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Ecotoxicology and Human Environmental Health
Genotoxicity Assessment of Drinking Water Disinfection By-products (DBPs) by DNA Damage and Repair Pathway Profiling Analysis Jiaqi Lan, Sheikh Mokhlesur Rahman, Na Gou, Tao Jiang, Michael J Plewa, Akram Alshawabkeh, and April Z Gu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06389 • Publication Date (Web): 16 Apr 2018 Downloaded from http://pubs.acs.org on April 16, 2018
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Genotoxicity Assessment of Drinking Water
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Disinfection By-products (DBPs) by DNA
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Damage and Repair Pathway Profiling Analysis
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Jiaqi Lan1,2, Sheikh Mokhlesur Rahman 1, Na Gou1, Tao Jiang1, Micheal J. Plewa3, Akram
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Alshawabkeh1 and April Z.Gu1,4*
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1
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Avenue, Boston, Massachusetts 02115, US.
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Department of Civil and Environmental Engineering, Northeastern University, 360 Huntington
Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical
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College, Beijing, 100050, China
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Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, US.
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*corresponding author:
[email protected] Safe Global Water Institute, University of Illinois at Urbana-Champaign and the Department of
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, 14850, US.
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ABSTRACT Genotoxicity is considered a major concern for drinking water disinfection by-products
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(DBPs). Of over 700 DBPs identified to date, only a small number of has been assessed with
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limited information for DBP genotoxicity mechanism(s). In this study we evaluated genotoxicity
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of 20 regulated and un-regulated DBPs applying a quantitative toxicogenomics approach. We
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used GFP-fused yeast strains that examine protein expression profiling of 38 proteins indicative
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of all known DNA damage and repair pathways. The toxicogenomics assay detected
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genotoxicity potential of these DBPs in consistent with conventional genotoxicity assays
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endpoints. Furthermore, the high-resolution, real-time pathway activation and protein expression
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profiling, in combination with clustering analysis, revealed molecular level details in the
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genotoxicity mechanisms among different DBPs and enabled classification of DBPs based on
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their distinct DNA damage effects and repair mechanisms. Oxidative DNA damage and base
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alkylation were confirmed to be the main molecular mechanisms of DBP genotoxicity. Initial
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exploration of QSAR modeling using moleular genotoxicity endpoints (PELI) suggested that
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genotoxicity of DBPs in this study was mainly contributed by topological and quantum chemical
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descriptors. This study presents a toxicogenomics-based assay for fast and efficient mechanistic
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genotoxicity screening and assessment of large number of DBPs. The results help to fill in the
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knowledge gap in the understanding of the molecular mechanisms of DBP genotoxicity.
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INTRODUCTION
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Drinking water disinfection by-products (DBPs) are formed during the reaction of disinfectants
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(such as chlorine, chlorine dioxide, chloramine, UV and ozone) with naturally occurring organic
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matter (NOM) and other contaminants present in water.1 DBPs are therefore widely existed in
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drinking water at sub- µg/L (ppb) to low-to-mid- µg/L levels. Currently, there are over 700 DBPs
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reported in drinking water, and new DBPs are continuing to be uncovered. 1-3
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Great knowledge gaps exist for toxicological information and health impacts of DBPs.
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Humans are exposed to DBPs through multiple routes, including ingestion (the common route
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studied), inhalation and dermal exposures. 3 Literature review indicates that only approximately
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15% (~100) of identified DBPs have been assessed with in vitro bioassays and a few with
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chronic in vivo studies.2-4 Potential health risks of DBPs have been reported, including cancer
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and other adverse reproductive effects, such as early-term miscarriage and birth defects. 5-7 An
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association of specific cancers and exposure to disinfected water has emerged by
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epidemiological research. 5, 8, 9 Several toxicity mechanisms for DBPs have been implicated,
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including genotoxicity, oxidative stress, disruption of folate metabolism and cell cycle
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disruption.5, 8, 9 Genotoxicity is of particular importance because of its link to mutagenicity,
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carcinogenicity as well as cancer. 7, 10
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The genotoxicity of evaluated DBPs seemed to be dependent on the structure and the
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substituents. For example, among the halogenated DBPs, iodinated DBPs were observed to be
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more toxic than their brominated and chlorinated analogs. 11, 12 Nitrogen-containing DBPs were
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more genotoxic than the DBPs that do not contain nitrogen. 3, 13 Genotoxicity mechanisms of
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DBPs are also strongly related to their structures. For example, for halogenated DBPs, oxidative
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stress-induced DNA damage 14-21 and DNA alkylation 22-25 are two major mechanisms.
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Halonitriles may also induce genomic damage by cell cycle disruption and the induction of
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hyperploidy. 26 Nitrosamines may act as alkylating agents after metabolism, 27 and formaldehyde
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can form DNA–protein crosslinks. 24 The genotoxicity and mechanisms of most DBPs remain
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unknown.
