Microbial Communities and Polycyclic Aromatic Hydrocarbons

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Microbial Communities and Polycyclic Aromatic Hydrocarbons:

Exposure Related Adaptations in Environmental Microbiomes and Their

Potential for Bioremediation

Lauren Kathleen Redfern

Department of Civil and Environmental Engineering Duke University

Date:______Approved:

______Claudia K. Gunsch, Supervisor

______Patrick L. Ferguson

______Scott H. Harrison

______Richard T. Di Giulio

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil and Environmental Engineering in the Graduate School of Duke University

2017

ABSTRACT

Microbial Communities and Chemical Pollutants:

Exposure Related Adaptations in Prokaryotic Microbiomes and Their

Influence on Environmental and Ecological Health

Lauren Kathleen Redfern

Department of Civil and Environmental Engineering Duke University

Date:______Approved:

______Claudia K. Gunsch, Supervisor

______Patrick L. Ferguson

______Scott H. Harrison

______Richard T. Di Giulio

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil and Environmental Engineering in the Graduate School of Duke University

2017

Abstract

Bioremediation is a treatment strategy that involves the removal of chemical pollutants using biological agents. When compared to physico-chemical treatment approaches, bioremediation causes less site disturbance and offers a range of other economic and ecological benefits. Yet, this treatment approach is not routinely selected mainly because of the unpredictability of the biological agents in the heterogeneous environments encountered at sites. Generally, three main approaches are utilized to improve bioremediation efficacy. These approaches consist of: 1) allowing native bacteria to degrade the pollutant (bioattenuation); 2) stimulating indigenous bacteria to improve their natural degradative capacity (biostimulation); or 3) supplying exogenous microorganisms that are known to degrade a specific contaminant if no known degraders are present at the site (bioaugmentation). Genetic bioaugmentation is another bioremediation treatment strategy, which relies on the well-studied mechanism of horizontal gene transfer (HGT) in which plasmids from exogenous donors are transferred to indigenous recipients. This strategy circumvents the need for the exogenous strain to compete with indigenous microorganisms under site conditions, a challenge that is difficult to overcome. HGT is a widespread natural phenomenon that readily occurs especially under harsh environmental conditions such as that in heavily contaminated environments.

Although all of these approaches show promise, in general, little is known about how organisms assemble in contaminated environments, limiting the implementation of

bioremediation treatment strategies under field conditions. The work presented in this dissertation aims to address this challenge by characterizing and engineering prokaryotic microbiomes in soils contaminated with polycyclic aromatic hydrocarbons (PAHs). The overarching goals of this dissertation were to first utilize next-generation sequencing

(NGS) to characterize pollutant-exposed environmental microbiomes and then use these data to develop a generalizable framework, which combines metagenomic and physico- chemical data to inform bioremediation treatment strategy selection. Then, in order to effectively validate this framework, a method for monitoring catabolic plasmid conjugation was developed. This dissertation was broadly broken down into four objectives as described below.

The first objective was to investigate environmentally relevant adaptation events in sediment microbial communities and gut-associated microbial communities in Atlantic killifish exposed to PAHs. Sediment and gut samples were obtained from the contaminated Republic Creosote Co. site and the relatively un-contaminated Kings Creek

(KC) reference site and their microbiomes and metabolomes were comparatively analyzed. Overall, significant microbial community shifts were identified between sites, suggesting an environmental microbiome evolved to withstand high levels of PAHs.

Specifically, of the OTUs identified, 9 species were different between KC guts and KC sediment while 176 were different between Republic guts and Republic sediment. These data suggest that factors other than dietary influence affect the microbiota colonized in the Republic fish gut. With respect to the metabolome, the amino acid (AA)

concentrations were found to be higher for 19 out of 21 AAs in the Kings Creek samples when compared to the Republic samples. This indicates both a potential consequence of the microbial shifts and impact on metabolism between in the PAH-exposed fish subpopulations. Overall, this work provides insight into chemical-associated microbial community and metabolomics shifts and some of the potential resulting impacts.

The second objective was to develop a framework for precision bioremediation in which optimal microbial taxa could be identified for biostimulation, bioaugmentation and genetic bioaugmentation at a given site. Here, we developed an approach that combined

Illumina Miseq high throughput sequencing, chemical profiling and Spearman correlation analyses. This framework was developed using samples obtained from the Holcomb

Creosote Co. Superfund site, which is contaminated with both PAHs and heavy metals.

Using this framework, Geobacter was identified as a biostimulation target while

Mycobacterium and Sphingomonas were identified as strong potential biostimulation and genetic bioaugmentation targets, respectively. Based on availability and sequencing data,

Mycobacterium fredericksbergens and Sphingomonas aromaticivorans F199 (containing the pNL1 catabolic plasmid) were selected as components to a bioaugmentation cocktail to treat the Holcomb Creosote Superfund soils for PAHs. Though it is possible to identify prospective bioremediation targets using this methodology, this approach remains limited mainly due to the inadequate amount of fully sequenced site-specific environmental strains and catabolic plasmids available in databases. Additional research is needed especially using shotgun metagenomic sequencing, rather than amplicon-based

sequencing, to improve the identification of potential donor strains and catabolic plasmids.

The third objective consisted of developing a method to effectively monitor genetic bioaugmentation and validate the method in a series of lab-scale precision bioremediation scenarios. Previous methods used to monitor for HGT events have relied on using either fluorescent labeling or culturing. However, these monitoring techniques have been shown to be ineffective in complex matrices and for large-scale field applications. Herein, qPCR was optimized to effectively monitor plasmid conjugation in complex matrices on a large scale.

In the final inclusive objective, the microbial cocktail formed by the framework developed in Objective 2 and the monitoring technique developed in Objective 3 were validated in lab scale reactors using a realistic PAH-contaminated soil matrix obtained from the Holcomb Creosote Co. Superfund site. Overall, it was found that the targeted microbial consortium was able to improve bioremediation by constructing an engineered environmental microbiome capable of increasing the rate of PAH biodegradation without overwhelming the natural microbial community. In particular, there were overall increases in the class Bacilli and decreases in Betaproteobacteria sustained over time. In addition, the efforts to monitor genetic bioaugmentation were successful and HGT through plasmid conjugation was quantified. Specifically, the probes were able to detect conjugation of the NAH7 plasmid immediately following genetic bioaugmentation. Not surprisingly, the Inc-P9 plasmid was not maintained in the community, as it did not

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provide a strong enough consistent benefit towards survival to justify the metabolic load.

The probes were also successful in quantifying the pNL1 plasmid, though no sustained

HGT events were detected.

Overall, this dissertation provides significant advancements to the field of precision bioremediation. In particular, this dissertation work integrates metagenomic and chemical measurements using statistical methods to effectively identify bioremediation targets and providing tools to monitor bioremediation progress under field relevant conditions. It is anticipated that as environmental microbiome databases continue to be populated, the use of the framework developed here will be instrumental for the identification of targeted bioremediation treatment strategies.

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Dedication

To Kitty and Craig Redfern, my wonderful parents.

Table of Contents

Abstract ...... iv

Table of Contents ...... x

List of Tables ...... xv

List of Figures ...... xvi

Acknowledgements ...... xx

1. Introduction ...... 1

1.1 Objectives ...... 3

1.2 Research Hypotheses and Approach ...... 4

2. Background and Literature Review ...... 7

2.1 Hazardous Wastes ...... 8

2.1.1 Polycyclic Aromatic Hydrocarbons (PAHs) ...... 8

2.1.1.1 Naphthalene ...... 9

2.1.1.2 Creosote ...... 10

2.2 PAHs and Environmental Health ...... 10

2.2.1 The Impact of PAHs on Prokaryotic Communities ...... 11

2.2.2 Community Impacts of PAH mixtures ...... 13

2.3 Bioremediation of PAHs ...... 14

2.3.1 In situ bioremediation (ISB) of PAHs ...... 15

2.3.1.2 Aerobic ISB (ISB-a) ...... 16

2.3.2 Bacteria Used in Bioremediation of PAHs ...... 17

2.3.3 Aerobic Bacteria used in ISB-a of PAHs ...... 18

2.3.3 The role of plasmids in bioremediation of PAHs ...... 20 x

2.3.3.1 Plasmid incompatibility (Inc) groups ...... 22

2.3.3.2 Catabolic plasmids ...... 22

2.3.3.4 Barriers to bacterial horizontal gene transfer (HGT) events ...... 26

2.4 Genetic bioaugmentation ...... 27

2.4.1 Methods for monitoring HGT events during genetic bioaugmentation ...... 29

3. Exposure-related changes in the sediment microbiota, and gut-associated microbiome and metabolome of evolved subpopulations of Atlantic killifish in the Elizabeth River (ER) ...... 32

3.1 Introduction ...... 32

3.2 Material and Methods ...... 36

3.2.1 Sample Collection ...... 36

3.2.2 Metabolomic Quantification and Analysis ...... 37

3.2.3 DNA extraction ...... 38

3.2.4 16S rDNA Microbial Community Analysis ...... 39

3.2.5 Illumina Miseq Sequencing Data Analysis ...... 40

3.2.6 Diversity Analysis ...... 40

3.2.7 Vikodak Functional Analysis ...... 41

3.3 Results ...... 41

3.3.1 Metabolomic Analysis ...... 41

3.3.1.1 Amino Acids ...... 43

3.3.1.2 Sphingolipids, Acylcarnitines, and Glycerophospholipids ...... 44

3.3.1.3 Custom Metabolic Indicators ...... 45

3.3.2 Microbiome Analysis ...... 46

3.3.2.1 Prokaryotic Community Analysis ...... 46 xi

3.3.2.2 Identification of Potentially Responsive Taxa ...... 50

3.3.2.3 Identification of Potentially Important Taxa ...... 51

3.3.2.4 Predicting Functional Changes using Vikodak ...... 53

3.4 Discussion ...... 54

4. A Framework for Approaching Precision Bioremediation ...... 61

4.1 Introduction ...... 61

4.2 Methods and Materials ...... 64

4.2.1 Field Sampling ...... 64

4.2.2 Chemical Analysis ...... 68

4.2.3 DNA extraction ...... 68

4.2.4 16S rDNA Sequencing ...... 69

4.2.5 Illumina Miseq Sequencing Data Analysis ...... 70

4.2.6 Spearman Rank Correlation Analysis ...... 71

4.2.7 Statistical Analysis ...... 71

4.3 Results and Discussion ...... 72

4.3.1 Chemical Characterization ...... 72

4.3.2 16s rDNA Amplicon Sequencing Analysis ...... 81

4.3.3 Associations Between Chemical and Microbial Variants ...... 88

4.3.4 Choosing Targets for Biostimulation and Bioaugmentation ...... 90

4.3.5 Choosing Targets for Genetic Bioaugmentation ...... 92

4.3.5 Implementation of Metagenomic Analysis for Precision Bioremediation ...... 94

4.3.6 Implications for In Situ Bioremediation ...... 96

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5. Monitoring Horizontal Gene Transfer of Large Catabolic Plasmids for the Purpose of Evaluating Genetic Bioaugmentation Efficacy ...... 98

5.1 Introduction ...... 98

5.2 Methods and Materials ...... 102

5.2.1 Chemicals and Culture Inocula Preparation ...... 102

5.2.2 Microcosm Preparation ...... 104

5.2.2 DNA Extraction ...... 105

5.3 Results and Discussion ...... 111

5.3.1 Detection of Gene Transfer Events ...... 111

5.3.2 Impacts of HGT on the Biodegradation of Naphthalene and 1,4-dioxane ..... 115

5.4 Concluding Remarks ...... 121

6. Monitoring Microbial Community Adaptation Following the Implementation of Targeted Biostimulation and (Genetic) Bioaugmentation Strategies in PAH Lab-Scale Soil Reactors ...... 122

6.1 Introduction ...... 122

6.2 Methods and Materials ...... 124

6.2.1 Reactor Design and Set-up ...... 124

6.2.2 Natural Attenuation, Biostimulation, and Targeted Bioaugmentation Intervention ...... 127

6.2.3 Sampling ...... 129

6.2.4 DNA Extraction ...... 130

6.2.5 16S rDNA Amplicon Sequencing ...... 131

6.2.6 Sequencing Data Analysis ...... 132

6.2.7 qPCR ...... 132

6.2.8 Statistical Analysis ...... 134 xiii

6.3 Results ...... 134

6.3.1 Diversity of Soil Microbial Community ...... 134

6.3.2 Impacts of Bioaugmentation ...... 141

6.3.4 Catabolic Gene Dissemination ...... 145

6.4 Discussion ...... 149

Chapter 7: Conclusions and Engineering Significance ...... 154

7.1 Key Findings and Future Work ...... 154

7.2 Engineering Significance ...... 160

Appendix A– Supplementary Information for Chapter 3 ...... 162

Appendix B – Supplementary Information for Chapter 4 ...... 169

Appendix C – Supplementary Information for Chapter 5 ...... 171

Appendix D – Supplementary Information for Chapter 6 ...... 172

References ...... 173

Biography ...... 210

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List of Tables

Table 1: List of metabolites with the largest potential impact, indicated by an average VIP score above 1.25. In general, the higher the VIP score, the higher the impact. Replicate samples (N) from each site were run in triplicate. Metabolite groups are labeled above each section...... 42

Table 2: SHE analysis results. S is reported as the natural log of S [ln(S)]...... 49

Table 3: Collection data for each of the five sampling sites at Holcomb Creosote Co. Superfund Site...... 66

Table 5: Concentrations of Metals in Holcomb soil samples. Concentrations are reported in ppm with a standard deviation (‘SD’). “Loc 1-5” indicates the sampling location...... 77

Table 6: Shannon species diversity indices and species identified at Holcomb sampling locations. Locations are listed in increasing PAH concentrations...... 85

Table 7: Primers and Taqman™ Probes used to monitor gene transfer events in the microcosms...... 107

Table 8: Conditions used to measure naphthalene and 1,4-dioxane degradation after solid phase microextraction in the microcosms...... 110

Table 9: The primers and probes used in Taqman™ qPCR probe-based assays are tabulated below. The pNL1 primer and probe and the NAH7 primer and probe target the plasmids, while F199 and PpG7 monitor the strains...... 133

List of Figures

Figure 1: Chemical structure of four PAHs: naphthalene (2 ring), phenathrene (3 ring), pyrene (four ring), and tetracene (four ring)...... 8

Figure 2: General schematic for the aerobic degradation of hydrocarbons. Image adapted from Das and Chandran (2010)...... 19

Figure 3: Schematic detailing horizontal gene transfer through plasmid conjugation. .... 21

Figure 4: The naphthalene degrading plasmid, NAH7 from Sota et al. 2006 (Sota et al. 2006)...... 24

Figure 5: Naphthalene biodegradation pathway encoded by NAH7. Adapted from Seo et al. 2009 (Seo, Keum, and Li 2009) ...... 25

Figure 6: (A) Schematic of genetic bioaugmentation (diagram adapted from Dr. Claudia Gunsch) (B) Selective fluorescence detection of E. coli RP-1 transconjugants during genetic bioaugmentation (photo adapted from Dr. Karou Ikuma)...... 28

Figure 7: Atlantic Killifish from Elizabeth River, VA...... 34

Figure 8: Quantification data of identified amino acid metabolites (uM). Error bars represented standard deviation...... 44

Figure 9: (A) Bar graphs of relative abundance (%) at the Class taxonomic level. (B) PCoA with Bray-Curtis Index of OTU-level data showing Republic gut (red circles, labeled ‘Rep’), Kings Creek gut (red circles, labeled “KC”), Republic sediment (green circles, labeled “Rep”) and Kings Creek sediment (green circle, labeled “KC”)...... 48

Figure 10: Vikodak generated pathway analysis using the sequencing data...... 54

Figure 11: Tryptophan (Trp) and Kynurenine concentrations identified in Republic and Kings Creek Gut samples. Kynurenine is a Tryptophan metabolite synthesized by the tryptophan dioxygenase enzyme. An asterick indicates significant difference (p<0.05). 56

Figure 12: (A) An aerial view of the site sampling strategy for Holcomb Creosote Co. Superfund Site. The distillation shed (referred to in Table 3) is indicated with an ‘X’. Sampling site locations are indicated numerically. (B) A front-view of the former Holcomb Creosote Co. Superfund Site. The former distillation shed is indicated with a red arrow...... 67

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Figure 13: (A) Total additive concentration [mg/g] of all metals identified in Holcomb soil samples. (B) Concentration [mg/g dry weight] of the top 5 individual PAHs (excluding Iron) represented in the highest concentrations. (B) Total additive concentration [ug/g] of all PAHs identified in Holcomb soil samples. (D) Concentration [ug/g dry weight] of the top 5 individual PAHs presented in the highest concentration at Holcomb soil samples. All error bars represent standard deviation...... 79

Figure 14: Non-Metric Dimensional Scaling (NMDS) of all OTUs identified in Holcomb Creosote Co. Superfund site soils...... 82

Figure 15: Phylum-level relative abundance of taxa identified in the Holcomb soil samples. The legend order corresponds to the order of the stacked taxa...... 83

Figure 16: Bar graphs of phyla with significant shifts between sites. (A) Proteobacteria, (B) Bacteroidetes, (C) Cloroflexi, (D) Euryarchaeota, (E) Verrucomicrobia. Letters denote statistically significant difference; if any locations share a letter, they are not significantly different from one another...... 84

Figure 17: Order-level relative abundance of top 15 most abundant taxa identified in the Holcomb soil samples...... 86

Figure 18: Bar graphs of eight of the twelve most abundant taxa at the Order taxonomic level with significant shifts between sites: (A) Clostridiales, (B) Rhizobiales, (C) Burkholderiales, (D) Holophagales, (E) Rhodospirillales, (F) Rhodocyclales), (G) Flavobacteriales, (H) Syntrophobacterales. The sites are labeled by increasing PAH concentration. Error bars represent standard deviation. Letters denote significant difference; in an individual graph, if they share a letter then they are not different...... 87

Figure 19: The diversity of Sphingomonas species identified in Holcomb soils...... 93

Figure 20: A schematic of the precision bioremediation approach...... 94

Figure 21: (A-C) Gene copy number and (D-F) normalized plasmid concentrations over time in all microcosms with both CB1190 and PpG7. The bars labeled “pSED02” and “NAH7” represent the plasmids, while “CB1190” and “PpG7” represent the strains. For each graph, if any time points share a letter, they are not significantly different...... 112

Figure 22: (A-B) Gene copy number and (C-D) normalized plasmid concentration over time for catabolic plasmid in microcosms with only donor strains and P. stutzeri...... 114

Figure 23: Degradation of (top) 1,4-dioxane and (bottom) naphthalene by the seven co- contaminated microcosms. Error bars represent the standard deviation of triplicate microcosms. The arrow at Day 31 represents when 1,4-dioxane and naphthalene were re- spiked in the microcosms...... 117 xvii

Figure 24: (A) Preliminary drawing of reactor design, (B) One of the three glass reactors constructed in collaboration with Prism Glass (Raleigh, NC), (C) Reactors mounted in a chemical fume hood and filled with soil from Holcomb Creosote Co. Superfund site. . 126

Figure 25: (A) Mounted Reactors were sampled from both top and bottom sampling ports on both sides of the reactors. (B) Samples were taken perpendicular to where amendments were added from all four side-sampling ports...... 130

Figure 26: NMDS of all OTUs in the soil reactors over time...... 135

Figure 27: Phylum-level relative abundances of the reactor soil microbiome...... 136

Figure 28: Top 7 most abundant Class-level taxonomic data throughout all phases of the experiment. Error bars represent standard deviation between reactors...... 138

Figure 29: Shifts in Bacilli in the reactors over time. Error bars represent standard deviation between reactors...... 139

Figure 30: Relative abundance of the Bacillus, Leuconostoc, and Sporolactobacillus genus over time. Error bars represent the standard deviation between reactors...... 140

Figure 31: Relative abundance of the Clostridium genera over time...... 141

Figure 32: Class-level taxonomic data of the augmented microbes over all phases. Error bars represent standard deviation between reactors. Relative abundance of (A) Gammaproteobacteria, (B) Alphaproteobacteria, and (C) Actinobacteria...... 143

Figure 33: The relative abundance of targeted and augmented genera in the reactors monitored over time...... 144

Figure 34: Species of interest identified in reactors through Illumina Miseq sequencing. Error bars represent standard deviation (N=3)...... 145

Figure 35: Taqman™ probe-based assays were used to monitor horizontal gene transfer events through catabolic plasmid conjugation. (A) NAH7 plasmid and PpG7 strain concentrations over time, (B) normalized concentrations of the NAH7 plasmid to PpG7 strain concentrations, (C) the pNL1 plasmid and F199 strain concentrations over time, (D) normalized concentrations of the pNL1 plasmid to F199 strain concentrations over time...... 147

Figure 32: Sphingolipid concentrations identified in killifish guts. There was no difference (p>0.1) in Sphingolipid abundance between sampling locations...... 162

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Figure 33: All identified taxa at the Phylum taxonomic level in Republic (‘Rep’) gut and sediment samples and KC (‘Kings’) gut and sediment samples. There is a decrease in overall diversity in Republic gut samples...... 163

Figure 34: Heatmap of OTU-level taxonomic data using Pearson distance measurements and Ward clustering algorithms...... 164

Figure 35: Non-metric multidimensional scaling (NMDS) plot using Bray-Curtis distances of microbiota data (stress=0.01106). A two-way ANOSIM with 9,999 permutations (N) revealed significant shifts due to the sampling location (gut vs. sediment, p=0.008) and PAH-exposure (Republic vs. KC, p=0.009)...... 165

Figure 36: A Similarity Percentage Analysis (SIMPER), which identified who the main contributors to the dissimilarity between gut samples...... 166

Figure 37: Alpha and Beta Diversities for Republic gut and sediment samples and KC gut and sediment samples...... 167

Figure 38: All identified Vikodak pathways. Green represents up regulated, red represents down regulated, and black is neutral. The Republic gut samples show down regulation in almost all identified pathways...... 168

Figure 39: Sampling strategy at Holcomb Creosote Co. Superfund Site. Map adapted from EPA OSC...... 169

Figure 40: Rarefaction curve of OTUs identified in all five sampling locations. All samples reached a plateau, indicating sufficient sampling depth...... 170

Figure 41: Photo of Pseudonocardia dioxanivorans strain CB1190 illustrating the morphology...... 171

Figure 42: PCR products resulting from the pNL1 (top row) and F199 (bottom row) primers. pNL1 target amplicons are 126bp while F199 targets are 171 bp. The first lane of both sections contains 100 bp ladder (max fragment size 2,000bp) obtained from New England BioLabs (Ipswich, MA)...... 172

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Acknowledgements

First and foremost, I would like to thank my wonderful family for their support and guidance. Thank you to my Mom, for always believing in me and helping me along the way and to my Dad, for his unwavering support and love. They never doubted me, even when I have doubted myself. In addition, thank you to: Bryce for his humor and love and being a wonderful baby brother, Grandmommy for her constant encouragement and support, Mimi and Papa for always showing me how proud you are and for feeding me so well, my Aunt Kember and Uncle Bob for all of their help, love, and support. It is so great to have you in my corner. I love you all and I am lucky to have them.

I would especially like to thank my advisor, Dr. Claudia Gunsch. I am so lucky to have her as a mentor and role model. I am so grateful for everything she has done for me and I am excited to continue to learn from her.

I am incredibly grateful for the help and support of my committee. I would like to

Dr. Richard Di Giulio for his support and being a wonderful collaborator. I have really enjoyed working with him and his group and I am so thankful for his help. Thank you to

Dr. Lee Ferguson for his help and for being so kind and approachable. It is nice to have another SEC fan around here. Thank you to Dr. Scott Harrison for his responsiveness and sense of humor. I could not have asked for a more encouraging committee.

I also want to thank Dr. Lisa Alvarez-Cohen for allowing me to join her group at

Berkeley during my externship. I am so appreciative of the opportunity to work with her.

Thank you to Jennifer and Ned as well for all of their help and friendship during my time at Berkeley and beyond. They made my experience fantastic.

Thank you to the best lab mates of all time. Thank you especially to: Alex for your sense of humor and for always eating an orange after eating yogurt, Baby Carley for your friendship and hilarious anger, Billy for being a great friend and ridiculous like me,

Savannah for being such a great friend and support, and Emilie for your knowledge and friendship. Thank you especially to Courtney, for being the greatest lab mate and friend of all-time and sharing so many of my strange interests. Thank you to my wonderful collaborators: Nishad for being an amazing person and even better friend, and Janie for being the definition of a loyal and true friend. Thank you to Leyf and Dr. Peirce for being my first friends in Durham, accepting me into your family, and continuing to help me every step of the way.

Thank you to all of my friends who have helped me along the way: Gracie for always being there for me and showing me what a loyal friend looks like, Lauren

Guimond Sotomayor for being so kind and supportive, The Hausers for being my second family, Amanda for being the best co-parent and great friend, and Katie and Jenni and

Amy for their decades of friendship and love. Thank you to Hanna Queen for being a great friend and to Mark and Lisa Queen for accepting me into your family so many years ago. Thank you to my girls Hannah, Kara, Chelsea, and Lauren- you are the best friends I could ever ask for and I’m so thankful. Above all, thank you to Monica and Dawn

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Petrella- I am so lucky to have you and I would not be here today without your love and support. Thank you and I love you all!

Finally, I would also like to thank my beautiful husband, Osman. Thank you for loving me, supporting me, and being my best friend. You are the kindest, smartest man I know and I’m so lucky to be your partner.

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1. Introduction

The contamination of soil and sediments with polyaromatic hydrocarbons (PAHs) is of environmental concern due to their ubiquity and carcinogenicity. One method to remove PAHs from the environment is bioremediation, a treatment strategy that utilizes biological agents to remove or stabilize contaminants. Enhanced in situ bioremediation

(ISB) occurs when biologically based remediation efforts are applied directly to a contaminated site, and offers environmental and economic appeal when compared to other remediation methods. ISB can be categorized into two groups: (1) biostimulation, which is when nutrients are added to stimulate microbial growth in an effort to increase pollutant degradation, and (2) bioaugmentation, which occurs when exogenous microbes with increased capacity for biodegradation are amended at a site (Vogel 1996). Although much research has been performed in the field of bioremediation, there remains a general lack of fundamental understanding of how chemical pollutants alter environmental microbiomes. This is problematic, as the field of bioremediation is directly limited in its ability to design and optimize targeted strategies. For instance, due to improper strain selection, foreign microbes introduced during bioaugmentation often either lose catabolic activity or are unable to survive long-term (Thompson et al. 2005).

A potential way of addressing these failures is through genetic bioaugmentation, an ISB method in which environmentally relevant donor bacteria harboring self- transmissible catabolic plasmids are introduced to increase the pollutant degradation potential (Ikuma and Gunsch 2012a, Top 2002, Top and Springael 2003). Genetic

bioaugmentation is an ISB method in which environmentally relevant donor bacteria harboring self-transmissible catabolic plasmids are introduced to increase the pollutant degradation potential (Ikuma and Gunsch 2012a, Top 2002, Top and Springael 2003).

