Cell-based bioassays for the screening of chemical contaminants and residues in foods

4 Cell-based bioassays for the screening of chemical contaminants and residues in foods H. Naegeli, University of Zürich, Switzerland

Abstract: Cell-based bioassays provide functional sensors (‘cytosensors’) for the detection of chemical hazards. Living cells respond with characteristic reaction patterns to various stimuli, and toxicant-specific changes of cell function, morphological integrity and subcellular/molecular composition reveal the presence of contaminants or residues. Powerful biotechnological methods (reporter gene assays, transcriptomics, proteomics, metabolomics) measure sublethal biological endpoints, providing new opportunities for risk-based control of food quality and safety. Platforms involve luciferase/fluorescence reporter or calcium signaling assays, polymerase chain reaction (PCR) arrays, oligonucleotide microarrays (‘DNA chips’) and high-throughput ‘next generation’ sequencing technologies. This chapter discusses the predictive power of cytosensors for the identification of contaminants or residues and the significance of this whole-cell strategy for risk assessment applications in food production. Key words: DNA chip, microarray, transcriptomics, proteomics, metabolomics.

4.1

Introduction

Despite consumers’ general expectation that all dietary components should be free of foreign substances, many food-borne toxicants of natural or anthropogenic origin pose a potential threat to human health. Typical examples include bacterial toxins, phyto-, myco- and phycotoxins, residues of pesticides or veterinary drugs, growth promoters, heavy metals, persistent organochlorines or endocrine disruptors as well as contaminants generated during food processing or those released from food contact and packaging materials (Bradley et al. 2008; Borchers et al. 2009; Jackson 2009). Safety rules have been issued to limit consumers’ exposure to below scientifically established threshold levels defined as maximum

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residue limits (MRLs). For the correct enforcement of these regulations, sophisticated analytical systems have been established that aim at detecting an increasing spectrum of suspected analytes (Ingendoh et al. 2009; Ortelli et al. 2009; Mastovska et al. 2010). However, these target compounds are in most cases present in food at harmless concentrations and it is a challenging task to identify those products that represent a significant health risk. This mission is further complicated by the emerging concern that, even if a sample is fully compliant with existing regulations, that is, when each analyte remains below its legal MRL, the overall mixture of contaminants or residues may elicit higher-than-expected effects due to additive or synergistic interactions (Kortenkamp et al. 2007; Dip et al. 2009). Conversely, some potential threats may be overvalued and, therefore, overregulated because the routine procedures adopted to establish MRLs do not take into account antagonistic interactions between contaminants, residues and matrix components leading to lower-than-expected effects (Wangikar et al. 2004; Vettori et al. 2006; Hendriksen et al. 2007; Van der Heiden et al. 2009). In view of these complex cross-talks between chemicals and their manifold targets, the true impact on human health can only be inferred from biological tests (also known as bioassays) that examine the combined action of all natural and anthropogenic constituents of a particular sample.

4.2

Description of bioassays

In a wide perspective, the term ‘bioassay’ indicates any test system that takes advantage of biological molecules to detect a particular analyte. This general definition includes, for example, microbiological indicator strain tests to screen for antibiotics, immunological platforms which make use of specific antibodies, and receptor-binding assays with cellular receptors of different sources. However, the same term is also employed to describe effect-driven biological tests carried out to measure typical reaction patterns or responses at various levels of complexity, ranging from reconstituted biochemical systems to subcellular fractions, cultured living cells, freshly dissected body parts such as tissue slices, engineered tissue models or even whole organisms. In the context of this review, we limit the use of the term ‘bioassay’ to define screening methods relying on cultured living cells as sensors (or ‘cytosensors’) of residues or contaminants. In such whole-cell assays, entire pathways or complex biological networks of interest can be interrogated, as opposed to a single predefined interaction in the aforementioned immunological or biochemical tests. These cytosensor assays determine hazard-induced biological responses such as losses of membrane integrity, disruption of metabolic networks, induction of signaling cascades, interference with the cell division cycle or reprogramming of the genome and, hence, are able to respond to the presence of contaminants or residues in a physiologically pertinent manner (Banerjee and Bhunia 2009). Due to their capacity to predict in vivo effects, large batteries of such bioassays based on living cells have already been adopted in the drug discovery area, where they

