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MUTAGENICITY IN SURFACE WATERS: SYNERGISTIC EFFECTS OF CARBOLINE ALKALOIDS AND AROMATIC AMINES Melis Muz, Martin Krauss, Stela Kutsarova, Tobias Schulze, and Werner Brack Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b05468 • Publication Date (Web): 03 Jan 2017 Downloaded from http://pubs.acs.org on January 4, 2017

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Environmental Science & Technology

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MUTAGENICITY IN SURFACE WATERS: SYNERGISTIC EFFECTS OF

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CARBOLINE ALKALOIDS AND AROMATIC AMINES

3 4

Melis MUZ a,b*, Martin KRAUSS a, Stela KUTSAROVAc, Tobias SCHULZE a, Werner BRACK a,b

5 6

a

7

Permoserstraße 15, 04318 Leipzig, Germany

8

b

9

Research,Worringerweg 1, 52074 Aachen, Germany

Department Effect-Directed Analysis, Helmholtz Centre for Environmental Research - UFZ,

RWTH Aachen University, Department of Ecosystem Analyses, Institute for Environmental

10

c

11

8010 Bourgas, Bulgaria

Laboratory of Mathematical Chemistry, University “Prof. Assen Zlatarov”, 1 Yakimov Street,

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*Corresponding author:

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e-mail [email protected]; Phone: +49-341-235 1823

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1 ACS Paragon Plus Environment


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ABSTRACT

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Since decades mutagenicity has been observed in many surface waters with a possible link to the

18

presence of aromatic amines. River Rhine is a well-known example of this phenomenon but

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responsible compound(s) are still unknown. To identify the mutagenic compounds, we applied

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effect-directed analysis (EDA) utilizing novel analytical and biological approaches to a water

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sample extract from the lower Rhine. We could identify 21 environmental contaminants

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including two weakly mutagenic aromatic amines, and the known alkaloid co-mutagen

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norharman along with two related β-carboline alkaloids, carboline and 5-carboline, which were

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reported the first time in surface waters. Results of mixture tests showed a strong synergism of

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the identified aromatic amines not only with norharman, but also with carboline and 5-carboline.

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Additionally, other nitrogen-containing compounds also contributed to the mutagenicity when

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aromatic amines were present. Thus, co-mutagenicity of β-carboline alkaloids with aromatic

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amines is shown to occur in surface waters. These results strongly suggest that surface water

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mutagenicity is highly complex and driven by synergistic mechanisms of a complex compound

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mixture (of which many are yet unidentified) rather than by single compounds. Therefore,

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mixture effects should be considered not only from mutagens alone, but also including possible

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co-mutagens and non-mutagenic compounds.

33 34

TOC/Abstract Art

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INTRODUCTION

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Mutagenicity is frequently observed in surface waters due to the presence of chemicals of

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anthropogenic and natural origin 1. Mutagens entering freshwaters due to incomplete removal in

39

wastewater treatment systems and occurring in drinking water abstracted from polluted lakes and

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rivers result in adverse effects on aquatic 2, 3 and human life 4. Although several studies tried to

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identify the chemicals that cause the mutagenicity in the rivers, in only few cases individual

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chemicals could be identified as the cause of the observed effect 5-9, whereas in other studies the

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origin of mutagenicity was inconclusive and could not be explained by identified compounds 10-

44

12

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aromatic amines are an important compound class with many suspected or known environmental

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mutagens15-17. Aromatic amines are used as industrial chemicals and may be formed by

47

transformation of pesticides, dyes and nitroaromatic compounds18-22. Compounds like

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naphthylamines23, substituted anilines24,

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mutagenic/carcinogenic potentials. Furthermore, it was shown that heterocyclic aromatic amines

50

such

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methylimidazo[4,5-f]quinoline (IQ), 2-amino-alpha-carboline (AαC) or 2-amino-1-methyl-6-

52

phenylimidazo[4,5-b]pyridine (PhIP) originating from grilled/fried meat and fish are discharged

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by sewage effluents and contribute significantly to the mutagenicity of surface waters

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of these compound groups require metabolic activation by the N-oxidation to aryl-N-

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hydroxylamines, which in turn form nitrenium ions, the reactive electrophilic metabolite which

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covalently binds to DNA 29. Therefore, in water samples showing an increased mutagenicity with

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metabolic activation, the presence of mutagenic aromatic amines may be hypothesized. Many

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PAHs are also mutagenic after metabolic activation

. Along with polycyclic aromatic hydrocarbons (PAHs)

as

25

13

and nitro-aromatic compounds

14

,

or benzidine analogues26 are well-studied for their

2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline

30

(MeIQx),

2-amino-3-

27, 28

. All

and often predominate mutagenicity in



31

and sediments

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soils

, but their concentrations in water are generally very low due to their

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hydrophobicity.

