Mixture Toxicity Revisited from a Toxicogenomic ... - ACS Publications

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Mixture Toxicity Revisited from a Toxicogenomic Perspective Rolf Altenburger,*,†,‡ Stefan Scholz,† Mechthild Schmitt-Jansen,† Wibke Busch,† and Beate I. Escher‡ †

Department Bioanalytical Ecotoxicology, UFZ − Helmholtz Centre for Environmental Research, Permoser Street 15, 04318 Leipzig, Germany ‡ National Research Centre for Environmental Toxicology (Entox), The University of Queensland, 39 Kessels Road, Brisbane, QLD 4108, Australia S Supporting Information *

ABSTRACT: The advent of new genomic techniques has raised expectations that central questions of mixture toxicology such as for mechanisms of low dose interactions can now be answered. This review provides an overview on experimental studies from the past decade that address diagnostic and/or mechanistic questions regarding the combined effects of chemical mixtures using toxicogenomic techniques. From 2002 to 2011, 41 studies were published with a focus on mixture toxicity assessment. Primarily multiplexed quantification of gene transcripts was performed, though metabolomic and proteomic analysis of joint exposures have also been undertaken. It is now standard to explicitly state criteria for selecting concentrations and provide insight into data transformation and statistical treatment with respect to minimizing sources of undue variability. Bioinformatic analysis of toxicogenomic data, by contrast, is still a field with diverse and rapidly evolving tools. The reported combined effect assessments are discussed in the light of established toxicological dose−response and mixture toxicity models. Receptor-based assays seem to be the most advanced toward establishing quantitative relationships between exposure and biological responses. Often transcriptomic responses are discussed based on the presence or absence of signals, where the interpretation may remain ambiguous due to methodological problems. The majority of mixture studies design their studies to compare the recorded mixture outcome against responses for individual components only. This stands in stark contrast to our existing understanding of joint biological activity at the levels of chemical target interactions and apical combined effects. By joining established mixture effect models with toxicokinetic and -dynamic thinking, we suggest a conceptual framework that may help to overcome the current limitation of providing mainly anecdotal evidence on mixture effects. To achieve this we suggest (i) to design studies to establish quantitative relationships between dose and time dependency of responses and (ii) to adopt mixture toxicity models. Moreover, (iii) utilization of novel bioinformatic tools and (iv) stress response concepts could be productive to translate multiple responses into hypotheses on the relationships between general stress and specific toxicity reactions of organisms.

1. INTRODUCTION The combined effects provoked in organisms through exposure to mixtures of chemicals have been a longstanding research topic in pharmacological and toxicological sciences.1−4 Environmental policy has recognized mixture effects as a major issue in environmental risk assessment of chemicals. Therefore, a systematic review of the approaches toward environmental risk assessment, with respect to the inclusion of systematic mixture considerations, is being discussed by the European Commission.5 The last two decades have seen a series of studies in environmental toxicology addressing various scientific questions in mixture toxicology (reviewed in refs 6−11). Major progress in the environmental risk assessment of mixtures was achieved when hypotheses of expected combined effects, based on individual components activities, were compared against the actual mixture effects observed. Reductionism in experimental approaches for univariate response parameters allowed for the study of quantitative relationships and pattern searching in © 2012 American Chemical Society

combined effect observation. The standardization of bioassays, minimized variance, and optimization of designs generated sufficient discriminatory power. Our current understanding of primary molecular interaction at target sites and at the level of apical effects is still in line with the simple and powerful null hypothesis models as reference models for the quantitative prediction of noninteractive combined effects: concentration and response addition also called dose or LOEWE additivity and independent action or BLISS Independence, respectively.3 The supply of extrapolation models to risk regulators and managers for handling a pressing issue of risk assessment12 represents a success story. However, unresolved issues that pose formidable future research challenges for mixture toxicology Received: Revised: Accepted: Published: 2508

October 26, 2011 January 25, 2012 January 27, 2012 January 27, 2012 dx.doi.org/10.1021/es2038036 | Environ. Sci. Technol. 2012, 46, 2508−2522

Environmental Science & Technology

Critical Review

studies, and to address conceptual or experimental gaps that should be addressed in future work in toxicogenomic combined effect studies. We focus this review on the studies which provide an ecotoxicological perspective, but we include major contributions from the field of human toxicology. For work related to human toxicology the reader is referred to Sen et al.23 who earlier summarized studies that investigated mixtures in complex exposure settings, which were only partly resolved for their components. We also included reports on mixture toxicity analysis using assays that detect multiple univariate responses such as sets of reporter assays. Thus reports that monitored individual gene activities alongside other biological effects and had no major focus on mixtures themselves were omitted. Our intention was also to focus on the molecular response analysis of mixture toxicity, and we did not identify combined effect specific debates on phenotypic anchoring24 or adverse outcome pathways. Therefore, we abstained from detailed consideration of other than transcriptomic, proteomic, or metabolomic observation parameters. The paper sets out with developing a conceptual framework for the use of toxicogenomic tools in combined effect studies, followed by a summary of the mixtures, experimental design, and data handling of existing toxicogenomic mixture studies. Subsequently, data interpretation with respect to mixture models is discussed before we provide an outlook trying to suggest possible advancements to the field.

remain. For instance, the current evidence has been generated using highly standardized bioassay systems which tend to focus on short-term integral adverse effects. The predictivity of the currently used mixture toxicity models for components with long-term mixture effects, that provoke adverse effects through a variety of different toxicity pathways, remains unresolved.13 A second major challenge relates to the question of what mixtures provoke synergy?14 Also, the understanding of how primary molecular interactions are translated into apical effects that we consider in toxicological assessments would help in extrapolating mixture responses between different biological scales and across species. Moreover, an additional challenge occurs when specific environmental matrices are contaminated. Here, research encounters unresolved complex mixture exposure, e.g. through an effluent or contaminated feed and combined effects are assessed from a diagnostic perspective. In these situations assessment approaches would greatly profit if it was possible to use biological effects to provide guidance of what compounds could be identified as major drivers for compromised organism or population performance15 under mixture exposure. For some of the questions raised above toxicogenomics provide tools to improve our understanding and predictability of combined effects. Toxicogenomics allow observing the interplay between impacted environmental conditions and dynamic responses of organisms on a gene, protein, or at the metabolite level. In particular modern detection techniques are multivariate and nontargeted with respect to gene transcript and protein expression or metabolic responses. The potential for toxicogenomic methods to provide knowledge of modes of action of chemicals has been anticipated.16 This knowledge is critical in diagnostic assessment to identify agents causing toxicity in complex contaminated samples such as effluents. Also, there is hope that toxicogenomic methods will lead to an improved selection of end points suitable for specific risk assessment. The perspective of the ‘omics’ techniques to provide tools for advanced biomarkers has meanwhile gained experimental support.17,18 For example, Yang and co-workers17 studied individual chemicals and suggested to use the compound-specific transcriptional response patterns in a barcode-manner to identify exposure against specific compounds. Most recently, the promises of toxicogenomic approaches for understanding the combined effects of environmental mixtures, with respect to toxicokinetic and toxicodynamic processes, were summarized.19 The investigation of the effects of mixture exposure in organisms using novel toxicogenomic methodology has, in the meantime, become a major research activity. Starting from pioneering mixture studies,20,21 there is a dramatic increase of studies since 2006. Reviews now even call for progressing toward employment of systems biology approaches in ecotoxicology.22 It seems therefore timely to systematically summarize and review the progress in toxicogenomic response analysis of mixtures. The objective of this study is to provide a comprehensive review of the first steps made in addressing mixture toxicity issues with toxicogenomic approaches. In order to learn about the strengths and shortcomings of the chosen approaches we analyzed exposure conditions and biosystems used, ways of data generation, signal treatment, and analysis to deal with the apparent issues of variance and means of aggregating the information. Moreover, an attempt was made to cluster underlying perspectives on mixture toxicity concepts that drive the authors’ conclusive assessments on observed molecular combined effects. The ultimate goals are to highlight what has been gained so far in terms of mixture understanding, to provide guidance for the experimental design of future mixture toxicity

