PLANT UPTAKE AND METABOLISM OF ANTIMICROBIALS AND ANTIBIOTICS By Khang Vinh Huynh A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Biosystems Engineering Doctor of Philosophy 201 9 ABSTRACT PLANT UPTAKE AND METABOLISM OF ANTIMICROBIALS AND ANTIBIOTICS By Khang Vinh Huynh Targeted and untargeted metabolomics using high resolution mass spectrometry and multivariate statistical analysis, coupled with 14 C - labeled chemicals studies were used to identify novel phytometabolites and quantify the fate of common antimicrobials in pl ant tissues. Triclocarban (TCC), an antimicrobial that is commonly found in personal care products, was metabolized by jalapeno pepper plants during long - term exposure (12 weeks), leading to the - OH - TCC and 6 - OH - TCC) and phase II glycosylated OH - TCC. Importantly, the concentrations of TCC metabolites were more than 20 times greater than the concentrations of TCC in the above - ground tissues of the pepper plants after 12 weeks. Approximately 95.6% o f the TCC was present as metabolites in the fruits. For sulfonamides, upon exposure to the model plant Arabidopsis thaliana , sulfamethazine (SMT) and sulfamethoxazole (SMX) were also prone to extensive metabolism in plant tissues. Untargeted screening of e xtractable metabolites revealed that glycosylated conjugates were the most abundant metabolites, which accounted for 80 90% of the total metabolites in plant tissues. Other conjugates, such as pterin - and methylsalicyclate - , were present at lower concent rations. Phase I transformation products, such as hydroxyl - , acetyl - , desulfo, and desamino - , were identifed as minor metabolites in plant tissues. For tetracyclines, abiotic transformation and plant metabolism played the key roles in their fate during exp osure to Arabidopsis thaliana . Plant metabolism of chlortetracycline (CTC) also led to the formation of glycosylated conjugates and the corresponding 4 - epi isomers. More importantly, although CTC was solely added into the experimental reactors, other tetra cycline antimicrobials such as tetracycline, 4 - epi - tetracycline, demeclocycline, and 4 - epi - demeclocycline were detected in the plant tissues. Preliminary studies using soil columns planted with A. thaliana plants showed that phytometabolism of sulfonamides was probably similar with that under hydroponic conditions, with glycosylated conjugates identified as the major phytometabolites. The majority of the uptaken parent sulfonamides and metabolites were found in the plant roots, with limited root - to - shoot tr anslocation. In conclusion, this research indicates that untransformed antimicrobials only represent a small proportion of the total compounds taken up in plant tissues when transformed, conjugated, and sequestered phytometabolites are considered. Consequently, phytometabolism of antimicrobials in planted systems is a critical point for comprehensively addressing human exposure to contaminants of emergin g concerns through food chains. iv ACKNOWLEDGEMENTS Firstly, a profound gratitude goes to the Vietnam Education Foundation for inspiring me to pursue a Ph.D., as well as providing partial financial support during my Ph.D. program in the United States. I am also grateful to other organizations that have funded my graduate work, including the National Science Foundation (NSF) and the U.S. Department of Agriculture (USDA). I would like to express my sincere gratitude to my advisor, Dr. Dawn Reinhol d, for the continuous support of my graduate studies and all related research over the past five years, for her patience, dedication, and understanding even during the toughest times in the Ph.D. pursuit. Dr. Reinhold was always open whenever I ha d a question about my research or writing. She consistently allowed this research project to be my own work, but steered me in the right direction whenever she thought I needed it. Besides my advisor, I would also like to thank my committee members, Dr. S teven Safferman, Dr. Jade Mitchell, and Dr. Alison Cupples, for their insightful comments and encouragements, but also for the challenging questions that helped me widen my research from various perspective. My sincere thanks also go to Dr. Dan Jones, Dr. Tony Schilmiller, and other staff at the Mass Spectrometry and Metabolomics Core Facility at Michigan State University, who provided me access to the UPLC - QTOF - MS, GC - MS, Progenesis QI, and EZInfo software, for their insightful discussions and technical as sistance that have expanded my knowledge of the mass spectrometry and plant metabolomics. I am also thankful to Dr. Hui Li and Dr. Yingjie Zhang for v t he use of their LC - MS/MS. Without their precious support, it would not be possible to conduct this researc h. With a special mention to Emily Banach, Katerina Tsou, Niroj Aryal, Ronald Aguilar, of friendships and good collaboration. Special thanks to my Vietnamese f riends at Michigan State University, Gerrit and Joette Laseur, the Cooper family, and the Swada family, who make me feel I have a family in the United States. And finally, I would like to thank my family for all their love and encouragement. I am grateful to my Mom and late Dad, my brothers and sisters, and my in - laws who have supported me spiritually when I am half the globe away from home in the past five years. Most of all, words are powerless to express my gratitude to my wife Loan; thank you for your f aithful and unconditional love and support during the most challenging moments in my life. My son Van and new - Khang Huynh Michigan State University February 2019 vi TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ .......................... x LIST OF FIGURES ................................ ................................ ................................ ...................... xii KEY TO ABBREVIATION S ................................ ................................ ................................ ...... xvi CHAPTER 1. ................................ ................................ ................................ ................................ .. 1 INTRODUCTION AND SCOPE ................................ ................................ ................................ ... 1 CHAPTER 2. ................................ ................................ ................................ ................................ .. 4 LITERATURE REVIEW ................................ ................................ ................................ ............... 4 2.1. The status quo of use and regulations of antibiotics ................................ ........................ 5 2.1.1. Antimicrobial use in livestock ................................ ................................ .................. 9 2.1.2. Antimicrobial use in aquaculture ................................ ................................ ............ 11 2.1.3. Curtailing antimicrobial use and resistance ................................ ............................ 13 2.2. Sources and occurrence of antimicrobials in the environment ................................ ...... 17 2.2.1. Triclosan and triclocarban ................................ ................................ ....................... 17 2.2.1.1. Wastewater treatment plants ................................ ................................ ............ 18 2.2.1.2. Biosolids - amended soils ................................ ................................ .................. 20 2.2.2. Sulfonamides and tetracyclines ................................ ................................ ............... 25 2.2.2.1. Wastewater treatment plants ................................ ................................ ............ 26 2.2.2.2. Animal manure ................................ ................................ ................................ 31 2.2.2.3. Animal manure - amended soils ................................ ................................ ........ 35 2. 3. Environmental fate of antimicrobials following soil application ................................ ... 38 2.3.1. Triclosan and triclocarban ................................ ................................ ....................... 38 2.3. 2. Sulfonamides and tetracyclines ................................ ................................ ............... 45 2.3.2.1. Sulfonamides ................................ ................................ ................................ ... 45 2.3.2.2. Tetracyclines ................................ ................................ ................................ .... 48 2.4. Plant uptak e, metabolism of antimicrobials, and the risk to humans exposed to contaminated food crops ................................ ................................ ................................ ........... 55 2.4.1. Triclosan and triclocarban ................................ ................................ ....................... 55 2.4.2. Sulfonamides and tetracyclines ................................ ................................ ............... 60 2.4.3. Human health risk from consumption of contaminated food crops ........................ 66 2.5. The hidden role of plant root exudates in transformation of organic xenobiotics ......... 67 2.6. Metabolomics for identification of plant metabolites of organic xenobiotics ............... 70 2.6.1. Target screening ................................ ................................ ................................ ...... 72 2.6.2. Suspect screening ................................ ................................ ................................ .... 74 2.6.3. Non - target screening ................................ ................................ ............................... 76 APPENDIX ................................ ................................ ................................ ................................ ... 80 REFERENCES ................................ ................................ ................................ ............................. 82 vii CHAPTER 3. ................................ ................................ ................................ .............................. 102 UPTAKE AND METABOLISM OF TRICLOCARBAN BY HYDROPONICALLY GROWN PEPPER ................................ ................................ ................................ ................................ ...... 102 3.1. Introduction ................................ ................................ ................................ .................. 103 3.2. Materials and Methods ................................ ................................ ................................ . 106 3.2.1. Chemicals and plant material ................................ ................................ ................ 106 3.2.2. Long - term exposure of pepper plants to TCC ................................ ...................... 106 3.2.3. Short - term exposure to TCC for metabolite scr eening ................................ ......... 108 3.2.4. Sample preparation and chemical analysis ................................ ........................... 108 3.2.4.1. Quantification of TCC in plants and hydroponic media ................................ 108 3.2.4.2. Liquid Scintillation Analysis ................................ ................................ ......... 110 3.2.4.3. Screening for potential metabolites of TCC using UPLC - QTOF - MS E and LC - MS/MS ................................ ................................ ................................ ....................... 110 3.2.4.4. Statistical analysis ................................ ................................ .......................... 113 3.3. Results and Discussion ................................ ................................ ................................ . 114 3.3.1. Depletion of TCC in the hydroponic media ................................ .......................... 114 3.3.2. TCC exposure exerted no effect on growth of pepper plants ............................... 115 3. 3.3. Accumulation and d istribution of TCC and potential metabolites in plant tissues 116 3.3.4. Screening for TCC metabolites in plants and media ................................ ............ 122 APPENDIX ................................ ................................ ................................ ................................ . 133 REFERENCES ................................ ................................ ................................ ........................... 147 CHAPTER 4. ................................ ................................ ................................ .............................. 153 UPTAKE AND METABOLSIM OF SULFONAMIDES BY ARABIDOPSIS THALIANA ...... 153 4.1. Introduction ................................ ................................ ................................ .................. 154 4.2. Materials and Methods ................................ ................................ ................................ . 156 4.2.1. Chemicals ................................ ................................ ................................ .............. 156 4.2.2. Preparation of Arabidopsis thaliana seeds and culture media .............................. 156 4.2.3. Plant exposure to 14 C - SMX and unlabeled SMX ................................ ................. 157 4.2.4. Plant exposure to 14 C - SMT and unlabeled SMT ................................ .................. 158 4.2.5. Sample preparation ................................ ................................ ............................... 159 4.2.5.1. Hyd roponic media ................................ ................................ ......................... 159 4.2.5.2. Plant tissues ................................ ................................ ................................ ... 160 4.2.6. Quantif ication of SMX and SMT by LC - MS/MS ................................ ................ 160 4.2.7. Metabolite candidates screening by UPLC - QTOF - MS E and data processing ...... 161 4.2.8. Radioactivity analysis ................................ ................................ ........................... 163 4.2.9. Statistical analysis ................................ ................................ ................................ . 163 4.3. Results and Discussion ................................ ................................ ................................ . 163 4.3.1. Sulfamethoxazole (SMX) ................................ ................................ ..................... 163 4.3.1.1. Uptake of SMX by A. thaliana ................................ ................................ ...... 163 4.3.1.2. Distribution of the 14 C - radioactivity in the Arabidopsis - planted treatments . 164 4.3.1.3. Multivariate statistical analysis of the SMX dataset ................................ ..... 166 4.3.1.4. Identification of the unlabeled metabolites using high - resolution MS .......... 168 4.3.1.5. Identification of the 14 C - labeled metabolites on the LC - - RAM - MS ........... 171 4.3.1.6. Major transformation pathways of SMX in A. thaliana and implications to human health ................................ ................................ ................................ .................... 174 viii 4.3.2. S ulfamethazine (SMT) ................................ ................................ .......................... 183 4.3.2.1. Root uptake and translocation of SMT ................................ .......................... 183 4.3.2.2. Formation , distribution, and release of SMT metabolites ............................. 185 4.3.2.3. Screening for SMT transformation products ................................ ................. 188 4.3.2.4. Abiotic transformations of SMT ................................ ................................ .... 189 4.3.2.5. Transformation and conjugation pathways of SMT ................................ ...... 191 4.3.2.6. Implications to human and environmental health ................................ .......... 195 APPENDIX ................................ ................................ ................................ ................................ . 200 REFERENCES ................................ ................................ ................................ ........................... 239 CHAPTER 5. ................................ ................................ ................................ .............................. 247 UPTAKE AND METABOLSIM OF TETRACYCLINES BY ARABIDOPSIS THALIANA .... 247 5.1. Introduction ................................ ................................ ................................ ...................... 248 5.2. Materials and Methods ................................ ................................ ................................ ..... 250 5.2.1. Chemicals ................................ ................................ ................................ .............. 250 5.2.2. Preparation o f Arabidopsis thaliana seeds and culture media .............................. 250 5.2.3. Plant material and exposure to CTC and OTC ................................ ..................... 251 5.2.4. Sample preparation ................................ ................................ ............................... 252 5.2.5. Quantification of CTC, 4 - epi - CTC, 6 - iso - CTC, and OTC by LC - MS/MS .......... 253 5.2.6. Metabolite candidates screening by UPLC - QTOF - MS E and data processing ...... 254 5.2.7. Statistical analysis ................................ ................................ ................................ . 255 5.3. Resul ts and Discussion ................................ ................................ ................................ .... 256 5.3.1. Chlortetracycline (CTC) ................................ ................................ ....................... 256 5.3.1.1. The enol - keto forms of CTC, 4 - epi - CTC, and 6 - iso - CTC in the standard solutions ................................ ................................ ................................ ....................... 256 5.3.1.2. CTC and its isomers in media and accumulation in plant tissues ................. 257 5.3.1.3. Screening of the CTC metabolites using multivariate statistical analysis ..... 261 5.3.1.4. Metabolism of CTC by A. thaliana ................................ ............................... 263 5.3.2. Oxytetracycline (OTC) ................................ ................................ ......................... 272 5.3.2.1. OTC dissipation in media and accumulation in plant tissues ........................ 272 5.3.2.2. Screening of the OTC metabolites using multivariate statistical analysis ..... 275 5.3.2.3. Metabolism of OTC by A. thaliana ................................ ............................... 277 APPENDIX ................................ ................................ ................................ ................................ . 280 REFERENCES ................................ ................................ ................................ ........................... 295 CHAPTER 6. ................................ ................................ ................................ .............................. 301 QUANTIFICATION OF PHYTOMETABOLITES OF SULFONAMIDES IN SOIL ECOSYSEMS PLANTED WITH ARABIDOPSIS THALIANA ................................ ................. 301 6.1. Introduction ................................ ................................ ................................ ...................... 302 6.2. Materials and Methods ................................ ................................ ................................ ..... 303 6.2.1. Chemicals ................................ ................................ ................................ .............. 303 6.2.2. Soil columns and experimental setups ................................ ................................ .. 304 6.2.3. Sampling ................................ ................................ ................................ ............... 307 6.2.4. Sample preparation ................................ ................................ ............................... 307 6.2.5. LC - MS - - RAM and radioactivity analysis ................................ ........................... 308 ix 6.2.6. Statistical analysis ................................ ................................ ................................ . 309 6.3. Resul ts and Discussion ................................ ................................ ................................ .... 310 6.3.1. Dissipation of SMT and SMX in soils ................................ ................................ .. 310 6.3.2. Accumulation and metabolism of SMT and SMX in plant tissues ....................... 313 6.3.3. N 4 - glycosyl - SMT and N 4 - glycosyl - SMX in plant tissues ................................ .... 318 REFERENCES ................................ ................................ ................................ ........................... 321 CHAPTER 7. ................................ ................................ ................................ .............................. 325 CONCLUSION REMARKS ................................ ................................ ................................ ...... 325 7.1. Research contribution to the field ................................ ................................ .................... 326 7.2. Research limitations ................................ ................................ ................................ ......... 328 x LIST OF TABLES Table 2. 1. Current data on the antimicrobial classes recommend for use in human and veterinary medicine (as of December 2018). ................................ ................................ ................................ ... 7 Table 2. 2. Chemical structure and properties of TCS and TCC (Ying et al., 2007) .................... 18 Table 2. 3. Occurrence and concentrations of TCS and TCC in the influent, effluent, and biosolids produced in the WWTPs worldwide ................................ ................................ ............................. 22 Table 2. 4. Occurrence of TCS and TCC in agricultural soils receiving biosolids and/or reclaimed - ................................ ................................ ................... 24 Table 2. 5. Chemical structure and properties of interested antibiotics (Peiris et al., 2017, USEPA, 2017) ................................ ................................ ................................ ................................ ............. 26 Table 2. 6. Occurrence and concentrations of SMT and SMX in the influent, effluent, and biosolids produced in the WWTPs ................................ ................................ ................................ ............... 2 9 Table 2. 7. Occurrence and concentrations of CTC and OTC in the influent, effluent, and biosolids produced in the WWTPs ................................ ................................ ................................ ............... 30 Table 2. 8. Occurrence of SMT and SMX in animal manure ................................ ....................... 33 Table 2. 9. Occurrence of CTC and OTC in animal manure ................................ ........................ 34 Table 2. 10. Occurrence of SMT, SMX, CTC, and OTC in agricultural soils receiving animal - ................................ ................................ .......................... 37 Table 2. 11. Biotransformation products of SMT and SMX ................................ ........................ 50 Table 2. 12. Biotransformation products of CTC and OTC ................................ ......................... 53 Table 2. 13. Summary of reported TCS bioaccumulation in food crops ................................ ...... 58 Table 2. 14. Summary of reported TCC bioaccumulation in food crops ................................ ...... 59 Tab le 2. 15. Summary of reported SMT bioaccumulation in food crops ................................ ..... 62 Table 2. 16. Summary of reported SMX bioaccumulation in food crops ................................ ..... 63 Table 2. 17. Summary of reported CTC bioaccumulation in food crops ................................ ...... 64 Table 2. 18. Summary of reported OTC bioaccumulation in food crops ................................ ..... 65 xi Table 2. 19. General comparison of commercial mass analyzers in LC - MS instruments. Typical values for an m/z range of 300 400 are given. Specific instruments or configurations might achieve better figures of merits (Krauss et al., 2010). ................................ ................................ ............... 71 Table 3. 1. Mass - Spectral Information and Proposed Structures of TCC Transformation Products Identified by Waters Proge nesis QI 2.1 and MassLynx 4.1 Softwares. ................................ ..... 130 Table 4. 1. Mass - Spectral Information and Proposed Structures of the SMX - Transformation Products Identified by Waters Progenesis QI 2.1 and MassLynx 4.1 Software. ........................ 180 Table 4. 2. Mass - Spectral Information and Proposed Structures of the SMT - Transformation Products Identified by Waters Progenesis QI 2.1 and MassLynx 4.1 Software. ........................ 197 Table 5. 1. Mass - Spectral Information and Proposed Structures of the CTC - Transformation Products Identified by Waters Progenesis QI 2.1 and MassLynx 4.1 Software. ........................ 268 Table 5. 2. Mass - Spectral Information and Proposed Structures of the OTC - Transformation Products Identified by Waters Progenesis QI 2.1 and MassLynx 4.1 Software. ........................ 279 Table 6. 1. Basic properties of the sandy loam topsoil used in this study. ................................ . 304 Table 6. 2. The potential metabolites of SMT and SMX (as identified in Chapter 4), and their corresponding m/z monitored in this study. ................................ ................................ ................ 309 xii LIST OF FIGURES Figure 2. 1. Sale and distribution of antimicrobial drugs approved for use in food - producing animals actively marketed in the U.S.: (A): During 2009 - 2016, (B): Data reported in 2016 by drug class and species - specific estimated sales, (C): Percentage of domestic sales and distribution in 2016 by drug class and species - specific (U.S. FDA, 2017). ................................ ......................... 11 Figure 2. 2. A proposed biodegradation pathway for TCS by the Sphingopyxis strain KCY1 (Lee et al., 2012) ................................ ................................ ................................ ................................ ... 44 Figure 2. 3. Proposed pathway for TCC biodegradation by Sphingomonas sp. strain YL - JM2C (Mulla et al., 2016a) ................................ ................................ ................................ ...................... 45 Figure 2. 4. Proposed pathway of TCS metabolism in hair root cultures of horseradish (Macherius et al., 2014b) ................................ ................................ ................................ ................................ . 57 Figure 2. 5. Proposed metabolic pathways of TCC in jalapeno pepper plants (Huynh et al., 2018) ................................ ................................ ................................ ................................ ....................... 57 Figure 2. 6. A proposed workflow for screening xenobiotic metabolites (Bletsou et al., 2015). . 72 Figure 2. 7. A framework proposed by (Schymanski et al., 2014) for concisely and accurately communicating levels of confidence in high resolution mass spectrometric analysis. : MS 2 is intended to also represent other forms of MS fragmentation (e.g. MS e , MS n ). ................ 78 Figure 3. 1. Accumulation of TCC ( 14 C - labeled and non - labeled) in different plant tissues: (A): roots, (B): stems, (C): leaves and (D): fruits during long - term exposure (12 weeks). Plant samples were extracted by acetone:methanol (1:1) mixture using an accelerated solvent extractor (ASE). Fo r fruit samples, 14 C was non - detectable in the ASE extracts; therefore, data of the NaClO extracts (Supplementary Table 3.1) were used. Error bars represent means ± SE (n = 5). In each panel, columns marked by different letters are significantly different from each other ( p < 0.05). ...... 116 Figure 3. 