The augmentation of donor strains, along with the selective pressure of the PAHs, encourages horizontal gene transfer (HGT) through plasmid conjugation to indigenous microorganisms (Top 2002, Sørensen et al. 2005). Rather than the augmented donor strain, the resulting transconjugants, or the site-adapted bacteria that now harbor the catabolic plasmid conjugated from the donor strain, carry out the chemical biodegradation (Ikuma and Gunsch 2012a). Plasmid proliferation within the system’s indigenous microbial communities increases the abundance and fitness of degradative microbes and eliminates the need for costly maintenance of an exogenously bioaugmented strain (Top 2002).

While genetic bioaugmentation shows great promise in simplified systems, its field implementation is limited due to our lack of understanding concerning how microbiomes assemble and function in various ecological settings. As a result, the ability to identify potential donor or recipient strains is greatly reduced. Generally, strains selected for bioaugmentation are chosen from the few microbes that have been fully sequenced, rather than the best potential strain. This is even more pronounced with genetic bioaugmentation, which requires additional genetic information concerning the donor strain’s catabolic plasmid and the potential site-adapted donor strains. In addition, these changes need to be characterized to ensure increased biodegradation potential rather

than ecosystem instability. A further limitation is that there exists no validated monitoring method to assess the successful implementation of genetic bioaugmentation in complex environments. A method to characterize and monitor genetic bioaugmentation is therefore crucial and recent advancements in next-generation sequencing (NGS) have allowed us to address some of these limitations. The focus of this dissertation is to use

NGS to engineer more efficacious microbial communities for the enhanced ISB of environmental contaminants.

1.1 Objectives

To date, very little research has been performed using NGS to investigate environmental microbiomes. The research presented here uses this tool to quantify changes in microbial populations after PAH exposure and assess its utility as a complement to traditional bioremediation. In the following objectives, we use NGS to develop a model framework for engineering and monitoring an environmental microbiome with increased bioremediation potential.

The specific objectives of this dissertation were to:

1.) Determine pollution-related adaptations in the sediment microbial community,

and gut-associated microbiome and metabolome of Atlantic killifish (Fundulus

heteroclitus) along the PAH-contaminated Elizabeth River.

2.) Develop a framework to inform a bioremediation treatment strategy selection

using Holcomb Creosote Co. Superfund site as a model PAH-contaminated site.

3.) Design a method to detect and quantify horizontal gene transfer of catabolic

plasmids in complex matrices.

4.) Validate the bioremediation framework and monitoring technique in lab scale

reactors using a realistic PAH-contaminated soil matrix from Holcomb Creosote

Co. Superfund site.

Successful completion of these objectives will facilitate a better understanding of how environmental microbiomes are altered in response to chemical pollutant exposure and provide a method for using and monitoring adaptations to increase biodegradation potential during bioremediation. These advancements may lead to increased adoption of targeted bioremediation at PAH-contaminated hazardous waste sites. Additional long- term outcomes include improved in situ remediation techniques for various pollutants, modifications to contaminated sites, and progress in fundamental pollutant adaptation response research.

1.2 Research Hypotheses and Approach

This project focused on using NGS to monitor the population dynamics of PAH- exposed prokaryotic microbiomes in a variety of hosts. It was hypothesized that long- term exposure to chemical pollutants selects for certain microbial communities and

encourages plasmid conjugation, and that these adaptations may be exploited to improve bioremediation.

The first objective was to assess adaptations in response to prolonged PAH exposure in the gut-associated microbiome and metabolome of evolved subpopulations of the Atlantic Killifish in the Elizabeth River, Virginia. It is hypothesized that in identifying microbial adaptions in response to chronic chemical pollutant exposure, we can identify critical microbes responsible for the PAH exposure-related adaptation. Each component of this objective was aimed to improve understanding of the process by which prokaryotic communities adapt to chemical pollutant exposure and how this may affect their hosts.

The second objective focused on developing a generalizable framework for targeted bioremediation at the Holcomb Creosote Co. Superfund site. The success of bioremediation using a consortium of biostimulants, bioaugmentation, and genetic bioaugmentation candidates depends on identifying the current microbiome and understanding conditions that enable the transfer and expression of a catabolic plasmid in sites with co-contamination. It was hypothesized that by characterizing the inherent pollutant-related shifts in the environmental microbiome of the soil across a gradient of co-contamination, it is possible to identify bioremediation targets and thereby increase the success of bioremediation. To do so, the soil-associated microbiomes from Holcomb soils were characterized through Illumina Miseq sequencing, after which a correlation analysis and literature review was conducted to classify possible recipient strains for

biostimulation, bioaugmentation, and genetic bioaugmentation. From there, potential donor strains for bioaugmentation and genetic bioaugmentation were identified and a targeted bioremediation consortium was constructed. This resulted in the development of a generalizable framework for employing precision bioremediation. The long-term aim of this objective was to develop a framework to employ NGS to improve in-situ bioremediation (ISB).

The third objective was to determine if a qRT-PCR method could be utilized to monitor the transfer of large catabolic plasmids as a potential tool for monitoring genetic bioaugmentation. It was hypothesized that HGT events occur during genetic bioaugmentation which thereby increase chemical biodegradation potential and that these transfer events could be monitored using targeted qPCR probes. This approach was validated with Pseudomonas putida G7 (PpG7) which carries the IncP-9 NAH7 plasmid and is known to degrade naphthalene, as well as the Pseudomonas dioxanivorans strain

CB1190 (CB1190), a strain which carries the pSED02 plasmid and is known to utilize

1,4-dioxane as a sole carbon and energy source (Parales et al. 1994, Mahendra and

Alvarez-Cohen 2005, 2006, Mahendra et al. 2007). This objective provides a novel way to monitor genetic bioaugmentation in a complex environment.

The fourth objective was to validate the targeted consortium outlined in Objective

2 (Chapter 4) and the monitoring technique developed in Objective 3 (Chapter 5). This was attempted in lab scale reactors filled with PAH-contaminated soil from Holcomb

Creosote Co. Superfund Site. It was hypothesized that by implementing the framework

outlined in Objective 2 and the tool outlined in Objective 3, a soil microbiome with increased degradation capacity could be engineered and gene transfer events could be monitored. In monitoring changes in microbial community populations, the dissemination of the added catabolic genes, and the degradation of the PAHs over time, this objective sought to assess the efficacy and efficiency of the developed framework and monitoring efforts.

2. Background and Literature Review

Bioremediation has become an increasingly popular treatment method for PAHs.

Numerous studies have shown that bioremediation may be used to treat PAHs, however, many other studies have reported failures. In addition, current methods for detecting gene transfer events are limited and new monitoring techniques are needed. This chapter presents a brief overview of the current scientific literature that addresses the central

topics of this dissertation. Specific reviews may be found at the beginning of the remaining objective-based research chapters.

2.1 Hazardous Wastes

2.1.1 Polycyclic Aromatic Hydrocarbons (PAHs)

The suite of chemicals known as polycylic aromatic hydrocarbons (also known as polycyclic organic matter or polynuclear aromatic hydrocarbons) are organic compounds composed of fused aromatic rings (ASTDR, Figure 1).

Naphthalene

Pyrene

Phenanthrene Tetracene

Figure 1: Chemical structure of four PAHs: naphthalene (2 ring), phenathrene (3 ring), pyrene (four ring), and tetracene (four ring).

PAHs are produced by the incomplete combustion of organic compounds which occur naturally or from anthropogenic sources (Shokralla et al. 2012). PAHs are increasingly prevalent in environmental ecosystems as a result of growing industrial 8

activities and expanding urbanization (Engraff et al. 2011). PAHs are widely distributed and contamination has been widely documented in terrestrial (Edwards 1983) and aquatic environments (Fetter and Fetter Jr 1999, MacKay and Gschwend 2001, Manoli and

Samara 1999), including in groundwater (Fetter and Fetter Jr 1999, MacKay and

Gschwend 2001, Manoli and Samara 1999), sediment (Neff, Stout, and Gunster 2005), and soils (Blumer and Youngblood 1975). PAHs are among the most common pollutants found at contaminated industrial sites and are mutagenic and carcinogenic to humans and animals (discussed more in a later section) (Samanta, Singh, and Jain 2002, Machala et al. 2001).

2.1.1.1 Naphthalene

Naphthalene is a common model PAH as it is the simplest PAH consisting of two fused benzene rings (Figure 1). Naphthalene’s Henry’s Law constant is 4.68 x 10-4 atm m3/mol and smells like mothballs (Mackay, Shiu, and Sutherland 1979). Naphthalene is found in abundance in crude oil and creosote and is a known carcinogen (M. Abdo 2001).

Naphthalene is a groundwater contaminant commonly found in wood preservation facilities (Goerlitz et al. 1985, Lesser-Carrillo 2014) and landfill leachate (Kjeldsen et al.

2002).

2.1.1.2 Creosote

PAHs in mixtures can be more toxic than the individual components, making creosote of greater concern (Engraff et al. 2011). Creosote, a bulk distillate of coal tar, is a complex mixture of PAHs, phenolics, and other heterocyclics (Hale and Aneiro 1997).

Creosote contamination is generally associated with soils, surface waters, and groundwater (Mueller, Chapman, and Pritchard 1989a). It is estimated that there are 700 sites in the US where wood treatment has been conducted (Mueller, Chapman, and

Pritchard 1989a) and it is believed that active wood treatment facilities generate around

840-1530 dry metric tons annually of hazardous waste sludge (Mueller, Chapman, and

Pritchard 1989a). Due to the complexity and large-scale historical usage of creosote, much of the research presented here has focused on biodegradation of components found in creosote.

2.2 PAHs and Environmental Health

Many PAHs are known to be toxic and disruptive to environmental health (Nisbet and LaGoy 1992) and are immunotoxic and carcinogenic (Eisler 1987). A wide range of

PAH-induced ecotoxicological effects in a diverse suite of biota, including microorganisms, terrestrial plants, aquatic biota, amphibians, reptiles, birds and terrestrial mammals have been reported (Delistraty 1997). PAHs are able to form DNA adducts through the reactive diol epoxide metabolite (Baird, Hooven, and Mahadevan 2005,

Haugen et al. 1986, Harvey 1991). Lipophilic PAHs can also accumulate in the

membrane lipid bilayer and compromise the membrane’s integrity (Sikkema, De Bont, and Poolman 1995). Planar aromatic hydrocarbons induce cytochrome P450 1A proteins via the intracellular aryl hydrocarbon receptor (AHR) (Poland and Knutson 1982). It is believed that PAHs disturb other aspects of the immune system but data are contradictory and depend on the mode of exposure, the dose used or the species studied (Reynaud and

Deschaux 2006). Overall, effects of PAHs have been documented on survival, growth, metabolism and tumor formation, i.e. acute toxicity, developmental and reproductive toxicity, cytotoxicity, genotoxicity and carcinogenicity (Delistraty 1997).

2.2.1 The Impact of PAHs on Prokaryotic Communities

Currently, there exists limited full-scale or in-depth knowledge on how PAHs impact prokaryotic environmental microbiomes. Most of the current data are from culture-based, indirect, or low-throughput sequencing techniques. Previous studies have used culturing and plating methods (Mueller, Chapman, and Pritchard 1989b, Mishra et al. 2001, Carmichael and Pfaender 1997), denaturing gradient gel electrophoresis

(DGGE) (Simarro et al. 2013), single-strand conformation polymorphism (SSCP) (Martin et al. 2012), and Sanger sequencing (Bastiaens et al. 2000) to investigate PAH bioremediation and exposure-related adaptations, all of which greatly underrepresents the taxonomic diversity and do not provide in-depth knowledge. One study (Slater et al.

2008) found minor sediment community changes after PAH exposure, but assumed decreased bacterial populations were indicated exclusively by phospholipid fatty acid

(PLFA) concentrations and were not able to indicate any specific taxa that were affected.

Another study looked at the Chattanooga Creek Superfund site (a site contaminated with coal tar creosote) and found that a population of pyrene-degrading Mycobacteria comprised a stable portion of the particle-associated bacterial (PAB) community by using terminal restriction fragment length polymorphism (T-RFLP, (DeBruyn and Sayler

2009)). Though T-RFLP is considered better than many other techniques, it gives less in- depth information regarding community structure than next-generation sequencing (NGS)

(also known as massively parallel sequencing). The advent of NGS has revolutionized our ability to sequence and investigate environmental microbiomes (Reis-Filho 2009,

Shokralla et al. 2012). This has critical impacts for evaluating and improving bioremediation (Stenuit et al. 2008).

Though we now have the ability to fully characterize microbial populations and evaluate community responses to variables, few studies to date have actually used NGS technologies to investigate environmental microbial communities and their response to chemical pollutants. One recent study (Covino et al. 2016) found co-compost may help shift a microbial community towards increased creosote biodegradation using a first- generation NGS, 454-pyrosequencing, which has limitations such as errors, cost, and yield (Goodwin, McPherson, and McCombie 2016). Newer NGS technologies, such as the Illumina Miseq (Illumina, San Diego, CA) used in this work, have yet to be utilized to explore creosote exposure and the resulting effects on environmental microbiomes.

2.2.2 Community Impacts of PAH mixtures

While there have been limited studies that identified the toxicity of PAHs and heavy metals in co-contamination, they focused on a single metal and PAH (Gust 2006), not specifically on microbial communities. This lack of research is of concern as PAHs may have synergistic toxicity reactions with other classes of contaminants (Engraff et al.

2011). Further, while it is known that certain microbes harbor plasmids that can degrade

PAHs (Sayler et al. 1990), little is known about degradation potential when there are multiple chemical selective pressures. This is important because when PAHs are found with co-contaminants, their ability to exert a strong selective pressure and/or their ability to be biodegraded is more complex. Of particular importance to this dissertation is the fact that PAHs are often found in creosotes and oils and are therefore likely to co-occur with other contaminants such as creosols, pentachlorophenol (PCP), and CCA (copper, chrome, and arsenate) (Mueller, Chapman, and Pritchard 1989a). PAHs are found with additional co-contaminants at hazardous waste sites; for instance, research wastes are known to contain both naphthalene and 1,4-dioxane from assays involving the liquid scintillation cocktails, membranes, and tissue preservation (Mohr, Stickney, and

DiGuiseppi 2010). This is a potential issue as the cyclic ether 1,4-dioxane is also a probable carcinogen (Kano et al. 2009) and groundwater pollutant (Isaacson, Mohr, and

Field 2006). In addition, 1,4-dioxane is a solvent widely used in industrial organic products such as cosmetics and shampoos, detergents, textiles, pesticides and fumigants, aircraft de-icing fluids and antifreezes, and paints and dyes (Mohr, Stickney, and

DiGuiseppi 2010) so it may also be found to co-occur with PAHs in landfill leachate. 13

2.3 Bioremediation of PAHs

Environmental contamination by petroleum hydrocarbons can lead to problems such as contaminated water, unusable land, and toxic human exposures (Teaf 2008,

Edwards 1983). Complete in situ remediation is difficult for PAHs, partially due to their hydrophobic nature and ecological toxicity, and partially due to the large remaining research gaps. Currently, there are only limited sustainable remediation options available for PAH remediation. In the past few decades, commonly used technologies for the remediation of PAHs in soil include mechanical removal, burying, evaporation, dispersion, and washing (Das and Chandran 2010). These methods are generally expensive, labor-intensive, and less effective, prompting a need for new or improved remediation strategies. Due to this, developing improved methods to quickly and cheaply remediate petroleum hydrocarbons is of pronounced importance. The bioremediation of

PAHs is of particular interest due to their ubiquity, recalcitrance, and toxicity (Bamforth and Singleton 2005).

Another option that has gained considerable attention over the past decade is bioremediation, which is a cost-effective and sustainable treatment option that relies on microorganisms to degrade contaminants (April, Foght, and Currah 1999).

Bioremediation represents one of the dominant and most cost-effective (Leahy and

Colwell 1990) mechanisms by which PAHs can be removed from the environment (Ulrici

2000). Bioremediation may be achieved by allowing degradation to occur naturally by

the native bacteria (bioattenuation), stimulating indigenous bacteria to degrade the pollutant (biostimulation) (Margesin and Schinner 1997), or seeding micro-organisms with the desired catabolic traits (bioaugmentation) (Vogel 1996, Pepper et al. 2002).

Biostimulation may include such things as the addition of rate limiting nutrients and/or electron acceptors (e.g., nitrogen, carbon) or harnessing local degraders to provide a more hospitable environment (van Cauwenberghe and Roote 1998, Rittmann 1994, Jørgensen

2007). Bioaugmentation generally includes selecting strains that degrade the contaminant of concern and introducing it to the contaminated site (Gast et al. 2003, Thompson et al.

2005). More, bioaugmentation may also incorporate biostimulation, augmenting both conditions and microbes. As bacteria are the most active agents in environmental petroleum degradation (Rahman et al. 2003, Brooijmans, Pastink, and Siezen 2009), they are the focus of the most of this work.

2.3.1 In situ bioremediation (ISB) of PAHs

Biostimulation may be applied in situ (ISB) or ex situ, with increasing attention focused on improving in situ methods due to the increased simplicity and lower impact

(van Cauwenberghe and Roote 1998, Rittmann 1994, Jørgensen 2007). The success of

ISB depends on the ability to establish and maintain conditions that favor enhanced biodegradation rates in the contaminated environment (Das and Chandran 2010).

Therefore, when using bioaugmentation, bacteria used to augment sites with PAH contamination must be adapted to survive the toxic chemical levels as well as to produce

surfactants in order to make the PAHs more accessible (Mrozik and Piotrowska-Seget

2010, Forsyth, Tsao, and Bleam 1995, Gentry, Rensing, and Pepper 2004, Gast et al.

2003). While increased biodegradation of xenobiotic pollutants by bioaugmentation has been demonstrated in several studies (Briglia et al. 1990, Brodkorb and Legge 1992,

Middeldorp, Briglia, and Salkinoja-Salonen 1990, Bento et al. 2005), there have been many other studies that have shown failures due to how degrading bacteria adapt to site conditions, predation, and competition (Thompson et al. 2005, Bouchez et al. 2000,

Goldstein, Mallory, and Alexander 1985, Vogel and Walter 2001). As a result of these repeated failures, the focus of recent efforts has been on identifying new degrading bacteria or developing ways to address some of its limitations through molecular techniques.

2.3.1.2 Aerobic ISB (ISB-a)

One type of ISB is aerobic (ISB-a), where oxygen is used as the electron acceptor.

ISB-a may be where oxygen and/or other nutrients and co-metabolites are added to the subsurface in order to stimulate the growth and activity of indigenous microbes or it may be combined with bioaugmentation (Wolicka et al. 2009, Wilson 2003, Vidali 2001).

Though a few industrial sites have ISB-a remediation plans, it has not been adopted widely. In addition, ISB-a is currently limited in practice and often considered ineffective for the removal of most PAHs from contaminated soil (Wilson and Jones 1993, Wilson

2003). As a result, the adoption of ISB-a at soil and sediment contaminated sites, such as

former wood treatment facilities (i.e., sites with a mixture of PAHs and landfills) is still rare.

2.3.2 Bacteria Used in Bioremediation of PAHs

Some PAH-degrading microorganisms, primarily bacteria, are capable of directly using PAHs as a carbon and energy source, whereas others can capacity to degrade while living on a widely available substrate (co-metabolism) (Ghosal et al. 2016). Such co- metabolism does not always result in growth of the microorganism; sometimes the co- substrate, i.e. the PAH, is only partially degraded depending on the available enzymes

(Ghanbari, Kneifel, and Domig 2015). For PAHs, the contribution of the co-metabolic degradation processes increases as the number of rings in the PAH-molecule increases, since far fewer microorganisms are capable of using the HMW PAHs as carbon and energy sources (Heitkamp and Cerniglia 1989, Cerniglia 1993). Historically, gram- positive bacteria are the typical aromatic degraders, but their single strain use is rare due to the inefficacy and inability to compete (Mrozik and Piotrowska-Seget 2010). As a result, they are often added in a microbial consortium with synergistic benefits; different microbes contain distinct catabolic pathways and any chemical intermediates generated by one pathway may be fully or further degraded by other strains (Heinaru et al. 2005,

Jacques et al. 2008). Commonly seeded PAH-degrading microorganisms are in the genera Pseudomonas, Flavobacterium, Sphingobium, and Alcaligenes (Mrozik and

Piotrowska-Seget 2010) and are often applied as consortia and/or with additional

amendments for biostimulation. There are a wide range of additional potential PAH- bioremediation prokaryotes thoroughly reviewed by Prince et al. 2010 Das and Chandran

2010, and Seo et al. 2009. Along with those already mentioned, a few of the PAH- degrading genera that may be used in bioremediation are: Microbacterium, Kurthia,

Micrococcus, Bacillus, Burkholderia, Aspergillus, Mycobacterium, Brevibacillus,

Arthrobacter, Stenotrophomonas, Deinococcus, and Aspergillus (Prince, Gramain, and

McGenity 2010, Mishra et al. 2001, Mueller, Cerniglia, and Pritchard 1996). Further, many of these genera are known to harbor catabolic plasmids that assist in PAH biodegradation, discussed more in the next section.

2.3.3 Aerobic Bacteria used in ISB-a of PAHs

Though many anaerobic PAH degraders have been identified, the work presented here focuses on aerobic microorganisms and ISB-a. Through ISB-a, aerobic microbes are able to use the PAHs as part of their metabolic processes, converting them to carbon dioxide, water, and microbial cell mass (Bouwer and Zehnder 1993) (Figure 2). The bacterial degradation of PAHs generally begins with a dioxygenase attack on one of the aromatic rings to form a cis-dihydrodiol, which is subsequently dehydrated to catechol.

These general metabolic pathways have been verified for a large number of bacteria and fungi, in pure cultures, during seemingly unwieldy degradation of a variety of individual

PAHs, as reviewed by Cerniglia, Sutherland et al. (Cerniglia and Sutherland 2001), and

Kanaly & Harayama (Kanaly and Harayama 2000).

As reviewed by Das and Chandran 2010 (Das and Chandran 2010), the initial attack of the hydrocarbon is an oxidative process and is generally catalyzed by oxygenases. After which, the pollutant is transformed to intermediates of a central metabolic process such as the tricarboxylic acid cycle (TCA) cycle. The resulting central precursor metabolites, such as acetyl-CoA and pyruvate, can then be used for the biosynthesis of cell biomass and eventually result in microbial growth.

Hydrocarbon O2

Oxygenase attack

Pathway degradation

TCA Cycle

+ NH4 CO2 3- PO4 Biosynthesis 2- Respiration SO4 O2 Fe3+ Cell biomass

H2O

Microbial growth

Figure 2: General schematic for the aerobic degradation of hydrocarbons. Image adapted from Das and Chandran (2010).

Due to the oxygen requirement for the initial hydrocarbon attack, ISB-a for PAHs generally requires an additional input of oxygen for its terminal electron acceptor in an 19

effort to enhance biodegradation. ISB-a is therefore often combined with in situ chemical oxidation (ISCO) where oxygen is added to ensure adequate oxygen is available and is no longer limiting in PAH degradation (Sutton et al. 2011). Generally, 3 moles of oxygen per 1 mole of hydrocarbon are recommended to maximize biodegradation potential (U.S.

EPA, 2004). Along with a terminal electron acceptor (e.g., O2), sufficient amounts of nitrogen (N), phosphorus (P), and potassium (K)- also referred to as NPKs- are required

(Das and Chandran 2010, Carmichael and Pfaender 1997).

2.3.3 The role of plasmids in bioremediation of PAHs

It has previously been documented that HGT events and later mutations have contributed to the rapid adaptation of bacteria (Springael and Top 2004, Top and

Springael 2003, Frost et al. 2005). One major method of HGT is plasmid conjugation.

Plasmids are extra-chromosomal DNA molecules that contain their own transcriptional promoters, allowing them to replicate and express themselves autonomously (Schumann

2001). Many plasmids undergo HGT events naturally when a donor cell extends a pilus to a recipient cell in the proximity (Willetts and Skurray 1980).

DONOR CELL RECIPIENT CELL

PLASMID PILUS BRIDGE

Figure 3: Schematic detailing horizontal gene transfer through plasmid conjugation.

The pilus bridge allows the two cells to have direct contact and the plasmid is then replicated in the recipient cell (Anthony et al. 1994, Ou and Anderson 1970).

Afterward, the cells detach and both cells are now ostensibly able to continue to propagate the plasmid (Figure 3) (Ou and Anderson 1970, Schumann 2001). This spread of genetic information throughout microbiomes has environmental implications such as an increased chemical biodegradation potential.

2.3.3.1 Plasmid incompatibility (Inc) groups

Plasmids are categorized in incompatibility (Inc) groups (Novick 1987, Kittell and Helinski 1993, Couturier et al. 1988). The Inc designation relates to host range transferability (Suzuki et al. 2010). Generally, there are broad host range (sometimes called promiscuous) or narrow host range (Norman, Hansen, and Sørensen 2009) plasmids. As the name implies, broad host range plasmids are typically found across different bacterial taxa, while narrow host range plasmids typically have very limited potential recipient strains (Sayler et al. 1990, Couturier et al. 1988). The IncF and IncI groups are considered to have a narrow host range, the IncH and IncN plasmids are considered to have an intermediate host range, while IncP is known to have a broad host range (Couturier et al. 1988, Suzuki et al. 2010). In general, broad host range plasmids may be the most useful for bioremediation, as they are more likely to transfer rapidly in an environmental microbiome. Due to their promiscuity, the IncP is one of the best studied broad host range conjugative plasmid containing genes for the degradation of xenobiotics (a foreign chemical substance) and other contaminants (Dennis 2005).

2.3.3.2 Catabolic plasmids

Of particular interest to this work is the dissemination of catabolic plasmids and the potential to enhance bioremediation (Sayler et al. 1990) through environmental microbiomes. Catabolic plasmids play a large role in the spread of catabolic pathways in bacterial communities, allowing microbial populations to adapt to new xenobiotics (Top

and Springael 2003). Many IncP plasmids that carry genes encoding enzymes for degrading organic xenobiotics have been isolated (Top and Springael 2003). A few notable examples are the TOL plasmid that breaks down xylenes and toluene (Abril et al.

1989), and the NAH7 plasmid that breaks down naphthalene (Sota et al. 2006) (Figure 4).

Many of these characterized catabolic plasmids originate in Pseudomonas species and, due to their large size, are typically present in low copy numbers (Duetz and van Andel

1991, Thomas and Smith 1987). It is believed that the in situ spread of plasmids and the increase in xenobiotic catabolism will raise the overall community degradation potential, but currently there are still limitations and concerns about the long-term survival of the exogenous donor strain (Mrozik and Piotrowska-Seget 2010, Ikuma and Gunsch 2012b).

2.3.3.2.1 The NAH7 plasmid A large portion of this dissertation used the NAH7 plasmid as a model catabolic plasmid. The NAH7 plasmid is a well-studied IncP-9 (promiscuous) catabolic plasmid

(Figure 4). It is a self-transmissible plasmid capable of completely degrading naphthalene

(Sota et al. 2006).