2. TOOLS AND CONCEPTUAL FRAMEWORK Toxicogenomic Response Detection. Following the sequencing of whole genomes, for a variety of different organisms (www.genomesonline.org), the understanding of the relationships between the structure of the genome and the gene function such as nongenomic modification of the genome (epigenetics), transcription into RNA (transcriptomics), translation into proteins (proteomics), and ultimately altered metabolic activities under varying conditions (metabolomics) are great challenges. Major methodological progress has been made in the development of analytical platforms that allow access to increasingly comprehensive detection of DNA transcripts,25 proteins,26 or metabolites27 and epigenetic modifications.28 Equally, tools are continuously being developed that allow handling such multiple responses and their statistical and biological interpretation.29 Transcriptomics in our context refers to the analysis of different levels of gene activity under varying conditions. Nontargeted approaches aim at a non-a-priory analysis of the differential expression of the entire set of genes (transcriptome) using microarray or RNA-sequencing as the currently most popular techniques.25,30 The latter is becoming more important since it provides a digital quantification (estimation of the number of transcripts similar to qRT-PCR, see below) and can additionally detect noncoding RNA genes from genomic regions hitherto considered as transcriptionally silent.31 Sequenced genomes are not necessarily required for microarray or sequencing-based approaches. This represents a major advantage regarding the diversity of organisms, although the lack of functional annotation of genes in nonmodel organisms currently limits the interpretation of data and cross-species extrapolations. Proteomics provides the scope to study a comprehensive nontargeted complement of proteins of a biosystem including their posttranslational modifications and variants.26 Typically, top-down approaches of total cell lysate separation are capable 2509

dx.doi.org/10.1021/es2038036 | Environ. Sci. Technol. 2012, 46, 2508−2522


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as the combined effect of a mixture of independently acting compounds is still expected to be quantitatively larger than that of any of the components alone. The guiding assumptions and the models for the relationship between the components individual effects and their expected combined effects are provided in Table 1. The two alternative reference models, however, provide quantitatively accurate predictions of the joint effects only if the mixture components do not show any interactions. In cases where interaction between the mixture components occur observable responses may deviate to be larger or smaller than expected for either concentration additive or independent action effects (Table 1). For interactive combined effects we, however, currently do not have generic models to describe, let alone predict, the outcomes. Both at the level of primary molecular interactions between chemical and biomolecules, as well as at the end of the toxicodynamic response chain, i.e. the apical outcome of toxicity assays, concentration addition, and independent action are well supported by experimental evidence. This knowledge should help to develop a conceptual framework for toxicogenomic mixture studies. Figure 1 displays an attempt to structure our existing understanding on mixture effects for the novel approaches employed in toxicogenomic studies. Going from left to right in the scheme, we illustrate toxicokinetic and toxicodynamic processes that can be seen as determining the causality of a concentration−response relationship. While the potential to improve environmental effect assessment through consideration of the internal dose level has been appreciated, and in fact led to a better understanding of the mechanisms of toxicokinetic interactions occurring for various mixtures,37 it is the advent of toxicogenomic techniques that opens the route to better understand the sequence of events within a cell or organism leading to apical effects. Also, multivariate molecular response detection might be instrumental to discriminate responses from different chemicals as similar or dissimilar acting and thus open novel routes to use mixture studies as probes for pharmacological action.38 The challenges to meet those prospects, however, are also apparent. For one, detected signals need to be attributed to defined response chains, and second we need to understand the crosstalk and convergence of pathways, as joint responses might switch between independent and concentration additive, or noninteractive and interactive, respectively, along different steps in the sequence. Designing toxicogenomic mixture studies requires sufficient understanding of the exposure regime and it is anchoring to expected combined effects at the level of primary molecular interaction or the apical effects. This would help to formulate a

of detecting only a fraction of a cellular proteome. The large dynamic range of protein occurrence probably marks the largest challenge when identifying proteins relevant for specific perturbations. Metabolomics32 aims to comprehensively detect small biomolecules like sugars, amino acids, or fatty acids from cells or tissues by NMR33- or mass spectroscopic34- based methods. Studies on organism-environment interactions and ecotoxicological studies were performed with a selection of aquatic and terrestrial organisms.27 As the molecules of primary metabolism are not restricted to defined species, metabolomics can be applied to all current model species in ecotoxicology, even if they are not sequenced. There is large variation in numbers of metabolites for different organisms, ranging from hundreds to several thousands.35 Conceptually, toxicologists have taken up these technological approaches, and others, by transforming previous thinking about the interaction between chemicals and biosystems, in terms of mechanisms and modes of action, into one of adverse outcome pathways.36 Mixture Toxicity Analysis. Thinking and experimentation on the combined effects from the exposure to mixtures of compounds dates back several decades.2 Major progress in environmental toxicology resulted from the introduction of receptorbased thinking of pharmacology. Particularly, reference models to formulate expectable combined effects are compared against experimental observations.12 Key was the hypothesis derived from the so-called sham experiment and the categories of target sites and modes of action. The ‘sham’ experiment is a thought experiment wherein the simplest mixture is a mixture of an individual compound with itself. Clearly, the expectation for the responses from such a mixture experiment is that increasing doses due to mixture exposure should lead to increasing effect. Moreover, the concentration-effect relationship, as derived from dilution-type experiments for that compound, should be retrieved irrespective of how many fractions are applied in the dosing regime. The usefulness of this idea is convincing when thinking about compounds interacting with the same molecular target site. Under the name of dose or concentration addition it became a widely accepted reference model in pharmacological research and environmental toxicology and applies to all mixtures of compounds that act according to a common mode of action. For mixtures of compounds that provoke their biological action through different target sites, responses are expected to be independent according to the statistical idea of independence. The derived reference model is called independent action or response addition. The latter term avoids misunderstanding

Figure 1. Conceptual frame for toxicogenomic mixture studies. For the null hypothesis of noninteracting compounds, mixture models would suggest that combined toxicogenomic responses can be explained by concentration addition or independent action models. 2510



Critical Review

Table 1. Reference Models Used in Mixture Toxicologya target/mechanism same noninteractive mode of action

interactive

different

n ∑i=1 (cSi/ECx(Si))

X=1− − Fi(pSi·(ECxmix))) independent action

no quantitative prediction model

no quantitative prediction model

=1 concentration addition

n ∏i=1 (1

a

Abbreviations used: cSi, concentration of substance i (Si) in the mixture; ECx, effect concentration at the response level x; F, function describing the relation between concentration and response for the individual component; pSi, fraction of substance i (Si) in the mixture; X, expected combined response; mix, mixture.