2. Concentrations of TCC and metabolites (expressed as µg 14 C - TCC equivalent/g dw) in (A): ro ots, (B): stems, (C): leaves and (D): fruits during long - term exposure (12 weeks). In this trial, the first fruit sampling event occurred at the 6 th week. Root and stem data for week 6 th and week 9 th were not available since sacrificial sampling was only p erformed after 3 and 12 weeks. ................................ ................................ ........................... 119 - OH TCC, 6 - OH TCC and M329) and phase II metabolites (M49 1a and M491b) in pepper plants during short - term metabolite screening trial. Initial exposure (0 - 7 days) and second exposure (7 - 28 days) were performed at TCC concentrations of 174.8 and 487.4 µg/L, respectively. M491a and M491b were only detected xiii in plant roots. Due to their trace concentrations, peak areas at Rt = 8.39 and 8.54 min (Figure S14) were combined and presented as M491a; likewise, peak areas at Rt = 8.44 and 8.59 min (Figure S15) were combined and presented as M491b. ......... 124 Figure 3. 4. Proposed metabolic pathways of TCC in hydroponically grown jalapeno pepper plants. Accurate m/z of the m etabolites were obtained using UPLC - QTOF - MS E , and their structures were - TCC in plant tissues was likely due to uptake of TCC impurities (dashed arrows). ............................... 125 Figure 4. 1. Distribution of the applied 14 C - radioactivity in Arabidopsis treatments (A), and temporal variation in concentrations of 14 C - SMX and its major metabolites detected in media and - represents plant samples, respectively. Error b ars represent standard error of duplicates. ........ 164 Figure 4. 2. PCA score plots (A and B), OPLS - DA score plots (C and D) and S - plots (E an d F) derived from the UPLC - QTOF - MS E datasets of the control and SMX - exposed plants and media 2 with 95% confidence. The dashed rectangles on the S - plots include m/z variables that signi ficantly contribute to the difference between the control and SMX - ................................ ................ 166 Figure 4. 3. Proposed transform ation pathways of SMX in A. thaliana plant. The occurrence of N 4 - OH SMX (TP270) was not unequivocally confirmed due to lack of adequate mass - spectral information (dashed arrow). The percentage represents the fractions of each metabolite observed over 10 da ys of exposure. ................................ ................................ ................................ ........... 174 Figure 4. 4. Temporal variation of the major metabolites of SMX in plant tissues and culture media over 10 days of exposure. Error bars represent standard error of triplicates. For some points, the error bars were shorter than the height of the symbol and were not displayed on graphs. ......... 174 Figure 4. 5. Extractable SMT concentrations in A. thaliana plant tissues (A), and mass balance of SMT in the culture media and plant tissues (B) over 21 days of exposure. Error bars represent standard error of tri plicates; some error bars are obscured by data symbols. ............................. 183 Figure 4. 6. Formation and release of 14 C - SMT metabolites by A. thaliana in whole - plant exposure. (A): distribution of 14 C - SMT and its phytometabolites in various compartments, with radiolabel detection revealed two major extractable metabolites (M1 and M2) upon exposure of A. thaliana to 14 C - SMT; (B) and (C): temporal variation in 14 C - radioactivity of M1, M2 and 14 C - SMT in plant tissues (B) and media (C) over 10 days of exposure. Error bars represent standard error of duplicates. ................................ ................................ ................................ ................................ ... 185 Figure 4. 7. Proposed transformation pathways of SMT in model plant A. thaliana and hydroponic media based on the metabolites identified in this study, and other transformation pathways of SMT in aqueous media previously descri bed by (García - Galán et al., 2012), (Nassar et al., 2017), and (Fu et al., 2018). All of the metabolites detected in plant tissues were concurrently present in the culture media, suggesting that A. thaliana plants likely excreted a fraction of the metaboli tes following uptake and metabolism. The dashed arrows indicate that the corresponding metabolites xiv were also detected in the abiotic control media; consequently, their presence in plant tissues may also due to uptake of the abiotic transformation products from the media. ................................ 191 Figure 5. 1. Temporal variations of CTC and its isomers in the A. thaliana plant tissues and culture media over 12 days of exposure. Data are presented as mean ± SE of triplicates, with some error bars are obscured by data symbols. ................................ ................................ ............................. 257 Figure 5. 2. PCA score plots (A), OPLS - DA score plots (B), and S - plots (C) derived from the UPLC - QTOF - MS E datasets of the control and CTC - exposed plants (1, 4, 7, and 12 days). The 2 with 95% confidence. The m/z variables on the S - plot that significantly contributed to the differences between control plants and CTC - exposed plants ( p ................................ ............... 261 Figure 5. 3. XenoSite prediction of the glycosylation positions on the structures of CTC isomers. The scales from 0.0 to 1.0 indicate higher possibility of conjugation reactions (Dang et al., 2016). ................................ ................................ ................................ ................................ ..................... 264 Figure 5. 4. Temporal variations of the metabolites TP641 (glycosyl - CTC), TP445 (TC), and TP465 (DMC) in the A. thaliana plant tissue s and culture media over 12 days of exposure. Data are presented as mean ± SE of triplicates, with some error bars are obscured by data symbols. 267 Figure 5. 5. Temporal variations of OTC in the A. thaliana plant tissues and planted media (A), and in the control media (B) over 12 days of exposure. Data are presented as mean ± SE of triplicates, with some error bars are obscured by data symbols. ................................ ................ 272 Figure 5. 6. PCA score plots (A), OPLS - DA score plots (B), and S - plots (C) derived from the UPLC - QTOF - MS E datasets of the control and OTC - exposed plants (1, 4, 7, and 12 days). The 2 with 95% confidence. The m/z variables on the S - plot that significantly contributed to the differences between control plants and OTC - exposed plan ts ( p ................................ ............... 275 Figure 6. 1 Soil column design for studying the fate of SMT and SMX in soil systems planted with A. thaliana . ................................ ................................ ................................ ................................ .. 306 Figure 6. 2. Percentage of the extractable 14 C (A) and parent SMT/SMX (B) in soils after 7 days of exposure compared to the initially added concentrations of approximately 3 nCi/g and 3 µg/g, respectively. Error bars represent standard error of triplicates. ................................ .................. 310 Figure 6. 3. Distribution of 14 C - radioactivity into extractable and bound residues in plant tissues (A and B) and concentrations of parent SMT and SMX in A. thaliana plant t issues (C and D) after 7 days of exposure in the experimental soil columns. Error bars represent standard error of triplicates. ................................ ................................ ................................ ................................ .... 313 Figure 6. 4. Distribution of 14 C - radioactivity (in percentage) into extractable and bound residues in plant tissues after 7 days of exposure. (A): 14 C - SMT treatments, (B): 14 C - SMX treatments. Error bars represent standard error of triplicates. ................................ ................................ ................. 317 xv Figure 6. 5. Distribution of N 4 - glycosyl - SMT (A) and N 4 - glycosyl - SMX (B) in A. thaliana plant tissues after 7 days of separate exposure to SMT and SMX in the experimental soil columns. 