Chemical and bioanalytical screenings for the unravelling of micropollutants from River Danube

Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigte Dissertation

vorgelegt von

Muhammad Arslan Kamal Hashmi, M.Sc. aus Gujrat, Pakistan

Berichter: Prof. Dr. rer. nat. Henner Hollert PD Dr. rer. nat. Werner Brack

Tag der mündlichen Prüfung: 26-Juni-2019

Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek online verfügbar.

i

ii

Erklärung

Die vorliegende Dissertation wurde im Department wirkungsorientierte Analytik des UFZ - Helmholtz Zentrum für Umweltforschung in Zusammenarbeit mit dem Lehr- und Forschungsgebiet für Ökosystemanalyse des Institutes für Umweltforschung (Biologie V) der RWTH Aachen unter Betreuung von Herrn Prof. Dr. Henner Hollert und Herrn PD Dr. Werner Brack angefertigt.

Hiermit versichere ich, dass ich die vorliegende Doktorarbeit selbstständig verfasst und keine anderen als die angegebenen Hilfsmittel verwendet habe. Alle Textauszüge und Grafiken, die sinngemäß oder wörtlich aus veröffentlichten Schriften entnommen wurden, sind durch Referenzen gekennzeichnet.

Muhammad Arslan Kamal Hashmi Aachen, den 26 Juni 2019

iii

iv

Abstract

Surfactants and endocrine disrupting chemicals (EDCs) can be found in surface waters at various concentration levels. Surfactants can be called as more frequently occurring contaminants in the surface water ranging as high as g/L concentration, while EDCs are often found in surface waters at ng/L or lower concentration range. Various chemical and bioanalytical screening approaches were used to detect the frequently occurring contaminants in high concentrations as well as potent bioactive pollutants with low concentrations in the river Danube. Multistep chemical screenings with LC-HRMS and LC-MS were used to detect and identify the chemicals from the samples while effect-based monitoring for various biological endpoints was done by using mammalian cell-based in-vitro reporter gene bioassays.

Multistep fractionation was also applied for the reduction of environmental mixture complexity and to uncover various biological effects.

In Chapter 2, water samples from Danube river were obtained from Joint Danube Survey 3

(JDS3) sampling campaign. Semi-automated nontarget screening (NTS) based prioritization and identification of high-intensity micropollutants successfully revealed many compounds present in the whole stretch of Danube. Most of the compounds detected were surfactants used in large quantities in many products used at domestic and industrial scale. Micropollutants concentration variation did not reveal any clear trend and occurred in the River Danube in low concentrations due to great dilution rate with river water.

In Chapter 3, estrogenic and androgenic effects of EDCs were detected by in-vitro reporter gene bioassays. The masking effects that interfered with biological responses was removed by the application of a successful primary fractionation procedure. In target chemical analysis, the estrogenic and androgenic human reproductive hormones were detected and upon confirmation were found to be the main contributors of the endocrine disruption in river water.

In Chapter 4, progestogenic and activity in parent sample was initially masked

v by cytotoxicity which was unraveled by primary fractionation and onward complexity reduction of active fraction was achieved by a second round of fractionation. mixture fractionation was performed for the detection of highly potent progestogenic pharmaceutical responsible for respective effect in the sample. Mass-balance approaches for Chapter 3 and

Chapter 4 confirmed that detected compounds were almost quantitatively driving the respective effects. Combination of effect-based tools, fractionation and chemical screening in effect- directed analysis (EDA) was demonstrated to be a powerful approach in environmental monitoring studies to detect various biological responses and to identify the main drivers of the activity.

vi

Zusammenfassung

Tenside und hormonaktive Substanzen (EDC) können in verschiedenen Konzentrationen in

Oberflächengewässern auftreten. Tenside sind in der Regel häufig vorkommende

Verunreinigungen im Oberflächenwasser, in einem Konzentrationsbereich von Gramm pro

Liter. EDCs treten in einem viel niedrigeren Konzentrationsbereich in Gewässern auf, im

Bereich Nanogramm pro Liter. Verschiedene chemische und bioanalytische

Untersuchungsverfahren wurden nun eingesetzt, um häufig auftretende Schadstoffe sowie niedrig konzentrierte bioaktive Substanzen in der Donau nachzuweisen. Mehrstufige chemische Screening Verfahren mittels LC-HRMS und LC-MS wurden zum Nachweis und zur Identifizierung der Chemikalien in Wasserproben verwendet, während die effektbasierte

Überwachung für verschiedene biologische Endpunkte durch Verwendung von säugetierzellbasierten in-vitro Reportergen-Wirkungstests erfolgte. Anwendung fand ebenfalls die Mehrstufenfraktionierung, um die Komplexität der Mischung zu reduzieren und verschiedene biologische Effekte aufzudecken.

In Kapitel 2 wurden Wasserproben genutzt, die im Rahmen des Joint Danube Survey 3 (JDS3)

Exkursion entnommen wurden. Die Priorisierung und Identifikation von Peaks hoher Intensität auf der Basis eines halbautomatischen Non-Target-Screening (NTS) Verfahrens führte zur

Entdeckung einiger sonst nicht überwachter chemischer Verbindungen entlang der Donau. bei den meisten der nachgewiesenen Chemikalien handelt es sich um Tenside, die in großen

Mengen und in einer Vielzahl von Produkten in Haushalt und Industrie verwendet werden. Die

Konzentrationen der meisten Mikroverunreinigungen waren relativ gering und zeigten keine deutlichen Trends in der Donau, was unter anderem auf die hohe Verdünnungsrate zurückzuführen ist.

In Kapitel 3 wurden östrogene und androgene Wirkungen von EDCs durch in-vitro-

Reportergen-Wirkungstests nachgewiesen und mittels wirkungsorientierte Analytik (EDA) auf

vii stoffliche Ursachen hin untersucht. Maskierungseffekte, welche biologische Reaktionen beeinflussen, wurden durch die Anwendung eines erfolgreichen primären

Fraktionierungsverfahrens beseitigt. In der chemischen Zielanalytik wurden östrogene und androgene humane Reproduktionshormone nachgewiesen und nach Bestätigung festgestellt, dass sie die Hauptverursacher endokriner Störungen im Flusswasser sind.

In Kapitel 4 wurde die progestogene und Glucocorticoid-Aktivität in Stichproben anfänglich durch Zytotoxizität maskiert, welche durch Primärfraktionierung aufgehoben wurde. Die weitere Vereinfachung der aktiven Fraktion wurde durch eine zweite Fraktionierungsrunde erreicht. Die Fraktionierung von Steroidmischungen wurde zum Nachweis hochpotenter progestogener Arzneimittel durchgeführt, die für die jeweilige Wirkung in der Probe verantwortlich sind. Massenbilanz-Ansätze in Kapitel 3 und Kapitel 4 bestätigten, dass nachgewiesene Verbindungen meist die Ursache der jeweiligen Auswirkungen waren. Die

Kombination wirkungsbasierter Instrumente, Fraktionierung und chemischer Untersuchungen in der EDA erwies sich als leistungsfähiger Ansatz für Umweltmonitoringstudien, um verschiedene biologische Reaktionen zu erkennen und die Haupttreiber der Aktivität zu identifizieren.

viii

ix

Contents

Abstract…………………………………………………………………………. V

List of Figures…………………………………………………………………... XIV

List of Tables……………………………………………………………………. XVI

Abbreviations and Symbols……………………………………………………. XVIII

Chapter 1. Introduction 1

1.1. Humans and Environment………………………………………………….. 1

1.2. Chemical screening………………………………………………………… 4

1.3. Surfactants………………………………………………………………….. 5

1.4. Endocrine disruption……………………………………………………….. 6

1.5. Freshwater wildlife…………………………………………………………. 9

1.6. In-vitro biological analysis…………………………………………………. 10

1.7. Effect-directed analysis (EDA)…………………………………………….. 12

1.8. Objectives and outline of the thesis………………………………………… 14

1.9. References………………………………………………………………….. 16

Chapter 2. Nontarget screening based prioritization and identification of 26 micropollutants from water samples of a large European river Danube.

2.1. Introduction………………………………………………………………… 28

2.2. Materials and methods……………………………………………………... 30

2.2.1. Chemicals and reagents……………………………………………….. 30

2.2.2. Sampling……………………………………………………………….. 30

2.2.3. Chemical analysis……………………………………………………… 31

2.2.3.1. Sample preparation…………………………………………….. 31

x

2.2.3.2. Liquid chromatography-high resolution mass spectrometry 31 (LC-HRMS)

2.2.4. Automated peak detection……………………………………………... 31

2.2.5. Prioritization for site-specific and frequent peaks…………………….. 32

2.2.6. Isotope peaks, adducts and homologues………………………………. 32

2.2.7. NTS and identification………………………………………………… 32

2.2.8. Micropollutants patterns analysis……………………………………… 33

2.3. Results and discussion……………………………………………………… 34

2.3.1. Frequency scores (FS)…………………………………………………. 34

2.3.2. Rarity scores (RS)…………………………………………………….. 34

2.3.3. Homologue series……………………………………………………… 35

2.3.4. Identification of compounds from JDS3……………………...... 36

2.3.4.1. Identification of compounds with high FS in ESI+…………… 36

2.3.4.2. Identification of compounds with high RS in ESI+……………. 37

2.3.4.3. Identification of compounds with high FS in ESI-…………….. 38

2.3.4.4. Identification of compounds with high RS in ESI-……………. 38

2.3.5. Micropollutant patterns along Danube………………………………… 39

2.4. Conclusions………………………………………………………………… 43

2.5. Acknowledgements………………………………………………………… 43

2.6. References………………………………………………………………….. 44

Chapter 3. Effect-Directed Analysis (EDA) of Danube River water sample 49 receiving untreated municipal wastewater from Novi Sad, Serbia.

3.1. Introduction………………………...………………………………………. 51

3.2. Materials and methods……………………………………………………... 53

3.2.1. Chemicals and reagents………………………………………………... 53

3.2.2. Sampling……………………………………………………………….. 54

xi

3.2.3. Fractionation…………………………………………………………… 54

3.2.4. Biological Analysis……………………………………………………. 55

3.2.5. Chemical Analysis……………………………………………………... 56

3.3. Results and discussion……………………………………………………… 59

3.3.1. Quality assurance (QA/QC)…………………………………………… 59

3.3.2. ERα-mediated response………………………………………………... 62

3.3.3. AR-mediated response………………………………………………… 66

3.3.4. Oxidative stress response (OSR)………………………………………. 69

3.4. Conclusions………………………………………………………………… 69

3.5. Acknowledgements………………………………………………………… 70

3.6. References………………………………………………………………….. 70

Chapter 4. Advanced effect-directed analysis unraveling water contamina- 77 tion with and at trace concentrations in a major European river.

4.1. Introduction………………………………………………………………… 79

4.2. Materials and methods……………………………………………………... 81

4.2.1. Study site and sampling………………………………………………... 81

4.2.2. Study design…………………………………………………………… 81

4.2.3. Fractionation…………………………………………………………… 82

4.2.4. Biological analysis…………………………………………………….. 83

4.2.5. Chemical analysis……………………………………………………… 84

4.2.5.1. Liquid chromatography-high resolution mass spectroscopy 84 (LC-HRMS) Nontarget (NT) screening

4.2.5.2. LC-HRMS Target Screening…………………………………… 85

4.2.5.3. Gas chromatography-high resolution mass spectroscopy (GC- 85 HRMS)

4.3 Results and Discussion……………………………………………………… 86

xii

4.3.1 Biological analysis……………………………………………………... 86

4.3.1.1. Quality control/Quality assurance (QC/QA)…………………… 86

4.3.1.2. PR- and GR-mediated responses……………………………….. 87

4.3.2 Chemical analysis………………………………………………………. 88

4.3.2.1 Nontarget (NT) screening……………………………………….. 88

4.3.2.2 Steroid mixture fractionation…………………………………… 89

4.3.2.3 Target screening………………………………………………… 89

4.3.3 Confirmation…………………………………………………………… 90

4.4 Acknowledgements…………………………………………………………. 95

4.5 References………………………………………………………………….. 96

CHAPTER 5. Conclusions…………………………………………………….. 103

Appendix A. Supplementary Information for Chapter 2…………………… 110

Appendix B. Supplementary Information for Chapter 3…………………… 118

Appendix C. Supplementary Information for Chapter 4…………………… 143

Acknowledgements……………………………………………………………... 166

Scientific Contribution…………………………………………………………. 168

Author’s Contribution…………………………………………………………. 170

Curriculum Vitae………………………………………………………………. 172

xiii

List of Figures

1.1: General flow diagram of an effect directed analysis (EDA) approach 12

2.1. Frequency scores of JDS3 peaks in ESI+ and ESI- mode 34

2.1.1. Frequency scores of top 1% JDS3 peaks in ESI+ and ESI- mode 34

2.2. Rarity scores of JDS3 peaks in ESI+ and ESI- mode 35

2.2.1. Rarity scores of top 1% JDS3 peaks in ESI+ and ESI- mode 35

2.3. Compounds with high FS detected and confirmed in ESI+ mode from 37 JDS3 data sample set.

2.4. Compounds with high FS detected and confirmed in ESI- mode from 38 JDS3 data sample set

2.5. Cluster analysis of JDS3 data sample set by using KmL method 39

2.6. Cluster analysis in the form of heatmap plotted for top 500 high 41 variance peaks from JDS3 sample set

2.7. Representation of frequency scores (FS) and rarity scores (RS) in the 41 form of boxplots for the selected peaks used for clustering for ESI+ and ESI- modes for JDS3 samples.

3.1. Experimental design of this study 53

3.2. Bioanalytical equivalent concentrations (BEQbio) of ERα (A), AR (B) 61 and OSR (C).

3.3. Estrogenic responses EEQ (ng/L) of fractions (EEQbio_Fi) of the water 65 sample (EEQbio_PS) explained by designed chemical mixtures (EEQbio_mixture) along with relative contribution of individual chemicals in EEQchem

3.4. Androgenic responses R1881-EQ (ng/L) of fractions (R1881-EQbio_Fi) 68 of the water sample (R1881-EQbio_PS) explained by designed chemical mixtures ( R1881-EQbio_mixture) along with relative contribution of individual chemical in R1881-EQchem

4.1 Simplified flow diagram of the work carried out in this study to 81 describe various steps.

xiv

4.2 Progestogenic and glucocorticoid mediated responses from samples, (i 87 & ii) represent bioanalytical equivalent concentrations (BEQ) from primary fractions for PR- and GR-activity respectively.

4.3. Flow diagram of developed alternative approach for the detection and 89 identification of ultra-trace endocrine disruptors in the environment.

4.4 Progestogenic responses (ng/L) of fractions (Promegestone_EQbio_Fi) 92 are explained by designed chemical mixtures (Promegestone_EQbio_mixture) as well as relative contribution of individual chemicals in the mixture (Promegestone_EQchem)

B-4.1 Responses from fractionation blanks processed through freeze-drying 127 (FBfd) and through solid phase extraction (FBspe) for ERα and AR

B-5.1 Activity in recombination of fractions processed through solid phase 128 extractions (Rspe) and through freeze drying (Rfd) and PS for ERα and AR

B-6.1 Agonistic and antagonistic ERα responses & cytotoxicity of F8, F9, 130 F11, F15, F16, Rspe and PS relative to agonistic (E2) and antagonistic () reference compounds respectively

B-7.1 Agonistic and antagonistic androgenic responses & cytotoxicity of F16, 130 F18, F19, Rspe and PS relative to agonistic (R1881) and antagonistic ( acetate) reference compounds respectively

B-8.1 Oxidative stress response of Rspe and PS relative to reference 131 compounds (tBHQ)

C-2 Two-step fractionation (primary and secondary) by using C18 and 146 aminopropyl columns. The fractions that showed biological responses are highlighted in white.

C-3 Flow chart of the experimental work and biological analysis carried out 147 in this study

C-3.1 Flow chart of the chemical analysis performed in this study 148

C-6 Sigmoidal and linear dose responses of fractionation blanks and samples 153 for receptor (PR)

C-7 Sigmoidal and linear dose response curves of fractionation blanks and 156 samples for (GR)

xv

List of Tables

2.1 list of JDS3 samples from Danube River and tributaries taken in 30 August-September 2013

3.1 EC10 and ECIR1.5 and BEQbio (EEQ, R1881-EQ and tBHQ-EQ) along 60 with standard error of mean (SEM) of ERα, AR in agonist mode and OSR.

3.2 Estrogenic and androgenic target compounds (ng/L) detected by 62 targeted screening from estrogenic (ERαA ) and androgenic (ARB ) active fractions. All other fractions did not contain any of the target chemicals above the detection limit

4.1 Progestogenic and glucocorticoid mediated responses from samples as 86 EC10±SE (REF) and BEQbio (-EQbio and -EQbio ) in ng/L.

4.2 Progestogens and glucocorticoids (in ng/L) detected in this study 90 along with compounds (, and ) detected in a previous study.

A-1 Settings for processing of liquid chromatography-high resolution 111 mass spectrometry (LC-HRMS) data in MZmine 2.21

A-2. List of compounds with highest Frequency score with peak intensities 112 detected in JDS3 water sample in ESI+ mode

A-3. List of compounds with highest Rarity score with peak intensities 114 detected in JDS3 water sample in ESI+ mode

A-4 List of compounds with highest Frequency score with peak intensities 115 detected in JDS3 water sample in ESI- mode

A-5 List of compounds with highest Rarity score with peak intensities 116 detected in JDS3 water sample in ESI- mode

B-1 Summary of bioassays performed 119

B-2.1 Targeted list for AR screening and list of compounds used as internal 122 standard (IS) for AR targeted screening

B-3.1 Effect concentrations (EC10) and relative effect potencies (REP, this 126 study) of detected compounds relative to ERα reference compound (E2) and AR reference compound R1881.

xvi

B-7.1 Androgenic agonistic and antagonistic response of Rspe and PS 131

B-9.1 Responses of ERα active environmental fractions and chemical 132 mixtures in terms of EC10±SEM (REF)

B-9.2 Effect of ERα active fractions explained by chemical mixtures in terms 132 of EEQbio±SEM (ng/L)

B-9.3 Responses of AR active environmental fractions and chemical mixtures 133 in terms of EC10±SEM (REF).

B-9.4 Effect of AR active fractions explained by chemical mixtures in terms 133 of R1881-EQbio±SEM (ng/L)

B-10.1 Compounds (ng/L) detected in targeted screening in ERα and AR 134 active and in neighboring fraction.

C-1 Description of progestogenic and glucocorticoid receptors (PR and GR 144 respectively) bioassays performed in this study.

C-5.1 Settings for processing of liquid chromatography-high resolution mass 151 spectrometry (LC-HRMS) data in MZmine 2.21

C-5.2 Settings for processing of gas chromatography-high resolution mass 151 spectrometry (GC-HRMS) data in MZmine 2.26

C-6.1 Comparison of EC10±SE (REF) values from samples for PR-mediated 154 response.

C-6.2 Comparison of Promegestone-EQbio and Promegestone-EQbio_mixture 154 (ng/L) and effect explained (percentage) for PR

C-8.1 List of compounds in steroid mixture, respective detection limits 157 (ng/mL water extract at concentrations factor (CF) of 1000) by using LC- HRMS and compounds affinities with their respective ligands (AR, receptor; PR, ; GR, glucocorticoid receptor).

C-9 Effect concentrations (EC10) and relative effect potencies (REP, this 160 study) of detected compounds relative to progesterone receptor (PR) reference compound promegestone and glucocorticoid receptor (GR) reference compound dexamethasone

C-10 Single compound concentration (ng compound/L) in active fractions 162 required to elicit EC10 response at PR bioassay.

C-11 Single compound concentration (ng compound/L) in active fractions 164 required to elicit EC10 response in the GR bioassay.

xvii

Abbreviations and Symbols

µL microliter

a.u. arbitrary units

AEO alcohol ethoyxylates

AES alkyl ether sulfates

AR

AS alkyl sulfates

BEQbio bioanalytical equivalent concentration-biological

BEQchem bioanalytical equivalent concentration-chemical

Br Bromine

C Carbon

CA Concentration addition

CAS Chemical abstract service

CF Concentration factor

Cl Chlorine

DDT Dichlorodiphenyltrichloroethane

DEA diethanolamides

DEHP bis(2-ethylhexyl)phthalate

DES

DHSS dihexyl sulfosuccinate

DHT Dihydrotestosterone

DIA data-independent acquisition

E1

E2 Estradiol

E3 17β-

EC Effect concentration

EDA Effect-directed analysis

xviii

EDC Endocrine disruption compound

EE2 17α-

EEQ Estradiol equivalent concentration

ERα receptor alpha

ESI Electrospray ionization

F Fraction

FBfd Fractionation blanks processed through freeze drying

FBspe Fractionation blanks processed through solid phase extraction

FS Frequency score

GC-HRMS Gas chromatography-high resolution mass spectrometry

GR Glucocorticoid receptor

H Hydrogen

HCD high energy collisional dissociation

HDX hydrogen deuterium exchange

HMMM hexa(methoxymethyl)melamine

HPLC High performance liquid chromatography

HRX Hydrophobic polystyrene-divinylbenzene xpert

JDS3 3rd Joint Danube Survey

KmL K-means for Longitudinal

LAS linear alkylbenzene sulphonates

LC-HRMS Liquid chromatography-high resolution mass spectrometry

LC-MS Liquid chromatography-mass spectrometry

LOD Limit of detection

LVSPE Large volume solid phase extraction

MA acetate

MF Molecular formula

xix

MP Medroxyprogesterone

MPA Medroxyprogesterone acetate

MR receptor

MS-SIM mass spectrometry-selected monitoring

MTBE methyl tert-butyl ether

N Nitrogen ng/L Nanogram/Liter

NS1 Novi Sad reference site upstream of UMWW inlet

NS2 Novi Sad contaminated site, downstream of UMWW inlet

NT screening Nontarget screening

O Oxygen oC degree Celsius

OSR Oxidative stress response

P Phosphorus

P4 Progesterone

PBBs Polybrominated biphenyls

PCBs Polychlorinated biphenyls

PCPs personal care products

PR Progesterone receptor

PS Parent sample

QA Quality assurance

QC Quality control

R Recombination of fractions

REF Relative enrichment factor

REP Relative effect potency

Rfd Recombination of fractions processed through freeze drying rkm River kilometer

xx

RP-HPLC Reverses phase-high performance liquid chromatography

RS Rarity score

Rspe Recombination of fraction processed through solid phase extraction

RT, rt Retention time

S Sulfur

SAS secondary alkane sulfonates

SEM standard error of mean

SPC sulfophenyl carboxylic acid

SSL Surfactant suspect list

STP Sewage treatment plant tBHQ Tert-butylhydroquinone

UMWW Untreated municipal wastewater

WWTP Wastewater treatment plant

xxi

xxii

Chapter 1

Introduction

1.1. Humans and Environment: Humans, for their survival depend on nature due to the wealth of resources provided by nature to the humans, which is estimated approximately 125 trillion US dollars annually. Food production, health, security, medical treatment and many other things make humans to heavily depend on biodiversity (WWF, 2018). Over the last 50 years, ecosystems have faced drastic changes due to growing human demands for various kinds of resources resulting in the extensive loss of biodiversity (MA, 2005). The drivers affecting the biodiversity are either direct or indirect. Major indirect drivers include increase in human population, basic requirements for food, living, health, social relationships and security which lead to the drivers directly effecting the biodiversity including overexploitation of resources, land usage, technological advancements and their utilization, agricultural activities, climate change and pollution (MA, 2005).

Over many decades, the loss incur to biodiversity due to human activities is immense.

Around 20% of the world coral reefs have been lost and another 20% have been degraded while

35% of mangrove areas have been lost (MA, 2005). Incredible loss of species has been recorded due to increasing population, rapid deforestation, land use change from forests to cropland and due to urbanization. Species extinction rate has increased over 1000 times comparatively and around 10-30% of mammals, birds and amphibian species are facing the danger of extinction

(MA, 2005). Overall 60% and maximum up to 89% decline have been observed among the population sizes of thousands of vertebrate species in various parts of the world since 1970 to

2014 (WWF, 2018).

1

Since ancient times, humans have always been living near the water sources. Water is a source of life and its presence on the earth not only affects the climate but it also facilitates the movement of species as well as many socio-economic activities depend on it. Surface water is the most accessible natural resource and it also accommodates wide-ranging ecosystem activities thus supporting the lives of thousands of aquatic species (WWF, 2018). Freshwater

(rivers, lakes, wetlands) constitutes only around 0.01% of the overall earth surface but it is habitat of over 125,000 species of various aquatic organisms (Balian et al., 2008) like fish, reptiles, molluscs, mammals, insects and plants (WWF, 2018) which represent 10% of overall counted species on earth. The freshwater vertebrates contribute 35% to the overall 52000 vertebrate species discovered so far (Balian et al., 2008). These species are strongly at risk due to many stressors including pollution, habitat loss and intrusion of invasive species (MA,

2005), which caused 83% of freshwater species decline since 1970 (WWF, 2018).

One of the major stressors responsible for the deterioration of surface water quality is biogenic in the form of excretion of feces and urine along with chemical pollution due to industrialization from point and nonpoint sources. The excretion from humans and live stock is proportional to the population and (UNEP/WHO, 1996) and in developing countries approximately two million tons of excreta in the form of untreated sewage is added into surface waters daily (CBD, 2010) due to the lack of facilities for proper sanitation and sewage treatment in most parts of the underdeveloped countries (UNEP/WHO, 1996). The fecal pollution in the surface waters may affect humans and aquatic organisms due to the presence of intestinal microorganisms and other biologically active substances (UNEP/WHO, 1996). Addition of untreated sewage in the surface waters is a major cause of concern due to the drinking water abstraction for domestic use as approximately half of the world big cities are situated near surface water streams which fulfill their domestic water requirements from surface waters

2

(CBD, 2010). It was forecasted that till year 2025 approximately 65% of the world population will live in areas with poor sanitation facilities resulting in increased stresses on freshwater

(CBD, 2010).

Due to industrialization and technological advancements the discharge of persistent organic and inorganic pollutants into the surface waters has increased which can put humans and aquatic life health at risk due to chemical exposure. It was estimated that in developing countries around 70% of untreated industrial waste is added into surface waters daily (CBD,

2010) resulting in the presence of huge numbers of pollutants which are being detected in environmental pollution and monitoring studies (Ginebreda et al., 2018; Kolarević et al., 2016).

The presence of wide range of contaminants with various health hazards on living organisms made European Union (EU) to implement a comprehensive legislation regarding the protection of EU surface waters and to protect the living organisms from contaminants’ impacts, known as water framework directive (WFD) with main objective to safeguard the

“good status” of surface waters by attaining the highest possible condition of chemical and ecological aspects (“WFD Directive 2000/60/EC,” 2000). The highest possible chemical aspects are required to achieve through the screening of limited number of compounds in surface waters including watch list and priority substances. The compounds in watch list are monitored in the surface waters of EU which further helps in the prioritization of the substances of concern present in priority list while priority list substances are supposed not to be present in the surface waters above the concentrations which are prescribed in environmental quality standards (EQS) by WFD (“WFD Directive 2013/39/EU,” 2013). The presence of complex mixtures of chemicals with chemical and biological activity including pharmaceuticals, , dioxins, surfactants, industrial chemicals, chemicals from personal care products and many more can be found in surface waters across the world (Aït-Aïssa et al., 2010; Creusot et al., 2014; Exner and Färber, 2006; Long and Bonefeld-Jørgensen, 2012; McKinlay et al.,

3

2008; Muz et al., 2017a; Orton et al., 2014; Rosal et al., 2010; Sanderson et al., 2004; van der

Linden et al., 2008). Exposure of these chemicals even at low concentrations may cause unwanted health impacts on humans and on aquatic organisms. Within the last decade chemical screening techniques have been developed to address this complex contamination

1.2. Chemical screening: Contaminants present in surface waters may be detected with high resolution mass spectrometry linked to liquid chromatography (LC-HRMS) and/or gas- chromatography (GC-HRMS) (Krauss, 2016). Various screening methods i.e., target and nontarget screening (NTS) are employed to identify the compounds (Krauss et al., 2010). With target screening hundreds of chemicals can be detected and quantified on the basis of reference standards. For the detection of unknown contaminants where no prior knowledge of the compounds is available, NTS is the suitable screening approach to be followed (Hollender et al., 2017). NTS can be used to record compulsive chemical fingerprinting. Individual peaks are prioritized because of signal intensity, the frequency of detection. The occurrence of heteroatoms can be subjected to compound identification based on exact mass, retention time and fragmentation. Candidate compound lists are generated by using in-silico fragmentation programs by using MetFrag (Ruttkies et al., 2016) connected with big chemical databases including ChemSpider (Royal society of Chemistry) and PubChem (NCBI). Compiled candidate lists for concerned unknown masses may contain large numbers of candidates and in order to reduce their number and to select plausible candidates various methods are applied i.e., ionization behavior (Gallampois et al., 2015), toxicity prediction (Nendza et al., 2013), as well as hydrogen deuterium exchange and pH-dependent LC retention (Muz et al., 2017b).

Onward selected plausible candidate compounds are confirmed with reference standards.

Generally environmental monitoring studies target the specific classes of compounds of their interest like pesticides (Lissalde et al., 2011), pharmaceuticals, personal care products (Bulloch et al., 2015) and priority substance in case of fulfilling regulatory requirements (“WFD

4

Directive 2013/39/EU,” 2013). While pesticides and pharmaceuticals are frequently detected, other compound groups such as surfactants typically occurring as complex mixtures of homologues and natural and synthetic hormones occurring and being biologically active at very low concentrations are overlooked. Surfactants are ubiquitous in the environment due to their immense use in daily life and are present in high concentration in the environment, while endocrine disrupting chemicals (EDCs) in the form of steroidal natural and synthetic hormones are excreted to the environment and are present in the environment in ng/L or lower concentrations.

1.3. Surfactants: Surfactant also termed as surface-active agents are heavily used in a variety of applications like in domestic applications in the form of detergents, industrial usage

(petroleum industry), soil remediation, firefighting foams, biological systems (usage in respiratory ailments, in hydrogels), in health and personal care products (like cosmetics, germicidal property of fabric softeners and detergents), in food industry (edible coatings to chocolates, confectionary, margarine), in agricultural products in the form of pesticides, herbicides, fungicides and many others purposes (Schramm et al., 2003). The surface active substances can be defined as substances which adsorb on the surface resulting in the change of free energies of those surfaces. The molecular structure of surfactant generally contain hydrophobic (water-insoluble but oil-soluble) and hydrophilic (water-soluble) structures

(Rosen, 2004) which reduce the surface tension between surfaces.

The major groups of surfactants use in today’s life are anionic, cationic, zwitterionic and nonionic while anionic surfactant contained with negative charge, cationic surfactant with positive charge, zwitterionic having with both positive and negative charge and nonionic with no charge (Porter, 1991; Steber, 2007). Surfactants are used in huge quantities i.e., hundreds of thousands of tonnes per year (Rosen, 2004) more than half of the total usage of surfactants is accounted for alkylbenzene sulfonates, alcohol ethoxylates, sulfates and ethersulfates which

5

are used in soaps, laundry, domestic and personal care products (Schramm et al., 2003). Due to their huge usage in the manufacturing of products, there ubiquitous presence in the environment and especially in surface waters is expected. Surfactants are known to produce homologue series consisting of compounds of similar structures but with increasing molecular weights. Each unit of such series is termed as a homologue of its next neighboring unit (Bahl and Bahl, 2008). These homologue units in case of surfactant could be i.e., methylene (CH2), methylene oxide (CH2O), polyethylene glycol (PEG) or ethylene oxide (C2H4O), polypropylene glycol (PPG) (C3H6O) and many others depending on the type and structure of the parent compound. In surface waters, individual surfactants in concentrations ranging from gram/L to mg/L range found to have various impacts on fish, amphibians, daphnia and other aquatic life (Ivanković and Hrenović, 2010; Olkowska et al., 2011) as well as in mixture form

(Rosal et al., 2010). Along with other effects surfactants are also known to exhibit the potential to disrupt the endocrine system of aquatic organisms thus acting as EDCs as well (Petrović and

Barceló, 2000).

1.4. Endocrine disruption: Endocrine disruption from endogenous and exogenous sources are present in surface waters in low concentrations (contrary to surfactants concentrations) generally ranging from ng/L to sub-ng/L. These compounds after finding their way (through various sources) into the living organisms have the ability to bind to specific receptors and cause adverse effects (Escher and Hermens, 2002) by disruption of endocrine system, which in turn can cause sub-lethal effects in the form of physiological and reproductive effects (WHO,

2005). In surface waters, EDCs belonging to both biological as well as anthropogenic sources were found to cause a range of effects. EDCs include natural hormones which are produced endogenously and excreted into the environment while anthropogenic EDCs include man-made chemicals including pharmaceuticals (synthetic hormones) (Creusot et al., 2014; Sanderson et al., 2004), pesticides (McKinlay et al., 2008), industrial chemicals and personal care products

6

(Orton et al., 2014). It was observed that more than 90% of total hormonal load in the form of

EDCs is released to the environment through untreated sewage (Chang et al., 2009).

The endocrine system plays a vital role in living organisms by secreting various hormones, which travel through the circulatory system to their targeted tissues where targeted cells respond to them through specific receptors. Hormones bind to the receptors in a hormone- receptor-complex which afterwards moves to the nucleus acting as a transcription factor, binding to a specific site on DNA, regulating the gene transcription and triggering endocrine activity (Contrò et al., 2015). The superfamily of nuclear receptors includes a subclass of nuclear steroid receptors which among others is comprised of estrogen (ER), androgen (AR), progesterone (PR), glucocorticoid (GR) and mineralocorticoid receptor (MR). These receptors regulate the gene expression and are involved in , development, reproductive functions, nervous system and homeostasis of the living organisms (Wilkinson et al., 2008).

Any interference or disturbance in the normal functioning of hormonal system can cause serious problems and health issues for living organisms.

Endocrine disruption and its impacts on living organisms is a cause of concern (Ihara et al.,

2014; Rocha et al., 2016; Vajda et al., 2011) due to the ability of EDCs to mimic hormones.

According to the International Programme on Chemical Safety, endocrine disruptors (IPCS) can be defined as “An endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, or its progeny, or (sub) populations” ; and “A potential endocrine disruptor is an exogenous substance or mixture that possesses properties that might be expressed to lead to endocrine disruption in an intact organism, or its progeny, or (sub) populations” (IPCS, 2002).

Many endogenous and exogenous compounds have the ability to behave like EDCs including but not limited to pharmaceuticals and personal care products (PPCPs), industrial and domestic compounds, surfactants, pesticides (Bolong et al., 2009; Diamanti-Kandarakis et al., 2009; Yu

7

et al., 2013) while natural sources of EDCs are humans, animals and plants (Diamanti-

Kandarakis et al., 2009; Lintelmann et al., 2003; Patisaul and Jefferson, 2010). Naturally cholesterol is the main precursor through which main are derived endogenously but other than these natural steroids, many steroidal pharmaceuticals are also prepared synthetically and are consumed for various treatments. Both natural and synthetic hormones ultimately find their way into the environment (Lintelmann et al., 2003).

Estrogens and are the main hormones responsible for female and male reproductive system respectively (Dobrzyn et al., 2017). Estrogens have their main role in female reproductive system but also contribute to the health of cardiovascular system, skeletal system, mood and behavior. Estrogens are also involved in many health disorders in living organisms which include various types of cancers (breast and ovarian), Alzheimer, Parkinson’s disease, heart related issues, endometriosis and diabetes (Wilderer and Kramer, 2011).

Androgens are responsible for maintaining the male reproductive system. During embryogenesis androgens are involved in the development of male phenotype and, at the time of puberty, achievement of sexual maturity and at later stages control the male reproductive function. Apart from its above mentioned functions, androgens also have some roles in non- reproductive tissues like musculoskeletal tissues and skin. Androgens are also held responsible for some health related issues including prostate cancer (Heemers and Tindall, 2006).

Progesterone is a female reproductive hormone which has important roles in menstruation, pregnancy and fetal development (Lintelmann et al., 2003). Progesterone itself is a natural hormone (P4) while synthetic often termed as progestogens are used as pharmaceuticals (Shing‐Shing et al., 2000). Glucocorticoids regulate the glucose metabolism and play an important role in diabetes, obesity, immune system, central nervous system (CNS), digestive, renal and reproductive systems (Escher et al., 2014; Katzung et al.,

2012). Natural hormones (estrogens, androgens, progestogens and glucocorticoids), in their

8

synthetic form, are used as pharmaceuticals to treat many health related issues like hormone related treatments, cancers, reproductive issues, contraceptives as well as in personal care products (Lintelmann et al., 2003; Runnalls et al., 2010; Shing‐Shing et al., 2000) The usage of synthetic hormones as pharmaceuticals is country-specific and ranges from few kg to thousands of kilograms per annum depending on the type of treatment and quantity of medicine prescribed (Besse and Garric, 2009; Fent, 2015; Kugathas et al., 2012; Macikova et al., 2014;

Runnalls et al., 2010)

1.5. Freshwater wildlife: The first ever incidence of endocrine disruption in surface waters was reported in Florida, USA, where strongly masculinized female mosquito fish were found downstream of a surface water stream receiving pulp and paper mill effluents (PPME) (Howell et al., 1980). Further many incidences of endocrine disruption were recorded with a range of effects like physiological and reproductive effects in aquatic organisms in surface waters receiving wastewater in many parts of the world such as USA, Italy, Czech Republic, Brazil and France (Burkina et al., 2018; Geraudie et al., 2017; Hewitt et al., 2008; Ibor et al., 2016;

Iwanowicz et al., 2019; Tancioni et al., 2015; Yamamoto et al., 2017). EDCs in the ng/L range may cause complete breakdown of fish populations (Kidd et al., 2007). Municipal wastewater effluents released into the surface waters or rivers are also known to contain EDCs which impact on sex specification like feminization and/or masculinization caused by estrogens and androgens in aquatic organisms which may lead to adverse impacts at population level resulting in imbalance in the aquatic ecosystem (Hou et al., 2018; Huang et al., 2016). Effects caused by progestogens on aquatic organisms in the environment are also related to the reproductive health of wild life (Orlando and Ellestad, 2014). Progestogens were found to have effects on egg development, decline in fertility, decreased vitellogenesis (Säfholm et al., 2012) impact at genetic level (DeQuattro et al., 2012; Lorenz et al., 2011), reduced sperm motility (Murack et al., 2011) as well as suppression of immune system (Pietsch et al., 2009). Glucocorticoids in

9

the surface waters are known to bioaccumulate in the fish plasma resulting in stress as well as causing various physiological and reproductive effects (Kugathas et al., 2013; Macikova et al.,

2014; Salas-Leiton et al., 2012)

In wastewater hormones are present in mixtures due to the presence of many compounds causing combined effects of all compounds following the models of concentration addition or independent action (Petersen and Tollefsen, 2011). Estrogenic hormones are known for their effect as concentration addition manner (Brian et al., 2005), where compounds in a mixture behave similarly (Kortenkamp and Altenburger, 1999), while in independent action, compounds present in a mixture impart their effect through different mechanisms of actions

(Hadrup et al., 2013). Concentration addition of hormones with same mechanism of action in mixtures have been observed in many studies like in case of ER (Brian et al., 2005; Hashmi et al., 2018), anti-AR (Orton et al., 2014) and PR (Zucchi et al., 2014). Compounds with antagonistic effects may reduce the effect of each other (Ihara et al., 2014). In a recent study, various hormones in a mixture at higher concentrations with different modes of action were found to be acting independently (Thrupp et al., 2018).

1.6. In-vitro biological analysis: In ecotoxicological studies, for the detection of biological effects of pollutants, two types of biological analysis are performed, i.e., in-vivo and in-vitro.

Whole organism(s) (like rodents, fish, fish embryos) in-vivo tests are typically performed to observe effects of pollutants on survival and/or reproduction while in case of in-vitro biological analysis, cell based bioassays are used which can cover a variety of biological endpoints based on various modes of actions. Many in-vitro assays are characterized by their reliability, cost- effectiveness, sensitivity, low labor intensity and time consumption (Escher and Leusch, 2011),

(Froment et al., 2016). There are many in-vitro bioassays using various cell lines to detect the biological responses from water samples such as chemical activated luciferase expression

(CALUX) (Leusch et al., 2017), yeast estrogenic and androgenic screens (YES and YAS)

10

(Routledge and Sumpter, 1996; Sohoni and Sumpter, 1998), and many others. Focusing on human health risk assessment, mammalian cell-based bioassays may be used in the screening of environmental water samples for the detection of EDCs with regards to receptor-mediated effects from estrogens, androgens, progestogens and glucocorticoids (Bain. et al., 2014; Brand et al., 2013; Conley et al., 2017; Escher et al., 2014; König et al., 2017; Leusch et al., 2017).

GeneBLAzer in-vitro reporter gene bioassays are sensitive to detect hormonal responses in agonist and antagonist mode from water samples at very low concentrations (Leusch et al.,

2017).