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The standard and most reliable genotoxicity tests are in vivo assays, however, they are
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resource-intensive and time-consuming, therefore cannot meet the demand for evaluating a large
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number of potential genotoxic DBPs.28, 29 In vitro genotoxicity assays, including Ames test,
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comet test and micronucleus test, require relatively shorter testing time (several days), but often
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yield inconsistent or false results compared to in vivo outcomes 7, 30, 31 due to the inherent
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limitations of the target and DNA damage effects they can detect.7, 30 In recent years, high-
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throughput genotoxicity assessment has been reported where the activation of single or selected
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biomarkers indicative of DNA damage recognition and repair are used to indicate potential
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genotoxicity.7, 32-37 Our group has recently developed and validated a new quantitative
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toxicogenomics-based assay based on real time protein expression profiling of known DNA
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damage and repair pathways ensemble. 38-41 Compared to other biomarkers-based tests, our assay
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derives quantitative endpoints that correlate with conventional genotoxicity endpoints and
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promises to be a cost-effective and mechanistic genotoxicity assessment assay. 40, 41
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In this study, we employed the newly developed quantitative toxicogenomics
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genotoxicity assay to perform a mechanistic genotoxicity assessment and profiling of 20 DBPs
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representing nine different chemical classes of DBPs. The results provided new genotoxicity
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information and insights of underlying DNA damaging mechanisms at molecular level for these
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20 DBPs. The high-resolution protein expression profiles of DNA damage and repair pathways
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also enabled DBP classification and further exploration of association between DBP chemical
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structure and the genotoxicity mechanisms.
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MATERIALS AND METHODS
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Chemicals
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20 DBPs were selected that belong to nine different chemical classes (Table 1,
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manufacturer information in Table S1). Each DBP was evaluated across six-log sub-cytotoxic
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concentration range (Table S1). The maximum non-cytotoxic concentration was pre-determined
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(> 95% cell survival tested by growth inhibition in yeast for 24 hours, Figure S1).
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Yeast Whole Cell Array and Real Time Protein Expression Analysis upon DBP Exposures
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The whole cell assay library consists 38 in frame GFP fusion proteins (Table S2) of
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Saccharomyces.cerevisiae (Invitrogen, no. 95702, ATCC 201388), constructed by
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oligonucleotide-directed homologous recombination to tag each open reading frame (ORF) with
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Aequrea victoria GFP (S65T) in its chromosomal location at the 3’ end, 42 covering all the seven
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known DNA damage repair pathways. The library expresses full-length, chromosomally tagged
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green fluorescent protein fusion proteins, 42 which makes the GFP signal reflect protein
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expression directly.
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Details of the proteomics assay for using GFP-tagged yeast cells were described in our
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previous reports. 38-41 Briefly, the yeast strains were grown in clear bottom black 384-well plates
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(Costar) with Synthetic Dextrose base (SD base) with Dropout (DO) supplement –His (Clontech,
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CA, US) for 4-6 h at 30 °C to reach early exponential growth. 10µL DBP sample aliquots in PBS
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or control (PBS only) were added to each well to obtain the target concentrations (Table S1). The
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plates were then placed in a Micro plate Reader (Synergy TM H1 Multi-Mode, Biotech, Winooski,
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VT) for absorbance (OD600 for cell growth) and GFP signal (filters with 485-nm excitation and
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535-nm emission for protein expression) measurements every 5 min for 2 hours after double
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orbital shake (425 cpm) for 1 minute. All tests were performed in dark in triplicate. Considering
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that all the DBPs were tested at concentrations much lower than their solubility, the evaporation
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and loss of the volatile DBPs during the 2-hr assay was not considered in this study. 43,44
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Protein Expression Profiling Data Processing and Quantitative Molecular Endpoints
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Derivation
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Temporal protein expression profiling data of yeast library were processed as described
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previously. 39, 41, 45 Temporal OD and GFP raw data are first corrected by background OD and
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GFP signal of blank media control with or without chemical, respectively. The protein
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expression level P for each protein biomarker (ORF) i, in treatment x, and at time point t is
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normalized by cell density as: Pi,x,t = (GFPi,x,t-corrected/ODi,x,t-corrected) (1)
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Where, GFPi,x,t-corrected is the GFP reading of protein i, in treatment x, at time t, corrected
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by the GFP reading in the blank media control at time t; ODi,x,t-corrected is the OD reading of
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protein i, in treatment x, at time t, corrected by the OD reading in the blank media control at time
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t.
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The altered protein expression in relative to untreated control (without chemical) for a
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given protein ORFi in treatment x, at time t, due to chemical exposure, also referred as induction
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factor I, is calculated as:
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Ii,x,t = Pi,x,t /Pi,untreated,t (2)
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Where, Pi,x,t = (GFPcorrected/ODcorrected)treatment x , is the altered protein expression GFP level
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for protein (ORF) i for treatment x, at time t, in the treated experimental condition with chemical
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exposure; Pi,untreated,t = (GFPcorrected/ ODcorrected) untreated control, is the altered protein expression GFP
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level for protein (ORF) i for treatment x, at time t, in the un-treated control without chemical
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exposure. P values of both treated experiments and untreated controls are normalized against
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internal control (housekeeping protein PGK146).
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To quantify the chemical-induced protein expression level changes of a treatment, Protein
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Effect Level Index (PELI) was derived as a quantitative molecular endpoint. 38-41 The
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accumulative altered protein expression change over the 2-hr exposure period for a given protein
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(ORF) i was calculated as:
, =
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!"# %
(3)
Where, t is the exposure time. For up-regulated protein, I (up-regulated) = I, when I≥1; for proteins that showed down-regulation, I (up-regulated) =1 when I