319 xvi KEY TO ABBREVIATIONS - TCC - tetrachlorocarbanilide ASE Accelerated Solvent Extraction CTC Chlortetracycline DCC - dichlorocarbanilide DPM Disintegration Per Minute HPLC High Performance Liquid Chromatography HR - MS High Resolution - Mass Spectrometry LC - MS Liqui d Chromatography - Mass Spectrometry LC - MS/MS Liquid Chromatography - Tandem Mass Spectrometry LSC Liquid Scintillation Counter Me - TCS Methyl - triclosan MRM Multiple Reaction Monitoring OPLS - DA Orthogonal Partial Least Squares - Discrimi nant Analysis OTC Oxytetracycline PCA Principal Component Analysis PPCPs Pharmaceuticals and Personal Care Products RT Retention time TCC Triclocarban TCS Triclosan SMT Sulfamethazine SMX Su l famethoxazole xvii SPE Solid Phase Extraction UPLC - QTOF - MS Ultra Performance Liquid Chromatography - Quadrupole Time of Flight - Mass Spectrometry U.S. CDC United States Center for Diseases Control U.S. EPA United States Environmental Protection Agency U.S. FDA United States Food and Drug Administration VAs V eterinary A ntimicrobials WHO World Health Organization WWTPs Wastewater Treatment Plants 1 CHAPTER 1. INTRODUCTION AND SCOPE 2 Ubiquitous occurrence of antimicrobials used in human and veterinary medicine have prompted growing concerns about antimicrobial contamination and resistance in the environment. When reaching the environment, antimicrobials undergo various degradation pathways, resulting in the formation of transformation products which are potentially bioactive or even more toxic than the parent compounds. However, the impacts of these transformation products on the ecosystems are still largely unknown. U ptake and accumulation of antimicrobials by food crops grown in contaminated soils has been well - documented in the literature. Therefore, unintentional human exposure to antimicrobials through consum ing contaminated food crops is obviously of great concern. While in the past, studies and risk assessments related to plant uptake and accumulation of these xenobiotics mainly addressed the unaltered parent compounds, recent research has shifted toward the formation, fate, and toxicity of their conjugated metabolites. It has been reported that these metabolites may account for more than 90% of all chemical species in the plant tissues. Nevertheless, their fate once ingested by humans and animals, with respe ct to back - transformation ( from metabolites to parent s ) potential, is also largely unknown. The overall aim of t his research is to elucidate the metabolic pathways , including quantify ing the production of metabolites of antimicrobials in plants. The propo sed research will evaluate the central hypothesis that plants will bioaccumulate antimicrobials upon exposure and subsequently metabolize these compounds through several transformation and conjugation reactions, forming metabolites that are readily reverte d to the parent compound during human and animal digestion . Accordingly, the obtained results will address the gaps of existing research on the fate of antimicrobials in planted systems, through use of state - of - the - art instrumentation (e.g. HPLC - high resol ution mass spectrometer) in combination with targeted and un targeted 3 metabolomic approaches, which will facilitate the detection, confirmation and quantification of the antimicrobials and their transformation products at trace levels. In order to determine the role and relevance of phytometabolism in fate of antimicrobials, the following specific aims will be achieved: 1) Elucidate the fate and metabolites of the antimicrobial triclocarban in crop plants, with a case study on hydroponically grown j alapeno pepp er plants . The working hypothesis , based on a current literature review and preliminary results, is that pepper plants will bioaccumulate considerable concentrations of triclocarban and subsequently transform the antimicrobial via the with carbohydrates, or direct conjugation with glutathione. 2) Elucidate the fate of antibiotics (sulfonamides and tetracyclines) in model plant Arabidopsis thaliana under hydroponic conditions . The working hypothesis , based on a current literature review, is that A. thaliana will predominantly metabolize antibiotics through conjugation with glucopyranosides followed by further conjugation with mal onyl and oligoglycoside molecules . A dditionally, phase I transformations will be limited to hydroxylation prior to conjugation. 3) Quantifying the production of phytometabolites of antimicrobials in soil ecosystems planted with Arabidopsis thaliana . The worki ng hypothesis is that plant uptake and phytometabolism will outcompete microbial and chemical degradation of antibiotics, thereby representing the largest mass loss of antibiotics from soils. 4 CHAPTER 2. LITERATURE REVIEW 5 2.1. The status q uo of use and regulations of antibiotics I did not invent penicillin. Nature did that. I only discovered it by accident Alexander Fleming (1881 1955) The discovery of penicillin by Sir Alexander Fleming in 1928 started the modern era of antibiotics, which has saved millions of lives and earned Dr. Fleming the 1945 Nobel Prize in Physiology/Medicine together with Howard Florey and Ernst Chain, who devised methods for mass production of penicillin (Tan and Tatsumura, 2015) . According to the current definitions of the World Health Organization (WHO), antibiotics are agents or substances that are produced from microorganisms that can act against another liv ing microorganisms , while antimicrobials are derived from any source s (microorganisms, plants, animals, synthetic or semisynthetic) that act against any type of microorganism . Accordingly, all antibiotics are antimicrobials, but not all antimicrobials a re antibiotics (World Health Organization, 2017) . Since 2005, WHO has published a list of important antimicrobials for human medicine, w ith regular revision, to be used as a basis for promoting their prudent use. The 5 th revision list (published in April 2017) contains 32 drug classes (291 individual drugs) categorized me antimicrobials are used only in humans, some in both humans and animals, and some only for animals (World Health Organization, 2017) . Notably, most antimicrobials used in animals are medically important for humans. Quinolones, 3 rd and higher generation cephalosporins, macrolides and ketolides, glycopeptides, and polymyxins (colistin) are considered highest priority amongst the important antimicrobials (World Health Organization, 2017) . Of the 291 drugs on the WHO list of medically important antimicrobials, only 38 are c urrently recommended for use in food - producing animals (World Health Organization, 2017) . In 2016, there were 42 antimicrobials being ap proved and 6 actively marketed for use in food - producing animals in the United States, of which 31 are categorized as important to humans medicine (U.S. FDA, 2017) . These antimicrobial drug classes and their corresponding active ingredients are shown in the Supplementary Table 2.1. Surveillance data on national and global trends of antimicrobial consumption over time is vitally important since the data will assist in formin g policies that optimize the use of antimicrobials, as well as minimizing antimicrobial resistance. According to a recent study by Klein et al., between 2000 and 2015, global antimicrobial consumption in humans increased by 65%, from 21.1 to 34.8 defined d aily doses (DDDs), while consumption rate increased 39%, from 11.3 to 15.7 DDDs per 1000 inhabitants per day, primarily driven by rising consumption in low - and middle - income countries. The massive increase in antimicrobial consumption in these countries c orrelates with the growing gross domestic product per capita and has been converging towards high - income country levels (Klein et al., 2018) . 7 Table 2. 1 . Current data on the antimicrobial classes recommend for use in human and veterinary medicine (as of December 2018). Antimicrobial classes Both humans and veterinary medicine (WHO) 1 Veterinary use only (WHO) 1 Approved for used in food animals (U.S.) 2 Critically Important Antimicrobials Aminoglycosides 14 1 5 Ansamycins 5 Carbapenems and other penems 7 Cephalosporins (3rd, 4th, and 5th gen) 28 3 2 Glycopeptides and lipoglycopeptides 5 1 Glycylcyclines 1 Lipopeptides 1 Macrolides and ketolides 18 7 7 Monobactams 2 Oxazolidinones 4 Penicillins (natural, aminopenicillins, and antipseudomonal) 29 1 Phosphonic acid derivatives 1 Polymyxins 2 1 Quinolones and fluoroquinolones 29 7 2 Drugs used solely to treat tuberculosis or other mycobacterial diseases 15 Highly Important Antimicrobials Amidinopenicillins 2 Amphenicols 2 1 1 8 Table 2.1 ( cont ) . Current data on the antimicrobial classes recommend for use in human and veterinary medicine (as of December 2018). Antimicrobial classes Both humans and veterinary medicine (WHO) 1 Veterinary use only (WHO) 1 Approved for used in food animals (U.S.) 2 Cephalosporins (1st and 2nd gen) and cephamycins 26 1 Lincosamides 2 1 2 Penicillins (antistaphylococcal) 5 4 Pseudomonic acids 1 Riminofenazines 1 Steroid antibacterials 1 Streptogramins 2 1 1 Sulfonamides, dihydrofolate reductase inhibitors and combinations 26 2 3* Sulfones 2 Tetracyclines 12 3 Important Antimicrobials Aminocyclitols 1 Cyclic polypeptides 1 Nitrofurantoins 4 1 Nitroimidazoles 3 Pleuromutilins 1 2 Currently not used in humans (food animals only) 9 Total 253 38 31 1 (World Health Organization, 2017) ; 2 (U.S. FDA, 2017) *Including ormetoprim, sulfadimethoxine, and sulfamethazine 9 2.1.1. Antimicrobial use in livestock Agricultural sectors represent the largest shares of the gl obal antimicrobial consumption, with veterinary antimicrobials (VAs) extensively used worldwide to protect or improve animal health, and to stimulate growth and maximize profits (Kuppusamy et al., 2018, O'Neill, 2015) . Antimicrobials administered to healthy animals to enhance growth is potentially the most controversial use, which has been banned in many countries. However, implementing and monitoring of these bans are often insufficient, especially in the low - and middle - income countries (Schar et al., 2018) . The actual amount of antimicrobials used in food - producing animals globally is difficult to estimate because of poor surveillance and data collect ion in many countries (Kuppusamy et al., 2018) . It has been estimated that more than 50% of the medically important antimicrobials sold in most countries are used in animals (O'Neill, 2015) , while that in the U.S. is approximately 80% (Ventola, 2015) . A recent survey of the 28 European Union countries revealed that 3,821 tons of active antimicrobial substances were sold for human use, while 8,927 tons for food - produ cing animals in 2014 (ECDC/EFSA/EMA, 2017) . The average consumption (expressed as mg/kg of estimated biomass) in food - producing animals was 152 mg/kg, compared to 