ralstonia sp. in both compost and activated sludge (Pauwels et al., 2008; Weber et al., 2005),

Sphingomonas sp. in activated sludge, artificial sandy aquifer and soil (Ke et al., 2007;

Kuris et al., 2010; Yu et al., 2007). This accounts for the increase in the number of these microbial species in the estrone treatment. Pseudomonas sp., Sphingomonas sp. and

Bacillus sp. has been shown in the literature to be resistant to the effect of triclosan and partially degrade triclosan (Heath et al., 2002). In this study, we saw an increase in the relative abundance from the genus Bacillus in the triclosan treatment. Pseudomonas sp. was found in the soil containing the binary mixture. A unique OTU from the genus

Janthinobacterium was found only in the triclosan treatment that means it is resistant to triclosan effect and could be capable of degrading triclosan. This cannot be concluded until further studies has been done to ascertain the function and mode of action of this specie.

Microorganisms can degrade or transform steroidal hormone using metabolic and co-metabolic mechanisms (Yu et al., 2007; Yu et al., 2013). In metabolic mechanism, microbial species use the steroid as a source of carbon in order to aid microbial growth whereas in the co-metabolic mechanism, the compound is degraded by extracellular enzymes produced by the bacteria and thus do not benefit in terms of energy or carbon source from this process. (Yu et al., 2013). Sphingomonas sp. utilizes estrone as its sole

Texas Tech University, Ezinne Adabaram Osuji, December 2016 carbon source under aerobic condition (Yu et al., 2013).

Observed degradation pattern from the binary mixture of the antimicrobial triclosan on the steroid estrone includes; the microorganisms degrading estrone are resistant to the effect of the presence of triclosan and vice versa. From our observations, triclosan may not have strong antimicrobial effect on the soil microbial community or the microbial species that are resistant to the effect of triclosan are the same species degrading estrone. One interesting observation from this research is that the estrone treatment caused a reduction in some microbial species. Specifically, some OTUs from the families Micromonsporaceae, Phycisphaerae, Pirellulaceae and from the genus

Kaistobacter had greatly diminished abundances while some OTU from families

Chthoniobacteriaceae, Oxalobacteriaceae and Rhodospirillaceae; and from genus’

Planctomyces, Devosia and Sinorhizobium were completely undetected in the microbial community after estrone treatment. This observation buttresses the assertion made as early as 1935 showing that estrogens have anti-microbial effects (Frazier et al., 1935;

Brownlee et al., 1943). In this study, we show that estrone has antimicrobial effect to the soil microbial community. A large and diverse soil microbial community was able to recover from the effect of these contaminants through being resistant to the effect of the estrone, triclosan or the binary mixture and/or forming a new microbial community. The metagenomic analysis confirms the trend that was shown in the substrate utilization profiling (Biolog ecoplate) analysis where we showed that at day 0 there was little to no substrate activity or substrate richness whereas as day 90 the substrate activity and richness were restored. In the PCA analysis it showed that there was a clumping together

Texas Tech University, Ezinne Adabaram Osuji, December 2016 of all microbial community in their substrate utilization whereas in day 90 they were distinctly apart. This goes further to show that microbial species are quickly able to overcome the effect of these contaminants in the soil environment possibly because it has an inherently high assortment of microbial species.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

CHAPTER IV

CONCLUSION

Due to the growing world concern over water and the need to recycle water especially for irrigation purpose, little attention is being paid to the persistence of these compounds in the soil which can contaminate the ground water. Several conclusions have been given regarding the degradation rate of estrone and triclosan, however this rate may vary greatly from place to place and given different environmental conditions. From this study, we can say that estrone and triclosan are degraded by soil microbial communities.

Soil temperature, microbial biomass and soil moisture condition seem to play a vital role in the process of degradation of these compounds. Estrone and Triclosan in the soil are resistant to and appear to be degraded by an assortment of microorganism found in the soil. The fact that Triclosan, an antimicrobial agent was degraded by some subset of the soil microbial community even if it exhibited anti-microbial effects on other portions of the community is a great feat for the environment. Further studies are needed to understand the interaction dynamics between estrone and triclosan. What mechanism is being used by the bacterial communities in the degradation process vis-à-vis the proteins being used, determine different soil temperature and conditions that will affect degradation and also to observe the interaction of other PPCPs mixture found in the environment with one another.

