Altenburger 2012

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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, GermanyNational 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 hasraised expectations that central questions of mixture toxicologysuch as for mechanisms of low dose interactions can now beanswered. This review provides an overview on experimentalstudies from the past decade that address diagnostic and/ormechanistic questions regarding the combined effects ofchemical mixtures using toxicogenomic techniques. From2002 to 2011, 41 studies were published with a focus onmixture toxicity assessment. Primarily multiplexed quantifica-tion of gene transcripts was performed, though metabolomicand proteomic analysis of joint exposures have also beenundertaken. It is now standard to explicitly state criteria forselecting concentrations and provide insight into data trans-formation and statistical treatment with respect to minimizing sources of undue variability. Bioinformatic analysis of toxicogenomicdata, by contrast, is still a field with diverse and rapidly evolving tools. The reported combined effect assessments are discussed inthe light of established toxicological doseresponse and mixture toxicity models. Receptor-based assays seem to be the mostadvanced toward establishing quantitative relationships between exposure and biological responses. Often transcriptomic responsesare discussed based on the presence or absence of signals, where the interpretation may remain ambiguous due to methodologicalproblems. The majority of mixture studies design their studies to compare the recorded mixture outcome against responses forindividual 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 toxicokineticand -dynamic thinking, we suggest a conceptual framework that may help to overcome the current limitation of providing mainlyanecdotal evidence on mixture effects. To achieve this we suggest (i) to design studies to establish quantitative relationships betweendose and time dependency of responses and (ii) to adopt mixture toxicity models. Moreover, (iii) utilization of novel bioinformatictools 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 exposureto mixtures of chemicals have been a longstanding researchtopic in pharmacological and toxicological sciences.14 Environ-mental policy has recognized mixture effects as a major issue in

environmental risk assessment of chemicals. Therefore, a sys-tematic review of the approaches toward environmental riskassessment, with respect to the inclusion of systematic mixtureconsiderations, is being discussed by the European Commission.5

The last two decades have seen a series of studies in environ-mental toxicology addressing various scientific questions inmixture toxicology (reviewed in refs611). Major progress inthe environmental risk assessment of mixtures was achieved

when hypotheses of expected combined effects, based onindividual components activities, were compared against theactual mixture effects observed. Reductionism in experimentalapproaches for univariate response parameters allowed for thestudy of quantitative relationships and pattern searching in

combined effect observation. The standardization of bioassays,minimized variance, and optimization of designs generatedsufficient discriminatory power. Our current understanding ofprimary molecular interaction at target sites and at the level ofapical effects is still in line with the simple and powerful nullhypothesis models as reference models for the quantitative

prediction of noninteractive combined effects: concentrationand response addition also called dose or LOEWE additivityand independent action or BLISS Independence, respectively.3

The supply of extrapolation models to risk regulators andmanagers for handling a pressing issue of risk assessment12

represents a success story. However, unresolved issues that poseformidable future research challenges for mixture toxicology

Received: October 26, 2011Revised: January 25, 2012Accepted: January 27, 2012Published: January 27, 2012

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remain. For instance, the current evidence has been generatedusing highly standardized bioassay systems which tend to focuson short-term integral adverse effects. The predictivity of thecurrently used mixture toxicity models for components withlong-term mixture effects, that provoke adverse effects through a

variety of different toxicity pathways, remains unresolved.13 Asecond major challenge relates to the question of what mixturesprovoke synergy?14 Also, the understanding of how primarymolecular interactions are translated into apical effects that weconsider in toxicological assessments would help in extrapolatingmixture responses between different biological scales and acrossspecies. Moreover, an additional challenge occurs when specificenvironmental matrices are contaminated. Here, research en-counters unresolved complex mixture exposure, e.g. through aneffluent or contaminated feed and combined effects are assessedfrom a diagnostic perspective. In these situations assessmentapproaches would greatly profit if it was possible to use biologicaleffects to provide guidance of what compounds could be identi-fied as majordrivers for compromised organism or populationperformance15 under mixture exposure.

For some of the questions raised above toxicogenomics pro-vide tools to improve our understanding and predictability ofcombined effects. Toxicogenomics allow observing the interplay

between impacted environmental conditions and dynamicresponses of organisms on a gene, protein, or at the metabolitelevel. In particular modern detection techniques are multivariateand nontargeted with respect to gene transcript and proteinexpression or metabolic responses. The potential for toxicoge-nomic methods to provide knowledge of modes of action ofchemicals has been anticipated.16 This knowledge is critical indiagnostic assessment to identify agents causing toxicity in com-plex contaminated samples such as effluents. Also, there is hopethat toxicogenomic methods will lead to an improved selection ofend points suitable for specific risk assessment. The perspective ofthe omics techniques to provide tools for advanced biomarkershas meanwhile gained experimental support.17,18 For example,

Yang and co-workers17 studied individual chemicals and suggestedto use the compound-specific transcriptional response patterns in a

barcode-manner to identify exposure against specific compounds.Most recently, the promises of toxicogenomic approaches forunderstanding the combined effects of environmental mixtures,

with respectto toxicokinetic and toxicodynamic processes, weresummarized.19 The investigation of the effects of mixture exposurein organisms using novel toxicogenomic methodology has, in themeantime, become a major research activity. Starting frompioneering mixture studies,20,21 there is a dramatic increase ofstudies since 2006. Reviews now even call for progressing towardemployment of systems biology approaches in ecotoxicology.22 Itseems therefore timely to systematically summarize and review theprogress in toxicogenomic response analysis of mixtures.