124 mg/kg in humans. However, the antimicrobial consumption was lower in food - producing animals than in humans in 18 out of 28 countries included in the survey, with only eight countries had higher antimicrobial consumption in food - producing animals than in human (ECDC/EFSA/EMA, 2017) . for animals (Qiao et al., 2018) . In 2013, the total antimicrobial consumption in China was approximately 162,000 tons, of which 84,240 tons were used in animals (Zhang et al., 2015) . That was approximately six times larger than the total sale and distribution of antimicrobials approved for 10 used in food - producing animals in the U.S. in 2016 (approximately 14,000 to ns) (Figure 1A) (U.S. FDA, 2017) . Amoxicillin, florfenicol, lincomycin, penicillin, and enrofloxacin were the major VAs used in China, with the consumption rates >4,000 tons (Qiao et al., 2018, Van Boeckel et al., 2015, Zhang et al., 2015) . In 2016, domestic sales of tetracyclines in the U.S. accounted for 70% of the medically important antimicrobials used in animals, followed by penicillins (10%), macrolides (7%), sulfonamides (sulfas) (4%), aminoglycosides (4%), and lincosamides (2%); each fluoroquinolones and cephalosporins accounted for less than 1% of the total sales (Figure 1B). Additionally, most of the domestic sales was intended for use in cattle (43%), followed by swine (37%), t urkeys (9%), chickens (6%), and other species (4%) (Figure 1C). In European countries, the veterinary antimicrobial classes with highest consumption in 2014 were tetracyclines (33%), penicillins (25%), and sulfonamides (11%) (ECDC, 2015) . Using statistical models combining maps of livestock densit ies, economic projections of demands for meat products, and current estimates of antimicrobial consumption in high - income countries, Van Boeckel et al. projected a 67% increase in the global consumption of antimicrobials for food animals between 2010 and 2 030, from approximately 63,000 tons to 105,000 tons, mainly - middle - income countries. The projected increase is set to double for the major emerging economies, in cluding Brazil, Russia, India, China, and South Africa (BRICS countries). The authors also estimated that the global average annual consumption of antimicrobials per kilogram of animal produced was 45 mg/kg, 148 mg/kg, and 172 mg/kg for cattle, chicken, an d pigs, respectively (Van Boeckel et al., 2015) . 11 Figure 2. 1 . Sale and distribution of antimicrobial drugs approved for use in food - producing animals actively marketed in the U.S.: (A): During 2009 - 2016, (B): Data reported in 2016 by drug class and species - specific estimated sales, (C): Percentage of domestic sales and distribution in 2016 by drug class and species - specific (U.S. FDA, 2017) . kg = kilogram of active ingredient. * This category includes the following: Cattle, Swine , and Other. ** This category includes the following: Cattle, Swine, Chicken, Turkey, and Other. - specific sales estimates for which there were fewer than three distinct sponsors actively marketing products domest ically are not independently reported. This category includes the following: Swine, Chicken, and Other. - specific sales estimates for which there were fewer than three distinct sponsors actively marketing products domestically are not independently reported. This category includes the following: Cattle and Other. 2.1.2. Antimicrobial use in aquaculture Aquaculture is the fastest growing food - production industry that accounts for nearly half of the fish consumed by human s worldwide (Okocha et al., 2018) . The rapid growth of aquaculture requires uses of intensive and semi - intensive practices, leading to higher densities of animals in 12 limited spaces of water, substantially promoting the conditions that favor the development of contagious diseases (Santos and Ramos, 2018) . Therefore, antimicrobials have been widely used in aquaculture to prevent and treat bacterial diseases in fish and invertebrates (Cabello, 2006) . Antimicrobials used in aquaculture are mainly administered in feed, occasionally by bath (immersion of the animals in closed containers with antimicrobials), and to groups with sick, healthy, and carrier individuals (Cabello et al., 2016) . It has been reported that as much as 80% of the administered antimicrobials in aquaculture are dispersed into water and sediments close to the application sites (Santos and Ramos, 2018) . Additionally, ingested antimicr obials are excreted largely intact, together with their metabolites. The residual antimicrobials and their metabolites persist in the aquatic sediments for months at sufficiently high concentrations to exert selection on the bacterial communities in the aq uatic environments (Cabello et al., 2016) . Aquacult ural use of antimicrobials has been restricted in developed countries in order to cope with the potential selection for antimicrobial resistance in humans (Cabello et al., 2013) . In European countries, oxytetracycline, florfenicol, sarafloxacin, erythromycin, an d sulfonamides are authorized for use in aquaculture, while oxytetracycline, florfenicol, and sulfadimethoxine/ormetoprim are authorized for used in aquaculture in the U.S. (Santos and Ramos, 2018) . Except from these two regions, data on the amounts and classes of antimicrobials used in aquaculture are scarce, especially in countries where control is less stringent or lacking (Cabello et al., 2013) . For example, China, accounting for 67% of the total worldwide aquaculture, does not require veterinar y prescriptions for use of antimicrobials in animals. Despite its efforts to implement stricter regulations to the aquaculture industry in the last few years, several reports still indicate that the enforcement of these regulation is lax (Chen et al., 2012a) . Likewise, antimicrobial sale and usage in India, the second largest aquaculture producer with 8% of the total worldwide 13 production, are not regulated (Done et al., 2015) . Detection of nitrofurans in aquacultural products imported to the U.S. from China, as well as chloramphenicol and metronidazole imported from China, Indonesia, Taiwan, Thailand, and Vietnam , to European markets further demonstrate d the lax for control of antimicrobial use in these top aquacultural producers (Cabello et al., 2013) . Moreover, data regarding antimicrobial consumption in agricultural and aquaculture vary largely depending on the regions and differences between countries coul d be very pronounced. For example, the amount of antimicrobials used to produce 1 tons of salmon in Chile was approximately 279 g while in Norway, only 4.8 g of antimicrobials was used for the same amount of salmon (Santos and Ramos, 2018) . N orway, the Netherlands, and Denmark can be considered the models in minimizing the use of antimicrobials in food - producing animals, without negative impacts on the quality and safety of the food, as well as without a damaging economic impact (Santos and Ramos, 2018) . 2.1.3. Curtailing antimicrobial use and resistance Recent studies and reports have revealed positive associations between antimicrobial consumption and resistance to different classes of antimicrobials in both humans and animals (ECDC/EFSA/EMA, 2017) the ability of a microorganism (like bacteria, viruses, and some parasites) to stop an antimicrobial (such as antibiotics, anti virals, and antimalarials) from working against it. As a result, standard naturally through adaptation to the environment or through exposure to synthetic antimicro bials that are inappropriately and excessively used in anthropogenic activities. The U.S. Center for Diseases Control estimates that 30% of all antimicrobials prescribed by outpatient clinics in the U.S. are unnecessary or incorrectly (CDC, 2017) . Misuse of antimicrobials i s even more 14 problematic in clinical centers in developing countries. In China, about 75% of patients with seasonal influenza are prescribed antibiotics, and the rate for inpatients is 80% (Qiao et al., 2018) . In addition to inappropriate use of antimicrobials in human medicine, overuse of these drugs in food - producing animals and aquaculture has prompted serious concerns about antimicrobial contamination and resistance in the ecosystems. Due to their highly bioactive nature, the presence of antimicrobials, even at trace concentrations, has been l inked to alteration of the composition of bacterial communities, causing or promoting antimicrobial resistance. More importantly, some last - resort antimicrobials for humans are also being extensively used in animals, such as colistin (polymyxins) (Santos and Ramos, 2018) . In 2014, the consumption of polymyxins in food - producing animals greatly exceeded their use in human medicine (10 mg/kg versus 0.03 mg/kg of estimated biomass, respectively) (ECDC/EFS A/EMA, 2017) . Recent outbreaks in hospitals with carbapenemase - producing Enterobacteriaceae and multidrug - resistant Pseudomonas and Acinetobacter species have led to the re - introduction of colistin as a last - resort antimicrobial (Santos and Ramos, 2018) . In 2015, Liu et al. published the first evidence of a plasmid - mediated mcr - 1 gene that conferred colistin resistance in China (Liu et al., 2016) . The authors reported the presence of this gene in 20% of the test animals and in 1% of the human population, clea rly indicating that the use of colistin in animals has resulted in the selection of this resistance , and that the gene can be transferred to humans (Liu et al., 