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Since the 1970s, mutagenicity that increases with metabolic activation has been observed in

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different in vitro mutagenicity assays 33, 34 in samples from the lower stretch of the River Rhine.

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Although some aromatic amines were detected in the river 35-37, the causative chemical(s) for the

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observed effect remained unidentified up to now. These findings raise concerns, since the River

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Rhine serves as a source of drinking water for 20 million people in Germany and the Netherlands

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38

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Effect directed analysis (EDA) is a valuable tool to unveil the chemicals contributing to adverse

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effects of an environmental sample by integrating effect testing, fractionation and non-target

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analysis of active fractions to identify the chemicals driving the observed effects

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EDA studies were successful in identifying compounds responsible for mutagenic effects in

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sediments

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contributions were found such as phenylbenzotriazoles (PBTAs)5, 43 or benzidine derivatives (5-

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nitro-DCB)7, the causes of mutagenicity remain largely inconclusive in many surface waters 10 or

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wastewater effluents

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liquid chromatography-mass spectrometry (LC-MS) data and to the limited information of

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analytical data for a reliable compound identification. However, it might be also hypothesized

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that mutagenicity in water samples is less clearly related to individual pre-dominant mutagens,

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but the result of mixture effects involving a larger number of chemicals.

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In this study we make a new attempt to unravel surface water mutagenicity by combining EDA

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based on Ames testing with different Salmonella strains with novel analytical and prediction

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tools. A recently developed derivatization method44 was applied for peak selection and pH

.

41, 42

39, 40

. Several

. Although in some cases a limited number of novel key toxicants with major

11

. This might be attributed to the lack of extensive spectral libraries for


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dependent LC retention and hydrogen-deuterium exchange experiments were conducted to

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reduce candidate numbers. In addition to the well-known nitrenium calculation model45, 46, the

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tissue metabolic simulator AMES mutagenicity model (TIMES version 11.11)47 was used to

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discriminate possible mutagenic chemicals. Finally the mutagenicities of confirmed candidates

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were evaluated for single compounds and in mixture effect experiments.

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MATERIALS AND METHODS

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Details on the solvents and reagents used are given in the Supporting Information (SI), section

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S1.

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Sampling and sample preparation

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The sample was taken in May 2014 from the Lower Rhine at the Dutch monitoring station in

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Lobith over a period of 42 hours. A large volume solid phase extraction (LVSPE) instrument was

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used to collect 800 L of water on site using three stacked sorbent cartridges containing 160 g of

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Chromabond HR-X, 100g of Chromabond HR-XAW, and 100 g of Chromabond HR-XCW

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(Macherey Nagel, Düren, Germany) in the direction of the water flow48. Sorbents were freeze

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dried and eluted. The eluates were combined and the final volume was reduced to 500 mL by

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means of a rotary evaporator. Additionally, a blank simulating a sample of 1000 L equivalent

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was prepared. More details on the sample preparation are given in the SI, section S1.1.

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Mutagenicity Assay

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The Ames test utilizing several strains with characteristic metabolic pathways or different

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mutations yields information regarding specific groups of mutagens and was shown to be a

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successful approach in the identification of environmental mutagens

49

. In our study, the Ames 5

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fluctuation assay (Ames II test) was performed using strain TA98 with and without metabolic

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activation by S9 and strain YG1024 only with metabolic activation by S9 as described in

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Reifferscheid et al.50 with slight modifications. YG1024 is characterized by an elevated o-

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acetyltransferase activity making it more sensitive for aromatic amines, when S9 is included 51.

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The mutagenic activity was determined in triplicates for every test and calculated by fitting the

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results to an exponential equation reaching a maximum of 48 (Eq. S1). Mutagenicity is

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quantified by the slope of this curve at the origin52 and given as number of revertants per liter of

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water equivalent. More details on the test are given in the SI, section S1.2.

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Fractionation

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An aliquot of the LVSPE extract corresponding to 62.5 L of water was fractionated on a C18 LC

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column (Agilent Zorbax Extend-C18, 9.4 x 250 mm, particle size 5 µm). In total 27 two-minute

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fractions were collected. The fractions were diluted with H2O to have a maximum content of 5%

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of MeOH and extracted by solid phase extraction using 200 mg of HR-X in between two PE frits

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in glass cartridges. Cartridges were eluted using 10 mL of MeOH:EtAc (1:1, v:v), evaporated to

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dryness under a mild nitrogen stream and re-dissolved in 1 mL of MeOH for further biological

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and chemical analysis. All fractions were tested at two concentrations corresponding to relative

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enrichment factors (REF) of 1000 and 2000 with both strains with S9 activation. More details on

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the fractionation procedure are given in the SI, section S1.3.