The objective of this study is to provide a comprehensivereview of the first steps made in addressing mixture toxicityissues with toxicogenomic approaches. In order to learn aboutthe strengths and shortcomings of the chosen approaches weanalyzed exposure conditions and biosystems used, ways of datageneration, signal treatment, and analysis to deal with theapparent issues of variance and means of aggregating theinformation. Moreover, an attempt was made to cluster underly-ing perspectives on mixture toxicity concepts that drive theauthors conclusive assessments on observed molecular com-

bined effects. The ultimate goals are to highlight what has beengained so far in terms of mixture understanding, to provideguidance for the experimental design of future mixture toxicity

studies, and to address conceptual or experimental gaps thatshould be addressed in future work in toxicogenomic combinedeffect studies.

We focus this review on the studies which provide anecotoxicological perspective, but we include major contribu-tions from the field of human toxicology. For work related tohuman toxicology the reader is referred to Sen et al.23 whoearlier summarized studies that investigated mixtures in com-plex exposure settings, which were only partly resolved for theircomponents. We also included reports on mixture toxicityanalysis using assays that detect multiple univariate responsessuch as sets of reporter assays. Thus reports that monitoredindividual gene activities alongside other biological effects andhad no major focus on mixtures themselves were omitted. Ourintention was also to focus on the molecular response analysisof mixture toxicity, and we did not identify combined effectspecific debates on phenotypic anchoring24 or adverse outcomepathways. Therefore, we abstained from detailed considera-tion of other than transcriptomic, proteomic, or metabolomicobservation parameters.

The paper sets out with developing a conceptual frameworkfor 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 mixturemodels is discussed before we provide an outlook trying tosuggest possible advancements to the field.

2. TOOLS AND CONCEPTUAL FRAMEWORK

Toxicogenomic Response Detection. Following thesequencing of whole genomes, for a variety of differentorganisms (www.genomesonline.org), the understanding of therelationships between the structure of the genome and the genefunction such as nongenomic modification of the genome(epigenetics), transcription into RNA (transcriptomics), trans-lation into proteins (proteomics), and ultimately alteredmetabolic activities under varying conditions (metabolomics)are great challenges. Major methodological progress has beenmade in the development of analytical platforms that allow accessto increasingly comprehensive detection of DNA transcripts,25

proteins,26 or metabolites27 and epigenetic modifications.28

Equally, tools are continuously being developed that allowhandling such multiple responses and their statistical and

biological interpretation.29

Transcriptomics in our context refers to the analysis of differentlevels of gene activity under varying conditions. Nontargeted ap-proaches aim at a non-a-priory analysis of the differential expressionof the entire set of genes (transcriptome) using micro-array or RNA-sequencing as the currently most popular tech-niques.25,30 The latter is becoming more important since it provides

a digital quantification (estimation of the number of transcriptssimilar to qRT-PCR, see below) and can additionally detect non-coding RNA genes fromgenomic regions hitherto considered astranscriptionally silent.31 Sequenced genomes are not necessarilyrequired for microarray or sequencing-based approaches. Thisrepresents a major advantage regarding the diversity oforganisms, although the lack of functional annotation of genesin nonmodel organisms currently limits the interpretation of dataand cross-species extrapolations.

Proteomics provides the scope to study a comprehensivenontargeted complement of proteins of a biosystemincludingtheir posttranslational modifications and variants.26 Typically,top-down approaches of total cell lysate separation are capable

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of detecting only a fraction of a cellular proteome. The largedynamic range of protein occurrence probably marks the largestchallenge when identifying proteins relevant for specificperturbations.

Metabolomics32 aims to comprehensively detect smallbiomolecules like sugars, amino acids, or fattyacids from cellsor tissues by NMR33- or mass spectroscopic34- based methods.Studies on organism-environment interactions and ecotoxico-logical studies wereperformed with a selection of aquatic andterrestrial organisms.27As the molecules of primary metabolismare not restricted to defined species, metabolomics can beapplied to all current model species in ecotoxicology, even ifthey are not sequenced. There is large variation in numbers ofmetabolites for different organisms, ranging from hundreds toseveral thousands.35

Conceptually, toxicologists have taken up these technologicalapproaches, and others, by transforming previous thinkingabout the interaction between chemicals and biosystems, interms of mechanismsand modes of action, into one of adverseoutcome pathways.36

Mixture Toxicity Analysis.Thinking and experimentationon the combined effects from the exposure to mixtures of com-pounds dates back several decades.2 Major progress in environ-mental toxicology resulted from the introduction of receptor-

based thinking of pharmacology. Particularly, reference modelsto formulate expectable combined effects are compared againstexperimental observations.12 Key was the hypothesis derivedfrom the so-called sham experiment and the categories of targetsites and modes of action. The shamexperiment is a thoughtexperiment wherein the simplest mixture is a mixture of anindividual compound with itself. Clearly, the expectation for theresponses from such a mixture experiment is that increasingdoses due to mixture exposure should lead to increasing effect.Moreover, the concentration-effect relationship, as derivedfrom dilution-type experiments for that compound, should beretrieved irrespective of how many fractions are applied in thedosing regime. The usefulness of this idea is convincing whenthinking about compounds interacting with the same moleculartarget site. Under the name of dose or concentration addition it

became a widely accepted reference model in pharmacologicalresearch and environmental toxicology and applies to all mixturesof compounds that act according to a common mode of action.