GeneBLAzer assays use human cancer cell lines which contain ligand binding domain

(LBD) of respective mammalian nuclear receptor fused to DNA-binding-domain (DBD) of

GAL4 gene. Beta-lactamase reporter gene is incorporated under the transcriptional control of an upward activator sequence (UAS) which is stably integrated in the cell line and upon ligand binding gene expression was carried out by beta-lactamase reporter gene. GeneBLAzer bioassays use Fluorescence Resonance Energy Transfer (FRET) technique for ratiometric reporter response by using FRET substrate (Qureshi, 2007). Bioassay response is achieved by fluorescence (F) readings of blue and green emissions of stimulated cells control and unstimulated cells control. Blue and green emissions along with cell viability/cytotoxicity were read at various wavelengths i.e., blue emission (excitation wavelength: 409 nm & emission wavelength: 460 nm), green emission (excitation wavelength: 409 nm & emission wavelength:

530 nm) and cell viability/cytotoxicity (excitation wavelength: 590 nm & emission wavelength:

665 nm) (Jones and Padilla-Parra, 2016; Qureshi, 2007).

11

1.7. Effect-directed analysis (EDA): Detection of biological effects of contaminants and identification of causative agents can be done by applying one of the two main approaches, i.e., effect directed analysis (EDA) and toxicity identification evaluation (TIE) (Burgess et al.,

2013). In TIE, for biological responses, whole organism tests are performed and effects are seen in the form of survival, growth and reproduction (Burgess et al., 2013), while TIE has a specific focus on ecological relevance. EDA focuses to optimize the diagnostic power, often using mode of actions, specific in-vitro assays together with fractionation and in-depth target and nontarget chemical analysis. EDA can be divided into three major parts, i.e., biological analysis, fractionation and chemical analysis and all these three parts work in collaboration with each other in an iterative manner until the achievement of objective (Brack, 2003).

Figure 1.1: General flow diagram of an effect directed analysis (EDA) approach (Brack,

2003)

12

EDA study starts with acquiring the sample of concern which can be water (Chen et al.,

2016; Thomas et al., 2009) sediments (Grung et al., 2011; Houtman et al., 2006), (Houtman et al., 2004; Rostkowski et al., 2011; Simon et al., 2013), and others (Vrabie et al., 2012). After processing the sample(s) in the laboratory, samples are tested for their effect or toxicity on biological endpoint(s) of concern by using in-vitro (Escher et al., 2014; Escher and Leusch,

2011; Leusch et al., 2017; Neale et al., 2017) and/or in-vivo (Massei et al., 2019; Murack et al.,

2011) biological analysis. As ambient water samples contain chemicals at very low concentrations and in order to detect the chemicals present and to record toxicological responses or toxicity out of the water samples as dose-response curves, samples are enriched

(relative enrichment factor (REF)) (Escher et al., 2014). To reduce mixture complexity, samples are fractionated typically using LC applying a suitable stationary phase and mobile phase. This reduction of the complexity of the sample in a stepwise manner and isolation of fractions for toxicity testing helps unraveling toxicological responses of the sample in fractions and enhance the chances of successful detection of chemicals in case of chemical analysis

(Brack et al., 2016).

Each individual fraction is tested on the same biological endpoint and upon getting response from one or more fractions; active fractions are identified and selected for further investigation. In order to check for activity recovery during fractionation, equal aliquots from all individual fractions are combined into one (recombination of fractions) and tested on the same biological analysis along with individual fraction. Activity recovered may be directly compared to the sample’s activity before fractionation. The active fraction(s) are then chemically analyzed by using LC-MS and/or GC-MS (Krauss, 2016) and target, suspect and/or nontarget screening (Krauss et al., 2010). Upon identification of compounds, confirmation of the toxicant from chemical analysis and biological analysis could be done either by using reference standards for chemical analysis and also for biological analysis dose-response curves

13

can be obtained from reference standards and compared with environmental samples (Brack et al., 2008).

1.8. Objectives and outline of the thesis: Current chemical monitoring ignores the vast majority of chemicals including many compounds occurring frequently and in high concentrations but also many biologically highly potent ones occurring in traces such as EDCs.

However, promising approaches for a more holistic monitoring are available such as chemical screening, effect-based monitoring and EDA to address these contaminants.

The overall goal of this study is to demonstrate that these approaches actually provide new insights into the contamination beyond priority substances and other classes of compounds which are normally monitored in the surface waters. This study is conducted on the second biggest river of Europe, the River Danube, which flows through many countries and millions of inhabitants rely on Danube River resources. River Danube is not only used as a source of drinking water but also for domestic, agricultural and industrial water usages along with serving as a navigational route for transportation as well as for various recreational activities

(Schmedtje et al., 2005). These activities contribute to the deterioration of river water quality due to various point and nonpoint sources of pollution and bye-products. By the application of chemical screening, effect-based tools and EDA, this study aimed to get the answers of various questions relevant for ecotoxicology. The questions this study addressed were:

1. What are frequently occurring compounds at high concentrations in River Danube? Are they priority substances as per identified by EU WFD or something new. NTS would be applied to get the answer of this question.

2. Assuming the specific relevance of endocrine disruption: would it be possible to detect EDCs present at very low concentrations in surface waters and their activity with effect-based in-vitro methods?

14

3. if effect-based tools successfully detect the EDCs activity, would it be possible to detect the main causative drivers with EDA?

In Chapter 2, prioritization and identification of frequently occurring chemicals from water samples of a big European river under a big sampling campaign (Joint Danube survey 3,

JDS3) was performed and many high intensity compounds were detected and identified by using NTS approach. In Chapter 3 & 4, application of effect-based tools for the detection of highly bioactive EDCs was applied for the assessment of various hormonal markers and further for the detection of causative compounds responsible for biological responses full application of EDA on water samples from River Danube was done.

Chapter 2 (in preparation) will be submitted in an international peer reviewed journal,

Chapter 3 has been already published in an international peer-reviewed journal “Science of the

Total Environment”, while Chapter 4 (in preparation) will be submitted to Environmental

Science and Technology.

15

1.9. References: Aït-Aïssa, S., Laskowski, S., Laville, N., Porcher, J.M., Brion, F., 2010. Anti-androgenic activities of environmental pesticides in the MDA-kb2 reporter cell line. Toxicol. Vitr. 24, 1979–1985. doi:10.1016/j.tiv.2010.08.014 Bahl, B.S., Bahl, A., 2008. Advanced Organic Chemistry, S. Chand and Company, India. Bain., P.A., Williams., M., Kumar., A., 2014. Assessment of multiple hormonal activities in wastewater at different stages of treatment. Environ. Toxicol. Chem. 33, 2297–2307. doi:10.1002/etc.2676 Balian, E., Segers, H., Lévêque, C., Martens, K., 2008. The Freshwater Animal Diversity Assessment: An overview of the results, Hydrobiologia. doi:10.1007/s10750-007-9246- 3 Besse, J.P., Garric, J., 2009. Progestagens for human use, exposure and hazard assessment for the aquatic environment. Environ. Pollut. 157, 3485–3494. doi:10.1016/j.envpol.2009.06.012 Bolong, N., Ismail, A.F., Salim, M.R., Matsuura, T., 2009. A review of the effects of emerging contaminants in wastewater and options for their removal. DES 239, 229–246. doi:10.1016/j.desal.2008.03.020 Brack, W., 2003. Effect-directed analysis: A promising tool for the identification of organic toxicants in complex mixtures? Anal. Bioanal. Chem. 377, 397–407. doi:10.1007/s00216-003-2139-z Brack, W., Ait-Aissa, S., Burgess, R.M., Busch, W., Creusot, N., Di Paolo, C., Escher, B.I., Mark Hewitt, L., Hilscherova, K., Hollender, J., Hollert, H., Jonker, W., Kool, J., Lamoree, M., Muschket, M., Neumann, S., Rostkowski, P., Ruttkies, C., Schollee, J., Schymanski, E.L., Schulze, T., Seiler, T.-B., Tindall, A.J., De Aragão Umbuzeiro, G., Vrana, B., Krauss, M., 2016. Effect-directed analysis supporting monitoring of aquatic environments — An in-depth overview. Sci. Total Environ. 544, 1073–1118. doi:http://dx.doi.org/10.1016/j.scitotenv.2015.11.102 Brack, W., Schmitt-Jansen, M., Machala, M., Brix, R., Barceló, D., Schymanski, E., Streck, G., Schulze, T., 2008. How to confirm identified toxicants in effect-directed analysis. Anal. Bioanal. Chem. 390, 1959–1973. doi:10.1007/s00216-007-1808-8 Brand, W., de Jongh, C.M., van der Linden, S.C., Mennes, W., Puijker, L.M., van Leeuwen, C.J., van Wezel, A.P., Schriks, M., Heringa, M.B., 2013. Trigger values for investigation of hormonal activity in drinking water and its sources using CALUX bioassays. Environ. Int. 55, 109–118. doi:https://doi.org/10.1016/j.envint.2013.02.003 Brian, J. V, Harris, C.A., Scholze, M., Backhaus, T., Booy, P., Lamoree, M., Pojana, G., Jonkers, N., Runnalls, T., Bonfà, A., Marcomini, A., Sumpter, J.P., 2005. Accurate Prediction of the Response of Freshwater Fish to a Mixture of Estrogenic Chemicals. Environ. Health Perspect. 113, 721–728. Bulloch, D.N., Nelson, E.D., Carr, S. a., Wissman, C.R., Armstrong, J.L., Schlenk, D., Larive, C.K., 2015. Occurrence of Halogenated Transformation Products of Selected Pharmaceuticals and Personal Care Products in Secondary and Tertiary Treated Wastewaters from Southern California. Environ. Sci. Technol. 49, 2044–2051. doi:10.1021/es504565n Burgess, R.M., Ho, K.T., Brack, W., Lamoree, M., 2013. Effects-directed analysis (EDA)

16

and toxicity identification evaluation (TIE): Complementary but different approaches for diagnosing causes of environmental toxicity. Environ. Toxicol. Chem. 32, 1935–1945. doi:10.1002/etc.2299 Burkina, V., Zamaratskaia, G., Sakalli, S., Giang, P.T., Kodes, V., Grabic, R., Velisek, J., Turek, J., Kolarova, J., Zlabek, V., Randak, T., 2018. Complex effects of pollution on fish in major rivers in the Czech Republic. Ecotoxicol. Environ. Saf. 164, 92–99. doi:https://doi.org/10.1016/j.ecoenv.2018.07.109 CBD, 2010. Convention on Biological Diversity (CDB). 2010. Living in harmony with nature. Secretariat of the Convention on Biological Diversity, Montreal, Canada. https://www.cbd.int/undb/media/factsheets/undb-factsheets-en-web.pdf 1–70. Chang, H., Wan, Y., Hu, J., 2009. Determination and Source Apportionment of Five Classes of Steroid Hormones in Urban Rivers. Environ. Sci. Technol. 43, 7691–7698. doi:10.1021/es803653j Chen, X.-W., Zhao, J.-L., Liu, Y.-S., Hu, L.-X., Liu, S.-S., Ying, G.-G., 2016. Evaluation of estrogenic activity in the Pearl River by using effect-directed analysis. Environ. Sci. Pollut. Res. 23, 21692–21702. doi:10.1007/s11356-016-7377-7 Conley, J.M., Evans, N., Cardon, M.C., Rosenblum, L., Iwanowicz, L.R., Hartig, P.C., Schenck, K.M., Bradley, P.M., Wilson, V.S., 2017. Occurrence and In Vitro Bioactivity of Estrogen, Androgen, and Glucocorticoid Compounds in a Nationwide Screen of United States Stream Waters. Environ. Sci. Technol. 51, 4781–4791. doi:10.1021/acs.est.6b06515 Contrò, V., Basile, J.R., Proia, P., 2015. Sex steroid hormone receptors, their ligands, and nuclear and non-nuclear pathways. AIMS Mol Sci 2, 294–310. Creusot, N., Aït-Aïssa, S., Tapie, N., Pardon, P., Brion, F., Sanchez, W., Thybaud, E., Porcher, J.-M., Budzinski, H., 2014. Identification of Synthetic Steroids in River Water Downstream from Pharmaceutical Manufacture Discharges Based on a Bioanalytical Approach and Passive Sampling. Environ. Sci. Technol. 48, 3649–3657. doi:10.1021/es405313r DeQuattro, Z.A., Peissig, E.J., Antkiewicz, D.S., Lundgren, E.J., Hedman, C.J., Hemming, J.C.., Barry, T.P., 2012. Effects of progesterone on reproduction and embryonic development in the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 31, 851–856. doi:10.1002/etc.1754 Diamanti-Kandarakis, E., Bourguignon, J.-P., Giudice, L.C., Hauser, R., Prins, G.S., Soto, A.M., Zoeller, R.T., Gore, A.C., 2009. Endocrine-Disrupting Chemicals: An Endocrine Society Scientific Statement. Endocr. Rev. 30, 293–342. doi:10.1210/er.2009-0002 Dobrzyn, K., Smolinska, N., Kiezun, M., Szeszko, K., Maleszka, A., Kaminski, T., 2017. The effect of estrone and estradiol on the expression of the adiponectin system in the porcine uterus during early pregnancy. Theriogenology 88, 183–196. doi:http://dx.doi.org/10.1016/j.theriogenology.2016.09.023 Escher, B.I., Allinson, M., Altenburger, R., Bain, P. a., Balaguer, P., Busch, W., Crago, J., Denslow, N.D., Dopp, E., Hilscherova, K., Humpage, A.R., Kumar, A., Grimaldi, M., Jayasinghe, B.S., Jarosova, B., Jia, A., Makarov, S., Maruya, K. a., Medvedev, A., Mehinto, A.C., Mendez, J.E., Poulsen, A., Prochazka, E., Richard, J., Schifferli, A., Schlenk, D., Scholz, S., Shiraishi, F., Snyder, S., Su, G., Tang, J.Y.M., Burg, B. Van Der, Linden, S.C. Van Der, Werner, I., Westerheide, S.D., Wong, C.K.C., Yang, M.,

17

Yeung, B.H.Y., Zhang, X., Leusch, F.D.L., 2014. Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays. Environ. Sci. Technol. 48, 1940–1956. doi:10.1021/es403899t Escher, B.I., Hermens, J.L.M., 2002. Modes of Action in Ecotoxicology: Their Role in Body Burdens, Species Sensitivity, QSARs, and Mixture Effects. Environ. Sci. Technol. 36, 4201–4217. doi:10.1021/es015848h Escher, B.I., Leusch, F., 2011. Bioanalytical Tools in Water Quality Assessment. doi:10.1002/ieam.1340 Exner, M., Färber, H., 2006. Perfluorinated Surfactants in Surface and Drinking Waters. Environ. Sci. Pollut. Res. - Int. 13, 299–307. doi:10.1065/espr2006.07.326 Fent, K., 2015. Progestins as endocrine disrupters in aquatic ecosystems: Concentrations, effects and risk assessment. Environ. Int. 84, 115–130. doi:https://doi.org/10.1016/j.envint.2015.06.012 Froment, J., Thomas, K. V, Tollefsen, K.E., 2016. Automated high-throughput in vitro screening of the acetylcholine esterase inhibiting potential of environmental samples, mixtures and single compounds. Ecotoxicol. Environ. Saf. 130, 74–80. doi:https://doi.org/10.1016/j.ecoenv.2016.04.005 Gallampois, C.M.J., Schymanski, E.L., Krauss, M., Ulrich, N., Bataineh, M., Brack, W., 2015. Multicriteria Approach To Select Polyaromatic River Mutagen Candidates. Environ. Sci. Technol. 49, 2959–2968. doi:10.1021/es503640k Geraudie, P., Gerbron, M., Minier, C., 2017. Endocrine disruption effects in male and intersex roach (Rutilus rutilus, L.) from French rivers: An integrative approach based on subcellular to individual responses. Comp. Biochem. Physiol. Part B Biochem. Mol. Biol. 211, 29–36. doi:https://doi.org/10.1016/j.cbpb.2017.05.006 Ginebreda, A., Sabater-Liesa, L., Rico, A., Focks, A., Barceló, D., 2018. Reconciling monitoring and modeling: An appraisal of river monitoring networks based on a spatial autocorrelation approach - emerging pollutants in the Danube River as a case study. Sci. Total Environ. 618, 323–335. doi:https://doi.org/10.1016/j.scitotenv.2017.11.020 Grung, M., Næs, K., Fogelberg, O., Nilsen, A.J., Brack, W., Lübcke-von Varel, U., Thomas, K. V, 2011. Effects-directed analysis of sediments from polluted marine sites in Norway. J. Toxicol. Environ. Health. A 74, 439–454. doi:10.1080/15287394.2011.550555 Hadrup, N., Taxvig, C., Pedersen, M., Nellemann, C., Hass, U., Vinggaard, A.M., 2013. Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro. PLoS One 8, e70490. Hashmi, M.A.K., Escher, B.I., Krauss, M., Teodorovic, I., Brack, W., 2018. Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia. Sci. Total Environ. 624, 1072–1081. doi:https://doi.org/10.1016/j.scitotenv.2017.12.187 Heemers, H. V, Tindall, D.J., 2006. Androgen Receptor (AR) Coregulators: A Diversity of Functions Converging on and Regulating the AR Transcriptional Complex. Endocr. Rev. 28, 778–808. doi:10.1210/er.2007-0019 Hewitt, L.M., Kovacs, T.G., Dubé, M.G., MacLatchy, D.L., Martel, P.H., McMaster, M.E.,

18

Paice, M.G., Parrott, J.L., van den Heuvel, M.R., van der Kraak, G.J., 2008. Altered reproduction in fish exposed to pulp and paper mill effluents: Roles of individual compounds and mill operating conditions. Environ. Toxicol. Chem. 27, 682–697. doi:10.1897/07-195.1 Hollender, J., Schymanski, E.L., Singer, H.P., Ferguson, P.L., 2017. Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go? Environ. Sci. Technol. 51, 11505–11512. doi:10.1021/acs.est.7b02184 Hou, L.-P., Yang, Y., Shu, H., Ying, G.-G., Zhao, J.-L., Fang, G.-Z., Xin, L., Shi, W.-J., Yao, L., Cheng, X.-M., 2018. Masculinization and reproductive effects in western mosquitofish (Gambusia affinis) after long-term exposure to . Ecotoxicol. Environ. Saf. 147, 509–515. doi:https://doi.org/10.1016/j.ecoenv.2017.08.004 Houtman, C.J., Booij, P., Jover, E., Pascual del Rio, D., Swart, K., van Velzen, M., Vreuls, R., Legler, J., Brouwer, A., Lamoree, M.H., 2006. Estrogenic and dioxin-like compounds in sediment from Zierikzee harbour identified with CALUX assay-directed fractionation combined with one and two dimensional gas chromatography analyses. Chemosphere 65, 2244–2252. doi:10.1016/j.chemosphere.2006.05.043 Houtman, C.J., Van Oostveen, A.M., Brouwer, A., Lamoree, M.H., Legler, J., 2004. Identification of estrogenic compounds in fish bile using bioassay-directed fractionation. Environ. Sci. Technol. 38, 6415–6423. doi:10.1021/es049750p Howell, W.M., Black, D.A., Bortone, S. a, Copeia, S., Dec, N., Black, D. a N.N., 1980. Abnormal Expression of Secondary Sex Characters in a Population of Mosquitofish , Gambusia affinis holbrooki : Evidence for Environmentally-Induced Masculinization Abnormal Expression of Secondary Sex Characters in a Population of Mosquitofish , Gambusia 676–681. Huang, G.-Y., Liu, Y.-S., Chen, X.-W., Liang, Y.-Q., Liu, S.-S., Yang, Y.-Y., Hu, L.-X., Shi, W.-J., Tian, F., Zhao, J.-L., Chen, J., Ying, G.-G., 2016. Feminization and masculinization of western mosquitofish (Gambusia affinis) observed in rivers impacted by municipal wastewaters. Sci. Rep. 6, 20884. doi:10.1038/srep20884 Ibor, O.R., Adeogun, A.O., Fagbohun, O.A., Arukwe, A., 2016. Gonado-histopathological changes, intersex and endocrine disruptor responses in relation to contaminant burden in Tilapia species from Ogun River, Nigeria. Chemosphere 164, 248–262. doi:https://doi.org/10.1016/j.chemosphere.2016.08.087 Ihara, M., Ihara, M.O., Kumar, V., Narumiya, M., Hanamoto, S., Nakada, N., Yamashita, N., Miyagawa, S., Iguchi, T., Tanaka, H., 2014. Co-occurrence of estrogenic and antiestrogenic activities in wastewater: Quantitative evaluation of balance by in vitro ERα reporter gene assay and chemical analysis. Environ. Sci. Technol. 48, 6366–6373. doi:10.1021/es5014938 IPCS, 2002. Global assessment of the state-of-the-science of endocrine disruptors. Geneva, International Programme on Chemical Safety, World Health Organization and United Nations Environment Programme. Ivanković, T., Hrenović, J., 2010. Surfactants in the Environment. Arch. Ind. Hyg. Toxicol. 61, 95–110. doi:https://doi.org/10.2478/10004-1254-61-2010-1943 Iwanowicz, L.R., Pinkney, A.E., Guy, C.P., Major, A.M., Munney, K., Blazer, V.S., Alvarez, D.A., Walsh, H.L., Sperry, A., Braham, R., Sanders, L.R., Smith, D.R., 2019. Temporal

19

evaluation of estrogenic endocrine disruption markers in smallmouth bass (Micropterus dolomieu) reveals seasonal variability in intersex. Sci. Total Environ. 646, 245–256. doi:https://doi.org/10.1016/j.scitotenv.2018.07.167 Jones, D.M., Padilla-Parra, S., 2016. The β-Lactamase Assay: Harnessing a FRET Biosensor to Analyse Viral Fusion Mechanisms. Sensors (Basel). 16, 950. doi:10.3390/s16070950 Katzung, B.G., Masters, S.B., Trevor, A.J., 2012. Basic and Clinical Pharmacology, 2012th ed. McGraw Hill, New York. Kidd, K.A., Blanchfield, P.J., Mills, K.H., Palace, V.P., Evans, R.E., Lazorchak, J.M., Flick, R.W., 2007. Collapse of a fish population after exposure to a synthetic estrogen. Proc. Natl. Acad. Sci. 104, 8897–8901. doi:10.1073/pnas.0609568104 Kolarević, S., Kračun-Kolarević, M., Kostić, J., Slobodnik, J., Liška, I., Gačić, Z., Paunović, M., Knežević-Vukčević, J., Vuković-Gačić, B., 2016. Assessment of the genotoxic potential along the Danube River by application of the comet assay on haemocytes of freshwater mussels: The Joint Danube Survey 3. Sci. Total Environ. 540, 377–385. doi:https://doi.org/10.1016/j.scitotenv.2015.06.061 König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Kortenkamp, A., Altenburger, R., 1999. Approaches to assessing combination effects of oestrogenic environmental pollutants. Sci. Total Environ. 233, 131–140. doi:https://doi.org/10.1016/S0048-9697(99)00228-4 Krauss, M., 2016. Chapter 15 - High-Resolution Mass Spectrometry in the Effect-Directed Analysis of Water Resources, in: Sandra Pérez, P.E., Damià, B. (Ed.), Comprehensive Analytical Chemistry. Elsevier, pp. 433–457. Krauss, M., Singer, H., Hollender, J., 2010. LC – high resolution MS in environmental analysis : from target screening to the identification of unknowns 943–951. doi:10.1007/s00216-010-3608-9 Kugathas, S., Runnalls, T.J., Sumpter, J.P., 2013. Metabolic and Reproductive Effects of Relatively Low Concentrations of Beclomethasone Dipropionate, a Synthetic Glucocorticoid, on Fathead Minnows. Environ. Sci. Technol. 47, 9487–9495. doi:10.1021/es4019332 Kugathas, S., Williams, R.J., Sumpter, J.P., 2012. Prediction of environmental concentrations of glucocorticoids: The River Thames, UK, as an example. Environ. Int. 40, 15–23. doi:https://doi.org/10.1016/j.envint.2011.11.007 Leusch, F.D.L., Neale, P.A., Hebert, A., Scheurer, M., Schriks, M.C.M., 2017. Analysis of the sensitivity of in vitro bioassays for androgenic, progestagenic, glucocorticoid, thyroid and estrogenc activity: Suitability for drinking and environmental waters. Environ. Int. 99, 120–130. doi:https://doi.org/10.1016/j.envint.2016.12.014 Lintelmann, J., Katayama, A., Kurihara, N., Shore, L., Wenzel, A., 2003. Endocrine disruptors in the environment (IUPAC Technical Report). Pure Appl. Chem. 75, 631– 681. Lissalde, S., Mazzella, N., Fauvelle, V., Mazellier, P., Legube, B., 2011. Liquid

20

chromatography coupled with tandem mass spectrometry method for thirty-three pesticides in natural water and comparison of performance between classical solid phase extraction and passive sampling approaches 1218, 1492–1502. doi:10.1016/j.chroma.2011.01.040 Long, M., Bonefeld-Jørgensen, E.C., 2012. Dioxin-like activity in environmental and human samples from Greenland and Denmark. Chemosphere 89, 919–928. doi:10.1016/j.chemosphere.2012.06.055 Lorenz, C., Contardo-Jara, V., Trubiroha, A., Krüger, A., Viehmann, V., Wiegand, C., Pflugmacher, S., Nützmann, G., Lutz, I., Kloas, W., 2011. The Synthetic Gestagen Disrupts Sexual Development in Xenopus laevis by Affecting Gene Expression of Pituitary Gonadotropins and Gonadal Steroidogenic Enzymes. Toxicol. Sci. 124, 311–319. MA, 2005. Millennium Ecosystem Assessment, Ecosystems and Human Well-being: Synthesis. Island Press, Washington, DC. Macikova, P., Groh, K.J., Ammann, A.A., Schirmer, K., Suter, M.J.-F., 2014. Endocrine Disrupting Compounds Affecting Signaling Pathways in Czech and Swiss Waters: Potential Impact on Fish. Environ. Sci. Technol. 48, 12902–12911. doi:10.1021/es502711c Massei, R., Hollert, H., Krauss, M., von Tümpling, W., Weidauer, C., Haglund, P., Küster, E., Gallampois, C., Tysklind, M., Brack, W., 2019. Toxicity and neurotoxicity profiling of contaminated sediments from Gulf of Bothnia (Sweden): a multi-endpoint assay with Zebrafish embryos. Environ. Sci. Eur. 31, 8. doi:10.1186/s12302-019-0188-y McKinlay, R., Plant, J.A., Bell, J.N.B., Voulvoulis, N., 2008. Endocrine disrupting pesticides: Implications for risk assessment. Environ. Int. 34, 168–183. doi:https://doi.org/10.1016/j.envint.2007.07.013 Murack, P.J., Parrish, J., Barry, T.P., 2011. Effects of progesterone on sperm motility in fathead minnow (Pimephales promelas). Aquat. Toxicol. 104, 121–125. doi:10.1016/j.aquatox.2011.04.006 Muz, M., Dann, J.P., Jäger, F., Brack, W., Krauss, M., 2017a. Identification of Mutagenic Aromatic Amines in River Samples with Industrial Wastewater Impact. Environ. Sci. Technol. 51, 4681–4688. doi:10.1021/acs.est.7b00426 Muz, M., Krauss, M., Kutsarova, S., Schulze, T., Brack, W., 2017b. Mutagenicity in Surface Waters: Synergistic Effects of Carboline Alkaloids and Aromatic Amines. Environ. Sci. Technol. 51, 1830–1839. doi:10.1021/acs.est.6b05468 Neale, P.A., Altenburger, R., Aït-Aïssa, S., Brion, F., Busch, W., Aragão Umbuzeiro, G., Denison, M.S., Pasquier, D., Hilscherová, K., Hollert, H., Morales, D.A., Novák, J., Schlichting, R., Seiler, T.-. B., Serra, H., Shao, Y., Tindall, A.J., Tollefsen, K.E., Williams, T.D., Escher, B.I., 2017. Development of a bioanalytical test battery for water quality monitoring: fingerprinting identified micropollutants and their contribution to effects in surface water. Water Res 123. Nendza, M., Gabbert, S., Kühne, R., Lombardo, A., Roncaglioni, A., Benfenati, E., Benigni, R., Bossa, C., Strempel, S., Scheringer, M., Fernández, A., Rallo, R., Giralt, F., Dimitrov, S., Mekenyan, O., Bringezu, F., Schüürmann, G., 2013. A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH. Regul. Toxicol. Pharmacol. 66, 301–314.

21

doi:https://doi.org/10.1016/j.yrtph.2013.05.007 Olkowska, E., Polkowska, Ż., Namieśnik, J., 2011. Analytics of Surfactants in the Environment: Problems and Challenges. Chem. Rev. 111, 5667–5700. doi:10.1021/cr100107g Orlando, E.F., Ellestad, L.E., 2014. Sources, concentrations, and exposure effects of environmental gestagens on fish and other aquatic wildlife, with an emphasis on reproduction. Gen. Comp. Endocrinol. 203, 241–249. doi:https://doi.org/10.1016/j.ygcen.2014.03.038 Orton, F., Ermler, S., Kugathas, S., Rosivatz, E., Scholze, M., Kortenkamp, A., 2014. Mixture effects at very low doses with combinations of anti-androgenic pesticides, antioxidants, industrial pollutant and chemicals used in personal care products. Toxicol. Appl. Pharmacol. 278, 201–208. doi:http://dx.doi.org/10.1016/j.taap.2013.09.008 Patisaul, H.B., Jefferson, W., 2010. The pros and cons of phytoestrogens. Front. Neuroendocrinol. 31, 400–419. doi:http://dx.doi.org/10.1016/j.yfrne.2010.03.003 Petersen, K., Tollefsen, K.E., 2011. Assessing combined toxicity of agonists in a primary culture of rainbow trout (Oncorhynchus mykiss) hepatocytes. Aquat. Toxicol. 101, 186–195. doi:10.1016/j.aquatox.2010.09.018 Petrović, M., Barceló, D., 2000. Determination of Anionic and Nonionic Surfactants, Their Degradation Products, and Endocrine-Disrupting Compounds in Sewage Sludge by Liquid Chromatography/Mass Spectrometry. Anal. Chem. 72, 4560–4567. doi:10.1021/ac000306o Pietsch, C., Neumann, N., Knopf, K., Wuertz, S., Kloas, W., 2009. Progestogens cause immunosuppression of stimulated carp (Cyprinus carpio L.) leukocytes in vitro. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 150, 16–24. doi:https://doi.org/10.1016/j.cbpc.2009.02.003 Porter, M.R., 1991. Handbook of Surfactants. Springer, Boston, MA. doi:https://doi.org/10.1007/978-1-4757-1293-3_5 Qureshi, S. a., 2007. beta-Lactamase: An ideal reporter system for monitoring gene expression in live eukaryotic cells. Biotechniques 42, 91–95. doi:10.2144/000112292 Rocha, M.J., Cruzeiro, C., Reis, M., Pardal, M.Â., Rocha, E., 2016. Pollution by endocrine disruptors in a southwest European temperate coastal lagoon (Ria de Aveiro, Portugal). Environ. Monit. Assess. 188, 101. doi:10.1007/s10661-016-5114-9 Rosal, R., Rodea-Palomares, I., Boltes, K., Fernández-Piñas, F., Leganés, F., Petre, A., 2010. Ecotoxicological assessment of surfactants in the aquatic environment: Combined toxicity of docusate sodium with chlorinated pollutants. Chemosphere 81, 288–293. doi:10.1016/j.chemosphere.2010.05.050 Rosen, M.J., 2004. Surfactants and Interfacial Phenomena, 3rd ed. John Wiley & Sons, Inc., Hoboken, New Jersey, The City of New York. Rostkowski, P., Horwood, J., Shears, J. a., Lange, A., Oladapo, F.O., Besselink, H.T., Tyler, C.R., Hill, E.M., 2011. Bioassay-directed identification of novel antiandrogenic compounds in bile of fish exposed to wastewater effluents. Environ. Sci. Technol. 45, 10660–10667. doi:10.1021/es202966c Routledge, E.J., Sumpter, J.P., 1996. Estrogenic activity of surfactants and some of their degradation products assessed using a recombinant yeast screen. Environ. Toxicol.

22

Chem. 15, 241–248. doi:10.1002/etc.5620150303 Runnalls, T.J., Margiotta-Casaluci, L., Kugathas, S., Sumpter, J.P., 2010. Pharmaceuticals in the Aquatic Environment: Steroids and Anti-Steroids as High Priorities for Research. Hum. Ecol. Risk Assess. An Int. J. 16, 1318–1338. doi:10.1080/10807039.2010.526503 Ruttkies, C., Schymanski, E.L., Wolf, S., Hollender, J., Neumann, S., 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3. doi:10.1186/s13321-016-0115-9 Säfholm, M., Norder, A., Fick, J., Berg, C., 2012. Disrupted Oogenesis in the Frog Xenopus tropicalis after Exposure to Environmental Progestin Concentrations1. Biol. Reprod. 86, 1–7,1–126,126. Salas-Leiton, E., Coste, O., Asensio, E., Infante, C., Cañavate, J.P., Manchado, M., 2012. Dexamethasone modulates expression of genes involved in the innate immune system, growth and stress and increases susceptibility to bacterial disease in Senegalese sole (Solea senegalensis Kaup, 1858). Fish Shellfish Immunol. 32, 769–778. doi:https://doi.org/10.1016/j.fsi.2012.01.030 Sanderson, H., Johnson, D.J., Reitsma, T., Brain, R.A., Wilson, C.J., Solomon, K.R., 2004. Ranking and prioritization of environmental risks of pharmaceuticals in surface waters. Regul. Toxicol. Pharmacol. 39, 158–183. doi:https://doi.org/10.1016/j.yrtph.2003.12.006 Schmedtje, U., Bachmann, J., Behrendt, H., Birk, S., Biza, P., D’Eugenio, J., Van Gils, J., Grath, J., Hamchevici, C., Hansen, W., Interwies, E., Kampa, E., Lindinger, H., Liska, I., Popescu, L., Popovici, M., Pottgiesser, T., Sigmund, G., Sommerhaeuser, A., 2005. The Danube River Basin District River basin characteristics, impact of human activities and economic analysis required under Article 5, Annex II and Annex III, and inventory of protected areas required under Article 6, Annex IV of the EU Water Framework D. Schramm, L.L., Stasiuk, E.N., Marangoni, D.G., 2003. Surfactants and their applications. Annu. Reports Sect. “C” Phys. Chem. 99, 3–48. Shing‐Shing, Y., Suh‐Yuh, W., Tai‐Ping, L., S., O.J., R., S.M., Troy, D., W., S.M., 2000. Improvement in Quality‐of‐Life Measures and Stimulation of Weight Gain After Treatment with Oral Suspension in Geriatric Cachexia: Results of a Double‐Blind, Placebo‐Controlled Study. J. Am. Geriatr. Soc. 48, 485–492. doi:10.1111/j.1532-5415.2000.tb04993.x Simon, E., van Velzen, M., Brandsma, S.H., Lie, E., Løken, K., de Boer, J., Bytingsvik, J., Jenssen, B.M., Aars, J., Hamers, T., Lamoree, M.H., 2013. Effect-Directed Analysis To Explore the Polar Bear Exposome: Identification of Thyroid Hormone Disrupting Compounds in Plasma. Environ. Sci. Technol. 47, 8902–8912. doi:10.1021/es401696u Sohoni, P., Sumpter, J.P., 1998. Several environmental oestrogens are also anti-androgens. J. Endocrinol. 158, 327–339. Steber, J., 2007. 3 - The Ecotoxicity of Cleaning Product Ingredients, in: Johansson, I., Somasundaran, P.B.T.-H. for C. of S. (Eds.), . Elsevier Science B.V., Amsterdam, pp. 721–746. doi:https://doi.org/10.1016/B978-044451664-0/50022-X Tancioni, L., Caprioli, R., Al-Khafaji, H.A., Mancini, L., Boglione, C., Ciccotti, E., Cataudella, S., 2015. Gonadal Disorder in the Thinlip Grey Mullet (Liza ramada, Risso 1827) as a Biomarker of Environmental Stress in Surface Waters. Int. J. Environ. Res. Public Health. doi:10.3390/ijerph120201817

23

Thomas, K. V., Langford, K., Petersen, K., Smith, a. J., Tollefsen, K.E., 2009. Effect- directed identification of naphthenic acids as important in vitro xeno-estrogens and anti- androgens in North Sea offshore produced water discharges. Environ. Sci. Technol. 43, 8066–8071. doi:10.1021/es9014212 Thrupp, T.J., Runnalls, T.J., Scholze, M., Kugathas, S., Kortenkamp, A., Sumpter, J.P., 2018. The consequences of exposure to mixtures of chemicals: Something from ‘nothing’ and ‘a lot from a little’ when fish are exposed to steroid hormones. Sci. Total Environ. 619– 620, 1482–1492. doi:https://doi.org/10.1016/j.scitotenv.2017.11.081 UNEP/WHO, 1996. Water Quality Monitoring - A Practical Guide to the Design and Implementation of Freshwater Quality Studies and Monitoring Programmes 1–22. Vajda, A.M., Barber, L.B., Gray, J.L., Lopez, E.M., Bolden, A.M., Schoenfuss, H.L., Norris, D.O., 2011. Demasculinization of male fish by wastewater treatment plant effluent. Aquat. Toxicol. 103, 213–221. doi:10.1016/j.aquatox.2011.02.007 van der Linden, S.C., Heringa, M.B., Man, H.-Y., Sonneveld, E., Puijker, L.M., Brouwer, A., van der Burg, B., 2008. Detection of Multiple Hormonal Activities in Wastewater Effluents and Surface Water, Using a Panel of Steroid Receptor CALUX Bioassays. Environ. Sci. Technol. 42, 5814–5820. doi:10.1021/es702897y Vrabie, C.M., Sinnige, T.L., Murk, A.J., Jonker, M.T.O., 2012. Effect-directed assessment of the bioaccumulation potential and chemical nature of A h receptor agonists in crude and refined oils. Environ. Sci. Technol. 46, 1572–1580. doi:10.1021/es2036948 WFD Directive 2000/60/EC, 2000. 1–72. WFD Directive 2013/39/EU, 2013. WHO, 2005. Ecosystems and human well-being : health synthesis : a report of the Millennium Ecosystem Assessment 1–63. Wilderer, P., Kramer, K., 2011. Bioassays for Estrogenic and Androgenic Effects of Water Constituents. Treatise Water Sci. 191–219. Wilkinson, J.M., Hayes, S., Thompson, D., Whitney, P., Bi, K., 2008. Compound profiling using a panel of cell-based assays. J. Biomol. Screen. Off. J. Soc. Biomol. Screen. 13, 755–765. doi:10.1177/1087057108322155 WWF, 2018. Living Planet Report - 2018: Aiming Higher. Grooten, M. and Almond, R.E.A. (Eds). WWF, Gland, Switzerland. Yamamoto, F.Y., Garcia, J.R.E., Kupsco, A., Oliveira Ribeiro, C.A., 2017. Vitellogenin levels and others biomarkers show evidences of endocrine disruption in fish species from Iguaçu River - Southern Brazil. Chemosphere 186, 88–99. doi:https://doi.org/10.1016/j.chemosphere.2017.07.111 Yu, Y., Wu, L., Chang, A.C., 2013. Seasonal variation of endocrine disrupting compounds , pharmaceuticals and personal care products in wastewater treatment plants. Sci. Total Environ. 442, 310–316. doi:10.1016/j.scitotenv.2012.10.001 Zucchi, S., Mirbahai, L., Castiglioni, S., Fent, K., 2014. Transcriptional and Physiological Responses Induced by Binary Mixtures of and Progesterone in Zebrafish (Danio rerio). Environ. Sci. Technol. 48, 3523–3531. doi:10.1021/es405584f NCBI PubChem. http://pubchem.ncbi.nlm.nih.gov/, National Center for Biotechnology Information, Bethesda, MD, USA. (accessed March 15, 2019)

24

Royal Society of Chemistry ChemSpider. http://www.chemspider.com. Royal Society of Chemistry, Cambridge. (accessed March 15, 2019)

25

Chapter 2

Nontarget screening based prioritization and identification of micropollutants from water samples of a large European river Danube

This chapter is based on the article in preparation:

Nontarget screening based prioritization and identification of micropollutants from water samples of a large European river Danube

Muhammad Arslan Kamal Hashmi, Werner Brack, Tobias Schulze, Martin Krauss

26

Abstract: Nontarget screening (NTS) on water samples is done to find out the unknowns from a specific classes or groups of compounds and while doing so the compounds belonging to other groups other than the field of concern who might present at high concentration may be overlooked. In this study, prioritization of micropollutants was done on 22 water samples from Europe’s second largest river Danube obtained during Joint Danube Survey 3 (JDS3). Samples were analyzed by using LC-HRMS respectively for ESI+ and ESI- modes and NTS was processed for the identification of micropollutants after prioritization of the peaks based on frequency scores (FS) and rarity scores (RS) along with automated generation of peak attributes information i.e., isotopes, adducts and homologue series. Tentative identification of micropollutants from JDS3 sample set based on highest FS and highest RS revealed many compounds mostly surfactants. Other than surfactants many other compounds were confirmed including 2nd highest frequently occurring compound triphenylphosphine and phthalates. Results from cluster analysis reveal that most of the pollutants present in JDS3 are with very low variation and results based on highly variant peaks indicative of over 80% peaks belong to ESI+ while remaining to ESI- mode. In cluster analysis high-variant compounds include hexa(methoxymethyl)melamine (HMMM), tri(butoxyethyl)phosphate, Valsartan, dimethachlor and metalaxyl in river Danube. Cluster analysis provides more clear site-specific pollution overview along with prioritization of peaks. The automated approach used in this study reduces the chances of over-looking the site-specific contamination which could facilitate the in-time mitigation measures of pollution prevention. Cluster analysis along with automated prioritizing approach may give a more comprehensive data analysis with site-specific origination of peaks and then micropollutants identification and confirmation.