Figure 4: The naphthalene degrading plasmid, NAH7 from Sota et al. 2006

Further, the naphthalene degrading enzyme system on NAH7 is involved in the degradation of higher-molecular-weight polycyclic aromatic hydrocarbons other than naphthalene, such as phenanthrene and anthracene (Sanseverino et al. 1993, Menn,

Applegate, and Sayler 1993). Naphthalene is primarily degraded to catechol by the nah genes located on the NAH7 plasmid and the catechol is further degraded by the alpha- ketadipate pathway (Dunn and Gunsalus 1973). The nah genes are divided into two 24

operons: the upper operon, nah1, converts naphthalene to salicylate and the lower operon, nah2, converts salicylate to acetaldehyde and pyruvate (Sokatch 2012, Sota et al. 2006,

Yen and Gunsalus 1982, Yen, Sullivan, and Gunsalus 1983, Dunn and Gunsalus 1973).

Figure 5: Naphthalene biodegradation pathway encoded by NAH7. Adapted from Seo et al. 2009 (Seo, Keum, and Li 2009)

The nah operons on NAH7 have been shown to be situated within a class II (Tn3- like) transposon designated Tn4655 (Tsuda and Iino 1990). The NAH7 plasmid also contains genes enabling the chemotaxis to naphthalene (Grimm and Harwood 1999).

Interestingly, due to the pervasiveness and persistence of this naphthalene-catabolic plasmid, it is believed that it may have played a role in the adaptation of microbial communities at PAH contaminated sites (Stuart-Keil et al. 1998) that has yet to be characterized or monitored.

2.3.3.4 Barriers to bacterial horizontal gene transfer (HGT) events

Though HGT events and, more broadly, the exchange of mobile gene elements

(MGEs) occur spontaneously in the environment, there are selective barriers that must be overcome (Top and Springael 2003). For instance, often a strong selective pressure must be applied and the acquired phenotype must be favorable (Top and Springael 2003).

Additional factors include specific growth rates in donor cells (Smets, Rittmann, and

Stahl 1993), metabolic activity (Johnsen and Kroer 2007), cell density/cell-to-cell contact occurrence (Hill and M Top 1998), phylogenetic relatedness (Pál, Papp, and Lercher

2005, Sorek et al. 2007), G+C content and similarity in G+C content (Pinedo and Smets

2005, Thomas and Nielsen 2005). In addition to microbial-based considerations, there are also environmental factors such as carbon substrate availability (Ikuma, Holzem, and

Gunsch 2012), temperature (Das and Chandran 2010), and chemical bioavailability

(Boopathy 2000). The standard donor-to-recipient ratio that is considered to be most

effective is 1:103 (Pinedo and Smets 2005), though, like all other influencing factors, this is likely variable for each situation.

2.4 Genetic bioaugmentation

Until recently, strain selection for bioaugmentation has been based entirely on degradation ability, with no consideration as to their potential ability to survive and thrive in sites to which they were applied (Thompson et al. 2005). Even the most well suited microbes may be unable to survive due to stressful site conditions, competition for resources, or co-contaminants (as discussed in previous sections). As a result, augmented microorganisms decrease quickly after inoculation (Mrozik and Piotrowska-Seget 2010,

Gentry, Rensing, and Pepper 2004) culminating in a remediation failure. One method of potentially addressing these failures and ensuring increased biodegradation potential is genetic bioaugmentation, where augmented exogenous microbes interact and exchange genes with the bacterial communities endogenous to the contaminated site (Ikuma and

Gunsch 2012a, 2013, Ikuma, Holzem, and Gunsch 2012, Ikuma and Gunsch 2012b). This approach relies on the well-studied mechanism of HGT (discussed above) and the transfer of relevant genes between exogenous and indigenous strains. HGT occurs genetic adaption mechanisms (plasmid conjugation) between exogenously bioaugmented bacterial strains and endogenous strains (indigenous strains) already adapted to the contaminated site’s adverse conditions (Figure 6).

A B

As a result, this approach does not require long-term survival of the exogenous strain and takes advantage of natural bacterial adaptation (Sayler et al. 1990). In doing so, the inherent microbial community naturally develops degradation capacities without requiring survival of the donor strain (Ikuma and Gunsch 2012a). This natural adaptation is critical, as these are not GEMs and will not be heavily regulated by the U.S. EPA.

Genetic bioaugmentation can be a more effective method for ISB-a, though more research is needed to ensure the success of genetic bioaugmentation. For instance, though certain factors influencing plasmid conjugation are known, factors influencing genetic bioaugmentation have limited evidence. The most important factors for the success of genetic bioaugmentation are (1) high transfer rates of catabolic plasmids to the site- adapted bacterial community and (2) the functional contaminant-degrading phenotype in

transconjugants after HGT of catabolic plasmids (Ikuma and Gunsch 2012a, Top and

Springael 2003). Further, concerns remain about the long-term fate of plasmids after community adoption. Plasmids are likely lost from host cells when the selective pressure is removed, mostly due to the unnecessary metabolic load on the host, as demonstrated by

Smets et al. 1994 (Smets, Rittmann, and Stahl 1994) and Ikuma and Gunsch 2013 (Ikuma and Gunsch 2013). Additional studies and improved models are recommended to better understand the long-term fate of genetic bioaugmentation.

The viability of genetic bioaugmentation has previously been confirmed with the

TOL plasmid (Ikuma and Gunsch 2013), a 117 kb plasmid that encodes for the catabolism of xylenes, toluene, as well as related chemicals (Abril et al., 1989). Conjugal transfer of the TOL plasmid from P. putida to other strains has been reported (Smets,

Rittmann, and Stahl 1994, Pinedo and Smets 2005). Ikuma and Gunsch (2014) (Ikuma and Gunsch 2013) tested the functionality of TOL transconjugants in soil microcosms as well as soil reactors and confirmed the potential of genetic bioaugmentation.

2.4.1 Methods for monitoring HGT events during genetic bioaugmentation

Historically, three approaches have been used to monitor plasmid conjugation.

The first approach is culture-based, which consists of using selective media to enumerate transconjugants (Bellanger, Guilloteau, Bonot, et al. 2014). This method is severely limited by the fact that fewer than 1% of environmental bacteria are cultivable, and even

fewer are able to express selective markers (Bellanger, Guilloteau, Bonot, et al. 2014,

Sørensen et al. 2005).

The second approach is fluorescent-based. As reviewed by Sørensen et al

(Sørensen et al. 2005), this approach requires a molecular monitoring system (e.g., genes that encode for fluorescent proteins) to track plasmid conjugation. Briefly, a fluorescent reporter, such as gfp (green fluorescent protein) or rfp (red fluorescent protein), may be detected using microscopy to visualize in situ plasmid conjugation (Bellanger,

Guilloteau, Bonot, et al. 2014, Sørensen et al. 2005). Previous work with genetic bioaugmentation has relied on an in situ reporter system for the conjugation of the gfp- labeled TOL plasmid (Ikuma and Gunsch 2013, Ikuma and Gunsch 2012b, Ikuma,

Holzem, and Gunsch 2012). The gfp labeling technique, though promising for the lab, presents several problems when environmentally applied. For instance, it is not appropriate for complex environmental matrices such as soil or manure. Nor is it effective with more than one or possibly two strains and it is constrained by the need to be able to genetically modify the plasmid (Bellanger, Guilloteau, Bonot, et al. 2014).

The third and newest method is the molecular-based approaches. Recently, several research groups have developed qPCR-based approaches to monitor plasmid conjugation and dissemination (Bellanger, Guilloteau, Bonot, et al. 2014, Bonot and

Merlin 2010, Merlin et al. 2011, Haug et al. 2011, Haug et al. 2010). Briefly, this method relies on monitoring the genetic evolution of both the plasmid and host DNA over time.

By normalizing plasmid-associated genes to strain-associated genes over time, increases

in plasmids above a certain threshold may be inferred to be the result of a plasmid conjugation event. This method offers several benefits: non-culture based/non-disruptive and has the ability to be used with multiple strains. These methods have all focused on tracking plasmids with antibiotic resistance genes and there has been no work done on large catabolic plasmids, such as the NAH7 or TOL plasmids.

3. Exposure-related changes in the sediment microbiota, and gut-associated microbiome and metabolome of evolved subpopulations of Atlantic killifish in the Elizabeth River (ER)

3.1 Introduction

Gut microbial communities have been shown to have substantial biological roles and may modulate organismal response to shifts in their abiotic environment (Nayak

2010, Ghanbari, Kneifel, and Domig 2015). Physiological effects of shifts in gut microbial communities are directly linked to changes in gut metabolite composition

(Nicholson et al. 2012, Hosokawa et al. 2006) and consequently overall host metabolic phenotype. A key example of this is how termites require gut microbial symbionts to degrade cellulose (Ohkuma 2003). Gut-associated microbes produce diet-dependent metabolites, such as amino acids, and diet-independent metabolites, such as lipopolysaccharide (Brestoff and Artis 2013). An increasing amount of research demonstrates that gut microbial communities are critical in determining numerous aspects of overall organismal health and immunity, including metabolic diversity and metabolic disorders (Tilg and Kaser 2011, Brestoff and Artis 2013), and may play a critical role in organismal response to anthropogenic environmental change.

Despite increasing evidence of the relationship between the host and the microbiome, little is known about how environmental pollutants influence the microbiome and the consequent effects for the host. Studies demonstrate that symbiotic gut bacteria show trends of co-evolution with their fish hosts (Sullam et al. 2012) and that microflora can potentially affect chemical transformation, bioavailability and bioactivity

(Ni et al. 2014, Gómez and Balcázar 2008). Here, we test this hypothesis by characterizing the intestinal microbiome and metabolome of Fundulus heteroclitus, a type of fish in the Chordota phylum that has evolved resistance to a highly toxic class of compounds in comparison to fish from a clean reference site.

The Elizabeth River (ER) is a sub-estuary off the Atlantic Ocean in the southern part of the Chesapeake Bay (Virginia, USA). Parts of the river are contaminated from former wood treatment facilities with creosote, a complex mixture of primarily un- substituted polycyclic aromatic hydrocarbons (PAHs) and some heterocyclic and phenolic PAHs (Clark et al. 2013, Bieri et al. 1986). PAHs are highly toxic, with effects reported across a wide range of organisms (Delistraty 1997), including in fish(Logan

2007, Eom et al. 2007) . The site of the former Republic Creosoting Co. has some of the highest detectable areas in term of PAH contamination ever measured (Douben 2003).

Researchers have shown that the levels of PAHs present at that site can induce significant developmental deformities, cause DNA damage (Jung et al. 2011, Reid et al. 2016, Di

Giulio and Clark 2015), and lead to carcinogenic liver lesions. Remarkably, a population of Atlantic killifish, Fundulus heteroclitus (commonly known as mummichogs) (Figure

7) has evolved resistance to the toxic effects of PAHs and successfully inhabits the contaminated site.

Figure 7: Atlantic Killifish from Elizabeth River, VA.

These PAH-resistant subpopulations present a unique natural experiment to examine evolution driven by anthropogenic pollution (Di Giulio and Clark 2015, Clark et al. 2013, Clark and Di Giulio 2012, Wills, Jung, et al. 2010, Jung et al. 2009, Bacanskas,

Whitaker, and Di Giulio 2004, Wills, Matson, et al. 2010, Meyer, Nacci, and Di Giulio

2002, Nacci, Champlin, and Jayaraman 2010, Meyer, Smith, et al. 2003, Meyer et al.

2005). They have been studied extensively in this context (Meyer and Di Giulio 2003, Di

Giulio and Clark 2015, Wills, Jung, et al. 2010, Vogelbein, Unger, and Gauthier 2008) and Di Giulio and Clark (Di Giulio and Clark 2015) have provided a comprehensive review of the mechanisms and consequences of PAH adaptation in ER killifish. While several studies have shown that altered PAH metabolism via modifications in the aryl hydrocarbon receptor (AHR) pathway and up regulation of antioxidant defense system are associated with this adaptive response (Di Giulio and Clark 2015), shifts in other biological processes including changes in epigenetic and commensal microbial communities may also play a role underlying PAH-resistance and have not been examined. It is possible that the intestinal prokaryotic communities and metabolome of

ER fish have either evolved alongside their PAH-adapted fish hosts or the composition has shifted.

To investigate the potential role of microbial communities in modulating the adaptive PAH-response or in response to chronic PAH-exposure, here we examined the sediment and gut microbial composition in the Republic site and killifish inhabiting this site in comparison to a reference site (Kings Creek – KC). Studies show that gut bacteria respond to their environment and affect host physiology as well as the metabolism of their neighboring species through the synthesis of a wide variety of amino acids (AA) and their metabolites (Dai, Wu, and Zhu 2011). Despite studies examining the gut metabolite composition in the context of the commensal microbiome in humans, this relationship has not been investigated in most taxa, including in fish. Here, we also evaluated the gut metabolite composition to derive physiological relevance of shifts in gut microbial communities. Metabolites are generated in the gastrointestinal tract as a result of the interactive chemical communication network between the host and its resident microbiota

(Turroni et al. 2016). As these metabolites can represent the “currency” between the microbes and the host, the analysis of metabolite composition (via metabolomics analysis) coupled with the gut microbial composition (via amplicon based metagenomic analysis) may help unlock the complex relationship between the microbiota and organismal health. In the context of understanding organismal responses to anthropogenic environmental change, defining the environmental influences on the gut microbiome and metabolome presents an opportunity to monitor ecological and physiological responses

and may help forecast their susceptibility and potential to adapt (Ley, Peterson, and

Gordon 2006).

3.2 Material and Methods

3.2.1 Sample Collection

Adult killifish were collected at the KC (37° 18′ 16.2″ N, 76° 24′ 58.9″ W) and

Republic Creosoting location (36° 47′ 39.65″ N, 76° 17′ 31.94″ W) discussed in Clark et al. (Clark et al. 2013). The two sites are tributaries of different river systems and were selected because any mixing between the fish populations is highly unlikely. Reference killifish were collected at the reference KC location while the PAH-exposed killifish were collected at the former Republic Creosoting Company. The KC and Republic

Creosoting Company sites average 526 and 113,886 ng/g PAHs/dry sediment (Xia and

Wishart 2011a, Ownby et al. 2002) , respectively. All fish collections were performed within 4 hours of each other. After collection, the fish were field sorted based on sex and size. Ten male fish from each site were dissected following methods outlined in

Jayasundara et al. (Jayasundara et al. 2015). The mid- to hind-portion of the guts (i.e., intestinal tract from the duodenum to the rectum) were removed and immediately placed in liquid nitrogen (Grosell, Farrell, and Brauner 2010, Ringø et al. 2003). Upon returning to the lab, samples were stored at -80°C until they were used for DNA extraction or metabolomics analysis. Multiple sediment grabs were also collected at each site, immediately subjected to mechanical homogenization and divided into three individual

storage containers. The sediment samples were stored on ice in a cooler until they were returned to the lab. All samples were stored in individual aliquots at -80°C until DNA isolation. The time between collection and storage did not exceed 18 hours.

3.2.2 Metabolomic Quantification and Analysis

Gut samples were homogenized and an aliquot was taken for quantitative analysis using the Biocrates Absolute IDQ™-kit p150 (BIOCRATES Life Sciences, AG,

Innsbruck, Austria). This metabolomic characterization method relies on flow injection analysis using electrospray ionization liquid chromatography–mass spectrometry (ESI-

LC–MS/MS) to quantify 163 known small molecule metabolites simultaneously

(Dhungana et al. 2016). Absolute metabolite values are deduced by reference to appropriate internal standards. The Biocrates dataset contains acylcarnitines (Cx:y), hydroxylacylcarnitines [C(OH)x:y] and dicarboxylacylcarnitines (Cx:y-DC); amino acids

(AA); sphingomyelins (SMx:y) and sphingomyelin-derivatives [SM(OH)x:y]; and glycerophospholipids (PC). Statistical significance was determined using the Holm-Sidak method using an alpha value of 5%. Each metabolite was analyzed individually without assuming a consistent standard deviation. The Biocrates data were analyzed using

MetaboAnalyst, a specialty program for metabolomics data analysis and interpretation

(Xia et al. 2015, Xia et al. 2012, Xia and Wishart 2011b, a). Variable Important in

Projection (VIP) scores were calculated for each metabolite. VIP scores consist of a weighted sum of squares of the partial least squared (PLS) loadings taking into account

the amount of explained Y-variation. The metabolites with VIP scores over 1.25 were assessed to determine the most influential metabolites to use for pathway analysis using the MetaboAnalyst pathway analysis tool. Using this tool, potential pathway impact scores (0-1) were calculated for all predicted pathways to determine which pathways were most likely influenced (Xia and Wishart 2011a).

3.2.3 DNA extraction

The fish gut samples were gently brought to room temperature. Then, 400 µL of

1X phosphate buffered saline (PBS) was added to each tube and vortexed on high for 5 seconds. To extract the microbes adhered to the intestinal lining (Ringø et al. 2001), fish gut samples were also sonicated in a Bransonic® 1510 Ultrasonic Cleaner (Branson

Ultrasonics Corporation, Danbury, CT) on ice for 15 minutes. The supernatant was poured off into a new tube, and 400 µL fresh 1X PBS was added to the remaining gut sample and sonicated on fresh ice again for an additional 15 minutes. The resulting supernatant was added to the first supernatant and spun down at 10,000xg for 10 minutes.

The DNA was then extracted using the PowerLyzer® PowerSoil® DNA Isolation Kit as instructed by the manufacturer (MO BIO Laboratories, Carlsbad, CA), except that the tubes were vortexed horizontally for 20 minutes on the VWR VX-2500 Multitube

Vortexer (VWR, Radnor, PA) rather than the instructed 10 minutes on the MO BIO

Vortex Adapter. DNA concentrations were measured in triplicate using a Qubit® 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA) with dsDNA BR reagents

according to manufacturer’s instructions. Genomic DNA samples were stored at −20°C until further use.

3.2.4 16S rDNA Microbial Community Analysis

Primers were designed to target the V3 and V4 regions of 16S rDNA. This set of primers was designed based on the primer sets in Takahashi et al. (Takahashi et al. 2014) which target all prokaryotes including archaeal species, a group of microorganisms rarely included in fish microbiome analysis (Ni et al. 2014). The forward primer sequence was

5’-CCTACGGGNBGCASCAG -3’ and the reverse primer was 5’-

GACTACNVGGGTATCTAATCC-3’. DNA was amplified in a one-step 27-cycle PCR using the 5 PRIME Hot MasterMix (Qiagen, Valencia, CA). PCR conditions used for this amplification consisted of the following: 94°C for 3 minutes, 27 cycles of 94°C for

30 seconds; 53°C for 40 seconds and 72°C for 1 minutes; and a final elongation step at

72°C for 5 minutes. Following PCR, all amplicon products were purified using

Agencourt® AMPure XP PCR purification (Agencourt Bioscience Corporation, Beverly,

MA, USA). Sequences were dual-indexed using 20 (12 forward, 8 reverse) distinct indices. Finally, samples were cleaned up once more using Agencourt® AMPure XP beads and quantified. Samples were then pooled to construct the sequencing library, and sequenced by Illumina Miseq using V3 chemistry to generate pair-ended 2 × 300 paired end reads (Illumina, San Diego, CA).

3.2.5 Illumina Miseq Sequencing Data Analysis

The raw mate-paired fastq files were quality-filtered, demultiplexed, and analyzed using the Illumina BaseSpace application and the 16S Metagenomic Application

(Illumina, San Diego, CA). Briefly, the reads are classified against the GreenGenes database (May 2013 release) for taxonomic assignment and sorted into Operational

Taxonomic Units (OTUs). The BaseSpace application uses a 99.65% and 98.24% similarity threshold for genus and species level classifications, respectively (Illumina

Support, 2014). Data handling and statistical analyses were performed using R 3.1.3,

GraphPad Prism (GraphPad Software, La Jolla, CA), and Paleontological Statistics

Software Package (PAST) (Hammer, Harper, and Ryan 2001).

3.2.6 Diversity Analysis

Using the Individual Rarefaction setting in PAST, rarefaction curves with a 95% confidence interval were obtained and showed that species saturation was reached for all samples. The samples in PAST were then subjected to SHE analysis which helps to evaluate community structure (Buzas, Hayek, and Culver 2007). This method examines the distribution of cumulative sample values of species richness (S), the information function (H) and evenness (E) with increasing number of individuals (N). OTU data were visualized using Bray-Curtis similarities in non-metric dimensional scaling (NMDS) plots. In addition, data were analyzed with multivariate statistical methods to compare the prokaryotic communities from the different sampling locations. The effect of PAH-

gradient exposure on the microbial communities was tested with the use of the nonparametric analysis of similarity (ANOSIM) (Clark 1993). Individual differences in taxa were analyzed by two-way analysis of variance (ANOVA) (St and Wold 1989) with

Tukey’s multiple comparison test on the average of the triplicate samples. Results were considered significant when p < 0.05 unless otherwise indicated.

3.2.7 Vikodak Functional Analysis

Vikodak (Nagpal, Haque, and Mande 2016) was used to predict metabolite composition based on community composition. In addition, amplicon data was analyzed using the same program. Vikodak enables inference of functional potential for microbial communities from 16S metagenomic datasets (Nagpal, Haque, and Mande 2016). This package provides predictions on which pathways may be affected. Vikodak was chosen over PICRUSt due to its known efficiency in correctly inferring the functional potential of environments that are known to be physiologically diverse (Akutagawa et al.).

3.3 Results

3.3.1 Metabolomic Analysis

The metabolites with the highest variable importance of projection (VIP) scores and therefore highest potential impact on the host are shown in Table 1. In general, the acylcarnitines, amino acids, and glycerophospholipids show the most significant change.

A more in depth description of specific impacts on each class of metabolites is provided below.

Acylcarnitines Amino Acids C10:2 1.36 Alanine 1.46 C14 1.42 Arginine 1.41 C14:1-OH 1.51 Asparagine 1.54 C14:2-OH 1.48 Aspartic Acid 1.31 C16:1-OH 1.53 Glutamine 1.38 C16:2-OH 1.14 Glutamic Acid 1.33 C3 1.5 Histidine 1.44 C3:1 1.56 Isoleuicine 1.49 C6 (C4:1-DC) 1.31 Leucine 1.48 C9 1.54 Lysine 1.45 Biogenic Amines Metionine 1.47 Carnosine 1.27 Phenyalanine 1.44 Creatinine 1.27 Proline 1.32 Glycerophospholipids Serine 1.47 lysoPC a C18:1 1.48 Threonine 1.48 lysoPC a C20:3 1.38 Tryptophan 1.45 PC aa C32:0 1.27 Tyrosine 1.47 PC aa C32:1 1.31 Valine 1.48 PC aa C32:2 1.47 Essential AA 1.48 PC aa C32:3 1.52 Non essential AA 1.43 PC aa C34:3 1.35 Glucogenic AA 1.38 PC aa C36:6 1.26 Sphingolipids PC ae C36:0 1.45 SM C18:0 1.34 PC ae C38:0 1.26 SM C24:1 1.32 PC ae C38:1 1.3

3.3.1.1 Amino Acids

AAs, a major component in the diet and anabolism of proteins that can regulate energy and protein homeostasis (Dai, Wu, and Zhu 2011), were found to be the class of metabolites with the largest VIP scores suggesting that their synthesis was the most affected by differential pollutant loads between sampling sites. Overall, the AAs were found to be higher in total concentrations and a majority of the identified AAs exhibited significant difference in concentration between the KC and Republic gut samples (Figure

8). The AA concentrations were found to be higher for 19 out of 21 AAs in the KC samples when compared to the Republic samples. The two AAs that were not significantly different between sampling locations were citrulline (Cit) and ornithine

(Orn) (p=0.66 and 0.05, respectively). Cit and Orn are both in the urea cycle. Orn is also a precursor for Cit, and arginine (Arg), an AA which can be broken down into Cit and is known to be related to Orn synthesis. Essential AAs, or the total AAs that cannot be synthesized de novo were significantly lower in Republic gut samples (p<10-5). Using

Bray-Curtis distances, overall AA data shows distinct clustering between sampling locations. Shifts in AAs between sampling sites were confirmed by ANOSIM (9,999 permutations).

Amino Acids Rep Citrulline Kings Ornithine Tryptophan Arginine Histidine Asparagine Phenylalanine Methionine Proline Aspartic acid Tyrosine Threonine Glycine Serine Isoleucine Glutamic acid Valine Alanine Lysine Glutamine Leucine 1 10 100 1000 10000 100000 Concentration (µM)

Figure 8: Quantification data of identified amino acid metabolites (uM). Error bars represented standard deviation.

3.3.1.2 Sphingolipids, Acylcarnitines, and Glycerophospholipids

The sphingolipid metabolite profiles show some differences in concentration between the subpopulations. Of the 15 sphingolipid metabolites, 5 were significantly higher (p<0.05) in Republic samples and included SM (OH) C22:1, SM (OH) C24:1, SM

C18:0, SM C24:1, and SM C26:1. Sphingolipids, also known as glycosylceramides, play structural roles in cellular membranes (Bartke and Hannun 2009) and have also been shown to be involved in numerous signaling cascades (Spiegel and Milstien 2002). 44

Twenty two of the 40 acylcarnitines (~55%) were significantly higher in KC samples (p values ranging from 0.002 to 1.2x10-13) while two (C5 and C5:1-DC; p<0.003 and p<0.015, respectively) were higher in the Republic gut samples. Acylcarnitines have been shown to play a critical metabolic function as they are involved in the transfer of fatty acids into the mitochondria for β-oxidation (Reuter and Evans 2012) and may be a metabolic biomarker for renal impairment (Breit and Weinberger 2016). Finally, of the

90 quantified glycerophospholipids, 19 (~21%) were significantly higher in KC gut samples while 30 (~33%) were found to be higher in Republic gut samples.

Glycerophospholipids (a.k.a, phosphoglycerides) are a form of phospholipids with a glycerol-base that has been shown to interact with important pathways such as cholesterol regulation (Schmid, Schmid, and Natarajan 1990) and neurological function (Farooqui,

Horrocks, and Farooqui 2000). These classes of metabolites were the only ones that showed an overall trend of being significantly higher in Republic samples.

3.3.1.3 Custom Metabolic Indicators

Thirty-four of the 81 custom metabolic indicators measured using the Absolute

IDQ showed disruption in the Republic samples suggesting impacts on the metabolism in killifish at that location. In particular, C2+C3/C0, an indicator of overall β-oxidation, was twice as high in KC samples (p=0.003) suggesting differences in energy generation. In addition, there were significant decreases in Republic samples in the

Spermidine/Putrescine and Spermine/Spermidine (p<0.001) ratios. Both of these ratios

are related to various metabolic functions within organisms such as mice (Pegg 2008,

2014). Finally, total sugars, grouped as hexoses in the Biocrates analysis, were higher in

KC guts (p<10-7). Taken together, these results indicate clear differences in metabolic capabilities between locations. This is noteworthy in relating back to cost of resistance previously seen in the ER killifish.

3.3.2 Microbiome Analysis

3.3.2.1 Prokaryotic Community Analysis

In aggregate, a total of 2,064 unique OTUs were identified at the KC and

Republic sampling sites. Two archaeal phyla (Crenarchaeota and Euryarchaeota) and 25 bacterial phyla (majority from Proteobacteria, Actinobacteria, Firmicutes, and

Bacteroidetes) are represented. Approximately 50-70% of the filtered reads were sorted to at least the Genus level. An average of 981.5 ± 75.5 and 913.8 ± 28.9 OTUs were identified in the Republic guts and KC guts, respectively. These values are similar to those of the sediment, which had an average of 984.75 ± 96.4 identified OTUs in

Republic Sediment and 1036.7 ± 59.6 OTUs in the KC Sediment. These differences in

OTUs were not significantly different (p>0.05). Overall, OTUs were identified within 28 phyla, 68 classes, 140 orders, 305 families, and 1086 genera. The dominant phyla, in order, were Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Chloroflexi,

Euryarchaeota, Acidobacteria, and Verrucomicrobia.