For mixtures of compounds that provoke their biologicalaction through different target sites, responses are expected to beindependent according to the statistical idea of independence.The derived reference model is called independent action orresponse addition. The latter term avoids misunderstanding

as the combined effect of a mixture of independently actingcompounds is still expected to be quantitatively larger thanthat of any of the components alone. The guiding assumptionsand the models for the relationship between the componentsindividual effects and their expected combined effects are pro-

vided in Table1. The two alternative reference models, how-ever, provide quantitatively accurate predictions of the joint effectsonly if the mixture components do not show any interactions. Incases where interaction between the mixture components occurobservable responses may deviate to be larger or smaller thanexpected for either concentration additive or independent actioneffects (Table1). 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 betweenchemical and biomolecules, as well as at the end of the toxi-codynamic response chain, i.e. the apical outcome of toxicityassays, concentration addition, and independent action are wellsupported by experimental evidence. This knowledge shouldhelp to develop a conceptual framework for toxicogenomicmixture studies. Figure1displays an attempt to structure ourexisting understanding on mixture effects for the novel ap-proaches employed in toxicogenomic studies. Going from leftto right in the scheme, we illustrate toxicokinetic and toxi-codynamic processes that can be seen as determining the cau-sality of a concentrationresponse relationship. While thepotential to improve environmental effect assessment throughconsideration of the internal dose level has been appreciated,and in fact led to a better understanding of the mechanisms oftoxicokinetic interactions occurring for various mixtures,37 it isthe advent of toxicogenomic techniques that opens the route to

better understand the sequence of events within a cell ororganism leading to apical effects. Also, multivariate molecularresponse detection might be instrumental to discriminateresponses from different chemicals as similar or dissimilar actingand thus open novel routes to use mixture studies as probes forpharmacological action.38 The challenges to meet those pro-spects, however, are also apparent. For one, detected signals needto be attributed to defined response chains, and second we needto understand the crosstalk and convergence of pathways, as

joint responses might switch between independent and con-centration additive, or noninteractive and interactive, respec-tively, along different steps in the sequence.

Designing toxicogenomic mixture studies requires sufficientunderstanding of the exposure regime and it is anchoring toexpected combined effects at the level of primary molecularinteraction or the apical effects. This would help to formulate a

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




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testable hypothesis and deal with the potential complexity inthe outcome.

After the dose level, the combined effect outcome may alsodepend on the concentration ratio of the mixture components.Figure2illustrates the three most common designs for a binary

mixture, whereby the same experimental effort is allocated. (1)A mixture can be composed of one component studied in adilution series in the presence of one fixed amount of another,or a dilution series of one component in the presence of anumber of fixed amounts of another were used, which can belabeled as a n design in the former or n n design in thelatter case.1 (2) Alternatively, the components can be mixed

based at a constant mixture ratio and than be diluted, which iscalled diagonal or ray design.1 If the mixture ratio is derivedfrom equal toxic units (TU), i.e. exposure concentration ofcomponent 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 designif dilutions or fractions of the constitutive effect concentrationare studied. (3) Furthermore, we find designs where themixture and individual concentrations are varied to systemati-cally cover the whole surface of possible mixture compositions(surface design). The optimal design of a mixture to bestudied depends on the objective1,39 as will be discussedfurther below.

3. TOXICOGENOMIC MIXTURE STUDIES

Scope of Analysis.The scope of studies analyzed is ratherdiverse ranging from purely exploratory, toward those searchingfor correlations among different responses, those that try toprovide causal explanations for pharmacological cascades, andfinally explicit extrapolation or modeling perspectives. Thecommon theme across all referred papers is, however, that theauthors derive an assessment on the observed mixture effectthat is based on a comparison with an anticipated effect for the

mixture of concern e.g. from the mixture components, anothermixture, or another response observation. The molecularresponses detected for the mixture of interest are thereforecompared against a reference. Very often the reference conceptis not explicitly stated so we will come back to it after havinganalyzed the studies in more detail. It may be helpful to trydistinguishing between the following intentions: (i) In asituation of complex contamination one may wish to identify amajor driver of biological effect. Here the perspective of lookingat toxicogenomic data is diagnostic by searching for patternssimilar to those from the reference case.18,40,41 (ii) By contrast,to understand the biological chain of effect after an interactionof a chemical with a biosystem, one may choose an interactionor mode of action scope trying to decipher signals that are

novel compared to the reference case.4245

(iii) Finally, a morequantitative view on the data would allow extrapolation toother mixture compositions, which is whenexplicit efforts tolook at signal intensities come into focus.21,4648

The 41 papers retrieved are summarized in Tables 24andTable SI-1, grouping the papers according to the mixture typestudied and thereafter in chronological and then alphabeticalorder of the authors. Mixture type refers to binary mixtures as thesimplest mixtures, multiple component mixtures, and complexmixtures, the latter comprising also unresolved mixture composi-tion e.g. from a contaminated site. The mixture type chosendepends on the focus of the study as discussed above. Tables24summarize the bioassay used, observations made, and interpre-tations derived. Table SI-1 provides an overview on the more

technical details of how the mixture study was performed withrespect to exposure conditions, data variance, and treatment issues.

Mixtures Studied.Eighteen of the studies report on com-bined effects from binary mixtures, i.e. mixtures of two com-pounds. Multiple mixtures are covered in another sixteenstudies, three of which also compare jointeffects from binaryand multiple component exposure.63,68,69 Multiple mixturescontained morethanfour but less than ten components. Seveninvestigations18,40,7377 studied complex mixtures, typicallyenvironmental samples, where the individual components arenot chemically defined. These studies were selected as theycontained explicit consideration of mixture assessment ratherthan regarding the investigated exposure situation as a single

Table 1. Reference Models Used in Mixture Toxicologya

target/mechanism

same different

mode ofaction

noninteractive i=1n (cSi/ECx(Si)) = 1concentration addition

X= 1 i=1n (1 Fi(pSi(ECxmix)))

independent action

interactive no quantitative

prediction model

no quantitative

prediction modelaAbbreviations used: cSi, concentration of substancei (Si) in the mixture; ECx, effect concentration at the response level x;F, function describing therelation between concentration and response for the individual component; pSi, fraction of substancei (Si) in the mixture; X, expected combinedresponse; mix, mixture.