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provide a high-throughput preclinical research tool to identify potential toxicants at early stages of drug development, thereby reducing the need for costly in vivo animal experiments and restricting the risk of unexpected toxicities (Michelini et al. 2010). 4.2.1 Cell viability and cytotoxicity assays Rapid assays for the assessment of cell viability or cytotoxicity are based on toxicant-induced losses of cellular structure or function. As a simple principle, many of these assays rely on the property of molecular probes to diffuse across damaged lipid membranes but not through intact cellular compartments. Typically, these tests are performed with optical detection systems, either by monitoring the direct incorporation of dyes such as trypan blue or propidium iodide or, indirectly, by quantifying the release of intracellular enzymes such as alkaline phosphatases or lactate dehydrogenases (Banerjee and Bhunia 2009). A well-established probe to measure overall metabolic activity is the redox indicator resazurin, also known as alamar blue (Fritzsche and Mandenius 2010). This non-fluorescent compound is reduced to the fluorescent resorufin product by the action of reductase enzymes in healthy cells. A further popular tool to monitor cell vitality is the use of colorimetric indicators like MTT or WST-1, which are converted to a purple product by the action of dehydrogenases in viable cells (Ngamwongsatit et al. 2008). Cell death by apoptosis can be detected, for example, by exploiting the affinity of annexin V for phosphatidylserine, which is flipped across the cell membrane, from the inner cytoplasmic leaflet to the outer cell surface layer, during early stages of apoptotic body formation. Because a large number of compounds are potentially able to damage the structural or functional integrity of living cells, or trigger apoptotic death programs, relying solely on such generic hallmarks of viability or cell death would not allow any distinctions between toxicants that exert different modes of action. 4.2.2 Calcium signaling measurements Cellular functions are regulated by common signaling pathways in essentially all tissues. Many short-term adaptation responses, including the reaction to toxic insults, are mediated by rapid changes in the intracellular level of free calcium, which is used as a ubiquitous messenger in signaling cascades. The availability of fluorescent calcium indicators (for example Fluo-4) or calcium-activated photoproteins (for example aequorin) enables the real-time monitoring of such toxicant-induced calcium fluctuations. An important application of these calcium assays is the detection of stimuli mediated by G protein-coupled receptors (GPCRs), representing a large family of sensor proteins (> 800 members in humans) that are embedded in the plasma membrane of most cells. They bind specifically to a range of agonist molecules and, thereafter, convey sensory information into downstream signaling pathways involving calcium as an intracellular messenger (Michelini et al. 2010). Thus, the engineering of mammalian cells that express dedicated

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receptors linked to calcium signaling shows considerable promise in the development of new cytosensor assays and, in the future, this approach could be extended to the detection of food toxicants. A proof-of-principle example is the construction of genetically engineered B-lymphocytes and mast cells that express pathogen-specific antibodies or receptors on their outer membrane (Rider et al. 2003; Curtis et al. 2008). In these cytosensor systems, the specific binding of pathogen molecules to surface recognition proteins initiates a downstream signal transduction cascade that, by increasing the intracellular free calcium, induces a readily measurable bioluminescent signal. Another recent application of this biosensor technology is the detection of sweeteners by exploiting taste receptors on the cell membrane (Toda et al. 2011). 4.2.3 Reporter gene assays More elaborated cytosensor assays employ mammalian cells to transduce and amplify receptor-mediated stimuli, thus generating ultimate responder signals that are amenable to easy detection. This strategy has been exploited for the development of a series of specialized reporter gene assays that, in some cases, have already been validated as screening tools for food safety (Bovee et al. 1998; Legler et al. 1999; Plotan et al. 2011). Briefly, mammalian cells can be engineered to produce a particular reporter gene product in response to receptor activation by a given stimulus. For that purpose, the reporter sequence of choice has to be placed under the regulation of transcriptional control elements that are sensitive to the quantity of the analyte to be measured. Exposing the cells to particular analytes (for example estrogens, androgens or dioxins) then results in increased expression of the reporter protein. In most cases, this reporter gene technology is based on the transient or stable introduction of ectopic genetic constructs, in the form of plasmids, where a reporter sequence is combined with selected regulatory elements. Gene expression is then triggered upon the binding of a specific type of transcription factor to these regulatory elements. Due to the aforementioned amplification through intracellular transduction cascades, these types of bioassays display high sensitivity, reliability and practicability. In addition, they usually possess a wide dynamic range and are amenable to automation for high-throughput screening. Unlike receptor-binding assays, carried out with purified receptor proteins or cell lysates, reporter gene assays are also able to distinguish between receptor agonists and antagonists (Sonneveld et al. 2006). Overall, mammalian cells have been shown to be more sensitive than yeast-based reporter gene assays (Legler et al. 2002). Ideally, the reporter gene product exhibits an enzymatic activity prone to direct monitoring, and at the forefront of most of these assays is the use of luciferase enzymes that catalyze a bioluminescent reaction, measurable in cell lysates upon the addition of appropriate reagents. A valuable alternative is to take advantage of a fluorescent reporter protein (Zhao et al. 2010). To increase the robustness of reporter gene assays, an internal reference signal should be introduced in order