27

2.1. Introduction: The Danube is the second largest river (2780 km long) of Europe with a catchment area of 801,500 km2, spanning across 19 countries with 81 million inhabitants. Along its course the Danube river receives a huge burden of chemical pollution (von der Ohe et al., 2011) due to many point and diffuse sources. Point sources of pollution might include discharge of untreated municipal wastewater (UMWW), wastewater treatment plants (WWTPs), as well as industrial wastewaters while diffuse sources include urban and agricultural (Kaisarevic et al., 2009; Schmedtje et al., 2005; Wittmer et al., 2010). This results in a complex chemical mixture in the river water, containing pharmaceuticals and personal care products (Meador et al., 2016), pesticides (Tang et al., 2014), heavy metal (Woitke et al., 2003), fuel hydrocarbons (Literathy, 2006) and many more that may cause toxicological effects (Busch et al., 2016; Kittinger et al., 2015; König et al., 2017; Mai et al., 2010; Muz et al., 2017b; Zheng et al., 2012). Screening of chemicals present in surface waters is a challenging task due the enormous number of chemicals production and utilization daily. Daily more than 100,000 chemicals are used (Brack, 2011) while the overall number of chemicals registered in the Chemical Abstract System (CAS) has reached more than 146 million (CAS). Most anthropogenic chemicals are not only present in the environment as parent compounds but they also produce transformation products (Celiz et al., 2009; Ying, 2006) which are also cause of concern to be screened due to their increased variety.

Environmental monitoring typically addresses specific compounds or compound classes of concern such as WFD Priority Substances or River Basin Specific Pollutants (Herrero et al., 2013; Koh et al., 2004; Maggioni et al., 2013; Moschet et al., 2013), while other compounds present in the mixture are not covered. In order to screen for all detectable chemicals, nontarget screening (NTS) may be used to address all known and unknown chemical pollutants present in the mixture (Alygizakis et al., 2019; Gago-Ferrero et al., 2015; Hollender et al., 2017; Krauss et al., 2010; Schymanski et al., 2015; Wang et al., 2018). Although NTS is challenging and time consuming, it has potential to detect new, previously unreported compounds (Gago-Ferrero et al., 2015; Hug et al., 2014; Muschket et al., 2017).

The principal steps of NTS involve LC-MS or GC-MS analysis, generation of peak lists, derivation of molecular formulas, compilation of candidate compounds lists and then shortlisting plausible candidates from lists using different criteria (Hug et al., 2014; Muz et al., 2017a, 2017b). For efficient NTS automated workflows have been developed (Krauss et al, submitted). Since a full identification of all NTS peaks is currently impossible, procedures are required to prioritize peaks of major interest for identification. Criteria for prioritization may

28

rely on peak attributes like isotope, adducts information and homologues series that are obtained during analysis. A specific focus may be on frequent but also on rare (site-specific) high intensity peaks. Recently, frequency and rarity scores have been developed to support prioritization (Krauss et al., submitted). Clustering might help to identify peaks from common sources (Hollender et al., 2017). In environmental studies for pollution monitoring and mitigation in rivers and estuaries, various types of clustering in order to see particular trends in pollution patterns have been increasingly applied addressing temporal (Carpenter et al., 2019), spatial (Carpenter and Helbling, 2018) and longitudinal patterns (Alygizakis et al., 2019; Beckers et al., 2018).

The aim of this study was to prioritize widely occurring versus site-specific organic micropollutants along the Danube River and some of its main tributaries and to assess whether the patterns of these compounds change along the course of the Danube. To this end, we used LC-HRMS non-target screening data from 22 river water samples collected during the Joint Danube survey 3 (JDS3) in 2013. Additionally, by using clustering any specific pollution trend or pattern along the course of Danube would be tried to find out.

29

2.2. Materials and methods:

2.2.1. Chemicals and reagents: Analytical standards were obtained from different vendors and were of at least 97% purity. Methanol of LC-MS grade purity were bought from Sigma- Aldrich/Honeywell, LC-MS grade water from Thermo Fisher, LC-MS grade formic acid, ammonium acetate and ammonium bicarbonate as well as deuterium oxide (99.7% purity) and methanol-d1 (99.5% purity) were obtained from Sigma-Aldrich.

2.2.2. Sampling: Samples were taken at 22 sites (Table 2.1) within the Joint Danube Survey 3 (JDS3) (ICPDR, 2015) between 13 August and 25 September 2013. by using a large volume solid phase extraction device (LVSPE) (Neale et al., 2015; Schulze et al., 2017). Blanks were also prepared along with the sample preparation (Neale et al., 2015).

Table 2.1: list of JDS3 samples from Danube River and tributaries taken in August-September 2013 (ICPDR, 2015). *rkm, river kilometer distance to Danube delta in case of Danube (sampling location) while for Tributary (sampling location) relative kilometer distance to Danube mouth. sr#, serial numbers of the main Danube samples used in clustering as tributaries were not clustered with main Danube stream. Sr Sample Sampling Site Sampling Sampling # Name (sr#) location (*rkm) Date 1 JDS08 (1) Oberloiben Danube (rkm 18-08-2013 2008) 2 JDS22 (2) Budapest downstream Danube (rkm 26-08-2013 1632) 3 JDS27 (3) Hercegszanto Danube (rkm 30-08-2013 1434) 4 JDS29 Drava Tributary (rkm 31-08-2013 1.4) 5 JDS30 (4) Downstream Drava (Erdut/Bogojevo) Danube (rkm 31-08-2013 1367) 6 JDS32 (5) Upstream Novi Sad Danube (rkm 02-09-2013 1262) 7 JDS33 (6) Downstream Novi Sad Danube (rkm 03-09-2013 1252) 8 JDS35 Tisa Tributary (rkm 1.0 03-09-2013 9 JDS36 (7) Downstream Tisa/Upstream Sava Danube (rkm 04-09-2013 (Belegis) 1200 10 JDS37 Sava Tributary (rkm 7.0 04-09-2013 11 JDS39 (8) Downstream Pancevo Danube (rkm 06-09-2013 1151 12 JDS41 Velika Morava Tributary (rkm 07-09-2013 0.0) 13 JDS44 (9) Irongate reservoir (Golubac/Koronin) Danube (rkm 09-09-2013 1040) 14 JDS53 (10) Downstream Zimnicea/Svishtov Danube (rkm 550) 15-09-2013 15 JDS55 (11) Downstream Jantra Danube (rkm 532) 16-09-2013 16 JDS57 (12) Downstream Ruse/Giurgiu Danube (rkm 488) 18-09-2013 17 JDS59 (13) Downstream Arges, Oltenita Danube (rkm 429) 19-09-2013 18 JDS60 (14) Chiciu/Silistra Danube (rkm 378) 19-09-2013

30

19 JDS63 Siret Tributary (rkm 22-09-2013 1.0) 20 JDS64 Prut Tributary (rkm 22-09-2013 1.0) 21 JDS65 (15) Reni Danube (rkm 130) 22-09-2013 22 JDS67 Sulina – Sulina arm Danube (rkm 31) 25-09-2013

2.2.3. Chemical analysis

2.2.3.1. Sample preparation. All LVSPE extracts were stored in methanol at a concentration factor of 1000 at -20°C. An aliquot was prepared for analysis at a concentration factor of 500 in methanol: water 70:30 after addition of an internal standard mixture containing 40 isotope- labelled compounds, which was used for quality control.

2.2.3.2. Liquid chromatography-high resolution mass spectrometry (LC-HRMS): All samples were analyzed by LC-HRMS using an Ultimate 3000 LC system (Thermo Scientific) coupled to a quadrupole-Orbitrap MS (QExactive Plus, Thermo Scientific). LC separation was done by using a Kinetex EVO C18 column (50x2.1 mm, 2.6 µm particle size, Phenomenex) with a gradient of water (A) and methanol (B), both containing 0.1% of formic acid. The flow rate was 300 µL/min and the injection volume was 5 µL. A mobile phase with 5% B was kept for one minute, subsequently increasing B linearly to 100% within 12 minutes and remaining constant for 11 minutes. Finally, the system was re-equilibrated for the next run. Samples were run for data independent acquisition (DIA) mode with electrospray ionization in positive and negative (ESI+ and ESI-) modes in separate runs. Settings used for mass spectrometry (MS) were, i.e., ESI source of MS was operated at 300°C, automatic gain control (AGC) at 3e6, maximum IT was 120 ms, transfer capillary temperature was 300°C, spray voltage at 3.8 kV (ESI+) and 3.5 kV (ESI-), sheath gas flow rate was 45 a.u. (ESI+) and 25 a.u. (ESI-) and auxiliary gas flow rate was 1 a.u.

2.2.4. Automated peak detection: ProteoWizard (Kessner et al., 2008) was used to convert the Thermo .raw data file from profile mode to .mzML centroid mode files. For peak detection, files were processed using MZmine 2.21 (Pluskal et al., 2010). Settings are given in the Appendix A (A-1). From MZmine, peak lists with m/z, retention times, and intensities for ESI+ and ESI- modes were exported to csv files and peak lists for sample and blanks were further processed in R, v3.3.0 (R Development Core Team, 2014).

31

2.2.5. Prioritization for site-specific and frequent peaks: For the full set of JDS3 samples including the main stream and its tributaries, frequency scores (FS) and rarity scores (RS) were calculated in order to identify ubiquitous or site-specific peaks, respectively. Rarity scores (RSi, Eq.1) and frequency scores (FSi, Eq.2) for each peak i detected in the JDS3 data set were calculated separately for ESI+ and ESI- mode according to Krauss et al. (submitted):

푚푎푥𝑖푚푢푚 𝑖푛푡푒푛푠𝑖푡푦 (𝑖) 푡표푡푎푙 푛푢푚푏푒푟 표푓 푠푎푚푝푙푒푠 푅푆 = × (Eq. 1) 𝑖 푚푒푑𝑖푎푛 𝑖푛푡푒푛푠𝑖푡푦 (𝑖) 푛푢푚푏푒푟 표푓 푝표푠𝑖푡𝑖푣푒 푑푒푡푒푐푡푠 푎푏표푣푒 푡ℎ푟푒푠ℎ표푙푑

푎푣푒푟푎푔푒 𝑖푛푡푒푛푠𝑖푡푦 (𝑖) × 푛푢푚푏푒푟 표푓 푝표푠𝑖푡𝑖푣푒 푑푒푡푒푐푡푠 푎푏표푣푒 푡ℎ푒 푡ℎ푟푒푠ℎ표푙푑 퐹푆 = (Eq. 2) 𝑖 푡ℎ푟푒푠ℎ표푙푑 𝑖푛푡푒푛푠𝑖푡푦

The threshold intensity for each peak (i) for the calculation of rarity and frequency scores was set to 104.

2.2.6. Isotope peaks, adducts and homologues: The R package nontarget version 1.9 (Loos 2012) was used for the detection of isotopologue peaks adducts and homologue series from the peaks lists to support the identification of prioritized peaks. Isotopologue peaks (13C, 15N, 34S, 37 81 + + + + Cl, and Br) with possible adducts for ESI + mode (M+H , M+Na , M+K , M+NH4 , + 2+ 2+ 2+ + + [M+CH3OH+H] , [M+2H] , [M+H+NH4] , [M+2Na] , [2M+Na] , [2M+NH4] ) and possible adducts for ESI- mode (M-H, [M+formate], [M-2H]2, [2M-H]) were screened. For homologue series, mass differences representing CH2, CH2O, C2H4O, C3H6O, C2H6SiO, CF2, and C2H4 were screened among the peaks. Results were compiled as monoisotopic peaks with associated isotopologues, adducts and homologues.

2.2.7. NTS and identification: For processing NS1, NS2 and JDS3 samples, a surfactants suspect list (SSL) present in the water samples in ESI+ and ESI- modes was generated by literature search (Gago-Ferrero et al., 2015; E L Schymanski et al., 2014). Peaklists were searched for the masses present in SSL. For prioritized peaks, molecular formulas were determined using QualBrowser (Thermo XCalibur 2.2) considering the respective adducts and isotope patterns. For all ion masses, MS/MS spectra were recorded. To this end, a full scan at R = 35,000 was combined with four data-dependent MS2 scans at R = 35,000 using higher energy collisional dissociation (HCD) with stepped collision energies of 35 and 70 a.u. and an inclusion list of the ion masses of interest. Based on molecular formulas and MS/MS spectra candidate lists were obtained from ChemSpider (Royal society of Chemistry) and/or PubChem (NCBI) using MetFrag (Ruttkies et al., 2016) and predicted MS/MS spectra were ranked against experimental ones. For further short-listing of candidate structures pH-dependent LC

32

retention and hydrogen deuterium exchange LC-HRMS were applied (Muz et al., 2017a) (A- 2). Finally, reference standards were obtained for confirmation of plausible candidates.

2.2.8. Micropollutants patterns analysis: Cluster analysis of JDS3 data (only Danube sampling sites were used for clustering, tributaries sites data was not used) for any possible pollution pattern along the course of river Danube was done by using R Studio (version 1.1.423) and R (version 3.4.4). Clustering was done by using two different methods, i.e., by using K-means for Longitudinal (KmL) method (Genolini et al., 2015; Genolini and Falissard, 2010) and by using heatmap.2 function (Warnes et al., 2016). In KmL, the data points are taken as variable trajectories and KmL clusters them together in distinct group according to their similarities, while heatmap.2 function clusters the data points based on their similarities and dissimilarities after the measurement of distance between data points. Along with heatmap.2, hclust method was used to generate hierarchal clusters for the heatmap. Peaklists of the samples were merged with the help of an identifier (mass along with retention time of each mass (mass_rt)) by using R script. Before clustering through the heatmap.2 function, all peak intensities of each mass were normalized (z-score) according to eq. 3, while for clustering with KmL, peak intensities along with identifiers were used. Normalization of data is an integrated component of this procedure in R. For heatmap.2 function, peak intensities were normalized prior to analysis in order to eliminate the effect of different absolute concentrations rather than patterns. All normalized data were ranked according to their increasing variance (coefficient of variance (CV-value)). The cutoff value of >= 9.65E+17 (while masses with cutoff value lower than this were not chosen due to less variance) was set to get highly variance peaks for further cluster analysis. By doing so top 500 highly variance peaks were obtained, which were used for clustering.

표푟𝑖푔𝑖푛푎푙 푑푎푡푎 푝표𝑖푛푡 표푓 𝑖푛푑𝑖푣𝑖푑푢푎푙 푝푒푎푘−푚푒푎푛 표푓 푎푙푙 푑푎푡푎 푝표𝑖푛푡푠 표푓 𝑖푛푑𝑖푣𝑖푑푢푎푙 푝푒푎푘 푛표푟푚푎푙푖푧푒푑 푑푎푡푎 = 푠푡푎푛푑푎푟푑 푑푒푣𝑖푎푡𝑖표푛 표푓 푎푙푙 푑푎푡푎 푝표𝑖푛푡푠 표푓 𝑖푛푑𝑖푣𝑖푑푢푎푙 푝푒푎푘 (Eq. 3)

33

2.3. Results and Discussion: Peak lists of JDS3 samples including tributaries were processed and frequency and rarity scores were calculated for positive and negative ionization modes. For ESI+ and ESI- mode the total numbers of peaks were 62294 and 9314, respectively.

2.3.1. Frequency scores (FS): For ESI+ mode, about 16% of the detected peaks were characterized by FS from 1-10, 39% peaks with FS range 11-100 while 40% of the detected peaks exhibit FS ranging from 101 to 1000. FS of only 5% of the 3576 peaks with average intensities from 106 to 108 were above 1000. Out of these high FS peaks, 3082 peaks were between FS of 1001-10000, 465 with FS of 10001-100000 while 29 peaks had FS of more than 100000. For ESI- mode, 53% of peaks exhibit an FS of 1-10, 29% FS of 11-100 while FS range of 101-1000 there were approximately 15% detected peaks. Only around 2% peaks exhibited FS>1000 representing 266 peaks with average intensities ranging from 106 to 107. Out of these 266 peaks, 230 peaks exhibit FS 1001-10000, 35 peaks with FS of 10001-100000 while only the FS of one peak exceeded 100000 (Figure 2.1). Top 1% highest FS detected peaks in ESI+ and ESI show more or less same trend in the graph and were falling in 3 main FS categories from 1001-10000, 10001-100000 and over 100001 (Figure 2.1.1). In JDS3, a higher percentage of peaks exhibit high FS in ESI+ with overall signal intensities being higher compared to ESI-.

Figure 2.1: Frequency scores of JDS3 peaks Figure 2.1.1: Frequency scores of top 1% in ESI+ and ESI- mode JDS3 peaks in ESI+ and ESI- mode

2.3.2. Rarity scores (RS): A high RS indicates appearance of a peak at low number of sites with high intensity as compared to the other sites. The peaks with high rarity scores are considered unique or rarely occurring peaks among all and can indicate about site-specific pollution. In ESI+ mode, 73% of the peaks exhibited RS from 1-10, 16% peaks a RS from 11 to 100 while 9% of the detected peaks were ranging from 101 to 1000. Only a small proportion

34

(approximately 1.5%) of detected peaks was above RS of 1000 with a total number of detected peaks of 908 and average peak intensities from 106 to 107. Out of these high RS peaks (RS > 1000), 795 peaks exhibited RS of 1001-10000, 104 peaks of RS 10001-100000 while just 9 peaks were above an RS of 100000. In ESI- mode, 26% of detected peaks were ranging from RS 1-10, 30% exhibited an RS range of 11 to 100 while in the RS range of 101 to 1000 there were approximately 41% peaks. Only around 3% peaks exhibited an RS of 1000 representing 287 peaks with average peak intensities of 106 to 107. Out of these 287 peaks, 256 peaks were within the range of RS 1001-10000, 26 were within the range of 10001-100000, while only 5 peaks had an RS above 100000. For both ESI+ and ESI-, the peak intensities for RS of over 1000 appeared to the one with peak intensities of 106 and over (Figure 2.2).

The top 1% detected peaks in ESI+ and ESI- with highest RS showed more or less same trend as for FS (Figure 2.2.1 and Figure 2.1.1) so in this case a minimum threshold limit value of RS > 1000 was considered for specifying the peak as a unique peak as an indicator for site- specific pollution occurring in least number of samples. In ESI+ mode approximately 87% of the peaks with an RS above 1000 were detected in 1-4 JDS3 samples while remaining peaks were appearing in more than 4 samples. In ESI-, around 75% of rare peaks (RS > 1000), appeared in 1-4 JDS3 samples and remaining were found in more than 4 samples.

Figure 2.2: Rarity scores of JDS3 peaks in Figure 2.2.1: Rarity scores of top 1% JDS3 ESI+ and ESI- mode peaks in ESI+ and ESI- mode

2.3.3. Homologue series: The detection of homologue series is an indication for the presence of surfactants. In this study homologue series occur predominantly in ESI+ mode. In most cases, homologue series units detected had mass differences of 14.0156 (±0.0003), 30.0106 (±0.0003), 44.0260 (±0.0003) and 58.0419 (±0.0003). In ESI+ lists processed for NTS 47- 85% of the peaks were components of homologue series, while in ESI- mode only 0-21% of peaks belonged to homologue series. The most abundant homologue series units were 35

methylene (CH2, 14.0156), methylene oxide (CH2O, 30.0106), polyethylene glycol (PEG) or ethylene oxide (C2H4O, 44.0260) and polypropylene glycol (PPG) (C3H6O, 58.0419). Surfactants degrade into homologues while there were no homologues detected at all in ESI- RS list despite their tentative confirmation (Table A-5). The probable reason might be that in ESI- mode, the peaks shapes for most of the masses were hump shaped and MZmine might picked different retention times and this retention time difference might be the reason that no homologues could be detected.

2.3.4. Identification of compounds from JDS3: Detection and identification of the compounds with high FS and RS was done with NTS. Top 40 highest FS and RS peaks were selected for NTS, while during processing some peaks with missing additional data (no MS2 data, no compounds found for specific peaks from MetFrag) were excluded from the list thus making the final number of peaks processed through NTS variable.

2.3.4.1. Identification of compounds with high FS in ESI+: Top frequent 27 peaks out of 62294 peaks were subjected to tentative identification (Table A-2). Nine peaks were found to contain at least 34S isotope. The candidate compound lists for these peaks contained from 1 to 12552 compounds. The following compounds were confirmed tentatively (Schymanski et al., 2014a). Erucamide was confirmed applying the MS2 spectra from massbank of North America (MoNA) and is a nonsaturated aliphatic primary amide used in polymers, medicines, lubricants and in consumer products (Huang et al., 2018), (NCBI). Triphenylphospine oxide was confirmed (second highest frequency score) which is used in paints and coatings (NCBI). Tentatively identified compounds from SSL include surfactants like polyethylglycols (PEG- butylether 4 EO, PEG-butylether 5 EO, PEG-butylether 6 EO, PEG-butylether 7 EO) and alcohol ethoxylates (C13-AEO-7, C15-AEO-6, C15-AEO-7, C15-AEO-8, C15-AEO-9, C15- AEO-10) (Figure 2.3).

36

Figure 2.3: Compounds with high FS detected and confirmed in ESI+ mode from JDS3 data sample set. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate.

2.3.4.2. Identification of compounds with high RS in ESI+: Top 17 peaks with highest Rarity Score (Table A-3) were subjected to tentative identification and the number of MetFrag (Ruttkies et al., 2016) candidate compounds ranged from 1-6520. Many rare peaks exhibited high ion masses (936.7482, 964.7794, 992.7391, 950.8001). Neither ChemSpider (Royal society of Chemistry) and PubChem (NCBI) databases searching by using MetFrag (Ruttkies et al., 2016) nor SSL search did result in any hits. Some other peaks with masses of 158.9638, 362.9259, 430.9132, 294.9385, exhibited sodium formate clusters as well as in-source fragments of mass 226.9511, while couple of other masses (177.0544, 149.0231) were found to be the in-source fragments of diethyl phthalate. Compounds confirmed were phthalates (diethyl phthalate and bis(2-ethylhexyl)phthalate (DEHP)) and two surfactants alcohol ethoyxylates (C15-AEO-5 and C15-AEO-6) were also present in the category of rare peaks. The RT difference was observed among some compounds from SSL and this RT difference may be due to broad peaks of masses extending over many minutes causing MZmine peak picking encountered with slightly different retention times resulting in some artefact, which

37

results in misclassification of DEHP and C15-AEO-5 and C15-AEO-6 as rare peaks, otherwise these are in fact frequently occurring compounds

2.3.4.3. Identification of compounds with high FS in ESI-: Out of 9314 peaks top 14 peaks with highest FS were subjected to tentative identification (Table A-4). The application of MetFrag (Ruttkies et al., 2016) of these peaks revealed 1 to 7143 candidates per peak. Five peaks had a 37Cl isotope while 7 peaks appeared to be complexes of , calcium or iron. One peak (m/z 265.1479) was confirmed as laurylsulfate. An isopropylbenzenesulfonic acid isomer was detected and confirmed, which was also found by a previous study (Schymanski et al., 2015). Four peaks were tentatively confirmed as surfactants (C7-SPC, C14-AS, C10-LAS, C11-LAS) from SSL (Figure 2.4). SPC (sulfophenyl carboxylic acid) surfactants are the degradation products of linear alkylbenzene sulphonates (LAS) (Jiménez- Díaz et al., 2008), while LAS are anionic surfactants which are used in products to clean greasy and oily surfaces and used in huge amounts in soaps (Steber, 2007).

Figure 2.4: Compounds with high FS detected and confirmed in ESI- mode from JDS3 data sample set. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate.

2.3.4.4. Identification of compounds with high RS in ESI-: NTS of top 12 peaks with highest rarity score was done (Table A-5) and with 1 to 4991 candidates for the detected masses from

38

MetFrag (Ruttkies et al., 2016). Three peaks were level 1 confirmed including dichlorobenzoic acid isomers and 6:2 fluorotelomer sulfonic acid. In total, 7 peaks were tentative assigned as surfactants. The surfactants tentatively assigned with SSL were linear alkylbenzene sulphonates (LAS) (C10-LAS, C11-LAS, C12-LAS), alkyl sulfates (C12-AS, C14-AS), alkyl ether sulfates (C12-AE1S) and dihexyl sulfosuccinate (DHSS). LAS are also frequently occurring compounds (Table A-4) and their presence in rare peaks is result of an artefact due to broad peak shapes resulting in some misclassification during peak picking by MZmine as described above.

2.3.5. Micropollutant patterns along Danube: Cluster analysis by using KmL was performed, all peaks present in ESI+ and ESI- modes were merged and blank values or zeros were removed by R script. The data obtained after clustering with KmL did not show any clear trend along the Danube (Figure 2.5), which shows that many contaminants, may be found in rather similar concentration from upstream to the downstream of the Danube. More than 60% peaks present in Danube are clustered with group A, while remaining over 35% was clustered with group B. Both A and B clustered group have limited variation in the course of the Danube and both clustered groups could not predict any specific pattern of pollution from any sampling site.

JDS3 samples with their ascending order (from JDS08 to JDS67)

Figure 2.5: Cluster analysis of JDS3 data sample set.by using KmL method. X-axis represents JDS3 samples in their increasing order left to right (JDS08-JDS67 (Table 2.1 sr#)), while y-axis show peak heights automatically normalized by the method used.

39

The KmL cluster analysis indicates that very low variation of pollutants is prevailing along the Danube, cluster analysis with heatmap.2 function was performed on highly variant masses from JDS3 sample set and top 500 peaks (less than 1% of overall peaks present in JDS3 data sample set) with high coefficient of variance were plotted by heatmap.2 function (Figure 2.6) by using R. The vertical bars within the heatmap show the individual samples in the JDS3 sample with red color indicating high relative concentrations. The heat map exhibits clear grouping of those compounds with high variance along the River Danube. These groups of peaks are site-specific without any clear similarity between adjacent sampling sites. JDS32 (upstream of Novi Sad) is clearly distinguished from all other samples with specific unique peaks clustered together. However, also several other sites are characterized by clusters of site- specific peaks including JDS65, JDS59, JDS30 and JDS60. While JDS33 (Novi Sad downstream) and JDS67 (Sulina – Sulina arm) appeared to be similar to each other, the last 5 sampling sites, namely JDS53 (Downstream Zimnicea/Svishtov) and JDS57 (Downstream Ruse/Giurgiu), JDS60 (Chiciu/Silistra), JDS36 (Downstream Tisa/Upstream Sava (Belegis)) and JDS22 (Budapest downstream) were also similar to each other. It was also apparent from the heatmap that clusters did not follow the order from Danube upstream to downstream (JDS08 – JDS67) indicating no clear trend present in chemical pollutants pattern along the Danube.

More than 80% of the peaks present in the heatmap were belonging to ESI+ mode while remaining was from ESI- mode. Few target compounds (12 target and suspected compounds from SSL) were among the high variance peaks considered for clustering including hexa(methoxymethyl)melamine (HMMM with frequency score 37592)), tri(butoxyethyl)phosphate, valsartan, dimethachlor and metalaxyl and 2 compounds confirmed with SSL, namely C16-AEO-14 and C16-AEO-16. HMMM is used as coatings in automotive and in beverage industry. HMMM has already been known for its toxic effects on daphnia and has already been known to be present in German rivers (Dsikowitzky and Schwarzbauer, 2015), while valsartan is a medicine used as antihypertensive agent (Lou-Meda et al., 2019), dimethachlor is a and biocide (Reemtsma et al., 2013) and metalaxyl a fungicide (Kubicki et al., 2019). Only four compounds belonging to SSL were present in negative ionization mode i.e., C6-SPC, C11-SPC, STA-2C and C11-DATS with frequency score ranging from 1400 to 4291.

All of the masses present in the heatmap were having high FS as compared to RS. Various isotopes were also detected to be the part of the peaks which were 34S, 15N, 81Br, 37Cl.

40

Maximum number of isotopes found in the clustered peaks were 69 34S, 20 15N, 1 81Br and 11 37Cl. The ranges of FS and RS in clustering for both in ESI+ and ESI- modes are given in Figure 2.7. The presence of diverse nature of compounds along with surfactant makes the cluster analysis more important and essential in order to have a comprehensive overview of the site specific pollution and to designate various sites depending on the types of contaminants released in to the surface waters.

Figure 2.6: cluster analysis in the form of heatmap plotted for top 500 high variance peaks from JDS3 sample set.

Figure 2.7: Representation of frequency scores (FS) and rarity scores (RS) in the form of boxplots for the selected peaks used for clustering for ESI+ and ESI- modes for JDS3

41

samples. Figure elaborates that peaks with high FS from ESI+ and ESI- modes are dominating the cluster as compared to RS of the peaks from ESI+ and ESI- modes.

42

2.4. Conclusions: In this study we applied a semi-automated workflow for prioritization of micropollutants from water samples of one of the big European river Danube. Most of the peaks present in Danube exhibited low variation along the river course with a set of chemicals with high intensity in all samples prioritized based on a high frequency score. Also rarity scores, isotopes and homologue series were used to prioritize micropollutants that are released in high concentrations in the environment but had not been in focus generally before, i.e., like surfactants and other industrial compounds which are used in huge amounts. Most occurring peaks at most of the sites were dominated by surfactants and their degrading products in JDS3, while also some rare peaks with high site-specific intensities were surfactants. Cluster analysis along with semi-automated NTS approach enhanced the scope of environmental monitoring studies giving an overview of large number of sampling sites with respect to their similarities and dissimilarities based on site specific contamination.

2.5. Acknowledgements:

This work was funded by ICPDR and SOLUTIONS project (grant agreement 603437), supported by the EU Seventh Framework Programme. The authors acknowledge the support for a doctoral research (personal reference # 91549557) for Muhammad Arslan Kamal Hashmi from Higher Education Commission (HEC) of Pakistan and German Academic Exchange Service (DAAD), Germany. The QExactive Plus LC-HRMS used is part of the major infrastructure initiative CITEPro (Chemicals in the Terrestrial Environment Profiler) funded by the Helmholtz Association. Chemaxon (Budapest, Hungary) is acknowledged for providing an academic license of JChem for Excel, Marvin, and the Calculator Plugins. R Core Team R: A language and environment for statistical computing, 3.4.3; R Foundation for Statistical Computing: Vienna, Austria, 2017.

43

2.6. References:

Alygizakis, N.A., Gago-Ferrero, P., Hollender, J., Thomaidis, N.S., 2019. Untargeted time- pattern analysis of LC-HRMS data to detect spills and compounds with high fluctuation in influent wastewater. J. Hazard. Mater. 361, 19–29. doi:https://doi.org/10.1016/j.jhazmat.2018.08.073 Beckers, L.-M., Busch, W., Krauss, M., Schulze, T., Brack, W., 2018. Characterization and risk assessment of seasonal and weather dynamics in organic pollutant mixtures from discharge of a separate sewer system. Water Res. 135, 122–133. doi:https://doi.org/10.1016/j.watres.2018.02.002 Brack, W., 2011. Effect-directed analysis of complex environmental contamination. Springer Science & Business Media. Busch, W., Schmidt, S., Kühne, R., Schulze, T., Krauss, M., Altenburger, R., 2016. Micropollutants in European rivers: A mode of action survey to support the development of effect-based tools for water monitoring. Environ. Toxicol. Chem. 35, 1887–1899. doi:10.1002/etc.3460 Carpenter, C.M.G., Helbling, D.E., 2018. Widespread Micropollutant Monitoring in the Hudson River Estuary Reveals Spatiotemporal Micropollutant Clusters and Their Sources. Environ. Sci. Technol. 52, 6187–6196. doi:10.1021/acs.est.8b00945 Carpenter, C.M.G., Wong, L.Y.J., Johnson, C.A., Helbling, D.E., 2019. Fall Creek Monitoring Station: Highly Resolved Temporal Sampling to Prioritize the Identification of Nontarget Micropollutants in a Small Stream. Environ. Sci. Technol. 53, 77–87. doi:10.1021/acs.est.8b05320 Celiz, M.D., Tso, J., Aga, D.S., 2009. Pharmaceutical metabolites in the environment: Analytical challenges and ecological risks. Environ. Toxicol. Chem. 28, 2473–2484. doi:10.1897/09-173.1 Dsikowitzky, L., Schwarzbauer, J., 2015. Hexa(methoxymethyl)melamine: An Emerging Contaminant in German Rivers. Water Environ. Res. 87, 461–469. doi:10.2175/106143014X14060523640919 Gago-Ferrero, P., Schymanski, E.L., Bletsou, A.A., Aalizadeh, R., Hollender, J., Thomaidis, N.S., 2015. Extended Suspect and Non-Target Strategies to Characterize Emerging Polar Organic Contaminants in Raw Wastewater with LC-HRMS/MS. Environ. Sci. Technol. 49, 12333–12341. doi:10.1021/acs.est.5b03454 Genolini, C., Alacoque, X., Sentenac, M., Arnaud, C., 2015. kml and kml3d: R Packages to Cluster Longitudinal Data. J. Stat. Software; Vol 1, Issue 4. Genolini, C., Falissard, B., 2010. KmL: k-means for longitudinal data. Comput. Stat. 25, 317–328. doi:10.1007/s00180-009-0178-4 Herrero, P., Borrull, F., Marcé, R.M., Pocurull, E., 2013. Pressurised liquid extraction and ultra-high performance liquid chromatography-tandem mass spectrometry to determine endogenous and synthetic glucocorticoids in sewage sludge. Talanta 103, 186–193. doi:10.1016/j.talanta.2012.10.030

44

Hollender, J., Schymanski, E.L., Singer, H.P., Ferguson, P.L., 2017. Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go? Environ. Sci. Technol. 51, 11505–11512. doi:10.1021/acs.est.7b02184 Huang, Y., Xiong, Y., Liu, C., Li, L., Xu, D., Lin, Y.-H., Nan, C.-W., 2018. Single- crystalline 2D erucamide with low friction and enhanced thermal conductivity. Colloids Surfaces A Physicochem. Eng. Asp. 540, 29–35. doi:https://doi.org/10.1016/j.colsurfa.2017.12.060 Hug, C., Ulrich, N., Schulze, T., Brack, W., Krauss, M., 2014. Identi fi cation of novel micropollutants in wastewater by a combination of suspect and nontarget screening. Environ. Pollut. 184, 25–32. doi:10.1016/j.envpol.2013.07.048 ICPDR, 2015. Joint Danube Survey 3, A Comprehensive Analysis of Danube Water Quality. ICPDR – International Commission for the Protection of the Danube River. ICPDR Secretariat Vienna International Centre,Vienna, Austria. Jiménez-Díaz, I., Ballesteros, O., Vílchez, J.L., Navalón, A., 2008. Determination of sulfophenyl carboxylic acids in agricultural groundwater samples by CE with ultraviolet absorption detection. Electrophoresis 29, 516–525. doi:10.1002/elps.200700403 Kaisarevic, S., Varel, U.L. V, Orcic, D., Streck, G., Schulze, T., Pogrmic, K., Teodorovic, I., Brack, W., Kovacevic, R., 2009. Effect-directed analysis of contaminated sediment from the wastewater canal in Pancevo industrial area, Serbia. Chemosphere 77, 907–913. doi:10.1016/j.chemosphere.2009.08.042 Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P., 2008. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24. doi:10.1093/bioinformatics/btn323 Kittinger, C., Baumert, R., Folli, B., Lipp, M., Liebmann, A., Kirschner, A., Farnleitner, H.A., Grisold, J.A., Zarfel, E.G., 2015. Preliminary Toxicological Evaluation of the River Danube Using in Vitro Bioassays. Water. doi:10.3390/w7051959 Koh, C.H., Khim, J.S., Kannan, K., Villeneuve, D.L., Senthilkumar, K., Giesy, J.P., 2004. Polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs) and 2,3,7,8-TCDD equivalents (TEQs) in sediment from the Hyeongsan River, Korea. Environ. Pollut. 132, 489–501. doi:10.1016/j.envpol.2004.05.001 König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Krauss, M., Singer, H., Hollender, J., 2010. LC – high resolution MS in environmental analysis : from target screening to the identification of unknowns 943–951. doi:10.1007/s00216-010-3608-9 Kubicki, M., Lamshöft, M., Lagojda, A., Spiteller, M., 2019. Metabolism and spatial distribution of metalaxyl in tomato plants grown under hydroponic conditions. Chemosphere 218, 36–41. doi:https://doi.org/10.1016/j.chemosphere.2018.11.069

45

Literathy, P., 2006. Monitoring and assessment of oil pollution in the Danube River during the transnational Joint Danube Survey. Water Sci. Technol. 53, 121–129. doi:10.2166/wst.2006.305 Lou-Meda, R., Stiller, B., Antonio, Z.L., Zielinska, E., Yap, H.-K., Kang, H.G., Tan, M., Glazer, R.D., Valentin, M.A., Wang, L., 2019. Long-term safety and tolerability of valsartan in children aged 6 to 17 years with hypertension. Pediatr. Nephrol. 34, 495– 506. doi:10.1007/s00467-018-4114-0 Maggioni, S., Balaguer, P., Chiozzotto, C., Benfenati, E., 2013. Screening of endocrine- disrupting phenols, herbicides, steroid estrogens, and estrogenicity in drinking water from the waterworks of 35 Italian cities and from PET-bottled mineral water. Environ. Sci. Pollut. Res. 20, 1649–1660. doi:10.1007/s11356-012-1075-x Mai, W.J., Yan, J.L., Wang, L., Zheng, Y., Xin, Y., Wang, W.N., 2010. Acute acidic exposure induces p53-mediated oxidative stress and DNA damage in tilapia (Oreochromis niloticus) blood cells. Aquat. Toxicol. 100, 271–281. doi:10.1016/j.aquatox.2010.07.025 Meador, J.P., Yeh, A., Young, G., Gallagher, E.P., 2016. Contaminants of emerging concern in a large temperate estuary. Environ. Pollut. 213, 254–267. doi:http://dx.doi.org/10.1016/j.envpol.2016.01.088 Moschet, C., Götz, C., Longrée, P., Hollender, J., Singer, H., 2013. Multi-Level Approach for the Integrated Assessment of Polar Organic Micropollutants in an International Lake Catchment: The Example of Lake Constance. Environ. Sci. Technol. 47, 7028–7036. doi:10.1021/es304484w Muschket, M., Di Paolo, C., Tindall, A.J., Touak, G., Phan, A., Krauss, M., Kirchner, K., Seiler, T.B., Hollert, H., Brack, W., 2017. Identification of Unknown Antiandrogenic Compounds in Surface Waters by Effect-Directed Analysis (EDA) Using a Parallel Fractionation Approach. Environ. Sci. Technol. doi:10.1021/acs.est.7b04994 Muz, M., Dann, J.P., Jäger, F., Brack, W., Krauss, M., 2017a. Identification of Mutagenic Aromatic Amines in River Samples with Industrial Wastewater Impact. Environ. Sci. Technol. 51, 4681–4688. doi:10.1021/acs.est.7b00426 Muz, M., Krauss, M., Kutsarova, S., Schulze, T., Brack, W., 2017b. Mutagenicity in Surface Waters: Synergistic Effects of Carboline Alkaloids and Aromatic Amines. Environ. Sci. Technol. 51, 1830–1839. doi:10.1021/acs.est.6b05468 Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K., Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I., 2015. Linking in Vitro Effects and Detected Organic Micropollutants in Surface Water Using Mixture-Toxicity Modeling. Environ. Sci. Technol. 49, 14614–14624. doi:10.1021/acs.est.5b04083 Pluskal, T., Castillo, S., Villar-Briones, A., Orešič, M., 2010. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395. doi:10.1186/1471-2105-11-395 R Development Core Team, 2014. R: A Language and Environment for Statistical Computing. R Found. Stat. Comput. Vienna Austria 0, {ISBN} 3-900051-07-0.