This research showed that there was a decrease in substrate activity & richness at the initial introduction of the estrone, triclosan and the binary mixture in both the conditioned and unconditioned soils. There was similar substrate utilization pattern at day 31

Texas Tech University, Ezinne Adabaram Osuji, December 2016

0 whereas a divergent utilization pattern at day 90. There was a shift in metabolic capabilities of the microbial communities at day 90. Prior exposure of the soil to estrone or triclosan did not change the rate of degradation. There was a compositional change through time as seen with the control treatment. There was a community discomposition between day 0 and day 90 as revealed by the metagenomic analysis

Future Studies

The results from this research work has led to more unanswered questions such as; Which of these diverse microbial community is actually doing the work of degrading these compounds in the soil having in mind that not all the microbial communities in there might be actively degrading estrone and triclosan? What is the mechanism by which these microbial species do the work of degrading these compounds? More research work is required to elucidate these questions. Some of which includes; transcriptomics analysis the transcriptome reflects the genes that are actively expressed at any given moment thus identifying mRNA from actively transcribed genes., Proteomics analysis to identify the proteins that are involved in the biological process.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

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Of Hazardous Materials, 317169-179. doi:10.1016/j.jhazmat.2016.05.066 Kurisu, F., Ogura, M., Saitoh, S., Yamazoe, A., and Yagi, O. (2010). Degradation of natural estrogen and identification of the metabolites produced by soil isolates of Rhodococcus sp. and Sphingomonas sp. Journal of Bioscience and Bioengineering 109, 576-582. doi:10.1016/j.jbiosc.2009.11.006 Kurisu, F., Zang, K., Kasuga, I., Furumai, H., & Yagi, O. (2015). Identification of estrone-degrading Betaproteobacteria in activated sludge by microautoradiography fluorescent in situ hybridization. Letters In Applied Microbiology, 61(1), 28-35. doi:10.1111/lam.12407 Li, Rongxia, et al. "A competitive photoelectrochemical assay for estradiol based on in situ generated CdS-enhanced TiO 2." Biosensors and Bioelectronics 66 (2015): 596-602. Li, Y. X., Han, W., Yang, M., Feng, C. H., Lu, X. F., & Zhang, F. S. (2012). Migration of natural estrogens around a concentrated dairy-feeding operation. Environmental monitoring and assessment, 184(8), 5035-5041. doi.org/10.1007/s10661-011- 2319-9 Lide, D. R. (Ed.). (2008). CRC handbook of chemistry and physics. 88TH Edition 2007- 2008. CRC Press, Taylor & Francis, Boca Raton, FL 2007, p. 3-232 Liu, J. L., & Wong, M. H. (2013). Pharmaceuticals and personal care products (PPCPs): a review on environmental contamination in China. Environment international, 59, 208-224.dx.doi:10.1016/j.envint.2013.06.012 Lozupone, C., & Knight, R. (2005). UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Applied & Environmental Microbiology, 71(12), 8228- 8235. doi:10.1128/AEM.71.12.8228-8235.2005 Lozupone, C., & Knight, R. (2005). UniFrac: a new phylogenetic method for comparing microbial communities. Applied and environmental microbiology, 71(12), 8228- 8235. doi:10.1128/AEM.71.12.8228–8235.2005 Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J., & Knight, R. (2011). UniFrac: An effective distance metric for microbial community comparison. ISME Journal, 5(2), 169-172. doi:10.1038/ismej.2010.133 Lubliner, B., Redding M., & Ragsdale D. (2010). Pharmaceutical and personal care products in municipal wasterwater and their removal by nutrient treatment technologies. Washington state department of ecology, Olympia, WA. Publication number 10-03-004. www.ecy.wa.gov/biblio/1003004.html. McBain, A. J., Rickard, A. H., & Gilbert, P. (2002). Possible implications of biocide accumulation in the environment on the prevalence of bacterial antibiotic resistance. Journal of Industrial Microbiology and Biotechnology, 29(6), 326-330. doi:10.1038/sj.jim.7000324