Figure 2. Options for designing mixtures in combined effect studies,illustrated for a binary mixture of chemicals at various concentrationsusing a constant experimental effort; As an example theoretical design

points for binary mixtures with identical number of observationsaccording to n n design (squares), ray design (circles), and surfacedesign (crosses) are shown. Forn n and surface design the mixtureratio varies and might refer to dilution series of the components or elseselected observation points on the two-dimensional plane (modifiedafter Altenburger et al. 2003).




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stressor, which is a common alternative view when studyingcomplex unresolved mixtures in environmental samples.Inorganic compounds, i.e., essential and nonessential metals,

were studied in fourteen of the 41 papers. Most papers onmetals also included some organic compounds, andonlyfive ofthe 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 comprisedof compounds such as pesticides (ten studies, mainly insec-ticides), compounds with endocrine agonist or antagonistactivities (eight studies), polyaromatic hydrocarbons (fourstudies), fluorinated and brominated flame retardants (twostudies), other persistent organic chemicals (POPs) and dioxins(four studies), and compounds used as biologically activeingredients in pharmaceuticals (three studies). Mixture com-position was often commented in terms of knowledge about thecomponents modes of action, indicating implicit anticipation ofan expected (di)similar joint effect.

Exposure Conditions. Exposure time was in the range ofone to seven days equivalent to the exposure duration of mostshort-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 mostlyaquatic organisms.51,69,72,73,75,76 In mammalian toxicologystudies, by contrast, exposure periods are commonly intendedto reflect at least subchronic exposure of 1428 days usingrodent bioassays.63,67,71 The latter are of specific interest as theymay provide scope for phenotypic anchoring of molecularresponses to chronic toxicity outcomes in higher organisms.

When utilizing transcriptomic methodologies for effect char-acterization, the intention typically is to retrieve information onsublethal effects, which for the experimental design results inthe question of how exposure concentrations are to be selected.

Most of the analyzed studies provide explicit considerationsto that end (Table SI-1). Most frequently authors selectedtheir mixture concentrations as defined fractions of effect con-centrations known for standard apical effects such as lethality,reproductive or viability responses, so that, on the one hand,observed molecular responses can arguably be consideredas subacute effects, while on the other hand, the responsescan be interpreted in context with consented adverse effects(i.e., phenotypic anchoring). Other strategies comprised thederivation of experimental exposure concentrations fromenvironmental occurrence or body burden, dilution seriestesting, or other previous experience. Overall, for the selectionof concentrations authors indicated a high degree of awarenessof potentially resulting interpretation problems. Unfortunately,

this caution is not equally seen for assuring that the con-centrations selected are actually achieved and maintainedduring the experiment. Mostly nominal concentrations are re-ported, which for substances or mixtures with low watersolubility, high volatility, high lipophilicity, potential fortransformation, or toxicokinetic interaction between com-pounds may lead to deviations from the expected exposuresituations.Table SI-1provides retrieved information for thosestudies where analytical efforts have been made to either

val idat e exposure41,60,61,69 or even determine internaldose.54,57,66,72,75 Chemical mixture components studied wereapplied simultaneously, with only two exceptions that dealt

with the effect of a sequential exposure of PFOS followed byTab

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3riversedimentsamples

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Caenorhab

ditiselegans

youngadults

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higherchemicalburde

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highernumberofdiff.expressedgenes

74

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77

aFortheabbreviationsseeGlossary.




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Cd60 and estradiol investigated together and in sequence with asuspected agonist drug compound.43

Mixture Design. For the mixture ratios employed variousdesigns were realized. Most often mixtures were composed ofcomponents at the same concentrations as studied individually(13 studies,a + bdesign,Table SI-1). It has been demonstratedthat one may only detect antagonistic responses with certaintyusing this design if more than one mixture component iseffective.1Authors employing this design for studying low leveleffects thus have to make sure that the components are indeedpresent at individual no effect concentration which typically islower than a NOEC level.78 Thea norn ndesign (refer toFigure2) was employed in five studies.The surface design wasemployed in only two of the studies.61,68A ray design was usedin twelve studies, six of these employed toxic units to define themixture ratios. However, the toxic units employed were derivedfrom other response parameters, typically from apical effects. Inaddition, we found six studies that used whole mixtures derivedfrom environmental samples of a partly unknown compositionand six studies that derived their defined mixture compositionfrom exposure considerations. Eleven out of the 41 studiestested dilution series with more than two dilution levels duringthe course of investigation.