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Derivatization

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Mutagenic fractions with increased activity on YG1024 compared to TA98 were subjected to a

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derivatization method for amines described in Muz et. al44. Briefly, an aliquot of 30 µL was

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taken from each fraction. Volumes of 10 µL of the derivatization reagent 4-fluoro-7-nitro-2,1,36 ACS Paragon Plus Environment

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benzoxadiazole (NBD-F) and 2 µL of 20 mM ammonium acetate buffer (pH 5.7) were added and

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the mixture was heated to 80°C for 30 minutes. All aliquots were derivatized with two different

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concentrations of reagent (0.8 and 10 mM NBD-F).

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LC-HRMS Analysis and Data Evaluation

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Derivatized and underivatized samples were analyzed by LC-HRMS using a Thermo Ultimate

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3000 LC system equipped with a ternary pump, autosampler and a column oven connected to an

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LTQ Orbitrap XL (Thermo). A phenyl-hexyl column (Accucore PhenylHexyl 150 x 3 mm, 2.6

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µm particle size, Thermo) was used for chromatographic separation at 30°C. HRMS analyses

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were conducted in positive ion mode with a heated electrospray ionization source. Full scan

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spectra were acquired in the mass range of m/z 100-1000 at a nominal resolving power of

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100,000 (at m/z 400). Product ion spectra (MS/MS) were acquired with a data-dependent method

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triggered for the two highest intensity peaks recorded at the full scan analysis. Details are given

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in the SI, section S1.4.

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Peak lists of derivatized and underivatized samples from the full scan chromatograms were

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obtained using the software MZmine 2.1053. Detailed software settings are given in the SI,

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section S1.5. A data matrix was created to find the analyte derivative matches by using the

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accurate mass difference that resulted from the addition of an NBD-F molecule (163.0018 ±

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0.0008, corresponding to 5 ppm). Separate matrices were prepared for the peak lists obtained

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with different NBD-F concentrations. The derivatives were confirmed by the occurrence of 1-8

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diagnostic fragments observed in the MS/MS spectra.

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Masses of interests in the active fractions were selected which can be categorized as: (i) masses

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of confirmed derivatives and (ii) masses that match with a potential derivative peak that is only

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present in the derivatized sample but did not trigger an MS/MS. Both derivatized and 7 ACS Paragon Plus Environment


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underivatized fractions were re-run with a Q-Exactive Plus instrument (Thermo). Details are

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given in the SI, section S1.4. Molecular formulas of the peaks of interest were assigned using the

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XCalibur software (Thermo) by limiting the elements according to isotope patterns. A workflow

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summarizing the steps until molecular formula assignment is given in Figure S1 in the SI.

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Candidate structure lists from the analyte MS/MS spectra with the highest number of meaningful

153

fragments were obtained using the software MetFrag 2.2 (command line version

154

ChemSpider (Royal Society of Chemistry) as the compound search database. Details of the

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settings for MetFrag are given in the SI, section S1.6. A score cut-off of 0.7 was used and the

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remaining candidates were sorted according to the number of references in ChemSpider giving

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an indication on the potential commercial and environmental relevance of the compound. For the

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lists without candidates with a high number of references (>10), 0.6 was used as the score cut-

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off. Additionally, the spectra of the analytes were compared with the spectra present in

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MassBank

161

confirmation was done and is reported according to the confidence levels proposed by

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Schymanski et al.

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procedure above were confirmed with reference standards and reported as confirmed structures

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(level 1). Analytes having mass spectra in full agreement with MassBank spectra and plausibility

165

as environmental pollutants were directly accepted for final confirmation with standards. For the

166

confirmation of a compound, not only the match of retention time (RT) and MS/MS of the

167

analyte but also of the NBD-F derivatives were compared.

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Confirmed compounds were then tested for their mutagenicity with both Ames strains in the

169

presence of S9 metabolic activation.

55

54

) with

for the same molecular formula when available. Compound identification and

56

. Whenever possible, plausible candidates selected according to the


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Complementary Approaches for Candidate Selection and Confirmation

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In cases without outstanding candidates with spectra closely similar to Massbank

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those with more than 100 references showing commercial and probable environmental relevance,

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two additional methods were applied to reduce the number of plausible candidates for a given

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molecular formula.

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The applied workflow for the selection or exclusion of candidates for generated molecular

176

formulas and the subsequent mutagenicity prediction and evaluation of confirmed compounds is

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summarized in Figure 1.

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spectra or

178 179

Figure 1. Applied workflow summarized for compound identification and mutagenicity

180

evaluation.

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The candidate satisfying the criteria of both methods and having the highest number of

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references was accepted as the most plausible structure and a confirmation with reference

184

standards was done.

185 186

pH-dependent LC retention

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The LC retention of ionizable molecules such as amines depends strongly on their charge and

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thus on the pH of the eluent. Thus, we compared the retention of the unknowns of interest at a

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mobile phase pH of 2.6, 6.4, and 10.0.

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As most aromatic amines should have pKa values between 2.6 and 5.5, their retention times are

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expected to be lower at pH 2.6 and higher at pH 6.4 and 10.0, while aliphatic amines with pKa

192

values above 8 are expected to have similar retention times at pH 2.6 and 6.4, but higher ones at

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pH 10.0, and neutral compounds should have similar retention times at all three pH values.