46

doi:10.1038/sj.hdy.6800737 Reemtsma, T., Alder, L., Banasiak, U., 2013. A multimethod for the determination of 150 pesticide metabolites in surface water and groundwater using direct injection liquid chromatography–mass spectrometry. J. Chromatogr. A 1271, 95–104. doi:https://doi.org/10.1016/j.chroma.2012.11.023 Ruttkies, C., Schymanski, E.L., Wolf, S., Hollender, J., Neumann, S., 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3. doi:10.1186/s13321-016-0115-9 Schmedtje, U., Bachmann, J., Behrendt, H., Birk, S., Biza, P., D’Eugenio, J., Van Gils, J., Grath, J., Hamchevici, C., Hansen, W., Interwies, E., Kampa, E., Lindinger, H., Liska, I., Popescu, L., Popovici, M., Pottgiesser, T., Sigmund, G., Sommerhaeuser, A., 2005. The Danube River Basin District River basin characteristics, impact of human activities and economic analysis required under Article 5, Annex II and Annex III, and inventory of protected areas required under Article 6, Annex IV of the EU Water Framework D. Schulze, T., Ahel, M., Ahlheim, J., Aït-Aïssa, S., Brion, F., Di Paolo, C., Froment, J., Hidasi, A.O., Hollender, J., Hollert, H., Hu, M., Kloß, A., Koprivica, S., Krauss, M., Muz, M., Oswald, P., Petre, M., Schollée, J.E., Seiler, T.-B., Shao, Y., Slobodnik, J., Sonavane, M., Suter, M.J.-F., Tollefsen, K.E., Tousova, Z., Walz, K.-H., Brack, W., 2017. Assessment of a novel device for onsite integrative large-volume solid phase extraction of water samples to enable a comprehensive chemical and effect-based analysis. Sci. Total Environ. 581–582, 350–358. doi:http://dx.doi.org/10.1016/j.scitotenv.2016.12.140 Schymanski, E.L., Jeon, J., Gulde, R., Fenner, K., Ruff, M., Singer, H.P., Hollender, J., 2014. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 48, 2097–2098. doi:10.1021/es5002105 Schymanski, E.L., Singer, H.P., Longrée, P., Loos, M., Ruff, M., Stravs, M.A., Ripollés Vidal, C., Hollender, J., 2014. Strategies to characterize polar organic contamination in wastewater: exploring the capability of high resolution mass spectrometry. Env. Sci Technol 48. doi:10.1021/es4044374 Schymanski, E.L., Singer, H.P., Slobodnik, J., Ipolyi, I.M., Oswald, P., Krauss, M., Schulze, T., Haglund, P., Letzel, T., Grosse, S., Thomaidis, N.S., Bletsou, A., Zwiener, C., Ibáñez, M., Portolés, T., Boer, R. De, Reid, M.J., Onghena, M., 2015. Non-target screening with high-resolution mass spectrometry : critical review using a collaborative trial on water analysis 6237–6255. doi:10.1007/s00216-015-8681-7 Steber, J., 2007. 3 - The Ecotoxicity of Cleaning Product Ingredients, in: Johansson, I., Somasundaran, P.B.T.-H. for C. of S. (Eds.), . Elsevier Science B.V., Amsterdam, pp. 721–746. doi:https://doi.org/10.1016/B978-044451664-0/50022-X Tang, J.Y.M., Busetti, F., Charrois, J.W.A., Escher, B.I., 2014. Which chemicals drive biological effects in wastewater and recycled water? Water Res. 60, 289–299. doi:http://dx.doi.org/10.1016/j.watres.2014.04.043 von der Ohe, P.C., Dulio, V., Slobodnik, J., De Deckere, E., Kühne, R., Ebert, R.U., Ginebreda, A., De Cooman, W., Schüürmann, G., Brack, W., 2011. A new risk assessment approach for the prioritization of 500 classical and emerging organic microcontaminants as potential river basin specific pollutants under the European Water

47

Framework Directive. Sci. Total Environ. 409, 2064–2077. doi:10.1016/j.scitotenv.2011.01.054 Wang, Y., Yu, N., Zhu, X., Guo, H., Jiang, J., Wang, X., Shi, W., Wu, J., Yu, H., Wei, S., 2018. Suspect and Nontarget Screening of Per- and Polyfluoroalkyl Substances in Wastewater from a Fluorochemical Manufacturing Park. Environ. Sci. Technol. 52, 11007–11016. doi:10.1021/acs.est.8b03030 Wittmer, I.K., Bader, H.-P., Scheidegger, R., Singer, H., Lück, A., Hanke, I., Carlsson, C., Stamm, C., 2010. Significance of urban and agricultural land use for biocide and pesticide dynamics in surface waters. Water Res. 44, 2850–2862. doi:https://doi.org/10.1016/j.watres.2010.01.030 Woitke, P., Wellmitz, J., Helm, D., Kube, P., Lepom, P., Litheraty, P., 2003. Analysis and assessment of heavy metal pollution in suspended solids and sediments of the river Danube. Chemosphere 51, 633–642. doi:https://doi.org/10.1016/S0045-6535(03)00217- 0 Ying, G.-G., 2006. Fate, behavior and effects of surfactants and their degradation products in the environment. Environ. Int. 32, 417–431. doi:https://doi.org/10.1016/j.envint.2005.07.004 Zheng, W., Wang, X., Tian, D., Zhang, H., Tian, W., Andersen, M.E., Zheng, Y., Sun, X., Jiang, S., Cao, Z., He, G., Qu, W., 2012. Pollution Trees: Identifying Similarities among Complex Pollutant Mixtures in Water and Correlating Them to Mutagenicity. Environ. Sci. Technol. 46, 7274–7282. doi:10.1021/es300728q Loos, M. (2012) nontarget: Detecting, combining and filtering isotope, adduct and homologue series relations in high-resolution mass spectrometry (HRMS) data. NCBI PubChem. http://pubchem.ncbi.nlm.nih.gov/, National Center for Biotechnology Information, Bethesda, MD, USA. (accessed February 11, 2019) Royal Society of Chemistry ChemSpider. http://www.chemspider.com. Royal Society of Chemistry, Cambridge. (accessed February 11, 2019) Warnes, G.R., Bolker, B., Bonebakker, L., Gentleman, R., Liaw, W.H.A., Lumley, T., Maechler,M., Magnusson, A., Moeller, S., Schwartz, M., Venables, B., 2016. gplots: Various R Programming Tools for Plotting Data. R package version 3.0.1. https://CRAN.Rproject.org/package=gplots MoNA. Fiehnlab massbank of north America. http://mona.fiehnlab.ucdavis.edu. (accessed February 17, 2019) CAS. Chemical Abstract System, a division of the American chemical society. https://www.cas.org/support/documentation/chemical-substances (accessed, February 26, 2019)

48

Chapter 3

Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia

This chapter is based on the article: Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia

Muhammad Arslan Kamal Hashmi, Beate I Escher, Martin Krauss, Ivana Teodorovic, Werner Brack

Science of the Total Environment, Volume: 624, December-2017, Pages: 1072-1081

49

Abstract: The release of untreated municipal wastewater (UMWW) to surface waters with a multitude of pollutants may have adverse effects on aquatic wildlife including endocrine disruption. River water from the Danube River downstream of the emission of UMWW in Novi Sad, Serbia, exhibited estrogenicity and androgenicity as well as activation of oxidative stress response (OSR). Effect-directed analysis (EDA) was performed in order to identify major compounds causing these effects by using reversed-phase high performance liquid chromatography (RP-HPLC) collecting thirty fractions at two- minute intervals. Biological analysis by reporter-gene assays revealed pronounced agonistic estrogenic (ERα) and androgenic (AR) effects in a few distinct fractions along with moderate oxidative stress response distributed over all fractions. Targeted chemical analysis using LC-MS/MS and LC- HRMS/MS identified estrone (E1), estradiol (E2), estriol (E3), and 17α-ethinylestradiol (EE2) as bioactive molecules in the ERα-active fractions and , dihydrotestosterone (DHT), progesterone and medroxyprogesterone in the AR-active fractions. The detected chemicals in each active fraction were mixed in the ratio of their occurrence and tested in the bioassays. The identified chemicals were quantitatively confirmed as the causative agents in the active fractions with no substantial contribution attributable to xenobiotics. OSR was found to be the cumulative effect of the mixture of many compounds present in the sample rather than the mixture effect dominated by individual chemicals. This study confirmed the suitability of EDA for receptor-mediated modes of action where few chemicals will cause the majority of the effect, while for those endpoints where many - if not all- chemicals contribute to the mixture effect, EDA cannot improve the interpretation of the observed bioactivity.

50

3.1. Introduction: Municipal wastewater released to rivers and lakes may contribute to water quality degradation and cause adverse effects to aquatic ecosystems and human health. This holds particularly true for untreated municipal wastewater (UMWW) as discharged by several big cities in the lower course of the River Danube, including Novi Sad (Serbia). Globally, over 80% of the wastewater is released into surface waters without any treatment (WWAP, 2012). Thus, particularly sub-lethal and chronic effects on aquatic life are a widespread phenomenon in Europe (Malaj et al., 2014) and probably worldwide.

Disruption of the endocrine system is among the most problematic sub-lethal effect to aquatic wildlife (Howell et al., 1980; Jobling et al., 1998) as well as to human health (Kabir et. al., 2015). Feminization and masculinization of fish downstream of wastewater effluent discharges into rivers have been frequently observed (Sumpter, 1998; Huang et al., 2016). Endocrine disrupting chemicals (EDCs) acting via the estrogen or androgen receptor include natural compounds as well as xenobiotics, both being frequently present in wastewater. Xenobiotics causing endocrine disruption include, but are not limited to pharmaceuticals, personal care products (PCPs), surfactants, pesticides and many other industrial chemicals (Bolong et al., 2009; Diamanti-Kandarakis et al., 2009; Yu et al., 2013). Dichlorodiphenyltrichloroethane (DDT), , chlorpyrifos, , phthalates, bisphenol-A, polychlorinated biphenyls (PCBs), polybrominated biphenyls (PBBs), dioxins, diethylstilbestrol (DES) and many others also exhibit hormonal activities (Lintelmann et al., 2003; Diamanti-Kandarakis et al., 2009). These so-called xenobiotics are typically of lower relative effect potency but also occur at much higher concentrations than natural hormones and might therefore add up to the burden of the effect of chemical cocktails in water.

Natural and synthetic steroidal hormones are excreted through urine and feces of humans and animals and enter the aquatic environment with treated and untreated wastewater. Natural hormones are produced endogenously, while synthetic steroids are used in therapeutic applications (Belhaj et al., 2016; Zaccaroni et al, 2016). Phytohormones emitted during food processing and consumption may also add up to the load of endocrine disruptors in the environment (Lintelmann et al., 2003; Diamanti-Kandarakis et al., 2009; Patisaul and Jefferson, 2010).

Previous studies linked measurable effects with causative chemicals via mass balance approaches (Escher et al., 2011; Maletz et al., 2013; Neale et al., 2015; Neale et al., 2017b; König et al., 2017) or effect directed analysis (EDA) (Brack and Schirmer, 2003, Thomas et al., 2004, Weiss et al., 2009; Muz et al, 2017). It was observed that natural and synthetic

51

steroids often dominated estrogen and androgen-receptor-mediated effects of surface water extracts in-vitro (Vajda et al., 2011; Rocha et al., 2016) while the endocrine disrupting effects of most xenobiotics were less pronounced, despite their concentrations might exceed those of steroids by several orders of magnitude (Legler et al., 1999; Legler et al., 2002).

The oxidative stress response (OSR) has been suggested to be a more general indicator of the adverse effects of environmental pollution and is an adaptive stress responses to a multitude of effects that involve reactive oxygen species or impaired redox balance of the cell, but also genotoxicity (Simmons et al., 2009; Dasgupta and Klein, 2014). Despite extensive chemical analysis, in previous studies the quantified chemicals failed to explain more than a small fraction (typically less than 1 %) of observed OSR (Escher et al., 2013; Yeh et al., 2014; Tang et al., 2014; Neale et al., 2015; Neale et al., 2017b). Thus, OSR may be triggered either by individual toxicants that were not considered in target analysis or by the complex mixture involving many chemicals. EDA, as a sequential approach to reduce the complexity of environmental samples by fractionation and biological analysis, is expected to be able to discriminate between the responses to complex mixture and individual chemicals.

UMWW from Novi Sad has been shown to be a source of xenobiotics, steroids and phytohormones to River Danube and the effluent discharge has been demonstrated to cause an increase in the biological activity of water extracts in different cell-based assays. The previous study by König et al. (2017) suggested a strong contribution of steroidal hormones to endocrine disruption potency in the Danube River downstream of Novi Sad. The present study applied EDA (Brack, 2003, Brack et al., 2016) on river water impacted by UMWW from Novi Sad using a high resolution fractionation approach together with in-vitro testing and sensitive chemical analysis to confirm cause-effect relationships with natural steroids and to possibly identify additional endocrine disruptors in Danube river waters impacted by discharge of untreated wastewaters. At the same time it shall be tested whether OSR is caused by distinct fractions or an effect of the complex mixture. As is outlined in Figure 3.1, we tested not only the biological activity in the parent sample (PS) and the fractions but also reconstituted mixtures of the detected chemicals to assess if the identified and quantified chemicals can explain the entire bioactivity of each fraction. The designed mixture experiment also helped to assure the validity of the concentration addition (CA) model to describe the mixture interaction of the components in the PS and the different fractions. Finally, all fractions were recombined and tested in the bioassays to assure that the process of fractionation did not lead to loss of bioactive compounds or contamination.

52

Figure 3.1: Experimental design of this study: LVSPE, large volume solid phase extraction; PS, parent sample; RP-HPLC, reverse phase-high performance liquid chromatography; Rspe, recombination of fractions processed through solid phase extraction; Rfd, recombination of fractions processed through freeze drying; F, fraction; BEQbio, bioanalytical equivalent concentration-biological; BEQchem, bioanalytical equivalent concentration-chemical; C, individual chemical; REP, relative effect potency; double-headed arrows represent comparison.

3.2. Materials and Methods

3.2.1. Chemicals and reagents

The chemicals i.e., 17β-estradiol (E2), R1881 (), tamoxifen, , estrone (E1), estriol (E3), 17α-ethinylestradiol (EE2), testosterone, dihydrotestosterone (DHT), , medroxyprogesterone, progesterone, tamoxifen, tert-butylhydroquinone (tBHQ) were obtained from Sigma-Aldrich. (RU-486), and epiandrosterone were purchased from Steraloids and Acros Organics respectively. Most of the chemicals had over 98% purity. Solvents were obtained from Sigma-Aldrich in LC-MS grade purity.

53

3.2.2. Sampling

The sample was taken in November 2014, downstream of UMWW outlet from Novi Sad, Serbia into the River Danube. Approximately 850 L of water were extracted on-site by large volume solid phase extraction (LVSPE) (Schulze et al., 2017), eluted, concentrated and recovered in to 850 mL of extract in methanol (concentration factor of 1000). The study site and sampling procedure were described in detail in König et al., 2017.

3.2.3. Fractionation

The extract was fractionated using reversed phase-high performance liquid chromatography (RP-HPLC). HPLC was operated under the control of Chromeleon 6.7 (Dionex) software and was comprised of Rheodyne manual valve, Varian Prostar 210 Pump and Foxy 2000 fraction collector (Teledyne Isco Inc., Lincoln, USA). Dionex UVD 340U UV/VIS detector was used for the recording of chromatograms at a wavelength of 210 nm. A sample comprised of 20 L water equivalent was prepared in 2 mL of methanol and water (50:50 v/v) mixture. Aliquots of 100 µL were fractionated on an octadecyl silica (C18) column (Nucleodur C18 Gravity, size: 250 x 10 mm, 5 µm particle size, Macherey-Nagel, Dueren, Germany). A gradient elution was performed using water containing 0.1% formic acid and methanol, containing 0.1% formic acid at a flow rate of 2.36 mL/minute. The gradient started at 50% methanol, held for 4 minutes, linearly increasing to 95% methanol within 44 minutes and maintained for next 20 minutes before returning back to the initial conditions for 18 minutes re-equilibration. Thirty fractions of two minute intervals were collected. Fractionation-blanks (FB) were also obtained and processed prior to the sample fractionation. A recombination of fractions (R) was made from equal volumes of all 30 fractions and processed in the same way as the fractions.

All fractions were concentrated in a BUCHI Syncore 12 channel parallel evaporator at 40°C, shaken at 200 rotations per minute (RPM) at a pressure of 33.7 kPa. Since these fractions still contained substantial (and variable) amounts of water after all solvent was removed, solid phase extraction was used to concentrate all aqueous sample fractions, fractionation blanks (FBspe) and a recombination of fractions (Rspe) after dilution with LC-MS grade water to less than 5% of methanol. Glass SPE cartridges (6 mL) were packed with 200 mg of pre-cleaned HRX sorbent and conditioned sequentially with 5 mL of LC-MS grade acetone, ethyl acetate and methanol and 10 mL of LC-MS grade water with a flow rate of 5 mL/min. After fraction extraction, cartridges were dried under a gentle nitrogen stream for 30 minutes and eluted with 5mL of methanol:ethyl acetate (1:1, v:v), 2 mL of methanol containing 1% formic acid and 2

54

mL of methanol containing 2% 7M ammonia. The extracts were adjusted to pH 7, evaporated to dryness and reconstituted in methanol for further analysis. In parallel to SPE, freeze-drying as a method for water removal was also tested for biological activity recovery (B-2 and Figure B-5.1).

3.2.4. Biological analysis

Reporter-gene assays were used to test for agonistic and antagonistic hormonal activities (estrogen receptor alpha (ERα) and androgen receptor (AR)) and for OSR (AREc32). For ERα and AR, GeneBLAzer ERα UAS-bla GripTite and AR-UAS-bla GripTite 293 cell lines encoding for -lactamase were used, while for OSR, AREc32-MCF7 cells with luciferase gene construct were applied. The GeneBLAzer bioassays were performed as described by König et al., 2017 and Neale et al., 2017a with some modifications and AREc32 was previously described by Escher et al., 2012. A brief description of the assay procedure for bioassays performed is given in supplementary information (B-1).

For biological analysis, the concentrations of environmental samples were expressed in relative enrichment factors (REF) (B I. Escher et al., 2014). In all bioassays, serial dilutions of samples were tested to derive the inhibitory concentration for cytotoxicity (IC10,cyt). Only REFs lower than IC10,cyt were included in the concentration-effect modeling for the activation of the reporter gene. As many environmental samples did not produce full concentration-effect curves or effects were masked by cytotoxicity it was not possible to report the results as EC50 values, so for consistency, EC10 (REF) was chosen for agonism, ECSR0.2 (REF) for antagonism and ECIR1.5 (REF) for OSR (Escher et al., 2014). Furthermore, confirmations of biological activity from detected chemicals in fractions were done by designing chemical mixtures by using chemical standard compounds. Chemicals were mixed as per their concentration and composition in the active fractions, applied on respective bioassays and dose response curves were drawn.

The evaluation of concentration-effect curves was done by using Microsoft Excel and Graph Pad Prism (GraphPad Prism version 6.05 for Windows, Graph Pad Software, La Jolla California USA) and equations for data evaluation were used as described by Escher et al., 2014. Bioanalytical equivalent concentrations (BEQ) (Eq. 1) were used to characterize the biological potency (BEQbio) of the parent sample (BEQbio_PS), fractions (BEQbio_Fi) and designed chemical mixtures (BEQbio_mixture) (Figure 3.1).

55

The BEQ that would be expected from the presence of quantified chemicals (BEQchem) were derived by multiplying relative effect potencies (REPs (Eq. 2), calculated from effect concentrations retrieved from the ToxCast database (USEPA 2015) (Table B-3.1) with the concentration of each detected chemical i and summing up all BEQchem contributions of all chemicals (Eq. 3). BEQbio and BEQchem will be compared with each other in order to see how much of the water sample’s effect was explained by the detected chemicals (Figure 3.1).

퐸퐶10 (푟푒푓푒푟푒푛푐푒 푐표푚푝표푢푛푑)(푛푔/퐿) 퐵퐸푄푏𝑖표(푛푔⁄퐿) = (Eq. 1) 퐸퐶10(푠푎푚푝푙푒)(푅퐸퐹)

퐸퐶10 (푟푒푓푒푟푒푛푐푒 푐표푚푝표푢푛푑)(푛푔/퐿) 푅퐸푃𝑖 = (Eq. 2) 퐸퐶10(𝑖)

푛 퐵퐸푄푐ℎ푒푚(푛푔⁄퐿) = ∑𝑖=1 푅퐸푃𝑖 ∗ 푐𝑖 (Eq. 3)

3.2.5. Chemical Analysis

The sample preparation for ERα and AR active fractions is described in B-2.1. A target analysis of ERα active and neighboring fractions for known natural and synthetic estrogenic compounds was done by using LC-HRMS (Ultimate 3000 LC system coupled to a QExactive MS (Thermo Scientific)), by adopting a method based on dansyl chloride derivatization (B-2.1) (Backe, 2015). For LC separation, a phenyl-hexyl column (150 x 3 mm, 2.6 µm particle size) was used.

Gradient elution with water (eluent A) containing 1 mM ammonium fluoride (NH4F) and methanol (eluent B) was carried out at a flow rate of 350 µL/min. The column was eluted with 45% B for one minute followed by a linear increase to 85% after seven minutes. This mobile phase composition was held for 14.5 minutes. Afterwards the LC column was re-equilibrated to the initial conditions.

For mass spectrometry-selected ion monitoring (MS-SIM), ESI+ mode with full scan resolution at 35,000 was used. Automatic gain control (AGC) was set at 3e6 with a maximum injection time of 120 ms, and scan ranges from 300-700 m/z and 700-1100 m/z were used for screening. The TraceFinder 3.2 software (Thermo) was applied for data evaluation and quantification.

Androgenic fractions were analyzed by LC-MS/MS (Agilent 1260 LC system coupled to ABSciex QTrap 6500 MS) for a list of natural and synthetic compounds with androgenic

56

activity (B-2.1 and Table B-2.1). For LC separation a Kinetex C18 column (100 x 3 mm, 2.6 µm particle size) equipped with a pre-column (5 x 3 mm) of the same type and an in-line filter was used. Gradient elution was carried out with methanol (eluent B) and water (eluent A) both containing 0.1% of formic acid with a flow rate of 350 µL/min at 30oC. The gradient elution started with 20% B (0-1 minute), increased linearly to 90% B (1-5 minutes), to 95% B (5-14 minutes) and remained constant for the next 2.2 minutes. Afterwards, the LC column was re- equilibrated to initial conditions.

The MS was operated in scheduled multiple reaction monitoring (sMRM) mode. A Turbo V ion source was used in positive ESI mode with the following settings: spray voltage -3.6 kV, temperature at 380 oC, nebulizer gas at 60 a.u., heater gas at 60 a.u., curtain gas at 50 a.u. and entrance potential at -10 V. An internal standard mixture comprised of isotope-labeled compounds was used for calibration and quantification (concentration ranged from 0.1-500 ng/L) (Table B-2.1). Data evaluation and quantification was done by MultiQuant 3.0 software (ABSciex).

Micropollutants were screened in all ERα and AR active and neighboring fractions with LC- HRMS/MS (Ultimate 3000 LC system coupled to a QExactive MS) using separate runs in positive and negative ion mode.

For LC separation, a methanol:water gradient (eluent A and B, respectively, both containing 0.1% of formic acid) on a Kinetex C18 EVO column (50 x 2.1 mm, 2.6 µm particle size, Phenomenex) was used. The flow rate was 300 µL/min. After 1 minute at 5% B, the fraction of B was linearly increased to 100% within 12 minutes and 100% B were kept for 11 min. Afterwards, the flow was diverted to waste and the column was rinsed for 2 minutes using isopropanol:acetone 50:50 / eluent B / eluent A (85% / 10% / 5%) and the column finally re- equilibrated to starting conditions. The ESI source and the transfer capillary were operated at 300 °C, the spray voltage was 3.8 kV (positive mode) or 3.5 kV (negative mode) the sheath gas flow rate was 45 a.u. and the auxiliary gas flow rate 1 a.u. All runs combined a full scan experiment (100-1000 m/z) at a nominal resolving power of 70,000 (referenced to m/z 200) and data-independent (DIA) MS/MS experiments at a nominal resolving power of 35,000. For DIA, data was acquired using isolation windows of 50 mass units (i.e., m/z ranges 97-147, 144- 194, 191-241, 238-288, 285-335, 332-382, 379-429, 426-476) and 280 mass units (i.e., m/z ranges 460-740, 730-1010), respectively. For data evaluation the software Trace Finder 3.2 (Thermo) was used, employing internal calibration with the isotope-labelled compound with the closest retention time for each analyte.

57

For quantification, full scan extracted ion chromatogram (7 ppm window) was used while for confirmation one or two diagnostic MS/MS fragments and isotope patterns were compared to those of reference standards.

58

3.3. Results and discussion

3.3.1. Quality Assurance (QA/QC)

Fractionation blanks (FBspe and FBfd) were tested with the ERα and AR bioassays and neither cytotoxicity nor ERα/AR activation was observed up to a REF of 100 (Figure B-4.1).

Cytotoxicity of Rspe and PS was observed only above REF 50 and 25 respectively, while none of the individual fractions (fraction 1-30) showed any sign of cytotoxicity up to REF of 300.

Bioanalysis of Rspe and Rfd exhibited similar ERα activity recoveries for freeze-drying and SPE relative to PS while two times higher AR activities were recovered from SPE as compared to freeze-drying (Figure B-5.1). Fractionation followed by SPE recovered (Table 3.1) more than 75% of both androgenicity and estrogenicity relative to PS so SPE method was applied in this study for the processing of fractions after fractionation.

Effect concentrations (EC10) of active fractions for ERα, AR and for OSR (ECIR1.5) are given in Table 3.1. BEQbio (estradiol-EQ (EEQ), R1881-EQ and tBHQ-EQ) values along with standard error of mean (SEM) for ERα, AR and OSR in active fractions, Rspe and PS are given in Table 3.1, while BEQbio are plotted in Figure 3.2.

59

Table 3.1. EC10 and ECIR1.5 and BEQbio (EEQ, R1881-EQ and tBHQ-EQ) along with standard error of mean (SEM) of ERα, AR in agonist mode and OSR. ERα AR Oxidative Stress Response Active (OSR) Fractions Average EC10 ± EEQbio ± SEM Average EC10 ± R1881-EQbio ± Average ECIR1.5 tBHQ-EQbio ± SEM (REF) (ng/L) SEM (REF) SEM (ng/L) ± SEM (REF) SEM (µg/L) F4 ------352±45 2.96±1.33 F5 ------240±3.5 4.35±1.87 F8 4.31±0.21 0.26±0.05 ------F9 37.1±1.34 0.03±0.005 ------F11 23.8±0.83 0.047±0.01 ------F15 2.02±0.13 0.56±0.10 -- -- 188±87 5.55±3.5 F16 47.7±1.66 0.024±0.004 30.9±0.63 0.90±0.06 -- -- F18 -- -- 21.2±0.65 1.31±0.09 -- -- F19 -- -- 185.8±8.34 0.15±0.01 424±244 2.5±1.76 Rspe 1.27±0.09 0.89±0.16 23.4±1.2 1.19±0.09 6.27±0.8 166±75 PS 1.02±0.03 1.11±0.19 17.8±0.98 1.56±0.13 11.5±1.4 91±41

60

Figure 3.2: Bioanalytical equivalent concentrations (BEQbio) of ERα (A), AR (B) and OSR (C) on fractions (F), recombination of fractions-solid phase extraction (Rspe), and parent sample (PS). Sum of F represents the arithmetic sum of the responses of all individual fractions per assay.

61

Table 3.2: Estrogenic and androgenic target compounds (ng/L) detected by targeted screening from estrogenic (ERαA ) and androgenic (ARB ) active fractions. All other fractions did not contain any of the target chemicals above the detection limit. Compound CAS # F8A F9A F11A F15A F16A,B F18 F19 PSA,B Name B B Estrone (E1) 53-16-7 ------4.0 0.24 -- -- 4.7 17-β estradiol 50-28-2 ------0.81 0.17 -- -- 0.92 (E2) Estriol (E3) 50-27-1 33 2.2 ------22 17-α 57-63-6 ------0.05 ------ethinylestradiol (EE2) 486-66- 251 106 ------287 8 446-72- -- -- 123 ------118 0 Testosterone 58-22-0 ------2.69 -- -- 1.3 Androsterone 53-41-8 ------114 78 Progesterone 57-83-0 ------3.3 3.4 Epiandrosterone 481-29------119 -- 81.5 8 Dihydrotestostero 521-18------5.8 7.9 ne (DHT) 6 Medroxyprogester 520-85------0.60 -- -- one 4 A ERα active fraction, B AR active fraction.

3.3.2. ERα-mediated response

The PS and five fractions (F8, F9, F11, F15 and F16) exhibited agonistic response on ERα (Figure 3.2, concentration effect curves depicted in Figure B-6.1), while none of them showed any antagonistic effect. F8 and F15 were most responsive giving full sigmoidal dose response curves.

Target screening of ERα-active and neighboring fractions revealed natural and synthetic female reproductive hormones i.e., E1, E2, E3, EE2 and the plant isoflavonoids i.e., daidzein and genistein, in concentrations ranging from 0.05 to more than 250 ng/L (Table 3.2) along with many xenobiotics including pharmaceuticals and industrial chemicals (Table B-10.1).

Among reproductive hormones, E3 was detected in higher concentrations of 33 and 2.2 ng/L in F8 and F9, respectively, while the concentrations of E1 and E2 (4.0 and 0.81 ng/L respectively in F15) were one order of magnitude lower. Similar concentrations of E1, E2, E3

62

and EE2 have already been found in various studies from sewage treatment plants (STPs) across the world and reported by (Manickum and John, 2014).

For confirmation, all active fractions were simulated with neat standard mixtures based on the composition reported in Table 3.2 and tested for effects on ERα. The potencies of these chemical mixtures (BEQbio_Fi_mixture) were found to be in a good agreement with the potencies of the fractions (BEQbio_Fi) (Figure 3.3 and B-9).

In PS, E1, E2, E3, daidzein, genistein and other compounds (Table 3.2) were detected and

EEQbio_PS_mixture could explain 39±15% of EEQbio_PS. The contribution of other chemicals (Table B-10.1) may not be excluded but is not compelling because none of the other chemicals in the fractions has been identified as potent ERα agonist. In F8, E3 and daidzein were detected in concentrations of 33 ng/L and 251 ng/L, respectively, and for fraction F8 EEQbio_F8_mixture explained 142±63% of EEQbio_F8. In F9, 2.2 ng/L E3 and 106 ng/L of daidzein were present and EEQbio_F9_mixture accounted for 97±39% of EEQbio_F9. Despite the presence of daidzein in higher concentrations in F8 and F9, the overall contribution of daidzein to EEQbio_mixture was less than 1% and 4% in both fractions respectively (Figure 3.3) on the basis of effect concentration values of this study (Table B-3.1).

In F11, the only bioactive compound detected was genistein and genistein explains only about

5±2% of EEQbio_F11_mixture. Remaining unexplained estrogenic activity might be attributed to other present below detection limit of LC-HRMS. It was shown previously that xenoestrogens present in a mixture even at low concentrations such as no observed effect concentrations (NOEC) levels may contribute to mixture effects (Rajapakse et al., 2002; Kortenkamp, 2007).

The compounds E1, E2 and EE2 were detected in F15 in concentrations of 4, 0.81 and 0.05 ng/L, respectively (Table 3.2), and EEQbio_F15_mixture of the chemical mixture accounted for

159±67% of EEQbio_F15 with a relative contribution of 1.5%, 95% and 3.5%, respectively (Figure 3.3 and Table B-9.2).

In F16, E1, E2 and testosterone were detected and EEQbio_F16_mixture explained 158±67%

EEQbio_F16 with a contribution of 0.5% and 99.5 % respectively for E1 and E2 while testosterone contribution was approximately 0.0006% in EEQchem (Figure 3.3). In this overestimation E2 contribution is over 99.5% which might be attributed to the more potent E2.

In this study, E2 and EE2 were more potent (Table B-3.1) compared to ToxCast (USEPA,

2015). Estradiol equivalent concentrations-chemical (EEQchem) in this study very well

63

correlating with EEQbio_mixture of fraction 8, 9, 11 and 15 but it is approximately 3 to 4 times more than EEQbio_mixture of PS and F16 respectively. Presence of agonists and antagonists together in the mixture have counteracting effects (Ihara et al., 2014) which may have resulting in low EEQbio_PS_mixture as compared to EEQchem. Estradiol (E2) and genistein present in a mixture were also found to have antagonistic mixture interaction rather than concentration addition, although both chemicals act as ER-receptor agonist (Charles et al, 2002). The more potent E2 in this study may be the reason for high EEQchem as compared to EEQbio_F16_mixture.

It has been well established that estrogenic steroids exert their effect in a concentration addition manner (Brian et al., 2005) and this stands true in this study as cumulative EEQbio of all 5 active

ERα fractions equals to the EEQbio_PS thus confirming the concentration addition effect of ERα steroids (Table B-9.2).

64

Figure 3.3: Estrogenic responses EEQ (ng/L) of fractions (EEQbio_Fi) of the water sample (EEQbio_PS) explained by designed chemical mixtures (EEQbio_mixture) along with relative contribution of individual chemicals in EEQchem

E2 is the most potent natural estrogenic hormone (Shore et al., 2004; USEPA 2015), which in the living organisms is transformed into E1 and further transforms into E3 (Liu et al., 2012). During pregnancy, E3 is released in highest amounts as compared to E1 and E2 (Johnson et al., 2000). Estrogenic steroids were found to cause intersex in freshwater fish at concentrations of 1-10 ng E2/L (Jobling et al., 2005). The synthetic estrogenic hormone EE2 used as a main component in the contraceptive pills (Zaccaroni et al, 2016) was detected at a concentration of 53 pg/L and is known to induce effects in aquatic organisms (0.1 to 0.5 ng/L) (Purdom et al., 1994). Exposure to low doses of EE2 can have behavioral, physiological and morphological effects in the mammals (Zaccaroni et al, 2016). Along with female reproductive hormones,

65

phytohormones i.e., daidzein and genistein were detected in higher concentrations i.e., more than 200 ng/L and 100 ng/L, respectively. These compounds are particularly found in soy products and are known to have estrogenic (Vitale et al, 2013), anti-estrogenic and anti- androgenic activities as reported in the ToxCast (USEPA, 2015). As daidzein was present in F8 and F9 along with E3 and no anti-estrogenic activity was detected in F8 and F9, it might be possible that anti-estrogenic activity of daidzein was cancelled out by E3 estrogenic activity.

3.3.3. AR-mediated response

The PS showed both agonistic and antagonistic responses on the AR (Figure 3.2 and Table B- 7.1), while the fractions 16, 18 and 19 triggered only agonistic responses. Target screening for AR-active chemicals detected testosterone, DHT, medroxyprogesterone, epiandrosterone, progesterone and androsterone at concentrations of below 1 to more than 100 ng/L in PS, F16, F18 and F19 (Table 3.2 and Table B-10.1).

Testosterone, E1 and E2 were found in fraction 16 in concentrations of 2.69, 0.24 and 0.17 ng/L, respectively. In F18 5.8 ng/L DHT was detected while weak androgenic metabolites androsterone and epiandrosterone were found in higher concentrations (114 and 119 ng/L, respectively) than testosterone and DHT. The natural and synthetic progestagens, progesterone and medroxyprogesterone, known to exhibit androgenic activity were found in F18 and F19 in concentrations of 3.30 and 0.60 ng/L, respectively (Table 3.2).

Standard chemical mixtures of androgens and estrogens present in PS (R1881-EQbio_PS_mixture)

(Table 3.2) explained 117±20% of R1881-EQbio_PS (Figure 3.4 and Table B-9.4) with a contribution of 21.6%, 70.8%, 7.5% and 0.004 % potency from testosterone, DHT, progesterone and E2, respectively, according to effect concentrations derived in this study (Table B-3.1).

R1881-EQbio_F16_mixture of chemical mixture of testosterone, E1 and E2 resembling F16 explained 147±22% of R1881-EQbio_F16 (Figure 3.4 and Table B-9.4) with approximately all effect from testosterone and just 0.002% from E2. DHT, medroxyprogesterone and epiandrosterone were detected in F18 and R1881-EQbio_F18_mixture explained 85±13% of R1881-

EQbio_F18 with relative contributions of 81.8% and 18.2% from DHT and medroxyprogesterone respectively (Figure 3.4 and Table B-9.4).

Progesterone and androsterone were detected in F19 with R1881-EQbio_F19_mixture explaining

31±6% of R1881-EQbio_F19 (Figure 3.4 and Table B-9.4). The unexplained effect of F19 may be attributed to other androgens or progestagens, which were not included in AR target

66

screening list. However mass balances on the basis of individually detected chemicals with the relative effect potencies from this study confirmed that most of the androgenic activity in environmental fractions was caused by the detected chemicals.

R1881-EQchem of F16 is very well correlating with R1881-EQbio_F16_mixture while for PS and F18 it is approximately double as of R1881-EQbio_F18_mixture and R1881-EQbio_PS_mixture. R1881-

EQchem for F19 is approximately five times higher than the R1881-EQbio_F19_mixture. In PS and

F18, alone DHT contribution towards R1881-EQchem is over 70% and over 81%, respectively, and this R1881-EQchem overestimation is uncertain. In F19, whole R1881-EQchem is from progesterone and it looked like progesterone alone is more potent as compared to in mixture form. Androsterone alone did not give any response in AR bioassay but any possible role of androsterone in a mixture form with progesterone still need to be explored.

As mentioned earlier, only PS showed antagonism on AR along with agonism while none of the fractions showed any antagonistic effect on AR. In order to recover the antagonistic potencies, the fractions were tested with REFs up to 3000 but all fractions were found to be either cytotoxic or non-antagonistic. The anti-androgenic response of PS (Table B-7.1) might be attributed to the presence of genistein and daidzein in estrogenic active fractions i.e., F8, F9 and F11 (Table 3.2, Table B-10.1), as these compounds are also known to exert their effect antagonistically on AR despite known to have estrogenic activity (USEPA, 2015). In PS, daidzein and genistein were present in higher concentrations (Table 3.2) but in the fractions, anti-androgenic response could not be detected in AR bioassays despite testing of individual fraction at much higher REFs. The cumulative R1881-EQbio of F16, F18 and F19 is 50% more than that of R1881-EQbio_PS which is understandable due to the presence of antiandrogens in PS which appeared to be suppressing the overall effect of androgens in PS. The presence of many compounds (Table B-10.1) with antiandrogenic response might be the cause that we observed AR antagonistic response from PS and after fractionation these compounds eluted in various fractions resulting in no antiandrogenic response from any fraction. In target screening of PS for xenobiotics approximately 15% of the compounds found were rated as antiandrogenic as per ToxCast (USEPA, 2015).

67

Figure 3.4: Androgenic responses R1881-EQ (ng/L) of fractions (R1881-EQbio_Fi) of the water sample (R1881-EQbio_PS) explained by designed chemical mixtures ( R1881-EQbio_mixture) along with relative contribution of individual chemical in R1881-EQchem

Similar to estrogens, androgens can cause health hazards (Tölgyesi et al., 2010) although they are much less investigated in the environment. Physiological and reproductive effects in fish by androgens including masculinization have been demonstrated in the rivers receiving wastewater from pulp and paper mill (Howell et al., 1980; Parks et al., 2001). In our study, the natural hormones testosterone and DHT were found to dominate most of the androgenic effect. Exposure to DHT may effect levels of estrogenic hormones, vitellogenesis along with many other potential adverse effects on the fish reproductive system (Rutherford et al., 2015; Bhatia and Kumar, 2016).

The progestagens progesterone and medroxyprogesterone also contributed to the androgenic effects of tested fractions. Progesterone is produced naturally while the synthetic progestagen medroxyprogesterone is used in contraceptives as well as for hormone replacement therapy

68

(HRT) in humans and as a muscle growth promoter in animals (Liu et al., 2012; Kumar et al., 2015). Upon entering into the environment, progestagens have the capability to cause reproductive issues in aquatic life including fish and amphibians (Ellestad et al., 2014). Progesterone and synthetic progestagens are quite promiscuous and have binding affinity to the progesterone receptor (PR) but also act through many other nuclear hormones receptors including AR consequently showing also androgenic hormonal activities (Besse and Garric, 2009; Sitruk-Ware and Nath, 2010; Africander et al., 2014).

3.3.4. Oxidative stress response (OSR)

Oxidative stress response of PS and Rspe was quite pronounced as compared to the individual fractions (Figure 3.2, Table 3.1 and Figure B-8.1), and only four fractions (F4, F5, F15, F19) showed minute OSR. OSR was found to agree well between PS and Rspe within the uncertainty of the method. Cumulative tBHQ-EQbio_Fi of all four active fractions was only 16% of tBHQ-

EQbio_PS, and it looks that the remaining unexplained oxidative stress response has been distributed over many or all of the thirty fractions and could not be detected by AREc32 bioassay. This finding confirms earlier mixture modeling studies that oxidative stress may not be the result of single chemicals effects but the effect of many chemicals in the mixture (Beate I. Escher et al., 2013). However, approximately one third of compounds detected in target screening in this study (Table B-10.1) were found to be exhibiting OSR on various bioassays as per ToxCast (USEPA, 2015).

3.4. Conclusions

Natural and synthetic hormonal steroids were found to explain the endocrine disruption potential of untreated municipal wastewater (UMWW). These compounds may have severe effects on the reproduction and thus populations of wildlife, but also on human health. Despite being mostly natural, high concentrations of these compounds depend on the agglomeration of humans excreting these compounds. While excretion cannot be avoided, steroids can and should be efficiently degraded by wastewater treatment.