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Russell, A. D. (2004). Whither triclosan? Journal of Antimicrobial Chemotherapy, 53(5), 693-695. Russo, J., and Irma H. Russo. "The role of estrogen in the initiation of breast cancer." The Journal of steroid biochemistry and molecular biology 102.1 (2006): 89-96. doi:10.1016/j.jsbmb.2006.09.004 Schug, Thaddeus T., et al. "Endocrine disrupting chemicals and disease susceptibility." The Journal of steroid biochemistry and molecular biology127.3 (2011): 204-215. Singer, H., Müller, S., Tixier, C., & Pillonel, L. (2002). Triclosan: occurrence and fate of a widely used biocide in the aquatic environment: field measurements in wastewater treatment plants, surface waters, and lake sediments. Environmental Science & Technology, 36(23), 4998-5004. doi:10.2175/106143005X41636 Singh, R., Kottapalli, R., Vasylyeva, T. (2016). Gut Microbiome Characteristics in Type 2 Diabetes Patients with Advanced Chronic Kidney Disease. Frontiers in Microbiology, In press Snyder, S. A., Westerhoff, P., Yoon, Y., & Sedlak, D. L. (2003). Pharmaceuticals, personal care products, and endocrine disruptors in water: implications for the water industry. Environmental Engineering Science, 20(5), 449-469 Sobek, E. A., & Zak, J. C. (2003). The soil FungiLog procedure: method and analytical approaches toward understanding fungal functional diversity. Mycologia, 95(4), 590-602. Staddon, W. J., Duchesne, L. C., & Trevors, J. T. (1997). Impact of clear-cutting and prescribed burning on microbial diversity and community structure in a Jack pine (Pinus banksiana Lamb.) clear-cut using Biolog Gram-negative microplates. World Journal of Microbiology and Biotechnology, 14(1), 119-123. doi: 10.1023/A:1008892921085 Suzuki, Y., & Maruyama, T. (2006). Fate of natural estrogens in batch mixing experiments using municipal sewage and activated sludge. Water Research, 40(5), 1061-1069. doi:10.1016/j.watres.2005.12.043 Svenningsen, H., Henriksen, T., Priemé, A., & Johnsen, A. R. (2011). Triclosan affects the microbial community in simulated sewage-drain-field soil and slows down xenobiotic degradation. Environmental Pollution, 159(6), 1599-1605. doi:10.1016/j.envpol.2011.02.052 Tan, K. H., Hajek, B. F., Barshad, I. E., & Klute, A. (1986). Methods of soil analysis: Part 1-Physical and mineralogical methods. Methods of soil analysis. Parte I: Physical and mineralogical methods. Ternes, T. A., Stumpf, M., Mueller, J., Haberer, K., Wilken, R. -., & Servos, M. (1999). Erratum: Behavior and occurrence of estrogens in municipal sewage treatment

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plants - I. investigations in germany, canada and brazil (the science of the total environment 225 (1998) (81-90) PII: S0048969799003349). Science of the Total Environment, 228(1), 87. doi:10.1016/S0048-9697(99)00057-1 Thomas, T., Gilbert, J., & Meyer, F. (2012). Metagenomics - a guide from sampling to data analysis. (2012). Microbial Informatics & Experimentation, 2(1), 3-14. doi:10.1186/2042-5783-2-3 US Environmental protection agency. Water Recycling and Reuse: The environmental benefits.Water Division Region IX - EPA 909-F-98-001 Retrieved from https://www3.epa.gov/region9/water/recycling/ US Food and Drug Adminstration. (2016) Retrieved from http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm517478.ht m USGS, 2015. US Geological Survey, 2015. Emerging Contaminants In The Environment. Retrieved from: http://toxics.usgs.gov/regional/emc/ Waller, N. J., & Kookana, R. S. (2009). Effect of triclosan on microbial activity in australian soils. Environmental Toxicology and Chemistry, 28(1), 65-70. doi:10.1897/08-224.1 Weber, S., Leuschner, P., K€ampfer, P., Dott, W., and Hollender, J. (2005). Degradation of estradiol and ethinyl estradiol by activated sludge and by a defined mixed culture. Applied Microbiology and Biotechnology 67, 106-112. doi:10.1007/s00253-004-1693-4 Wintgens, T., Melin, T., Schäfer, A., Khan, S., Muston, M., Bixio, D., & Thoeye, C. (2005). The role of membrane processes in municipal wastewater reclamation and reuse. Desalination, 178(1-3), 1-11. doi:10.1016/j.desal.2004.12.014 Wooley, J. C., Godzik, A., & Friedberg, I. (2010). A primer on metagenomics. PLoS Comput Biol, 6(2), e1000667. •DOI: 10.1371/journal.pcbi.1000667 Writer, J. H., Ryan, J. N., Keefe, S. H., and Barber, L. B. (2012). Fate of 4-nonylphenol and 17b-estradiol in the Redwood River of Minnesota. Environmental Science and Technology 46, 860-868. DOI: 10.1021/es2031664 Xia, K., Bhandari, A., Das, K., & Pillar, G. (2005). Occurrence and fate of pharmaceuticals and personal care products (PPCPs) in biosolids. Journal of environmental quality, 34(1), 91-104. doi:10.2134/jeq2005.0091 Xu, N. A. N., Johnson, A. C., Jürgens, M. D., Llewellyn, N. R., Hankins, N. P., & Darton, R. C. (2009). Estrogen concentration affects its biodegradation rate in activated sludge. Environmental Toxicology and Chemistry, 28(11), 2263-2270. doi:10.1897/08-577.1 Ying, G. G., & Kookana, R. S. (2005). Sorption and degradation of estrogen‐like‐ endocrine disrupting chemicals in soil. Environmental Toxicology and Chemistry, 41