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Candidate structures in disagreement with expected retention behavior at different pH were

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rejected. The acidic and basic pKa values of candidate structures were calculated using the

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Calculator Plugins JChem for Excel (version 6.3.0, Chemaxon)57. Those candidates showing pKa

197

values compatible with the observed RT shifts were further processed. Details on the method and

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criteria for candidate selection based on observed retention time shifts and calculated pKa values

199

are given in the SI, section 1.7.

200 201

Hydrogen Deuterium Exchange

202

Hydrogen deuterium exchange (HDX) LC-HRMS analysis was used to determine the number of

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exchangeable hydrogen atoms (i.e., typically those connected to heteroatoms) of the selected

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unknowns of interest and candidate lists were limited to those molecules corresponding to that


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58-60

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number to gain information about the presence and number of polar functional groups

. For

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HDX experiments, a 50-min LC gradient elution program was applied. Eluent A was replaced by

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deuterium oxide (99.9 atom % D, Sigma-Aldrich) with 0.1% formic acid and eluent B was

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replaced by methan-d1-ol (99.8 atom % D, Sigma-Aldrich). Aliquots were analyzed with a

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nominal resolving power of 140,000 (at m/z 200) in the mass range of 100-700 m/z using a

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QExactive Plus instrument. The HDX full scan spectra were searched manually using the

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Xcalibur QualBrowser for peaks with an m/z value corresponding to an increasing number of

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hydrogens exchanged based on the “normal” run m/z. For each candidate structure, the number

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of heteroatom-attached hydrogens was calculated by the substructure search function of JChem

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for Excel57 and candidates fitting with the observed exchanged hydrogen counts were further

215

evaluated.

216 217

Mutagenicity Prediction via Nitrenium Ion Stability for Primary Amines

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In accordance with Ford et al

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or heterocyclic aromatic primary amine (ArNH2) relative to the baseline compound-aniline

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(PhNH2) was used to predict their mutagenicity. Heat of formation values (∆Hf) of the

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compounds and their respective nitrenium ions were calculated by the semi empirical model

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PM7 by using MOPAC

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equation S2 in SI. A negative value of the enthalpy change indicates a more stable nitrenium ion

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for the aromatic amine (ArNH+) compared to the non-mutagenic reference compound aniline

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(PhNH+), which correlates positively with the mutagenic activity. Compounds with a ∆∆E < 0.0

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kcal/mol were accepted as probably Ames positive. Details are given in the SI, section S1.8.

61

45

and Bentzien et al 46, the stability of nitrenium ions of aromatic

and the difference in enthalpy (∆∆E) was obtained according to



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TIMES Ames Mutagenicity Model

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TIMES Ames mutagenicity model is a SAR approach that predicts the DNA mutagenicity of

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chemicals to any of the Salmonella typhimirium strains that are typically used in an Ames test47.

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There are two types of TIMES Ames models - with and without rat liver S9 metabolic activation.

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In order to provide predictions for a parent chemical and its stable S9 metabolites, TIMES Ames

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with S9 activation model combines reactivity of chemicals to DNA and the S9 metabolic

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simulator. So far this model has not been used as a candidate selection criterion. The

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mutagenicity of the candidates and their S9 metabolites was predicted according to the model

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version “TIMES in vitro Ames mutagenicity S9 activated” v.11.11.

236 237

RESULTS AND DISCUSSION

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Mutagenicity Testing and Identification of Active Fractions for Candidate Mutagen

239

Identification

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The water extract exhibited a mutagenic response with and without S9 in TA98. Metabolic

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activation by S9 enhanced the response of TA98 strain, while the response of YG1024 was 4.6

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times higher than of TA98 (both with S9) (Fig. S2) confirming the contribution of mutagenic

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amines51. The mutagenic activities were calculated as: 461, 101 and 24 rev L-1 water eq. with

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strains YG1024, TA98 with S9 and TA98 without S9, respectively. The processed blank did not

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show mutagenicity with either of the strains.

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Among the collected fractions, nine showed mutagenicity with TA98 with S9. In total, 14

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fractions exerted an increased response with YG1024 and were accounted as active fractions.

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Recovery of the mutagenic activity after fractionation was confirmed through both strains

249

comparing the activity of a recombination of all fractions with the original water extract (RAW)


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using both strains with S9 (see Fig. 2). Results of all fractions are given in Table S2 in the SI.