3. TOXICOGENOMIC MIXTURE STUDIES Scope of Analysis. The scope of studies analyzed is rather diverse ranging from purely exploratory, toward those searching for correlations among different responses, those that try to provide causal explanations for pharmacological cascades, and finally explicit extrapolation or modeling perspectives. The common theme across all referred papers is, however, that the authors derive an assessment on the observed mixture effect that is based on a comparison with an anticipated effect for the mixture of concern e.g. from the mixture components, another mixture, or another response observation. The molecular responses detected for the mixture of interest are therefore compared against a reference. Very often the reference concept is not explicitly stated so we will come back to it after having analyzed the studies in more detail. It may be helpful to try distinguishing between the following intentions: (i) In a situation of complex contamination one may wish to identify a major driver of biological effect. Here the perspective of looking at toxicogenomic data is diagnostic by searching for patterns similar to those from the reference case.18,40,41 (ii) By contrast, to understand the biological chain of effect after an interaction of a chemical with a biosystem, one may choose an interaction or mode of action scope trying to decipher signals that are novel compared to the reference case.42−45 (iii) Finally, a more quantitative view on the data would allow extrapolation to other mixture compositions, which is when explicit efforts to look at signal intensities come into focus.21,46−48 The 41 papers retrieved are summarized in Tables 2−4 and Table SI-1, grouping the papers according to the mixture type studied and thereafter in chronological and then alphabetical order of the authors. Mixture type refers to binary mixtures as the simplest mixtures, multiple component mixtures, and complex mixtures, the latter comprising also unresolved mixture composition e.g. from a contaminated site. The mixture type chosen depends on the focus of the study as discussed above. Tables 2−4 summarize the bioassay used, observations made, and interpretations derived. Table SI-1 provides an overview on the more technical details of how the mixture study was performed with respect to exposure conditions, data variance, and treatment issues. Mixtures Studied. Eighteen of the studies report on combined effects from binary mixtures, i.e. mixtures of two compounds. Multiple mixtures are covered in another sixteen studies, three of which also compare joint effects from binary and multiple component exposure.63,68,69 Multiple mixtures contained more than four but less than ten components. Seven investigations18,40,73−77 studied complex mixtures, typically environmental samples, where the individual components are not chemically defined. These studies were selected as they contained explicit consideration of mixture assessment rather than regarding the investigated exposure situation as a single

testable hypothesis and deal with the potential complexity in the outcome. After the dose level, the combined effect outcome may also depend on the concentration ratio of the mixture components. Figure 2 illustrates the three most common designs for a binary

Figure 2. Options for designing mixtures in combined effect studies, illustrated for a binary mixture of chemicals at various concentrations using a constant experimental effort; As an example theoretical design points for binary mixtures with identical number of observations according to n × n design (squares), ray design (circles), and surface design (crosses) are shown. For n × n and surface design the mixture ratio varies and might refer to dilution series of the components or else selected observation points on the two-dimensional plane (modified after Altenburger et al. 2003).

mixture, whereby the same experimental effort is allocated. (1) A mixture can be composed of one component studied in a dilution series in the presence of one fixed amount of another, or a dilution series of one component in the presence of a number of fixed amounts of another were used, which can be labeled as a × n design in the former or n × n design in the latter case.1 (2) Alternatively, the components can be mixed based at a constant mixture ratio and than be diluted, which is called diagonal or ray design.1 If the mixture ratio is derived from equal toxic units (TU), i.e. exposure concentration of component over effect concentration of the same compound, this is sometimes specifically referred to as equitoxic design, though the toxicity does not have to be equivalent in this design if dilutions or fractions of the constitutive effect concentration are studied. (3) Furthermore, we find designs where the mixture and individual concentrations are varied to systematically cover the whole surface of possible mixture compositions (surface design). The optimal design of a mixture to be studied depends on the objective1,39 as will be discussed further below. 2511


90 min, 7 dilutions, 8 binary mixtures 24 h

2 d, semistatic

4d, binary mixtures with Ni

14 d

24 h, ip. injection

4 d, semistatic

2 h, binary mixtures with Cd, 5 dilutions 4 d, 4 dilutions

azothymine, Cd, CuCl2, H2O2, MMS, Mito C, NaN3, nalidixic acid, paraquat, PCP BAP, BbF, DBahA, DBaIP, FA

EE2, ZM

Ni, Cd, Pb

cyproterone, 17β-trenbolone

Cd, BAP

imidacloprid, thiacloprid

Cd, Cu, Pb

2512

7, 14 d

4 d, semistatic

7 d, semistatic, 3 dilutions 7 + 3 d, simultaneous, sequential, semistatic 24 h, 3 dilutions

Cu, Cd

chlorpyrifos, Ni

EE2, ZM

a

For the abbreviations see Glossary.

chlorpyrifos, Cu

PFOS, Cd

3d

chlorpyrifos, diazinon

pyrene, fluoranthene

NP, TCB

2 d, simultaneous, sequentially 2 d, ip. injections

ICI 182,780, estradiol

exposure regime

4 d, 3 dilutions

NP, PCB-77

mixture

individual treatment signal strength

individual treatment gene expression, treatment related metabolite concentrations individual treatment gene expression

individual treatment metabolite abundance treatment and concentration dependent gene expression

1

2DGE/MS proteome

cDNA array (1.7 k) diff. expression, 2DGE/MS proteome

target gene expression, 9 stress genes

custom-made cDNA microarray (1,189) diff. expression, NMR (25+ signals), and GC-MS metabolom (108 signals) 60mer microarray (22 k) diff. expression

metabolites detected by 1H NMR

Pimephales promelas

Solea senegalensis liver

Mytilus galloprovincialis digestive gland

Chlamydomonas reinhardtii Daphnia magna juveniles

target gene express., thyroid function, and oxidative stress response GeneChip zebrafish array, 15 k diff. expression

Danio rerio olfactory tissues

target gene expression, 5 genes (qPCR)

1.7 k cDNA array (MytArray) diff. expression

H NMR metabolic profile of male urine samples

custom-made cDNA microarray diff. expression

Pimephales promelas early life stage Danio rerio early life stage

Perna viridis juveniles, soft tissue Mytilus galloprovencincialis digestive tissue

Caenorhabditis elegans

individual treatment expression pattern and intensity individual treatment related expression increase/decrease individual treatment expression, treatment related fold induction

Operon rat oligunucleotide array (5.7 k), diff. expression microarray 2 k and 22 k, diff. expression