Bioassays and End Points. With respect to biosystemsused for effect detection, studies on fish organs especially liverand gonads contributed most to the existing evidence onmultiple responses amounting to almost half of the studiesreviewed here. In particular, studies on organs from exposedmodel systems like zebrafish (Danio rerio) or rodents domina-ted the picture. Other biosystems used, encompass inverte-

brates (Daphnia and molluscs), cell culture systems, or genereporter systems with bacterial cells. There is currently only onestudy available that employed a plant system, namely theunicellular green algae Chlamydomonas reinhardtii.54

Half of the studies we considered analyzed differential geneexpression employing microarrays, mostly from commercial

suppliers, while twelve studies used targeted multiple geneexpression observation and in particular qPCR techniques formixture response detection. Targeted in this context refers toeither the selection ofdifferent genes fromasimilar functionlike the innate immune70 or thyroid system,60 else they repre-sent established stress response signaling (e.g., ref 72). Thelatter is also the common strategy for gene reporter basedassays,21,47 though novel approaches also offer scope for anontargeted perspective.77 Moreover, qPCR is commonlyemployed to confirm findings from microarray experiments.For other toxicogenomic techniques such as metabol-omics51,55,57,71 or proteomics52,53,67 we have identified onlypioneering studies.

Signal Treatment. A major challenge in performing experi-

ments on biosystems where multivariate responses are to bedetected is capturing sources of undue variation. Details of how theconsidered studies accounted for this are collated in Table SI-1.Some studies tried to focus their replicates on the expectedlargest confounder be that variation between individuals orreplicate samples, others try to utilize the obtained variance toderive signal filters. All papers confirmed that the qPCR find-ings overall were consistent with the findings from the micro-arrays. Not all were confirmed in independent experiments,and onlyone based this statement on an explicit correlationanalysis.61

Variance of responses was also considered in signaltreatment. Apart from procedures like normalization techniques

to obtain comparable quantities from different samples ormeasurements, the single most frequently used technique wassignificance testing. The tests are constructed to select thosesignals from the multivariate responses that, compared tocontrols, show significant differences in either direction, i.e.larger or smaller. Mostly, authors did account for a falsediscovery rate by performing multiple testing on a sample.

Above all, it seems an almost consented approach in processingtranscriptome data from microarrays to apply an additionalfilter prior to, or subsequently to, statistical testing, based onthe ratio of signals from treatment versus control. Authors thusdemonstrated their scepticism in the common strategy ofsignificance testing, however without providing specific reasons.Furthermore, these filters sometimes appear to be applied inorder to reduce the number of data points for subsequentconsiderations. The stated thresholds in transcriptome studiestypically use an arbitrarily defined minimum fold change, whichtends to lie between 1.3 to 1.8 fold.

For untargeted toxicogenomic approaches it is essential toorganize data for display and analysis in aggregated formreadying them for interpretation. To that end three steps can bediscerned in the considered literature. First, all authors used

graphical techniques such as Venn diagrams, heat maps, or foldexpression graphs to display the structure of their originalfindings normalized only for control observations. In a secondstep statistical methods such as hierarchical clustering, linearregression on signal strength against concentration or time

variation, correlation or multivariate techniques such as prin-ciple component analysis (PCA) or supervised techniques likepartial least-squares analysis (PLS) were employed to detectmajor trends in the multivariate data. Finally, in the more re-cent publications, bioinformatics tools such as clustering ongene ontology terms, gene set and gene set enrichment analysisor 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 quiteimpressive, 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 deducedfrom the existing studies.

The reported observations can be summarized as consideringthe occurrence/absence of a signal (qualitative response) or theanalysis may be based on the signal intensity (quantitativeresponse). More than two-thirds of the studies, reportedqualitatively different treatments, while the classical toxico-

logical concept of dose-graduation played a much smallerrole (i .e ., being reported in one-third of the stud-ies).21,41,42,4548,54,58,59,65,73,77 The time-dependence of responses was considered in only one of the studies.69 Whilestudies based on nontargeted techniques, like the microarrays,commonly utilized a qualitative approach, studies thatemployed reporter assay responses or qPCR determinedmRNA levels, more frequently evaluated their data utilizingquantitative approaches. Taking a diagnostic perspectiveauthors typically intended to identify components of themixtures that drive the observable effect pattern. With anextrapolative scope, studies were undertaken to generate anexpectation for the mixture response based on responses of




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mixture components when studied alone. The mechanism-based focus finally can be summarized as striving for a causalexplanation of the sequence of molecular events provokedthrough mixture exposure.

Assessment Terminology.Almost all assessments on theobserved mixture effects that are reported, e.g. synergistic,additive, or antagonistic responses or even surprisingly, wefound, comprise a comparative statement, i.e. the experimentalobservation is compared against an expectation of the authors.However, the majority of papers did not provide an explicithypothesis on what an expected combined effect from amixture exposure would look like. Thus, it was often difficult tocomprehend which findings stipulated the conclusionsregarding mixture interaction. Moreover, terminology used todescribe findings was often used with different meanings. Thefrequently used term interaction, e.g., could be found to bear atleast the following four connotations: (1) signifying a specifictype of response that is elucidated (e.g., induced pathway)(e.g. ref64), (2) referring to all responses provoked by morethan a single compound (e.g. ref50), (3) depicting a responsethat is more than no effect (low dose effect) (e.g. ref63), or (4)demarking deviation of responses from a mixture model, e.g.concentration additive (e.g. ref54), effect addition (e.g. ref49),or agonism/antagonism (e.g. ref51). Thus, if confusion is to

be avoided in the future, clarity as to the meaning of spe-cifically employed assessment terminology is essential, as e.g.suggested byGreco, Bravo, and Parson3 or by Hertzberg andMacDonell.79

Mixtures selected are often commented in terms of knowledgeabout the components modes of action, thus implicitly anticipa-ting expected combined effects as concentration additive, inde-pendently acting or deviating. Indeed none of the studies linkedthe findings to existing evidence on combined effects for thespecific compound mixtures and effect typologies (e.g., forpesticides,80 endocrine active compounds,8 or metals81).