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These results revealed the presence of a larger number of mutagenic compounds and mixtures

252

than expected with a wide range of lipophilicity and thus were not helpful to relate the observed

253

effects to individual compounds. However, fractionation was crucial for selective analytical

254

identification of aromatic amines. The derivatization yield for most amines showed a high

255

dependency on matrix load44, which could be significantly reduced by fractionation serving as an

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efficient clean up strategy without any significant losses in overall mutagenicity. Based on these

257

findings we followed the strategy to identify candidate mutagens in the fractions, but to consider

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them as components of one mixture representing the parent water extract. 48 44

TA98 REF1000 TA98 REF2000 YG1024 REF1000 YG1024 REF2000

40

number of revertants (max=48)

36 32 28 24 20 16 12 8 4 0

Fractions(min)

259 260

Figure 2. Mutagenicity of active fractions with TA98 and with YG1024 strain with addition of

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S9.



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Screening for parent candidate-derivative pairs

263

Based on the mass difference before and after derivatization with NBD-F (163.0018 ± 5 ppm), in

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total 237 candidate parent-derivative matches were found in the active fractions. Eleven and 148

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matches were found in the matrices created with the peak lists of 0.8 mM and 10 mM NBD-F

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derivatized fractions, respectively, and 39 matches were detected in the matrices of both

267

concentrations. From all candidates, 83 derivatives were confirmed with diagnostic fragments.

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Among them 78 showed more than one diagnostic fragment, while 23 of the candidates exhibited

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no diagnostic fragment in the MS/MS spectra and 23 of them were detected also in the

270

underivatized fractions. Both sets of candidates were eliminated from further data processing.

271

The remaining 63 matches included 37

272

more than one analyte peak but one visible derivative peak and eight matches with more than one

273

derivative peak but one visible analyte peak. The candidates with confirmed derivatives (83)

274

were evaluated manually in XCalibur and molecular formulas for 70 analyte peaks could be

275

assigned based on accurate mass and isotope patterns. For six out of these a Chemspider search

276

revealed no aromatic compounds and thus these were removed from further evaluations. A

277

number of 13 peaks were eliminated due to broad, dispersed or not well separated peak shapes

278

and unclear links between the analyte peak and the detected derivative.

279

Compound Identification and Confirmation

13

C isotope peak-derivative matches, 18 matches with

280 281

Out of 64 candidates with confirmed derivatives and successfully assigned molecular formula,

282

21 micropollutants from different compound classes that are commonly detected in surface

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waters and rather new carboline alkaloids were identified and confirmed with reference standards

284

as shown in Table 1. A more detailed summary is given in Table S3 in SI. Among the identified 14 ACS Paragon Plus Environment

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compounds, eight of them without MassBank spectra similarity had in total 705 candidates with

286

a score higher than 0.7 and 279 of them exhibited more than 10 references. Candidate exclusion

287

using pH-dependent retention reduced the number of possible structures to 427, of which 142

288

had more than 10 references. The use of HDX for candidate exclusion selected 274 possible

289

candidate structures, 95 of them with more than 10 references. 125 candidates with a score

290

higher than 0.7 were in compliance with HDX and pH-dependent retention with only 50 of them

291

with more than 10 references.

292

The identified compounds include eight pharmaceuticals: 4-aminoantipyrine, candesartan,

293

sotalol, sitagliptin, metoprolol, lamotrigine, 2- and 4-phenylpyridine, five industrial chemicals:

294

4-dimethylaminopyridine

295

quinolinol and isoquinolone, two agricultural chemicals: metamitron-desamino and isopentenyl

296

adenine, two industrial aromatic amines: o-toluidine and 2,6-xylidine used as dye precursors and

297

four alkaloids: cotinine and the three carboline isomers norharman, carboline and 5-carboline.

298

After confirming the presence of metamitron-desamino, metamitron was searched manually and

299

found in the same fraction and confirmed with the reference standard. All the reference standards

300

were also derivatized and used for confirmation of the respective derivative peaks when possible.

301

The concentrations of the confirmed compounds were semi-quantified with a comparison of peak

302

areas found in the fractions with the peak areas of the reference standards. The MS/MS spectra

303

for all the analyte peaks and the corresponding reference standards are given in SI, section S2.2.

304

In addition to the positively identified chemicals a large number of peaks remained unidentified

305

(Table S4) although the applied filtering approach reduced the number of plausible candidate

306

structures to a large extent. Many of the remaining candidates were predicted positive with either

307

one of the mutagenicity models and in some cases, the only candidate left was predicted positive

(4-DMAP),

1H-benzotriazole,

5-methyl-1H-benzotriazole,

6-



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with both models. These results showed that there are many unidentified compounds with

309

mutagenic potential in the active fractions that could be contributing to the overall mutagenicity.

310

Among the chemical signals that were detected in the mutagenic fractions, but could not be

311

positively identified, several peaks were found having the same exact mass as the carboline

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isomers norharman, carboline and 5-carboline and showing common fragments with the

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carboline isomers in the MS/MS spectra. Four to eight common fragments were observed for

314

each peak as summarized in Table S5 in SI. Furthermore, these unknowns showed the same pH

315

dependent LC retention shifts and the same number of exchangeable hydrogen atoms in the HDX

316

method, suggesting that these unknowns are further closely related isomers. Possible candidate

317

compounds are listed in Table S6 in SI.