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24(10), 2640-2645. doi: 10.1897/05-074R.1 Ying, G., & Kookana, R. S. (2007). Triclosan in wastewaters and biosolids from Australian wastewater treatment plants. Environment International, 33(2), 199- 205. doi:10.1016/j.envint.2006.09.008 Ying, G., & Kookana, R. S. (2007). Triclosan in wastewaters and biosolids from Australian wastewater treatment plants. Environment International, 33(2), 199- 205. doi: 10.1016/j.envint.2006.09.008 (b) Ying, G.G., Yu, X.Y., & Kookana, R. S. (2007). Biological degradation of triclocarban and triclosan in a soil under aerobic and anaerobic conditions and comparison with environmental fate modelling. Environmental Pollution, 150(3), 300-305. doi: 10.1016/j.envpol.2007.02.013 Yu, C. P., Roh, H., and Chu, K. H. (2007). 17b-Estradiol-degrading bacteria isolated from activated sludge. Environmental Science and Technology 41, 486-492. DOI: 10.1021/es060923f Yu, Y., Wu, L., and Chang, A. C. (2013). Seasonal variation of endocrine disrupting compounds, pharmaceuticals and personal care products in wastewater treatment plants. Science of the Total Environment 442, 310-0316. doi: 10.1016/j.scitotenv.2012.10.001 Zak, J. C., Willig, M. R., Moorhead, D. L., & Wildman, H. G. (1994). Functional diversity of microbial communities: a quantitative approach. Soil Biology and Biochemistry, 26(9), 1101-1108. Doi:10.1016/0038-0717(94)90131-7 Zhang, C., Li, Y., Wang, C., Niu, L., & Cai, W. (2016). Occurrence of endocrine disrupting compounds in aqueous environment and their bacterial degradation: A review. Critical Reviews in Environmental Science and Technology, 46(1), 1-59. Doi:10.1080/10643389.2015.1061881 Zhang, X., Li, Y., Liu, B., Wang, J., & Feng, C. (2014). The effects of estrone and 17β- estradiol on microbial activity and bacterial diversity in an agricultural soil: Sulfamethoxazole as a co-pollutant. Ecotoxicology and environmental safety, 107, 313-320. doi:10.1016/j.ecoenv.2014.06.010

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FIGURES AND TABLES

Figure 1. Environmental Fate of PPCPs taken from Lubliner et al., (2010). https://fortress.wa.gov/ecy/publications/documents/1003004.pdf 43

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Figure 2. Different views of the Lubbock Land Application Site (LLAS). Located at 4602 County Rd 6700, Lubbock, TX 79403

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Figure 3. Plot of Substrate activity of conditioned & unconditioned soil. * indicates statistical difference p<0.05 by 1-way ANOVA between day 0 treatment and day 90, + indicates statistical difference p<0.05 by 1-way ANOVA followed by Tukey’s multiple comparison test between day 90 control and day 90 of the treatment groups. Vertical bars ± standard error of the means (n=3). EX denotes the conditioned soil, UX is the unconditioned soil. C, E, T, E/T denotes control, estrone, triclosan, and the mixture of estrone and triclosan respectively.

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Figure 4. Plot of Substrate richness of conditioned & unconditioned soil. * indicates statistical difference p<0.05 by 1-way ANOVA between day 0 treatment and day 90, + indicates statistical difference p<0.05 by 1-way ANOVA followed by Tukey’s multiple comparison test between day 90 control and day 90 of the treatment groups. Vertical bars ± standard error of the means (n=3). EX denotes the conditioned soil, UX is the unconditioned soil. C, E, T, E/T denotes control, estrone, triclosan, and the mixture of estrone and triclosan respectively.