Undertaking to make conceptual assumptions transparent

the assumptions used for assessment of the observed mixtureeffects were identified, both for approaches that are interpretingthe occurrence of signals (qualitative assessment) and interpre-tations that are based on signal intensities (quantitative assess-ment).

Qualitative Assessment.The occurrence of signals underexposure was compared between treatments and subsequentlythe arguments were built on the group of signals retrieved forthe mixture and whether they were included in the referencetreatments (e.g., components), totally, in part, or not at all. Theinterpretation is typically oriented toward the exposure iden-tification of dominating causative chemicals or response char-acterization as similar or dissimilar. The Venn diagram is atechnique of displaying observations that is often employed to

that end. An example taken from ref50is displayed in Figure3.We see that Ni and Cd exposure led to a number of similarlydifferentially regulated gene transcripts in Daphnia magna. Ofthese, two-thirds (n = 22) reoccurred under the mixtureexposure at half of the concentrations of both components,

while less than half the signals that were compound specificresponses (n= 10) re-emerge under the mixture exposure. Anadditional 85 transcripts were uniquely differentially expressedin the mixture. Next, it would now be interesting to annotatethe signals and allocate them to specific pathways and interpretstress and toxic responses.

Major shortcomings of this approach are that the sta-tistics and filtering approaches used to select signals impact on

what we discuss as outcome during interpretation. At leastthe different statistical tests used in conjunction with oftenarbitrarily chosen cut off values should make us aware thatresults 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-foldexpression is demonstrated in ref77. Another way to overcomesome limitations of this approach could be to aggregate theinformation prior to interpretation. Most statistical and bio-informatic interpretation tools discussed above strive for that,

basically by trying to group, cluster, or else logically organizethe information, e.g. to allocate various signals to a commonpathway. These approaches sometimes also work with theoriginal responses without prior application of filters on theresponses observed.

Another approach was to interpret the number of signalsoccurring in relation to the degree of contamination, as Menzeland co-workers74 did when comparing differently contaminatedsediments using microarray gene profiling in Caenorhabditis

elegans. Underlying notions could either be that the degree ofcontamination results in different dosages in the exposed bio-systems and that a higher dose leads to higher number ofresponding genes or to higher number of chemicals with similarconsequences. For a complex mixture of naphthenic acids itcould indeed be shown that a multiple reporter system showedclear increase insignal numbers with increasing dose.77 Also,

Judson et al.82 reported that the number of respondingpathways, studied by a multitude of receptor-based assays,increased with increasing doses for various chemicals. Bycontrast, Tilton et al.61 showed various types of responses inthe number of differentially expressed genes upon exposure ofzebrafish to three different chlorpyrifos concentrations. Thus,

we need to understand the relation between dose and generesponses for individual components prior to addressing mixturesof variable compound composition.

Hendriksen and co-workers63 proposed yet another view onthe interpretation of altered occurrence of signals when com-paring individual compounds and their mixtures. In theirstudies of mixtures from benzene, trichloroethylene, and methylmercury on transcriptomic responses from liver and kidneytissue of rats exposed for 14 days, they deduced from geneontology based identification of affected pathways, thatspecific responses resulting from exposure to individualcompounds were replaced by general stress responses uponmixture exposure.63

Figure 3.Venn diagrams for differentially expressed gene transcripts inNi and Cd exposed and coexposed Daphnia magna (fromVandenbrouck et al. 2009).




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This links to a debate on the role of common stress responsepathways in dealing with exposure to environmental con-taminants.83,84

Quantitative Assessments. Signal intensity based inter-pretations follow the idea that the intensity of the exposureshould be monotonously related to dose and to the change indifferentially expressed signal intensity. This concept is ratherfundamental to toxicological work, and indeed several papersdemonstrate evidence that an increase in concentration of atoxicant will give rise to corresponding changesin the expressionof transcriptomic,21,41,42,70,73 proteomic,85 and metabol-omic signals.86 It is, however, also known that individual trans-criptomic and proteomic signals may not strictly adhere to amonotonous behavior but instead may show U-shaped con-centration dependencies or highly variable responses.54,86

Hutchins and co-workers54 e.g. showed qPCR detected genetranscription intensity for various genes in Chlamydomonasunder Cu2+ and Pb2+ exposure demonstrating classic concen-tration dependencies at lower concentrations but inverted trendsat higher exposure levels. Various arguments have explained suchpatterns, such as cytotoxicity induced at higher concentrationsthat overlay stress or specific responses. Quantitative mixtureassessment typically relies on monotonous changes in responseas represented in sigmoidal concentration response relationships,thus U-shaped curves present specific challenges. It is strikingthat apart from purely descriptive approaches such as linearregressions performed by refs 46,73, and 77 the modeling ofdose response relationships has not been used for combinedeffect predictions using the established reference models.

Time patterns of transcriptomic response intensities can beregarded as equally important as their dose-dependencies. Thefew studies that have actually incorporated observations of timedependence for toxicity description show results that are parallelto the prevalent thinking of cumulative damage occurrence intoxicology (e.g., refs54,87, and88).

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 eithernot account for the established approaches or displayed grossmisconceptions of it. Thus, the vast majority of studies evalu-ated their mixture observations through direct comparison withthe responses using individual components (Tables24).