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to normalize the response and separate specific reactions from non-specific interferences. This is normally achieved by the inclusion of a second reporter gene that is constitutively expressed and whose activity depends only on general parameters like transfection efficiency or cell vitality. A wide battery of commercial dual reporter assays, which operate by the sequential detection of firefly and Renilla luciferase activity in the same sample, have been developed to address this important normalization issue (see, for example, http://www.promega.com or http://www.sabiosciences.com). Under physiologic conditions, when regulated by natural promoter sequences, gene expression results from a combinatorial assembly of multiple transcription factors, which are activated by many receptors and their respective signal transduction systems (New et al. 2003). To avoid an excessive pathway promiscuity and, hence, increase the overall assay specificity and accuracy, the regulatory promoter sequences that control expression of luciferases or other reporter genes are heavily simplified to include only those minimal elements that allow the binding of just one kind of transcription factor. An important consequence of this promoter reduction strategy is that the resulting artificial construct is not able to recapitulate the overall biological responses driven in living cells by multifactorial signaling networks (Rotwein et al. 1993). Recent work in our laboratory attempted to circumvent this possible drawback of advanced reporter gene assays by exploiting new molecular tools for the assessment of downstream effects of regulatory networks. These novel tools allow the monitoring of genomic effects at the transcriptome, proteome or even metabolome level. Indeed, we propose that, by the application of such ‘-omics’ techniques, it has become feasible to develop multi-endpoint assays yielding broad molecular fingerprints that predict more efficiently than traditional single-endpoint bioassays the biological activity of toxic insults or other cellular stressors (Aardema and MacGregor 2002).

4.3 Transcriptomics fingerprinting technologies A main purpose of this chapter is to illustrate how the transcriptomics fingerprinting of easy-to-culture human cells may be exploited to detect hazardous food constituents, toxic contaminants and residues. Transcriptomics is defined as the systematic investigation of RNA molecules in a given biological system. This nucleic acid analysis exploits the strict substrate specificity or template fidelity of DNA-metabolizing enzymes such as restriction endonucleases, DNA polymerases, RNA polymerases, reverse transcriptases or DNA ligases. In addition, transcriptomics methods utilize the universal base pairing properties (adenine–thymine and guanine–cytosine) driving the spontaneous hybridization of complementary nucleic acid polymers. To date, reverse-transcriptase polymerase chain reactions (RT-PCR) and DNA microarrays constitute the most popular transcriptomics techniques, but these traditional methods may soon be replaced by more advanced biotechnological methods involving ‘next generation sequencing’. An outline of different transcriptomics platforms is presented below.

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4.3.1 Real-time RT-PCR PCR is a thermal cycling technology whereby heat-stable DNA polymerases are used to amplify a given DNA sequence. Each amplification cycle comprises the following individual steps: heat denaturation of double-stranded DNA, annealing of specific oligonucleotide primers and template-directed elongation of these primers by thermostable DNA polymerases. PCR methods have been extended to the monitoring of RNA transcripts by first converting target RNA sequences into complementary DNA (cDNA) molecules by means of an enzyme known as reverse transcriptase, which copies the RNA sequence into a perfectly matching DNA product. Real-time RT-PCR represents a highly quantitative version of this technique whose main feature is that the resulting cDNA is detected while the PCR amplification advances in real time, for example by measuring the progressive release of a fluorescent probe. Finally, RNA levels are determined by assessing the PCR cycle number at which the liberated fluorescence exceeds a threshold over background values (Kubista et al. 2006). Gene expression changes induced by a particular stimulus are quantified by comparing the concentration of each RNA target with that of an endogenous housekeeping control that is constitutively expressed at steady levels (Livak and Schmittgen 2001). Applications of RT-PCR in the area of food toxicology include, for example, the monitoring of gene expression changes caused by enzyme inducers, dioxins, dioxin-like chemicals or endocrine disruptors such as xenoestrogens (Baba et al. 2005; Dip et al. 2008a; 2008b). Real-time RT-PCR assays, as a premier tool for gene expression analyses, are characterized by high precision, specificity, sensitivity, a wide dynamic range and reproducibility. Initially, this method was applicable only for the analysis of a small number of candidate transcripts but, more recently, sets of RT-PCR arrays on multi-well plates have become commercially available. These combined RT-PCR arrays allow the determination of multiple RNA sequences simultaneously (Mauerer et al. 2009).