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Figure 5. Principal component analysis (PCA) of the conditioned and unconditioned soil at day 0 and day 90. PCA analysis is based on the 168h data which was corrected with the control analyzed and grouped into 6 carbon guilds. (A) Day 0: PC1 accounted for 45.2% of the variance in the data set while PC2 accounted for 26.6%. (B) Day 90: PC1 accounted for 53.8% variance while PC2 accounted for 23.1% variance

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Figure 6. Degradation of estrone in soil using exponential degradation. Data point is the triplicate of the measured values. Vertical bars ±standard error of the means (n=3)

Figure 7. Degradation of triclosan in soil using exponential degradation. Data point is the triplicate of the measured values. Vertical bars ±standard error of the means (n=3)

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120

100

20 % Remaining %

0 0 10 20 30 40 50 60 70 80 90 Time (Days)

Figure 8. Killed controls plot for the estrone treatment and the estrone component of the binary mixture treatment at different time points. Vertical bars ±standard error of the mean (n=3). Brs not seen fall within the dimension of the symbol

120

100

60 Ex T Ux T % Remaining % 40 Ex ET - T Ux ET - T 20

0 0 10 20 30 40 50 60 70 80 90 Time (Days)

Figure 9. Killed controls plot for the triclosan treatment and triclosan component of binary mixture treatment at different time points. Vertical bars ±standard error of the mean (n=3). Bars not seen fall within the dimension of the symbol

Texas Tech University, Ezinne Adabaram Osuji, December 2016

(A)

Actinobcteria;Acidimicrobiales 9% Day-0 Control Acidobacteria;Unknown 7% Firmicutes;Bacillus 6% Planctomycetes;Phycisphaerae 3% Actinobacteria;Gaiellaceae 3% Planctomycetes;Pirellulaceae 3% Acidobacteria;Chloracidobacteria1 2% 9% Bacteroidetes;Cytophagaceae 2% 7% Acidobacteria;Unknown 2% Firmicutes;Bacillaceae 2% 6% Proteobacteria;Skermanella 2% 48% 3% 3% Planctomycetes;Planctomyces 2% 2% Actinobacteria;Rubrobacter 1% 2% 2% Actinobacteria;Acidiomicrobiales1 1% 2% 2%2% Proteobacteria;Piscirickettsiaceae 1% 1%2% 1%1%1%1%1%1%1% Chloroflexi;Unknown1 1% Proteobacteria;Sinobacteraceae 1% Proteobacteria;Rhodospirillaceae 1% Bacteroidetes;Chitinophagaceae 1% Proteobacteria;Myxococcales 1% Others 48% (B)

Firmicutes;Bacillus 23% Day -90-Control Firmicutes;Bacillaceae 6% Proteobacteria;Sphingomonas 5% Bacteroidetes;Chitinophaga 5% Proteobacteria;Sphingomonadaceae 4% Fibrobacteres;Bacillales 4% Proteobacteria;Sinorhizobium 4% Proteobacteria;Balneimonas 2% 23% Proteobacteria;Kaistobacter 2% 30% Planctomycetes;Planctomyces 2% Proteobacteria;Rhizobiaceae 2% Actinobacteria;Streptomyces 2% 6% Planctomycetes;Unknown 2% 1%1% 5% Verrucomicrobia;Chthoniobacteraceae 1% 1%1% 1%1% 5% Planctomycetes;Pirellulaceae 1% 1%1% 1%2% 4% Actinobacteria;Micromonosporaceae 1% 2%2%2%4%4% Firmicutes;Virgibacillus 1% Bacteroidetes;Flavisolibacter 1% Actinobacteria;Gaiellaceae 1% Proteobacteria;Xylophilus 1% Others 30%

Texas Tech University, Ezinne Adabaram Osuji, December 2016

(C)

Firmicutes;Bacillus 24% Day -90-E Firmicutes;Bacillales 7% Firmicutes;Bacillaceae 7% Proteobacteria;Sphingomonas 5% Actinobacteria;Gaiellaceae 4% Actinobacteria;Streptomyces 4% Actinobacteria;Micromonosporaceae 4% Actinobacteria;Couchioplanes 2% 24% Firmicutes;Ammoniphilus 2% 29% Actinobacteria;Actinomadura 2% Planctomycetes;Phycisphaerae 2% Firmicutes;Virgibacillus 2% 7% Planctomycetes;Pirellulaceae 1% 1%1% 1%1% Actinobacteria;Solirubrobacterales 1% 1%1% 7% 1% Proteobacteria;Sinobacteraceae 1% 1%2% 2%2% 5% Proteobacteria;Hyphomicrobiaceae 1% 2%2%4%4%4% Firmicutes;Paenibacillus 1% Proteobacteria;Kaistobacter 1% Firmicutes;Brevibacillus 1% Actinobacteria;Acidiomicrobiales1 1% Others 29%

(D)