318


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Table 1. Summary of identified compounds in the active fractions. All were confirmed by

320

reference standards. Found in

Formula

Fraction

Candidates with

MassBank

# of Candidates

Compound

Rank / Score

a score > 0.7

Similarity

complying with

Name

(Ref#) in

( ref#>10)

both LC methods

Use/origin

MetFrag Lists

(Ref >10) 4-dimethyl 4_5

C7H10N2

273 (85)

no

33 (3)

1 / 0.96 (520)

catalyst

beta blocker drug

aminopyridine

4_5

C12H20N2O3S

*

yes

-

sotalol

1 / 0.36 (1554)

6_7

C10H12N2O

925 (61)

yes

-

(S)-(-)-cotinine

3 / 0.95 (136)

alkaloid, human nicotine metabolite

14_15

C24H20N6O3

59 (4)

yes

-

14_15

C11H13N3O

1901 (74)

yes

-

candesartan

1 / 0.72 (1090)

antihypertensive drug

1 / 0.85 (721)

anti-inflammatory drug

4-amino antipyrine 1H14_15

C6H5N3

30 (14)ǁ

yes

-

corrosion 1 / 0.67 (635)

benzotriazole

16_17

C15H25NO3

670 (8)

yes

-

16_17

C10H9N3O

439 (38)

yes

-

metoprolol

inhibitor

1 / 0.92 (3665)

metamitron-

beta blocker drug

transformation product of 2 / 0.74 (96)

desamino

metamitron

16_17

C10H10N4O

-

-

-

metamitron

-

herbicide

16_17

C9H7NO

129 (41)

no

14 (14)

6-quinolinol

9 / 0.70 (175)

chelating agent

16_17

C9H7NO

61 (28)

no

16 (6)

isoquinolone

3 / 0.86 (234)

chelating agent

16_17

C11H8N2

133 (35)

no

18 (9)

5-carboline

3 / 0.61 (64)

alkaloid

18_19

C7H9N

26 (4)

yes

-

o-toluidine

3 / 0.98 (523)

dye precursor

¥



Page 18 of 30

Cont`d Found in

Formula

Fraction

Candidates with

MassBank

# of Candidates

Compound

Rank / Score

a score > 0.7

Similarity

complying with

Name

(Ref#) in

( ref#>10)

both LC methods

Use/origin

MetFrag Lists

(Ref >10)

18_19

C16H15F6N5O

8 (2)

yes

-

sitagliptin

1 / 1.0 (120)

antidiabetic drug

18_19

C9H7Cl2N5

26 (4)

yes

-

lamotrigine

1 / 0.94 (2011)

anticonvulsant drug

18_19

C7H7N3

32 (16)

yes

-

5-methyl

corrosion 1 / 0.70 (195)

benzotriazole

22_23

C8H11N

32 (16)

yes

-

22_23

C10H13N5

83 (6)

yes

-

2,6-xylidine

inhibitor

3 / 0.93 (285)

dye precursor

1 / 1.0 (104)

plant growth regulator

isopentenyl adenine ¥

26_27

C11H8N2

147 (35)

28_29

C11H8N2

28_29

C11H9N

no

18 (6)

norharman

1 / 0.61 (323)

alkaloid

157 (43)

no

16 (6)

carboline

2 / 0.62 (82)

alkaloid

39 (6)

no

5 (3)

¥

4-phenyl

monoamine oxidase 2 / 0.70 (180)

pyridine

(MAO) inhibitor

2-phenyl 28_29

C11H9N

39 (6)

no

5 (3) pyridine

321 322 323 324 325 326

monoamine oxidase 1 / 0.71 (324) (MAO) inhibitor

* For the mass 272.1194, only 12 candidates had a reference number higher than 10 with scores between 0.15-0.36. Therefore, no score cut-off was applied. ǁ

For the mass 119.0483, with a score cut off 0.7, the sole candidate with a reference number higher than 10 was phenylazide. Since the structure

of phenylazide is not favorable for yielding a derivative, 0.6 was used as the score cut off. ¥

For the mass of 168.0687, with a score cut off 0.7, no environmental relevance was found for the sole candidate with a reference number higher

than 10. Therefore, 0.6 was used as the score cut off.


Page 19 of 30


327

Contribution of identified compounds and mixtures thereof to sample

328

mutagenicity

329

In order to identify probable causes of observed mutagenicity, literature was searched for data on

330

mutagenicity of the identified compounds. Ames mutagenicity was predicted by both the TIMES

331

and the nitrenium ion stability models. The aromatic amines o-toluidine and 2,6-xylidine were

332

predicted positive with both models and also have been reported as weak mutagens in the Ames

333

test with strains TA98 and TA100, respectively, with metabolic activation in previous studies 25,

334

62

335

were predicted to be Ames positive with S9 according to the TIMES model. One should

336

emphasize that the predictions did not belong to the model domain, which decreases their

337

reliability.