Wistar albino rat, liver slices from males Pimephales promelas gonads Daphnia magna juveniles

individual treatment gene expression, treatment dependent fold induction

treatment and concentration related expression increase/decrease individual treatment expression of target mRNA

individual treatment pattern of responses, proteome responses did not confirm gene expression findings concentration dependent fold induction

individual treatment protein expression

concentration dependent fold induction

individual treatment expression levels

individual treatment expression levels

Lac Z expression from 14 stress gene promoters in individual assays

target gene expression, 5 genes (RT-PCR)

gene reporter constructed in E. coli

observation concentration dependent % change

SSH (300 clones), target gene expression, 12 genes (qPCR)

molecular response target gene expression, 6 genes (qPCR)

rainbow trout hepatocytes sea bream liver and testis Salmo salar hepatocytes

biosystem

Table 2. Combined Effect Observation and Assessment for Binary Mixturesa evaluation

against expected mixture effect based on independent action for gene expression change

against effects of individual components at expression and pathway level against effects of individual components against expected mixture effect based on sum of TU for phenotypic effect against effect of individual compounds against effects of individual compounds

against expected mixture effect based on independent action for gene transcription against effects of individual components

against expected mixture effect based on EC25S1 + EC25S2 = EC50mixture

against expected mixture effect based on sum of TU for immobility response against effects of cyproterone, comparison with known antagonist against effects of individual compounds

against effect of individual components against effects of individual components, comparison with known antagonists against expected mixture effects based on concentration addition and independent action against effects of individual components and sum of effects against EE2 effects

against NP effect

authors’ assessment

exposure to a mixture causes unique transcriptional signature

58 interactive effects, common responses, mixture effect biased toward chlorpyrifos, additional mixture response features mixture exposure resulted in different expression pattern potential increase of Cd toxicity by pre-exposure to PFOS

61

60

59

57

56

55

54

53

52

51

50

44

48

47

49

43

42

ref

Cu dominated metabolic profile changes

mixture responses can be both similar and distinct from those of components depending on mixture ratio intense unexpected crosstalk between gene pathways

synergistic and antagonistic interactions depending on gene

antagonistic or synergistic effects, enhanced effects through specific responses rather than cumulative damage different toxicodynamics may occur even for components with the same mode of action

antagonist behavior, interactive effect at low concentration

concentration addition and independent action reasonably predict combined effects, MoA not relevant for predictivity antagonism as compared to expected addition of individual effects mixture exposure provides distinction between different modes of estrogenic action interactive molecular responses as mixtures showed additionally affected pathways

combined exposure causes reduction in PCB-77 and NP gene induction pharmacologic behavior agonistic and antagonistic crosstalk between ER and AhR signaling, complex mode of ER-AhR interaction

Environmental Science & Technology Critical Review


14 d repeated oral dose; binary and ternary mixture 4 d, 1−3 dilutions

3d

24, 72 h, 4 doses, multiple mix. injected in muscle

methylmercury, benzene, TCE

mixture of 13 a.i. of pharmaceuticalsa

E2, EE2, BPA, NP, MES, BP, NP1EC

2513

a

Daphnia magna 6−8 days old

target gene expression, AhR metabolism, and stress response (11 proteins) target gene expression, 15 genes (qPCR), microarray (15 k)

individual treatment transcript copies concentration dependent and treatment related fold changes in expression profiles

individual treatment changes in metabolites and proteins

metabolites detected by 1H NMR, proteins detected by antibody array (225 antibodies)

mouse blood and liver extracts from 14 weeks C57 BL/6J males and females Danio rerio liver tissue

28 d, repeated gavage

63 d, 3 dosage via food, multiple PBDD mixture 24 h, 8 multiple mixtures

individual treatment relative transcript levels

target gene expression, innate immunerelated genes (6)

Danio rerio embryos

3 d, multiple mixture, semistatic

time dependent fold induction

individual treatment gene transcripts, treatment de pendent fold expression

individual treatment differentially expressed protein spots

individual treatment changes in gene induction increase/ decrease and fold induction

suppression subtractive hybridization (786 clones), qPCR, 3 time points

rare minnow cDNA microarray (1,773), diff. expression

proteome pattern

GRASP 16k salmonid microarray, diff. expression

concentration dependent relative transcript expression

individual treatment gene transcripts, treatment dependent fold induction individual treatment increase/decrease in gene induction concentration dependent gene cluster response patterns individual treatment increase/ decrease

concentration dependent induction concentration dependent increase in gene expression

against regression based sum of effects for components, and against groundwater sample

against TBDD and TBDD

against untreated control

comparison of components effects relative to single compounds

against effects of binary mixture

against effects of individual components, and different mixture ratios

against mixture effects of subgroups

against individual compounds profiles regarding altered genes, and pattern that suggest nonadditivity

comparison to other response patterns in HEK293 cells and zebrafish liver tumors against effects form BPA and E2 alone, and other effects

gene expression changes predictable for defined mixtures, mixture effects prevented prediction of composition of complex mixtures

comparable AhR gene induction in mixture

effects of mixture at concentrations below NOELs for components

mixture increased immune toxicity

complex mixture could be more toxic than binary

consistent gene set vs unique expression pattern for mixtures, complex crosstalk

effects of NCM markedly different from MeHg, PCB, OC and not predicted from subgroups

mixtures can have both similar and distinct effects, effect at lower concentrations than for individual components no single compound dominated the profile, but mixtures are not simply additive, Cr and EE2 act synergistic on mitochondrial dysfunction

convergent responses in different cell lines and disease models

more or less than response additive

strong antagonistic effects and synergistic interaction; shift from specific to stress responses

against expected mixture effects based on sum of profiles of components against effects of individual components

against effects of individual components

no evidence for synergistic activation of gene expression mixture can inhibit cell proliferation by interference with functional pathways and cell cycle progression unpredicted combined effects, TCDD signature closest to mixture

against effects of individual compounds against untreated control, and other effects

authors’ assessment metal mixture showed unique expression pattern

evaluation against As-induced gene responses

observation individual treatment gene ex pression, treatment de pendent fold induction

Platichthys f lesus juveniles liver samples

Sprague−Dawley rats, analysis in pups brain cerebellum and hippocampus rare minnow hepatocytes

Oncorhynchus mykiss liver from males, sexually immature

target gene expression, 5 genes (qPCR)

Mytilus galloprovencincialis

salmonid microarray 1,273 genes, diff. expression

Salmo trutta lacustris one year old liver 50mers microarray (14 k) diff. expression

16k rat array

male F344 rat, liver lobe, kidney

zebrafish liver (ZFL) cells

GRASP 16 k salmonid microarray, diff. expression

rainbow trout hepatocytes

gene reporter assays constructed in HepG2cells HEK293 cell culture

molecular response 1,2 k Atlas human cancer array, 2,4 k NEN human oncogen/tumor suppressor array, NEN kinase/phosphatase array CAT-ELISA activity from 13 stress gene promoters in individual assays target gene expression, 5 genes (qPCR)

Same mixture used as in ref 45. For the abbreviations see Glossary.