4.3.2 DNA microarrays The diversity and scale of transcriptomics analyses have exploded with the development of oligonucleotide arrays, also known as DNA microarrays or DNA chips. In its conventional form, this technology is based on a small solid surface displaying hundreds or thousands of microscopic spots, each containing short single-stranded DNA probes with a specific nucleotide sequence that is complementary to one of the many RNA molecules generated by the cells of a particular organism. For transcriptomics applications, the RNA sequences isolated from biological samples are first converted either to labeled cDNA or, depending on the platform of choice, to labeled complementary RNA (cRNA), and then hybridized to the oligonucleotide probes immobilized on microarray chips. Subsequently, expression profiles are determined by scanning the chip surface for hybridization intensities, whereby the signal at each particular microarray location reflects the abundance of a respective RNA molecule. Appropriate bioinformatics

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tools have been developed for the acquisition and management of the enormously rich datasets obtained from microarray experiments. These algorithms allow hybridization quality controls, noise corrections, signal alignments, data normalization, averaging and filtering as well as intersample comparisons and statistical evaluations (Burczynski 2003). Gene ontology databases can be consulted to interpret the microarray data by linking gene expression changes to biochemical pathways and presumed cellular functions. Two main types of array formats have been employed for transcriptional fingerprinting. High-density arrays, which attempt to cover the entire transcriptome of a given organism, are mostly suitable to explore general gene expression profiles associated with diseases or to identify novel transcriptional biomarkers associated with exposure to hormones, drugs or toxic chemicals (Naciff et al. 2002; Frasor et al. 2003; Moggs et al. 2004; Riedmaier et al. 2009). In contrast, low-density microarrays are focused on a selected group of RNA targets and, hence, provide a more costeffective option for expression studies targeting known biological endpoints (Lobenhofer et al. 2002; Ise et al. 2005; Parveen et al. 2009). 4.3.3 Unbiased sequencing strategies A major disadvantage of DNA microarrays is that they offer only a narrow dynamic range for the quantitative comparison of expression changes, where RNA amounts often span several orders of magnitude. Also, microarrays will not be able to detect RNA targets for which no oligonucleotide probe has been included in the DNA chip design. These problems are counteracted by sequencingbased methods that provide a digital rather than continuous count of RNA quantities and offer a totally unbiased access to deciphering the cells’ transcriptome. Sequencing-based transcriptomics methods were first pioneered by a technique known as serial analysis of gene expression (SAGE), which consists of two basic steps. First, a short fragment of each individual cDNA molecule, generated by DNA cleavage with specific restriction enzymes, is used as a tag to identify the corresponding mRNA transcript. In a second step, after PCR amplification, these short oligonucleotide tags are ligated to each other and subjected to sequencing. The resulting end-to-end chains, or concatenates, allow the detection of multiple transcripts per sequencing reaction (Velculescu et al. 1995; 1997; Patino et al. 2002). Ultimately, SAGE provides both a list of expressed sequence tags and a quantitative readout of the number of times each tag is present. During recent years, new highly quantitative strategies, known as next generation (or whole transcriptome) sequencing, have been introduced to analyze the complete transcriptome of biological systems. These powerful techniques have been developed to detect virtually every RNA species in a given sample by massive parallel sequencing (Wall et al. 2009). The automated SOLiD platform, for example, uses an adapter sequence as the attachment point for a PCR-based amplification of cDNA molecules. The sequencing itself is carried out on a solid support by exploiting the preference of DNA ligases, enzymes that join free DNA ends, for complementary nucleotide partners (Janitz 2008; Hashimoto et al.