Firmicutes;Bacillus 19% Day -90- T Proteobacteria;Janthinobacterium 9% Actinobacteria;Micromonosporaceae 8% Firmicutes;Bacillaceae 4% Proteobacteria;Sinorhizobium 3% Actinobacteria;Couchioplanes 3% Firmicutes;Bacillales 3% Actinobacteria;Gaiellaceae 3% 19% Planctomycetes;Phycisphaerae 3% 28% Actinobacteria;Acidiomicrobiales1 2% Proteobacteria;Oxalobacteraceae 2% 9% Planctomycetes;Pirellulaceae 2% Firmicutes;Virgibacillus 2% 1%1% 1% TM7;Unknown 2% 1%1% 8% 1% Verrucomicrobia;Chthoniobacteraceae 2% 2%2% 4% 2%2% 3% Actinobacteria;Streptomyces 1% 2%3%3%3%3% Proteobacteria;Kaistobacter 1% Proteobacteria;Sinobacteraceae 1% Proteobacteria;Hyphomicrobiaceae 1% Firmicutes;Paenibacillus 1% Others 28%

Texas Tech University, Ezinne Adabaram Osuji, December 2016

(E)

Day -90- ET Firmicutes;Bacillus 12% Actinobacteria;Acidiomicrobiales1 8% Proteobacteria;Pseudomonas 6% Actinobacteria;Gaiellaceae 5% Actinobacteria;Couchioplanes 4% Firmicutes;Virgibacillus 3% Firmicutes;Bacillales 3% 12% Planctomycetes;Pirellulaceae 3% Actinobacteria;Micromonosporaceae 3% 8% 38% Planctomycetes;Phycisphaerae 3% 6% Firmicutes;Bacillaceae 2% Actinobacteria;Streptomyces 2% 5% Proteobacteria;Hyphomicrobiaceae 2% 4% Acidobacteria;Unknown 1% 3% Actinobacteria;Solirubrobacterales 1% 1% 3% 1%1% 3% 1%1% 3% Actinobacteria;Mycobacterium 1% 1%1%1%2%2%2% Proteobacteria;Sinobacteraceae 1% Proteobacteria;Bradyrhizobium 1% Actinobacteria;Micrococcaceae 1% Actinobacteria;Rubrobacter 1% Others 38%

Figure 10 (A-E). Plot showing the top 20 abundant OTUs in each treatment group with their mean relative abundances shown. (A) Day 0 control (B) Day 90 control (C) Day 90 Estrone treatment (D) Day 90 Triclosan treatment (E) Day 90 Estrone and Triclosan mixture treatment

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Day 90 ET

Day 90 T

Day 90 E

Day 90 C

Day 0 C

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Acidobacteria;Chloracidobacteria1 Acidobacteria;Chloracidobacteria2 Acidobacteria;Solibacterales Acidobacteria;Unknown Acidobacteria;Unknown Acidobacteria;Unknown Actinobacteria;Acidiomicrobiales1 Actinobacteria;Acidiomicrobiales2 Actinobacteria;Actinomadura Actinobacteria;Couchioplanes Actinobacteria;Gaiellaceae Actinobacteria;Intrasporangiaceae Actinobacteria;Micrococcaceae Actinobacteria;Micromonosporaceae Actinobacteria;Mycobacterium Actinobacteria;Nocardioidaceae Actinobacteria;Rhodococcus Actinobacteria;Rubrobacter Actinobacteria;Solirubrobacterales Actinobacteria;Solwaraspora Actinobacteria;Streptomyces Actinobacteria;Streptomycetaceae Actinobacteria;Verrucosispora Actinobcteria;Acidimicrobiales Bacteroidetes;Adhaeribacter Bacteroidetes;Chitinophaga Bacteroidetes;Chitinophagaceae Bacteroidetes;Cytophagaceae Bacteroidetes;Flavisolibacter Bacteroidetes;Flavobacterium Bacteroidetes;Pontibacter Bacteroidetes;Sphingobacteriales Chloroflexi;Caldilineaceae Chloroflexi;Thermomicrobia Chloroflexi;Unknown1 Chloroflexi;Unknown2 Chloroflexi;Unknown3 Fibrobacteres;Bacillales Firmicutes;Alicyclobacillus Firmicutes;Ammoniphilus Firmicutes;Bacillaceae Firmicutes;Bacillales Firmicutes;Bacillus Firmicutes;Brevibacillus Firmicutes;Clostridium Firmicutes;Paenibacillus Firmicutes;Planococcaceae Firmicutes;Virgibacillus Gemmatimonadetes;Unknown Gemmatimonadetes;Unknown Planctomycetes;Gemmata Planctomycetes;Phycisphaerae Planctomycetes;Pirellulaceae Planctomycetes;Planctomyces Planctomycetes;Unknown Proteobacteria;Alcaligenaceae Proteobacteria;Arthrospira Proteobacteria;Balneimonas Protoebacteria;Betaproteobacteria Proteobacteria;Bradyrhizobium Proteobacteria;Comamonadaceae Proteobacteria;Devosia Proteobacteria;Enterobacteriaceae Proteobacteria;Erythrobacteraceae Proteobacteria;Hyphomicrobiaceae Proteobacteria;Janthinobacterium Proteobacteria;Kaistobacter Proteobacteria;Luteimonas Proteobacteria;Mesorhizobium Proteobacteria;Mycoplana Proteobacteria;Myxococcales Proteobacteria;Oxalobacteraceae Proteobacteria;Phyllobacteriaceae Proteobacteria;Piscirickettsiaceae Proteobacteria;Pseudomonas Proteobacteria;Ralstonia Proteobacteria;Ramlibacter Proteobacteria;Rhizobiaceae Proteobacteria;Rhodoplanes Proteobacteria;Rhodospirillaceae Proteobacteria;Rhodospirillales Proteobacteria;Sinobacteraceae Proteobacteria;Sinorhizobium Proteobacteria;Skermanella Proteobacteria;Sphingomonadaceae Proteobacteria;Sphingomonas Proteobacteria;Steroidobacter Proteobacteria;Xanthomonadaceae Proteobacteria;Xylophilus TM7;Unknown Verrucomicrobia;Akkermansia Verrucomicrobia;Chthoniobacteraceae Verrucomicrobia;Pedosphaerales Verrucomicrobia;Verrucomicrobiaceae Others