2016, Santos and Ramos, 2018) . Antimicrobial resistance is a global public health threat that requires aggressive actions from all stakeholders. In May 2015, the WHO endorsed a global action plan to tackle antimicrobial resistance. The global action plan sets out five strategic objectiv es: (i) to improve awareness and understanding of antimicrobial resistance; (ii) to strengthen knowledge through surveillance and research; (iii) to reduce the incidence of infection; (iv) to optimize the use of antimicrobial agents; 15 and (v) develop the ec onomic case for sustainable investment that takes account of the needs of all countries and increase investment in new medicines, diagnostic tools, vaccines , and other interventions. On September 25 th , 2018, the U.S. Centers for Disease Control and Prevent ion and U.S. Department of Health and Human Services launched the Antimicrobial Resistance Challenge at the United Nations General Assembly meeting. The challenge calls on governments, private industry , and civil society to commit to taking actionable step s that further progress in combating antibiotic resistance around the world. In 2014, the White House announced the National Strategy for Combating Antibiotic - Resistant Bacteria. The plan outlines five main goals for combating antibiotic resistance. Each g oal has accompanying milestones to be achieved by 2020. The goals serve as a roadmap for the federal agencies working to preserve antibiotic efficacy. Many countries have already taken actions to reduce the use of antimicrobials in food - producing animals. In 2015, California was the first state in the U.S. to pass legislation that goes far beyond federal rules. California S enate Bill No. 27 ( January 1 st , 2018 ) antimicrobials and ban non - therapeutic use of a ntimicrobials for disease prevention and growth promotion in livestock (State of California, 2015) . In May 2017, the State of Maryland also adopted a law banning routine antimicrobial use for livestock on farms (State of Maryland, 2017) . At the federal level, regulations by the U.S. FDA , that went into effect in January 1 st , 2017, required retail establishments that sell medica lly important antimicrobials for use in feed or water for food - producing animals to change the marketing status from over - the - counter (OTC) to prescription (Rx) or to veterinary feed directive (VFD) (U.S. FDA, 2016a) . In Europe, the European Union banned the use of antimicrobial growth promoters in animal feed in 2006. Several countries have also launched national programs and reporting systems fo r surveillance of 16 antimicrobial consumption and resistance in bacteria from animals, foods, and humans (e.g. DANMAP in Denmark, Nethmap in the Netherlands). In order to effectively control the antimicrobial prescribing patterns, the Ministry of Health of C hina launch a National Action Plan in 2011 for antimicrobial stewardship targeting antimicrobial misuse in public hospitals (Bao et al., 2015) . The Action Plan, which was a combination between managerial and professional strategies, effectively reduced the frequency and the intens peri - operative antimicrobial treatment in clean surgeries (Bao et al., 2015) . Antimicrobial prescription to hospitalized patients dropped from 68% in 2011 to 58% by the end of 2012 . A dditionally, t he percentage of outpatients who were prescribed antimicrobial drugs also dropped from 25% to 15% in the same period (Qiao et al., 2018) . Chinese government have also launched regulations on antimicrobial use in animals since the early 2000s (Xie et al., 2018) . For example, use of chlo ramphenicol, metronidazole, and nitrofuran in animal production was banned in 2002 (Mo et al., 2017) . The direct injections of oxytetracycline and chlortetracycline hydroxide were also banned in 2007. In 2014, prescription for veterinary antimicrobials was also required following impleme - prescription Drugs for (Xie et al., 2018) . Nevertheless, farmers can still purchase antimicrobial drugs directly from chemical companies, as well as via the internet (including the banned chloramphenicol and metronidazole), without a prescr iption (Mo et al., 2017) . Additi onally, limited legislation or regulations are currently available to control the misuse of antimicrobials in disease prevention and growth promotion (Xie et al., 2018) . 17 2.2. Sources and occurrence of antimicrobials in the environment Overuse of antimicrobials in human medicine and food - producing animals has prompted concerns about occurrence of antimicrobial residues and metabolites in the environment, and the potential adverse impacts on ecological and human health. Antimicrobials enter the environment primarily through excreted wastes of animals and hu mans after medication, disposal of unused or expired drugs, manufacturing and hospital wastewater, field application of reclaimed wastewater, biosolids, and manure containing antimicrobial residues and metabolites (Pan a nd Chu, 2017a, Sarmah et al., 2006a) . Antimicrobials prescribed for human use are eventually excreted into domestic sewage and are discharged to wastewater treatment plants (WWTPs) along with their metabolites. Elimination of antimicrobials in WWTPs was found to be relatively low; consequent ly, biosolids and sewage effluents are considered the main sources of these antimicrobials and metabolites (Pan and Chu, 2017a) . Moreover, negative removal efficiencies for antibiotics are commonly reported (García - Galán et al., 2011a, Göbel et al., 2007, Gros et al., 2010) . This ob servation can be attributed to the presence of transformation products (e.g. acetylated metabolites) that are totally or partially cleaved by bacteria and reverted back to the parent forms during wastewater treatment processes (García - Galán et al., 2012a) . Likewise, the excretion of urine and feces from medicated animals and amendment of contaminated manures on agricultural soils as fertilizer serve as the major pathways by which antimicrobials and their metabolites enter the environment (Pan and Chu, 2017a) . 2.2.1. Triclosan and triclocarban Triclosan (5 - chloro - 2 - [2,4 - dichl oro - phenoxy] - - trichlorocarbanilide; TCC) are structurally similar antimicrobial agents (Table 2.2) commonly found in household and personal care products. The amount of TCS and TCC incorporated into 18 consumer products ty pically ranges from 0.1 0.3% (w/w), exhibiting a broad - spectrum activity against bacteria, molds , and yeasts (Clarke and Smith, 2011) . It had been estimated that U.S. annual disposal of TCS and TCC into wastewater totals more than 300 and 330 tons/year, respectively (Halden and Paull, 2005) . However, in 2016, the U.S. Food and Drug Administration (FDA) i ssued a final rule on elimination of over - the - counter consumer antiseptic wash products containing TCS and TCC due to lack of scientific evidence that they are more effective than plain soap and water in preventing human illness and reducing infection (U.S. FDA, 2016b) . As a result, emission of both TCS and TCC into the municipal wastewater treatment systems in the U.S. is expected to decline in the coming years. Table 2. 2 . Chemical structure and properties of TCS and TCC (Ying et al., 2007) . Properties TCS TCC Structure CAS No 3380 - 34 - 5 101 - 20 - 2 Formula C 12 H 7 Cl 3 O 2 C 13 H 9 Cl 3 N 2 O M.W 289.5 315.6 Boiling point ( o C) 373.62 434.57 Melting point ( o C) 136.79 182.04 pKa 7.9 12.7 Water solubility (mg/L) 4.621 0.6479 Vapor pressure (mm Hg at 25 o C) 4.65 x 10 - 6 3.61 x 10 - 9 Log K ow 4.7 4.9 2.2.1.1. Wastewater treatment plants Given their wide use in personal care products, TCS and TCC are among the most frequently detected pollutants in both influents, effluents, and biosolids of WWTPs worldwide (Table 2.3). As such, municipal WWTPs are considered the main pollution source of T CS and TCC due to discharge of reclaimed wastewater and reuse of biosolids as fertilizer on agricultural soils. As shown in Table 2.3, the concentrations of TCS and TCC in the influents fluctuated and 19 greatly differed among countries, from below method lim it of detection to several part - per - billion (µg/L). The highest concentrations were found in the U.S., with TCS and TCC concentrations in the effluents reached up to 86,161 ng/L and 36,221 ng/L, respectively (Kumar et al., 2010) . These data were from stu the use of TCS and TCC in consumer wash products in the U.S (U.S. FDA, 2016b) . Concentrations of both antimicrobials in WWTPs across the U.S. are expected to decrease when the ban came into effect in 2016; however, no published data with samples collected after 2016 to date could be found . The levels of TCS and TCC enter ing WWTPs also varied substantially among different sampling locations within the same country (e.g. China), most likely attributed to variations in sampling procedures, wastewater treatment technologies, and urbanization levels of the cities (Wang et al., 2018) . In effluents, the levels of TCS and TCC were much lower than in the influents reported in most of the studies, mostly varying from below limit of detectio n to a few hundreds of ng/L. However, negative removal efficiencies were also reported in recent studies. For example, Wang et al. reported higher concentrations of both TCS and TCC in the effluents of WWTPs located in the southeast of China, likely due to conversion of their metabolites/conjugates to the parent compounds during wastewater treatment processes (Wang et al., 2018) . Negative