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2009). A pool of all possible oligonucleotides of defined length, but containing different fluorescent labels, are annealed and ligated: the preference of the DNA ligase for matching sequences results in a signal informative of the nucleotide at each individual position. After multiple rounds of template-mediated ligation, accompanying computational tools dissect the parallel fluorescent readouts generated from millions of nucleic acid fragments to reveal both the nucleotide sequence and the exact copy number of each RNA species. Although the costs associated with this new fascinating technology are still prohibitive for routine screening applications, it represents a promising strategy for the assessment of expression profiles. A possible development towards more practical and affordable solutions, with possible applications for screening assays in the food safety area, is the combination of parallel sequencing with tailored capture arrays that preselect and, hence, limit the number of targets to be detected. Another interesting development is single-polymerase sequencing exploiting fluorescently tagged substrates. In this new type of sequencer, each base is identified in real time by monitoring a distinguishable color that flashes as the respective base is incorporated into the growing DNA strand (Mardis 2008).

4.3.4 Chemical group-specific expression signatures The advent of high-density transcriptomics methods like DNA microarrays has revolutionized the way toxicological problems are addressed. In fact, this new comprehensive approach allows large-scale RNA profiling, such that it becomes possible to examine the function of virtually every gene of a given organism and thereby relate chemical hazards to genome-wide expression changes (Afshari et al. 1999). Based on this whole-genome coverage, major efforts have been undertaken during the last decade to test the prediction that living cells respond to different categories of toxic stress with distinct expression fingerprints (Aardema and MacGregor, 2002). Distinctive expression profiles, referred to as ‘signatures’, have been provided for the liver tissue of rats exposed to hepatotoxic compounds like benzene, carbon tetrachloride or allyl alcohol (Waring et al. 2001; Heijne et al. 2003). In rodents, characteristic transcriptional profiles of hepatic cells have also been identified for a wide range of phase I enzyme inducers (Hamadeh et al. 2002; Burczynski, 2003) and genotoxic agents as well as non-genotoxic carcinogens including peroxisome proliferators or other chemicals promoting oxidative stress (Burczynski et al. 2000; McMillan et al. 2004). Similarly, characteristic expression fingerprints have been established in target tissues (mammary gland, uterus, ovaries) of laboratory animals exposed to hormones or endocrine disruptors (Naciff et al. 2002; Moggs et al. 2004). Analogous studies have been performed at a lower level of biological organization, with cultured cell lines exposed, for example, to coumarin (Kienhuis et al. 2006), xenoestrogens (Lobenhofer et al. 2002; Frasor et al. 2003), polychlorinated biphenyls (PCBs), dioxins (Buterin et al. 2005) or genotoxic chemicals (Zhang et al. 2010). This pioneering work showed that, in combination with appropriate pattern recognition

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algorithms (see below), the analysis of gene expression changes represents a promising new tool to predict toxicities, study mechanisms of action, discover new biomarkers of exposure, screen drugs for off-target effects or monitor industrial chemicals for unwanted health hazards (Natsoulis et al. 2005). An important strength of this multivariate approach is that it is best suited to detect the combined, synergistic or antagonistic effects of chemicals (van Delft et al. 2005; Hendriksen et al. 2007). Indeed, many transcriptomics studies aim at deciphering the health impact of nutritional constituents, supplements, vitamins or additives in an integrated context of multiple stress factors (Herzog et al. 2004; Kitajka et al. 2004; Vittal et al. 2004; Kuntz et al. 2007). In view of the proven feasibility of an effect-driven substance classification using transcriptional profiling, we postulated that this molecular fingerprinting strategy would be applicable to the identification of food-borne chemical hazards. We hypothesized that it should be possible to establish a simple cellular system, or cytosensor, where changes of gene expression patterns serve as an indicator of adverse or unwanted effects. In this test system, treatment with reference standards displaying known activities will generate expression fingerprints on transcriptomics platforms representing typical cytosensor reactions to chemical injuries. Some changes of gene expression will reflect a generic response to cellular damage, but additional transcriptional fingerprints will be unique to a particular category of chemicals. Treatment of the same cytosensor with mixtures extracted from food samples will then yield transcriptional patterns that, by comparison with established reference fingerprints, may be used as a signature that warns of specific classes of hazardous contaminants or residues. The key principle of this approach is that a chemical (or a group of related chemicals) will be defined not by alterations in the function of a single gene (as with reporter gene assays or other single-endpoint tests) but, instead, by the combinatorial transcriptional change representing the fingerprint of particular substances. It should be pointed out that this detection principle holds true independently of whether or not the transcriptional changes (describing ‘what may happen’) translate to shifts in the composition of cellular proteins (describing ‘what makes it happen’) or metabolite content (describing ‘what has actually happened’).