TNT, 2,4-DNT, 2,6DNT, DNB, TNB, RDX

biosystem human keratinocytes (RHEK-1)

62 d, semistatic binary and multiple mixture

2d, binary and multiple mixtures

PFOA, PFNA, PFDA, PFDoA, PFOS, FTOH (8:2) Roundup (Glyp), AMPA, Mecoprop, Acetochlor, 2,4-D E2, EE2, atrazine, nonylphenol permethrin Alachlor, Mancozeb, Endosulfan, Captan, Diazinon, Maneb 12 PBDEs

NCM, MeHg, PCB, OC

7 d, EE2 aqua., BDE and Cr as bolus arterial sequential (Δ3d) injections 21 d, oral feed of dams

EE2, BDE-47, Cr−VI

Cd, CCl4, pyrene

24 h

EE2, TCDD, Paraquat, NQO

mixture of 13 a.i. of pharmaceuticals

2 d, ternary mixture, 5 dilutions 2 and 24 h, 3 dilutions

Cd, Pb, Cr

exposure regime

24 h

As, Cr, Cd, Pb

mixture

Table 3. Combined Effect Observation and Assessment for Multiple Mixtures

41

72

71

70

69

68

67

66

65

64

46

63

62

45

21

20

ref

Environmental Science & Technology Critical Review


77

76

linear regression for number of affected genes a

For the abbreviations see Glossary.

concentration dependent gene promoter activation and fractional response green fluorescent protein (GFP) fluorescence gene reporter assay in E. coli for 1900 promoterzs

∑ of POPs from 7 groups HCH, chlordanes, DDT, PCB, PBDE, HBCD, HCB effluent sample from WWTP

technical mixture of naphthenic acids

5 months

kerosene, gas oil, heavy fuel oil, crude oil

3 h, 3 dilutions

fathead minnow array (22 k) Pimephales promelas testis and liver tissue 21 d, semistatic

individual treatment gene expression

comparison of mixtures regarding functional clusters and gene association networks against effects of EE2 and 11-KT or 17βtrenbolone 48/72 h exposure

GRASP 16k salmonid microarray, diff. expression 65mer microarray (16 k), diff. expression Oncorhynchus mykiss juveniles unspecified tissue Danio rerio female fish liver and ovarian tissue 4 d, semistatic

individual treatment gene transcripts, treatment dependent fold expression individual treatment gene expression

microarray (20 k), com., diff. expression

14 and 21d, 3 dilutions 4d 3 river sediment samples

stressor, which is a common alternative view when studying complex unresolved mixtures in environmental samples. Inorganic compounds, i.e., essential and nonessential metals, were studied in fourteen of the 41 papers. Most papers on metals also included some organic compounds, and only five of the reviewed papers were focused solely on metals20,21,50,54,57 while the other studies contained metals in mixtures with organic substances. Mixtures of organic compounds comprised of compounds such as pesticides (ten studies, mainly insecticides), compounds with endocrine agonist or antagonist activities (eight studies), polyaromatic hydrocarbons (four studies), fluorinated and brominated flame retardants (two studies), other persistent organic chemicals (POPs) and dioxins (four studies), and compounds used as biologically active ingredients in pharmaceuticals (three studies). Mixture composition was often commented in terms of knowledge about the components modes of action, indicating implicit anticipation of an expected (di)similar joint effect. Exposure Conditions. Exposure time was in the range of one to seven days equivalent to the exposure duration of most short-term bioassays used in ecotoxicity evaluation of chemicals. Rarely, as in the case of the gene reporter assays47,77 the exposure time was shorter than a day, and only occasionally (six studies) long-term exposure durations of more than a week were being employed in ecotoxicological studies with mostly aquatic organisms.51,69,72,73,75,76 In mammalian toxicology studies, by contrast, exposure periods are commonly intended to reflect at least subchronic exposure of 14−28 days using rodent bioassays.63,67,71 The latter are of specific interest as they may provide scope for phenotypic anchoring of molecular responses to chronic toxicity outcomes in higher organisms. When utilizing transcriptomic methodologies for effect characterization, the intention typically is to retrieve information on sublethal effects, which for the experimental design results in the question of how exposure concentrations are to be selected. Most of the analyzed studies provide explicit considerations to that end (Table SI-1). Most frequently authors selected their mixture concentrations as defined fractions of effect concentrations known for standard apical effects such as lethality, reproductive or viability responses, so that, on the one hand, observed molecular responses can arguably be considered as subacute effects, while on the other hand, the responses can be interpreted in context with consented adverse effects (i.e., phenotypic anchoring). Other strategies comprised the derivation of experimental exposure concentrations from environmental occurrence or body burden, dilution series testing, or other previous experience. Overall, for the selection of concentrations authors indicated a high degree of awareness of potentially resulting interpretation problems. Unfortunately, this caution is not equally seen for assuring that the concentrations selected are actually achieved and maintained during the experiment. Mostly nominal concentrations are reported, which for substances or mixtures with low water solubility, high volatility, high lipophilicity, potential for transformation, or toxicokinetic interaction between compounds may lead to deviations from the expected exposure situations. Table SI-1 provides retrieved information for those studies where analytical efforts have been made to either validate exposure 41,60,61,69 or even determine internal dose.54,57,66,72,75 Chemical mixture components studied were applied simultaneously, with only two exceptions that dealt with the effect of a sequential exposure of PFOS followed by

only some expression changes consistent with model estrogen, suggesting complex interactions system suitable for detection of NAs over a large range of conc.

75

18

unique expression profiles for different oils, but cluster patterns emerge compared to compounds irrespective of component ratio and conc. changes in key regulator genes

74 higher chemical burden coincides with higher number of diff. expressed genes

comparison of different sites, knowledge on gene function and responses to single chemicals comparison among oils, comparison against other compounds effects

against effects of individual components

Pimephales promelas 150 d post hatch liver and gonad Caenorhabditis elegans young adults

individual treatment gene expression

73

40

effluent samples from 2 WWTPs

authors’ assessment

induction suggest that flounders from polluted site respond to PAH contamination diagnostic effect pattern comparison of low vs high polluted sites, knowledge on gene function

evaluation observation

exposure and sex dependent gene expression, differences for male fish only concentration dependent increase/decrease custom-made cDNA array (0.16 k), diff. expression target gene expression, 12/21 genes

molecular response biosystem

Platichthys f lesus liver tissue, feral fish life history

exposure regime mixture

Table 4. Combined Effect Observation and Assessment for Complex Mixturesa

Critical Review

as present at two river sites

ref


2514



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Cd60 and estradiol investigated together and in sequence with a suspected agonist drug compound.43 Mixture Design. For the mixture ratios employed various designs were realized. Most often mixtures were composed of components at the same concentrations as studied individually (13 studies, a + b design, Table SI-1). It has been demonstrated that one may only detect antagonistic responses with certainty using this design if more than one mixture component is effective.1 Authors employing this design for studying low level effects thus have to make sure that the components are indeed present at individual no effect concentration which typically is lower than a NOEC level.78 The a × n or n × n design (refer to Figure 2) was employed in five studies. The surface design was employed in only two of the studies.61,68 A ray design was used in twelve studies, six of these employed toxic units to define the mixture ratios. However, the toxic units employed were derived from other response parameters, typically from apical effects. In addition, we found six studies that used whole mixtures derived from environmental samples of a partly unknown composition and six studies that derived their defined mixture composition from exposure considerations. Eleven out of the 41 studies tested dilution series with more than two dilution levels during the course of investigation. Bioassays and End Points. With respect to biosystems used for effect detection, studies on fish organs especially liver and gonads contributed most to the existing evidence on multiple responses amounting to almost half of the studies reviewed here. In particular, studies on organs from exposed model systems like zebrafish (Danio rerio) or rodents dominated the picture. Other biosystems used, encompass invertebrates (Daphnia and molluscs), cell culture systems, or gene reporter systems with bacterial cells. There is currently only one study available that employed a plant system, namely the unicellular green algae Chlamydomonas reinhardtii.54 Half of the studies we considered analyzed differential gene expression employing microarrays, mostly from commercial suppliers, while twelve studies used targeted multiple gene expression observation and in particular qPCR techniques for mixture response detection. Targeted in this context refers to either the selection of different genes from a similar function like the innate immune70 or thyroid system,60 else they represent established stress response signaling (e.g., ref 72). The latter is also the common strategy for gene reporter based assays,21,47 though novel approaches also offer scope for a nontargeted perspective.77 Moreover, qPCR is commonly employed to confirm findings from microarray experiments. For other toxicogenomic techniques such as metabolomics51,55,57,71 or proteomics52,53,67 we have identified only pioneering studies. Signal Treatment. A major challenge in performing experiments on biosystems where multivariate responses are to be detected is capturing sources of undue variation. Details of how the considered studies accounted for this are collated in Table SI-1. Some studies tried to focus their replicates on the expected largest confounder be that variation between individuals or replicate samples, others try to utilize the obtained variance to derive signal filters. All papers confirmed that the qPCR findings overall were consistent with the findings from the microarrays. Not all were confirmed in independent experiments, and only one based this statement on an explicit correlation analysis.61 Variance of responses was also considered in signal treatment. Apart from procedures like normalization techniques