338

The identified compounds were tested separately and in mixtures with relative concentrations in

339

agreement with the original sample composition. Absolute test concentrations up to 1000-fold

340

above the concentration in the sample were used to establish the concentration-response models

341

as shown in S2.4 in SI (Figs. S3-5). The test mixtures were designed by grouping the chemicals

342

as 1) aromatic amines with known mutagenic potency (o-toluidine and 2,6-xylidine), 2) possible

343

mutagens predicted by TIMES (metamitron, lamotrigine and 4-phenylpyridine), 3) norharman,

344

carboline and 5-carboline that might act as co-mutagens63-67, and 4) all other identified

345

compounds predicted to be non-mutagenic (others). All compounds and mixtures were tested

346

with YG1024 with S9 activation (Fig. 3).

. In addition to o-toluidine and 2,6-xylidine, 4-phenylpyridine, lamotrigine and metamitron



Page 20 of 30

400 350

10-3 rev / L water eq.

300 250 200 150 100 50 0

347 348

Figure 3. Mutagenicity of artificial mixtures designed according to the relative composition of

349

the parent sample in 10-3revertants (rev) per L water equivalent derived from the slope of full

350

concentration response relationships of strain YG1024 with S9. o-toluidine: OTO, 2,6-xylidine:

351

XYL, norharman: NH, carboline: CB, 5-carboline: 5CB, possible mutagens predicted by

352

TIMES (metamitron, lamotrigine and 4-phenylpyridine): pms, other compounds predicted to be

353

non-mutagenic: others

354 355

No mutagenicity was observed for any of the single compounds identified by either TA98 or

356

YG1024 with S9, up to 1000-fold enhanced concentrations compared to original water samples.

357

This held also true for the mixture of the two aromatic amines o-toluidine/2,6-xylidine, for

358

norharman alone and in combination with other carbolines. o-toluidine with norharman resulted

359

in significant mutagenicity of 10 × 10-3 rev/L water eq which increased to 14 × 10-3 rev/L water


Page 21 of 30


360

eq when 2,6-xylidine was added. The mutagenicity was enhanced by a factor of 14 for o-

361

toluidine to 139 × 10-3 rev/L water eq. and by a factor of 16 for o-toluidine/2,6-xylidine to 230 ×

362

10-3 rev/L water eq. if all three identified carbolines were present in the relative concentrations

363

found in the water sample. Co-exposure of YG1024 to o-toluidine/2,6-xylidine together with the

364

three TIMES-predicted mutagenic substances did not result in any mutagenic response up to a

365

concentration 1000-fold above the original water concentrations. Along with norharman 120 ×

366

10-3 rev/L water eq. were observed, 8 times more than without TIMES-predicted mutagenic

367

substances. Involving all three carbolines the effect was doubled (250 × 10-3 rev/L water eq.).

368

The mixture of identified compounds (without carbolines) exhibited some mutagenicity (19 × 10-

369

3

370

rev/L water eq. respectively) and strongly increased by norharman to 168 × 10-3 rev/L water eq..

371

However, it should be considered that the concentrations of carboline and 5-carboline where

372

about one order of magnitude below the norharman concentration. All compounds together at

373

relative concentrations resembling the original sample exhibited a mutagenic effect of 302 × 10-3

374

rev/L water eq. This is still three orders of magnitude below the effect of the raw sample. Thus,

375

the identified compounds still only explain a minor fraction of measured mutagenicity but they

376

clearly illustrate the synergism of mutagenic effects of typical water contaminants even without

377

components with a significant individual effect.

378

The results suggest a particularly strong synergistic effect between carbolines and aromatic

379

amines. This effect has been shown for norharman and some other β-carboline alkaloids

380

including harman, harmine, harmol, harmaline and harmalol found in plants, tobacco smoke,

381

well-cooked foods68 and roasted coffee69 together with aniline70,

382

isomers66. There are different mechanisms proposed, all starting with a reaction of the aromatic

rev/L water eq.) that was slightly increased by carboline and 5-carboline (29 and 43 × 10-3

71

and different toluidine



Page 22 of 30

383

amine with the pyrrole nitrogen under the catalysis of cytochrome P450 oxygenases to (in the

384

case of norharman and aniline) aminophenylnorharman67,

385

further activated by N-hydroxylation and acetylation resulting in a nitrenium ion that is the final

386

electrophile that forms DNA-adducts67 or (2) forms hydroxamino- and nitrosophenylnorharman

387

causing oxidative DNA damage71. Aminophenylnorharman has been reported as a carcinogen in

388

mouse and rat73, 74 and was detected in human urine samples75.