In-vitro bioassays have a vital role in the monitoring of pollutants causing specific effects for characterization of water quality. The results of environmental samples by biological analysis are in a good agreement with the results obtained by chemical analysis and confirmation of the detected compounds responsible for these effects explains very well not only the estrogenicity but also the much less investigated androgenicity, while xenobiotics did not play a major role as endocrine disruptors in our study. However, this does not preclude that they might still pose

69

a risk under different exposure scenarios where concentration of natural hormones are not as dominant as in UMWW. OSR was confirmed as a result of mixture of chemicals as a combined effect of many chemicals in a mixture rather than the effect of individual chemicals.

Effect-directed analysis (EDA) combining sensitive in-vitro biotests with chemical target analysis for a broad range of steroids provided a quantitative assignment of measurable effects to causes. This might be in contrast to the paradigm that EDA involves mainly non-target analysis, however, sensitivity and specificity of non-target screening methods for steroids is still not sufficient for a reliable detection of these compounds. Thus, for the identification of endocrine disruptors specialized target analysis is still required supplemented by non-targeted methods for the detection of additional candidate compounds.

3.5. Acknowledgments

The authors acknowledge the support for a doctoral research (personal reference # 91549557) for Muhammad Arslan Kamal Hashmi from Higher Education Commission (HEC) of Pakistan and German Academic Exchange Service (DAAD), Germany. This work was funded by SOLUTIONS project (grant agreement 603437), supported by the EU Seventh Framework Programme. The authors thank to Mr. Jörg Ahlheim for sampling, Rita Schlichting, Maria König and Christin Kühnert for technical help in the cell-based bioassays. Chemaxon (Budapest, Hungary) is acknowledged for providing an academic license of JChem for Excel, Marvin, and the Calculator Plugins.

3.6. References:

Africander, D. J., Storbeck, K., & Hapgood, J. P. (2014). Journal of Steroid Biochemistry and Molecular Biology A comparative study of the androgenic properties of progesterone and the progestins , medroxyprogesterone acetate ( MPA ) and acetate ( NET- A ). Journal of Steroid Biochemistry and Molecular Biology, 143, 404–415. http://doi.org/10.1016/j.jsbmb.2014.05.007 Backe, W. J. (2015). An Ultrasensitive (Parts-Per-Quadrillion) and SPE-Free Method for the Quantitative Analysis of Estrogens in Surface Water. Environmental Science and Technology, 49(24), 14311–14318. http://doi.org/10.1021/acs.est.5b04949 Belhaj, D., Athmouni, K., Jerbi, B., Kallel, M., Ayadi, H., & Zhou, J. L. (2016). Estrogenic compounds in Tunisian urban sewage treatment plant: occurrence, removal and ecotoxicological impact of sewage discharge and sludge disposal. Ecotoxicology, 25(10), 1849–1857. http://doi.org/10.1007/s10646-016-1733-8 Besse, J. P., & Garric, J. (2009). Progestagens for human use, exposure and hazard assessment for the aquatic environment. Environmental Pollution, 157(12), 3485–3494. http://doi.org/10.1016/j.envpol.2009.06.012 Bhatia, H., & Kumar, A. (2016). Does anti-androgen, cancel out the in vivo effects

70

of the androgen, dihydrotestosterone on sexual development in juvenile Murray rainbowfish (Melanotaenia fluviatilis)? Aquatic Toxicology, 170, 72–80. http://doi.org/http://dx.doi.org/10.1016/j.aquatox.2015.11.010 Bolong, N., Ismail, A. F., Salim, M. R., & Matsuura, T. (2009). A review of the effects of emerging contaminants in wastewater and options for their removal. DES, 239(1–3), 229– 246. http://doi.org/10.1016/j.desal.2008.03.020 Brack, W. (2003). Effect-directed analysis: A promising tool for the identification of organic toxicants in complex mixtures? Analytical and Bioanalytical Chemistry, 377(3), 397–407. http://doi.org/10.1007/s00216-003-2139-z Brack, W., Ait-Aissa, S., Burgess, R. M., Busch, W., Creusot, N., Di Paolo, C., … Krauss, M. (2016). Effect-directed analysis supporting monitoring of aquatic environments — An in- depth overview. Science of The Total Environment, 544, 1073–1118. http://doi.org/http://dx.doi.org/10.1016/j.scitotenv.2015.11.102 Brack, W., & Schirmer, K. (2003). Effect-Directed Identification of Oxygen and Sulfur Heterocycles as Major Polycyclic Aromatic Cytochrome P4501A-Inducers in a Contaminated Sediment. Environmental Science & Technology, 37(14), 3062–3070. http://doi.org/10.1021/es020248j Brian, J. V., Harris, C. A., Scholze, M., Backhaus, T., Booy, P., Lamoree, M., … Sumpter, J. P. (2005). Accurate prediction of the response of freshwater fish to a mixture of estrogenic chemicals. Environmental Health Perspectives, 113(6), 721–728. http://doi.org/10.1289/ehp.7598 Charles, G. D., Gennings, C., Zacharewski, T. R., Gollapudi, B. B., & Carney, E. W. (2002). Assessment of Interactions of Diverse Ternary Mixtures in an Estrogen Receptor-α Reporter Assay. Toxicology and Applied Pharmacology, 180(1), 11–21. http://doi.org/https://doi.org/10.1006/taap.2001.9346 Dasgupta, A., & Klein, K. (2014). Chapter 1 - Introduction to Free Radicals and the Body’s Antioxidant Defense BT - Antioxidants in Food, Vitamins and Supplements (pp. 1–18). San Diego: Elsevier. http://doi.org/http://dx.doi.org/10.1016/B978-0-12-405872- 9.00001-X Diamanti-Kandarakis, E., Bourguignon, J.-P., Giudice, L. C., Hauser, R., Prins, G. S., Soto, A. M., … Gore, A. C. (2009). Endocrine-Disrupting Chemicals: An Endocrine Society Scientific Statement. Endocrine Reviews, 30(4), 293–342. http://doi.org/10.1210/er.2009- 0002 Ellestad, L. E., Cardon, M., Chambers, I. G., Farmer, J. L., Hartig, P., Stevens, K., … Orlando, E. F. (2014). Environmental gestagens activate fathead minnow (Pimephales promelas) nuclear progesterone and androgen receptors in vitro. Environmental Science and Technology, 48(14), 8179–8187. http://doi.org/10.1021/es501428u Escher, B. I., Allinson, M., Altenburger, R., Bain, P. a., Balaguer, P., Busch, W., … Leusch, F. D. L. (2014). Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays. Environmental Science and Technology, 48(3), 1940–1956. http://doi.org/10.1021/es403899t Escher, B. I., Dutt, M., Maylin, E., Tang, J. Y. M., Toze, S., & Roland, C. (2012). Water quality

71

assessment using the AREc32 reporter gene assay indicative of the oxidative stress response pathway, 2877–2885. http://doi.org/10.1039/c2em30506b Escher, B. I., Lawrence, M., Macova, M., Mueller, J. F., Poussade, Y., Robillot, C., … Gernjak, W. (2011). Evaluation of Contaminant Removal of Reverse Osmosis and Advanced Oxidation in Full-Scale Operation by Combining Passive Sampling with Chemical Analysis and Bioanalytical Tools. Environmental Science & Technology, 45(12), 5387– 5394. http://doi.org/10.1021/es201153k Escher, B. I., Van Daele, C., Dutt, M., Tang, J. Y. M., & Altenburger, R. (2013). Most oxidative stress response in water samples comes from unknown chemicals: The need for effect- based water quality trigger values. Environmental Science and Technology, 47(13), 7002– 7011. http://doi.org/10.1021/es304793h Howell, W. M., Black, D. A., Bortone, S. a, Copeia, S., Dec, N., & Black, D. a N. N. (1980). Abnormal Expression of Secondary Sex Characters in a Population of Mosquitofish , Gambusia affinis holbrooki : Evidence for Environmentally-Induced Masculinization Abnormal Expression of Secondary Sex Characters in a Population of Mosquitofish , Gambusia, (4), 676–681. Huang, G.-Y., Liu, Y.-S., Chen, X.-W., Liang, Y.-Q., Liu, S.-S., Yang, Y.-Y., … Ying, G.-G. (2016). Feminization and masculinization of western mosquitofish (Gambusia affinis) observed in rivers impacted by municipal wastewaters. Scientific Reports, 6, 20884. http://doi.org/10.1038/srep20884 Ihara, M., Ihara, M. O., Kumar, V., Narumiya, M., Hanamoto, S., Nakada, N., … Tanaka, H. (2014). Co-occurrence of estrogenic and antiestrogenic activities in wastewater: Quantitative evaluation of balance by in vitro ERα reporter gene assay and chemical analysis. Environmental Science and Technology, 48(11), 6366–6373. http://doi.org/10.1021/es5014938 Jobling, S., Nolan, M., Tyler, C. R., Brighty, G., & Sumpter, J. P. (1998). Widespread Sexual Disruption in Wild Fish. Environmental Science & Technology, 32(17), 2498–2506. http://doi.org/10.1021/es9710870 Jobling, S., Williams, R., Johnson, A., Taylor, A., Gross-Sorokin, M., Nolan, M., … Brighty, G. (2005). Predicted exposures to steroid estrogens in UK rivers correlate with widespread sexual disruption in wild fish populations. Johnson, A. C., Belfroid, A., & Di Corcia, A. (2000). Estimating steroid oestrogen inputs into activated sludge treatment works and observations on their removal from the effluent. Science of The Total Environment, 256(2–3), 163–173. http://doi.org/http://dx.doi.org/10.1016/S0048-9697(00)00481-2 Kabir, E. R., Rahman, M. S., & Rahman, I. (2015). A review on endocrine disruptors and their possible impacts on human health. Environmental Toxicology and Pharmacology, 40(1), 241–258. http://doi.org/http://dx.doi.org/10.1016/j.etap.2015.06.009 König, M., Escher, B. I., Neale, P. A., Krauss, M., Hilscherová, K., Novák, J., … Brack, W. (2017). Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environmental Pollution, 220, Part, 1220–1230. http://doi.org/http://dx.doi.org/10.1016/j.envpol.2016.11.011

72

Kortenkamp, A. (2007). Ten years of mixing cocktails: A review of combination effects of endocrine-disrupting chemicals. Environmental Health Perspectives, 115(SUPPL1), 98– 105. http://doi.org/10.1289/ehp.9357 Kumar, V., Johnson, A. C., Trubiroha, A., Tumová, J., Ihara, M., Grabic, R., … Kroupová, H. K. (2015). The challenge presented by progestins in ecotoxicological research: A critical review. Environmental Science and Technology, 49(5), 2625–2638. http://doi.org/10.1021/es5051343 Legler, J., Dennekamp, M., Vethaak, A. D., Brouwer, A., Koeman, J. H., van der Burg, B., & Murk, A. J. (2002). Detection of estrogenic activity in sediment-associated compounds using in vitro reporter gene assays. Science of The Total Environment, 293(1–3), 69–83. http://doi.org/http://dx.doi.org/10.1016/S0048-9697(01)01146-9 Legler, J., van den Brink, C. E., Brouwer, A., Murk, A. J., van der Saag, P. T., Vethaak, A. D., & van der Burg, B. (1999). Development of a stably transfected estrogen receptor- mediated luciferase reporter gene assay in the human T47D breast cancer cell line. Toxicological Sciences, 48(1), 55–66. Lintelmann, J., Katayama, A., Kurihara, N., Shore, L., & Wenzel, A. (2003). Endocrine disruptors in the environment (IUPAC Technical Report). Pure and Applied Chemistry, 75(5), 631–681. Liu, S., Ying, G. G., Zhou, L. J., Zhang, R. Q., Chen, Z. F., & Lai, H. J. (2012). Steroids in a typical swine farm and their release into the environment. Water Research, 46(12), 3754– 3768. http://doi.org/10.1016/j.watres.2012.04.006 Liu, S., Ying, G., Zhao, J., Zhou, L., Yang, B., Chen, Z., & Lai, H. (2012). Occurrence and fate of androgens , estrogens , glucocorticoids and progestagens in two different types of municipal wastewater treatment plants †, 482–491. http://doi.org/10.1039/c1em10783f Malaj, E., von der Ohe, P. C., Grote, M., Kühne, R., Mondy, C. P., Usseglio-Polatera, P., … Schäfer, R. B. (2014). Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale. Proceedings of the National Academy of Sciences of the United States of America, 111(26), 9549–54. http://doi.org/10.1073/pnas.1321082111 Maletz, S., Floehr, T., Beier, S., Klümper, C., Brouwer, A., Behnisch, P., … Hollert, H. (2013). In vitro characterization of the effectiveness of enhanced sewage treatment processes to eliminate endocrine activity of hospital effluents. Water Research, 47(4), 1545–1557. http://doi.org/10.1016/j.watres.2012.12.008 Manickum, T., & John, W. (2014). Occurrence, fate and environmental risk assessment of endocrine disrupting compounds at the wastewater treatment works in Pietermaritzburg (South Africa). Science of the Total Environment, 468–469, 584–597. http://doi.org/10.1016/j.scitotenv.2013.08.041 Muz, M., Krauss, M., Kutsarova, S., Schulze, T., & Brack, W. (2017). Mutagenicity in Surface Waters: Synergistic Effects of Carboline Alkaloids and Aromatic Amines. Environmental Science & Technology, 51(3), 1830–1839. http://doi.org/10.1021/acs.est.6b05468 Neale, P. A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M. S., Deutschmann, B., … Escher, B. I. (2015). Linking in Vitro Effects and Detected Organic Micropollutants in Surface Water Using Mixture-Toxicity Modeling. Environmental Science and

73

Technology, 49(24), 14614–14624. http://doi.org/10.1021/acs.est.5b04083 Neale, P. A., Altenburger, R., Aït-Aïssa, S., Brion, F., Busch, W., de Aragão Umbuzeiro, G., … Escher, B. I. (2017). Development of a bioanalytical test battery for water quality monitoring: Fingerprinting identified micropollutants and their contribution to effects in surface water. Water Research, 123, 734–750. http://doi.org/https://doi.org/10.1016/j.watres.2017.07.016 Neale, P. A., Munz, N. A., Aїt-Aїssa, S., Altenburger, R., Brion, F., Busch, W., … Hollender, J. (2017). Integrating chemical analysis and bioanalysis to evaluate the contribution of wastewater effluent on the micropollutant burden in small streams. Science of The Total Environment, 576, 785–795. http://doi.org/http://dx.doi.org/10.1016/j.scitotenv.2016.10.141 Parks, L. G., Lambright, C. S., Orlando, E. F., Guillette, L. J., Ankley, G. T., & Gray, L. E. (2001). Masculinization of Female Mosquitofish in Kraft Mill Effluent-Contaminated Fenholloway River Water Is Associated with Androgen Receptor Agonist Activity. Toxicological Sciences, 62(2), 257–267. http://doi.org/10.1093/toxsci/62.2.257 Patisaul, H. B., & Jefferson, W. (2010). The pros and cons of phytoestrogens. Frontiers in Neuroendocrinology, 31(4), 400–419. http://doi.org/http://dx.doi.org/10.1016/j.yfrne.2010.03.003 Purdom, C. E., Hardiman, P. A., Bye, V. V. J., Eno, N. C., Tyler, C. R., & Sumpter, J. P. (1994). Estrogenic Effects of Effluents from Sewage Treatment Works. Chemistry and Ecology, 8(4), 275–285. http://doi.org/10.1080/02757549408038554 Rajapakse, N., Silva, E., & Kortenkamp, A. (2002). Combining xenoestrogens at levels below individual no-observed-effect concentrations dramatically enhances steroid hormone action. Environmental Health Perspectives, 110(9), 917–921. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1240992/ Rocha, M. J., Cruzeiro, C., Reis, M., Pardal, M. Â., & Rocha, E. (2016). Pollution by endocrine disruptors in a southwest European temperate coastal lagoon (Ria de Aveiro, Portugal). Environmental Monitoring and Assessment, 188(2), 101. http://doi.org/10.1007/s10661- 016-5114-9 Rutherford, R., Lister, A., Hewitt, L. M., & MacLatchy, D. (2015). Effects of model aromatizable (17α-) and non-aromatizable (5α-dihydrotestosterone) androgens on the adult mummichog (Fundulus heteroclitus) in a short-term reproductive endocrine bioassay. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 170, 8–18. http://doi.org/http://dx.doi.org/10.1016/j.cbpc.2015.01.004 Schulze, T., Ahel, M., Ahlheim, J., Aït-Aïssa, S., Brion, F., Di Paolo, C., … Brack, W. (2017). Assessment of a novel device for onsite integrative large-volume solid phase extraction of water samples to enable a comprehensive chemical and effect-based analysis. Science of The Total Environment, 581–582, 350–358. http://doi.org/http://dx.doi.org/10.1016/j.scitotenv.2016.12.140 Shore, L. S., Reichmann, O., Shemesh, M., Wenzel, A., & Litaor, M. I. (2004). Washout of accumulated testosterone in a watershed. Science of the Total Environment, 332(1–3), 193–202. http://doi.org/10.1016/j.scitotenv.2004.04.009

74

Simmons, S. O., Fan, C., & Ramabhadran, R. (2009). Cellular Stress Response Pathway System as a Sentinel Ensemble in toxicological screening, 1–98. Retrieved from file:///2009/Unknown/2009 Simmons et al 2009 Cellular.pdf Sitruk-Ware, R., & Nath, A. (2010). The use of newer progestins for contraception. Contraception, 82(5), 410–417. http://doi.org/http://dx.doi.org/10.1016/j.contraception.2010.04.004 Sumpter, J. P. (1998). Xenoendocrine disrupters — environmental impacts. Toxicology Letters, 102–103, 337–342. http://doi.org/http://dx.doi.org/10.1016/S0378-4274(98)00328-2 Tang, J. Y. M., Busetti, F., Charrois, J. W. A., & Escher, B. I. (2014). Which chemicals drive biological effects in wastewater and recycled water? Water Research, 60, 289–299. http://doi.org/http://dx.doi.org/10.1016/j.watres.2014.04.043 Thomas, K. V, Balaam, J., Hurst, M. R., & Thain, J. E. (2004). Identification of in vitro estrogen and androgen receptor agonists in North Sea offshore produced water discharges. Environmental Toxicology and Chemistry, 23(5), 1156–1163. http://doi.org/10.1897/03- 239 Tölgyesi, Á., Verebey, Z., Sharma, V. K., Kovacsics, L., & Fekete, J. (2010). Simultaneous determination of , androgens, and progesterone in river water by liquid chromatography-tandem mass spectrometry. Chemosphere, 78(8), 972–979. http://doi.org/10.1016/j.chemosphere.2009.12.025 Vajda, A. M., Barber, L. B., Gray, J. L., Lopez, E. M., Bolden, A. M., Schoenfuss, H. L., & Norris, D. O. (2011). Demasculinization of male fish by wastewater treatment plant effluent. Aquatic Toxicology, 103(3–4), 213–221. http://doi.org/10.1016/j.aquatox.2011.02.007 Vitale, D. C., Piazza, C., Melilli, B., Drago, F., & Salomone, S. (2013). Isoflavones: estrogenic activity, biological effect and bioavailability. European Journal of Drug Metabolism and Pharmacokinetics, 38(1), 15–25. http://doi.org/10.1007/s13318-012-0112-y Weiss, J. M., Hamers, T., Thomas, K. V., Van Der Linden, S., Leonards, P. E. G., & Lamoree, M. H. (2009). Masking effect of anti-androgens on androgenic activity in European river sediment unveiled by effect-directed analysis. Analytical and Bioanalytical Chemistry, 394(5), 1385–1397. http://doi.org/10.1007/s00216-009-2807-8 WWAP. (2012). World Water Assessment Programme. The United Nations World Water Development Report 4: Managing Water under Uncertainty and Risk, Paris, UNESCO. Yeh, R. Y. L., Farré, M. J., Stalter, D., Tang, J. Y. M., Molendijk, J., & Escher, B. I. (2014). Bioanalytical and chemical evaluation of disinfection by-products in swimming pool water. Water Research, 59, 172–184. http://doi.org/http://dx.doi.org/10.1016/j.watres.2014.04.002 Yu, Y., Wu, L., & Chang, A. C. (2013). Seasonal variation of endocrine disrupting compounds , pharmaceuticals and personal care products in wastewater treatment plants. Science of the Total Environment, The, 442, 310–316. http://doi.org/10.1016/j.scitotenv.2012.10.001 Zaccaroni, M., Seta, D. Della, Farabollini, F., Fusani, L., & Dessì-Fulgheri, F. (2016). Developmental Exposure to Very Low Levels of Ethynilestradiol Affects Anxiety in a

75

Novelty Place Preference Test of Juvenile Rats. Neurotoxicity Research, 30(4), 553–562. http://doi.org/10.1007/s12640-016-9645-1 USEPA. 2015. Interactive Chemical Safety for Sustainability (iCSS) Dashboard v2. http:// actor.epa.gov/dashboard/ (accessed 25 Oct 2017)

Hansch, C., Leo, A., D. Hoekman., 1995. Exploring QSAR – Hydrophobic, Electronic, and Steric Constants. Washington, DC: American Chemical Society., p. 158 in “https://pubchem.ncbi.nlm.nih.gov” (accessed 17th Feb 2017)

76

Chapter 4

Advanced effect-directed analysis unraveling water contamination with progestogens and glucocorticoids at trace concentrations in a major European river

This chapter is based on the article in preparation:

Advanced effect-directed analysis unraveling water contamination with progestogens and glucocorticoids at trace concentrations in a major European river

Muhammad Arslan Kamal Hashmi, Martin Krauss, Beate I Escher, Ivana Teodorovic, Werner Brack 77

Abstract: Effect-based monitoring is increasingly applied to detect and – in conjunction with chemical analysis - to identify endocrine disrupting compounds (EDCs) in the environment. While this approach of effect-directed analysis has been successfully demonstrated for estrogenicity and androgenicity, data on progestogens and glucocorticoids driving endocrine disruption are quite limited. In this study, progestogenic and glucocorticoid activities were investigated in the Danube river water receiving untreated wastewater from Novi Sad, Serbia. After a two-step fractionation all fractions were tested with reporter gene bioassays for agonistic and antagonistic hormonal responses at progestogenic and glucocorticoid hormone receptors as well as target and non-target analytical screening of active fractions. Due to masking effects by cytotoxic mixture components, the effects could not be detected in the raw water extract but were unraveled only after fractionation. Target chemical screening of the fraction that was active in the progesterone receptor (PR)-assay by liquid chromatography-high resolution mass spectrometry (LC-HRMS) revealed progesterone and megestrol acetate as predominant drivers of PR-mediated activity along with medroxyprogesterone, dihydrotestosterone, androsterone and epiandrosterone. was detected at sub- ng/L concentration in the active fraction in the glucocorticoid receptor (GR)- assay but could not explain any significant observed GR activity. The present study indicates that effect-based monitoring is a powerful tool to detect EDCs in the aquatic environment but fractionation may be required to avoid masking effects of mixture components. Future EDA studies are required to better understand the occurrence of EDCs and masking compounds in different lipophilicity windows in order to finally reduce fractionation requirements for monitoring to a smart clean- up.

78

4.1. Introduction: The presence of endocrine disrupting compounds (EDCs) in surface waters may cause adverse effects on aquatic organisms (Jobling and Tyler, 2003; Sumpter and Jobling, 2013). These compounds include natural hormones as well as pharmaceuticals, veterinary drugs and their metabolites, which may enter the environment in complex mixtures through direct disposal, urine and feces (Sanderson et al., 2004). In addition, many synthetic chemicals such as and the recently discovered fluorescent dye 7-diethylamino-4-methyl coumarin may contribute to endocrine disruption in aquatic ecosystems (Muschket et al., 2018; Sumpter, 1998).

So far, estrogenic and androgenic EDCs received most attention (Hashmi et al., 2018; Ihara et al., 2014; Liney et al., 2005; Sumpter, 1995) although there is increasing awareness that other EDCs such as natural and synthetic progestogens and glucocorticoids also enter the environment in significant amounts (Chang et al., 2011; Creusot et al., 2014; Schriks et al., 2010) and may impact fish reproduction and metabolism (Kugathas et al., 2012a; Kugathas and Sumpter, 2011; Runnalls et al., 2013). Synthetic progestogens are consumed in larger amounts than synthetic estrogens or androgens (Kumar et al., 2015; Runnalls et al., 2010; Shen et al., 2018). They are used with estrogens in contraceptives and to treat various hormone-related diseases as well as in animals for muscle growth (Chang et al., 2009; Lintelmann et al., 2003; Liu et al., 2012), while glucocorticoids are primarily used to treat inflammations in the context of asthma, joint pain, or skin problems (Chang et al., 2009). Glucocorticoids are also present in personal care products (PCPs) like in many topical creams and lotions for skin related issues (Sanderson et al., 2004; Weizel et al., 2018).

Both progestogens and glucocorticoids act through binding to a wide range of receptors including progesterone (PR), estrogen (ER), androgen (AR), glucocorticoid (GR) and mineralocorticoid (MR) receptors (Kumar et al., 2015). The presence of progestogens and glucocorticoids in the environment can be detected by chemical trace analysis (Ammann et al., 2014; Jia et al., 2016; Schriks et al., 2010; Shen et al., 2018) or biologically via PR and GR binding using a variety of reporter gene assays (Creusot et al., 2014; Jia et al., 2015; Wangmo et al., 2018). Since in contrast to estrogens GR- and PR-ligands are rarely monitored, only limited information is available on the occurrence and concentrations of these compounds in the environment (Fent, 2015). For the River Thames total GR agonist concentrations of 30 to 850 ng/L have been predicted based on consumption, metabolism and excretion and removal in wastewater treatment (Kugathas et al., 2012b), while GR activity ranged from 39 to 155 ng/L in the effluents from secondary wastewater treatment plants (Jia et al., 2016). Measured

79

concentrations in industrial and hospital wastewaters in the Netherlands approached several hundred ng/L (Schriks et al., 2010). In a study on Czech and Swiss rivers concentrations in water of up to tens of ng/L were measured for glucocorticoids (Macikova et al., 2014). Natural and synthetic progestogens have been detected in treated wastewater effluents and river water typically in the range of below and several ng/L, in individual cases up to more than 100 ng/L (Chang et al., 2011; Fent, 2015; Shen et al., 2018).

Glucocorticoids and progestogens are groups of known and probably unknown chemicals following the same toxicological pathways with receptor binding as initiating event. Due to new chemicals entering the market and the continuous discovery of new compounds in the environment chemical analysis cannot prevent against overlooking any relevant chemicals. Thus, effect-based monitoring is suggested to address similarly acting chemicals according to their relative potency as a mixture (Brack et al., 2017; Neale et al., 2017). This promising concept has been demonstrated successfully at different European river basins (Kase et al., 2018; Neale et al., 2015) but also in a nationwide screen in the United States (Conley et al., 2017). In-depth investigation of endocrine disruption at the River Danube downstream of Novi Sad emitting untreated wastewater to the river water revealed significant ER- and AR-agonistic and antagonistic effects and the corresponding effect drivers (Hashmi et al., 2018; König et al., 2017), while no GR- and PR-mediated effects could be detected in the water extracts probably due to masking effects. Similar failure of effect detection has been observed for PR-activity in municipal WWTP effluents in the Czech and the Slovak republic (Šauer et al., 2018). In a previous study (Weiss et al., 2009) demonstrated that fractionation of highly complex mixtures may eliminate such masking effects. Combining effect-based monitoring and fractionation with chemical analysis of active fractions in effect-directed analysis (EDA) is a powerful tool to identify the drivers behind the measured effects (Brack et al., 2016; Hashmi et al., 2018; Muschket et al., 2018; Thomas et al., 2004).

Thus, hypothesizing that together with estrogens and androgens also progestogens and glucocorticoids will be discharged to the River Danube from Novi Sad, we explored whether fractionation may help to unravel masked endocrine disruption potency of raw water extracts. In case of the presence of PR and GR-mediated activity in the fractions, possible drivers of this activity should be identified by using EDA.

80

4.2. Material and Methods:

4.2.1. Study site and sampling: The study was carried out 238 m downstream of the discharge of untreated municipal wastewater (general collector 1, GPS coordinates 45°15′12.5″ N, 19°51′21.0″ E) into River Danube from the city of Novi Sad, Serbia, under low flow, dry weather conditions. A large volume solid phase extraction (LVSPE) device (Schulze et al., 2017) was used to extract 850 Liters of Danube water overnight, which were concentrated in

850 mL methanol (enrichment factorsolid phase extraction (EFspe) of 1000) as described previously

(König et al., 2017). EFspe was calculated by using following equation:(Escher et al., 2014)

Vwater 퐸퐹spe= (Eq. 1) Vextract

4.2.2. Study design: The study design (Figure 4.1) and detailed flow charts are given in Figure C-3 and Figure C-3.1, while biological effects from samples and chemicals were explained by using a study design described previously (Hashmi et al., 2018) with some modifications.

Biological activity (BEQbio) of parent sample (PS), recombination of primary fractions (R), recombination of secondary fractions (R_F18), primary and secondary fractions and designed mixtures were tested on bioassays in order to characterize the portion of biological activity explained by detected chemicals (BEQchem) and reconstituted mixtures (BEQbio_mixture).

Figure 4.1: Simplified flow diagram of the work carried out in this study to describe various steps (details can be found in C-3); A, sampling and fractionation; B, biological analysis, C, chemical analysis; PS, parent sample; NT, nontarget; BEQbio, bioanalytical equivalent concentration-

81

biological; BEQchem, bioanalytical equivalent concentration-chemical; BEQbio-_mixture, bioanalytical equivalent concentration-biological of chemical mixture; PR, receptor, GR, glucocorticoid receptor, GC-HRMS, gas chromatography-high resolution mass spectrometry; big two-sided arrows represents comparisons between BEQs.

4.2.3. Fractionation. The parent sample (PS) extract was fractionated by a two-step procedure using reversed phase-high pressure liquid chromatography (RP-HPLC) (C-2) operated by the Chromeleon 6.8 (Dionex) software. The HPLC system consisted of a Rheodyne manual injection valve, a Varian Prostar 210 Pump, a Dionex UltiMate 3000 automated fraction collector (Thermo Scientific) and a Dionex UVD 340U UV/VIS detector, which was used at a wavelength of 210 nm for the recording of chromatograms. For primary fractionation, an aliquot corresponding to 20 L of water extract was prepared in 2 mL of 50:50 methanol:water and aliquots of 100 µL were fractionated by using a Nucleodur C18 Gravity column. Primary fractionation has already been described previously (Hashmi et al., 2018).

For secondary fractionation, an aliquot of primary fraction F18 corresponding to 3 L of water equivalent was concentrated into 2 mL of methanol:water (30:70) and aliquots of 400 µL were fractionated using an aminopropyl column (Unison NH2, 150 x 10 mm, 3µm particle size, Imtakt Corp., Kyoto, Japan) with methanol [B]:water [A] as a mobile phases, both containing 0.1% of formic acid. AP has been identified in a previous study as highly orthogonal to C18 (Muschket et al., 2018). Gradient elution was carried out with a flow rate of 2.8 mL/min. For the first minute, the mobile phase contained 30% of solvent B, which was increased linearly to 95% at 29 min, remained constant for 10 minutes and was followed by 20 min of re- equilibration to the initial conditions.

In total, 30 primary fractions, 28 secondary fractions as well as fractionation blanks were collected and processed for further analysis. All fractions were concentrated by a Syncore 12 channel parallel evaporator (Büchi) at 40oC, shaken at 200 rounds per minute (RPM) at a pressure of 33.7 kPa for evaporation of methanol. For concentrating the fractions further, a solid phase extraction procedure (Hashmi et al., 2018) was employed using the same sorbent as for the LVSPE procedure.

A steroid mixture comprised of commercially available progestogens and glucocorticoids (C- 8) was also fractionated (steroid mixture fractionation) by using the same 2-step-fractionation method. Mass balances of primary and secondary fractionation were performed by

82

recombination of equal aliquots of primary and secondary fractions (R and R_F18 respectively) and by comparison of effects of recombined and parent sample.

4.2.4. Biological Analysis: For biological analysis stably transfected mammalian cell lines (GeneBLAzer PR-UAS-bla HEK293T and GR-UAS-bla HEK 293T) were used for PR- and GR-mediated responses due to their sensitivity to detect biological responses from surface waters (Leusch et al., 2017). The detailed procedure for biological analysis is described in C- 1.

Responses from bioassays were obtained in the form of sigmoidal dose response curves ranging from 0 to 100% effect. For all samples, effect concentrations at 10% (EC10) from dose response curves were expressed by using relative enrichment factors (REF) (Escher et al., 2014). REF

(Eq. 3) is a combination of dilution factor of sample in the bioassay (dilution factorbioassay) (Eq.

2) multiplied by enrichment factorspe (Eq. 1).

푉표푙푢푚푒 표푓 푒푥푡푟푎푐푡 푎푑푑푒푑 푡표 푏𝑖표푎푠푠푎푦 푑푖푙푢푡푖표푛 푓푎푐푡표푟 = (Eq. 2) 푏𝑖표푎푠푠푎푦 푡표푡푎푙 푣표푙푢푚푒 표푓 푏𝑖표푎푠푠푎푦

REF = 푑푖푙푢푡푖표푛 푓푎푐푡표푟푏𝑖표푎푠푠푎푦 ∗ 퐸퐹푠푝푒 (Eq. 3)

Concentrations causing 10% effect (EC10) (Eq. 5) for PR- and GR-activation were derived from the linear portion of the concentration-response curves by taking slope (s) as an adjustable parameter from dose-response curve (Eq. 4) as described previously (Escher et al., 2018, 2014; König et al., 2017). The values of dose response curves causing sharp decline of dose-response curves at higher concentrations were taken as cytotoxic values and dose-response curves were drawn after exclusion of cytotoxic values (Macova et al., 2010).

1 10 log 퐸퐶 = − 푙표푔 ( ) (Eq. 4) 10 푠 90

10% 퐸퐶 = (Eq. 5) 10 (푅퐸퐹) 푠푙표푝푒 (푠)

Biological activity of the samples was described by bioanalytical equivalent concentrations

(BEQbio in ng/L) (e.g., for the parent sample (BEQbio_PS), for fractions (BEQbio_Fi)), which were derived by dividing the effect concentration (EC10 in ng/L) of the reference compound (promegestone and dexamethasone for PR and GR-bioassays respectively) with effect concentration (EC10 in REF) of the respective sample (Eq. 6).

ng EC10 (reference compound)( ) BEQ (ng⁄L)= L (Eq. 6) bio EC10(sample)(REF)

83

Relative effect potencies (REPs) of each tested chemical were derived by dividing the EC10

(ng/L) of the reference compound with the EC10 of the tested chemical i (Eq. 7). Error propagation was performed as described in ref (Escher et al., 2018).

ng EC (reference compound)( ) 10 L REPi= ng (Eq. 7) EC (i)( ) 10 L

Individual chemicals responses in the mixture were expressed as BEQchem (ng/L), which are a result of multiplication of REP of the respective chemical with the concentration of the detected chemical (i). According to the concept of concentration addition, BEQchem of the individual mixture components are summed up to predict mixture activity from detected chemicals (Eq. 8).

n BEQchem(ng⁄L)= ∑i=1 REPi *ci (Eq. 8)

The contribution of detected candidate chemicals (Table 4.2) to biological activity of the samples was confirmed by testing simulated chemical mixtures based on detected chemicals concentrations (BEQbio_mixture).

4.2.5. Chemical analysis

A detailed flow chart of the chemical analysis performed in this study is given in Figure C-3.1.

4.2.5.1. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) Nontarget (NT) screening.

LC-HRMS analysis was done by using an Ultimate 3000 LC system (Thermo Scientific) coupled to a quadrupole-orbitrap MS (QExactive Plus, Thermo Scientific). Sample preparation and analytical settings of LC-HRMS are given in SI (C-4). NT screening of PR-active secondary fraction F18.4 was carried out as described previously (Muz et al., 2017a). Briefly, samples (F18.4 and blanks) were initially screened with LC-HRMS in full scan mode at a resolving power of 140,000 (referenced to m/z 200) with ESI+ and ESI- ionization with a scan range of 100–1000 m/z. Raw data files were processed by using MZmine 2.21 (Pluskal et al., 2010) with the settings given in the SI (C-5). Peak lists and masses were exported to csv files for further processing in MS Excel. A stepwise filtering procedure was performed (Hug et al., 2014) and the number of peaks was reduced by removal of common peaks in all samples. Remaining peaks were sorted according to their peak intensity. Plausible molecular formulas for the selected masses were generated by using QualBrowser (Thermo XCalibur 2.2) while keeping in mind the protonation and deprotonation in ESI+ and ESI- modes, adjusting the

84

numbers of atoms based on the isotopologue peak intensities and including C, H, N, O, S, P, F, Cl and Br. For further processing, a data-dependent acquisition (ddMS2) of plausible masses in the active fraction was generated on a compound inclusion list comprising the respective . Data was obtained by combining a full scan at R = 35,000 with four ddMS2 scans using higher energy collisional dissociation (HCD) with stepped collision energies of 35 and 70 a.u. at R = 35,000. Subsequently, candidate lists of plausible compounds were generated based on molecular formulas and MS/MS spectra information using MetFrag (Ruttkies et al., 2016) querying the ChemSpider compound database (http://www.chemspider.com/). Along with the match of predicted and experimental MS2 spectra the number of data sources and references (as proxies for the commercial importance of a compound) were considered as additional criteria for ranking of candidates.

For the confirmation of potential candidates from NT screening, two additional methods were applied, namely pH-dependent LC retention and hydrogen deuterium exchange (HDX) analysis (C-4). The analysis was run at full scan of 70,000 resolution with both ionization modes (ESI+ and ESI-) and HRMS/MS (ddMS2) data was obtained by using higher energy collisional dissociation (HCD) with stepped collision energies of 15, 35 and 55. Fragment ions generated through different collision energies were saved in one spectrum and candidate lists were generated by using MetFrag.

4.2.5.2. LC-HRMS Target screening. Samples and blanks containing 700 mL water equivalent were reduced to dryness by using gentle stream of nitrogen and reconstituted in a final volume of 50 µL of LC-MS grade methanol and LC-MS grade water to a CF of 14,000 (70:30, methanol:water) for further analysis. Target chemical analysis was done by using LC- HRMS with all specifications as described for NT screening. For mass spectrometry (MS), samples were run in ESI+ mode with full scan resolution of 140,000 and an injection volume of 5 µl. For data evaluation TraceFinder 3.2 (Thermo Scientific) was used. Chemicals were quantified based on full-scan extracted ion chromatogram with 7 ppm window and confirmed with three to four diagnostic MS/MS fragments. Patterns of these fragments were compared with reference standards.

4.2.5.3. Gas chromatography-high resolution mass spectrometry (GC-HRMS). The PR- and GR-active secondary fraction 18.4 and the neighboring fractions as well as blank samples were additionally screened using GC-HRMS on a GC-QExactive (Thermo). Sample preparation, GC-HRMS settings and data evaluation are given in SI (C-4).

85

4.3. Results and Discussion.

4.3.1. Biological analysis.

4.3.1.1. Quality control/Quality assurance (QA/QC). None of the fractionation blanks from any fractionation step showed any progestogenic or glucocorticoid response or cytotoxicity up to a REF of 600 (C-6, C-7). Cytotoxicity of the parent sample (PS) and the recombination of primary fractions (R) was observed from a REF of 25 and 50, respectively. No visible progestogenic and glucocorticoid response could be observed in samples PS and R. Primary fractions (F1-F30) were tested at REF of 300-450 on PR- and GR-bioassays for both agonists and antagonists and some fractions showed agonistic response but neither visible antagonistic response nor cytotoxicity (Table 4.1, Figure 4.2, C-6, C-7).

Cytotoxicity was observed after the recombination of secondary fractions (R_F18) above a REF of 200 on PR and GR along with loss of PR-activity as compared to F18, while individual secondary fractions did not show any cytotoxicity up to REF 450 (C-6). Progestogenic activity recovered from R_F18 was approximately 28% of the activity of F18 while R_F18 recovered approximately 59% of the GR-activity as compared to F18 (Table 4.1).

Table 4.1: Progestogenic and glucocorticoid mediated responses from samples as EC10±SE (REF) and BEQbio (Promegestone-EQbio and Dexamethasone-EQbio ) in ng/L.

PR GR Samples EC10 ± SE Promegestone- EC10 ± SE Dexamethasone- (REF) EQbio ± SE (ng/L) (REF) EQbio ± SE (ng/L)

F18.4 268 ± 13 0.088 ± 0.005 -- --

Recombination of secondary 293 ± 12 0.080 ± 0.004 272 ± 17 0.56 ± 0.04 fractions (R_F18)

F18 81 ± 2 0.29 ± 0.01 161 ± 5 0.95 ± 0.05

F19 213 ± 8 0.11 ± 0.01 -- --

Parent sample (PS) Cytotoxic -- Cytotoxic --

Recombination of primary Cytotoxic -- Cytotoxic -- fractions (R)

86

Figure 4.2: Progestogenic and glucocorticoid mediated responses from samples, (i & ii) represent bioanalytical equivalent concentrations (BEQ) from primary fractions for PR- and GR-activity respectively, (i.i & ii.i) BEQ from secondary fractions. R, recombination of primary fractions; PS, parent sample; R_F18, recombination of secondary fractions; Sum of F and Sum of F18.F denoted to arithmetic sum of the effects of all primary and secondary fractions respectively.