to obtain comparable quantities from different samples or measurements, the single most frequently used technique was significance testing. The tests are constructed to select those signals from the multivariate responses that, compared to controls, show significant differences in either direction, i.e. larger or smaller. Mostly, authors did account for a false discovery rate by performing multiple testing on a sample. Above all, it seems an almost consented approach in processing transcriptome data from microarrays to apply an additional filter prior to, or subsequently to, statistical testing, based on the ratio of signals from treatment versus control. Authors thus demonstrated their scepticism in the common strategy of significance testing, however without providing specific reasons. Furthermore, these filters sometimes appear to be applied in order to reduce the number of data points for subsequent considerations. The stated thresholds in transcriptome studies typically use an arbitrarily defined minimum fold change, which tends to lie between 1.3 to 1.8 fold. For untargeted toxicogenomic approaches it is essential to organize data for display and analysis in aggregated form readying them for interpretation. To that end three steps can be discerned in the considered literature. First, all authors used graphical techniques such as Venn diagrams, heat maps, or fold expression graphs to display the structure of their original findings normalized only for control observations. In a second step statistical methods such as hierarchical clustering, linear regression on signal strength against concentration or time variation, correlation or multivariate techniques such as principle component analysis (PCA) or supervised techniques like partial least-squares analysis (PLS) were employed to detect major trends in the multivariate data. Finally, in the more recent publications, bioinformatics tools such as clustering on gene ontology terms, gene set and gene set enrichment analysis or network connections were utilized to query observations for biologically meaningful summarized information. In principle, this last step aggregates multivariate response information into biologically defined bins, such as biochemical pathways, which, in turn, depends on access to adequate database information.

4. COMBINED EFFECT ASSESSMENT While the number of mixture studies performed seems quite impressive, none of them explicitly tested mixture hypotheses. Consequently, it is mainly information on the way of designing, performing, and evaluating mixture studies that can be deduced from the existing studies. The reported observations can be summarized as considering the occurrence/absence of a signal (qualitative response) or the analysis may be based on the signal intensity (quantitative response). More than two-thirds of the studies, reported qualitatively different treatments, while the classical toxicological concept of dose-graduation played a much smaller role (i.e., being reported in one-third of the studies). 21,41,42,45−48,54,58,59,65,73,77 The time-dependence of responses was considered in only one of the studies.69 While studies based on nontargeted techniques, like the microarrays, commonly utilized a qualitative approach, studies that employed reporter assay responses or qPCR determined mRNA levels, more frequently evaluated their data utilizing quantitative approaches. Taking a diagnostic perspective authors typically intended to identify components of the mixtures that drive the observable effect pattern. With an extrapolative scope, studies were undertaken to generate an expectation for the mixture response based on responses of 2515



Critical Review

mixture components when studied alone. The mechanismbased focus finally can be summarized as striving for a causal explanation of the sequence of molecular events provoked through mixture exposure. Assessment Terminology. Almost all assessments on the observed mixture effects that are reported, e.g. ‘synergistic’, ‘additive’, or ‘antagonistic’ responses or even ‘surprisingly, we found’, comprise a comparative statement, i.e. the experimental observation is compared against an expectation of the authors. However, the majority of papers did not provide an explicit hypothesis on what an expected combined effect from a mixture exposure would look like. Thus, it was often difficult to comprehend which findings stipulated the conclusions regarding mixture interaction. Moreover, terminology used to describe findings was often used with different meanings. The frequently used term interaction, e.g., could be found to bear at least the following four connotations: (1) signifying a specific type of response that is elucidated (e.g., induced pathway) (e.g. ref 64), (2) referring to all responses provoked by more than a single compound (e.g. ref 50), (3) depicting a response that is more than no effect (low dose effect) (e.g. ref 63), or (4) demarking deviation of responses from a mixture model, e.g. concentration additive (e.g. ref 54), effect addition (e.g. ref 49), or agonism/antagonism (e.g. ref 51). Thus, if confusion is to be avoided in the future, clarity as to the meaning of specifically employed assessment terminology is essential, as e.g. suggested by Greco, Bravo, and Parson3 or by Hertzberg and MacDonell.79 Mixtures selected are often commented in terms of knowledge about the components modes of action, thus implicitly anticipating expected combined effects as concentration additive, independently acting or deviating. Indeed none of the studies linked the findings to existing evidence on combined effects for the specific compound mixtures and effect typologies (e.g., for pesticides,80 endocrine active compounds,8 or metals81). Undertaking to make conceptual assumptions transparent the assumptions used for assessment of the observed mixture effects were identified, both for approaches that are interpreting the occurrence of signals (qualitative assessment) and interpretations that are based on signal intensities (quantitative assessment). Qualitative Assessment. The occurrence of signals under exposure was compared between treatments and subsequently the arguments were built on the group of signals retrieved for the mixture and whether they were included in the reference treatments (e.g., components), totally, in part, or not at all. The interpretation is typically oriented toward the exposure identification of dominating causative chemicals or response characterization as similar or dissimilar. The Venn diagram is a technique of displaying observations that is often employed to that end. An example taken from ref 50 is displayed in Figure 3. We see that Ni and Cd exposure led to a number of similarly differentially regulated gene transcripts in Daphnia magna. Of these, two-thirds (n = 22) reoccurred under the mixture exposure at half of the concentrations of both components, while less than half the signals that were compound specific responses (n = 10) re-emerge under the mixture exposure. An additional 85 transcripts were uniquely differentially expressed in the mixture. Next, it would now be interesting to annotate the signals and allocate them to specific pathways and interpret stress and toxic responses. Major shortcomings of this approach are that the statistics and filtering approaches used to select signals impact on

Figure 3. Venn diagrams for differentially expressed gene transcripts in Ni and Cd exposed and coexposed Daphnia magna (from Vandenbrouck et al. 2009).