The flaw in the approach of comparing the quantitativemixture outcome directly with those of the components can bedemonstrated by recalling the previously introduced thoughtexperiment: The simplest mixture of all is that of combining adefined amount of a substance with a defined amount of thesame substance. When looking at the expected quantitativeeffect of this so-called sham combination one clearly wouldnot expect the combined effect to be equal to that of each

individual dose (for illustration cf. ref 39). This, however, iswhat most of the papers considered here suggested implicitly.Rather, it is consented in mixture toxicology that the expectedcombined effect should be derived from a function taking intoaccount the activities of all individual components whichtypically calls for testing the mixture at lower dosage levels thanused for the individual components. For the case of mixing onecompound with itself e.g. the dilution principle proves helpfulin providing a reasonable mixture effect expectation. For aneffect of interest, take dilutions (fractions) of the components.In the example of the sham combination, if the compoundprovokes a certain effect at the defined concentration, twotimes half that concentration is expected to give rise to the

same effect, as is true for a mixture ratio of one-third to two-thirds of that effect concentration or three times one-third andso on. Such an evaluation should be feasible for 11 of thestudies, because they considered dilution series with more than2 concentration levels.

In the studies reviewed here, the models introduced above asconcentration or dose addition have explicitlybeen adopted by

refs47and50. The work of Dardenne et al.47

reports the mix-ture effects for 8 different binary mixtures investigated in dilu-tion series on 14 different stress gene reporters. It is certainlypioneering in its attempt to pursue a strictly quantitative per-spective and demonstrates good agreement between expectedand observed mixture responses for the various mixtures andstress gene inductions. However, the sensitivity of the reportedmixture responses may raise concerns whether the reportedresponses could also be explained by the individual compoundsactivities alone. The strategy chosen by Vandenbrouck et al.50

is, again, based on the idea of testing the concentration additionprediction for the mixture outcome. This could not be con-firmed for microarray-detected expression responses of

Daphnia magna juveniles exposed to two binary metal mixtureswith nickel. A problem here is that fractional toxic units wereperceived as effect measures upon which the comparison ofcomponents and mixture responses is then based. Thisassumption would be true only if linear and parallel doseresponse functions existed for the substances considered, which

was not tested or confirmed in the study, but as we havediscussed is unlikely for low effect levels.

Alternatively, one may derive a quantitative estimate of anexpected combined effect from the multiplication of the re-sponse probabilities for the mixture components (see Table1).The technical advantage of employing independent action as areference model for the observations considered here is thatexplicit mixture effects can be calculated from any effect estimate

for the components without prior modeling of a concentra-tion response relationship. Tilton et al.61 and Hutchins et al.54

have taken advantage of this model property. Hutchins and co-workers54 studied transcriptome signatures for nine targetedstress genes in Chlamydomonas exposed to two binary metalmixtures of copper and lead each with cadmium. Observed andpredicted fold changes were compared for six gene transcripts,and predictions according to independent action provided agood estimate of what was actually observed for five of them,irrespective of the mixture if one allows for some variance. In theremaining case the independent action model predicted higherthan observed fold changes. Tilton and co-workers61 investigatedthe effect of a chlorpyrifos and copper mixture on microarraydetected transcriptome profiles in olfactory tissues from short-term exposed Danio rerio. The calculatedn-fold changes in ex-pression rates were compared for all signals retrieved against theresponses expected for an independent combined effect. Overallthe majority of observed responses were smaller than expected

by independent action. A problem possibly encountered heremay stem from the lack of knowledge about the underlyingconcentration response relationship which makes it impossible tocalculate mixture prediction other than independent action.Further, if there is lack of information on the maximum effectsfor the individual responses one may calculate mixtureexpectations that would not even be detectable for the individualcomponents.




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5. OUTLOOK: FROM ANECDOTAL EVIDENCE TOHYPOTHESIS DRIVEN MIXTURE STUDIES

The current evidence to support the published mixture assess-ments from toxicogenomic studies is as yet mainly observa-tional. How can this be advanced in future studies? As a firststep we recommend that experimenters provide an explicithypothesis of what they expect for the mixture of interest.

Then, as an alternative to the currently popular repeated doseregime and statistical testing of signal variance, we suggest touse graded exposure situations and regression-based analysis.The development of these approaches would follow the trendin toxicology where risk assessment moves from NOEC esti-mates toward benchmark concentrations.78

Potential scepticism as to whether consistent patterns ofsignals with covariance in concentrations may be achievablemight be overcome by recalling the pioneering studies of Michaelisand Menten or Hill that those authors used to formulate quan-titative models on the relationship between the concentration of asubstance and the molecular responses of biosystems. Also, firstpromising 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 othersuccessful efforts known which do not stem from the mixture liter-ature but describe concentration dependence of transcriptomic,proteomic, or metabolomic signals (e.g., refs 8587)and evenemploy regression techniques for their description.85,87

With advancing a quantitative perspective on transcriptomic,proteomic, or metabolomic responses we could also calculateexplicit combined effect predictions using the two referencemodels available. As we would expect responses detected byomics analysis to be attached to separate or convergingpathways, the reward in adopting both reference modelscould be that toxicogenomic studies may allow detectingand distinguishing similar and dissimilar joint responses intoxicological action.38,89

Another determinant for future progress can be seen in usingmore balanced approaches toward the exposure, effect, andmodeling parts of toxicogenomic mixture studies. Consideringthe resources spent for toxicogenomic response studies, it isnotorious to state that exposure conditions should not only becarefully selected but also analytically validated. This will be anessential step prior to considering implementing any toxi-cogenomic data interpretation into chemical risk assessment. If

we furthermore intend to elucidate the sequence of biologicalresponses as we may when referring to interactions withpathways, the challenge becomes also to separate toxicokineticfrom toxicodynamic responses. This means we would needadvanced consideration of a compounds uptake and fate withinan organism to be able to discern whether an observed time-

dependent effect is due to an increase in dose over time orconsequence of enhancement through a specific molecularinteraction. Suggestions for incorporation of dosimetry have

been made already for cell-based response analysis90 and maybe implemented for other bioassays as well.