4.3.1.2. PR- and GR-mediated responses. Neither PS nor R showed any progestogenic response but cytotoxicity, while two primary fractions, F18 and F19, were biologically active (Figure 4.2). The progestogenic response from F18 was more than twice as high as the response to F19. Progestogenic response to fractions but no response to PS and R indicate that PR activity was masked by cytotoxicity or antagonistic effects. Fractionation was employed to differentiate between these two different causes and reduce the complexity of environmental mixtures resulting in the removal of masking effects (Brack et al., 2016) and all individual fractions were tested for agonistic and antagonistic response. Two adjacent fractions showed agonistic response but no antagonistic response in other fractions. As no antagonistic responses were observed in any of the fractions it was concluded that the progestogenic effect in the PS was likely masked by cytotoxicity and not by other chemical constituents acting antagonistically.

Similar to the progestogenic response, no GR effect of PS and R could be observed due to cytotoxicity at REFs of 25 and 50, respectively. One fraction was GR-responsive i.e., F18, the same as for AR (Hashmi et al., 2018) and PR (Figure 4.2). The GR-activity could not be

87

recovered in any of the secondary fractions (F18.1 to F18.28) but in the recombination (R_F18) thereof. This can be interpreted as a spread of the active compound on the aminopropyl column over many fractions below the detection limit (Figure 4.2). While the aminopropyl column, used due to its capability to isolate steroids including progestogens and glucocorticoids (Shao et al., 2005; Yang et al., 2009) successfully isolated megestrol acetate in one secondary fraction, this phase failed to resolve glucocorticoids in one or few fractions. This finding may be due to poor chromatographic performance for the glucocorticoids of concern or suggests the presence of many glucocorticoids of low individual activity distributing over many fractions, while the activity in the parent fraction was a mixture effect.

4.3.2. Chemical Analysis:

4.3.2.1. Nontarget (NT) screening: NT screening of secondary fraction F18.4 was carried out (Figure C-3.1). Peak lists and masses were generated by processing the raw data files of samples in MZmine 2.21 (C-5) and common peaks and masses present in blanks were removed from the active sample by using MS Excel. The majority of the peaks were low intensity peaks while only few peaks had high intensity up to 1  106 for both ESI+ and ESI- mode. Peaks with intensities above 1 104 were selected for further processing, thus leaving 36 peaks out of 6556 total peaks in ESI+ mode and 52 peaks out of 2391 in ESI- mode.

Molecular formulas were generated for the masses with high intensity peaks and candidate lists were retrieved from ChemSpider database for each parent mass by using MetFrag website (Ruttkies et al., 2016). Candidate lists for parent masses ranged from 3 – 3713 candidates with an average number of 679 candidates per mass for ESI+ (in total 16964). For ESI-, candidate lists for parent masses (total 46735) ranged from 7 – 19873 candidates with the average number of 1869 candidates per parent mass. Candidates were further characterized by the number of data sources, the MetFrag score as well as the potential environmental relevance of the candidate. Hydrogen-deuterium exchange (HDX) and pH-dependent-LC-retention methods (Muz et al., 2017a) were also applied to filter the candidate list and to select plausible compounds. Three probable candidates were chosen depending on their MetFrag score, their usage and commercial availability but none of them was present in active fraction.

GC-HRMS screening of the active and neighboring inactive fractions as well as blanks was not able to detect any peak present only in the active fraction, thus no candidate compounds were obtained.

88

4.3.2.2. Steroid mixture fractionation. Since NT screening did not reveal any plausible candidates we hypothesized that the PR- and GR-activity might be driven by highly potent steroid-like EDCs below the detection limits of NT screening. Thus, to inform target analysis on possible candidates in active fractions F18 and F18.4 a chemical mixture comprised of more than 40 steroids (50 ng/mL each, (C-8), mostly commercially available progestogens and glucocorticoids, was subjected to the used primary and secondary fractionation procedure and analyzed using LC-HRMS. From this a targeted list of steroids (C-10) eluting into F18 and F18.4 and in neighboring fractions was compiled.

4.3.2.3. Target screening: In order to ensure that detection limits of target analysis were below effect concentrations (EC) of progestogens (C-10) and glucocorticoids (C-11), the required detection limits for the chemicals from the steroid mixture fractionation target list were compared to individual effect concentrations (EC) assuming that the whole effect in the active fraction is driven by one chemical. Specific attention was given to the compounds with high REPs (C-9). For PR-response, this criterion was met by megestrol acetate, , , medroxyprogesterone acetate and (highlighted in red color in C- 10), while for GR-response none of the compound fell under this category (C-11). If the compounds highlighted in green (C-10) would be the drivers of the effect, they would need to be present at concentrations well above the detection limits of the LC-HRMS screening. In order to be able to detect the highly potent chemicals analytically at their EC, the samples were enriched up to concentration factor (CF) of 14,000 for target analysis (Figure. 4.3).

Figure 4.3: Flow diagram of developed alternative approach for the detection and identification of ultra-trace endocrine disruptors in the environment.

89

In the active fractions F18 and F18.4 megestrol acetate and hydrocortisone were both detected in the sub ng/L range (Table 4.2). While megestrol acetate from F18 has been recovered almost completely in F18.4, only about one fourth of hydrocortisone was found in this secondary fraction. In F18 three additional steroids were detected including dihydrotestosterone, medroxyprogesterone and epiandrosterone at concentrations of 5.8, 0.6 and 119 ng/L, respectively.

Table 4.2: Progestogens and glucocorticoids (in ng/L) detected in this study along with compounds (dihydrotestosterone, medroxyprogesterone and epiandrosterone) detected in a previous study (Hashmi et al., 2018). Compound name CAS # F18.4 (ng/L) F18 (ng/L) F19 (ng/L) PS (ng/L) Megestrol acetate 595-33-5 0.53 0.66 -- 0.19 Hydrocortisone 50-23-7 0.08 0.30 -- -- Dihydrotestosterone 521.18-6 -- 5.8 -- 7.9 Medroxyprogesterone 520-85-4 -- 0.60 -- -- Epiandrosterone 481-29-8 -- 119 -- 82 Progesterone 57-83-0 -- -- 3.3 3.4 Androsterone 53-41-8 -- -- 114 78 Testosterone 58-22-0 ------1.3 Estrone 53-16-7 ------4.7 Estradiol 50-28-2 ------0.92 Estriol 50-27-1 ------22 Daidzein 486-66-8 ------287 Genistein 446-72-0 ------118

4.3.3. Confirmation. Based on the analysis of the active secondary fraction F18.4 with concentrations of megestrol acetate of 0.53 and hydrocortisone of 0.08 ng/L, a mixture of both chemicals was tested (Promegestone-EQbio_mixture_F18.4) and was found to explain 91.7 ± 14.9% of the overall PR-activity of this secondary fraction (Promegestone-EQbio_F18.4). While the contribution of hydrocortisone was found to be negligible, megestrol acetate was identified as the driver of the effect on the basis of individual progestogenic potencies of both compounds (Figure 4.4). This result is in agreement with the confirmation on the level of the primary fraction F18, although 50% of the activity of F18 remained unexplained. Three more steroids were identified and quantified in F18 including dihydrotestosterone (5.8 ng/L),

90

medroxyprogesterone (0.6 ng/L) and epiandrosterone (119 ng/L) (Hashmi et al., 2018). However, their contribution to the mixture effect would be less than 0.1% of the measured mixture BEQ.

In F19, progesterone and androsterone were the compounds detected at concentrations of 3.3 and 114 ng/L respectively, and Promegestone-EQbio_mixture_F19 explained 190.9 ± 17.3 % of the

Promegestone-EQbio_F19, which is within the uncertainty of the method of about a factor of 2. Similar to F18 there was only one driver of the effect, namely progesterone (Figure 4.4).

To summarize, progesterone, megestrol acetate and to a lesser extent dihydrotestosterone were identified as drivers of progestogenic activity in the River Danube water sample impacted by untreated wastewater from Novi Sad. Due to masking by cytotoxicity, this endocrine disruption potency could be only detected after fractionation.

GR-mediated activity was detected in F18, which has already been identified as progestogenic. Since no promising candidates could be identified, a mixture of the five steroids detected in this fraction (Table 4.2) has been tested in the respective concentrations but it could not explain any dexamethasone-EQbio_F18 in the fraction. Thus, we were not able to identify the drivers of GR-mediated effects. This is in agreement with a nationwide screen of glucocorticoid compounds in the United States (Conley et al., 2017), that identified GR-agonism in effect- based monitoring at 9 out of 35 sites without being able to associate the effects with the compounds driving this effect. Contrary to this in a study a mixture the glucocorticoid activity from secondary effluents wastewater treatment plants were attributed to mixture of 12 hormones present at very low concentration. The detected glucocorticoids were able to explain the entire effect produced by GR bioassay especially (Jia et al., 2016). It is quite possible that compounds with glucocorticoid activity might present in the active fraction at very low concentrations below the LOD of LC-HRMS. Thus, there is a need for additional efforts to identify drivers of GR-mediated effects in the aquatic environment. EDA is a promising tool but obviously requires further advancements to meet this challenge.

In this study glucocorticoid activity of approximately 1 ng/L dexamethasone EQ has been detected from river Danube which is in consistent with many other environmental monitoring studies like low concentrations of glucocorticoid activity (sub ng/L to ng/L) have been detected. For instance in Chinese rivers 0.02-4.2 ng/L (Chang et al., 2007) and from surface waters of Netherland and river Rhine 0.3-1.3 and 0.4-2.7 ng/L Dexamethasone EQ (Schriks et al., 2013; van der Linden et al., 2008) respectively. Glucocorticoids at low concentrations are found to

91

upregulate as well as down regulate the specific genes responsible for various functions like hepatic, nervous system and pancreatic system of zebra fish (Chen et al., 2017, 2016) Further at higher concentrations glucocorticoids exposure may lead to weight loss in fish, increased stress levels as well as physiological and reproductive effects (Kugathas et al., 2013; Salas- Leiton et al., 2012).

Figure 4.4: Progestogenic responses (ng/L) of fractions (Promegestone_EQbio_Fi) are explained by designed chemical mixtures (Promegestone_EQbio_mixture) as well as relative contribution of individual chemicals in the mixture (Promegestone_EQchem).

92

The present study demonstrates the relevance of progestogenic and glucocorticoid steroids in surface waters impacted by untreated wastewater. Thus, PR- and GR-mediated effects should be monitored along with ER- and AR-mediated effects. The typical approach of detecting activity of endocrine disruptors in-vitro in water extracts poses the risk of overlooking the effects if masked by others such as cytotoxic or antagonistic compounds. Similar findings were previously reported for GR-(Šauer et al., 2018) and AR-(Weiss et al., 2009) mediated effects. More fractionation and EDA studies are required to unravel, whether there are any generalizable rules for the occurrence of EDCs and masking compounds that might help reduce required fractionation efforts towards an effective clean up allowing for the safe effect-based detection of EDCs.

Non-target screening has been shown in many EDA studies (Muschket et al., 2018; Muz et al., 2017a, 2017b) to be the method of choice to detect and identify candidates in active fractions. In the case of compounds that are extremely potent at ultra-trace concentrations such as steroidal hormones, this approach runs the risk to fail due to insufficient detection limits of non-target screening. The present paper indicates highly sensitive target screening of known candidates as an option to address this problem. Comparison of detection limits and effect concentrations of individual candidates is helpful to adapt sample preparation and pre- concentration in order to ensure that, if a distinct compound would be the driver of the effect, it would be detected with the method applied. As shown for GR-mediated effects, a target screening-based approach may fail if the right candidate is not included in the list or if the effect is driven by a mixture of many compounds present at extremely low concentrations in the sample.

In agreement with a study running in parallel on Czech and Slovak WWTP effluents (Šauer et al., 2018), megestrol acetate, a pharmaceutical that is used to treat wasting syndromes, breast and endometrial cancer, has been identified as driver of progestogenic effects in river water impacted by wastewater effluents. Although this compound has been previously detected in waste- and surface waters (Chang et al., 2011; Zhang et al., 2017) environmental data are extremely scarce and more monitoring efforts are required. At higher concentrations, megestrol acetate has been found to affect zebrafish reproduction individually (Han et al., 2014) and in binary mixtures with 17α-ethinylestradiol (Hua et al., 2016). In an in-vivo study Chinese rare minnow was exposed to nominal concentrations of 1 ng/L and 10 ng/L megestrol acetate for six months and effects on growth, lowered reproductive hormones in male and female fish as well as gene expression related to male and female gonads were found at 10 ng/L concentration

93

(Hua et al., 2018). While at concentration of 1ng/L megestrol acetate was found to have impact on oogenesis of Chinese rare minnow (Hua et al., 2018). Further data on effects of megestrol acetate on a wider range of fish and other organisms are extensively lacking. Progesterone has been shown to decrease the fertility of female fathead minnow (DeQuattro et al., 2012) as well as a variety of physiological responses and gene expression changes in western mosquitofish (Hou et al., 2017).

The present study has been performed at one specific hot spot in the Danube River. Similar hot spots of untreated wastewater impacts can be found at many sites all over the world. The concentrations of PR- and GR- mediated responses detected in this study are environmental relevant and upon exposure to these environmental relevant concentrations for longer period of time may cause endocrine disruption in the aquatic organisms. Thus, more efforts are required for monitoring and identifying PR- and GR-active compounds integrating effect-based and chemical analytical tools as well as collecting in-vitro and in-vivo effect data for these chemicals in order to allow an assessment on a larger scale.

We could demonstrate that both natural and synthetic hormones occur in mixtures and both contribute significantly to measured effects. Since the options to reduce the excretion of the former and the use of the latter appear quite limited, best practice wastewater treatment is required in order to avoid endocrine disruption impacting on aquatic ecosystems and impeding production of drinking water without risks for human health.

94

4.4. Acknowledgements: This work was funded by SOLUTIONS project (grant agreement 603437), supported by the EU Seventh Framework Programme. The doctoral research grant for Muhammad Arslan Kamal Hashmi was provided by Higher Education Commission (HEC) of Pakistan and German Academic Exchange Service (DAAD), Germany. Chemaxon (Budapest, Hungary) is acknowledged for providing an academic license of JChem for Excel, Marvin, and the Calculator Plugins. We thank to Jörg Ahlheim for sampling, Dr. Tobias Schulze for technical help in fractionation and Margit Petre for processing the samples. We also thank Rita Schlichting, Maria König, Jenny John and Christin Kühnert for providing technical help for performing the bioassays. The instruments for chemical analysis and bioassays were provided by the major infrastructure CITEPro (Chemicals in the Environment Profiler) funded by the Helmholtz Association.

95

4.5. References:

Ammann, A.A., Macikova, P., Groh, K.J., Schirmer, K., Suter, M.J.F., 2014. LC-MS/MS determination of potential endocrine disruptors of cortico signalling in rivers and wastewaters. Anal. Bioanal. Chem. 406, 7653–7665. doi:10.1007/s00216-014-8206-9 Brack, W., Ait-Aissa, S., Burgess, R.M., Busch, W., Creusot, N., Di Paolo, C., Escher, B.I., Mark Hewitt, L., Hilscherova, K., Hollender, J., Hollert, H., Jonker, W., Kool, J., Lamoree, M., Muschket, M., Neumann, S., Rostkowski, P., Ruttkies, C., Schollee, J., Schymanski, E.L., Schulze, T., Seiler, T.-B., Tindall, A.J., De Aragão Umbuzeiro, G., Vrana, B., Krauss, M., 2016. Effect-directed analysis supporting monitoring of aquatic environments — An in-depth overview. Sci. Total Environ. 544, 1073–1118. doi:http://dx.doi.org/10.1016/j.scitotenv.2015.11.102 Brack, W., Dulio, V., Ågerstrand, M., Allan, I., Altenburger, R., Brinkmann, M., Bunke, D., Burgess, R.M., Cousins, I., Escher, B.I., Hernández, F.J., Hewitt, L.M., Hilscherová, K., Hollender, J., Hollert, H., Kase, R., Klauer, B., Lindim, C., Herráez, D.L., Miège, C., Munthe, J., O’Toole, S., Posthuma, L., Rüdel, H., Schäfer, R.B., Sengl, M., Smedes, F., van de Meent, D., van den Brink, P.J., van Gils, J., van Wezel, A.P., Vethaak, A.D., Vermeirssen, E., von der Ohe, P.C., Vrana, B., 2017. Towards the review of the European Union Water Framework Directive: Recommendations for more efficient assessment and management of chemical contamination in European surface water resources. Sci. Total Environ. 576, 720–737. doi:https://doi.org/10.1016/j.scitotenv.2016.10.104 Chang, H., Hu, J., Shao, B., 2007. Occurrence of natural and synthetic glucocorticoids in sewage treatment plants and receiving river waters. Environ. Sci. Technol. 41, 3462–3468. doi:10.1021/es062746o Chang, H., Wan, Y., Hu, J., 2009. Determination and Source Apportionment of Five Classes of Steroid Hormones in Urban Rivers. Environ. Sci. Technol. 43, 7691–7698. doi:10.1021/es803653j Chang, H., Wan, Y., Wu, S., Fan, Z., Hu, J., 2011. Occurrence of androgens and progestogens in wastewater treatment plants and receiving river waters: Comparison to estrogens. Water Res. 45, 732–740. doi:10.1016/j.watres.2010.08.046 Chen, Q., Jia, A., Snyder, S.A., Gong, Z., Lam, S.H., 2016. Glucocorticoid activity detected by in vivo zebrafish assay and in vitro glucocorticoid receptor bioassay at environmental relevant concentrations. Chemosphere 144, 1162–1169. doi:https://doi.org/10.1016/j.chemosphere.2015.09.089 Chen, Q., Li, C., Gong, Z., Chan, E.C.Y., Snyder, S.A., Lam, S.H., 2017. Common deregulated gene expression profiles and morphological changes in developing zebrafish larvae exposed to environmental-relevant high to low concentrations of glucocorticoids. Chemosphere 172, 429–439. doi:https://doi.org/10.1016/j.chemosphere.2017.01.036 Conley, J.M., Evans, N., Cardon, M.C., Rosenblum, L., Iwanowicz, L.R., Hartig, P.C., Schenck, K.M., Bradley, P.M., Wilson, V.S., 2017. Occurrence and In Vitro Bioactivity of Estrogen, Androgen, and Glucocorticoid Compounds in a Nationwide Screen of United States Stream Waters. Environ. Sci. Technol. 51, 4781–4791. doi:10.1021/acs.est.6b06515 Creusot, N., Aït-Aïssa, S., Tapie, N., Pardon, P., Brion, F., Sanchez, W., Thybaud, E., Porcher,

96

J.-M., Budzinski, H., 2014. Identification of Synthetic Steroids in River Water Downstream from Pharmaceutical Manufacture Discharges Based on a Bioanalytical Approach and Passive Sampling. Environ. Sci. Technol. 48, 3649–3657. doi:10.1021/es405313r DeQuattro, Z.A., Peissig, E.J., Antkiewicz, D.S., Lundgren, E.J., Hedman, C.J., Hemming, J.C.., Barry, T.P., 2012. Effects of progesterone on reproduction and embryonic development in the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 31, 851–856. doi:10.1002/etc.1754 Escher, B.I., Allinson, M., Altenburger, R., Bain, P. a., Balaguer, P., Busch, W., Crago, J., Denslow, N.D., Dopp, E., Hilscherova, K., Humpage, A.R., Kumar, A., Grimaldi, M., Jayasinghe, B.S., Jarosova, B., Jia, A., Makarov, S., Maruya, K. a., Medvedev, A., Mehinto, A.C., Mendez, J.E., Poulsen, A., Prochazka, E., Richard, J., Schifferli, A., Schlenk, D., Scholz, S., Shiraishi, F., Snyder, S., Su, G., Tang, J.Y.M., Burg, B. Van Der, Linden, S.C. Van Der, Werner, I., Westerheide, S.D., Wong, C.K.C., Yang, M., Yeung, B.H.Y., Zhang, X., Leusch, F.D.L., 2014. Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays. Environ. Sci. Technol. 48, 1940–1956. doi:10.1021/es403899t Escher, B.I., Neale, P.A., Villeneuve, D.L., 2018. The advantages of linear concentration– response curves for in vitro bioassays with environmental samples. Environ. Toxicol. Chem. 37, 2273–2280. doi:10.1002/etc.4178 Fent, K., 2015. Progestins as endocrine disrupters in aquatic ecosystems: Concentrations, effects and risk assessment. Environ. Int. 84, 115–130. doi:https://doi.org/10.1016/j.envint.2015.06.012 Han, J., Wang, Q., Wang, X., Li, Y., Wen, S., Liu, S., Ying, G., Guo, Y., Zhou, B., 2014. The synthetic progestin megestrol acetate adversely affects zebrafish reproduction. Aquat. Toxicol. 150, 66–72. doi:https://doi.org/10.1016/j.aquatox.2014.02.020 Hashmi, M.A.K., Escher, B.I., Krauss, M., Teodorovic, I., Brack, W., 2018. Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia. Sci. Total Environ. 624, 1072–1081. doi:https://doi.org/10.1016/j.scitotenv.2017.12.187 Hou, L., Xu, H., Ying, G., Yang, Y., Shu, H., Zhao, J., Cheng, X., 2017. Physiological responses and gene expression changes in the western mosquitofish (Gambusia affinis) exposed to progesterone at environmentally relevant concentrations. Aquat. Toxicol. 192, 69–77. doi:https://doi.org/10.1016/j.aquatox.2017.09.011 Hua, J., Han, J., Guo, Y., Zhou, B., 2018. Endocrine disruption in Chinese rare minnow (Gobiocypris rarus) after long-term exposure to low environmental concentrations of progestin megestrol acetate. Ecotoxicol. Environ. Saf. 163, 289–297. doi:https://doi.org/10.1016/j.ecoenv.2018.07.049 Hua, J., Han, J., Wang, X., Guo, Y., Zhou, B., 2016. The binary mixtures of megestrol acetate and 17α-ethynylestradiol adversely affect zebrafish reproduction. Environ. Pollut. 213, 776–784. doi:https://doi.org/10.1016/j.envpol.2016.03.031

97

Hug, C., Ulrich, N., Schulze, T., Brack, W., Krauss, M., 2014. Identi fi cation of novel micropollutants in wastewater by a combination of suspect and nontarget screening. Environ. Pollut. 184, 25–32. doi:10.1016/j.envpol.2013.07.048 Ihara, M., Ihara, M.O., Kumar, V., Narumiya, M., Hanamoto, S., Nakada, N., Yamashita, N., Miyagawa, S., Iguchi, T., Tanaka, H., 2014. Co-occurrence of estrogenic and antiestrogenic activities in wastewater: Quantitative evaluation of balance by in vitro ERα reporter gene assay and chemical analysis. Environ. Sci. Technol. 48, 6366–6373. doi:10.1021/es5014938 Jia, A., Escher, B.I., Leusch, F.D.L., Tang, J.Y.M., Prochazka, E., Dong, B., Snyder, E.M., Snyder, S.A., 2015. In vitro bioassays to evaluate complex chemical mixtures in recycled water. Water Res. 80, 1–11. doi:https://doi.org/10.1016/j.watres.2015.05.020 Jia, A., Wu, S., Daniels, K.D., Snyder, S.A., 2016. Balancing the Budget: Accounting for Glucocorticoid Bioactivity and Fate during Water Treatment. Environ. Sci. Technol. 50, 2870–2880. doi:10.1021/acs.est.5b04893 Jobling, S., Tyler, C.R., 2003. Endocrine disruption in wild freshwater fish. PURE Appl. Chem. 75, 2219–2234. doi:10.1351/pac200375112219 Kase, R., Javurkova, B., Simon, E., Swart, K., Buchinger, S., Könemann, S., Escher, B.I., Carere, M., Dulio, V., Ait-Aissa, S., Hollert, H., Valsecchi, S., Polesello, S., Behnisch, P., di Paolo, C., Olbrich, D., Sychrova, E., Gundlach, M., Schlichting, R., Leborgne, L., Clara, M., Scheffknecht, C., Marneffe, Y., Chalon, C., Tusil, P., Soldan, P., von Danwitz, B., Schwaiger, J., Palao, A.M., Bersani, F., Perceval, O., Kienle, C., Vermeirssen, E., Hilscherova, K., Reifferscheid, G., Werner, I., 2018. Screening and risk management solutions for steroidal estrogens in surface and wastewater. TrAC Trends Anal. Chem. 102, 343–358. doi:https://doi.org/10.1016/j.trac.2018.02.013 König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Kugathas, S., Runnalls, T., Sumpter, J., 2012a. Synthetic progestins and glucocorticoids affect fish reproduction and physiology. Toxicol. Lett. 211, S30. doi:https://doi.org/10.1016/j.toxlet.2012.03.134 Kugathas, S., Runnalls, T.J., Sumpter, J.P., 2013. Metabolic and Reproductive Effects of Relatively Low Concentrations of Beclomethasone Dipropionate, a Synthetic Glucocorticoid, on Fathead Minnows. Environ. Sci. Technol. 47, 9487–9495. doi:10.1021/es4019332 Kugathas, S., Sumpter, J.P., 2011. Synthetic Glucocorticoids in the Environment: First Results on Their Potential Impacts on Fish. Environ. Sci. Technol. 45, 2377–2383. doi:10.1021/es104105e

98

Kugathas, S., Williams, R.J., Sumpter, J.P., 2012b. Prediction of environmental concentrations of glucocorticoids: The River Thames, UK, as an example. Environ. Int. 40, 15–23. doi:https://doi.org/10.1016/j.envint.2011.11.007 Kumar, V., Johnson, A.C., Trubiroha, A., Tumová, J., Ihara, M., Grabic, R., Kloas, W., Tanaka, H., Kroupová, H.K., 2015. The challenge presented by progestins in ecotoxicological research: A critical review. Environ. Sci. Technol. 49, 2625–2638. doi:10.1021/es5051343 Leusch, F.D.L., Neale, P.A., Hebert, A., Scheurer, M., Schriks, M.C.M., 2017. Analysis of the sensitivity of in vitro bioassays for androgenic, progestagenic, glucocorticoid, thyroid and estrogenc activity: Suitability for drinking and environmental waters. Environ. Int. 99, 120–130. doi:https://doi.org/10.1016/j.envint.2016.12.014 Liney, K.E., Jobling, S., Shears, J.A., Simpson, P., Tyler, C.R., 2005. Assessing the Sensitivity of Different Life Stages for Sexual Disruption in Roach (Rutilus rutilus) Exposed to Effluents from Wastewater Treatment Works. Environ. Health Perspect. 113, 1299–1307. doi:10.1289/ehp.7921 Lintelmann, J., Katayama, A., Kurihara, N., Shore, L., Wenzel, A., 2003. Endocrine disruptors in the environment (IUPAC Technical Report). Pure Appl. Chem. 75, 631–681. Liu, S., Ying, G.G., Zhou, L.J., Zhang, R.Q., Chen, Z.F., Lai, H.J., 2012. Steroids in a typical swine farm and their release into the environment. Water Res. 46, 3754–3768. doi:10.1016/j.watres.2012.04.006 Macikova, P., Groh, K.J., Ammann, A.A., Schirmer, K., Suter, M.J.-F., 2014. Endocrine Disrupting Compounds Affecting Corticosteroid Signaling Pathways in Czech and Swiss Waters: Potential Impact on Fish. Environ. Sci. Technol. 48, 12902–12911. doi:10.1021/es502711c Macova, M., Escher, B.I., Reungoat, J., Carswell, S., Chue, K.L., Keller, J., Mueller, J.F., 2010. Monitoring the biological activity of micropollutants during advanced wastewater treatment with ozonation and activated carbon filtration. Water Res. 44, 477–492. doi:10.1016/j.watres.2009.09.025 Muschket, M., Di Paolo, C., Tindall, A.J., Touak, G., Phan, A., Krauss, M., Kirchner, K., Seiler, T.-B., Hollert, H., Brack, W., 2018. Identification of Unknown Antiandrogenic Compounds in Surface Waters by Effect-Directed Analysis (EDA) Using a Parallel Fractionation Approach. Environ. Sci. Technol. 52, 288–297. doi:10.1021/acs.est.7b04994 Muz, M., Dann, J.P., Jäger, F., Brack, W., Krauss, M., 2017a. Identification of Mutagenic Aromatic Amines in River Samples with Industrial Wastewater Impact. Environ. Sci. Technol. 51, 4681–4688. doi:10.1021/acs.est.7b00426 Muz, M., Krauss, M., Kutsarova, S., Schulze, T., Brack, W., 2017b. Mutagenicity in Surface Waters: Synergistic Effects of Carboline Alkaloids and Aromatic Amines. Environ. Sci. Technol. 51, 1830–1839. doi:10.1021/acs.est.6b05468 Neale, P.A., Ait-Aissa, S., Brack, W., Creusot, N., Denison, M.S., Deutschmann, B., Hilscherová, K., Hollert, H., Krauss, M., Novák, J., Schulze, T., Seiler, T.B., Serra, H., Shao, Y., Escher, B.I., 2015. Linking in Vitro Effects and Detected Organic

99

Micropollutants in Surface Water Using Mixture-Toxicity Modeling. Environ. Sci. Technol. 49, 14614–14624. doi:10.1021/acs.est.5b04083

Neale, P.A., Altenburger, R., Aït-Aïssa, S., Brion, F., Busch, W., de Aragão Umbuzeiro, G., Denison, M.S., Du Pasquier, D., Hilscherová, K., Hollert, H., Morales, D.A., Novák, J., Schlichting, R., Seiler, T.-B., Serra, H., Shao, Y., Tindall, A.J., Tollefsen, K.E., Williams, T.D., Escher, B.I., 2017. Development of a bioanalytical test battery for water quality monitoring: Fingerprinting identified micropollutants and their contribution to effects in surface water. Water Res. 123, 734–750. doi:https://doi.org/10.1016/j.watres.2017.07.016 Pluskal, T., Castillo, S., Villar-Briones, A., Orešič, M., 2010. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395. doi:10.1186/1471-2105-11-395 Runnalls, T.J., Beresford, N., Losty, E., Scott, A.P., Sumpter, J.P., 2013. Several Synthetic Progestins with Different Potencies Adversely Affect Reproduction of Fish. Environ. Sci. Technol. 47, 2077–2084. doi:10.1021/es3048834 Runnalls, T.J., Margiotta-Casaluci, L., Kugathas, S., Sumpter, J.P., 2010. Pharmaceuticals in the Aquatic Environment: Steroids and Anti-Steroids as High Priorities for Research. Hum. Ecol. Risk Assess. An Int. J. 16, 1318–1338. doi:10.1080/10807039.2010.526503 Ruttkies, C., Schymanski, E.L., Wolf, S., Hollender, J., Neumann, S., 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 8, 3. doi:10.1186/s13321-016-0115-9 Salas-Leiton, E., Coste, O., Asensio, E., Infante, C., Cañavate, J.P., Manchado, M., 2012. Dexamethasone modulates expression of genes involved in the innate immune system, growth and stress and increases susceptibility to bacterial disease in Senegalese sole (Solea senegalensis Kaup, 1858). Fish Shellfish Immunol. 32, 769–778. doi:https://doi.org/10.1016/j.fsi.2012.01.030 Sanderson, H., Johnson, D.J., Reitsma, T., Brain, R.A., Wilson, C.J., Solomon, K.R., 2004. Ranking and prioritization of environmental risks of pharmaceuticals in surface waters. Regul. Toxicol. Pharmacol. 39, 158–183. doi:https://doi.org/10.1016/j.yrtph.2003.12.006 Šauer, P., Stará, A., Golovko, O., Valentová, O., Bořík, A., Grabic, R., Kroupová, H.K., 2018. Two synthetic progestins and natural progesterone are responsible for most of the progestagenic activities in municipal wastewater treatment plant effluents in the Czech and Slovak republics. Water Res. 137, 64–71. doi:https://doi.org/10.1016/j.watres.2018.02.065 Schriks, M., Leerdam, J.A. van, Linden, S.C. van der, Burg, B. van der, Wezel, A.P. van, Voogt, P. de, 2010. High-Resolution Mass Spectrometric Identification and Quantification of Glucocorticoid Compounds in Various Wastewaters in The Netherlands 44, 4766–4774. doi:10.1021/ES100013X Schriks, M., Linden, S.C. Van Der, Stoks, P.G.M., Burg, B. Van Der, Puijker, L., Voogt, P. De, Heringa, M.B., 2013. Chemosphere Occurrence of glucocorticogenic activity in various surface waters in The Netherlands. Chemosphere 93, 450–454. doi:10.1016/j.chemosphere.2013.04.091

100

Schulze, T., Ahel, M., Ahlheim, J., Aït-Aïssa, S., Brion, F., Di Paolo, C., Froment, J., Hidasi, A.O., Hollender, J., Hollert, H., Hu, M., Kloß, A., Koprivica, S., Krauss, M., Muz, M., Oswald, P., Petre, M., Schollée, J.E., Seiler, T.-B., Shao, Y., Slobodnik, J., Sonavane, M., Suter, M.J.-F., Tollefsen, K.E., Tousova, Z., Walz, K.-H., Brack, W., 2017. Assessment of a novel device for onsite integrative large-volume solid phase extraction of water samples to enable a comprehensive chemical and effect-based analysis. Sci. Total Environ. 581–582, 350–358. doi:http://dx.doi.org/10.1016/j.scitotenv.2016.12.140 Shao, B., Zhao, R., Meng, J., Xue, Y., Wu, G., Hu, J., Tu, X., 2005. Simultaneous determination of residual hormonal chemicals in meat, kidney, liver tissues and milk by liquid chromatography–tandem mass spectrometry. Anal. Chim. Acta 548, 41–50. doi:https://doi.org/10.1016/j.aca.2005.06.003 Shen, X., Chang, H., Sun, D., Wang, L., Wu, F., 2018. Trace analysis of 61 natural and synthetic progestins in river water and sewage effluents by ultra-high performance liquid chromatography–tandem mass spectrometry. Water Res. 133, 142–152. doi:https://doi.org/10.1016/j.watres.2018.01.030 Sumpter, J.P., 1998. Xenoendocrine disrupters — environmental impacts. Toxicol. Lett. 102– 103, 337–342. doi:http://dx.doi.org/10.1016/S0378-4274(98)00328-2 Sumpter, J.P., 1995. Feminized responses in fish to environmental estrogens. Toxicol. Lett. 82–83, 737–742. doi:http://dx.doi.org/10.1016/0378-4274(95)03517-6 Sumpter, J.P., Jobling, S., 2013. The occurrence, causes, and consequences of estrogens in the aquatic environment. Environ. Toxicol. Chem. 32, 249–251. doi:10.1002/etc.2084 Thomas, K. V, Balaam, J., Hurst, M.R., Thain, J.E., 2004. Identification of in vitro estrogen and androgen receptor agonists in North Sea offshore produced water discharges. Environ. Toxicol. Chem. 23, 1156–1163. doi:10.1897/03-239 van der Linden, S.C., Heringa, M.B., Man, H.-Y., Sonneveld, E., Puijker, L.M., Brouwer, A., van der Burg, B., 2008. Detection of Multiple Hormonal Activities in Wastewater Effluents and Surface Water, Using a Panel of Steroid Receptor CALUX Bioassays. Environ. Sci. Technol. 42, 5814–5820. doi:10.1021/es702897y Wangmo, C., Jarque, S., Hilscherová, K., Bláha, L., Bittner, M., 2018. In vitro assessment of sex steroids and related compounds in water and sediments – a critical review. Environ. Sci. Process. Impacts 20, 270–287. doi:10.1039/C7EM00458C Weiss, J.M., Hamers, T., Thomas, K. V., Van Der Linden, S., Leonards, P.E.G., Lamoree, M.H., 2009. Masking effect of anti-androgens on androgenic activity in European river sediment unveiled by effect-directed analysis. Anal. Bioanal. Chem. 394, 1385–1397. doi:10.1007/s00216-009-2807-8 Weizel, A., Schlüsener, M.P., Dierkes, G., Ternes, T.A., 2018. Occurrence of Glucocorticoids, , and Progestogens in Various Treated Wastewater, Rivers, and Streams. Environ. Sci. Technol. 52, 5296–5307. doi:10.1021/acs.est.7b06147 Yang, Y., Shao, B., Zhang, J., Wu, Y., Duan, H., 2009. Determination of the residues of 50 anabolic hormones in muscle, milk and liver by very-high-pressure liquid chromatography–electrospray ionization tandem mass spectrometry. J. Chromatogr. B 877, 489–496. doi:https://doi.org/10.1016/j.jchromb.2008.12.054

101

Zhang, K., Zhao, Y., Fent, K., 2017. Occurrence and Ecotoxicological Effects of Free, Conjugated, and Halogenated Steroids Including 17α-Hydroxypregnanolone and Pregnanediol in Swiss Wastewater and Surface Water. Environ. Sci. Technol. 51, 6498– 6506. doi:10.1021/acs.est.7b01231

102

Chapter 5

Conclusions

Environmental pollutants with great variety are known to be present in aquatic environment which are discharged by many point and non-point pollution sources. Surfactants and EDCs occurrence in European waters have been previously detected and monitored by chemical and/or effect-based screenings (Grund et al., 2011; Micić and Hofmann, 2009) and their presence is known to pose a risk to aquatic organisms. Due to the immense usage of surfactants in domestic as well as at industrial level they were the most frequently detected contaminants at high concentrations in the whole stretch of River Danube as shown in Chapter

2. In Chapter 3 and Chapter 4, effect-based tools were successfully applied to detect various hormonal activities with regards to endocrine disruption in Danube river water samples and further main EDCs responsible for these hormonal activities were unraveled with the help of

EDA.

The overall goal of this study was to demonstrate that monitoring approaches such as chemical screening, effect-based tools and EDA provide new insights into the contamination beyond priority substances and other classes of compounds which are normally monitored in the surface waters. This goal was achieved by the application of various chemical screening approaches, i.e., target and nontarget screening (NTS), in-vitro bioassays for various biological markers, mixture complexity reduction by multistep fractionation and collectively by the application of EDA. The outcome of this study revealed that the contaminants detected with greatest intensity and highest frequencies in the River Danube identified were different from priority substances. It was shown that majority of the high intensity frequently occurring contaminants were surfactants dominating throughout the Danube River, while at low

103

concentration ranges steroidal hormones were detected as the cause of various biological responses.

The first objective of the study was to find out the frequently occurring compounds in high concentrations from Danube River and these compounds were found to be various kinds of surfactants and their degradation products occurring in homologue series throughout the whole stretch of Danube as shown in Chapter 2. The detected peaks with high intensity and frequency do not represent the compounds which are generally screened by monitoring studies i.e., priority substances or others commonly monitored. Semi-automated prioritization with

NTS approach successfully identified vast majority of high intensity frequently occurring compounds. NTS is oftenly used to detect unknowns from environmental samples but it can also be used to assess most frequent occurring contaminants from various sites as well as for the assessment of WWTP efficiency by analyzing the influents and effluents of WWTPs and site-specific pollutants. Clustering was done for detection of any pollution trend along the course of Danube and it detected no specific trend in Danube. The daily based use of NTS with prioritization can also be used to record any variability of contaminants in the surface waters and be used to measure the contaminants amounts passing through a specific point.

Prioritization by using semi-automated approaches can enhance the throughput and allow for its routine application in environmental monitoring studies.

For the achievement of second and third objectives of this study, effect-based tools, i.e., in-vitro bioassays were utilized and found to be successfully detecting various biological responses from EDCs present at very low concentrations in river water as apparent from

Chapter 3 and Chapter 4. Further application of EDA for the unraveling of main compounds responsible for observed hormonal effects was done which successfully detected and identified main causative drivers. The EDCs detected confirm and explain most of the hormonal activity from samples. Various biological activities (estrogenic, androgenic, progestogenic and

104

glucocorticoid) were detected and discriminated by effect-based monitoring and these activities were predominated by natural and synthetic steroids. Many other compounds were also detected including industrial chemicals, pharmaceuticals, personal care products, pesticides and herbicides in Chapter 3 (Appendix-B) along with the EDCs. During biological analysis masking effects were hampering the detection of endocrine disruption potential of EDCs due to cytotoxic chemicals. In case of chemical screening, detection of compounds at low levels was not possible due to background noise. In such scenario, clean-up of the environmental sample with fractionation was done which reduced the masking effects in a stepwise manner.

The success of an EDA study very much relies on successful fractionation. In Chapter 4 sufficient reduction in environmental sample complexity was not achieved with one round of fractionation thus secondary fractionation was done to isolate the active compound in active fraction and onward successful detection of megestrol acetate.