what we discuss as outcome during interpretation. At least the different statistical tests used in conjunction with often arbitrarily chosen cut off values should make us aware that results may be highly dependent on the method employed. A good practice to check the robustness of the findings e.g. with respect to the selection of a specific cut off value for n-fold expression is demonstrated in ref 77. Another way to overcome some limitations of this approach could be to aggregate the information prior to interpretation. Most statistical and bioinformatic interpretation tools discussed above strive for that, basically by trying to group, cluster, or else logically organize the information, e.g. to allocate various signals to a common pathway. These approaches sometimes also work with the original responses without prior application of filters on the responses observed. Another approach was to interpret the number of signals occurring in relation to the degree of contamination, as Menzel and co-workers74 did when comparing differently contaminated sediments using microarray gene profiling in Caenorhabditis elegans. Underlying notions could either be that the degree of contamination results in different dosages in the exposed biosystems and that a higher dose leads to higher number of responding genes or to higher number of chemicals with similar consequences. For a complex mixture of naphthenic acids it could indeed be shown that a multiple reporter system showed clear increase in signal numbers with increasing dose.77 Also, Judson et al.82 reported that the number of responding pathways, studied by a multitude of receptor-based assays, increased with increasing doses for various chemicals. By contrast, Tilton et al.61 showed various types of responses in the number of differentially expressed genes upon exposure of zebrafish to three different chlorpyrifos concentrations. Thus, we need to understand the relation between dose and gene responses for individual components prior to addressing mixtures of variable compound composition. Hendriksen and co-workers63 proposed yet another view on the interpretation of altered occurrence of signals when comparing individual compounds and their mixtures. In their studies of mixtures from benzene, trichloroethylene, and methyl mercury on transcriptomic responses from liver and kidney tissue of rats exposed for 14 days, they deduced from gene ontology based identification of affected pathways, that specific responses resulting from exposure to individual compounds were replaced by general stress responses upon mixture exposure.63 2516



Critical Review

same effect, as is true for a mixture ratio of one-third to twothirds of that effect concentration or three times one-third and so on. Such an evaluation should be feasible for 11 of the studies, because they considered dilution series with more than 2 concentration levels. In the studies reviewed here, the models introduced above as concentration or dose addition have explicitly been adopted by refs 47 and 50. The work of Dardenne et al.47 reports the mixture effects for 8 different binary mixtures investigated in dilution series on 14 different stress gene reporters. It is certainly pioneering in its attempt to pursue a strictly quantitative perspective and demonstrates good agreement between expected and observed mixture responses for the various mixtures and stress gene inductions. However, the sensitivity of the reported mixture responses may raise concerns whether the reported responses could also be explained by the individual compounds activities alone. The strategy chosen by Vandenbrouck et al.50 is, again, based on the idea of testing the concentration addition prediction for the mixture outcome. This could not be confirmed for microarray-detected expression responses of Daphnia magna juveniles exposed to two binary metal mixtures with nickel. A problem here is that fractional toxic units were perceived as effect measures upon which the comparison of components and mixture responses is then based. This assumption would be true only if linear and parallel dose− response functions existed for the substances considered, which was not tested or confirmed in the study, but as we have discussed is unlikely for low effect levels. Alternatively, one may derive a quantitative estimate of an expected combined effect from the multiplication of the response probabilities for the mixture components (see Table 1). The technical advantage of employing independent action as a reference model for the observations considered here is that explicit mixture effects can be calculated from any effect estimate for the components without prior modeling of a concentration response relationship. Tilton et al.61 and Hutchins et al.54 have taken advantage of this model property. Hutchins and coworkers54 studied transcriptome signatures for nine targeted stress genes in Chlamydomonas exposed to two binary metal mixtures of copper and lead each with cadmium. Observed and predicted fold changes were compared for six gene transcripts, and predictions according to independent action provided a good estimate of what was actually observed for five of them, irrespective of the mixture if one allows for some variance. In the remaining case the independent action model predicted higher than observed fold changes. Tilton and co-workers61 investigated the effect of a chlorpyrifos and copper mixture on microarray detected transcriptome profiles in olfactory tissues from shortterm exposed Danio rerio. The calculated n-fold changes in expression rates were compared for all signals retrieved against the responses expected for an independent combined effect. Overall the majority of observed responses were smaller than expected by independent action. A problem possibly encountered here may stem from the lack of knowledge about the underlying concentration response relationship which makes it impossible to calculate mixture prediction other than independent action. Further, if there is lack of information on the maximum effects for the individual responses one may calculate mixture expectations that would not even be detectable for the individual components.

This links to a debate on the role of common stress response pathways in dealing with exposure to environmental contaminants.83,84 Quantitative Assessments. Signal intensity based interpretations follow the idea that the intensity of the exposure should be monotonously related to dose and to the change in differentially expressed signal intensity. This concept is rather fundamental to toxicological work, and indeed several papers demonstrate evidence that an increase in concentration of a toxicant will give rise to corresponding changes in the expression of transcriptomic,21,41,42,70,73 proteomic,85 and metabolomic signals.86 It is, however, also known that individual transcriptomic and proteomic signals may not strictly adhere to a monotonous behavior but instead may show U-shaped concentration dependencies or highly variable responses.54,86 Hutchins and co-workers54 e.g. showed qPCR detected gene transcription intensity for various genes in Chlamydomonas under Cu2+ and Pb2+ exposure demonstrating classic concentration dependencies at lower concentrations but inverted trends at higher exposure levels. Various arguments have explained such patterns, such as cytotoxicity induced at higher concentrations that overlay stress or specific responses. Quantitative mixture assessment typically relies on monotonous changes in response as represented in sigmoidal concentration response relationships, thus U-shaped curves present specific challenges. It is striking that apart from purely descriptive approaches such as linear regressions performed by refs 46,73, and 77 the modeling of dose response relationships has not been used for combined effect predictions using the established reference models. Time patterns of transcriptomic response intensities can be regarded as equally important as their dose-dependencies. The few studies that have actually incorporated observations of time dependence for toxicity description show results that are parallel to the prevalent thinking of cumulative damage occurrence in toxicology (e.g., refs 54,87, and 88). The subsequent mixture interpretation may then be approached as is established for apical end points.9 Surprisingly, the literature reviewed, with few exceptions,47,50,54,61 did either not account for the established approaches or displayed gross misconceptions of it. Thus, the vast majority of studies evaluated their mixture observations through direct comparison with the responses using individual components (Tables 2−4). The flaw in the approach of comparing the quantitative mixture outcome directly with those of the components can be demonstrated by recalling the previously introduced thought experiment: The simplest mixture of all is that of combining a defined amount of a substance with a defined amount of the same substance. When looking at the expected quantitative effect of this so-called ‘sham combination’ one clearly would not expect the combined effect to be equal to that of each individual dose (for illustration cf. ref 39). This, however, is what most of the papers considered here suggested implicitly. Rather, it is consented in mixture toxicology that the expected combined effect should be derived from a function taking into account the activities of all individual components which typically calls for testing the mixture at lower dosage levels than used for the individual components. For the case of mixing one compound with itself e.g. the dilution principle proves helpful in providing a reasonable mixture effect expectation. For an effect of interest, take dilutions (fractions) of the components. In the example of the sham combination, if the compound provokes a certain effect at the defined concentration, two times half that concentration is expected to give rise to the 2517