4.4 Workflow of a transcriptomics fingerprinting-based screening strategy The application of transcriptomics as an effect-driven screening assay depends on the following crucial steps: (i)

Quick sample extraction taking into account the varying physical–chemical properties of different analytes. (ii) Full reconstitution of the resulting food extract in cell culture medium. (iii) Exposure of a responsive cytosensor, possibly in a miniaturized cell culture format.

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(iv) Monitoring of gene expression changes induced by the food extract. (v) Computational data analysis and pattern recognition search for the identification of cellular reactions indicative of hazardous substances in food.

4.4.1 Sample extraction A general obstacle to all types of in vitro assays based on cultured mammalian cells is their susceptibility to unspecific matrix effects, leading to generic responses like reduced cell vitality. An important challenge to improve the signal-to-noise ratio is, therefore, the development of multi-residue and multi-contaminant sample extraction methods that reduce the matrix complexity without causing any losses of biologically active analytes. In a previous study (Dip et al. 2008a), for example, persistent organic pollutants were isolated from breast milk samples of nursing mothers by subjecting the lipid fraction to gel permeation chromatography using cyclohexane/ethyl acetate as the solvent. The resulting mixture, which contained a wide range of PCBs and dioxins as well as other organic pollutants, exerted a dual transcriptional effect in human cells by inducing expression of the cytochrome P450 enzymes CYP1A1 and CYP1B1 and, simultaneously, inhibiting a typical estrogen receptor-mediated genetic program. Another example is the separation of phytoestrogens from infant formula, baby food and soymilk, achieved by liquid extraction (using either acetate buffer alone or acetate/acetone/ diethyl ether) followed by a dual (reverse and normal solid-phase) clean-up through C18 and SiOH cartridges (Antignac et al. 2009). Similarly, we recovered Fusarium mycotoxins (trichothecenes and zearalenone) from raw cereals, breakfast cereals and baby food by a combination of liquid-phase (acetonitrile/ water) and solid-phase extraction through MycoSep columns (Bowens et al. 2009; Lancova et al. 2009). To obtain the full range of biologically active analytes, an enzymatic (Helix pomatia preparation) and chemical deconjugation (acidic hydrolysis) step is commonly included to release the compounds of interest from phase II metabolites such as glucuronide or sulfate moieties (Antignac et al. 2009). However, extensive time- and cost-consuming pre-analytical cleaning steps should be avoided whenever possible to ensure final mixtures with a wide window of target analytes.

4.4.2 Reconstitution and cytosensor exposure To start the cytosensor assay, extracted residues need to be completely dissolved in cell culture medium. In view of its compatibility with mammalian cells, generally at concentrations of up to 0.3–0.8% (vol/vol), dimethyl sulfoxide is the solvent of choice for this reconstitution step. As a responsive cytosensor system, we have been using a widely available MCF-7 breast cancer cell line. These routinely cultured human cells of epithelial origin display a range of nuclear receptors, including those mediating the activity of glucocorticoids, progesterone, xenoestrogens, dioxins, dioxin-like PCBs and pro-oxidant chemicals (Soto et al.

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1995; Clement et al. 2007). MCF-7 cells also display the pregnane X receptor whose function is to regulate gene expression in response to many drugs and foreign substances (Honorat et al. 2008). In addition, despite their unlimited lifespan, MCF-7 cells retain a normal responsiveness to genotoxic stress; that is, upon activation of the p53 tumor suppressor they undergo transient cell cycle arrest, apoptosis or senescence (Ray et al. 2006). Of course, the response of such a cell line may not be fully extrapolated to whole organisms, but it nevertheless represents a biologically relevant and practical caliper of toxic effects. An important issue is that, in some cases, cytosensors like MCF-7 cells may be able to raise transcriptional responses to food toxicants only after a preceding metabolic activation process. Acrylamide, for example, is a heat-induced food contaminant generated during the Maillard browning reaction that, even at millimolar concentrations, induces only marginal changes of gene expression in cultured cells. However, characteristic expression fingerprints were generated by exposure of the same cells to glycidamide, which is the main active metabolite of acrylamide. In this case, the most prominent transcriptional responses involved the induction of a cluster of factors and enzymes that protect from oxidative stress, indicating that the Maillard browning reaction increases the pro-oxidant activity of processed food (Clement et al. 2007). This example illustrates that, to expand the range of applications in the food safety area, the cytosensor approach may be further developed with the inclusion of an enzymatic bioactivation step taking advantage of an appropriate battery of metabolic enzymes.