Steroidal EDCs at low concentrations (ng/L and/or sub-ng/L range) were the most bioactive compounds in the Danube River. In WFD, so far, there are no steroidal hormones listed as priority substance while two hormones 17α-ethinylestradiol (EE2) and 17β-estradiol

(E2) has been added in watch list as well as one transformation product of 17beta-estradiol, known as estrone (E1) (“WFD Directive 2015/495,” 2015). Due to high bioactivity of natural and synthetic steroidal hormones in surface waters, there is need to measure the concentrations of these substances on routine basis in EU surface waters. In this study, EDCs detected were shown to behave in concentration addition manner, which is generally thought to be applicable for the steroids present in a mixture with same mode of action, while in case of compounds with opposite mode of actions, general toxic effects in the form of toxicity may prevail.

Compounds listed in priority list of WFD belong to various categories of insecticide, herbicides and industrial chemicals and more than half of these priority substances, apart from their main activity, are known to have potential for endocrine disruption. In Chapter 4, many

105

pharmaceuticals were tested on in-vitro bioassays and many of them were highly bioactive at very low concentrations based on their effect concentrations and relative effect potencies

(Appendix C) and these extensively ignored pharmaceuticals used for various treatments i.e., many progestogens (megestrol acetate, dydrogesterone, norgestrel, gestodene, medroxyprogesterone) and glucocorticoids (desoximetason, ) are highly bioactive can play important role in endocrine disruption. These pharmaceuticals especially progestogens need to be monitored in aquatic environment because they have multiple binding affinities with other hormone receptors thus causing multiple effects simultaneously. In this study, progestogens were found to have ability to cross talk with other receptors, i.e., androgen receptor (Appendix B). Progestogens are also found to be active on estrogen and glucocorticoid receptors (Besse and Garric, 2009), thus, can have multiple effects on aquatic organisms. Environmental quality standards (EQS) so far are set for individual substances and there are currently no EQS available for mixtures. Hormonal effects of single compound may not provide real overview of the effects on living organism due to the fact that aquatic organisms are continuously exposed to mixtures of chemicals in the environment. It highlights the need to set up EQS for EDCs in the form of mixtures while keeping in consideration the identification of main drivers of hormonal activity and onward assessment of effects of these mixtures based on mixture EQS.

WWTPs have crucial role in the reduction of hormonal responses present in wastewater before releasing into the environment but due to the inefficient functioning of WWTPs, these may turn to point source of pollution as shown previously (Jobling et al., 2002). On other hand, efficient WWTPs equipped with modern technologies can considerably reduce the biological load well below the LOD. Substantial reduction of EDCs concentrations can be achieved by the application of secondary and tertiary treatments (Zhang et al., 2017).

106

In this study, biological screening with in-vitro analysis was sensitive enough to detect responses of EDCs present at very low concentration in the aquatic environment, while detection of EDCs at sub-ng/L level with chemical screening approaches may not be sensitive enough to detect and to quantify these EDCs. Derivatization method was applied for increasing the sensitivity of LC-HRMS for the detection of estrogens at low concentration (Chapter 3), while for other classes of EDCs present at very low concentrations further improvement in the sensitivity of analytical techniques as well as suitable method development for the enhancement of sensitivity need to be worked upon in the future. Sufficient sample enrichment played a decisive role in the detection of very low concentration EDCs as shown in Chapter

4. Most of the activity in Chapter 3 and Chapter 4 was from detected EDCs as confirmed by mass balance approach in which cumulative responses of individual chemicals in the form of bioanalytical equivalent concentrations were conforming to mixture effects of their respective active fractions. The results of this study are also in agreement with the results of a previous mass balance study (König et al., 2017) but this study also detected and identified other highly bioactive EDC causing endocrine disruption. The concentrations of some EDCs detected in

Chapter 3 and Chapter 4 were below the LOD of LC-HRMS and attributed to the high dilution due to high volumes of Danube river water and are reducing further downstream as shown previously (König et al., 2017). The non-detection of main causative compound(s) for one of the biological endpoint (glucocorticoid) in Chapter 4 may also be attributed to the dilution rates of Danube water which might have brought the concentration of respective compounds at such low concentrations which might be beyond the LOD of LC-HRMS despite the high enrichment of samples. In summary, all objectives of this study were very well achieved which represent that chemical screening, effect-based tools and EDA are ideal and successful tools for the detection and identification of contaminants as well as various

107

biological activities due to these contaminants and it can be used for regular pollution monitoring in various environmental compartments for further pollution mitigation measures.

108

References: Besse, J.P., Garric, J., 2009. Progestagens for human use, exposure and hazard assessment for the aquatic environment. Environ. Pollut. 157, 3485–3494. doi:10.1016/j.envpol.2009.06.012 Grund, S., Higley, E., Schönenberger, R., Suter, M.J.-F., Giesy, J.P., Braunbeck, T., Hecker, M., Hollert, H., 2011. The endocrine disrupting potential of sediments from the Upper Danube River (Germany) as revealed by in vitro bioassays and chemical analysis. Environ. Sci. Pollut. Res. 18, 446–460. doi:10.1007/s11356-010-0390-3 Jobling, S., Whitmore, J.G., Sumpter, J.P., Beresford, N., Coey, S., McAllister, B.G., Kime, D.E., Van Look, K.J.W., Henshaw, A.C., Brighty, G., Tyler, C.R., 2002. Wild Intersex Roach (Rutilus rutilus) Have Reduced Fertility1. Biol. Reprod. 67, 515–524. doi:10.1095/biolreprod67.2.515 König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Micić, V., Hofmann, T., 2009. Occurrence and behaviour of selected hydrophobic alkylphenolic compounds in the Danube River. Environ. Pollut. 157, 2759–2768. doi:https://doi.org/10.1016/j.envpol.2009.04.028 WFD Directive 2015/495, 2015. 1–3. Zhang, K., Zhao, Y., Fent, K., 2017. Occurrence and Ecotoxicological Effects of Free, Conjugated, and Halogenated Steroids Including 17α-Hydroxypregnanolone and Pregnanediol in Swiss Wastewater and Surface Water. Environ. Sci. Technol. 51, 6498– 6506. doi:10.1021/acs.est.7b01231

109

Appendix A

Supplementary Information for Chapter 2

Nontarget screening based prioritization and identification of micropollutants from water samples of a large European river Danube

110

Supplementary Information:

A-1: MZmine settings:

Table A-1. Settings for processing of liquid chromatography-high resolution mass spectrometry (LC-HRMS) data in MZmine 2.21.

MZmine step Settings

Peak detection Noise cutoff 2000

Chromatogram building Min. time span 0.2 min, min. height 20,000, mass tolerance 0.001 m/z or 7 ppm

Smoothing Filter width of 7

Peak deconvolution Local minimum search; chromatographic threshold 80%; search minimum in retention time range 0.15 min; minimum relative height 30%, minimum absolute height 20,000 a.u.; minimum ratio of peak top/edge 2.7; peak duration range 0.15-5 min

Peak list alignment Join aligner, m/z tolerance 0.001 or 7 ppm, weight for m/z 80; retention time tolerance 0.4 min; weight for RT 30.

Custom database search Against list of internal standards, m/z tolerance 0.001 m/z or 8 ppm; retention time tolerance 0.4 min absolute

A-2. pH-dependent LC retention and hydrogen deuterium exchange (HDX) methods.

Three different pHs were used for pH-Dependent LC retention times analysis i.e., (2.6, 6.4 and 10.0) by using gradient elution with methanol [B] and water [A] each contained with 0.1% formic acid (for pH 2.6). For pH 6.4 and 10.0, 2.5 mM ammonium acetate and 2.5 mM ammonium bicarbonate were used in methanol and water respectively. Kinetex EVO C18 column (50 x 2.1 mm, particle size 2.6 µm) was used for LC separation (Muz et al., 2017).

For hydrogen deuterium exchange (HDX) analysis, the mobile phase comprised of methanol

[B]: D2O [A] both containing 0.1% formic acid with same gradient setting as described above.(Muz et al., 2017) The analysis was carried out at full scan of 70,000 resolutions with both ionization modes (ESI+ and ESI-) and HRMS/MS (ddMS2) data was obtained by using higher energy collisional dissociation (HCD) with stepped collision energies of 15, 35 and 55.

111

Table A-2: List of compounds with highest Frequency score with peak intensities detected in JDS3 water sample in ESI+ mode. Adduct, isotope and homologue series assignment are taken from detection by the nontarget script; missing annotations of homologue series were added based on visual inspection of raw data. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate, 4=unequivocal molecular formula, 5=accurate mass. RT Frequen Part of Homologue Main ion (additional Isotopes Molecular formula ID # m/z [min Intensity cy Score series label (unit or Compound name Comment adducts) assigned of ion level ] (FS) increment)

8 + 1 381.2967 15.44 1.56×10 M+Na 285719 Yes (CH2, C2H4O) [C21H42O4] Glycerol stearate isomer 2

7 + 13 + 2 279.0928 11.08 6.24×10 M+H 252588 C [C18H15OP] Triphenylphosphine oxide 1 Confirmed 231511 3-Cyclohexyl-1,1- 3 171.1489 7.89 2.24×108 M+H+ Yes (CH ) [C H N O]+ 2 2 9 18 2 dimethylurea

8 + 13 + 4 419.3148 16.02 2.59×10 M+H 214744 C [C26H42O4] Dinonylphthalate 2

8 208893 Yes (CH2, CH2- + 5 353.2655 14.89 1.19×10 M+Na [C19H38O4] Palmitoylglycerol isomer 2 C2H4O)

8 13 + 6 376.3414 15.44 1.35×10 M+NH4 194376 C Yes (CH2) [C21H42O4] Glycerol stearate isomer 2

8 185726 Yes (CH2, CH2- + 7 348.3102 14.89 1.19×10 M+NH4 [C19H38O4] Palmitoylglycerol isomer 2 C2H4O) PEG-ter-dodecyl 8 484.3654 14.64 1.83×108 M+NH 13C, 34S Yes (C H O) [C H O S]+ 2 4 184896 2 4 24 50 6 mercaptoethanol

8 156825 13 Yes (CH2,CH2O, + 9 312.2375 9.20 1.72×10 M+NH4 C [C14H30O6] PEG-butylether 5 EO 2 C2H4O)

8 + 10 356.2637 9.53 1.55×10 M+NH4 141081 Yes (C2H4O) [C16H34O7] PEG-butylether 6 EO 2 140553 Glyceryl 1,3- 11 614.5707 17.56 8.13×107 M+NH 13C Yes (CH ) [C H O ]+ 2 4 2 37 72 5 Palmitostearate

8 + + 12 114.0913 2.75 1.81×10 M+H 124809 [C6H11NO] caprolactam 2

8 13 + 13 268.2114 8.80 1.36×10 M+NH4 117876 C Yes (CH2, C2H4O) [C12H26O5] PEG-butylether 4 EO 2

7 114873 13 Yes (CH2, C2H4O, + 14 554.4616 15.31 4.69×10 M+NH4 C [C29H60O8] C15-AEO-7 3 C15-AEO-7, Confirmed with SSL CH2-C2H4O)

8 114702 13 Yes (CH2, C2H4O, + 15 598.4877 15.31 4.46×10 M+NH4 C [C31H64O9] C15-AEO-8 3 C15-AEO-8, Confirmed with SSL CH2-C2H4O)

7 + 13 + 16 331.2836 14.89 7.10×10 M+H 109340 C Yes (CH2) [C19H38O4] Palmitoyl glycerol isomer 2

8 13 + 17 400.2899 9.80 1.15×10 M+NH4 106527 C Yes (CH2O, C2H4O) [C18H38O8] PEG-butylether 7 EO 3

7 104653 13 Yes (CH2, C2H4O, + 18 642.5136 15.31 4.00×10 M+NH4 C [C33H68O10] C15-AEO-9 3 C15-AEO-9, Confirmed with SSL CH2-C2H4O)

7 + 13 15 + 19 225.1957 11.08 9.63×10 M+H 102916 C, N [C13H24N2O] 1,3-Dicyclohexylurea 2

112

7 99878 Yes (CH2, CH2- + 20 586.5397 17.30 6.14×10 M+NH4 [C35H68O5] dipalmitoyl glycerol 2 C2H4O)

7 96353 13 Yes (CH2, C2H4O, + 21 510.4357 15.29 4.19×10 M+NH4 C [C27H56O7] C15-AEO-6 3 C15-AEO-6, Confirmed with SSL CH2-C2H4O)

7 13 34 + 22 364.3236 16.02 8.36×10 M+NH4 95808 C, S Yes (CH2) [C20H42O2S] Didecyl sulfone 2 92262 Tris(2,4-ditert- 23 663.4526 17.06 7.38×107 M+H+ 13C [C H O P]+ 2 42 63 4 butylphenyl)phosphate

7 89707 13 Yes (CH2, C2H4O, + 24 686.5395 15.30 3.29×10 M+NH4 C [C35H72O11] C15-AEO-10 3 C15-AEO-10, Confirmed with SSL CH2-C2H4O)

7 87812 13 Yes (CH2, C2H4O, + 25 526.4305 14.67 2.57×10 M+NH4 C [C27H56O8] C13-AEO-7 3 C13-AEO-7, Confirmed with SSL CH2-C2H4O)

7 + + 26 359.3148 15.44 6.68×10 M+H 87135 Yes (CH2) [C21H42O4] Glycerol Stearate isomer 2 84824 Confirmed. Massbank of North 7 + 13 + 27 338.3411 15.87 7.49×10 M+H C [C22H43NO] Erucamide 1 America spectrum, very likely. Standrd in stock.

113

Table A-3: List of compounds with highest Rarity score with peak intensities detected in JDS3 water sample in ESI+ mode. Adduct, isotope and homologue series assignment are taken from detection by the nontarget script; missing annotations of homologue series were added based on visual inspection of raw data. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate, 4=unequivocal molecular formula, 5=accurate mass. RT Rarity Part of Homologue Main ion (additional Isotopes Molecular formula ID # m/z [min Intensity Score series label (unit or Compound name Comment adducts) assigned of ion level ] (RS) increment)

8 + + 1 223.0961 10.36 3.57×10 M+H 229229 Yes (CH2O, C2H4O) [C12H14O4] Diethyl phthalate 2

7 + 13 + 2 226.9511 0.41 8.78×10 M+H 184371 C [C3H3O6Na4] Sodium formate cluster 2 159737 in-source frag of diethylphthalate 3 177.0544 10.36 2.50×108 M+H+ 13C [C H O ]+ 2 10 8 3 (223.0961)

8 + + 4 181.0492 10.48 1.97×10 M+H 119007 [C9H8O4] Aspirin 2

7 + 5 936.7482 17.22 5.11×10 M+NH4 107340 Yes (CH2) [C55H98O10] 2 frag is C18 H29 O2 101379 frag is C18 H29 O2, additional 6 964.7794 17.22 4.83×107 M+NH Yes (CH ) [C H O ]+ 2 4 2 57 102 10 isobaric peak to the right

7 + 7 992.7391 16.32 3.78×10 M+NH4 79406 [C57H98O12] 2 frag is C18 H29 O2

7 + 13 + 8 149.0231 10.35 9.63×10 M+H 57014 C [C8H4O3] 2 in-source fragment of # 223.0961 56817 Sodium formate cluster, in-source 9 158.9638 0.40 2.71×107 M+H+ 13C [C H O Na ]+ 2 2 2 4 3 frag of # 226.9511 56174 Sodium formate cluster, in-source 10 362.9259 0.40 2.67×107 M+H+ [C H O Na ]+ 2 5 5 10 6 frag of #226.9511

7 13 + 11 950.8001 17.72 2.54×10 M+NH4 53365 C Yes (CH2) [C57H104O9] Ricinolein 4 50749 Sodium formate cluster, in-source 12 430.9132 0.41 2.42×107 M+H+ [C H O Na ]+ 2 6 6 12 7 frag of #226.9511 45277 Sodium formate cluster, in-source 13 294.9385 0.41 2.16×107 M+H+ [C H O Na ]+ 2 4 4 8 5 frag of #226.9511

7 + 13 + 14 391.2835 15.62 2.10×10 M+H 43906 C Yes (CH2) [C24H38O4] DEHP 1 Confirmed

7 + + 15 299.2577 14.13 1.99×10 M+H 36078 Yes (CH2) [C18H34O3] keto or epoxy fatty acid 4

7 28049 Yes (CH2, C2H4O, + 16 471.3651 15.30 1.34×10 M+Na [C25H52O6] C15-AEO-5 3 C15-AEO-5, Confirmed with SSL CH2-C2H4O)

7 27875 Yes (CH2, C2H4O, + 17 515.3912 15.31 1.33×10 M+Na [C27H56O7] C15-AEO-6 3 C15-AEO-6, Confirmed with SSL CH2-C2H4O)

114

Table A-4: List of compounds with highest Frequency score with peak intensities detected in JDS3 water sample in ESI- mode. Adduct, isotope and homologue series assignment are taken from detection by the nontarget script; missing annotations of homologue series were added based on visual inspection of raw data. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate, 4=unequivocal molecular formula, 5=accurate mass. RT Frequen Part of Homologue Main ion (additional Isotopes Molecular formula ID # m/z [min Intensity cy Score series label (unit or Compound name Comment adducts) assigned of ion level ] (FS) increment)

8 - 13 34 - 1 265.1479 17.24 1.59×10 M-H 147228 C, S [C12H26O4S] Laurylsulfate 1 Confirmed

7 - 13 - 2 158.9776 0.41 3.64×10 M-H 73311 C [C3H3O6Mg] Magnesium complex 2

7 - 37 3 197.8073 0.55 4.10×10 M-H 68587 Cl FeCl4 Iron complex 2

7 - - 4 174.9552 1.43 4.19×10 M-H 55386 [C3H3O6Ca] Calcium complex 2

7 - 37 5 195.8104 0.55 3.23×10 M-H 49361 Cl FeCl4 Iron complex 2

8 - - 6 311.1689 20.11 1.69×10 M-H 34678 [C12H28O3S] C11-LAS 3 C11-LAS, Confirmed with SSL

8 - - 7 297.1530 17.64 1.88×10 M-H 33387 Yes (CH2) [C16H26O3S] C10-LAS 3 C10-LAS, Confirmed with SSL

7 - 37 8 199.8043 0.55 1.91×10 M-H 30377 Cl FeCl4 Iron complex 2

8 - - 9 293.1795 21.60 1.87×10 M-H 18085 [C14H30O4S] C14-AS 3 C14-AS, Confirmed with SSL

6 - 37 - 10 148.9487 0.45 5.34×10 M-H 17510 Cl [C2H2O4ClMg] Magnesium complex 2 16101 Isopropylbenezensulfonic confirmed (Schymanski et al., 11 199.0428 9.52 2.26×107 M-H- 13C Yes (CH ) [C H O S]- 1 2 9 12 3 acid isomer 2015)

8 - 13 37 - 12 188.9509 9.89 3.15×10 M-H 11596 C, Cl [C7H4Cl2O2] Dichlorobenzoic acid 2

7 - 13 34 - 13 285.0805 10.55 1.47×10 M-H 10990 C, S Yes (CH2) [C13H18O5S] C7-SPC 3 C7-SPC, Confirmed with SSL

6 - 13 - 14 159.9784 0.44 4.57×10 M-H 9305 C [C3H3O6Mg] Magnesium complex 2

115

Table A-5: List of compounds with highest Rarity score with peak intensities detected in JDS3 water sample in ESI- mode. Adduct, isotope and homologue series assignment are taken from detection by the nontarget script; missing annotations of homologue series were added based on visual inspection of raw data. The ID level is given according to (Schymanski et al., 2014a): 1= confirmed, 2=probable structure, 3=tentative candidate, 4=unequivocal molecular formula, 5=accurate mass. Rarity Part of Homologue RT Main ion (additional Isotopes Molecular formula ID # m/z Intensity Score series label (unit or Compound name Comment [min] adducts) assigned of ion level (RS) increment)

8 - - 1 311.1688 16.72 1.69×10 M-H 177807 [C17H28O3S] C11-LAS 3 C11-LAS, Confirmed with SSL 172188 6:2 fluorotelomer sulfonic 2 426.9679 14.53 8.20×107 M-H- [C H F O S]- 1 Confirmed 8 5 13 3 acid 163893 Dichlorobenzoic acid 3 188.9509 9.87 3.12×108 M-H- 13C, 37Cl [C H Cl O ]- 1 Confirmed 7 4 2 2 isomers

8 - - 4 293.1795 20.10 2.0×10 M-H 81052 [C14H30O4S] C14-AS 3 C14-AS, Confirmed with SSL

8 - - 5 265.1478 16.80 1.89×10 M-H 44193 [C12H29O4S] C12-AS 3 C12-AS, Confirmed with SSL

7 - - 6 325.1845 22.53 7.89×10 M-H 33146 [C18H30O3S] C12-LAS 3 C12-LAS, Confirmed with SSL 14663 Dichlorobenzoic acid 7 192.9450 9.84 3.49×107 M-H 3C [C7H4Cl2O2]- 1 Confirmed isomer

6 - 13 - 8 309.1744 17.72 8.40×10 M-H 11132 C [C14H30O5S] C12-AE1S 3 C12-AE1S, Confirmed with SSL 10112 Dichlorobenzoic acid 9 189.9543 9.85 2.41×107 M-H- 37Cl [C7H4Cl2O2]- 1 Confirmed isomer

7 - - 10 421.2269 19.62 2.29×10 M-H 9217 [C20H38O7S] DHSS 3 DHSS, Confirmed with SSL 144.9606 9.84 11 1.74×107 M-H- 7300 13C, 37Cl 2 in source frag of #426.9679 (-CO2)

7 - - 12 297.1530 15.88 1.69×10 M-H 7121 [C16H26O3S] C10-LAS 3 C10-LAS, Confirmed with SSL

116

References: Muz, M., Dann, J.P., Jäger, F., Brack, W., Krauss, M., 2017. Identification of Mutagenic Aromatic Amines in River Samples with Industrial Wastewater Impact. Environ. Sci. Technol. 51, 4681–4688. doi:10.1021/acs.est.7b00426 Schymanski, E.L., Jeon, J., Gulde, R., Fenner, K., Ruff, M., Singer, H.P., Hollender, J., 2014. Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence. Environ. Sci. Technol. 48, 2097–2098. doi:10.1021/es5002105 Schymanski, E.L., Singer, H.P., Slobodnik, J., Ipolyi, I.M., Oswald, P., Krauss, M., Schulze, T., Haglund, P., Letzel, T., Grosse, S., Thomaidis, N.S., Bletsou, A., Zwiener, C., Ibáñez, M., Portolés, T., Boer, R. De, Reid, M.J., Onghena, M., 2015. Non-target screening with high-resolution mass spectrometry : critical review using a collaborative trial on water analysis 6237–6255. doi:10.1007/s00216-015-8681-7

117

Appendix B

Supplementary Information for Chapter 3

Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia

118

B-1: Biological Analysis:

Table B-1: Summary of bioassays performed.

Bioassay and Day 1 (Seeding) Day 2 (Dosing) Day 3 (Reading) Method reference

ERα: Cell line: 1. 3500 cells/well (30 1. 17β-Estradiol (E2, agonist), 19.4 – 0.019 nM. 1. 8µL/well of fluorescence resonance ERα-UAS-bla µL/well) at 2. Tamoxifen (antagonist), 100 – 0.098 µM. energy transfer (FRET) substrate GripTite™ concentration of 2. reading at t0h (excitation wavelength 5 3. EC80 of E2, 0.273 nM (König et al., 2017; 1.17*10 cells/mL in 409 & 590 nm; emission wavelength 460, Neale et al., 2017) stimulated & 3. 10µL/well of sample in stimulated cells control 530 & 665 nm) and 2 hours of incubation unstimulated cells dissolved in assay medium (agonist response) in dark at room temperature. control. 4. 10µL/well of sample in stimulated cells control 3. reading at t2h (excitation wavelength 2. 30 µL/well of assay with EC80 concentration of E2 dissolved in assay 409 & 590 nm; emission wavelength 460, medium in cell free medium (antagonist response). 530 & 665 nm) control. 5. 10µL/well of assay medium without & with EC80 3. incubation for 24 concentration of E2 in respected unstimulated cells hours at 37oC & 5% control. CO2. 6. 10µL/well of assay medium in cell free control.

o 7. incubation for 22 hours at 37 C & 5% CO2.

AR: Cell line: AR- 1. 4000 cells/well (30 1. R1881 (metribolone, agonist), 69.5 – 0.068 nM 1. 8µL/well of FRET substrate UAS-bla GripTite™ µL/well in assay 2. Cyproterone Acetate (antagonist), 10.0 – 0.0098 2. reading at t0h (excitation wavelength (König et al., 2017; medium) at µM 409 & 590 nm; emission wavelength 460, Neale et al., 2017) concentration of 530 & 665 nm) and 2 hours of incubation 5 3. EC80 of Metribolone, 1.62 nM 1.34*10 cells/mL in in dark at room temperature. stimulated & 3. 10µL/well of sample in stimulated cells control unstimulated cells dissolved in assay medium (agonist response) control.

119

2. 30 µL/well of assay 4. 10µL/well of sample in stimulated cells control 3. reading at t2h (excitation wavelength medium in cell free with EC80 concentration of R1881 dissolved in 409 & 590 nm; emission wavelength 460, control. assay medium (antagonist response). 530 & 665 nm). 3. incubation for 24 5. 10µL/well of assay medium without & with hours at 37oC & 5% EC80 concentration of R1881 in respected CO2. unstimulated cells control. 6. 10µL/well of assay medium in cell free control.

o 7. incubation for 22 hours at 37 C & 5% CO2.

Oxidative stress 1. 2500 cells/well (30 1. tert-Butylhydroquinone (tBHQ), 49.8 – 0.0486 1. PrestoBlue Assay: assay medium response: Cell line: µL/well) at µM removed and added Prestoblue (10x AREc32 (MCF-7) concentration of 2. 10 µL/well of sample in stimulated cells control concentrated) 20µL/well & reading at t0h 4 (Escher et al., 2012; 8.34*10 cells/mL in dissolved in assay medium. (excitation wavelength 560 nm; emission stimulated cells control. wavelength 590 nm) Wang et al., 2006) 3. 10 µL/well of assay medium in unstimulated cells 2. 30 µL/well of assay 2. Incubation for one hour at 37oC & control. medium in unstimulated reading at t1h (excitation wavelength 560 o cells control. 4. incubation for 22 hours at 37 C & 5% CO2. nm; emission wavelength 590 nm). 3. incubation for 24 3. Luciferase Assay: Prestoblue was hours at 37oC & 5% removed and cells were washed with CO2. phosphate-buffered saline solution (PBS), 60 µL/well. 4. Lysis buffer 20µL/well and incubation for 10 minutes at room temperature. 5. Luciferase substrate, 40µL/well 6. Luminescence reading.

120

B-2: Freeze-Drying: Along with solid phase extraction of fraction blanks (FBspe) and recombination of fractions (Rspe), freeze drying of fractionation blanks (FBfd) and recombination of fractions (Rfd) was done by using CHRIST Alpha 2-4 LDplus freeze-dryer. Initially samples were kept at -20 oC freezer in glass round bottomed flasks to let them freeze and then were shifted to freeze-dryer and freeze-dried under vacuum at -70 oC and 0.003 mbar of pressure.

B-2.1: Chemical Analysis: Chemical screening of bioactive fractions was done by using LC- HRMS/MS for ERα active fractions and LC-MS/MS for AR active fractions respectively.

ERα: Sample preparation: Target analysis of ERα active fractions and neighboring fractions was done after dansyl chloride derivatization based on Backe, 2015. Briefly, 5 µL of internal standard solution (1 µg/mL) were added into 100 µL of the sample in a 2 mL auto-sampler vial and reduced to dryness under a gentle nitrogen stream at room temperature. Dried contents were reconstituted in 200 µL of sodium carbonate (50 mM). The vial was vortexed for 15 seconds and 200 µL of a freshly prepared dansyl chloride solution (0.2 mg/mL in acetone) were added into the sample vial and vortexed. Vials were placed at 60°C for three minutes. After cooling down, the solution was transferred to a 5 mL centrifuge tube and 1 mL of methyl tert-butyl ether (MTBE) was added. After vortexing and centrifugation at 3000  g for three minutes750 µL of the MTBE phase (upper layer) were transferred to a new 2 mL autosampler vial and reduced to dryness in a nitrogen stream. The extract was reconstituted in 75 µL of methanol:water (70:30), transferred to a 2 mL autosampler vial with glass insert and analyzed by using LC-HRMS/MS. Estrogens were quantified by internal calibration against Estradiol-d3 added prior to derivatization.

A matrix-matched calibration corresponding to 0.067 to 133 ng/L was done by spiking aliquots of an SPE extract of water from a pristine creek in the upper Harz mountains (Germany) containing none of the target compounds. Spiked extracts were derivatized and prepared as the samples.

AR: Sample preparation: Samples for AR target analysis were prepared by adding 100 µL of extract (CF1000) into a 2 mL auto sampler vial with glass insert. Afterwards 30 µL LC-MS grade methanol, 60 µL of LC-MS grade water and 10 µL of an internal standard solution (Table B-2.1) were added to each vial and final ratio of the sample for methanol:water was set to 70:30 for analysis on LCMS/MS. Calibration was done using solvent calibration standards prepared in a range correpsonding to of 0.1 to 200 ng/L in methanol:water 70:30.

121

Table B-2.1: Targeted list for AR screening and list of compounds used as internal standard (IS) for AR targeted screening

Analytes and Internal standard Retentio Internal Precurso Product (IS) for n time DP CE CXP Standards r ion m/z ion m/z quantification (min) (V) (V) (V)

p- 394.991 225.0 Toluenesulfonamide- 8.19 66 27 12 d4

p- 394.991 90.8 Toluenesulfonamide- 8.19 66 89 10 d4

p- 394.991 114.9 Toluenesulfonamide- 8.19 66 117 14 d4

Raloxifene 474.246 112.0 Carbamazepine-d10 8.691 111 37 18

474.246 84.1 Carbamazepine-d10 8.691 111 87 10

Anastrozole 294.201 225.0 Carbamazepine-d10 8.78 61 29 12

294.201 210.1 Carbamazepine-d10 8.78 61 45 10

4- Hydroxytamoxife 388.121 71.9 9.65 111 47 10 n DEET-d7

388.121 44.0 DEET-d7 9.65 111 101 20

Trenbolone 271.083 253.0 DEET-d7 9.94 166 29 12

271.083 199.0 DEET-d7 9.94 166 29 20

271.083 165.0 DEET-d7 9.94 166 69 16

4- 287.138 97.0 10.09 121 23 16 Androstenedione DEET-d7

287.138 109.0 DEET-d7 10.09 121 37 12

287.138 78.9 DEET-d7 10.09 121 59 20

Exemestane 297.178 278.9 DEET-d7 10.08 16 13 14

122

297.178 121.1 DEET-d7 10.08 16 25 14

297.178 91.1 DEET-d7 10.08 16 61 16

Gestoden 311.01 109.0 DEET-d7 10.13 111 31 10

311.01 90.9 DEET-d7 10.13 111 63 14

311.01 76.9 DEET-d7 10.13 111 83 10

Norethindrone 299.129 91.1 DEET-d7 10.09 141 67 12

299.129 77.0 DEET-d7 10.09 141 81 20

299.129 78.9 DEET-d7 10.09 141 63 22

Canrenone 341.101 106.9 DEET-d7 10.2 101 35 12

341.101 90.9 DEET-d7 10.2 101 81 10

Drospirenone 367.181 90.9 -13C3 10.13 96 81 10

367.181 97.0 Atrazine-13C3 10.13 96 29 18

Tamoxifen 372.116 72.0 Metolachlor-d6 10.36 146 27 10

372.116 44.0 Metolachlor-d6 10.36 146 85 12

Testosterone 289.0 96.9 Metolachlor-d6 10.36 111 27 10

289.0 108.8 Metolachlor-d6 10.36 111 29 12

289.0 78.9 Metolachlor-d6 10.36 111 63 10

Finasteride 373.044 305.0 Metolachlor-d6 10.4 141 41 16

373.044 317.2 Metolachlor-d6 10.4 141 31 16

Cyproterone 374.93 321.1 Metolachlor-d6 10.44 111 27 16

374.93 43.1 Metolachlor-d6 10.44 111 91 20

Levo-norgestrel 313.2 91.1 Metolachlor-d6 10.48 136 63 14

313.2 109.1 Metolachlor-d6 10.48 136 33 22

313.2 107.0 Metolachlor-d6 10.48 136 49 18

123

Epi-Androsterone 291.294 273.1 Metolachlor-d6 10.7 51 11 14

291.294 255.1 Metolachlor-d6 10.7 51 19 14

Medroxyprogeste 345.183 123.0 10.77 51 31 14 rone Metolachlor-d6

345.183 97.0 Metolachlor-d6 10.77 51 29 12

Dihydrotestostero 291.205 255.1 10.84 116 21 14 ne Metolachlor-d6

291.205 91.0 Metolachlor-d6 10.84 116 65 10

291.205 105.0 Metolachlor-d6 10.84 116 51 12

Progesterone 315.113 109.0 Progesterone-d9 11.05 111 31 14

315.113 79.1 Progesterone-d9 11.05 111 65 12

315.113 97 Progesterone-d9 11.05

Androsterone 291.1 273.1 Progesterone-d9 11.17 61 11 14

291.1 255.1 Progesterone-d9 11.17 61 19 12

291.1 91.0 Progesterone-d9 11.17 61 61 16

Tri-n-butylphosphate- 370.258 123.9 11.53 101 41 14 d27

Tri-n-butylphosphate- 370.258 77.1 11.53 101 95 20 d27

Tri-n-butylphosphate- 370.258 91.0 11.53 101 75 10 d27

Clopidogrel 321.893 212.1 Metolachlor-d6 10.54

321.893 184.1 Metolachlor-d6 10.54 p- Toluenesulfonami 176.097 95.0 -- 6.7 91 21 14 de-d4

176.097 158.9 -- 6.7 91 11 22

124

Carbamazepine- -- 246.927 204.1 9.255 121 29 10 d10

246.927 202.0 -- 9.255 121 47 10

DEET-d7 199.192 126.0 -- 9.63 121 23 6

199.192 98.0 -- 9.63 121 41 10

Atrazine-13C3 219.1 177.0 -- 9.68 106 23 8

219.1 69.9 -- 9.68 106 47 8

Metolachlor-d6 290.166 258.1 -- 10.66 56 19 12

290.166 182.1 -- 10.66 56 35 10

Progesterone-d9 324.083 99.9 -- 11.02 121 27 12

324.083 113.0 -- 11.02 121 33 18

Tri-n- -- butylphosphate- 294.303 101.9 11.45 66 25 26 d27

294.303 82.9 -- 11.45 66 85 10

125

B-3: Relative effect potencies (REPs):

Table B-3.1: Effect concentrations (EC10) and relative effect potencies (REP, this study) of detected compounds relative to ERα reference compound estradiol (E2) and AR reference compound R1881.

Chemical Name ERα, EC10 (M) REP (ERα) AR, EC10 (M) REP (AR) (this study) (this study)

Metribolone -- -- 9.75E-11 1 (R1881) (AR reference compound)

Estradiol (E2) (ERα 4.15E-12 1 5.83E-07 1.67E-04 reference compound)

Estrone (E1) 1.28E-09 3.24E-03 -- --

Estriol (E3) 3.00E-10 1.38E-02 -- --

Ethinylestradiol 6.80E-12 6.10E-01 2.54E-05 3.84E-06 (EE2)

Daidzein 4.76E-07 8.72E-06 -- --

Genistein 2.29E-07 1.81E-05 -- --

Testosterone 1.03E-05 4.03E-07 1.65E-10 5.91E-01

Dihydrotestosterone 1.84E-07 2.26E-05 3.04E-10 3.21E-01 (DHT)

Epiandrosterone 8.34E-07 4.98E-06 -- --

Progesterone -- -- 1.13E-09 8.63E-02

Androsterone 1.20E-05 3.46E-07 -- --

Medroxyprogesteron 1.17E-10 8.33E-01 e

126

B-4: Quality Assurance (QA/QC):

Figure B-4.1: Responses from fractionation blanks processed through freeze-drying (FBfd) and through solid phase extraction (FBspe) for ERα and AR

B-5: Activity Recovery:

127

Figure B-5.1: Activity in recombination of fractions processed through solid phase extractions (Rspe) and through freeze drying (Rfd) and PS for ERα and AR

128

B-6: ERα:

129

Figure B-6.1: Agonistic and antagonistic ERα responses & cytotoxicity of F8, F9, F11, F15, F16, Rspe and PS relative to agonistic (E2) and antagonistic (tamoxifen) reference compounds respectively.

B-7: AR:

Figure B-7.1: Agonistic and antagonistic androgenic responses & cytotoxicity of F16, F18, F19, Rspe and PS relative to agonistic (R1881) and antagonistic (cyproterone acetate) reference compounds respectively

130

Table B-7.1. Androgenic agonistic and antagonistic response of Rspe and PS

AR

Agonist Antagonist Sr Sample Name EC10 ± SEM ECSR0.2 ± SEM # (REF) (REF)

1 PS 17.8±0.98 19.9±6.61

2 Rspe 23.4±1.2 7.53±0.65

B-8: Oxidative Stress Response:

Figure B-8.1: Oxidative stress response of Rspe and PS relative to reference compounds (tBHQ)

131

B-9: Confirmation:

ERα:

Table B-9.1: Responses of ERα active environmental fractions and chemical mixtures in terms of EC10±SEM (REF)

ERα Fractions Env Sample (EC10 ± SEM Chemical Mixture (EC10 ± (REF)) SEM (REF))

F8 4.31±0.21 3.09±0.15

F9 37.1±1.34 39.3±3.05

F11 23.8±0.83 502.6±38.1

F15 2.02±0.13 1.27±0.04

F16 47.7±1.66 30.1±1.32

PS 1.02±0.03 2.64±0.06

Table B-9.2: Effect of ERα active fractions explained by chemical mixtures in terms of EEQbio±SEM (ng/L)

ERα Fractions Env Sample Chemical Mixture ΣEEQchem (EEQbio±SEM) (EEQbio_mixture±SEM)

F8 0.26±0.05 0.366±0.06 0.43

F9 0.03±0.005 0.029±0.005 0.03

F11 0.047±0.01 0.0022±0.0004 0.0022

F15 0.56±0.10 0.89±0.15 0.85

F16 0.024±0.004 0.038±0.007 0.169

PS 1.11±0.19 0.43±0.07 1.23

132

AR: Table B-9.3: Responses of AR active environmental fractions and chemical mixtures in terms of EC10±SEM (REF).