Other Actinobacteria Alphaproteobacteria Deltaproteobacteria Gammaproteobacteria Betaproteobacteria Clostridia Anaerolineae Bacilli King's Creek Republic Flavobacteriia Guts Guts Methanomicrobia Sphingobacteriia 30 25 20 15 10 5 5 10 15 20 25 30

King's Creek Republic Sediment Sediment A 30 25 20 15 10 5 5 10 15 20 25 30 Relative Abundance (%)

47 1.0

0.5 King's Creek

King's Creek King's Creek 1.0 SampleType a Gut 0.0 a Sediment King'sKing'sKing's CreekCreek Creek Republic

Axis.2 [33.5%] Axis.2 RepublicRepublic 0.5 King's Creek Republic RepublicRepublic KC King's Creek King's Creek SampleType a Gut -0.5 0.0 KC a Sediment King'sKing's Creek Creek RepublicREP

Axis.2 [33.5%] Axis.2 RepublicRepublic

Republic RepublicRepublic REP -0.5

-1.0

-1.0 -0.25 0.00 0.25 0.50 Axis.1 [53%] -0.25 0.00 0.25 B 0.50 Axis.1 [53%] B B

Results from the SHE analysis performed in PAST are shown in Table 2.

Republic guts had an average species richness (S) value of 7.05, a Shannon-Weiner Index

(H) value of 4.74, and evenness (E) of 0.3. KC guts had an average S value of 7.56, an H

value of 5.36, and E value of 0.26. Republic sediment had an average S value of 7.66, an

H value of 5.39, and E of 0.11. KC sediment has had an S value of 7.74, an H value of 48

5.45, and E value of 0.14. From there, in order to compare differences in diversity between samples, a series of Diversity t-tests were conducted. Overall, Republic guts were significantly lower in diversity when compared to KC guts (p<0.00001) and

Republic sediments were significantly lower in diversity when compared to KC sediments (p<0.0001) (Figure 9A). The highest average number of OTUs was found in the KC sediment samples, which also have the largest average Species Richness and

Shannon Diversity Index.

Table 2: SHE analysis results. S is reported as the natural log of S [ln(S)].

ln(S) H E

Republic Gut 7.05 4.74 0.30

KC Gut 7.56 5.36 0.26

Republic Sediment 7.66 5.40 0.11

KC Sediment 7.74 5.45 0.14

Based on Kruskal-Wallis, or one-way ANOVA based on rank, and a post-hoc

Dunn analysis, which is a non-parametric method to measure differences between groups and then analyze differences in specific sample pairs (Vargha and Delaney 1998), respectively, overall gut microbiota composition was significantly different between the

KC and Republic gut samples as well as between Republic gut and Republic Sediment

(p<0.0001). There was no significant difference in composition between KC guts and KC

sediment (p>0.05). An NMDS using Bray-Curtis distances exhibited distinct grouping of gut samples (stress: 0.01106) (Figure 9B). To confirm distinct shifts, a two-way

ANOSIM with 9,999 permutations (N) was performed and shows significant shifts due to the sampling location (gut vs. sediment, p=0.008) and PAH-exposure (Republic vs. KC, p=0.009). Overall, chronically PAH-exposed killifish gut bacterial communities were significantly different from those of the unexposed gut communities. In general, at a high taxonomic level, the Republic guts see increases in Actinobacteria, Alphaproteobacteria, and Gammaproteobacteria with decreases in Betaproteobacteria and overall diversity when compared to KC guts.

3.3.2.2 Identification of Potentially Responsive Taxa

In an attempt to identify potential PAH degraders, rather than examine overall differences in the communities as a whole, individual differences in taxa were analyzed.

The OTUs with significant differences (p<0.05) between gut samples or between the gut- sediment locations (i.e., KC vs. Republic gut, KC gut vs. KC Sediment, and Republic gut vs. Republic sediment) were then determined using Tukey’s multiple comparisons. Of the

OTUs identified to the species level, a total of 216 species were different between at least one of the comparisons. These 213 OTUs were then individually examined. Sixty-four were different between Republic guts and KC guts; 134 were different Republic sediment between KC sediment; 9 were different between KC guts and KC sediment; and 176 were different between Republic guts and Republic sediment. Six were Archaeal, from the

genus Methanogenium, Methanosaeta, and Methanobacterium. The remaining taxa were from various bacterial genera. The most abundant, in order, were: Pseudomonas,

Corynebacterium, Lactobacillus, Streptococcus, Sphingomonas, Mycobacterium,

Loktanella, and Bacillus. The remaining genera, including Tolumonas and

Janthinobacterium and Runella, appeared less than four times.

3.3.2.3 Identification of Potentially Important Taxa

In order to further identify specific taxa that have a potential relationship to contamination and/or adaptation, the 213 OTUs were further examined. Most of the top responding taxa have been previously idenfied as PAH degraders including Pseudomonas

(Haritash and Kaushik 2009, Cao, Nagarajan, and Loh 2009), Corynebacterium

(Bouknight and Sadoff 1975), Sphingomonas (Haritash and Kaushik 2009, Johnsen,

Wick, and Harms 2005, Cao, Nagarajan, and Loh 2009), Mycobacterium (Haritash and

Kaushik 2009, Johnsen, Wick, and Harms 2005), and Bacillus (Haritash and Kaushik

2009). Bacillus cereus (Hunter et al. 2005, Cao, Nagarajan, and Loh 2009) and

Mycobacterium vanbaalenii (Kweon et al. 2010), both known to degrade multiple PAHs, and Desulfosarcina ovata, known to degrade xylene (Weelink, van Eekert, and Stams

2010), were higher in Republic guts and sediment than their KC counterparts.

Microbacterium esteraromaticum, Mycobacterium flavescens, Sphingomonas haloaromaticamans, and Stenotrophomonas acidaminiphila have been previously shown to degrade aromatics (Balkwill et al. 1997, Mangwani et al. 2014, Gauthier et al. 2003,

Dean-Ross and Cerniglia 1996, Dean-Ross, Moody, and Cerniglia 2002), Pseudomonas syringae has been identified in a PAH degrading consortia (Shen, Stehmeier, and

Voordouw 1998, Aburto and Peimbert 2011), and Leucobacter komagatae is known to produce biosurfactants that may help increase the bioavailability of hydrocarbons

(Saimmai, Sobhon, and Maneerat 2012). All of the bacteria were identified to be higher in Republic sediments when compared to Republic guts and KC sediments.

Bacillus megaterium abundances were higher in KC guts and KC sediment than when compared to their Republic counterparts. These bacteria have previously been found intestinally, and are known to produce several amino acid dehydrogenases

(Sahayaraj and Balasubramanian 2016). These bacteria are thought to be important in tryptophan metabolism, an AA that is decreased in Republic guts compared to KC.

Another important class of bacteria that is abundant in KC intestines, and

Republic and KC sediment, but absent in Republic fish guts are Sphingobacteria.

Republic guts have 20-fold lower levels (0.3% in Republic guts vs. 6% in KC Guts, p=0.01) in the class Sphingobacteria. This class contains several genera including

Sphingobacterium, which are known to be associated with the production and usage of sphingolipids (Naka et al. 2003).

Known pathogens were also identified, including many species of Staphylococcus and Streptococcus. Staphylococcus warneri, Streptococcus dysgalactiae and

Streptococcus agalactia are known to cause infections in fish (Bowater et al. 2012, Evans et al. 2009, Nomoto et al. 2004, Gil et al. 2000) and were found to be higher in Republic

guts than Republic sediment except for S. warneri that was higher in Republic guts than

KC guts. Dietzia maris, which is considered pathogenic and has been found intestinally in fish (Koerner, Goodfellow, and Jones 2009), was also more abundant in Republic guts than Republic sediment and KC guts.

3.3.2.4 Predicting Functional Changes using Vikodak

In order to see if the changes in microbial community were associated with predicted functional changes, data were analyzed using Vikodak (Nagpal, Haque, and

Mande 2016), an online tool that offers a framework for using 16S metagenomic datasets to infer functional potential. The results predict reduction in AA and sphingolipid metabolism and biosynthesis (Figure 10). This indicates that there is a predicted down regulation in these pathways in the Republic guts. This is not surprising, as the concentrations of AAs are significantly decreased in Republic guts, as discussed earlier in this section.

REPUBLIC KING’S CREEK

Figure 10: Vikodak generated pathway analysis using the sequencing data.

Additional correlation and diversity information can be found in the Chapter 4 Appendix.

3.4 Discussion

This study demonstrates that long-term chemical pollutant exposure and adaptive responses to pollutants cause shifts in intestinal microbiota and metabolites, both of which exhibit a complex relationship with changing the host physiology. Though it is known that xenobiotics can disrupt the gut microbiome (Lu, Mahbub, and Fox 2015), to our knowledge, this is the first study that examines the gut metabolome and microbiome

simultaneously which the goal of establishing associations that have evolved in response to their adaptive resistance to PAH pollution.

Metabolites have previously been shown to be indicators of organismal health in various hosts and may have a bi-directional relationship with the host’s gut microbial community structure (Sonnenburg and Bäckhed 2016, Benedetti et al. 2016). In the present study, we detected lower levels of AAs in Republic guts, suggesting potential differences in protein synthesis, consumption patterns, variation in intestinal absorption, or pathway regulation and secretion of amino acids (Turroni et al. 2016), (Dai, Wu, and

Zhu 2011). We also detected shifts in the gut microbial communities in Republic guts, and indigenous gut microbiota play an important role in degradation of proteins into smaller peptides and AAs through proteases and peptidases (Macfarlane, Cummings, and

Allison 1986). When examined together, these shifts may explain some of the discrepancies in metabolic phenotype in PAH-resistant subpopulations in the ER (Meyer and Di Giulio 2003). AA homeostasis is known to be a major regulator of metabolism

(Mardinoglu et al. 2015) and AA-derived molecules produced by intestinal bacteria affect host health by regulating either host immunity and cell function or microbial composition and associated metabolism (Dai, Wu, and Zhu 2011). It is also known that gut microbes regulate the host glycine consumption, overall lipid metabolism, and play a role in metabolic disorders (Mardinoglu et al. 2015).

One interesting aspect of the reduced AA concentrations is the relationship to killifish immunity, particularly with regard to the aryl hydrocarbon receptor (AHR)

pathways. ER subpopulations have modified AHR protein, reduced AHR inducibility with AHR agonists and altered AHR signaling cascade (Meyer and Di Giulio 2003,

Meyer, Wassenberg, et al. 2003, Clark, Bone, and Di Giulio 2014a) (Clark, Bone, and Di

Giulio 2014b, Reid et al. 2016). Of particular relevance to the present study is the differential regulation of the AA tryptophan (Trp) that is decreased in Republic fish guts.

Tryptophan and Kynurenine 4 Kings Creek Republic

3 * M] µ

2 Concentration [

1 *

0 Trp Kynurenine

Trp has been implicated in at least five pathways involved with AHR activation

(Quintana and Sherr 2013). Trp is synthesized via the gut microbiome and is known to modulate the central nervous system inflammation, brain cell activity, and the differentiation of regularly T cells through the AHR (Rothhammer et al. 2016, Sridharan

et al. 2014). The activation of AHR by exogenous ligands such as Trp has important physiological effects (Quintana and Sherr 2013) and the reduced levels of Trp may be directly associated to altered AHR structure and activity. Overall, these data suggest complex associations between gut microbiota, AAs, the AHR pathway, and the adapted killifish. Furthermore, Trp is known to be metabolized through an aerobic catabolic pathway that is funneled through the intermediate catechol (Cao, Nagarajan, and Loh

2009). This same pathway is also used by a wide range of microorganisms for the degradation of the PAHs found at Republic including naphthalene and phenanthrene

(Clark, Bone, and Di Giulio 2014b). Thus, it is possible that the enzymes associated with the biodegradation of PAHs may also be up regulated at Republic, which in turn may also affect the metabolism of Trp. However, further analysis is required to verify if an association exists, as we did not measure gene expression or enzyme activity in the present study. In addition, B. megaterium, a bacterium with lower abundance in Republic fish gut samples compared to KC fish gut samples has previously been shown to use Trp as its sole carbon source (Bouknight and Sadoff 1975). It is unclear if the decreased levels of B. megaterium in Republic fish gut samples is a direct result of the overall shifts in AA concentrations, particularly Trp, but it is likely that the decreased Trp concentrations in Republic fish gut samples influenced the decreased abundance of B. megaterium.

Another prominent difference between Republic and KC fish is the reduced sphingolipid levels as well as significantly decreased abundance of Sphingobacterium in

Republic fish guts. Importantly, sphingolipid signaling have important biological outcomes, such as apoptosis and inflammation, and may allow some bacterial species to persist in the intestine and perform as symbionts (An et al. 2011). Sphingolipids are known to be largely of host origin (Heung, Luberto, and Del Poeta 2006) and a previous population genomic study showed that sphingolipid synthesis pathway is under selection in killifish with evolved-resistance to PAHs (Jayasundara et al, In review, 2017). While the precise relationship between sphingolipid metabolism and corresponding presence of

Sphingobacteria remains unclear, the data presented herein demonstrate a striking example of adaptive genomic changes in a vertebrate host modulating gut microbial composition.

The overall shifts in the microbiome hint at complex host and metabolic interactions. We initially hypothesized that the killifish gut microbial communities would be similar to the sediment because it has been hypothesized that killifish feed off of benthic invertebrates associated with plants found at the bottom of the river in close proximity to sediments (Lloyd et al. 2016). However, while the KC fish gut composition was largely similar to the KC sediment with only 9 identified species as different, the

Republic guts were significantly different than the Republic sediment with 176 species identified as different. These data suggest that factors other than dietary influence affect the microbiota colonized in the Republic fish gut. This difference may be a direct result of the increase in PAH-degrading species found in the Republic fish guts, such as B. cereus, M. vanbaalenii, M. esteraromaticum, M. flavescens, S. haloaromaticamans, S.

acidaminiphila, P. syringae, L. komagatae. Another explanation for this difference is that the heavy pollution conditions at the Republic site may have directly resulted in microbiome shifts and led to an increase in pathogens in the Republic fish guts as it is known that fish exposed to environmental stress, including chemical pollutants, are more susceptible to infection(Snieszko 1974). This hypothesis is further supported by the differences in Streptococcus or Lactobacillus strains between sampling sites and previous work showing that certain Streptococcus infections and lactic acid bacteria are known activators of the AHR(Vorderstrasse and Lawrence 2006, Head and Lawrence 2009,

Takamura et al. 2011). Therefore, the reduced AHR activation may leave the fish more susceptible to pathogenic infection, which may be partially demonstrated in the differential fish gut microbiomes between the two sites.

Finally, another aspect to consider concerning the contribution of the microbiome to PAH-resistance is the microbiome’s potential for rapid adaptation through horizontal gene transfer (HGT) events. It has been shown that the gut is an excellent environment for HGT to occur (Xu et al. 2007, Ley et al. 2008). As a result, some of the genes related to critical metabolic attributes, such as xenobiotic activation or metabolism, may be rapidly propagated (Hehemann et al. 2010, Smillie et al. 2011). The environmental selective pressure may force an adaptation in the prokaryotic gut community and eventually influence genetic and/or phenotypic changes in the fish host. This hypothesis would need to be further confirmed in follow up shotgun sequencing metagenomic studies.

In closing, this study revealed that the differences in exposure and adaptation are reflected in the gut prokaryotic communities structure and gut-associated metabolome.

Overall, we hypothesize that shifts in the microbiome and the metabolome are reflected in other physiological outcomes, though it remains unclear exactly how the microbiome and metabolome are associated. It is clear that in defining influences on the gut microbiome and metabolome, there is an opportunity to monitor ecological change, and, these data are critical to the development of models for disease susceptibility and adaptations (Ley, Peterson, and Gordon 2006).

4. A Framework for Approaching Precision Bioremediation

4.1 Introduction

Bioremediation is a treatment strategy that involves the removal of chemical pollutants through biological agents such as plants and microbes. When compared to physical-chemical treatment or dredging, bioremediation offers complete chemical pollutant destruction, reduced costs, and increased aesthetic and public acceptance(Azubuike, Chikere, and Okpokwasili 2016, Vidali 2001). Generally, bioremediation efficacy can be improved by inducing degradation via three main approaches: 1) allowing the native bacteria to degrade the pollutant (bioattenuation); 2) stimulating indigenous bacteria (biostimulation) (Margesin and Schinner 1997); or 3) seeding exogenous micro-organisms with the desired catabolic traits (bioaugmentation)

(Vogel 1996, Pepper et al. 2002, Iwamoto and Nasu 2001) . Biostimulation strategies may include either the addition of rate limiting nutrients and/or electron acceptors (e.g., nitrogen, carbon) or harnessing indigenous degraders to provide a more hospitable environment (Vidali 2001, Singh and Ward 2004). Bioaugmentation can be utilized in parallel with biostimulation, augmenting both the physico-chemical and microbiological environment simultaneously. While bioaugmentation can be effective, this approach often fails because the augmented exogenous microorganisms have difficultly surviving in the contaminated environment, as they tend to be unadapted for site conditions and unable to compete for resources with indigenous microbes (Thompson et al. 2005,

Mrozik and Piotrowska-Seget 2010, Bouchez et al. 2000). In addition, limited literature

exists concerning the efficacy of bioaugmentation at sites contaminated with multiple classes of chemicals, which may require the addition of a microbial cocktail to achieve complete biodegradation.

While bioremediation is commonly and preferably used when the mechanisms underlying pollutant biodegradation are well understood, such as in the case of chlorinated ethylenes (Aulenta, Majone, and Tandoi 2006, Hoelen and Reinhard 2004), it may not be the preferred treatment approach in other treatment scenarios because of the limited knowledge about the microorganisms capable of complete biodegradation. For instance, the bioaugmentation of polycyclic aromatic hydrocarbons (PAHs) and PAH- mixtures such as creosote, is still limited. Methods to improve the bioremediation of

PAHs are of particular interest due to their ubiquity, recalcitrance, and toxicity (Jacques et al. 2008, Teaf 2008). Several approaches has been proposed for the bioremediation of

PAHs, including using microorganisms adapted to elevated contaminant concentrations or those able to produce surfactants to increase PAHs bioavailability (Mrozik and

Piotrowska-Seget 2010, Forsyth, Tsao, and Bleam 1995, Gentry, Rensing, and Pepper

2004, Gast et al. 2003). However, these methods are still limited because incomplete information is available for strain/consortia selection, thus often resulting in incomplete or slow degradation (Thompson et al. 2005). A handful of bacterial strains have been characterized that are capable of mineralizing select PAHs such as anthracene (Chávez-

Gómez et al. 2003), benzo(a)pyrene (Zeng et al. 2013), naphthalene (Sota et al. 2006), fluorene, fluoranthene, and phenanthrene (Boldrin, Tiehm, and Fritzsche 1993). In

addition, environmental co-cultures and microbial communities have been shown to synergistically degrade PAHs (Wang et al. 2012, de Boer et al. 2005, Vila, Tauler, and

Grifoll 2015, Kotterman, Vis, and Field 1998, Thion et al. 2013). The enhanced co- culture degradation has been attributed to bacterial production of growth factors needed by other microbes, such as vitamins (Wang et al. 2012) as well as the complete degradation of small-molecule breakdown products that down-regulate enzymatic activity through a feedback loop (de Boer et al. 2005). Still our overall understanding of the

PAH-microbial interactions in complex communities remains limited and hampers the field deployment of bioremediation treatment strategies that target PAHs.

Due to recent metagenomic advances, in-depth investigations of the microbial diversity at contaminated sites may now be conducted. This opens up new avenues for identifying microbial taxa capable of carrying out specific biodegradative capabilities.

For instance, a potential way of addressing the shortcomings of bioaugmentation is through genetic bioaugmentation, an approach that relies on the well-studied mechanism of horizontal gene transfer (HGT) (Ikuma and Gunsch 2013, 2012a, Ikuma, Holzem, and

Gunsch 2012). HGT naturally and readily occurs when genetic adaptation is required for survival and this natural phenomenon could be useful for bioremediation to shift microbial communities in favor of degrading xenobiotics, persistent organic compounds, and emerging contaminants (Sørensen et al. 2005). Genetic bioaugmentation relies on targeting specific site prokaryotes identified through sequencing as recipients for catabolic plasmids and therefore requires knowledge about both the bioaugmented strain

and the existing site community (Ikuma and Gunsch 2012a). Metagenomic approaches show great potential for aiding in the implementation and optimization of genetic bioaugmentation and, more generally, for better describing environmental microbiomes for precision bioremediation implementation (Adrian and Löffler 2016). Thus, in this study, we aimed to associate contaminants and specific microbes in creosote- contaminated soil obtained from the Holcomb Creosote Superfund Site in western North

Carolina. Changes in both bacterial and archaeal communities were monitored with the goal of identifying taxa to target for biostimulation and/or bioaugmentation. To the authors’ knowledge, this is the first study to use high-throughput sequencing to identify taxa that may be useful for bioremediation at PAH contaminated sites.

4.2 Methods and Materials

4.2.1 Field Sampling

Soil samples were collected in May 2015 at the Holcomb Creosote Co. Superfund Site in Yadkinville, NC. This site is approximately 80-acres and previously operated as a wood- treatment facility. Open drip-pads and improper creosote waste storage resulted in 3-acres of heavily stained soil at the site (EPA, 2011). The sampling site was located directly behind and downhill from the former wood-treatment process area and was selected due to the likelihood of high-prolonged exposure to high levels of creosote exposure and PAH-contamination in this area. Due to emergency action by RCRA in 2011, the top layer of PAH-contaminated soils at the site, approximately 1 to 2 feet in depth, was excavated and the site was backfilled. Mulch was also placed on the area and sowed with K31 fescue and rye grass. Multiple soil samples were collected at depths ranging from 18 to 24 inches at five locations (Figure 12, Table 3). From each location, four replicate soil grabs ranging from 0.75 to 1 L were collected with a stainless-steel auger kit and placed in a glass container. The samples were then homogenized using a sterile stainless-steel trowel and aliquots were 64

place into 50 mL muffled amber vials for chemical analysis (Sigma Aldrich, St. Louis,

MO), 50 mL Falcon tubes (VWR, Radnor, PA) tubes for genomic analysis, and the remaining soil was stored in sterile glass containers. The auger buckets were decontaminated with DI water and ethanol before each sampling and replaced between sampling locations. Samples were placed on ice while in transport to the laboratory where they were stored at -80°C prior to DNA extraction.

Table 3: Collection data for each of the five sampling sites at Holcomb Creosote Co. Superfund Site.

Sampling Site GPS Approximate Soil pH Location Sampling Depth [inches] Location 1 +36.15796, 24 6.0 Around 60-70 feet behind Distillation -80.67520 Evaporator/Condenser Shed; Mid-section of downhill slope Location 2 +36.15791, 21 6.6 Around 60-70 feet behind Distillation -80.67519 Evaporator/Condenser Shed; Mid-section of downhill slope; Location 3 24 6.6 Around 60-70 feet behind Distillation +36.15788, Evaporator/Condenser Shed; -80.67516 Mid-section of downhill slope; Location 4 21 6.5 Around 55-65 feet behind Distillation +36.15789, Evaporator/Condenser Shed; -80.67519 Mid-section of downhill slope; Mid-point of sites 2 and 3 Location 5 +36.15789, 28 6.3 Around 70-75 feet behind Distillation -80.67516 Evaporator/Condenser Shed;

1 2

4 5

4.2.2 Chemical Analysis

Samples were analyzed for PAHs by the Duke University Superfund Center

Analytical Chemistry Core (ACC). Quantification of PAHs in the site soil samples was done according to methods outlined in Clark et al. (Clark et al. 2013). In addition, acid- extractable trace element contents (Cr, Ni, Cu, Zn, As, Ag, Cd, and Pb) were determined via microwave-assisted digestion of the samples in concentrated nitric acid. The extracts were then diluted in a mixture of 2% HNO3/0.5% HCl (v/v) and analyzed using an inductively coupled plasma mass spectrometer (ICP-MS). Acid extraction efficiency was verified by simultaneous extraction of a certified standard reference material (NIST SRM

2709a San Joaquin soil). In addition, Holcomb soil carbon and nitrogen contents were quantified by the Duke Environmental Stable Isotope Laboratory using a Carlo Erba

Elemental Analyzer (Thermo Fisher Scientific, Waltham, MA). Prior to being loaded onto the machine, soil samples were dried for 60 hours at 55ºC and ground using an autoclaved mortar and pestle.

4.2.3 DNA extraction

The soil samples were gently brought to room temperature, weighed out to approximately 0.3 g, and placed in 1.5 mL microcentrifuge tubes (VWR, Radnor, PA).

Pseudo-triplicate samples were taken from triplicate sample vials from each sampling location. Prior to DNA extraction, samples underwent a clay-wash to maximize DNA yield using the technique in Pietramellara et al. (Pietramellara et al. 2007). Briefly, the

soil was washed and thoroughly mixed with 10 mM Tris-HCl (pH 8) in order to buffer the soil matrix and protect from DNAse activity. The sample was then pelleted at 10,000x g for 60 seconds. This step was repeated two more times. The soil pellet was then washed once with 100 mM NaCl, pelleted, and washed once more with 100 mM NaPO3. The sample was then washed twice with a desorptive solution (2% SDS; 17 mM lactic acid, 3 mM KH2PO4, 27 mM Na2HPO4, 0.23 mM MgSO4, 11 mM NH4Cl, 19 µM CaCl2, 0.5

µM FeSO4, 86 mM sodium pyrophosphate, 57 mM EDTA). The final pellet was washed with 1X PBS. DNA was then extracted from the final soil pellet using the

PowerLyzer® PowerSoil® DNA Isolation Kit (MO BIO Laboratories, Carlsbad, CA).

DNA was extracted as instructed by the manufacturer, except that the tubes were vortexed horizontally for 20 minutes on the VWR VX-2500 Multitube Vortexer (VWR,

Radnor, PA). The eluted DNA concentrations were measured in triplicate using a Qubit®

2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA) with dsDNA BR reagents according to manufacturer’s instructions. Genomic DNA samples were stored at −20°C until further use.

4.2.4 16S rDNA Sequencing

A set of primers were designed based on the previously published primer sets by

Takahashi et al. (Takahashi et al. 2014). The forward primer sequence was 5’-

CCTACGGGNBGCASCAG -3’ and the reverse primer sequence was 5’-

GACTACNVGGGTATCTAATCC-3’. These primers were designed to span the V3-V4

regions of the 16S rRNA gene and were selected because they have been proven to successfully target both bacteria and archaea (Takahashi et al. 2014). DNA was amplified in a one-step 27-cycle PCR using the 5 PRIME Hot MasterMix (Qiagen, Valencia, CA).