A further difficulty will lie in finding strategies of how to dealwith time-dependent responses in mixture modeling as theestablished reference models do not account for this. Process-

based modeling could be one solution. Modeling biologicalaction for multiresponse outcomes could be approachedfrom aprocess-perspective as it is established in pharmacology91 andecotoxicology.92,93 Jusko et al.91 for instance have used arraydata 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-basedquantitative doseresponse models.

Regarding the qualitative perspective, improvements areexpected by putting observable molecular responses into scopethrough connecting different response levels as in system

biology approaches19 or linking them to phenotypicoutcomesas in the concept of adverse outcome pathways.36 With theprogress in analytical platforms and bioinformatic tools, how-ever, the main issue is probably not in generating more data butin providing more effective ways to digest these. Williamset al.94 provide an excellent example of how network modelingcan make sense of multivariate toxicogenomic responsesobtained from nonmodels, i.e. overcome current limitationsfor nonannotated organisms.

In this context it may be interesting to study whether themeans suggested for receptor-based high-throughput assays forthe derivation of effective doses for toxicity-related biologicalpathways95 could be adapted for microarray or other non-targeted techniques. Also, the notion that toxicity as an apicaloutcome in biosystems derives from an overwhelmed stressdefense system96 which can be identified through study of a

limited number of stress pathways rather than the identificationof primary interactions could help. Again, for experimentalstudies this would require extended focus on the time patternof responses. These latter considerations are not specific for acombined effect analysis.

ASSOCIATED CONTENT*S Supporting InformationSI - Table 1: mixture study design and data treatment. Thismaterial is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATIONCorresponding Author

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

NotesThe authors declare no competing financial interest.

ACKNOWLEDGMENTSPart of this study was supported by the Federal Environment

Agency (UBA) project FKZ 370956404. R.A. acknowledges thereceipt of a fellowship under the OECD Co-operative ResearchProgramme: Biological Resource Management for Sustainable

Agricultural Systems to the National Research Centre forEnvironmental Toxicology (Entox) at the University ofQueensland and Queensland Health, Brisbane, Australia. Forconstructive critics we thank four reviewers. Earlier versions ofthe manuscript have been commented by Till Luckenbach andDimitar Zitzkat; for language improvement we thank MaritaGoodwin.

GLOSSARYSubstance names

ATZ

atrazineBAP

benzo[a]pyreneBbF

benzo[b]fluoranthene


http://pubs.acs.org/http://pubs.acs.org/mailto:[email protected]:[email protected]://pubs.acs.org/http://pubs.acs.org/


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BDE-472,2,4,4,-tetrabromodiphenyl etherBP

benzophenoneBPA

bisphenol ACHP

chlorpyrifosCrVI

hexavalent chromiumDBahA

dibenzo[a,h]anthraceneDBaIP

dibenzo[a,I]pyreneDDT

dichlorodiphenyltrichloroethaneE2

17-estradiolEE2

17-ethynylestradiol

FAfluorantheneGlyp

glyphosate (active ingredient in the Roundup productformulations)HBCD

hexabromocyclododecaneHCBhexachlorobenzeneHCHhexachlorocyclohexaneICI 182,780

antiestrogen drug marketed under the trade name Fluvestrant

11-KT11-ketotestosteroneMeHgmethylmercuryMES

mestranolMito C

mitomycin CMMS

methylmethanesulfonateNQO

4-nitroquinoline-1-oxideMNNG

N-methyl-N-nitro-N-nitrosoguanidineNAs

naphthenic acidsNCM

Northern Contaminant Mixture: 27 persistent environmentalcontaminants as detected in blood of Canadian ArcticPopulation (composition found in Pelletier et al. 2009)including methyl mercury (MeHg), polychlorinated biphenyls(PCB), and organochlorine pesticides (OC)NP

nonylphenolNP1EC

nonylphenol monoethoxylate carboxylateOCorganochlorine pesticides (refer to Pelletier et al. 2009)PBDE

polybrominated dibenzo-p-dioxinsPCB

polychlorinated biphenyl

PCB-773,3,4,4-tetrachlorobiphenylPCP

pentachlorophenolPM

permethrinTBDD

2,3,7,8,-tetrabromodibenzo-p-dioxinTCB

3,3,4,4-tetrachlorobiphenylTCEtrichloroethyleneTCDD

2,3,7,8-tetrachlorodibenzo-p-dioxinZM

ZM 189,154 experimental anti-estrogen of Astra Zeneca

other

AhR

aryl hydrocarbon receptora.i.

active ingredientsCATchloramphenicol acetyltransferaseconc.concentration2DGE/MS

two dimensional gel electrophoresis with mass spectrometric

protein identificationdiff. expressiondifferential expressionEC25S1effect concentration for substance 1 elucidating 25% effectsER

estrogen receptorGOgene ontologyip.intraperitonealMOA

mode of actionNMR

nuclear magnetic resonanceqPCR

quantitative polymerase chain reactionRT-PCR

real time polymerase chain reaction, here taken to refer to

semiquantitative methodologies, such as band intensity

measures of PCR productsSSH

suppressive substractive hybridization




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TU

toxic unit, i.e. exposure concentration of component over effectconcentration of the same compoundTUS

sum of toxic units (TU)WWTPswaste water treatment plants

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