AR Fractions Env Sample (EC10 ± Chemical Mixture (EC10 ± SEM (REF)) SEM (REF))

F16 30.9±0.63 21.0±0.88

F18 21.2±0.65 24.9±1.04

F19 185.8±8.34 594.4±55.02

PS 17.8±0.98 15.2±0.54

Table B-9.4: Effect of AR active fractions explained by chemical mixtures in terms of R1881- EQbio±SEM (ng/L)

AR Fractions Env Sample (R1881- Chemical Mixture ΣR1881- EQbio±SEM) (R1881- EQchem EQbio_mixture±SEM)

F16 0.90±0.06 1.32±0.1 1.56

F18 1.31±0.09 1.11±0.08 2.23

F19 0.15±0.01 0.047±0.005 0.258

PS 1.56±0.13 1.83±0.13 3.5

133

B-10: Target screening of active and neighboring fractions:

Table B-10.1: Compounds (ng/L) detected in targeted screening in ERα and AR active and in neighboring fractions. (A ERα active fraction, B AR active fraction, C Neighboring fraction. a+ ERa agonist, a- ERa antagonist, b+ AR agonist, b- AR antagonist; o+ OSR, as per USEPA, 2015. SR # Name and biological CAS No F7C F8A F9A F10C F11A F12C F14C F15A F16A,B F17C F18B F19B PSA,B Description activity a+ , a- , b+ , b-; o+

1 Phenazone 60-80-0 4.2 2 ------20 Pharmaceutical

2 Bezafibrate 41859-67-0 ------1.7 ------Pharmaceutical

3 Propyphenazone 479-92-5 ------6.1 ------6.7 Pharmaceutical

4 Metoprolol_IP 51384-51-1 ------30 Pharmaceutical

5 Isoproturon 34123-59-6 ------4.7 ------5.2 Pesticide

6 Erythromycin 114-07-8 ------9.6 Pharmaceutical

7 Diazinon o+ 333-41-5 ------2.9 Insecticide

8 Furosemide o+ 54-31-9 7.9 ------13 Pharmaceutical

9 Chlorotoluron 15545-48-9 ------3.3 ------3.6 Pesticide

10 Propranolol 525-66-6 ------4.3 Pharmaceutical

11 Triethylcitrate o+ 77-93-0 -- 3.6 129 ------316 Industrial chemical

12 N-Acetyl-4- 83-15-8 ------98 Pharmaceutical aminoantipyrine_IP

13 Benzophenone-3 o+ 131-57-7 ------15 8.2 25 Personal care product

14 Estrone (E1) a+, b- 53-16-7 ------4 0.24 ------4.7 Natural product/hormone

134

15 17-β estradiol (E2) a+, a-, 50-28-2 ------0.81 0.17 ------0.92 Natural product/hormone b+, b-

16 Estriol (E3) a+, b- 50-27-1 -- 33 2.2 ------22 Natural product/hormone

17 17-α ethinylestradiol 57-63-6 ------0.05 ------Pharmaceutical/hormone (EE2) a+, b-

18 Dimethenamid a-, b-, o+ 87674-68-8 ------1 ------0.92 Herbicide

19 Flufenacet b-, o+ 142459-58------0.63 -- -- 0.77 Herbicide 3

20 Prosulfocarb 52888-80-9 ------0.55 Pesticide

21 Chloridazon 1698-60-8 ------1.25 Pesticide

22 Epoxiconazole b-, o+ 133855-98------0.63 -- -- 0.50 Fungicide 8

23 Diazepam a-, b- 439-14-5 ------0.50 Pharmaceutical

24 Tris(2- 115-96-8 -- -- 12 ------12 Industrial chemical chloroethyl)phosphate

25 Octocrylene a-, o+ 6197-30-4 ------7 Industrial chemical

26 Clarithromycin 81103-11-9 -- -- 1.1 1.4 1.8 ------67 Pharmaceutical

27 Desethylatrazine 6190-65-4 ------4.7 Pesticide (TP of atrazine)

28 Ketoprofen 22071-15-4 ------15 ------54 Pharmaceutical

29 Sulfamethoxazole 723-46-6 ------60 Pharmaceutical

135

30 Desethylterbutylazine 30125-63-4 ------2.9 0.38 ------4 Pesticide (TP of terbuthylazine)

31 Tonalid o+ 21145-77-7 ------27 Industrial chemical

32 Terbuthylazine a+ 5915-41-3 ------4.4 ------3.7 Pesticide

33 Terbutryn o+ 886-50-0 ------2.2 ------2.3 Pesticide / Biocide

34 Carbamazepine 298-46-4 ------35 ------43 Pharmaceutical

35 Triphenylphosphate b-, o+ 115-86-6 ------2.4 Industrial chemical

36 Progesterone a-, b+, b-, o+ 57-83-0 ------3.3 3.38 Natural product/hormone

37 DEET 134-62-3 ------10 ------13 Personal care product

38 Atrazine 1912-24-9 ------2.4 ------4.1 Pesticide

39 1H-Benzotriazole_BP 95-14-7 ------184 Food / stimulant

40 Propiconazole b-, o+ 60207-90-1 ------1.8 1.7 Fungicide

41 Diclofenac o+ 15307-86-5 ------34 24 59 Pharmaceutical

42 Mefenamic acid a-, o+ 61-68-7 ------1.6 Pharmaceutical

43 Metazachlor 67129-08-2 ------4.4 ------4.1 Pesticide

44 Metolachlor o+ 51218-45-2 ------2.5 1 -- 3.2 Pesticide

45 Roxithromycin 80214-83-1 ------1.2 Pharmaceutical

46 5-Methyl-1H- 136-85-6 10 ------126 Industrial chemical benzotriazole

136

47 Benzyldimethyldodecyla 1340-95-0 ------47 208 42 ------377 Biocide mmonium

48 Benzyldimethylhexadecy 10328•34•4 ------8.5 Biocide lammonium

49 Didecyldimethylammoni 2390-68-3 1.3 ------1.3 -- -- 1.3 3.6 11 1.5 30 Biocide um a-, b+, b-

50 Dimethyldioctadecylam 14357-21-2 ------4.9 -- 4.9 industrial chemical monium

51 Tebuconazole b-, o+ 107534-96------1.2 1 fungicide 3

52 Tri(butoxyethyl)phosphat 78-51-3 ------62 Industrial chemical e o+

53 Tri-isobutylphosphate o+ 126-71-6 ------19 Industrial chemical

54 Lauric isopropanolamide 8028-85-1 ------18 Personal care product

55 Hexa(methoxymethyl)me 3089-11-0 ------91 Industrial chemical lamine

56 Triphenylphosphine 791-28-6 ------12 ------21 Industrial chemical oxide o+

57 a+, a-, b+, b-, 65277-42-1 2.9 0.45 ------4.7 Pharmaceutical o+

58 N-Ethyl-o- 1077-56-1 -- 2 ------1.5 Industrial chemical toluenesulfonamide

59 2- 615-22-5 ------13 Industrial chemical (Methylthio)benzothiazol e

137

60 Benzothiazole o+ 95-16-9 ------61 Industrial chemical

61 2,6-Dichlorobenzamide 2008-58-4 9.7 ------Industrial chemical

62 Mycophenolic acid a-, b-, 24280-93-1 ------12 ------46 Pharmaceutical o+

63 Icaridin o+ 119515-38------3.9 ------10 Personal care product 7

64 2-Benzothiazolesulfonic 941-57-1 74 ------208 industrial chemical (TP acid of mercaptobenzothiazole)

65 ISO E Super o+ 54464-57-2 ------542 Personal care product

66 10,11-Dihydro-10,11- 58955-93-4 ------167 Pharmaceutical (TP of dihydroxycarbamazepine carbamazepine)

67 Cetirizine 83881-51-0 ------1.7 ------1.2 pharmaceutical

68 2- 68011-66-5 ------8.9 Pharmaceutical (TP of Hydroxycarbamazepine carbamazepine)

69 Clomazone o+ 81777-89-1 ------1.3 ------herbicide

70 Testosterone a+, b+, o+ 58-22-0 ------2.69 ------1.3 Natural product/ hormone

71 Dihydrotestosterone 521-18-6 ------5.8 -- 7.9 Natural product/ (DHT) a+, b+, o+ hormone

72 Androsterone 53-41-8 ------114 78.24 Natural product/ hormone

73 Epiandrosterone 481-29-8 ------119 -- 81.53 Natural product/ hormone

138

74 Genistein a+, a-, b-, o+ 446-72-0 ------19.56 122.9 ------118.1 Food product 5

75 Daidzein a+, b-, o+ 486-66-8 -- 251.4 105.8 3.75 ------287.3 Food product

76 a+, b- 80-09-1 ------7 industrial chemical

77 Flurtamone 96525-23-4 ------0.82 ------0.65 herbicide

78 Acetyl-sulfamethoxazole 21312-10-7 ------140 Pharmaceutical (TP sulfamethoxazole)

79 Citalopram 59729-33-8 ------0.55 pharmaxeutical

80 Azithromycin 83905-01-5 ------1.3 pharmaceutical

81 Bisoprolol 66722-44-9 ------21 pharmaceutical

82 EDDP a- 17109-49-8 ------5.3 pesticide

83 fluconazole 86386-73-4 ------6.1 Pharmaceutical

84 Glimepiride 93479-97-1 ------6.4 -- -- Pharmaceutical

85 Lorazepam 846-49-1 ------4.9 ------7 Pharmaceutical

86 Memantine 19982-08-2 ------2 Pharmaceutical

87 Oxazepam 604-75-1 ------8.8 1 ------25 Pharmaceutical

88 Sertraline 79617-96-2 -- -- 1.5 -- 1.5 ------Pharmaceutical

89 triethylphosphate 78-40-0 ------20 industrial chemical

90 Fluvoxamine 54739-18-3 23 ------3.9 Pharmaceutical

139

91 Losartan 114798-26------24 ------39 Pharmaceutical 4

92 Valsartan 137862-53------123 20 ------178 Pharmaceutical 4

93 Clopidogrel 113665-84------1.4 ------1 Pharmaceutical 2

94 Pentoxifylline 6493-05-6 ------12 Pharmaceutical

95 Medroxyprogesterone b+ 520-85-4 ------0.6 -- -- hormone

96 Enalapril 75847-73-3 ------20 Pharmaceutical

97 Acetamiprid 135410-20------1.9 Pesticide 7

98 Mebendazole o+ 31431-39-7 ------0.75 0.66 ------1.5 Pharmaceutical

99 Tetraglyme 143-24-8 ------6 industrial chemical

100 4- 1122-58-3 ------5.6 industrial chemical (Dimethylamino)pyridine o+

101 Benzocain 94-09-7 4.4 ------14 Pharmaceutical

102 Scopolamine-N-butyl 149-64-4 ------2.4 Pharmaceutical

103 7-Amino-4- 26093-31-2 -- 2 ------industrial chemical methylcoumarin

104 2(4- 4225-26-7 ------0.2 0.42 ------0.19 industrial chemical morpholinyl)benzothiazo le

140

105 7-Diethylamino-4- 91-44-1 ------13 ------11 industrial chemical methylcoumarin a+, b+, o+

106 Bentazone o+ 25057-89-0 ------0.75 5.7 ------3 Pesticide

107 Ibuprofen 15687-27-1 ------93 Pharmaceutical

108 Perfluorooctanesulfonic 1763-23-1 ------5.9 Industrial Chemical acid_DP a-, o+

109 Mecoprop 93-65-2 ------43 ------50 Pesticide/Biocide

110 Benzophenone-4 4065-45-6 ------82 26 ------70 Personal care product

111 Iopamidol_IP 60166-93-0 ------82 Industrial Chemical

112 Phthalamic acid 88-97-1 ------16 Industrial Chemical

113 Perfluorooctanesulfonami 754-91-6 ------0.94 ------Industrial Chemical de a+, a-, o+

114 Mesalazine 89-57-6 ------4.8 Pharmaceutical

115 Nitrendipin b- 39562-70-4 ------6.8 ------Pharmaceutical

141

References:

Backe, W.J., 2015. An Ultrasensitive (Parts-Per-Quadrillion) and SPE-Free Method for the Quantitative Analysis of Estrogens in Surface Water. Environ. Sci. Technol. 49, 14311– 14318. doi:10.1021/acs.est.5b04949 Escher, B.I., Dutt, M., Maylin, E., Tang, J.Y.M., Toze, S., Roland, C., 2012. Water quality assessment using the AREc32 reporter gene assay indicative of the oxidative stress response pathway 2877–2885. doi:10.1039/c2em30506b König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Neale, P.A., Altenburger, R., Aït-Aïssa, S., Brion, F., Busch, W., de Aragão Umbuzeiro, G., Denison, M.S., Du Pasquier, D., Hilscherová, K., Hollert, H., Morales, D.A., Novák, J., Schlichting, R., Seiler, T.-B., Serra, H., Shao, Y., Tindall, A.J., Tollefsen, K.E., Williams, T.D., Escher, B.I., 2017. Development of a bioanalytical test battery for water quality monitoring: Fingerprinting identified micropollutants and their contribution to effects in surface water. Water Res. 123, 734–750. doi:https://doi.org/10.1016/j.watres.2017.07.016 Wang, X.J., Hayes, J.D., Wolf, C.R., 2006. Generation of a Stable Antioxidant Response Element – Driven Reporter Gene Cell Line and Its Use to Show Redox-Dependent Activation of Nrf2 by Cancer Chemotherapeutic Agents 10983–10994. doi:10.1158/0008-5472.CAN-06-2298 USEPA. 2015. Interactive Chemical Safety for Sustainability (iCSS) Dashboard v2. http:// actor.epa.gov/dashboard/ (accessed 25 Oct 2017)

142

Appendix C

Supplementary Information for Chapter 4

Advanced effect-directed analysis unraveling water contamination with progestogens and glucocorticoids at trace concentrations in a major European river

143

Supplementary Information:

C-1: Biological Analysis:

Table C-1: Description of progestogenic and glucocorticoid receptors (PR and GR respectively) bioassays performed in this study.

Bioassay Day 1 Day 2 (Dosing) by pipetting robot Day 3 (detection of and (Seeding of Hamilton Star response) method cells) reference

PR: Cell 1. 4500 A. Make dilution series of reference A. Cytotoxicity assessment line: PR- cells/well compound and samples 1. Plates were read for cell UAS-bla (30 B. Dose 10 µL/well into cell plate viability by using IncuCyte HEK293T µL/well) at (making it a final well volume of 40 SE Live Cell Analysis (König et concentrati µL) with the following final System (Essen Bioscience) al., 2017) on of concentrations: directly after dosing and 5 1.5×10 after 24h of incubation. cells/mL in 1. 10 µL/well of Promegestone stimulated (agonist), final conc. 1.51 – 0.0015 & nM B. Quantification of unstimulate 2. 10 µL/well of Mifepristone (RU- reporter protein activity d cells 486) (antagonist), final conc. 13 – 2. 8 µL/well of control by 0.013 µM in presence of a constant fluorescence resonance using conc of EC80 of Promegestone, energy transfer (FRET) microplate 0.098nM substrate cell- 3. 10 µL/well of sample dissolved in dispenser 3. reading right after assay medium (agonist response) (MultiFlo, addition of FRET substrate BioTek). 4. 10 µL/well of sample in stimulated (excitation wavelength 409 cells control with EC80 concentration & 590 nm; emission 2. 30 of Promegestone dissolved in assay wavelength 460, 530 & µL/well of medium (antagonist response). 665 nm) by using Tecan assay Infinite M1000 Pro reader medium in 5. 10 µL/well of assay medium and 2 hours of incubation cell free without & with EC80 concentration of in dark at room control. Promegestone in respected unstimulated cells control. temperature. 3. 4. reading after 2 hours incubation 6. 10 µL/well of assay medium in cell (excitation wavelength 409 for 24 free control. & 590 nm; emission hours at o C. Incubation for 22 hours at 37 C & wavelength 460, 530 & 37oC & 5% 5% CO2. 665 nm) by using Tecan CO2. Infinite M1000 Pro reader

144

GR: Cell 1. 4000 A. Make dilution series of reference A. Cytotoxicity assessment line: GR- cells/well compound and samples 1. Plates were read for cell UAS-bla (30 µL/well B. Dose 10 µL/well into cell plate viability by using IncuCyte HEK in assay (making it a final well volume of 40 SE Live Cell Analysis 293T(Kön medium) at µL) with the following final System (Essen Bioscience) ig et al., concentrati concentrations: directly after dosing and 2017) on of after 24h of incubation. 1.34×105 1. 10 µL/well of Dexamethasone final cells/mL in conc. (agonist), 90 – 0.088 nM stimulated 2. 10 µL/well of Mifepristone (RU- B. Quantification of & 486) final conc. (antagonist), 25 – reporter protein activity unstimulate 0.024 nM in presence of a constant 2. 8 µL/well of d cells conc of EC80 of dexamethasone, fluorescence resonance control by 3.32nM energy transfer (FRET) using 3. 10 µL/well of sample dissolved in substrate microplate assay medium (agonist response) cell- 3. reading right after dispenser 4. 10 µL/well of sample in stimulated addition of FRET substrate (MultiFlo, cells control with EC80 concentration (excitation wavelength 409 BioTek). of Dexamethasone dissolved in assay & 590 nm; emission medium (antagonist response). wavelength 460, 530 & 2. 30 665 nm) by using Tecan µL/well of 5. 10 µL/well of assay medium Infinite M1000 Pro reader assay without & with EC80 concentration of and 2 hours of incubation medium in Dexamethasone in respected in dark at room cell free unstimulated cells control. temperature. control. 6. 10µL/well of assay medium in cell 4. reading after 2 hours 3. free control. (excitation wavelength 409 incubation o C. Incubation for 22 hours at 37 C & & 590 nm; emission for 24 5% CO2. wavelength 460, 530 & hours at 665 nm) by using Tecan 37oC & 5% Infinite M1000 Pro reader. CO2.

145

C-2: Two step fractionation:

Figure C-2: Two-step fractionation (primary (Hashmi et al., 2018) and secondary) by using C18 and aminopropyl columns. The fractions that showed biological responses are highlighted in white.

146

C-3: Study design:

Figure C-3: Flow chart of the experimental work and biological analysis carried out in this study

147

Figure C-3.1: Flow chart of the chemical analysis performed in this study; CF= concentration factor; GC-HRMS, gas chromatography-high resolution mass spectrometry.

148

C-4: Chemical analysis

Sample preparation for nontarget (NT) screening. Samples and blanks of 400 mL water equivalent were reduced to dryness by using a gentle stream of nitrogen and reconstituted in 130 µL of LC-MS grade methanol, 60 µL of LC-MS grade water and 10 µL of internal standard in methanol, resulting in a final volume of 200 µL (70:30, methanol:water) and a concentration factor (CF) of 2000 for liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis.

LC-HRMS.

For chemical screening, LC separation was done by using a Kinetex EVO C18 column (50x2.1 mm, 2.6 µm particle size, Phenomenex) with a gradient of water (A) and methanol (B), both containing 0.1% of formic acid. The flow rate was 300 µL/min and the column oven temperature was 40°C, while injection volume was 5 µL. A mobile phase with 5% B was kept for one minute, subsequently increasing B linearly to 100% within 12 minutes and remaining constant for 11 minutes. Finally, the system was re-equilibrated for the next run.

For mass spectrometry (MS), samples were run with electrospray ionization in positive and negative (ESI+ and ESI-) mode. MS settings used were as follows: ESI source of the MS operated at 300°C, the automatic gain control (AGC) at 3e6, maximum IT was 120 ms, the transfer capillary temperature 300°C, spray voltage 3.8 kV (ESI+) and 3.5 kV (ESI-), sheath gas flow rate 45 a.u. (ESI+) and 25 a.u. (ESI-) and auxiliary gas flow rate 1 a.u. pH-dependent LC retention and hydrogen deuterium exchange methods. pH-Dependent LC retention times analysis was done by using three different pH values (2.6, 6.4 and 10.0) along with gradient elution with methanol [B] and water [A] each containing 0.1% formic acid (pH 2.6), while 2.5 mM ammonium acetate and 2.5 mM ammonium bicarbonate were used in methanol and water for pH 6.4 and 10.0, respectively. LC separation was achieved with Kinetex EVO C18 column (50 x 2.1 mm, particle size 2.6 µm) (Muz et al., 2017). For hydrogen deuterium exchange (HDX) analysis, the mobile phase was switched to methanol [B]: D2O [A] both containing 0.1% formic acid with same gradient setting as described above.(Muz et al., 2017)

Gas chromatography-high resolution mass spectrometry (GC-HRMS).

Aliquots of 100 µL of extract (CF 1000) were evaporated to dryness and reconstituted in 100 µL of ethyl acetate. Compounds were separated on a DB-5MS capillary column (30 m x 250

149

µm x 0.25 µm) with helium (He) as carrier gas at a flow rate of 1 mL min-1. Extract aliquots of 1 µL were injected using programmed temperature vaporizing injection (starting at 60°C and ramped at 8°C/sec to 300°C). The gas chromatograph oven program started at 60°C, held for 1 minute, linearly increased to 300°C at 5°C/minute and was held for 15 minutes. The transfer line temperature was 280°C. Electron ionization was used at 70 eV and a source temperature of 250°C. The MS was operated in full scan mode with an m/z range of 70-810 at a nominal resolving power of 60,000 (referenced to m/z 200).

Data files were processed in MZmine 2.26 (Pluskal et al., 2010) for peak detection using the settings given in the SI (C-5). Occurrence of peaks was compared for the active and neighboring non-active fractions as well as fractionation and injection blanks to obtain candidate peaks for the active compounds.

150

C-5: MZmine settings:

Table C-5.1. Settings for processing of liquid chromatography-high resolution mass spectrometry (LC-HRMS) data in MZmine 2.21

MZmine step Settings

Peak detection Noise cutoff 2000

Chromatogram building Min. time span 0.2 min, min. height 20,000, mass tolerance 0.001 m/z or 7 ppm

Smoothing Filter width of 7

Peak deconvolution Local minimum search; chromatographic threshold 80%; search minimum in retention time range 0.15 min; minimum relative height 30%, minimum absolute height 20,000 a.u.; minimum ratio of peak top/edge 2.7; peak duration range 0.15-5 min

Peak list alignment Join aligner, m/z tolerance 0.001 or 7 ppm, weight for m/z 80; retention time tolerance 0.4 min; weight for RT 30

Custom database search Against list of internal standards, m/z tolerance 0.001 m/z or 8 ppm; retention time tolerance 0.4 min absolute

Table C-5.2. Settings for processing of gas chromatography-high resolution mass spectrometry (GC-HRMS) data in MZmine 2.26

MZmine step Settings

Peak detection Noise cutoff 2000

Chromatogram building ADAP Module (Myers et al., 2017): Min group size in # of scans: 8, group intensity threshold 2000 a.u., Minimum highest intensity 5000 a.u., m/z tolerance 0.001 m/z or 7 ppm

Smoothing Filter width of 7

Peak deconvolution Local minimum search; chromatographic threshold 80%; search minimum in retention time range 0.10 min; minimum relative height 20%, minimum absolute height 10,000 a.u.; minimum ratio of peak top/edge 2.2; peak duration range 0.02-4 min

Peak list alignment Join aligner, m/z tolerance 0.001 or 5 ppm, weight for m/z 80; retention time tolerance 0.2 min; weight for RT 30

Custom database search Against list of internal standards, m/z tolerance 0.001 m/z or 8 ppm; retention time tolerance 0.2 min absolute

151

C-6: PR-mediated responses:

152

Figure C-6: Sigmoidal and linear dose responses of fractionation blanks and samples for progesterone receptor (PR). The left plot is the concentration-response curve on the logarithmic scale from which cytotoxicity IC10 was derived. The right plot shows the concentration-response curve on a linear scale for the activation of PR only up to concentrations IC10. The dotted line indicates the level of 10% effect, which was the threshold for activity and was used to derive the effect concentration EC10. Data points (in the form of squares, circles, diamonds and triangles given in sigmoidal and linear dose

153

responses) represent various repeats of the respective sample. 2-6 repeats were performed for each sample depending on the availability of the sample volume

Table C-6.1: Comparison of EC10±SE (REF) values from samples for PR-mediated response.

Samples EC10 ± SE EC10 ± SE (REF)_Mixture (REF)

F18.4 268 ± 13 293 ± 26

R_F18 293 ± 12 --

F18 81 ± 2 159 ± 10

F19 213 ± 8 111 ± 3

Parent sample (PS) Cytotoxic 168 ± 20

Table C-6.2: Comparison of Promegestone-EQbio and Promegestone-EQbio_mixture (ng/L) and effect explained (percentage) for PR

Samples Promegestone-EQbio Promegestone- Effect explained by ± SE (ng/L) EQbio_mixture ± SE designed mixtures (ng/L) (%)

F18.4 0.088 ± 0.005 0.080 ± 0.008 91.7 ± 14.9

R_F18 0.080 ± 0.004 -- --

F18 0.29 ± 0.01 0.15 ± 0.01 51 ± 5.8

F19 0.11 ± 0.005 0.21 ± 0.009 190.9 ± 17.3

Parent sample (PS) Cytotoxic -- --

154

C-7: GR-mediated responses:

155

Figure C-7: Sigmoidal and linear dose response curves of fractionation blanks and samples for glucocorticoid receptor (GR). The left plot is the concentration-response curve on the logarithmic scale, No cytotoxicity was observed. The right plot shows the concentration- response curve on a linear scale for the activation of GR. The dotted line indicates the level of 10% effect, which was the threshold for activity and was used to derive the effect concentration EC10. Data points (in the form of squares, circles, diamonds and triangles given in sigmoidal and linear dose responses) represent various repeats of the respective sample. For secondary fractionation blank 1 analysis could be performed due to limited sample volume while for other samples 2-6 repeats were performed depending on the availability of the sample volume

156

C-8: Steroid mixture fractionation list. Table C-8.1: List of compounds in steroid mixture, respective detection limits (ng/mL water extract at concentrations factor (CF) of 1000) by using LC-HRMS and compounds affinities with their respective ligands (AR, androgen receptor; PR, progesterone receptor; GR, glucocorticoid receptor).

Detection limit in ng/mL, Nuclear Hormone SR # Compound Name visible Peak @ lowest conc Receptor Affinity

1 Norethindrone 0.2 PR

2 Progesterone 0.2 PR

3 Norgestimate 0.1 PR

4 0.5 GR

5 Dexamethasone 0.2 GR

6 0.5 GR

7 Testosterone 0.2 AR

8 0.2 antimineralocorticoid

9 4-Androstene-3,17-dione 0.2 AR

10 Androsterone 50 AR

11 Epiandrosterone 10 AR

12 0.5 GR

13 0.2 GR

14 Triamcinolone 0.5 GR

15 Dihydrotestosterone 20 AR

16 5alpha-androstan-17-beta-ol-one -- AR

17 Norgestrel 0.2 PR

18 0.2 Anti-estrogen

19 Medroxyprogesterone 0.1 PR

20 Gestoden 0.2 PR

21 Drospirenone 0.2 PR

157

22 Hydrocortisone 1 GR

23 Bethamethasone 0.05 GR

24 0.2 GR

25 0.2 GR

26 Cyproterone 0.2 PR, anti-AR

27 0.2 GR

28 0.2 GR

29 0.2 GR

30 0.1 AR

31 0.2 GR

32 Dydrogesterone 0.1 PR

33 Hydrocortisonacetate 0.2 GR

34 Medroxyprogesterone acetate 0.2 PR

35 Megestrol-17-acetate 0.1 PR, GR

36 11-Ketotestosterone 0.2 AR

37 17α-Hydroxyprogesterone 0.2 PR

38 17α-Methyltestosterone 0.2 AR

39 4-Androsten-11B-ol-3,17-dione 0.2 AR

40 0.2 GR

41 0.2 GR

42 0.1 GR

43 Desoximetasone 0.2 GR

44 Beclomethasone dipropionate 0.1 GR

45 Dienogestrel 0.05 PR

46 Fluticasone Furoate 0.05 GR

158

47 Beclomethasone 0.2 GR

48 0.02 GR

49 0.05 GR

159

C-9: Relative effect potencies (REP):

Table C-9: Effect concentrations (EC10) and relative effect potencies (REP, this study) of detected compounds relative to progesterone receptor (PR) reference compound promegestone and glucocorticoid receptor (GR) reference compound dexamethasone

Compound Name CAS # PR, EC10 REP GR, EC10 REP (M) (PR) (M) (GR)

Promegestone 34184-77-5 7.20E-11 1.00E+00 -- -- (PR reference compound)

Dexamethasone 50-02-2 -- -- 3.88E-10 1.00E+0 (GR reference compound) 0

Megestrol acetate 595-33-5 3.06E-10 2.35E-01 6.51E-07 5.96E-04

Hydrocortisone 50-23-7 -- -- 9.81E-08 3.96E-03

Dihydrotestosterone 521.18-6 6.03E-07 1.19E-04 -- --

Medroxyprogesterone 520-85-4 1.95E-06 3.70E-05 1.82E-07 2.13E-03

Epiandrosterone 481-29-8 ------

Progesterone 57-83-0 1.60E-09 4.50E-02 -- --

Androsterone 53-41-8 ------

Testosterone 58-22-0 1.41E-06 5.09E-05 -- --

Estrone 53-16-7 ------

Estradiol 50-28-2 2.65E-06 2.71E-05 -- --

Estriol 50-27-1 ------

Daidzein 486-66-8 ------

Genistein 446-72-0 ------

Norgestrel 797-63-7 9.74E-12 7.39E+00 -- --

Budesonide 51333-22-3 1.16E-09 6.19E-02 6.91E-10 5.62E-01

Cyproterone 2098-66-0 1.36E-06 5.27E-05 -- --

160

Amcinonide 51022-69-6 5.50E-08 1.31E-03 8.70E-09 4.46E-02

Dydrogesterone 152-62-5 1.92E-11 3.75E+00 -- --

Medroxyprogesterone acetate 71-58-9 1.89E-11 3.82E+00 1.21E-07 3.20E-03

17α Hydroxyprogesterone 68-96-2 1.28E-06 5.63E-05 -- --

17α Methyltestosterone 58-18-4 1.91E-07 3.77E-04 -- --

Fluticasone propionate 80474-14-2 2.66E-09 2.71E-02 6.74E-10 5.76E-01

Gestodene 60282-87-3 5.66E-12 1.27E+01 -- --

Drospirenone 67392-87-4 4.41E-09 1.63E-02 -- --

Fluocinonide 356-12-7 -- -- 4.44E-10 8.75E-01

Desoximetasone 382-67-2 -- -- 3.18E-10 1.22E+0 0

Beclomethasone dipropionate 5534-09-8 -- -- 6.91E-10 5.62E-01

Fluticasone Furoate 397864-44- -- -- 2.88E-10 1.35E+0 7 0

Beclomethasone 4419-39-0 -- -- 2.81E-09 1.38E-01

Prednicarbate 73771-04-7 -- -- 7.49E-09 5.19E-02

Rimexolone 49697-38-3 -- -- 1.02E-09 3.82E-01

161

C-10: Possible progestogens presence in active fractions based on effect concentration (EC) values:

Table C-10: Single compound concentration (ng compound/L) in active fractions required to elicit EC10 response at PR bioassay.

Compound Name CAS # EC10 (M) Detection Concentration Concentration Unit (ng/L) limit of (BEQchem in (BEQchem in LC-HRMS ng/L) of single ng/L) of single (ng/mL compound compound water required to required to extract at cause effect up cause effect up CF 1000) to EC10 in to EC10 in F18 F18.4

Megestrol acetate 595-33-5 3.06E-10 0.1 0.44 1.45 ng megestrol acetate/L

Dihydrotestosterone 521.18-6 6.03E-07 20 653 2166 ng dihydrotestosterone/L

Medroxyprogesterone 520-85-4 1.95E-06 0.1 2505 8310 ng medroxyprogesterone/ L

Progesterone 57-83-0 1.60E-09 0.2 1.88 6.2 ng progesterone/L

Norgestrel 797-63-7 9.74E-12 0.2 0.01 0.04 ng norgestrel/L

Budesonide 51333-22- 1.16E-09 0.2 1.9 6.2 ng budesonide/L 3

162

Cyproterone 2098-66-0 1.36E-06 0.2 1901 6307 ng cyproterone/L

Amcinonide 51022-69- 5.50E-08 0.2 103 342 ng amcinonide/L 6

Dydrogesterone 152-62-5 1.92E-11 0.1 0.022 0.07 ng dydrogesterone/L

Medroxyprogesterone 71-58-9 1.89E-11 0.2 0.027 0.09 ng acetate medroxyprogesterone acetate/L

17α 68-96-2 1.28E-06 0.2 1577 5232 ng 17α Hydroxyprogesterone hydroxyprogesterone/ L

17α Methyltestosterone 58-18-4 1.91E-07 0.2 215 715 ng 17α methyltestosterone/L

Fluticasone propionate 80474-14- 2.66E-09 0.2 5 16.5 ng fluticasone 2 propionate/L

Gestodene 60282-87- 5.66E-12 0.2 0.007 0.022 ng gestodene/L 3

Drospirenone 67392-87- 4.41E-09 0.2 6 20 ng drospirenone/L 4

Green filled: could be detected easily by LC-HRMS as their presence is above the detection limit. Red filled, compounds with high EC and REPs and present in the sample below detection limit so these compounds required enrichment of the sample to bring these compounds concentrations above the detection limit of LC-HRMS, CF= concentration factor

163

C-11: Possible glucocorticoid presence in active fractions based on effect concentration (EC) values:

Table C-11: Single compound concentration (ng compound/L) in active fractions required to elicit EC10 response in the GR bioassay.

Compound Name CAS # EC10 (M) Detection Concentration Unit (ng/L) limit of (BEQchem in LC-HRMS ng/L) of single (ng/mL compound water required to extract at cause effect up CF 1000) to EC10 in F18

Megestrol Acetate 595-33-5 6.51E-07 0.1 1560 ng megestrol acetate/L

Hydrocortisone 50-23-7 9.81E-08 0.2 222 ng hydrocortisone/L

Medroxyprogesterone 520-85-4 1.82E-07 0.1 391 ng medroxyprogesterone/L

Budesonide 51333-22-3 6.91E-10 0.2 2 ng budesonide/L

Amcinonide 51022-69-6 8.70E-09 0.2 27 ng amcinonide/L

Medroxyprogesterone 71-58-9 1.21E-07 0.2 291 ng medroxyprogesterone acetate acetate/L

Fluticasone propionate 80474-14-2 6.74E-10 0.2 2.1 ng fluticasone propionate/L

Green filled: Concentrations required to produce effects up to EC10, all concentrations were well above the detection limit of LC- HRMS , CF= concentration factor

164

References: Hashmi, M.A.K., Escher, B.I., Krauss, M., Teodorovic, I., Brack, W., 2018. Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia. Sci. Total Environ. 624, 1072–1081. doi:https://doi.org/10.1016/j.scitotenv.2017.12.187 König, M., Escher, B.I., Neale, P.A., Krauss, M., Hilscherová, K., Novák, J., Teodorović, I., Schulze, T., Seidensticker, S., Kamal Hashmi, M.A., Ahlheim, J., Brack, W., 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, Part, 1220–1230. doi:http://dx.doi.org/10.1016/j.envpol.2016.11.011 Muz, M., Dann, J.P., Jäger, F., Brack, W., Krauss, M., 2017. Identification of Mutagenic Aromatic Amines in River Samples with Industrial Wastewater Impact. Environ. Sci. Technol. 51, 4681–4688. doi:10.1021/acs.est.7b00426 Myers, O.D., Sumner, S.J., Li, S., Barnes, S., Du, X., 2017. One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks. Anal. Chem. 89, 8696–8703. doi:10.1021/acs.analchem.7b00947 Pluskal, T., Castillo, S., Villar-Briones, A., Orešič, M., 2010. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395. doi:10.1186/1471-2105-11-395

165

Acknowledgements

I would like to express my gratitude to my supervisors PD. Dr. Werner Brack and Dr. Martin Krauss for providing me an opportunity to work and conduct this study in department of WANA. I would like to thank you for your help and guidance. I appreciate your valuable comments giving me about the working procedures and problems faced. I am highly indebted to you for the successful completion of this study for PhD Degree. I would like to thank Prof. Dr. Henner Hollert for accepting me as a PhD student for RWTH Aachen University and evaluation of this thesis.

I would like to thank Higher Education Commission (HEC) of Pakistan and German Academic Exchange Service (DAAD) for providing personal grant for this PhD study (Personnel reference #91549557). All of the work performed in this study was funded by SOLUTIONS project (grant agreement 603437), supported by the EU Seventh Framework Programme. The instruments for chemical analysis and bioassays were provided by the major infrastructure CITEPro (Chemicals in the Environment Profiler) funded by the Helmholtz Association. Chemaxon (Budapest, Hungary) is acknowledged for providing an academic license of JChem for Excel, Marvin, and the Calculator Plugins.

I am deeply grateful to all WANA members especially, Dr. Tobias Schulze, Dr. Christin Müller, Jörg Ahlheim, Margit Petre, Dr. Hubert Schupke and Dr. Aleksandra Piotrowska for providing me support while working in the lab. I would like to special thanks to Jörg Ahlheim for his cooperation not only for this study but beyond this.

I would also like to thank Cell Toxicology department, in particular, thanks to Prof. Dr. Beate Escher for the guidance and discussions regarding the cell-based bioassays and cooperation for performing biological analysis. I would like to thank Dr. Rita Schlichting, Maria König and Christin Kühnert for providing technical help for work in bioassays lab.

I would like to thanks my friends in Germany, in WANA, in UK and in Pakistan for their quality time and support. I want to thank my office mates for making this time period more interesting with their cherished discussions.

At the end, my special thanks are due to my father, mother, life partner and other family members, who stayed with me through every thick and thin. I thank you all for your encouragement and support during the course of this journey.

166

The flowing river mirrors the red glow of dawn, The quiet of the evening mirrors the evening song,

The rose-leaf mirrors spring’s beautiful cheek; The chamber of the cup mirrors the beauty of the wine;

Beauty mirrors Truth, the heart mirrors Beauty; The beauty of your speech mirrors the heart of man.

Life finds perfection in your sky-soaring thought. Was your luminous nature the goal of existence?

When the eye wished to see you, and looked, It saw the sun hidden in its own brilliance.

You were hidden from the eyes of the world, But with your own eyes you saw the world exposed and bare.

Nature guards its secrets so jealously— It will never again create one who knows so many secrets.

Allama M Iqbal (1877-1938)

167

Scientific Contribution

Publications

* M. A. K. Hashmi, W. Brack, T. Schulze., M. Krauss. (in preparation). Nontarget screening based prioritization and identification of micropollutants from water samples of a large European river Danube

* M. A. K. Hashmi, B. I. Escher, M. Krauss, I. Teodorovic, W. Brack. 2018. Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia. Sci. Total Environ. 624, 1072–1081.

* M. A. K. Hashmi, M. Krauss, B. I. Escher, I. Teodorovic, W. Brack. (in preparation). Advanced effect-directed analysis unraveling water contamination with progestogens and glucocorticoids at trace concentrations in a major European river.

M. König, B. I. Escher, P. A. Neale, M. Krauss, K. Hilscherová, J. Novák, I. Teodorović, T. Schulze, S. Seidensticker, M. A. K. Hashmi, J. Ahlheim, W. Brack. 2017. Impact of untreated wastewater on a major European river evaluated with a combination of in vitro bioassays and chemical analysis. Environ. Pollut. 220, 1220–1230.

* Publications which are part of this PhD thesis.

Presentations in Conferences

M. A. K. Hashmi, M. Krauss, B. I. Escher, W. Brack. “Identification ofendocrine disruptors in river Danube”. Platform Presentation, NORMAN GA, 28-30 Nov 2018, Thessaloniki, Greece.

M. A. K. Hashmi, M. Krauss, B. I. Escher, W. Brack. “Identification of progestogens and glucocorticoids in river Danube using effect-directed analysis (EDA)”. Platform Presentation, SETAC GLB, 9-12 Sep 2018, Münster, Germany.

M. A. K. Hashmi, M. Krauss, B. I. Escher, I. Teodorović, W. Brack. “Identification of Gestagen(s) in Danube River impacted by untreated municipal wastewater effluents from Novi Sad, Serbia”. Poster Presentation, SETAC Europe, 12-18 May 2018, Rome, Italy.

168

M. A. K. Hashmi, B. I. Escher, M. Krauss, , W. Brack. “Effect-directed analysis (EDA) of Danube River water samples receiving UMWW from Novi Sad, Serbia”. Platform Presentation, SETAC Europe, 7-11 May 2017, Brussels, Belgium.

M. A. K. Hashmi, M. König, P. A. Neale, B. I. Escher, M. Krauss, T. Schulze, W. Brack. “Effect-directed analysis (EDA) of Danube River water samples receiving untreated municipal wastewater from Novi Sad, Serbia”. Poster Presentation, SETAC Europe, 22-26 May 2016, Nantes, France.

169

Author’s Contributions Paper I

Title: Nontarget screening based prioritization and identification of micropollutants from water samples of a large European river Danube

Authors: Muhammad Arslan Kamal Hashmi, Werner Brack, Tobias Schulze, Martin Krauss

Status: in preparation

Contribution: Muhammad Arslan Kamal Hashmi (>70%) designed research, data analysis, results discussion, wrote manuscript

Werner Brack (8%) results discussion, manuscript editing

Tobias Schulze (2%) sampling

Martin Krauss (20%) designed research, results discussion, manuscript editing

Paper II

Title: Effect-directed analysis (EDA) of Danube River water sample receiving untreated municipal wastewater from Novi Sad, Serbia.

Authors: Muhammad Arslan Kamal Hashmi, Beate Escher, Martin Krauss, Ivana Teodorovic, Werner Brack

Status: Published in 2018 in Science of the Total Environment, Vol. 624, pp 1072-1081

Contribution: Muhammad Arslan Kamal Hashmi (>70%) designed research, data analysis, results discussion, wrote manuscript

Beate Escher (9%) results discussion, manuscript editing

Martin Krauss (10%) results discussion, manuscript editing

Ivana Teodorovic (1%) manuscript editing

Werner Brack (10%) results discussion, manuscript editing

170

Paper III

Title: Advanced effect-directed analysis unraveling water contamination with progestogens and glucocorticoids at trace concentrations in a major European river.

Authors: Muhammad Arslan Kamal Hashmi, Martin Krauss, Beate Escher, Ivana Teodorovic, Werner Brack

Status: In preparation

Contribution: Muhammad Arslan Kamal Hashmi (>70%) designed research, data analysis, results discussion, wrote manuscript

Martin Krauss (15%) results discussion, manuscript editing

Beate Escher (5%) results discussion, manuscript editing

Ivana Teodorovic (1%) manuscript editing

Werner Brack (9%) results discussion, manuscript editing

171

Curriculum Vitae:

Name: Muhammad Arslan Kamal Hashmi Date of Birth: 01-December-1978 Country: Pakistan Address: Apartment 708, Strasse des 18 Oktober 33, 04103, Leipzig Email: [email protected]

Education Department of Effect-Directed Analysis (EDA) 2014 – 2019 PhD Helmholtz Centre for Environmental Research, UFZ- Leipzig, Germany & Institute of Biology V (Environmental Research), RWTH Aachen University, Germany

Funded by: Higher Education Commission of Pakistan (HEC), Islamabad, Pakistan & German academic exchange service (DAAD), Bonn, Germany Institute for Environment (IfE), 2007 – 2008 MSc Brunel University West London, United Kingdom Dissertation: An assessment of endocrine disruption potential of black liquor effluent from wheat straw pulp and paper manufacturing in Pakistan, using a lab-scale model College of Earth and Environmental Sciences (CEES), 2004 – 2006 MSc University of Punjab, Lahore, Punjab Pakistan Dissertation: Health, safety and environmental (HSE) hazards for pulp and paper manufacturing from wheat straw in Century Paper and Board Mill (CPBM).

172