PCR conditions used for this amplification consisted of: 94°C for 3 minute, 27 cycles of

94°C for 30 second; 53°C for 40 sec and 72°C for 1 min; and a final elongation step at

72°C for 5 min. Following PCR, all amplicon products were purified using Agencourt

Ampure beads (Agencourt Bioscience Corporation, Beverly, MA, USA). The sample library was then prepared according to the “Illumina MiSeq 16S Metagenomic

Sequencing Library Preparation” workflow (Illumina, San Diego, CA.). Briefly, samples were dual-indexed with barcoded primers based on Nextera XT N7XX and N5XX index sequences and chemistry. Samples were normalized, pooled, and run on a 2 x 300 bp paired-end MiSeq platform using V3 chemistry at Duke’s Center for Genomic and

Computational Biology (GCB). Sequencing data were uploaded to NCBI (STUDY:

PRJNA385836 (SRP106892)).

4.2.5 Illumina Miseq Sequencing Data Analysis

The raw mate-paired fastq files were quality-filtered, demultiplexed and analyzed using the Illumina BaseSpace App and 16S Metagenomic Analysis Pipeline (Hosokawa et al. 2006). Briefly, the reads were classified against the GreenGenes database for taxonomic assignment and sorted into Operational Taxonomic Units (OTUs). Using the

Individual Rarefaction setting in PastProject (Hammer, Harper, and Ryan 2001), a

rarefaction curve with a 95% confidence interval was obtained. The rarefaction analysis showed that species saturation was reached for all except one sample (second replicate from Holcomb site 2), which was discarded and not included in subsequent analyses.

4.2.6 Spearman Rank Correlation Analysis

To determine the relationships between chemicals and taxa, a series of Spearman

Rank Correlations were performed to target potential bioaugmentation and genetic bioaugmentation recipient strains as well as potential biostimulation targets. To do so, a relative abundances of identified taxa (at the genus taxonomic level) were correlated with concentrations of all chemical pollutants using the corrplots package in R (Wei 2013).

This corollary data illustrates trends in exposure response and was further analyzed for strongly impacted taxa.

4.2.7 Statistical Analysis

Sample diversities were assessed by comparing the α-diversities and a non-metric dimensional scaling (NMDS) plot using Bray-Curtis similarities was generated. The effect of PAH-gradient exposure on the microbial communities was tested with the use of the nonparametric analysis of similarity (ANOSIM), which indicates the degree of separation between groups due to environmental impacts (Ramette 2007, Clark 1993).

The overall site differences in PAHs and metal concentrations were analyzed by a multivariate, non-parametric analysis of variance (ANOVA) which tests for significant

differences between the variable means of multiple groups (Ramette 2007). Individual differences in taxa were analyzed by a one-way ANOVA with Tukey’s multiple comparison tests on the average of the triplicate samples (Tukey 1949). Results were considered significant when p < 0.05 unless otherwise indicated. Data handling and statistical analyses were performed using GraphPad Prism (Prism 1994) and Past Project

(Hammer, Harper, and Ryan 2001).

4.3 Results and Discussion

4.3.1 Chemical Characterization

The total PAH concentrations at the sample locations range from 322.2 ± 47.6 ng/g dry weight at Location 5 to 26,196.6 ± 4,013.8 ng/g dry weight at Location 3 (

Table 4). Due to its low relative PAH concentration, Location 5 was treated as the reference site for the present study. When compared to other PAH contaminated sites, the

Holcomb site falls in the moderately contaminated range and is comparable to some creosote-contaminated sites along the Elizabeth River (Clark et al. 2013) as well as previously studied railway junctions (Wiłkomirski et al. 2011). The Holcomb Creosote

Superfund site is located in a relatively rural area where one would expect background concentrations of PAHs to be quite low due to urbanization. Yet, Holcomb soil PAH levels, even at our reference sampling location, were slightly higher than measured PAH levels in urban soils which generally tend to have PAH concentrations below 210 ng/g

(Wang et al. 2004, Srogi 2007, Wilson and Jones 1993).

Table 4: Concentrations of all identified PAHs in Holcomb Creosote Co. Superfund site soil samples. “Loc 1-5” indicates the sampling location and average concentrations are reported in ng/g with standard deviation (‘SD’). Naphthalene 1-methylnaphthalene Benzo(e)pyrene Average SD N Average SD N Average SD N Loc 1 12.9 2.12 3 10.0 4.5 3 886.34 303.9 3 Loc 2 4.0 1.9 3 2.2 1.3 3 215.6 63.6 3 Loc 3 36.8 6.0 3 15.4 4.4 3 1291.7 259.5 3 Loc 4 14.9 8.8 3 4.3 0.5 3 471.8 73.5 3 Loc 5 0 0 3 1.2 0 3 24.6 1.5 3

2,6-dimethyl- Acenaphthylene naphthalene Benzo(a)pyrene Average SD N Average SD N Average SD N Loc 1 0 0 3 964.2 32.6 3 1016.9 210.2 3 Loc 2 12.1 5.0 3 128.0 15.6 3 185.1 45.4 3 Loc 3 0 0 3 570.1 47.3 3 1022.1 166.7 3 Loc 4 0 0 3 189.9 18.0 3 429.9 68.2 3 Loc 5 0 0 3 17.0 4.0 3 9.4 2.6 3

Acenaphthene Dibenzofuran Perylene Average SD N Average SD N Average SD N Loc 1 76.5 10.7 3 29.9 1.8 3 317.8 86.8 3 Loc 2 10.5 1.1 3 0 0 3 67.8 16.0 3 Loc 3 48.6 5.0 3 72.9 9.0 3 332.3 45.3 3 Loc 4 30.7 3.9 3 17.2 4.2 3 141.4 21.0 3 Loc 5 0 0 3 0 0 3 0 0 3

Fluorene Dibenzothiophene 3-methylcholanthrene

Average SD N Average SD N Average SD N Loc 1 99.5 29.1 3 14.0 3.94 3 23.0 11.7 3 Loc 2 14.0 2.6 3 2.9 0.8 3 4.3 2.9 2 Loc 3 124.1 20.4 3 37.6 2.7 3 28.1 3.4 3 Loc 4 46.5 3.1 3 9.6 1.1 3 13.3 1.3 3 Loc 5 0 0 3 0 0 3 0 0 3

Phenanthrene Anthracene Dibenzo(a,j)-- anthracene Average SD N Average SD N Average SD N Loc 1 148.2 47.3 3 126.9 26.4 3 126.9 26.4 3 Loc 2 119.2 133.4 3 22.5 6.8 3 22.5 6.8 2 Loc 3 532.4 67.9 3 240.0 45.9 3 240.0 45.9 3 Loc 4 117.6 15.6 3 59.5 10.2 3 59.5 10.2 3 Loc 5 0 0 3 0 0 3 0 0 3

Carbazole Dibenzo(a,l)pyrene Benzo(g,h,i)perylene Average SD N Average SD N Average SD N Loc 1 70.1 19.2 3 0 0 3 359.2 91.8 3 Loc 2 24.3 5.03 2 0 0 3 76.3 17. 2 Loc 3 504.9 47.5 3 3.2 0.3 3 677.1 184.7 3 Loc 4 97.4 12.8 3 0 0 3 197.4 25.1 3 Loc 5 5.6 0.8 3 0 0 3 7.08 1.0 3

2-methyl- Indeno(1,2,3-c,d)pyrene 1-methyl- phenanthrene phenanthrene Average SD N Average SD N Average SD N Loc 1 19.3 5.1 3 548.4 79.2 3 100.3 16.4 3 74

Loc 2 20.3 23.1 2 114.0 27.9 3 25.8 7.1 2 Loc 3 84.9 9.6 3 949.6 146.4 3 245.5 61.4 3 Loc 4 13.7 3.4 3 265.7 30.3 3 70.7 7.9 3 Loc 5 19.3 5.2 3 548.4 79.3 3 100.4 16.4 3

Fluoranthene Benzo(a)fluoranthene Pyrene Average SD N Average SD N Average SD N Loc 1 1258.5 230.7 3 263.0 84.7 3 1441.2 349.6 3 Loc 2 291.9 18.9 2 56.1 11.1 3 626.6 134.0 2 Loc 3 3295.1 617.8 3 331.2 42.6 3 3545.6 436.1 3 Loc 4 1206.5 183.2 3 119.5 16.1 3 1435.1 204.5 3 Loc 5 24.8 0.6 3 3.2 0.14 3 29.1 7.1 3

1,2-benzofluorene Benzo(k)fluoranthene Picene Average SD N Average SD N Average SD N Loc 1 751.9 201.6 3 760.9 240.9 3 74.7 15.6 3 Loc 2 224.9 58.9 2 142.0 17.9 3 14.8 5.5 2 Loc 3 990.3 206.6 3 1063.1 211.1 3 136.6 21.9 3 Loc 4 546.1 63.0 3 417.0 69.8 3 34.3 3.6 3 Loc 5 8.4 1.8 3 16.4 1.8 3 0 0 3

3,4-benzofluorene Benzo(c)phenanthrene Benzo(b)fluoranthene Average SD N Average SD N Average SD N Loc 1 145.0 25.5 3 230.7 64.9 3 1966.7 699.1 3 Loc 2 16.6 4.0 2 46.3 12.9 3 565.8 0 2 Loc 3 176.2 32.8 3 441.9 66.2 3 2940.1 420.1 3 Loc 4 90.8 1.5 3 184.8 18.3 3 1095.1 173.7 3

Loc 5 0 0 3 4.5 3.2 3 47.0 2.7 3

Retene 1,2-Benzanthracene Chrysene Average SD N Average SD N Average SD N Loc 1 25.2 8.9 3 1398.3 395.9 3 1549.6 250.6 3 Loc 2 8.9 5.2 2 227.8 77.8 3 327.9 77.5 2 Loc 3 31.6 1.0 3 1455.5 202.0 3 2448.7 279.4 3 Loc 4 10.6 3.0 3 998.1 138.1 3 1077.4 150.1 3 Loc 5 9.9 0 3 24.7 7.4 3 30.6 5.7 3

Dibenzo(a,h)anthrac Benzo(b)chrysene ene Average SD N Average SD N Loc 1 125.3 44.3 3 84.8 18.7 3 Loc 2 25.5 4.8 2 8.2 7.0 3 Loc 3 223.0 30.5 3 121.99 21.3 3 Loc 4 62.3 6.6 3 38.0 6.0 3 Loc 5 0 0 3 0 0 3

A statistical analysis reveals differences in both medium and high MW PAHs between sites. Location 3, the most contaminated location, was significantly higher

(p>0.05) in 11 PAHs when compared to all other locations: benzo(k)fluoranthene, anthracene, phenanthrene, pyrene, fluoranthene, chrysene, indeno(1,2,3-c,d)pyrene, benzo(g,h,i)perylene, benzo(e)pyrene, benzo(b)fluoranthene, and carbazole. Other locations had inconsistent fluctuations in chemical differences.

Table 5: Concentrations of Metals in Holcomb soil samples. Concentrations are reported in ppm with a standard deviation (‘SD’). “Loc 1-5” indicates the sampling location.

Cr Ni Cu Average SD N Average SD N Average SD N Loc 1 26.7 1.9 3 9.8 0.8 3 12.1 1.0 3 Loc 2 30.9 2.7 3 8.8 1.1 3 15.1 0.7 3 Loc 3 34.6 2.8 3 15.5 1.1 3 26.4 2.2 3 Loc 4 37.3 3.6 3 11.4 1.3 3 15.8 0.5 3 Loc 5 20.0 0.8 3 9.2 0.1 3 8.2 0.2 3

Zn As Ag Average SD N Average SD N Average SD N Loc 1 36.4 7.8 3 2.5 0.4 3 0.02 0.002 3 Loc 2 34.9 2.6 3 1.6 0.1 3 0.01 0.0005 3 Loc 3 362.2 113.1 3 3.7 0.7 3 0.03 0.003 3 Loc 4 38.4 1.7 3 1.8 0.1 3 0.01 0.001 3 Loc 5 31.5 2.0 3 3.0 0.1 3 0.02 0.001 3

Cd Pb U Average SD N Average SD N Average SD N Loc 1 0.03 0.02 3 11.4 1.02 3 0.6 0.02 3 Loc 2 0.04 0.01 3 12.0 1.1 3 0.7 0.07 3 Loc 3 2.5 0.4 3 19.4 0.5 3 0.7 0.06 3 Loc 4 0.02 0.00 3 10.7 0.4 3 0.7 0.07 3 Loc 5 0.01 0.00 3 8.5 0.1 3 0.4 0.01 3

Se Co Mn Average SD N Average SD N Average SD N Loc 1 0.3 0.01 3 9.4 1.6 3 430.6 6.2 3 Loc 2 0.3 0.02 3 4.3 0.3 459.53 5.6 3 Loc 3 0.4 0.03 3 7.9 0.2 3 8.71 0.2 3 Loc 4 0.3 0.02 3 5.5 0.4 3 9.4 0.07 3 Loc 5 0.3 0.01 3 14.5 0.4 3 430.6 6.2 3

V Fe Average SD N Average SD N Loc 1 59.7 5.2 3 24311.2 2120.0 3 Loc 2 75.1 6.4 3 32538.0 2240.9 3 Loc 3 70.4 2.7 3 53106.1 15924.6 3 Loc 4 76.9 3.0 3 34005.0 1469.4 3 Loc 5 34.6 1.1 3 14319.6 501.6 3

The total metal concentrations at the sample locations range from 14.5 ± 0.5 ppm weight at Location 5 to 53.6 ± 16.0 ppm weight at Location 3 (Table 3). Metals identified at the site showed a similar trend to that of the PAHs in that Location 3 was the highest in overall concentration (Table 3). This is unsurprising, as this area was likely exposed to the most creosote and wood-processing by-products. These concentrations are relatively low in terms of human exposure risks, with Zinc (Martin and Griswold 2009, de Vries et al. 2013) and Arsenic (As) being higher than EPA guidelines (Teaf et al. 2010). Some other metals (e.g., Vanadium (V)) were also measured at concentrations known to inhibit

microbes (Giller, Witter, and Mcgrath 1998, Brookes 1995, Brookes and McGrath 1984,

Larsson et al. 2013).

40 100 A B METALS PAHs

30 75

20 50

25 10 Total PAH concentration [ug/g] concentration PAH Total Total metal concentration [mg/g] concentration metal Total 0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3

0.5 5 Cr Anthracene Cu C Chrysene D Zn Pyrene 0.4 Pb 4 Benzo(e)pyrene Benzo(b)fluoranthene V

0.3 3

0.2 2 Top 5 PAHs PAHs 5 Top Top 5 Metals Metals 5 Top

0.1 1 Concentration [ug/g] Concentration [mg/g ]

0.0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Figure 13: (A) Total additive concentration [mg/g] of all metals identified (excluding Iron) in Holcomb soil samples. (B) Concentration [mg/g dry weight] of the top 5 individual PAHs represented in the highest concentrations. (B) Total additive concentration [ug/g] of all PAHs identified in Holcomb soil samples. (D) Concentration [ug/g dry weight] of the top 5 individual PAHs presented in the highest concentration at Holcomb soil samples. All error bars represent standard deviation.

A statistical analysis of these five sampling locations reveals that contamination levels are significantly different in terms of PAH concentrations and metal concentrations

(p=0.0002-0.05). The five PAHs with the highest measured concentrations included pyrene (4 rings), benzo(b)fluoranthene and benzo(k)fluoranthene (5 rings), chrysene (4 rings), and anthracene (3 rings) (Figure 13D). These top five PAHs represent between 6-

16% of the total PAHs detected in the sample, depending on the location. Iron (Fe) was found in the highest concentration at all sites by several magnitudes. The top five metals, after Fe, are Cr, Cu, Zn, Pb, and V (Figure 13C). These top five metals account for 13-

54% of the total metals, depending on the location.

Chemical characterization provides some clues as to where contamination arose and how contaminants have been transported on site. The carbon to nitrogen ratio (C:N) was relatively consistent between all sites, with an average of 16.19 and a standard deviation of 2.74 for all sites. However, a clear gradient of PAH and metal contamination was found at the site. In increasing total PAH concentrations: Location 5, Location 2,

Location 4, Location 1 and Location 3 (Figure 13B and D). While a total of 35 distinct

PAHs were measured in the soil, a relatively small number of PAHs (15-16 PAHs depending on sampling location) made up 90+% of the total PAHs. Not surprisingly, the mid to high MW PAHs were the largest contributors to the total PAHs. The relative abundance of mid and high MW did not drastically change between sampling locations.

Many of the PAHs appearing in the highest concentrations were mid- to high-molecular weight PAHs, such as chrysene and pyrene (Figure 13). Medium MW (2 to 3 ring PAHs)

made up approximately 24, 15, 13, 15, 17% of the total PAHs at Locations 5, 2, 4, 1, 3, respectively, while high MW (4+ rings) made up 76, 85, 87, 85, and 83% at Locations 5,

2, 4, 1, 3, respectively. Sampling Location 5 had total PAH concentrations of 322.2 ng/g.

There were low levels of metals at all of the five sites, with Location 3 having significantly elevated levels of Zinc (Zn). No other individual metals had significant increases between sites. Overall, Location 3 was elevated (p=0.012) in total metals. In increasing total metal concentrations: Location 5, Location 1, Location 4/Location 1, and

Location 3 (Figure 13C), though there were only significant differences between

Location 5 and Location 3.

4.3.2 16s rDNA Amplicon Sequencing Analysis

NMDS and subsequent ANOSIM analyses reveal marginal clustering based on sampling site location (p<0.1) (Figure 14).

81 There were shifts between sampling locations

NMDS- All iden/ﬁed OTUS

Loca/on 4

Loca/on 3 Loca/on 1 Loca/on 5 Loca/on 2

Figure 14: Non-Metric Dimensional Scaling (NMDS) of all OTUs identified in Holcomb Creosote Co. Superfund site soils.

Based on Similarity Percentage Analysis (SIMPER), Firmicutes was the main contributor to the dissimilarity between Location 4 and all other sites. Firmicutes relative abundance is slightly reduced at Location 4 (10.5% on average) relative to the other sampling locations (21% on average) (p<0.01 for all comparisons). No other sampling location had consistent contributors to the dissimilarity between sites. This is surprising as the PAH and metal profiles varied greatly between sampling locations and additional contributors were expected.

Further sequencing analysis reveals significant variations in microbial communities between sampling locations (Figure 15).

100 PHYLUM Armatimonadetes Chlamydiae Caldithrix Chlorobi 80 Thermi Tenericutes Planctomycetes Synergistetes Thermotogae Nitrospirae 60 Spirochaetes Crenarchaeota Verrucomicrobia Acidobacteria Euryarchaeota Chloroflexi Cyanobacteria 40 Bacteroidetes Actinobacteria Relative Abundance (%) Firmicutes Proteobacteria

0 Site 5 Site 2 Site 4 Site 1 Site 3

INCREASING PAH CONCENTRATION

Figure 15: Phylum-level relative abundance of taxa identified in the Holcomb soil samples. The legend order corresponds to the order of the stacked taxa. 83

A B C Proteobacteria Bacteroidetes Cloroflexi 50 10 8 a a a b b b b a 40 8 ab 6 a a

30 6 ab ac 4 20 4 b

c 2 10 2 Relative Abundance (%) Relative Abundance (%) Relative Abundance (%)

0 0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3

D Euryarchaeota E Verrucomicrobia 6 8 a a 6 4 a

4 b b b b 2 2 Relative Abundance (%) Relative Abundance (%) b b b 0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3

Proteobacteria appeared to be favored over Bacteroidetes at the middle chemical pollutant concentrations (Locations 4 and 1) but not at the highest concentrations

(Location 3). There was no observed decrease in Shannon Species Diversity Indices across the sampling locations (Table 3), thus these fluctuations cannot be attributed to changes in microbial diversity. Microorganisms in both the Proteobacteria and

Bacteroidetes phyla have been associated with PAH degradation previously. In particular, the Proteobacteria phylum contains many prominent PAH degraders including

Pseudomonads, Acinetobacter, and Sphingomonads (Prince, Gramain, and McGenity 84

2010). The Bacteroidetes phylum contains the Terrimonas and Mesoflavibacter genera which have recently been found to degrade benzo(a)pyrene (Song et al. 2015).

Table 6: Shannon species diversity indices and species identified at Holcomb sampling locations. Locations are listed in increasing PAH concentrations.

Shannon Species

Diversity Index

Location 5 2.0

Location 2 2.3

Location 4 2.1

Location 1 2.0

Location 3 2.3

In order to discern important variations in taxa of concern, further analysis to determine potential relationships was done at a lower taxonomic level (Figure 17).

ORDER Rhodospirillales Flavobacteriales Burkholderiales Desulfovibrionales 100 Holophagales Spirochaetales Acidobacteriales Methanosarcinales Bacillales Rhodocyclales 80 Syntrophobacterales Anaerolineales Rhizobiales Clostridiales Actinomycetales

40 Relative Abundance (%)

0 Site 5 Site 2 Site 4 Site 1 Site 3

INCREASING PAH CONCENTRATION

Figure 17: Order-level relative abundance of top 15 most abundant taxa identified in the Holcomb soil samples.

A Clostridiales B Rhizobiales C Burkholderiales D Holophagales 20 25 10 8 b b a d 20 a 8 15 6 b b a a 15 6 a 10 c c 4 10 4 b cd c 5 b 2 5 2 cd Relative Abundance (%) b a a a 0 0 0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3

E Rhodospirillales F Rhodocyclales G Flavobacteriales H Syntrophobacterales 6 6 5 6 b b b b b a b 4 bc 4 a a 4 c c 4 c 3 c a c a c a c c 2 2 2 ac 2

1 a

Relative Abundance (%) a a a a a a a a a a a a 0 0 0 0 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3 Loc 5 Loc 2 Loc 4 Loc 1 Loc 3

At the order level, more than 50% of the 15 most abundant taxa have been previously associated with aromatic degradation (Prince, Gramain, and McGenity 2010):

Clostridiales (Rabus et al. 2016), Rhizobiales (Herbst et al. 2013), Burkholderiales (Kim et al. 2014), Holophagales (Anderson et al. 2012), Rhodospirillales (Pagé, Yergeau, and

Greer 2015), Rhodocyclales (Hesselsoe et al. 2009), Flavobacteriales (Wong et al. 2015), and Syntrophobacterales (Rabus et al. 2016). Similarly to the phylum level, there were

significant variations between sampling locations, however no consistent trend emerged

(Figure 18A-F).

At the genus level, there were also significant variations in the distribution of the microbes. Location 5 was higher in Thermogemmatispora, Brachyspira, Thermosiphon,

Cystobacter, Chthoniobacter, Paenibacillus, and Rhodoplanes than all other sites.

Location 4 was higher in Treponema, Deinococcus, Ramlibacter, Actinomyces, Geothrix,

Candidatus Methylacidiphilum, and Candidatus Tammella than Location 1, Location 2, and Location 3. Location 3 and Location 2 showed a parallel trend in some of their abundances when compared to Location 1. Location 3 and Location 2 were lower in

Peptoniphilus, Treponema, Geobacter, Desulfovibrio, Paenibacillus, Edaphobacter,

Pelotomaculum, Uliginosibacterium, Candidatus Koribacter, Mycoplasma,

Acidimicrobium, Pelagicoccus, Actinokineospora, Kineosporia, Nitrospira than Location

1. Location 3 and Location 2 were higher in Methylosinus, Methanosarcina,

Rhodoplanes, Flavobacterium, Longilinea, Thermocladium, Hyphomicrobium,

Pedomicrobium, and Micromonospora than Location 1.

4.3.3 Associations Between Chemical and Microbial Variants

As C:N ratios and pH were relatively stable across samples, we hypothesized that microbial community shifts would most likely associate with the contaminant profile at each sampling location. While there may be additional environmental conditions at play, such as soil heterogeneity/local environments and moisture content, these parameters

were not considered herein. To identify potentially useful relationships between pollutants and taxa, a Spearman Rank Correlation was performed. The Spearman Rank

Correlation is a nonparametric (i.e., distribution free) approach to the correlation between two sets of measurements made on the same samples, ranking each set in order of magnitude (Prince, Gramain, and McGenity 2010, Zar 1998). In the work presented here, analytical measures in PAH/metal concentrations were correlated with relative abundance measurements in taxa using the corrplot package in R (Wei 2013). Correlations are reported as a correlation coefficient (ρ) with values between -1 and 1. The closer the ρ is to 1, the stronger the positive correlation, while the closer the ρ is to -1, the stronger the negative correlation. The metals found to have a strong influence, both positively and negatively, were Silver (Ag), Chromium (Cr), and Copper (Cu) taxa (ρ > 0.6 positive correlation or ρ < -0.6 negative correlation). Many of the PAHs appear to exhibit little (-

0.2 < ρ < 0.2) to no effect (ρ = 0) on correlation with community composition.

Armatimonadetes, Sphingomonas, Mycobacterium, Riskettsia, Thauera, Syntrophobacter,

Methlyonatum, Novosphingobium, Moorella, Pedosphaera, and Geobacter were positively correlated (ρ > 0.4) with many of the PAHs. Interestingly, when these taxa were not positively correlated, they were uncorrelated (-0.1 < ρ < 0.1), and had no negative correlation coefficients with PAHs. In addition, these taxa were positively correlated (ρ > 0.4) correlated with all metals except Cr and As. This suggests these taxa should be further investigated for bioremediation potential. Conversely, the taxa

Thermogemmatispora, Rhodoplanes, Bifidobacterium, Pedobacter, Brachyspira,

Rhodovibrio, Azorbizobium, Chthoniobacter, Tepidanaerobacter, and Thiocapsa were negatively correlated (ρ < -0.2) with many PAHs, or otherwise uncorrelated (ρ < 0).

Unsurprisingly, no single chemical pollutant exhibits the same directional influence on all taxa.

4.3.4 Choosing Targets for Biostimulation and Bioaugmentation

The variations between sites provide insight into potential bioremediation targets.

Further using the methodology described above, we identified putative biostimulation and bioaugmentation microbial targets. A target deemed to be an appropriate biostimulation candidate is ones that are well suited to survive at the contaminated site

(indicated by the correlation analysis) and responds positively to the increased nutrient supply provided in the context of site biostimulation. A strong bioaugmentation target is one which is fast growing, easily cultured, non-pathogenic, and able to withstand as well as degrade high concentrations of the target contaminant in diverse environments

(Mrozik and Piotrowska-Seget 2010). In addition to these parameters, it is important to consider the r-K ecological scheme, which assumes that evolution, will favor either adaptation to high rates of reproduction (r strategists) or optimal utilization of environmental resources (K strategists) should be considered (Margesin et al. 2003,

Pianka 1970, Meyers et al. 2007). K-strategists tend to have increased success in resource-limited situations and are usually more stable, permanent members of the

community (Margesin et al. 2003). As a result, both r- and K-strategists should be chosen for both rapid and long-term community adaptation.

In order to identify strong candidates, a correlation analysis on taxa and chemicals was performed in the corrplot package in R (Wei 2013). Then, the taxa positively correlated across PAHs and most metals were further investigated for their bioremediation potential. Taxa that were considered appropriate biostimulation and bioaugmentation candidates, as described above, were identified and their strategist scheme was determined based on existing literature. Using this information, Geobacter was identified as a biostimulation target at the Holcomb Creosote site. Geobacter are known to degrade aromatic hydrocarbons and remediate some metals (Prakash et al.