389

There is only one previous study that detected norharman and some chlorinated harmans in the

390

effluent of a sewage treatment plant76 while neither carboline nor 5-carboline have ever been

391

detected as environmental contaminants before. The results of the present study strongly suggest

392

that the synergistic effects of carbolines and aromatic amines might play an important role in

393

environmental mutagenicity and contribute to the explanation of mutagenic effects in river water.

394

It could be also shown that besides norharman also other less known compounds such as

395

carboline and 5-carboline strongly contribute to the synergistic effect. In previous studies it had

396

been shown that other β -carbolines like harman and harmin68, 77 and also 3-methylindole and

397

indole78 caused an enhancement in the mutagenicity of aromatic amines.

398

The occurrence of many different carbolines and indoles probably acting as co-mutagens in

399

addition to the possible mutagens (as listed in Table S5) might also help to explain parts of the

400

still substantial mismatch between the mutagenicity detected in the water sample fractions and

401

the mutagenicity exerted by identified chemicals. In the present study in 10 of the 14 active

402

fractions peaks with the exact mass of norharman were detected. At the same time the present

403

results indicate that if some aromatic amines that can undergo the reaction with carbolines are

404

present (without o-toluidine/2,6-xylidine no mutagenicity was observed even with norharman)

405

additional nitrogen-containing compounds can further enhance mutagenicity even if they do not

70, 72

. This intermediate is either (1)


Page 23 of 30


406

show any mutagenicity individually. The mechanism behind this finding is unknown. Mixture

407

mutagenicity assessment typically follows an effect addition model summing up the number of

408

revertants induced per amount of compound or fraction determined as the slope of linear

409

concentration-relationships and thus being also in agreement with the model of concentration

410

addition. This approach has been successfully applied for airborne particles79 and sediments80,

411

where mutagenicity is predominated by polycyclic aromatic hydrocarbons and related

412

compounds. Deviations have been reported for high concentrations or increasing mixture

413

complexity resulting in lower numbers of revertants than expected from additivity due to an

414

interference of inhibiting processes41. So far, mixture effects and particularly synergism in

415

mutagenicity of water samples have been rarely reported81 but may be hypothesized as one of the

416

reasons that explains why previous studies failed to identify individual compounds as the cause

417

of mutagenicity in river waters10,

418

YG1024 for aromatic amines- should be considered which is shown to act as an effective

419

diagnostic tool to unveil not only the mutagenicity of single compounds, but also mutagenicity of

420

mixtures involving these compound classes. Moreover, a systematic filtering of candidates using

421

HDX, pH dependent retention time shifts and number of references improved the candidate

422

selection significantly, suggesting this approach as useful for the identification of

423

environmentally relevant pollutants when mass spectral libraries lack a similar entry, although

424

biasing candidate lists towards more well-studied compounds.

425

The mixture mutagenicity experiments in the present study were driven by the attempt to explain

426

the mutagenic potency in the analyzed sample and thus based on a mixture design mimicking the

427

original water composition. However, it indicates that a rigorous investigation of mixture

428

mutagenicity involving aromatic amines, carbolines and related co-mutagens as well as other

52

. For that purpose, employing different strains -such as



Page 24 of 30

429

mutagenic and non-mutagenic water contaminants is required in order to understand the

430

mechanisms behind the mixture effects found in this study and to identify the drivers of the

431

effects in surface waters. At the same time, those drivers identified in the River Rhine including

432

particularly aromatic amines (e.g., from industrial processes) and carbolines, which might be

433

largely natural alkaloids, should be included into water monitoring in order to assess the

434

potential risks and the sources of these water contaminants for aquatic ecosystems and

435

particularly for drinking water abstraction.

436 437

SUPPORTING INFORMATION

438

Detailed information on sample preparation, fractionation, LC-HRMS analysis and additionally

439

bioassay results, spectra of identified compounds, unidentified possible mutagens and

440

concentration-response plots of designed mixtures are given in SI.

441 442

ACKNOWLEDGEMENTS

443

This work was funded by the EDA-EMERGE project (FP7-PEOPLE-2011-ITN, grant agreement

444

290100) and the SOLUTIONS project (grant agreement 603437), both supported by the EU

445

Seventh Framework Programme, and the ToxBox project funded by the German Federal

446

Ministry for Education and Research (BMBF) under the grant agreement 02WRS1282C. We

447

thank Jörg Ahlheim and Margit Petre for their technical support and Arnold Bahlmann for his

448

valuable ideas. We express our gratitude to Takehiko Nohmi and Masami Yamada from National

449

Institute of Health Sciences, Japan, who generously provide the YG1024 strains and also Canan

450

Karakoc, for her precious assistance in handling the strains. Chemaxon (Budapest, Hungary) is


Page 25 of 30


451

acknowledged for providing an academic license of JChem for Excel, Marvin and the Calculator

452

Plugins. A free academic license of MOPAC2016 was kindly granted by James J.P. Stewart.



Page 26 of 30

453

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454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497

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