Figure 11. Unique OTU with relative abundances of at least 0.5% (≥0.5%) for a combination of all the treatment groups at day 0 and day 90

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Day 0 Control Day 0 C

Day 90 Control

(A)

Day 90 Estrone

Day 0 Control

(B)

Texas Tech University, Ezinne Adabaram Osuji, December 2016 Day 0 Control

Day 90 Triclosan (C)

Day 90 Estrone/Triclosan

Day 0 Control (D)

Figure 12. (A-D). The Emperor Principal Coordinate analysis (PCoA) plot using weighted UniFrac distances for all treatment groups. Distinctive clustering by treatment type is seen. (A) Day 0 control treatment in red while Day 90 control treatment is shown in blue. (B) Day 0 control treatment in blue while the Day 90 estrone treatment is shown in red. (C) Day 0 control treatment in red while the Day 90 triclosan treatment is in blue. (D) Day 0 control treatment is shown in blue while the Day 90 binary mixture of estrone and triclosan is shown in red. 55

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Table 1. Grouping of 31 carbon substrates in the Biolog Ecoplate into six different guilds. Adapted from Choi & Dobbs (1999)

Phenolic Amines Carboxylic acids Polymers Amino acids Carbohydrates compounds 2-Hydroxy benzoic Phenylethylamine D-Glucosaminic acid Tween 40 L-Arginine D-Cellobiose acid 4-Hydroxy benzoic Putrescine D-Galacturonic acid Tween 80 L-Asparagine α-D-Lactose acid γ-Hydroxybutyric α-Cyclodextrin L-Phenylalanine β-Methyl-D-glucoside acid D-xylose Itaconic acid Glycogen L-Serine i-Erythritol α-Ketobutyric acid L-Threonine Glycyl-L- D-Malic acid D-Mannitol glutamic acid N-Acetyl-D- Pyruvic acid methyl glucosamine ester Glucose-1-phosphate D,L-α-glycerol phosphate D-Galactonic acid γ- lactone

Table 2. Definition of parameters used in the biolog ecoplate analysis.

Parameter Definition Analysis Substrate The number of substrates used from a microtiter plate that exhibit an Absorbance of greater or equal to 0.1 (≥0.1) Richness optical density 0.1. Substrate The amount of carbon in each microtiter plate well used by at least one Absorbance of greater than 0 (>0) Activity member of the soil microbial community.