2010, Kashefi and Lovley 2000, Staats, Braster, and Röling 2011) and were one of the most abundant genera identified at all 5 sampling sites suggesting these microorganisms have adapted to local environmental conditions. In addition, Saunders et al. (Saunders et al. 2008) found when Fe is present, the Geobacter can remediate As and the addition of molasses may help raise the dissolved As concentrations and encourage microbial growth. Strong bioaugmentation candidates were Mycobacterium (K-strategist) and

Thauera (r-strategist), as they are generally positively correlated with PAHs (ρ >0.4 for at least 10 PAHs), are known potential hydrocarbon degraders, and are present in a high abundance and wide diversity at all 5 sampling sites.

4.3.5 Choosing Targets for Genetic Bioaugmentation

Genetic bioaugmentation relies on targeting specific site bacteria to receive catabolic plasmids from augmented donor strains. As a result, the successful implementation of genetic bioaugmentation requires knowledge about both the bioaugmented strain and the existing site community. It has been shown that several factors influence HGT, including phylogenetic relatedness and similar GC contents (Pál,

Papp, and Lercher 2005). Advancements in high-throughput sequencing have made it feasible to characterize environmental microbiomes and the phylogeny of the microbial community. In addition to establishing potential relationships, such as those determined through Spearman Rank correlation, site-specific recipient strains that have the potential to accept plasmids from known plasmid-harboring donor strains must be identified.

(Heitkamp and Cerniglia 1989, Böttger et al. 1997, Biegert, Fuchs, and Heider 1996). A genetic bioaugmentation recipient candidate is one which has the same attributes as a bioaugmentation candidate, but also possesses a self-conjugating catabolic plasmid and a high phylogenetic relatedness to other abundant microorganisms at the site (Ikuma and

Gunsch 2012b). Using these criteria, a potential genetic bioaugmentation recipient target at Holcomb Creosote may be the Sphingomonads, as they are commonly used in bioaugmentation, commonly contain xenobiotic-degrading plasmids (Basta, Buerger, and

Stolz 2005, Basta et al. 2004), and there are over twenty species of Sphingomonads identified across the five sampling sites (Figure 19).

HOLCOMB SOILS

Sphingomonas sanguinis Sphingomonas Sphingomonas kwangyangensis Sphingomonas mali Sphingomonas fennica Sphingomonas yunnanensis Sphingomonas melonis Sphingomonas abaci Sphingomonas leidyia Sphingomonas haloaromaticamans Sphingomonas hunanensis Sphingomonas wittichii Sphingomonas suberifaciens Sphingomonas hankookensis Sphingomonas elodea Sphingomonas roseiflava Sphingomonas insulae Sphingomonas desiccabilis Sphingomonas ginsenosidimutans Sphingomonas dokdonensis Sphingomonas japonica Sphingomonas sanxanigenens Sphingomonas oligophenolica 0.00 0.05 0.10 0.15 Relative Abundance (%)

Figure 19: The diversity of Sphingomonas species identified in Holcomb soils.

One potential exogenous donor strain may be Sphingomonas sp. strain KS14 carries a plasmid that is able to utilize phenanthrene and naphthalene as its sole source of carbon and energy, and co-metabolically degraded pyrene using phenanthrene as secondary growth substrate (Cho and Kim 2001). Additionally, Sphingomonas aromaticivorans F199 contains a PAH-degrading catabolic plasmid shown to conjugate to another Sphingomonas species (Romine et al. 1999). Both strains, F199 and KS14, are potential donor strains.

4.3.5 Implementation of Metagenomic Analysis for Precision Bioremediation

Here we provide the beginning of a framework for application of metagenomic precision bioremediation and identifying microbial targets for biostimulation, bioaugmentation and genetic bioaugmentation (Figure 20).

SITE SAMPLING

Nucleic Acid Isolation Clay wash, gDNA isolation Chemical Analysis Analytical methods such as IC-PMS and GC-MS

Illumina Miseq, v3-v416S paired-end Sequencing 2 X 300bp reads

Spearman Rank Correlation Establish Relationships ANOSIM/ANOVA NMDS/PcOA

Select strains for bioaugmentation and genetic Strain bioaugmentation (plasmid-containing) based on Identification relationships, availability, and strain characteristics

Precision Bioremediation

Figure 20: A schematic of the precision bioremediation approach.

The proposed approach consists of a series of sequential steps. First, a site must be gridded and adequately sampled. Then, the samples must be analytically analyzed for chemical pollutant concentrations and total nucleic acids extracted. From there, the samples are sequenced using high-throughput sequencing such as the Illumina Miseq.

The taxonomic data is then used to evaluate the relationship between the chemical pollutants and taxa. For instance, a correlation between microbial diversity and the level of chemical pollutant(s) across a chemical gradient at a contaminated site can be established. By including multiple chemical pollutants, organisms may be selected that are not sensitive to the co-contaminant scenario. The strains that are positively correlated with a particular chemical or otherwise determined to be well suited to the polluted are then further analyzed to determine strong biostimulation, bioaugmentation, and genetic bioaugmentation targets. Because this final step is largely reliant on information available in databases such as Greengenes (DeSantis et al. 2006) or ExTaxon-e (Kim et al. 2012), this approach becomes more powerful as more data are known about environmental microbiomes. From there, a consortium of optimized microbes capable of surviving and fully biodegrading chemical pollutants can be developed. A combination of r- and K strategists should be considered for both rapid and long-term community adaption, respectively. Finally, it should be noted that while using amplicon-based metagenomic analyses such as that used herein, a shotgun sequencing approach which provides information about specific genetic functions present in a given microbiome will provide better direct insights into actual degradation potential of a microbial community.

However, this approach is significantly more costly and requires much more bioinformatic power (Ranjan et al. 2016).

4.3.6 Implications for In Situ Bioremediation

Recent advances in sequencing technologies provide new insights into the implementation of precision bioremediation strategies. Herein, we have developed a new framework that enables the consideration of co-contaminants (i.e., metals) when designing a bioremediation treatment scheme for PAH degradation. The power of this framework is heavily reliant on data available in searchable databases especially when working with amplicon based metagenomic analyses. Currently, it remains difficult to fully assemble genomes with shotgun sequencing using the Illumina HiSeq and MiSeq platforms (Koren et al. 2013). In the near future, 3rd generation platforms such as single- molecule sequencing from PacBio may facilitate this simpler full genome assembly and also minimize bioinformatics analysis steps (Koren et al. 2013). Finally, in order to account for additional environmental factors or focus on a group of chemical targets, more sophisticated and in-depth bioinformatics pipelines are needed. This requires that we continue to build our databases, perform more in silico testing, and start expanding our work in synthetic ecology similarly to that done in other fields of study. In the gut microbiome realm, it is now possible to identify a >95% percentage of the microbial population at a very low taxonomic level and a core group of microbes have been identified (Arumugam et al. 2011). In the oral microbiome realm, the development of the

Human Oral Microbiome Database (HOMD) that interlinks phenotypic, phylogenetic, genomic, clinical and bibliographic information for each taxon has played a critical role in the development of this field (Chen et al. 2010). Similarly, by continuing to build the

environmental microbiome databases, significant advances in the field of precision bioremediation will become possible.

5. Monitoring Horizontal Gene Transfer of Large Catabolic Plasmids for the Purpose of Evaluating Genetic Bioaugmentation Efficacy

5.1 Introduction

Bioremediation is a sustainable treatment approach that has environmental and economic appeal for remediating contaminated groundwater, soil and sediments. In general, bioremediation treatment strategies rely on two main approaches: biostimulation and bioaugmentation. Biostimulation strategies can be used if indigenous bacteria with the requisite degradation capabilities are present at a given site (Rittmann 1994). In biostimulation, nutrients or other substrates are provided to promote in situ biodegradation (Margesin and Schinner 1997). However, if no indigenous strains are present, exogenous microorganisms with the desired catabolic traits can be introduced, a process known as bioaugmentation (Vogel 1996, Pepper et al. 2002). While bioaugmentation approaches have been successfully used at a variety sites, this strategy is known to fail quite frequently when exogenous strains are unable to thrive and compete for survival with indigenous microorganisms (Bouchez et al. 2000, Goldstein, Mallory, and Alexander 1985). Genetic bioaugmentation is an alternative approach that has been proposed as a treatment strategy to address the challenges associated with biostimulation and bioaugmentation (Top, Springael, and Boon 2002, Top 2002, Ikuma and Gunsch

2012a). Genetic bioaugmentation relies on the horizontal gene transfer (HGT) of catabolic plasmids from an exogenous donor to an indigenous recipient, removing the need for the long-term survival of the exogenous strain under site conditions (Ikuma and

Gunsch 2012a). HGT is a naturally occurring phenomenon that is widespread among prokaryotes and occurs when microorganisms are under selective pressure, such as in the presence of either antibiotics or particularly recalcitrant carbon substrates (Syvanen 1994,

Davison 1999, Top 2002). Taking advantage of naturally occurring HGT events to engineer an environmental microbiome, genetic bioaugmentation has been proven effective with limited impacts on ecology in a model lab-scale column(Ikuma and

Gunsch 2012a, 2013). However, methods to accurately monitor and assess the efficacy of genetic bioaugmentation are limited.

Three monitoring approaches have been developed to measure rates of plasmid conjugation. The first approach is culture-based and consists of using selective growth media to enumerate transconjugants (Bellanger, Guilloteau, Bonot, et al. 2014). This method requires bacteria that are able to be cultured and express selective marker genes

(Bellanger, Guilloteau, Bonot, et al. 2014, Sørensen et al. 2005). Due to these requirements, the culture-based approach is very limited in application and does not distinguish between an increased number of transfer event (Sørensen et al. 2005). The second approach is fluorescent-based, which consists of inserting a reporter gene on a catabolic plasmid that is repressed in the donor cell but expressed in the recipient cell after successful transfer takes place (Sørensen et al. 2005). The presence of the fluorescent protein-labeled genes may be detected using microscopy, and a visualization of in situ plasmid conjugation may be observed (Bellanger, Guilloteau, Bonot, et al.

2014, Sørensen et al. 2005). Previous work in our research group has relied on this

approach, using an in situ reporter system to monitor the conjugation of the gfp-labeled

TOL plasmid (Ikuma and Gunsch 2013, Ikuma and Gunsch 2012b, Ikuma, Holzem, and

Gunsch 2012). While this method is effective in simple growth media, it is not practical for rapidly measuring HGT in complex media such as soils, and therefore is not ideal for monitoring HGT in situ field applications (Ikuma, Holzem, and Gunsch 2012). Use of this technique is further limited by the availability of only seven unique fluorescence proteins and difficulties associated with monitoring more than four fluorescence labels

(i.e., cyan, yellow, orange, and red) simultaneously (Shaner, Steinbach, and Tsien 2005).

Further still, the gfp expression can often be or become weak or absent and indistinguishable from background fluorescence, particularly over the long term

(Sørensen et al. 2005). Most importantly, this method is constrained by the need to be able to genetically modify the plasmid (Bellanger, Guilloteau, Bonot, et al. 2014). This severely limits the used of this option, as only a select few environmentally relevant plasmids can be cultured and manipulated.

The last method which has been developed is a molecular-based approach which utilizes quantitative real-time PCR (qPCR) to monitor plasmid conjugation and dissemination (Bellanger, Guilloteau, Bonot, et al. 2014, Bonot and Merlin 2010, Merlin et al. 2011, Haug et al. 2011, Haug et al. 2010). This method takes advantage of the mosaic structure of DNA [described in (Smith 1992)] to monitor the genetic evolution of both the plasmid and host DNA over time. This method takes advantage of the irregular placement of blocks of genes over time that has occurred as a result of natural

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evolutionary processes. Thus, through the development of assays which target these unique DNA regions, it is possible to normalize plasmid-associated genes to strain- associated genes, and any relative increase in genes associated with the plasmid can be inferred to be the result of a plasmid conjugation event (Bellanger, Guilloteau, Bonot, et al. 2014). This method offers numerous benefits as it is not affected by the biases associated with gene expression or culture-based methods, is applicable to a wide range of strains and plasmids and consortia, and is effective in complex matrices such as manure and soil (Bonot and Merlin 2010, Bellanger, Guilloteau, Bonot, et al. 2014). To the authors’ knowledge, this method has only been used to track plasmids with antibiotic resistance genes and there has been no research applying this method to monitor HGT events in the context of bioremediation. Thus, in this proof of concept study, we developed a qRT-PCR assay to monitor the HGT of the NAH7 and pSED02 plasmids.

NAH7 is a commonly studied large Inc-9 catabolic and self-transmissible plasmid carrying the genes necessary for complete mineralization of naphthalene to pyruvate, a common target for bioremediation (Sota et al. 2006). The pSED02 plasmid contains genes that are likely involved in the degradation of 1,4-dioxane (Grostern et al. 2012) and has been investigated for bioaugmentation (Kelley et al. 2001). These plasmids were selected as they have been well studied and provide genes for different classes of contaminants, which are known to co-occur in research and medical wastes as well as at sites such as landfills and Superfund sites (US EPA).

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The overarching goal of this study was to determine if a qRT-PCR method could be utilized to monitor the transfer of large catabolic plasmids as a potential tool for monitoring genetic bioaugmentation. Here, we describe our work to validate this approach with Pseudomonas putida G7 (ATCC 17485) (hereon referred to as PpG7) which carries the IncP-9 NAH7 plasmid and known to degrade naphthalene as well as

Pseudomonas dioxanivorans strain CB1190 (NCBI taxonomy ID 675635) (hereon referred to as CB1190), a strain which carries the pSED02 plasmid and is known to utilize 1,4-dioxane as a sole carbon and energy source (Parales et al. 1994, Mahendra and

Alvarez-Cohen 2005, 2006, Mahendra et al. 2007).

5.2 Methods and Materials

5.2.1 Chemicals and Culture Inocula Preparation

Stock solutions of naphthalene (99+% pure, Sigma Aldrich, St. Louis, MO) and

1,4-dioxane (99+% pure, scintillation grade, Sigma Aldrich, St. Louis, MO) were prepared in ammonia mineral salts (AMS) (Parales et al. 1994). A 100 mg/L 1,4-dioxane stock solution and a 10 mg/L naphthalene stock solution were prepared in AMS.

However, due to its low solubility, naphthalene was first dissolved in 40 mL 1,4-dioxane prior to AMS addition. One liter of AMS consisted of 100 mL of 10× salt solution,

1.0 mL of AMS trace elements, 1.0 mL of stock A, and 20 mL of 1.0 M phosphate buffer

(added after sterilization). The AMS 10× salts solution consisted of 6.6 g of (NH4)2SO4,

10.0 g of MgSO4·7H2O, and 0.15 g of CaCl2·2H2O. The AMS trace elements contained

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−1 −1 −1 −1 0.5 g.L FeSO4·7H2O, 0.4 g.L ZnSO4·7H2O, 0.02 g.L MnSO4·H2O, 0.015 g.L

−1 −1 −1 H3BO3, 0.01 g.L NiCl2·6H2O, 0.05 g.L CoCl2·6H2O, 0.005 g.L CuCl2·2H2O, and

0.25 g.L−1 EDTA. The AMS stock A contained 5.0 g.L−1 Fe-Na EDTA and 2.0 g.L−1

−1 −1 NaMoO4·2H2O. The 1 M phosphate buffer contained 113.0 g.L K2HPO4 and 47.0 g.L

KH2PO4.

In addition to CB1190 and PpG7, microcosms were run with multiple additional potential recipient strains including Pseudomonas stutzeri (ATCC 17588), Pseudomonas fluorescens (ATCC 13525) and Bacillus subtilis (ATCC 6633) which acted as co- inoculants. P. stutzeri ( a Gram-negative bacterium) was selected because it is closely phylogenetically related to PpG7 with a similar GC content (around 60%) (Cladera et al.

2004) and it has recently emerged as a potential HGT candidate (Lalucat et al. 2006). P. fluorescens (a Gram negative bacterium) and B. subtilis (a Gram postive bacterium) were selected because they are often environmentally competent or undergo natural transformation, are able to be cultivated, are easy to work with, and are non-pathogenic

(Seitz and Blokesch 2013). All strains were grown at ~20°C in 250 mL Erlenmeyer flasks containing 100 mL of AMS medium. PpG7 and CB1190 were grown in the presence of only 50 µM naphthalene and 2.5 mM 1,4-dioxane, respectively. P. stutzeri, P. fluorescens, and B. subtilis were all pre-grown in AMS with 1% pyruvate. Pyruvate was selected for this step because pyruvate is a known product of naphthalene and 1,4- dioxane biodegradation (Sota et al. 2006, Sales et al. 2011). Cells were harvested after 48 hours of growth in the late exponential phase, and the optical density was measured at

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600 nm with a spectrophotometer (Spectronic 20D1; Milton Roy Company). All bacterial cultures except CB1190 were diluted to 109 cells mL−1 (determined by hemocytometer counts), pelleted by centrifugation, and washed with AMS to remove any remaining pyruvate. The cell pellets were then re-suspended in 1 mL of AMS and augmented to their respective microcosm. Due to their phenotype, growth of CB1190 could not be assessed by spectrophotometry. Rather, CB1190 colonies were enumerated by plate count on AMS with 1.5% solid Bacto agar in a glass petri dish. The petri dish was inverted and a Whatman® glass microfiber filter (Sigma Aldrich, St. Loius, MO) was saturated with liquid 1,4-dioxane and placed on the opposite side of the petri dish from the agar as described elsewhere (Mahendra and Alvarez-Cohen 2005). The inverted petri dish was sealed with parafilm (VWR, Radnor, PA) and placed in a 30°C incubator where 1,4- dioxane was supplied in the vapor phase. The filter was re-amended with 1,4-dioxane every 5 days. After 10 days of growth, three colonies similar in size were selected and inoculated into microcosms.

5.2.2 Microcosm Preparation

All microcosms were prepared in 250 mL clear glass bottles and capped with

Teflon-lined MininertTM caps (Sigma-Aldrich, St. Louis, MO). The mouth of each bottle was wrapped with PTFE thread seal tape (VWR, Radnor, PA) to ensure a tight seal. Each reactor was filled with 100 mL of AMS medium and sterilized by autoclaving at 121°C and 15 psi for 45 minutes. After autoclaving, microcosms were dosed to a final

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concentration of 5 mg/L naphthalene and 50 mg/L 1,4-dioxane from the stock solutions.

These concentrations were selected as they are representative of levels observed in groundwater at contaminated sites (Mohr, Stickney, and DiGuiseppi 2010, Zenker,

Borden, and Barlaz 2003, Cline, Delfino, and Rao 1991). The seven conditions tested were: (1) PpG7 only, (2) CB1190 only, (3) PpG7 and CB1190, (4) PpG7 and P. stutzeri,

(5) CB1190 and P. stutzeri, (6) CB1190 and PpG7 and P. stutzeri, and (7) CB1190 and

PpG7 and P. stutzeri (ATCC 17588) and P. fluorescens (ATCC 13525) and B. subtilis

(ATCC 6633). Abiotic controls (not augmented with any microbial strains) received sterile AMS and were used to monitor for non biologically mediated transformations of naphthalene and 1,4-dioxane. All microcosms were prepared in triplicate for a total of 21 microcosms. Microcosms were maintained on a rotary table at 100 rpm for 8 weeks at room temperature (20 ± 2°C). After 31 days, a second addition of naphthalene and 1,4- dioxane was provided to the microcosms to return them to Day 0 concentrations (5 mg/L for naphthalene and 50 mg/L for 1,4-dioxane).

5.2.2 DNA Extraction

A 2 mL sample was collected from all microcosms weekly using a BD 5 mL syringe (Becton, Dickinson and Company, Franklin Lakes, NJ) and stored at -80°C until

DNA extraction. Total genomic DNA was isolated using the Qiagen Blood and Tissue

Kit (Qiagen, Valencia, CA) according to manufacturer’s instructions for Gram-positive bacteria except 100 µL rather than 200 µL of elution buffer was added to the center of

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filter membrane and the samples were incubated for 5 minutes and centrifuged for 60 seconds at 13,000 x g in an Eppendorf AG Centrifuge 5424 (Hamburg, Germany). The eluted DNA concentrations were measured in triplicate using a Qubit® 2.0 fluorometer with dsDNA BR reagents (Thermo Fisher Scientific, Waltham, MA) according to manufacturer’s instructions.

5.2.3 Quantitative PCR

Targeted qPCR assays were adapted for catabolic plasmids based on methods described by Bonot and Merlin (Bonot and Merlin 2010) for monitoring antibiotic resistance plasmids. Taqman®™ probes were developed based on available sequencing data (Sales et al. 2011, Sota et al. 2006) and designed to target the mosaic structure of the nah operon genes on the NAH7 plasmid and the pSED02 plasmid. As the nah operon on

NAH7 has been shown to be uniquely situated within a class II (Tn3-like) transposon designated Tn4655 (Tsuda and Iino 1990), the edge of this transposon was targeted. A similar approach was taken with the pSED02 plasmid, and the intersection of two relatively unique gene regions was targeted. For the pSED02 plasmid, the DNA region where the oxidoreductase FAD-binding protein gene meets the methane/phenol/toluene hydroxylase was targeted (between Psed_6977 and Psed_6978 in gene region 34kb,

NCBI). Additional strain-specific probes were designed to target unique 16S rDNA regions for both PpG7 and CB1190 (Table 7).

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Table 7: Primers and Taqman™ Probes used to monitor gene transfer events in the microcosms.

Name Primer (F/R) Sequence Type

NAH7 Forward ATTAGCGATTTCGTGGCTCT HPLC

Reverse GCTCCAGCACTACGGTTACA HPLC

Probe CCGGCCCTACTGGAAACCAGAA 5'FAM-3'TAMRA

PpG7 Forward TGTGTGAAGAAGGTCTTCGG HPLC

Reverse CGCTTGCACCCTCTGTATTA HPLC

Probe AACTCTGTGCCAGCAGCCGC 5'FAM-3'TAMRA

pSED02 Forward TTCCAGACTGGCTCAACTCA HPLC

Reverse AGTCAACCCACTCCTTGCTT HPLC

Probe ATTGTTGTCCCGCGCCACCT 5'FAM-3'TAMRA

CB1190 Forward CTTTAAGGGATTCGCTCCAC HPLC

Reverse CACACATGCTACAATGGCAA HPLC

Probe CACGGTCTCGCAGCCCTCTG 5'FAM-3'TAMRA

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The specificity of the primers sets were evaluated using gDNA isolated from pure cultures. All qPCR assays were performed on a Stratagene Mx3000P thermocycler

(Agilent, Richardson, TX). The qPCR reactions were performed in a total reaction volume of 20 µL. Due to the high GC content of CB1190 (73.1%), dimethyl sulfoxide

(DMSO) was added at 5% total volume as a disruptor (Bachmann, Lüke, and

Hunsmann 1990). Primers and probes were added to a final concentration of 250 nM, with 10-30 ng of extracted total community DNA. The reaction conditions consisted of

95°C for 5 minutes, followed by 45 cycles of denaturation at 95°C for 30 seconds and

60°C for 1 minute. qPCR reactions were performed in triplicate for each sample, including standards and negative controls. The coefficient of determination (R2) values relating threshold values (Ct) values to a standard curve were greater than 0.97 for all reported values.

5.2.3 Analytical Techniques

Liquid samples (20 µL samples) were collected from each microcosm weekly with a 100 µL Pressure-Lok syringe through the Mininert valves for chemical analysis.

Samples taken from each microcosm was then analyzed using a Waters Quattro micro™ GC (GC-MS) (Milford, MA). Dioxane and naphthalene concentrations were analyzed by solid phase microextraction (SPME) using a

Carboxen®/Polydimethylsiloxane (CAR/PDMS) fiber (Sigma-Aldrich, St. Louis, MO).

Samples were added to glass headspace vials (Agilent, Richardson, TX) with a threaded

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top, steel cap, and high temperature PTFE septa (Agilent, Richardson, TX). Microcosm samples were added at a maximum concentration of 1 mg/L (often requiring 1:1,000 dilution). Samples were added to 10 mL of nanopure water with 30% NaCl to help salt the compound out of the liquid phase. Dioxane-d8 was used as an internal standard. The fiber was conditioned in the needle heater at 270°C for 45-60 minutes. Machine controls and sample data storage were operated through the MassLynx software (Waters

Corporation, Milford, MA). The injection volume was set to 22 for all samples.

Additional conditions and settings for the GC-MS are reported in Table 8.

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Table 8: Conditions used to measure naphthalene and 1,4-dioxane degradation after solid phase microextraction in the microcosms.

GC-MS Conditions

Sample 20uL sample in 10mL water in 20mL vial 30% sodium chloride

SPME Fiber 80 µm Carboxen-PDMS metal fiber

Extraction Headspace, with agitation (500 rpm) 40 degrees 900 seconds Penetration of 22mm

Desorption 180 seconds

Column SPB-1

Oven 35°C, hold for 2.7 minutes; ramp to 145°C at 20°C/minute, then ramp to 180°C at 45°C/min hold 4

minutes. Injection Splitless, 250°C 1.7 min purge time; 25 mL/min purge flow.

Scan range m/z dioxane-d8 (96), dioxane (88, 58), naphthalene (121, 51)

Carrier gas Helium, 1.3 mL/min, constant

5.2.3 Data Analysis

GC-MS peak data was used to quantify chemical concentration using the

MassLynx software (Waters Corporation, Milford, MA). For qPCR data, the comparative threshold cycle (CT) method was used to compare starting quantification

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values to a standard curve. A two-way ANOVA was used to evaluate changes in naphthalene and 1,4-dioxane concentrations as well as qPCR gene abundance values over treatment and time in GraphPad Prism (Prism 1994). Additional data handling was performed in PAST (Hammer, Harper, and Ryan 2001).

5.3 Results and Discussion

5.3.1 Detection of Gene Transfer Events

The Taqman™ probe assays were successfully used to monitor plasmid concentrations and plasmid conjugation events in consortia consisting of 2 to 5 strains suggesting that this method could be used to assess HGT in the context of genetic bioaugmentation. Based on previously published results, the average copy number of target plasmid was expected to be approximately 101 to 102 copies per PpG7 cell (Park and Crowley 2006). As we did not normalize to cell numbers but rather to copies of a 16S rDNA region, we were unable to make direct comparisons. The plasmid concentrations during the initial phases of the experiment for the NAH7 plasmid ranged from 101 to 105 per mL (Figure 21). For the 1,4-dioxane-associated plasmid (pSED02), starting concentrations were difficult to determine due to variability in the initial growth of the

CB1190 strain. The three microcosms with both PpG7 and CB1190 [i.e., (1) PpG7 and

CB1190, and (2) PpG7 and CB1190 and P. stutzeri, and (3) PpG7 and CB1190] and P. stutzeri and B. subtilis and P. fluorescens (all strains) had relatively stable normalized plasmid profiles (Figure 21).