removal efficiencies were also observed for other pharmace uticals in earlier studies (García - Galán et al., 2011a, Göbel et al., 2007, Gros et al., 2010, Hedgespeth et al., 2012, Jelic et al., 2015) . Although the transformation products may not be as bioactive as the parent compounds, an estimation of the conjugated analytes in the influents is essential for subsequent mass balance calculation (Wang et al., 2018) . Due to their high octanol/water partitioning coefficients (log K ow of 4.8 and 4.9 for TCS and TCC at neutral pH, respectively) the two antimicrobials are primarily removed from was tewater by adsorption to biosolids. TCS and TCC are the most frequently detected organic 20 pollutants in biosolids worldwide. The national sewage sludge survey by the U.S. EPA indicated that 92.4% and 100% of biosolids samples in the U.S contained TCS and TC C, respectively (U.S. EPA, 2009) . Of the 72 phar maceuticals and personal care products ( PPCPs ) in the U.S. EPA archived biosolids collected from 32 states and the District of Columbia, TCS and TCC were the most a bundant contaminants with mean concentrations of 12,600 µg/kg dw for TCS and 36,000 µg/kg dw for TCC, respectively (McClellan and Halden, 2010) . Therefore, the maximum projected annual land application of TCS and TCC is 52 tons/year and 150 tons/year, respectively (McClellan and Halden, 2010) . However, emission of TCS and TCC through biosolids is also expected to wash products (U .S. FDA, 2016b) . The occurrence of TCS and TCC in biosolids have also been commonly reported in China (Sun et al., 2016, Wang et al., 2018) , Singapore (Tran et al., 2016) , South Korea (Ryu et al., 2014) , India (Subedi et al., 2015) , Canada (Kim et al., 2014) (Table 2.3). 2.2.1.2. Biosolids - amended soils Land application of treated biosolids is a common practice in many countries. In the U.S., approximately 60%, or 3.4 - 4.2 million tons, of biosolids produced have annually been applied to land as soil amendment (McClellan and Halden, 2010) . Fate of TCS and TCC in land applied biosolids has been reported in se veral studies (Al - Rajab et al., 2015, Chen et al., 2014, Fu et al., 2016, Higgins et al., 2 011) . H owever, most of these studies were conducted in laboratory settings or in small plots under well - controlled conditions , which are difficult for extrapolating to commercially applied fields (Lozano et al., 2018) - biosolids application, in which concentration s of TCC were found to be higher than those of TCS in most of the studies. Lozano et al. reported that TCC was highly persistent in biosolids - amended 21 soils several years after application (45.8 ± 6.09 and 72.4 ± 15.3 µg/kg dw after 7 and 8 years, respectiv ely) (Lozano et al., 2018) . 22 Table 2. 3 . Occurrence and concentrations of TCS and TCC in the influent, effluent, and biosolids produced in the WWTPs worldwide . Country Year of sampling TCS TCC References Influent (ng/L) Effluent (ng/L) Biosolids (µg/kg dw) Influent (ng/L) Effluent (ng/L) Biosolids (µg/kg dw) China 2016 5.5 7. The - NH 2 attached to the aromatic ring referred to as N 4 and deprotonated at pH 2.5. Therefore, most sulfonamides are positively ch arged under acidic conditions, neutral between 2.5 6, and negatively charged at alkaline conditions (Sarmah et al., 2006b) . Tetracyclines inhibit synthesis of b acterial protein by preventing the association of aminoacyl - tRNA with the bacterial ribosome (Chopra and Roberts, 2001) . Tetracyclines are characterized by a partially conjugated four - ring structure with several ionizable functional groups, and the charge of tetracycline molecules depends on the solution pH (Sarmah et al., 2006b) . At environmental pH, tetracyclines may exist as cation, zwitterion, or net negatively charge ion (Sarmah et al., 2006b) . 26 Table 2. 5 . Chemical structure and properties of interested antibiotics (Peiris et al., 2017, USEPA, 2017) . Properties SMT SMX CTC OTC Structure CAS No 57 - 68 - 1 723 - 46 - 6 57 - 62 - 5 79 - 57 - 2 Formula C 12 H 14 N 4 O 2 S C 10 H 11 N 3 O 3 S C 22 H 23 ClN 2 O 8 C 22 H 24 N 2 O 9 M.W 278.3 253.3 478.9 460.4 Boiling point ( o C) 451.2 414.0 764.0 781.7 Melting point ( o C) 189.8 172.4 335.9 344.2 pKa (at 25 o C) 2.65; 7.65 1.6; 5.7 3.33; 7.55; 9.33 3.22; 7.46; 8.94 Water solubility (mg/L) 1,500 610 4,200 17,000 Vapor pressure (mm Hg at 25 o C) 6.82 x 10 - 9 1.3 x 10 - 7 5.84 x 10 - 22 9.05 x 10 - 23 Log K OW 0.14 0.89 - 0.62 - 0.9 SMT: sulfamethazine, SMX: sulfamethoxazole, CTC: chlortetracycline, OTC: oxytetracycline. 2.2.2.1. Wastewater treatment plants Tetracyclines and sulfonamides administered to humans and animals are poorly absorbed in the digestive tract, with approximately 50 90% of the doses are excreted via urine and feces as parent and metabolized forms (e.g. acetyl and glucuronides) (Tran et al., 2016, Tran et al., 2018, Yuan et al., 2019) . Due to intensi ve use in humans, the mixture of antimicrobials and their metabolites has been continuously released into the municipal WWTPs (Ezzariai et al., 2018) . However, incomplete elimination of antimicrobials and their metabolites during wastewater treatment p rocesses leads to their ubiquitous presence in the effluents and treated sludge (biosolids). The occurrence of SMT, SMX, CTC, and OTC in WWTPs is summarized in Table 2.6 and Table 2.7. Sulfonamides were frequently detected in both WWTPs influents and eff luents worldwide. As showed in Table 2.6, SMT and SMX have been detected in WWTPs of Asia, Europe, North and South America, and Australia, in which concentrations of SMX were generally higher than 27 those of SMT. The highest concentrations of SMX in the effl uents were 3,100 ng/L in Canada (Guerra et al., 2014) and 2,260 ng/L in India (Subedi et al., 2015) , while the highest concentrations of SMT were detecte d in Singapore (1,814 ng/L) and China (176.4 ng/L). A study by Yuan et al. reported SMT concentrations in the effluent (88.9 ng/L) were approximately 5 - fold higher than in the influent (16 ng/L) (Yuan et al., 2019) . It has been reported that N 4 - acetyl - SMX can be back - transformed to the parent SMX during wastewater treatment processes (Göbel et al., 2007, Gobel et al., 2005) , suggesting a possible explanation to the negative removal efficiencies of SMT in 4 - acetyl - and N 1 - glucuronide - sulfonamides are the dominant metabolites of sulfonamides in humans (Vree et al., 1991) and their occurrence in municipal WWTPs has been reported in literature (García - Galán et al., 2008) . Because these metabolites can undergo back - transformation to release the bioactive parent compounds during treatment processes, their occurrence in WWTPs should also be monitored. Tetracy clines present a high range of concentrations in wastewater, particularly OTC. As shown in Table 2.7, OTC had higher detection frequency than CTC in WWTPs influent and effluent worldwide. The concentrations of OTC in the influent and effluent ranged from b elow detection limit to thousands of ng/L. Tran et al. reported the highest concentration of OTC (approximately 30,049 ng/L) in the influent of a WWTP in Singapore (Tran et al., 2016) . Highest concentration of CTC was also detected at the same WWTP, at 15,911 ng/L (Tran et al., 2016) . OTC were also found in several WWTPs located in China, and substantially varied among different sampling locations (Table 2.7). The lowest concentrations of tetracyclines were founds in WWTPs in North American countries (e.g. U.S. and Canada). Conve ntional WWTPs are not designed for effective elimination of antimicrobials; consequently, fractions of antimicrobials in the influents can go through wastewater treatment 28 processes unchanged and be sorbed to biosolids. Concentrations of SMT, SMX, CTC, and OTC in treated biosolids produced by WWTPs are presented in Table 2.6 and Table 2.7. Many studies have reported occurrence of tetracyclines (CTC and OTC) in biosolids at relatively high concentrations, mostly in Asian countries (e.g. China). Recently, Ashf aq et al. reported concentrations of CTC and OTC up to 2,150 µg/kg dw and 5,116 µg/kg dw, respectively, in biosolids sampled from WWTPs in China (Ashfaq et al., 2017, Ben et al., 2018) . Under environmental pH, tetracyclines mainly exist as cations and zwitterion forms and sorb strongl y onto suspended particles via electrostatic interactions. Tetracyclines also form complexes with divalent cations (e.g. Ca 2+, Cu 2+ , and Mg 2+ ) present in biosolids, resulting in their high concentrations in the particulate phase (Tran et al., 201 6) . It has been found that the concentrations of antimicrobials in WWTPs in each country depend largely on several factors, such as usage patterns, population size/density, weather conditions, treatment systems etc. (Tran et a l., 2018) . Data from recent studies suggest that consumption of these antimicrobials in humans is still high in many developing countries. Compared to tetracyclines, sulfonamides were detected at lower concentrations in biosolids, from below limit of detectio n to hundreds of µg/kg dw (Table 2.6). 29 Table 2. 6 . Occurrence and concentrations of SMT and SMX in the influent, effluent, and biosolids produced in the WWTPs . Country Year of sampling SMT SMX Reference Influent (ng/L) Effluent (ng/L) Biosolids (µg/kg dw) Influent (ng/L) Effluent (ng/L) Biosolids (µg/kg dw) China 2017 16 88.9 na 124.1 50.4 na (Yuan et al., 2019) Colombia 2016 na na na 439 827 123 558 446 831 279 434 na (Botero - Coy et al., 2018) China 2016