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Table 3. Mean utilization of carbon sources in all treatment groups at day 90 by optical densities (OD) read from the spectrophotometer after 168h of plate incubation. Ex C – Conditioned soil with the control treatment, Ex E – Conditioned soil with the estrone treatment, Ex T – Conditioned soil with the triclosan treatment, Ex E/T – Conditioned soil with the binary mixture of estrone and triclosan. Ux C – Unconditioned soil with the control treatment, Ux E – Unconditioned soil with the estrone treatment, Ux T – Unconditioned soil with the tricloan treatment, Ux E/T – Unconditioned soil with the estrone and triclosan treatment

Ex C 4.691 12.404 4.668 6.897 -0.025 0.758 Ex E 5.018 4.065 6.94 8.683 0.203 0.762 Ex T 1.379 10.416 1.128 0.916 -0.029 -0.009 Ex E/T 3.02 4.023 6.17 7.371 2.079 2.061 Ux C 1.994 3.874 7.442 7.859 0.225 0.132 Ux E 1.811 1.042 3.04 3.111 -0.02 0.127 Ux T 2.047 4.705 4.33 4.373 -0.007 0.121 Ux E/T 0.607 0.156 1.213 0.219 0.014 -0.011

Table 4. Mean values of Shannon’s diversity (H), evenness (E) and substrate richness (R) based on 168-h incubation of day 0 for the different treatment groups. Mean ± standard error, n=3

Index Formula Treatment (Day 0) Ex C Ex E Ex T Ex ET Ux C Ux E Ux T Ux ET Shannon’s H=−Σpi(lnpi) 0 0.423±0.267 0 0 0 0.230±0.410 0.813±0.338 0.507±0.201 diversity Shannon’s E=H/lnR 0 0 0 0 0 0 0.209±0.102 0.322±0.031 Evenness Richness R 0 1.333±0.816 0 0 0 0.667±0.408 3.886±1.871 1.576±0.816

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Table 5. Mean values of Shannon’s diversity (H), evenness (E) and substrate richness (R) based on 168-h incubation of day 0 for the different treatment groups. Mean ± standard error, n=3

Index Formula Treatment (Day 90) Ex C Ex E Ex T Ex ET Ux C Ux E Ux T Ux ET Shannon’s H=−Σpi(lnpi) 1.345±0.037 1.252±0.027 1.095±0.046 1.264±0.013 1.234±0.163 0.968±0.084 1.161±0.178 0.645±0.126 diversity Shannon’s E=H/lnR 0.062±0.002 0.077±0.003 0.103±0.002 0.062±0.004 0.077±0.001 0.127±0.006 0.093±0.009 0.198±0.049 Evenness Richness R 21.633±1.080 16.319±0.408 10.666±0.408 20.2444±1.08 16.043±1.871 7.639±0.816 12.435±0.816 3.263±0.408

Texas Tech University, Ezinne Adabaram Osuji, December 2016

Table 6. Calculated half-lives (days) of estrone, triclosan and their mixture. The r2 for the regression fit is indicated in parenthesis. Bracketed values represents [a, b] in the equation y = aebx for both the estrone and triclosan.

Treatment Calculated Half-life Estrone (E1) Triclosan Conditioned 6.8 (0.916) [63.99, -0.035] 26.7 (0.914) [74.94, -0.015] Unconditioned 6.1 (0.959) [66.16, -0.046] 25 (0.930) [74.14, -0.016] Conditioned (Mixture ET) 6.5 (0.802) [58.19, -0.023] 26.9 (0.907) [76.94, -0.016] Unconditioned (Mixture ET) 5.9 (0.916) [62.41, -0.037] 24.1 (0.916) [75.24, -0.017]

Table 7. Total unique OTUs, OTUs with relative abundance of at least 0.5% & 1.0%. OTUs contributing more than 10% and 50% to the total number of OTUs

Total OTUs with OTUs OTUs Unique IDs relative contributing OTUs representing (OTUs abundance with relative contributing more than Treatment contributing of at least abundance of 50% to the total 10% of the type >0%) 1.0% at least 0.5% number of OTU total OTU Day -0- C 674 28 53 19 0 Day -90- C 530 20 42 8 1 Day -90- E 527 17 38 7 1 Day -90- T 524 22 37 8 1 Day -90- ET 582 23 45 12 1

Table 8. Count of bacteria phylum in the analysis of OTUs with relative abundance greater than 0.5%

Phyla Day 0 C Day 90 C Day 90 E Day 90 T Day 90 ET Acidobacteria 6 1 1 2 2 Actinobacteria 9 10 13 11 13 Bacteroides 7 2 0 0 0 Chloroflexi 4 0 1 1 2 Fibrobacteria 0 1 0 0 0 Firmicutes 3 4 7 6 8 Gemmatimonadetes 2 0 0 0 0 Planctomycetes 4 3 2 3 3 Proteobacteria 15 19 12 12 14 Verrucomicrobia 2 2 1 1 2