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9 10 A PpG7 and CB1190 10 D 108 1 a 107 a a a a a 6 0.1 b 10 a 5 10 0.01 104 103 0.001 2 10 0.0001 Gene Copy Number 101 0 Plasmid normalized number 0.00001 10 pDioxane pNaphthalene pSED02 CB1190 NAH7 PpG7 B E 109 PpG7 and CB1190 and P. stutzeri 10 108 a a 1 a b 107 ab ab b 106 0.1 a 105 0.01 104 103 0.001 102

Gene Copy Number 0.0001 101 Plasmid normalized number 100 0.00001 pSED02 CB1190 NAH7 PpG7 pDioxane pNaphthalene

All strains 109 C 10 F 8 b 10 ab 1 ab b b ab 107 a 106 0.1 105 0.01 a 104 103 0.001 102 a

Gene Copy Number 0.0001 101

100 Plasmid normalized number 0.00001 pSED02 CB1190 NAH7 PpG7 pDioxane pNaphthalene

Day 14 Day 21 Day 28 Day 35 Day 56

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In the microcosm with PpG7 and CB1190 (Figure 21D), there no increase

(p>0.05) in normalized plasmid concentration throughout the experiment. In the microcosms with PpG7 and CB1190 and additional potential recipient strains (Figure

21E, 21F), NAH7 increased (p<0.01) in the initial phase of the experiment. This trend was seen with pSED02 in the microcosm that contained all strains (Figure 21C), but was not seen in the microcosms with PpG7 and CB1190 (Figure 21A) or in the microcosm with PpG7 & CB1190 & P. stutzeri (Figure 21B). For these two latter microcosms, there was a decrease in the pSED02 normalized plasmid concentrations from Day 21 to Day 28 which then increased again after Day 28. As expected, the microcosms with only PpG7 and only CB1190 did not show any increase in plasmids normalized to strain over time.

There was also no increase in normalized plasmid concentrations in the microcosm with CB1190 and P. stutzeri (Figure 22D). This result was also expected, as it is known that bacteria exchange genetic information more readily with phylogenetically related recipient strains and those with similar GC contents (Pál, Papp, and Lercher 2005,

Ikuma and Gunsch 2012a). The microcosm containing PpG7 and P. stutzeri also did not have a significant change in normalized plasmid growth over time (Figure 22C), nor did the CB1190 and P. stutzeri microcosm, with average normalized values ranging from

0.25 and 2.5 for all.

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A C 9 10 PpG7 and P. stutzeri 10 108 107 1 106 0.1 105 4 10 0.01

Gene Copy Number 103 102 0.001 101

Plasmid normalized number 0.0001 100 NAH7 PpG7 pDioxane pNaphthalene B D 109 10 CB1190 and P. stutzeri 108 107 1 106 0.1 105 104 0.01 103

Gene Copy Number 2 10 0.001 101

100 Plasmid normalized number 0.0001 pSED02 CB1190 pDioxane pNaphthalene

Day 14 Day 21 Day 28 Day 35 Day 56

Figure 22: (A-B) Gene copy number and (C-D) normalized plasmid concentration over time for catabolic plasmid in microcosms with only donor strains and P. stutzeri.

In Figure 22, the bars labeled “pSED02” and “NAH7” represent the plasmids, while “CB1190” and “PpG7” represent the strains; “pDioxane” represents the pSED02 plasmid normalized to CB1190, while “pNaphthalene” represents the NAH7 plasmid normalized to PpG7. The first sampling point, Day 14, did not have enough strain growth to be accurately detected for any microcosms except the one with the additional recipient strains.

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The abundance of catabolic plasmids NAH7 and CB1190 in the abiotic controls was found to be below detection, indicating that the controls remained abiotic throughout the duration of the experiment. These data corroborate the chemical profiling where no significant decrease was observed as discussed below and seen in Figure 23.

5.3.2 Impacts of HGT on the Biodegradation of Naphthalene and 1,4-dioxane

All of the initial naphthalene and most of the 1,4-dioxane were removed from all reactors except for the control by Day 30 (Figure 23). Neither naphthalene nor 1,4- dioxane showed a significant difference in removal (C/C0) between any of the microcosms except from the control throughout the experiment. After the chemical and nutrient amendment on Day 31, the naphthalene was removed approximately two-fold faster and the 1,4-dioxane was removed approximately 1.3-fold faster (on average for all microcosms with both PpG7 and CB1190) than during the first chemical spike.

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Control PpG7 and P. stutzeri Dioxane PpG7 CB1190 and stutzeri CB1190 PpG7 and CB1190 and P. stutzeri 75 PpG7 and CB1190 All

Dioxane [mg/L] 30

0 0 10 20 30 40 50 Days

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8 Control PpG7 and P. stutzeri Naph PpG7 CB1190 and P. stutzeri CB1190 PpG7 and CB1190 and P. stutzeri PpG7 and CB1190 All

Naphthalene conx [mg/L] 2

0 0 10 20 30 40 50 Days

Figure 23: Degradation of (top) 1,4-dioxane and (bottom) naphthalene by the seven co-contaminated microcosms. Error bars represent the standard deviation of triplicate microcosms. The arrow at Day 31 represents when 1,4-dioxane and naphthalene were re-spiked in the microcosms.

PpG7, the gram-negative pseudomonad that harbors the NAH7 plasmid, was able to remove naphthalene even in the presence of 1,4-dioxane (Figure 23). Although the relative amount of 1,4-dioxane removed was lower than naphthalene, the data suggest that PpG7 is able to remove 1,4-dioxane even in the absence of pSED02 albeit not preferentially. Ether bonds, such as those found in 1,4-dioxane, are known to be cleaved by both monooxygenases and dioxygenases (Beek 2006), thus it is possible that the 117

NAH7-associated dioxygenase could also be used to cleave 1,4-dioxane’s ring, however follow up studies should be conducted to verify this hypothesis.

CB1190, the Gram-positive actinomycete that harbors the pSED02 plasmid, was able to remove both 1,4-dioxane and naphthalene parent compounds. This confirms previous studies that show CB1190 degrades 1,4-dioxane (Grostern et al. 2012, Kelley et al. 2001, Sales et al. 2011, Mahendra and Alvarez-Cohen 2005, 2006, Mahendra,

Grostern, and Alvarez-Cohen 2013, Mahendra et al. 2007, Li et al. 2010) and can oxidize naphthalene when growing on 1,4-dioxane (Mahendra and Alvarez-Cohen 2005).

Similarly to PpG7, CB1190 also utilizes an oxygenase to initially attack the 1,4-dioxane ring structure albeit a monooxygenase rather than a dioxygenase (Mahendra and Alvarez-

Cohen 2006). Both mono- and dioxygenases have been shown to attack ring structures and therefore, the expression of these enzymes may explain the reduction in naphthalene and 1,4-dioxane concentrations in the CB1190 and PpG7 microcosms, respectively.

However, because the 1,4-dioxane is never completely removed in the PpG7 microcosm, it is possible that an intermediary by-product is formed, which inhibits further degradation or there may be other factors at play. These hypotheses were not expounded upon further in this study as they were considered to be beyond the scope of our intended goals.

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There were no significant differences in degradation between microcosms that persisted through the entirety of the experiment. The undetected differences in degradation may be explained by the fact that we did not quantify degradation products.

Consequently, these data do not show complete degradation but merely the removal of the parent compound. Almost all the microcosms followed similar removal patterns

(Figure 23).

The initial chemical removal rate for the microcosms containing both PpG7 and

CB1190 was 4.7 mg/L/day for 1,4-dioxane during the first week, removing over 60% of the 1,4-dioxane, but the removal rate remained relatively steady after this time point and only another 2 mg/L on average was removed during the remaining 21 days. For naphthalene, the initial chemical removal rate averaged ~0.18 mg/L/day for the first week, but fell to an average of 0.12 mg/L/day for the second and third weeks, before picking up again slightly to an average of 0.15 mg/L/day for the last week before re- amendment. After the second addition of 1,4-dioxane and naphthalene, the chemical removal rate for all microcosms with both PpG7 and CB1190 was on average 1.6 mg/L/day and 0.28mg/L/day for 1,4-dioxane and naphthalene, respectively. This increased removal rate after re-amendment is likely due to the addition of carbon and an established biomass presence as shown in Figure 21A. It is probable that the cell growth was slowed by the limited available substrate near the end of the month, and then rapidly increased when more were added. Further, it should be stated that cell growth was generally slow; turbidity was not visible in many of the microcosms until the second

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week (Days 7-14). This may be because of the co-contaminants and/or the fact the microcosms were run below the optimal strain growth temperatures of 30°C (Parales et al. 1994).

Also notable is the fact that PpG7 does not initially rapidly degrade naphthalene.

This may have been caused by naphthalene quickly partitioning into the available headspace (Henry’s Law constant of 4.68 x 10-4 atm m3/mol (Mackay, Shiu, and

Sutherland 1979)) while 1,4-dioxane was less volatile (Henry’s Law constant of 4.88 x

10-6 atm m3/mol (Howard et al. 1990)) and remained in solution, leading to the possible potential preferential removal of 1,4-dioxane. This hypothesis is supported by the fact that the microcosms with just PpG7 show some degradation of 1,4-dioxane relatively early on with a slower than expected degradation of naphthalene. Finally, it is possible that growth conditions were not ideal in the microcosms and that alternate substrates may be needed. Work from Li et al. (Li et al. 2010) showed that CB1190 degradation of 1,4- dioxane in microcosms was enhanced by adding 1-butanol. Others have also shown that the addition of an appropriate carbon source is critical to developing a functional phenotype after a plasmid conjugation event (Ikuma and Gunsch 2013). This suggests that adding additional growth substrates and/or providing more specialized conditions may further improve biodegradation.

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5.4 Concluding Remarks

Overall, we found statistically significant increases in normalized plasmid numbers throughout the initial weeks of the experiment, suggesting there were some

HGT events that were detected over the course of the experiment and that this method can detect them. Due to the relatively small-scale liquid microcosms, this method should be further validated in larger-scale reactors with more complex media.

The method presented herein provides a framework for monitoring the proliferation of target plasmids via HGT for genetic bioaugmentation of bacterial strains harboring catabolic plasmids. The use of Taqman™ probe assays eliminates the need for manipulation of strains and provide an opportunity to monitor strain growth and plasmid dissemination in a complex environment. To the authors’ knowledge, this is the first study describing this approach for quantifying HGT of catabolic plasmids.

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6. Monitoring Microbial Community Adaptation Following the Implementation of Targeted Biostimulation and (Genetic) Bioaugmentation Strategies in PAH Lab-Scale Soil Reactors

6.1 Introduction

Polycyclic aromatic hydrocarbons (PAHs) are a group of organic priority pollutants consisting of two or more fused benzene rings that are of concern due to their toxic, genotoxic, mutagenic and/or carcinogenic properties as well as their ubiquity and recalcitrance (Ghosal et al. 2016, Cerniglia 1993). Several remediation technologies have been developed in efforts to remove PAHs from the environment. One technology, in situ bioremediation (ISB), involves the removal of chemical pollutants using biological agents (Rittmann 1994). Due to its economic and ecological benefits, treatment approaches relying on ISB have been of great interest over the past few decades for

PAHs. Certain microorganisms, such as Pseudomonas putida G7 (PpG7) (Tsuda and Iino

1990), have been shown to transform PAHs into other organic compounds or to inorganic end products such as carbon dioxide and water (a process termed mineralization) (Wilson and Jones 1993). As discussed thoroughly in previous chapters, PAH-degrading microbes may either be stimulated or augmented depending on site conditions, however bioremediation often fails due to improper strain selection (Thompson et al. 2005).

Recently, additional methods to improve ISB have been developed, including biostimulation, bioaugmentation and genetic bioaugmentation. In particular, genetic bioaugmentation is an ISB method in which environmentally relevant donor bacteria harboring self-transmissible catabolic plasmids are introduced to increase the pollutant

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degradation potential (Ikuma and Gunsch 2012a, Top 2002, Top and Springael 2003).

The augmentation of donor strains along with the selective pressure of the PAHs encourages horizontal gene transfer (HGT) through plasmid conjugation to indigenous microorganisms (Top 2002, Sørensen et al. 2005). Using this approach, rather than the augmented donor strain, the resulting transconjugants (i.e, the site-adapted bacteria that now harbor the catabolic plasmid conjugated from the donor strain) carry out the chemical biodegradation (Ikuma and Gunsch 2012a). HGT events are known to occur more readily when strains are phylogenetically related and have similar G+C content

(Ikuma and Gunsch 2010, Bellanger, Guilloteau, Breuil, et al. 2014, Thomas and Nielsen

2005). However, to successfully select appropriate donor strains for genetic bioaugmentation, a characterization of the current indigenous microbial community is critical. Recent advancements in sequencing technology have made genetic bioaugmentation, and more broadly have improved our ability to implement precision bioremediation. Precision bioremediation refers to the use of sequencing technologies to apply a combination of biostimulation, targeted bioaugmentation, and genetic bioaugmentation in an effort to address the limitations associated with bioremediation

(Adrian and Löffler 2016). It was hypothesized that by applying precision bioremediation, it is possible to naturally shift the soil microbiome towards a community with increased biodegradation potential, thereby providing a more effective way to remediate PAHs.

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In the following chapter, we present efforts to apply precision bioremediation in reactors filled with creosote-contaminated soil from Holcomb Creosote Co. Superfund site. The targeted microbial consortium designed in Chapter 4 was augmented to soil reactors and HGT events of the two augmented catabolic plasmids, pNL1 and NAH7, were monitored using methods involving qPCR Taqman™ probes characterized in

Chapter 5. To examine the comparative efficacy of precision bioremediation, testing was done in 3 phases: Phase I consisted of natural attenuation, Phase II consisted of biostimulation, and Phase III consisted of targeted (genetic) bioaugmentation. In order to assess the efficacy and potential ecological impacts of each phase, the microbial community shifts resulting from each targeted bioremediation treatment approach were also characterized.

6.2 Methods and Materials

6.2.1 Reactor Design and Set-up

Reactors were designed and constructed first to monitor prokaryotic community responses to PAHs and evaluate the efficacy of the targeted amendments as well as the introduction of exogenous strains in a controlled lab-scale environment. The reactor design is shown in Figure 1. Reactors were constructed of glass and hand-blown by Prism

Research Glass (Raleigh, NC). Each reactor contained multiple top, side, and bottom sampling ports and was deep enough to allow for both aerobic and anoxic zones.

Reactors were mounted on a wooden frame approximately 8 inches from the ground in a

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chemical fume hood to allow for access to all sampling ports. Above the drainage port, the bottom of the reactor was covered with a micropore mesh (Vigoro, Sylacauga,

Alabama) to prevent any soil or debris loss. Each reactor was packed with a 5-7 cm deep base layer of large (0.5-1.0 inch in diameter) gravel (Kolorscape, TPC Drainage Rocks,

Atlanta, GA) in order to allow any excess amendment to pool at the bottom of the reactor for easier drainage or removal. Immediately above the large gravel layer, a ~ 5 cm layer of medium sized (0.2-0.5 inch in diameter) gravel (Kolorscape, TPC pea gravel, Atlanta,

GA) was added for a total packing height of ~10-12 cm. All gravel used in this study was sieved and autoclaved at 121 °C for 60 minutes prior to its addition into the reactors.

Due to the high clay content of the Holcomb Creosote Co. soil, ~15% by volume of sterile sand (Quikrete Sand, Atlanta, GA) was mixed with the original soil samples to prevent pooling, minimize short circuiting and increase flow through the columns was ideal. Sand was autoclaved at 121 °C for 60 minutes prior to addition to the reactors.

Holcomb Creosote Co. Superfund site soil characterization information is provided in

Chapter 4.

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A B

Top1 Top2

Bo)om1 Bo)om2

C 60#

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6.2.2 Natural Attenuation, Biostimulation, and Targeted Bioaugmentation Intervention

The reactors were subjected to three phases of treatment as outlined in the following section. Phase I lasted the first six weeks (Weeks 1-6) of the experiment. The goal during Phase I was to establish a natural attenuation baseline. During this phase, reactors only received carbon-free Dulbecco’s Modified Eagle’s Medium (referred to herein as Eagle’s Medium) (Sigma Aldrich, Saint Louis, MO). A peristaltic pump

(Masterflex, Cole-Parmer, Vernon Hills, IL) was mounted above the reactors and Eagle’s

Medium was sprayed over the reactors once a week intermittently at a rate of 8-10 mL/minute; each reactor received a total of approximately 100 mL of Eagle’s Medium twice a week.

Phase II intervention was carried out during the second six weeks of the experiment (Weeks 6-12) and consisted of a biostimulation intervention. At the beginning of Weeks 6 and 9, a 200 mL of 1:10 dilution of Black Strap Molasses (Golden

Barrel, Lancaster County, PA) in Eagle’s Medium was added to each of the reactors.

Black Strap Molasses was selected as the amendment as it is a rich carbon source and known to contain phenolic compounds (Quinn et al. 2007). Eight boreholes, the length of the reactor, were fashioned approximately 2 inches apart with an AMS 2.54 cm x 61.0 cm stainless steel soil corer (American Falls, ID). Each borehole was then filled with ~25 mL of the molasses solution.

Phase III was completed during the last six weeks of the experiment (Weeks 12-

18). Phase III consisted of the targeted (genetic) bioaugmentation intervention. At the

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beginning of Weeks 12 and 15, the engineered consortium developed in Chapter 4 was added to each reactor. In short, the engineered consortium consisted of four strains:

Novosphingobium aromaticivorans F199 (referred to as F199, ATCC 700278),

Mycobacterium fredericksbergense (USDA Agricultural Research Service, B-21426),

Pseudomonas putida G7 (PpG7, ATCC 17485), and Acinetobacter venetianus (ATCC

31012). Two of these strains were identified to be strong bioaugmentation candidates as described in Chapter 4 (F199 and M. frederickbergense) and the other 2 strains were selected as genetic bioaugmentation candidates (PpG7 and A. venetianus) based on the framework also outlined in Chapter 4. PpG7 and F199 harbor the NAH7 and pNL1 catabolic plasmids known to carry PAHs degrading genes, respectively.

Prior to augmentation, all strains were grown overnight on a shaker at 150 rpm at

30°C in Eagle’s Medium with 2% sodium pyruvate as the carbon source (Sigma Aldrich,

Saint Louis, MO). One hundred mL of each culture was added to 400 mL of fresh Eagle’s

Medium and combined for a total of 2 L. The consortium was augmented into the reactors by the same method used for biostimulation. Eight boreholes were created along the entire length of the reactor approximately 2 inches apart. Approximately 25 mL of the consortium was injected into each borehole. In total, each reactor received 200 mL of the consortium during each bioaugmentation event.

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6.2.3 Sampling

Soil samples were taken from each reactor immediately before and after each intervention. At each sampling time point, 4 to 5 cores were taken from the entire width of each reactor from the top and bottom sampling ports. Each core was ~2-5 mL for a total sample of ~15 mL of soil taken from each side sampling port. Samples were taken perpendicular to where biostimulation and bioaugmentation interventions were applied

(arrows depicted in Figure 25). This sampling strategy was used in an attempt to include as much of the diversity and heterogeneity within the reactor as possible. Samples were placed in 50 mL polypropylene centrifuge tubes (VWR, Radnor, PA). Samples from the top sampling ports (labeled Top1 & Top2 in Figure 24B) and bottom sampling ports

(labeled Bottom1 & Bottom2 in Figure 24B) were combined and homogenized.

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Amendment Additions

Sampling

A B

6.2.4 DNA Extraction

Soil samples were weighed out to ~0.25-0.35 g and placed in 1.5 mL Eppendorf tubes (VWR, Radnor, PA) for DNA extraction. Pseudo-triplicate samples were taken from three sample vials from each reactor in order to minimize any sampling or user error. Prior to DNA extraction, samples underwent a clay-wash using a technique adapted

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from Pietramellara et al. (Pietramellara et al. 2007) as described previously in Chapter 4.

After the clay wash, DNA was extracted using the PowerLyzer® PowerSoil® DNA

Isolation Kit (MO BIO Laboratories, Carlsbad, CA). DNA was extracted as instructed by the manufacturer; the exception being that the tubes were vortexed horizontally for 20 minutes on the VWR VX-2500 Multitube Vortex (VWR, Radnor, PA) rather than the instructed 10 minutes on the MO BIO Vortex Adapter. In addition, during the final step of the extraction protocol, the pseudo-triplicates were run through the same DNA-binding filter and eluted together. The eluted DNA concentrations were measured in triplicate using a Qubit® 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA) with dsDNA

BR reagents according to manufacturer’s instructions. Genomic DNA samples were stored at −20°C until they were submitted to sequencing.

6.2.5 16S rDNA Amplicon Sequencing

Library prep and sequencing were performed by Duke’s Center for Genomic and

Computational Biology (GCB, Duke University, Durham, NC). The sample library was prepared according to the “Illumina MiSeq 16S Metagenomic Sequencing Library

Preparation” workflow (Illumina, San Diego, CA.). Samples were normalized, pooled, and run on a 2 x 300 bp paired-end MiSeq platform using V3 chemistry.

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6.2.6 Sequencing Data Analysis

The raw mate-paired fastq files were quality-filtered, de-multiplexed and analyzed using the Illumina BaseSpace App and 16S Metagenomic Analysis Pipeline (Hosokawa et al. 2006). Briefly, the reads were classified against the GreenGenes database for taxonomic assignment and sorted into Operational Taxonomic Units (OTUs). Using the

Individual Rarefaction setting in PastProject (Hammer, Harper, and Ryan 2001), a rarefaction curve with a 95% confidence interval was calculated. Samples that did not reach saturation were removed from further analysis.

6.2.7 qPCR

Plasmid conjugation was monitored through Taqman®™ probe-based qPCR using methods previously described in Chapter 5. PpG7 primers and probe were previously described in Chapter 5 and the F199 primers and probes are shown in

Table 9. The F199 primers and probe were designed to target where the sigma54 specific transcriptional regulator (Fis family, location 640480949) meets the ring hydroxylating dioxygenase alpha subunit (location 640480950) as this was found to be a unique pNL1- specific region.

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Name Primer (F/R) Sequence Type pNL1 Forward GAACTTGAGCGGATCTTCGG HPLC Reverse CATCGTCTTCGCCCATGAAG HPLC Taqman Probe CGCCCGGCTTCGGAATCTGGC 5'FAM-3'TAMRA

F199 Forward TATTGGACAATGGGCGCAAG HPLC Reverse CGCCCAGTAATTCCGAACAA HPLC Taqman Probe CGTGCCAGCAGCCGCGGTAA 5'FAM-3'TAMRA

NAH7 Forward ATTAGCGATTTCGTGGCTCT HPLC Reverse GCTCCAGCACTACGGTTACA HPLC Taqman Probe CCGGCCCTACTGGAAACCAGAA 5'FAM-3'TAMRA

Pp16s Forward TGTGTGAAGAAGGTCTTCGG HPLC Reverse CGCTTGCACCCTCTGTATTA HPLC Taqman Probe AACTCTGTGCCAGCAGCCGC 5'FAM-3'TAMRA

qPCR reactions were carried out on a BioRad C1000 Touch™ Thermal Cycler

CFX96 ™ Real-Time System (Hercules, CA). The qPCR reactions were performed with a total reaction volume of 20 µL. Primers and probes were added to a final concentration of 250 nM with 2 µL of undiluted DNA (~20-40 ng of DNA). The reaction conditions were as follows: 95 °C for 10 minutes, followed by 45 cycles of denaturation at 95°C for

1 minute and 60°C for 15 seconds. qPCR reactions were performed in triplicate for each sample, including samples for a standard curve and negative control. An eight point

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standard curve and a negative control were run on each plate. Runs with an R2 value above 97% were analyzed. Gene copy numbers were normalized to 1 g of starting soil sample.

6.2.8 Statistical Analysis

Microbial diversity was assessed by non-metric analyses through non-metric dimensional scaling (NMDS) plots using Bray-Curtis similarities. The effect of treatment and time on the microbial communities were tested using two-way analysis of variation

(ANOVA) over treatment and time (St and Wold 1989). To identify specific taxonomic shifts, Tukey’s multiple comparison tests were run post-hoc (Tukey 1949). For qPCR data, a two-way ANOVA over treatment and time was performed. Experimental values are reported as the mean ± standard deviation. Data handling and statistical analyses were performed using the statistical R packages (R studio, version 3.1.2), GraphPad Prism

(Prism 1994), Past Project (Hammer, Harper, and Ryan 2001), and Microbiome Analyst

(Dhariwal et al. 2017). Differences were considered significant for p-values ≤ 0.05 and marginal significance was considered for p-values ≤ 0.10.

6.3 Results

6.3.1 Diversity of Soil Microbial Community

Differences in diversity were visually confirmed with NMDS analysis (Figure

26). Overall, the community structure was found to significantly shift by treatment

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(p<0.001) and time (p<0.001), determined by a 9,999 permutation two-way permutational analysis of variance (PERMANOVA) with Bray-Curtis similarity indices.

BIOSTIMULATION

BIOAUGMENTATION

Figure 26: Non-metric dimensional scaling (NMDS) analysis of all identified OTUs in reactors over all three treatments.

The communities consisted of 26 phyla, consistent across all treatment and time.

Core phyla were similar between all three reactors and all phases and were dominated by

Proteobacteria, Firmicutes, and Actinobacteria (Figure 27).

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100 Chrysiogenetes Fusobacteria Crenarchaeota Thermodesulfobacteria Caldithrix Euryarchaeota 75 Thermotogae Deferribacteres Armatimonadetes Gemmatimonadetes Cyanobacteria Thermi 50 Nitrospirae Spirochaetes Synergistetes Verrucomicrobia Tenericutes Chloroflexi Chlamydiae 25 Acidobacteria Relative Abundance (%) Planctomycetes Bacteroidetes Actinobacteria Firmicutes Proteobacteria 0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 27: Phylum-level relative abundances of the reactor soil microbiome.

There were significant fluctuations in the dominant taxa over time. In particular,

Proteobacteria decreased in abundance during Phase II (p<0.05) and between the start

(Week 6) and end (Week 18) of the interventions in the bottom of the reactors. No significant differences were observed in the top of the reactors during this same time period. Conversely, Firmicutes increased (p<0.05) during biostimulation and between the start and end of the interventions in the bottom of the reactors. Similarly to

Proteobacteria, Firmicutes showed no difference in the top of the reactors. Though

Actinobacteria increased (p<0.05) following biostimulation in both the top and the

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bottom of the reactors, there was no significant difference between the start and end of the experiment. Bacteroidetes were increased in the top of the reactors between the start and finish of the experiment, as was Planctomycetes. There were no other phyla with significant fluctuations.

At a lower taxonomic level, the class level, sequencing analysis revealed significant variations in microbial communities between treatment and time (Figure 28).

However, the starting communities at Week 6 in the top and bottom of the reactor were not significantly different than the ending community at Week 18. Overall, the top 7 classes represented over 75% of the total community and no single bacterial class dominated the samples.

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Clostridia Bacilli Betaproteobacteria Actinobacteria Deltaproteobacteria Alphaproteobacteria Gammaproteobacteria

100

Relative Abundance (%) 25

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 28: Top 7 most abundant Class-level taxonomic data throughout all phases of the experiment. Error bars represent standard deviation between reactors.