Critical Review

5. OUTLOOK: FROM ANECDOTAL EVIDENCE TO HYPOTHESIS DRIVEN MIXTURE STUDIES The current evidence to support the published mixture assessments from toxicogenomic studies is as yet mainly observational. How can this be advanced in future studies? As a first step we recommend that experimenters provide an explicit hypothesis of what they expect for the mixture of interest. Then, as an alternative to the currently popular repeated dose regime and statistical testing of signal variance, we suggest to use graded exposure situations and regression-based analysis. The development of these approaches would follow the trend in toxicology where risk assessment moves from NOEC estimates toward benchmark concentrations.78 Potential scepticism as to whether consistent patterns of signals with covariance in concentrations may be achievable might be overcome by recalling the pioneering studies of Michaelis and Menten or Hill that those authors used to formulate quantitative models on the relationship between the concentration of a substance and the molecular responses of biosystems. Also, first promising steps to that avail for toxicogenomic responses can be found46,77 that utilize linear regressions and describe many of their signals successfully in that way. Moreover, there are other successful efforts known which do not stem from the mixture literature but describe concentration dependence of transcriptomic, proteomic, or metabolomic signals (e.g., refs 85−87) and even employ regression techniques for their description.85,87 With advancing a quantitative perspective on transcriptomic, proteomic, or metabolomic responses we could also calculate explicit combined effect predictions using the two reference models available. As we would expect responses detected by ‘omics’ analysis to be attached to separate or converging pathways, the reward in adopting both reference models could be that toxicogenomic studies may allow detecting and distinguishing similar and dissimilar joint responses in toxicological action.38,89 Another determinant for future progress can be seen in using more balanced approaches toward the exposure, effect, and modeling parts of toxicogenomic mixture studies. Considering the resources spent for toxicogenomic response studies, it is notorious to state that exposure conditions should not only be carefully selected but also analytically validated. This will be an essential step prior to considering implementing any toxicogenomic data interpretation into chemical risk assessment. If we furthermore intend to elucidate the sequence of biological responses as we may when referring to interactions with pathways, the challenge becomes also to separate toxicokinetic from toxicodynamic responses. This means we would need advanced consideration of a compounds uptake and fate within an organism to be able to discern whether an observed timedependent effect is due to an increase in dose over time or consequence of enhancement through a specific molecular interaction. Suggestions for incorporation of dosimetry have been made already for cell-based response analysis90 and may be implemented for other bioassays as well. A further difficulty will lie in finding strategies of how to deal with time-dependent responses in mixture modeling as the established reference models do not account for this. Processbased modeling could be one solution. Modeling biological action for multiresponse outcomes could be approached from a process-perspective as it is established in pharmacology91 and ecotoxicology.92,93 Jusko et al.91 for instance have used array data from corticosteroid provoked effects in rat livers to cluster

responses into a limited number of time-dependent patterns, which they subsequently used to formulate process-based quantitative dose−response models. Regarding the qualitative perspective, improvements are expected by putting observable molecular responses into scope through connecting different response levels as in system biology approaches19 or linking them to phenotypic outcomes as in the concept of adverse outcome pathways.36 With the progress in analytical platforms and bioinformatic tools, however, the main issue is probably not in generating more data but in providing more effective ways to digest these. Williams et al.94 provide an excellent example of how network modeling can make sense of multivariate toxicogenomic responses obtained from nonmodels, i.e. overcome current limitations for nonannotated organisms. In this context it may be interesting to study whether the means suggested for receptor-based high-throughput assays for the derivation of effective doses for toxicity-related biological pathways95 could be adapted for microarray or other nontargeted techniques. Also, the notion that toxicity as an apical outcome in biosystems derives from an overwhelmed stress defense system96 which can be identified through study of a limited number of stress pathways rather than the identification of primary interactions could help. Again, for experimental studies this would require extended focus on the time pattern of responses. These latter considerations are not specific for a combined effect analysis.

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ASSOCIATED CONTENT

S Supporting Information *

SI - Table 1: mixture study design and data treatment. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +49.341.235.1522. Fax: +49.341.235.1787. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Part of this study was supported by the Federal Environment Agency (UBA) project FKZ 370956404. R.A. acknowledges the receipt of a fellowship under the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems to the National Research Centre for Environmental Toxicology (Entox) at the University of Queensland and Queensland Health, Brisbane, Australia. For constructive critics we thank four reviewers. Earlier versions of the manuscript have been commented by Till Luckenbach and Dimitar Zitzkat; for language improvement we thank Marita Goodwin.

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GLOSSARY

Substance names

ATZ atrazine BAP benzo[a]pyrene BbF benzo[b]fluoranthene 2518



Critical Review

nonylphenol monoethoxylate carboxylate OC organochlorine pesticides (refer to Pelletier et al. 2009) PBDE polybrominated dibenzo-p-dioxins PCB polychlorinated biphenyl PCB-77 3,3’,4,4’-tetrachlorobiphenyl PCP pentachlorophenol PM permethrin TBDD 2,3,7,8,-tetrabromodibenzo-p-dioxin TCB 3,3’,4,4’-tetrachlorobiphenyl TCE trichloroethylene TCDD 2,3,7,8-tetrachlorodibenzo-p-dioxin ZM ZM 189,154 experimental anti-estrogen of Astra Zeneca

BDE-47 2,2,4,4,-tetrabromodiphenyl ether BP benzophenone BPA bisphenol A CHP chlorpyrifos Cr−VI hexavalent chromium DBahA dibenzo[a,h]anthracene DBaIP dibenzo[a,I]pyrene DDT dichlorodiphenyltrichloroethane E2 17β-estradiol EE2 17α-ethynylestradiol FA fluoranthene Glyp glyphosate (active ingredient in the Roundup product formulations) HBCD hexabromocyclododecane HCB hexachlorobenzene HCH hexachlorocyclohexane ICI 182,780 antiestrogen drug marketed under the trade name Fluvestrant 11-KT 11-ketotestosterone MeHg methylmercury MES mestranol Mito C mitomycin C MMS methylmethanesulfonate NQO 4-nitroquinoline-1-oxide MNNG N-methyl-N′-nitro-N-nitrosoguanidine NAs naphthenic acids NCM Northern Contaminant Mixture: 27 persistent environmental contaminants as detected in blood of Canadian Arctic Population (composition found in Pelletier et al. 2009) including methyl mercury (MeHg), polychlorinated biphenyls (PCB), and organochlorine pesticides (OC) NP nonylphenol NP1EC

other

AhR aryl hydrocarbon receptor a.i. active ingredients CAT chloramphenicol acetyltransferase conc. concentration 2DGE/MS two dimensional gel electrophoresis with mass spectrometric protein identification diff. expression differential expression EC25S1 effect concentration for substance 1 elucidating 25% effects ER estrogen receptor GO gene ontology ip. intraperitoneal MOA mode of action NMR nuclear magnetic resonance qPCR quantitative polymerase chain reaction RT-PCR real time polymerase chain reaction, here taken to refer to semiquantitative methodologies, such as band intensity measures of PCR products SSH suppressive substractive hybridization 2519



Critical Review

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TU toxic unit, i.e. exposure concentration of component over effect concentration of the same compound TUS sum of toxic units (TU) WWTPs waste water treatment plants

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