After biostimulation there was a significant increase (p<0.05) in the class Bacilli in both the top and bottom of the reactors (Figure 29). This increased abundance did not persist following bioaugmentation throughout Phase III, suggesting a response to biostimulation. Within the class Bacilli, the responding genera were Bacillus,

Leuconostoc and Sporolactobacillus; increasing in abundance (p<0.05) during Phase II and presenting as high as 11.0 ± 8.5%, 14.3 ± 3.1%, and 16 ± 2.8%, respectively (Figure

30). Bacillus and Leuconostoc responded to the first stimulation event, while

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Sporolactobacillus responded to the second stimulation event as well as the first augmentation event.

Clostridia Bacilli Betaproteobacteria Actinobacteria Deltaproteobacteria Alphaproteobacteria Gammaproteobacteria 100

Relative Abundance (%) 25

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 29: Shifts in Bacilli in the reactors over time. Error bars represent standard deviation between reactors.

Along with Bacillus, Leuconostoc, and Sporolactobacillus, there were additional genera that responded to interventions. In particular, Clostridium was one of the most abundant genera in Phases II and III, increasing from an average of 1% ± 0.5% during

Phase I to as high as 16% ± 7% and 20% ± 11% during Phases II and III, respectively 139

(Figure 31). Clostridium showed fluctuations between the top and bottom sampling points, generally being elevated in the bottom of the reactors. This is not surprising as

Clostridium is an obligate anaerobe and the bottom of the reactor was more likely to become anaerobic.

30 Bacillus Leuconostoc Sporolactobacillus 25

10 Relative Abundance (%) 5

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 30: Relative abundance of the Bacillus, Leuconostoc, and Sporolactobacillus genus over time. Error bars represent the standard deviation between reactors.

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30 Clostridium

10 Relative Abundance (%) 5

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 31: Relative abundance of the Clostridium genera over time.

6.3.2 Impacts of Bioaugmentation

When looking specifically at the classes that were augmented,

Gammaproteobacteria (A. venetianus and PpG7), Alphaproteobacteria (F199),

Actinobacteria (M. frederiksbergense), there were some fluctuations in abundance but no significant shifts over time (p>0.05) (Figure 32). However, when considering a lower taxonomic level, significant shifts in the augmented taxa are observed (Figure 33). There were no significant shifts in Mycobacterium and Novosphingobium (p>0.1) though they were both maintained throughout the experiments. Geobacter relative abundance did not increase during biostimulation. In fact, Geobacter decreased marginally (p=0.1) from

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Day 0 abundance. Significant fluctuations (p>0.05) in Pseudomonas were observed over time and location in the reactor. Starting at Day 0, Pseudomonas had an abundance of 3.4

± 0.2% on average, decreased to an average of 0.26 ± 0.1% in the bottom of the reactors by Week 7 before rebounding to an average of 3.0 ± 0.8% in the bottom of the reactors during Weeks 15 and 16. Acinetobacter also showed significant fluctuations in the reactors. Acinetobacter increased in Phase III after bioaugmentation; starting at an average of 0.004 ± 0.001% in the top and bottom sampling ports prior to augmentation at

Week 12 and increasing to 1 ± 0.1% and 1.4 ± 0.4% in the top and bottom sampling, respectively by Week 13. By Week 15, Acinetobacter decreased again to an average of

0.09 ± 0.02% and 0.1 ± 0.1% before rebounding after the second bioaugmentation event to 4.7 ± 2.2% and 3 ± 2.3% in the top and bottom reactors, respectively. By the end of the experiment, Acinetobacter was again close to starting abundances.

Changes in the relative abundance of P. putida, M. frederiksbergense, and N. aromaticivorans (i.e., individual members of the augmented consortia) were assessed

(Figure 34). A. venetianus was not detected in the reactors. Though individual strains could not be identified, increases in species abundance were observed for N. aromaticivorans; showing increased (p<0.05) abundance from Day 0 to Weeks 13 and 15 in the top of the reactor. No other augmented genus had significant increases.

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Clostridia Bacilli Betaproteobacteria Actinobacteria Deltaproteobacteria Alphaproteobacteria A Gammaproteobacteria

100

25 Relative Abundance (%)

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B

BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Clostridia Bacilli Betaproteobacteria Actinobacteria Deltaproteobacteria Alphaproteobacteria Gammaproteobacteria

B 100

Relative Abundance (%) 25

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT Clostridia Bacilli Betaproteobacteria Actinobacteria Deltaproteobacteria Alphaproteobacteria C Gammaproteobacteria

100

25 Relative Abundance (%)

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

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15 Pseudomonas Novosphingobium Mycobacterium 12 Acinetobacter Geobacter

3 Relative Abundance (%) Abundance Relative

0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 33: The relative abundance of targeted and augmented genera in the reactors monitored over time.

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0.4 Pseudomonas putida Mycobacterium frederiksbergense Novosphingobium aromaticivorans

0.3

0.2

Relative Abundance (%) 0.1

0.0 D0 W6 T W7 T W9 T W6 B W7 B W9 B W10 T W12 T W13 T W15 T W16 T W18 T W10 B W12 B W13 B W15 B W16 B W18 B BIOSTIMULATE BIOSTIMULATE BIOAUGMENT BIOAUGMENT

Figure 34: Species of interest identified in reactors through Illumina Miseq sequencing. Error bars represent standard deviation (N=3).

6.3.4 Catabolic Gene Dissemination

The concentrations of the NAH7 and pNL1 catabolic plasmids and the strains that harbor the plasmids (i.e., PpG7 and F199, respectively) were monitored throughout the course of the experiment.

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A NAH7 PpG7 1010

109 NA Biostimulation * Bioaugmentation

108

107

106

105

104 Gene Copy Number 103

102

101

100 0 6T 6B 7T 7B 9T 9B 10T 10B 12T 12B 13T 13B 15T 15B 16T 16B 18T 18B

STIMULATE STIMULATE AUGMENT AUGMENT

B pNaphthalene 10000

NA Biostimulation * Bioaugmentation 1000

100

0.1

0.01

0.001

0.0001

0.00001 0 6T 6B 7T 7B 9T 9B 10T 10B 12T 12B 13T 13B 15T 15B 16T 16B 18T 18B

STIMULATE STIMULATE AUGMENT AUGMENT

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C pNL1 F199 1010

109 NA Biostimulation Bioaugmentation

108

107

106

105

104 Gene Copy Number 103

102

101

100 0 6T 6B 7T 7B 9T 9B 10T 10B 12T 12B 13T 13B 15T 15B 16T 16B 18T 18B

STIMULATE STIMULATE AUGMENT AUGMENT

D ppNL1 1000

NA Biostimulation Bioaugmentation 100

0.1

0.01

0.001

0.0001

0.00001 0 6T 6B 7T 7B 9T 9B 10T 10B 12T 12B 13T 13B 15T 15B 16T 16B 18T 18B

STIMULATE AUGMENT STIMULATE AUGMENT

When comparing starting concentrations, the pNL1 plasmid was found at ~1000 times higher initial concentrations (103-104 per g soil) than the NAH7 plasmid (101-102 per g soil). The PpG7 strain and F199 strain were found at similar starting concentrations

(around 104 per g soil).

NAH7 levels in reactor soils displayed an overall increase after Phase III (Figure

35A) with a significant increase (p<0.05) at Week 13 in the top of the reactors.

Normalized NAH7:PpG7 concentrations were also increased (p<0.05) after augmentation

(Figure 35B). pNL1 levels in reactor soils did not increase (p>0.1) after augmentation, nor during any other phase (Figure 35C). Normalized pNL1:F199 concentrations showed fluctuations but no consistently significant increase (Figure 35D). There was a marginal increase (p<0.1) in F199 strain abundance at Week 16 in the top sampling point. This sample was taken following the second bioaugmentation intervention, halfway through

Phase III. No consistent trend was identified between the top and bottom sampling ports that persisted over time. In addition, PpG7 was detected at higher average levels than the

F199 strain.

The qPCR data is consistent with the sequencing data in that PpG7 as well as the

P. putida species do not show a significantly increase in abundance throughout the experiment are not different between top and bottom sampling ports, while N. aromaticivorans increases slightly in abundance after Week 12 in the top of the reactors

(p<0.1) and F199 increases at Week 16 in the top of the reactors (Figure 38 and Figure

39).

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6.4 Discussion

In this chapter, a framework for incorporating next-generation sequencing in bioremediation as well as the use of qPCR probes for monitoring genetic bioaugmentation was successfully applied in a ISB case study. Overall, the work presented in this chapter supports the hypothesis that environmental microbiome can be engineered in a targeted fashion.

Though there were some significant fluctuations in the dominant taxa over time, most of the identified phyla were not affected. The stimulation resulted in less

Proteobacteria and more Firmicutes and Actinobacteria. The magnitude of these fluctuations was reduced during bioaugmentation and there were increases in

Bacteroidetes and Planctomycetes. At no point was any one phyla identified to be greater than 60% of the total community. The class Bacilli responded positively to the biostimulation intervention during Phase II. In particular, the Bacillus and

Sporolactobacillus genus were responsive to intervention. This is not surprising, as certain members of the class Bacilli have been shown to degrade petroleum hydrocarbons, including Bacillus strains (Das and Chandran 2010). The lack of response from Geobacter suggests that bacteria belonging to the Bacilli genus may be stronger bioremediation targets and more efforts are needed to determine how to effectively stimulate Geobacter. Bacteria in the Clostridium genus also responded to both biostimulation and bioaugmentation interventions. Though Clostridium are not generally regarded as bioremediation candidates, some anaerobic strains have recently been

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identified that can biodegrade hydrocarbons (von Netzer et al. 2013) and thus may represent a novel target for precision bioremediation.

Increases in abundances following bioaugmentation were observed in two of the augmented taxa: Acinetobacter spp. and N. aromaticvorans. Neither taxon maintained an increased abundance in the long term but rather quickly responded to the interventions before reducing again in abundance. M. frederiksbergense and P. putida did not show a significant increase and A. venetianus was never detected in the reactors suggesting it could not compete with other indigenous bacteria already present. The overall taxonomic shifts support the hypothesis that we may temporarily and subtly alter the environmental microbiome to increase biodegradation potential, but the shifts in the microbial community will not persist long-term. This is advantageous, as slight increases in PAH- degrading taxa may be more desirable than large-scale ecological disruptions that may affect nutrient cycling or other aspects of environmental ecological health. In particular, there were increases in multiple Bacilli genera and Acinetobacter after biostimulation and augmentation, respectively. The sustainable but minor responses in specific taxa may be ideal, especially if they still carry increased biodegradation capacity. It is also interesting to note that M. fredericksbergense, the K-strategist, appeared to be maintained at a stable but low abundance in the community throughout the duration of the experiment though with no significant increase or decrease in abundance. On the other hand, P. putida, N. aromaticivorans, and A. venetianus, the other augmented species, are r-strategists and showed less stable abundances. In particular, N. aromaticivorans species increased after

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bioaugmentation in the top of the reactors but was reduced in abundance again by the conclusion of the experiment. Transcriptomic or proteomic work is needed to measure the functional activity of these strains.

Finally, in terms of genetic bioaugmentation, most uptake events occurred during the initial augmentation intervention when selective pressure presumably is highest.

When monitoring the NAH7 plasmid, it appears that recipient strains rapidly took up the plasmid following augmentation but the plasmid concentration reaches a plateau. This finding suggests that some microorganisms initially take up the plasmid to determine if it carries advantageous traits but eventually release it and do not uptake it again once the selective pressure is decreased. Such a scenario has previously been documented with the removal of a strong selective pressure (Ikuma and Gunsch 2013). After the second augmentation intervention, with further reduction in selective pressure, the microbes may have determined that the plasmid is no longer beneficial for survival and no longer seek to take it up. For the second plasmid (pNL1), there was no detected increase overtime.

This was surprising as the pNL1 plasmid is known to encode genes for the catabolism of toluene, naphthalene, xylene, p-cresol, and additional aromatic compounds (Fredrickson et al. 1991). The pNL1 plasmid is the only plasmid from a Sphingomonas strain that has been sequenced, and it has been found that about one-third of the identified ORFs of this plasmid are associated with the catabolism or transport of aromatic compounds, including oxygenases (Basta, Buerger, and Stolz 2005, Romine et al. 1999). Though the conjugation of pNL1 to another Sphingomonas sp. has been demonstrated (Romine et al.

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1999), it does not appear to have been promoted in these reactors. It is possible the pNL1 plasmid did not undergo plasmid conjugation in the reactors because the transconjugants did not carry a favorable enough phenotype compared to other members of the community. The F199 strain was confirmed to be present in the initial soils, however it did not responded well to biostimulation, possibly being outcompeted by other strains, and although F199 did respond to the second bioaugmentation intervention, its increase did not persist.

One of the general drawbacks of the qPCR method used to quantify conjugation events is that it does not provide any information about the identity of the transconjugants. For instance, it is unknown if one genus is the main group of recipient strains (i.e., those responsible for accepting most of the plasmids during HGT events) or if it is relatively widespread throughout the community. Unfortunately, there are no currently available methods that allow for a more precise measurement. As many of the potential recipient strains are likely unculturable, improvements to the molecular methodology will likely be the only way to identify all the transconjugants. In the future, this method should be tested with more dynamic environmental parameters, such as in a mesocosms or in a field trial before full-scale adoption, and oxy-PAHs and other degradation by-products should be quantified. In addition, as previously discussed in other chapters, the ability to design qPCR and implement NGS in bioremediation is heavily influenced by the limited availability of environmental genomic data. Thus,

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future work should focus on improving the inadequate existing data through whole- genome shotgun sequencing.

While the work presented in this chapter was carried out on PAHs, the research presented herein has broad implications for engineering soil microbiomes for bioremediation in general. In particular, this work provides a framework for the incorporation of NGS in the design of ISB strategies. This framework allows for the identification and quantification of biostimulation and (genetic) bioaugmentation targets as well as their continued monitoring following the implementation of ISB strategies.

This dissertation work begins to fill an important research gap in the field of bioremediation.

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Chapter 7: Conclusions and Engineering Significance

This dissertation research provides key insights into prokaryotic microbiome assembly in PAH contaminated environments and into the development of strategies for engineering environmental microbiomes to enhance bioremediation performance.

Summaries of key findings as well as the engineering significance for the work carried out in the context of this dissertation are presented below.

7.1 Key Findings and Future Work

Conclusion 1: Long-term PAH exposure is linked to shifts in intestinal microbiota and metabolites, which have a complex relationship with the host physiology.

The work presented in this dissertation is the first to examine the gut metabolome and microbiome of pollution-adapted Elizabeth River (ER) killifish. Overall, the gut metabolome of the Republic (i.e., creosote-exposed) killifish subpopulation showed a significant decrease in gut-associated metabolites, which is reflected by decreased diversity in the gut-associated microbiome. The unexposed Kings Creek (KC) guts were similar in diversity to the KC sediment, differing only in the abundance of nine of the identified species. However, Republic killifish guts had abundance shifts in 176 identified species when compared to the Republic sediment. In addition, Republic killifish guts and sediment grabs were higher in PAH-degrading taxa (e.g., Pseudomonas,

Corynebacterium, Sphingomonas, Mycobacterium, and Bacillus genera) than KC samples. These data suggest that PAHs in the ER act as a significant selective pressure

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that selects for bacterial communities with less overall taxonomic diversity but increased biodegradation potential. Furthermore, this suggests there are more than simple dietary influences acting on the Republic fish gut microbiome.

Along with shifts in the environmental and gut microbiomes, this chapter also identified shifts in the gut-associated metabolome. For instance, the Republic fish presented an overall trend of decreased amino acid concentrations. As the indigenous gut microbiota play an important role in degradation of proteins into smaller peptides and

AAs through proteases and peptidases (Macfarlane, Cummings, and Allison 1986), this metabolome shift is likely associated to the gut microbiota. This is critical, as the changes in the microbiome as well as shifts in the metabolome are likely to affect the fish host, suggesting that the Republic fish may have decreased metabolic capacity or immunity.

Taken together, these results also suggest the adapted phenotype the Republic killifish display is related to the adaptations mirrored in their gut microbiome and metabolome.

Future work should be focused on sequencing a larger sample size over multiple seasons and years from various locations along the ER in order to account for environmental variability and identify additional host-microbe-metabolome trends. In addition, because we do not know the exact relationship and directionality between the microbiome, metabolome, and fish host, additional experiments should focus on clarifying this complex relationship.

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Conclusion 2: Using the data from NGS, a soil microbiome can be engineered through targeted bioremediation that has increased biodegradation potential.

Environmental contamination can lead to contaminated water, unusable land, and toxic human exposures. Developing methods to quickly and sustainably remediate contaminated environments is of pronounced importance. In this objective, the aim was to improve current bioremediation strategies to minimize long-term impacts and provide insights into how to best protect nearby ecosystems. Herein, we used high-throughput sequencing to investigate how long-term chemical pollutant exposure influences native environmental microbiomes, and developed a framework for determining which strains could serve as biostimulation, bioaugmentation or genetic bioaugmentation targets. While this approach was tested on PAH contaminated soils obtained from the Holcomb

Creosote Co. Superfund site, the resulting framework could be generally applied for improving bioremediation at any site.

In the Holcomb Creosote soils, shifts in putative aromatic degrading bacterial taxa

(e.g., Rhizobiales, Burkholderiales, Holophagales, Clostridiales, Rhodocyclales,

Flavobacteriales, and Syntrophobacterales) were associated with varying soil contamination levels. Microbes in the Geobacter genus were identified as strong biostimulation targets, as these taxa are known to degrade aromatic hydrocarbons, remediate some metals, were one of the most abundant genera, and were identified at all five sampling sites. Mycobacterium fredericksbergense (USDA ARS, B-21426) was identified as a promising bioaugmentation candidate as it is able to degrade PAHs, is

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readily available, inexpensive, easy to cultivate, has previously reported hydrocarbon degradation, and generally positively correlated with PAHs and metals, high abundance, and species diversity at all Holcomb locations. Finally, Sphingomonads were identified as promising targets for genetic bioaugmentation recipients as they were positively correlated across PAHs and are known to harbor large xenobiotic-degrading plasmids.

Sphingomonas strain KS14 and Sphingomonas aromaticivorans F199 were identified as two specific donor strains. These were selected because they are capable of conjugation and contain plasmids capable of degrading PAHs. More than twenty species of

Sphingomonads were identified across the five sampling sites, suggesting that they are well adapted to the site conditions.

Difficulty remains when making full site assessments and designing a bioremediation strategy due to the inadequate environmental microbiome databases.

Currently, due to the limited data, much of the environmental microbiome analysis functions at a high taxonomic level, which is inappropriate for choosing bioremediation targets. As a result, future work in whole genome shotgun metagenomic sequencing is needed. Along with genomic advancements, this framework for using NGS in bioremediation should be evaluated at additional sites, as it is also necessary to build the metadata associated with the whole-genome sequencing databases.

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Conclusion 3: HGT can be monitored in complex media using qPCR probes targeting highly specific DNA regions in plasmids and donor cells.

A qPCR TaqMan™ assay was developed and validated for monitoring the proliferation of catabolic plasmids via HGT in the context of genetic bioaugmentation.

This approach eliminates the need for genetically manipulating strains and provides an opportunity to monitor strain growth and plasmid dissemination in a complex environment. This method was validated in microcosms seeded with Pseudomonas dioxanivorans strain CB1190 (CB1190) Pseudomonas putida strain G7 (PpG7) containing naphthalene and 1,4-dioxane. Using this approach we were able to detect multiple gene transfer events over an 8 week period of time. This qPCR assay can be adapted to monitor genetic bioaugmentation of any other catabolic plasmids. These probes provide an opportunity to accurately monitor strain growth and plasmid dissemination in a complex environment and during events such as genetic bioaugmentation. The main limitation of this work is the limited amount of fully sequenced genomes and catabolic plasmids. This further emphasizes the need for increased efforts in whole-genome sequencing of environmental microbes.

Conclusion 4: Metagenomic characterization can be used to successfully engineer environmental microbiomes and improve PAH in situ bioremediation efficacy.

Efforts to engineer a soil microbiome capable of increased PAH degradation were validated in soil reactors filled with soil from the Holcomb Creosote Co. Superfund site.

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Based on the sequencing data, the environmental microbiome in the reactors had significant shifts over treatment and time. In particular, there were increases in multiple

Bacilli genera and Acinetobacter after biostimulation and augmentation, respectively.

Clostridium and also responded to both biostimulation and bioaugmentation. Of the augmented species, there was only an increase observed in N. aromaticivorans. M. frederiksbergense and P. putida did not show a significant increase and A. venetianus was not detected in the reactors. Generally, the shifts in the microbiome were only detected at lower taxonomic levels, indicating sustainable but minor responses in specific taxa rather than large disruptions in soil microbial communities. This is an important, as slight increases in PAH-degrading taxa are desirable, rather than large scale disruptions that may effect environmental health such as plant growth and nutrient cycling. In addition, probes were designed to successfully monitor the transfer of the introduced catabolic plasmids in a complex environment. The probes were able to detect HGT events in the NAH7 plasmids and the lack of HGT events from the pNL1 plasmid throughout natural attenuation, biostimulation, and precision bioremediation. In general, the NAH7 plasmid was immediately conjugated after genetic bioaugmentation, but the plasmid was not maintained in the community over time. This is likely because there was not a strong enough advantage with respect to the increased metabolic load. Overall, this work suggests that our ability to improve bioremediation remains severely limited due to the lack of environmental microbial data. In the future, more full genomic characterization of

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site-relevant strains should be conducted and the database for environmental microbiomes should be developed.

7.2 Engineering Significance

This dissertation aimed to identify pollution-related adaptions in prokaryotic communities and to develop strategies for engineering environmental microbiomes to improve bioremediation efficacy. This work demonstrates that developing a better understanding of pollutant-related microbiome shifts is critical in designing effective and targeted bioremediation strategies. It is clear that recent advancements in NGS improve our ability to identify which microbes should be targeted during bioremediation.

However substantial work is needed to scale this dissertation work to real world field applications. In particular, due to the heterogeneous conditions encountered at sites, for this framework to be successfully implemented, it is critical that environmental microbiome structure be thoroughly characterized at the sites. Future work should aim to identify the minimum amount of sampling required (which is likely a complex and multi- faceted problem), to develop a successful bioremediation strategy, and to work to scale up these methods beyond lab-scale reactors into mesocosms or field trials.

The work presented here provides a framework towards implementing amplicon- based NGS towards bioremediation, but this work needs to be followed by shotgun metagenomic and additional –omic (e.g., transcriptomic, proteomic) work. Though it is known that many microbes are metabolically diverse and able to degrade a wide variety

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of environmental contaminants (Mrozik and Piotrowska-Seget 2010), their use in bioremediation is limited by a lack of genomic data. As more environmental microbes are fully characterized, the greater the potential for more effective and site-specific bioremediation. In addition, by strengthening the environmental microbiome databases, there is potential for improved power in selecting donor and recipient strains through modeling and machine learning.

Finally, a more comprehensive understanding of how microbiomes adapt in the context of a changing environment is not only critical in the context of bioremediation but also to human health. As we deal with increasing environmental stress, such as climate change and the rise in antibiotic usage, understanding the role of the microbiome is increasingly important.

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Appendix A– Supplementary Information for Chapter 3

Sphingolipids SM C26:1 Republic SM C26:0 King's Creek SM C24:1 SM C24:0 SM C22:3 SM C20:2 SM C18:1 SM C18:0 SM C16:1 SM C16:0 SM (OH) C24:1 SM (OH) C22:2 SM (OH) C22:1 SM (OH) C16:1 SM (OH) C14:1 -50 0 50 100 150 Sphingolipid Conx [µM]

Figure 36: Sphingolipid concentrations identified in killifish guts. There was no difference (p>0.1) in Sphingolipid abundance between sampling locations.

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Identified Taxa- Phylum Level 100 Elusimicrobia Fibrobacteres Chrysiogenetes Fusobacteria Thermodesulfobacteria Caldiserica Deferribacteres Gemmatimonadetes Armatimonadetes Chlamydiae Caldithrix Chlorobi Thermi 50 Tenericutes Planctomycetes Synergistetes Thermotogae Nitrospirae Spirochaetes Crenarchaeota Relative Abundance (%) Verrucomicrobia Acidobacteria Euryarchaeota Chloroflexi Bacteroidetes Actinobacteria Firmicutes 0 Proteobacteria

Rep, Gut Kings, Gut Rep, Sediment Kings, Sediment Figure 37: All identified taxa at the Phylum taxonomic level in Republic (‘Rep’) gut and sediment samples and KC (‘Kings’) gut and sediment samples. There is a decrease in overall diversity in Republic gut samples.

163

SampleLocation SampleType 3 SampleLocation King's Creek 2 Republic

SampleType 1 Gut 0 Sediment

-1

-2

-3 Sample1 Sample2 Sample3 Sample7 Sample8 Sample9 Sample4 Sample5 Sample6 Sample10 Sample11 Sample12

Figure 38: Heatmap of OTU-level taxonomic data using Pearson distance measurements and Ward clustering algorithms.

164

Republic Gut Republic Sediment KC Gut KC Sediment

Figure 39: Non-metric multidimensional scaling (NMDS) plot using Bray-Curtis distances of microbiota data (stress=0.01106). A two-way ANOSIM with 9,999 permutations (N) revealed significant shifts due to the sampling location (gut vs. sediment, p=0.008) and PAH-exposure (Republic vs. KC, p=0.009).

165

Figure 40: A Similarity Percentage Analysis (SIMPER), which identified who the main contributors to the dissimilarity between gut samples.

166

Figure 41: Alpha and Beta Diversities for Republic gut and sediment samples and KC gut and sediment samples.

167

Republic guts are decreased in almost all iden3ﬁed pathways

Figure 42: All identified Vikodak pathways. Green represents up regulated, red represents down regulated, and black is neutral. The Republic gut samples show down regulation in almost all identified pathways.

168

Appendix B – Supplementary Information for Chapter 4

a b

d e

Sampling Locations a Site 1

b Site 2

c Site 3

d Site 4

e Site 5

Figure 43: Sampling strategy at Holcomb Creosote Co. Superfund Site. Map adapted from EPA OSC.

169

Figure 44: Rarefaction curve of OTUs identified in all five sampling locations. All samples reached a plateau, indicating sufficient sampling depth.

170

Appendix C – Supplementary Information for Chapter 5

Figure 45: Photo of Pseudonocardia dioxanivorans strain CB1190 illustrating the morphology.

171

Appendix D – Supplementary Information for Chapter 6

pNL1

F199

Figure 46: PCR products resulting from the pNL1 (top row) and F199 (bottom row) primers. pNL1 target amplicons are 126bp while F199 targets are 171 bp. The first lane of both sections contains 100 bp ladder (max fragment size 2,000bp) obtained from New England BioLabs (Ipswich, MA).

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Biography

Lauren Kathleen Redfern was born in Gainesville, Florida on January 7th 1990 to

Kitty and Craig Redfern. She received her B.S. in Agricultural and Biological

Engineering from the University of Florida in 2013. She went on to join the lab of Dr.

Claudia Gunsch at Duke University where she worked as a Superfund Trainee and received her Master’s Degree in 2016 and her Doctorate in 2017 in Environmental

Engineering. Lauren has been involved in number outreach organizations, including

SENSOR Saturday Academy and Duke Youth Programs, and is an active member of

ASABE and ASCE.

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