4.4.3 Monitoring and interpretation of gene expression changes When MCF-7 cells used as cytosensors are exposed to chemical stimuli, primary transcriptional responses usually take place within 3–24 hours. Thereafter, RNA is isolated from the harvested cells and analyzed with the aid of a suitable transcriptomics platform. For specific applications to food safety, we have adopted a cost-effective and miniaturized DNA microarray format (Clondiag, Jena, Germany) that fits into the bottom of standard laboratory tubes and, hence, does not require specialized equipment. This user-friendly and inexpensive microchip format consists of a small glass surface with a maximal capacity of 156 spots, where each oligonucleotide probe is printed in triplicate at different locations. After hybridization with biotin-labeled cDNA obtained from a selective amplification of target RNA sequences, expression fingerprints are visualized by the addition of a streptavidin–horseradish peroxidase conjugate, thus generating a blue precipitate that is amenable to simple colorimetric measurements. Finally, the detection of possible contaminants by expression fingerprinting requires computational tools to relate transcriptional effects to the presence of hazardous substances. Several algorithms are available to compare expression patterns with known compound signatures. For example, principal component analysis (PCA) is a dimensionality reduction technique that is commonly applied to classify complex data profiles (Hamadeh et al. 2002; van der Werf et al. 2006; Riedmaier et al. 2009). In a typical PCA display, each tested sample is visualized by one spot

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in two- or three-dimensional graphs where transcriptomes that are similar group together whereas transcriptional profiles that differ from each other appear further apart. Another pattern recognition tool is the hierarchical clustering of expression data, which is normally presented as a tree-like diagram known as a dendrogram, that reflects the degree of similarity between different transcriptomes. In many cases, the hierarchical clustering is combined with a heatmap display where different pseudocolors represent the expression intensity of each individual transcript. Additionally, expression fingerprints may be evaluated by the pairwise comparison of transcripts in linear regression analyses, where high R-values would be indicative of similar fingerprints and low R-values would imply diverging transcriptional effects (Ise et al. 2005; Buterin et al. 2006).

4.5 Applications of transcriptomics fingerprinting for the screening of chemical contaminants and residues in foods Recent reports from our own laboratory illustrate possible applications of screening methods based on the fingerprinting of an in vitro cytosensor like the one described in Section 4.4. An example is the detection of type A trichothecenes (T-2 and HT-2 toxins), representing a class of Fusarium mycotoxins, for which group MRLs are expected to be issued by EU authorities. Once confirmed by quantitative real-time RT-PCR, the information gained from high-density microarray analyses (Bowens et al. 2009) has been used to design a low-density microchip focused on the most prominent transcriptional changes induced by T-2 and HT-2 mycotoxins. Some of the major expression changes affect genes coding for chemokine ligand 2, death-associated protein kinase 3, interleukin 32, tumor necrosis factor alpha-induced protein 3 and lymphotoxin beta. Using this tailored microchip platform, the expression fingerprinting of MCF-7 cells has been shown to provide a prototype cytosensor with limits of detection lower than 20 ng/g for both T-2 and HT-2 in cereals (Lancova et al. 2009). Another combination of oligonucleotide probes on the same microchip arrays has been employed to measure the presence of zearalenone, which is a Fusarium mycotoxin that displays potent estrogenic activities. In this case, the major target genes include those coding for thymidylate synthetase, ribonucleotide reductase, cyclindependent kinase inhibitor and other cell cycle-associated factors. Using this microchip tailored for the monitoring of estrogenic activities, the limit of detection for zearalenone in maize is about 10 ng/ml. Importantly, even with these rather complex sample matrices like cereals, the transcriptomics bioassays could be performed after relatively simple, conventional one- or two-step extraction methods. Both high-density (Dip et al. 2008b) and low-density DNA microarrays (Ise et al. 2005) have been employed to analyze the transcriptional response induced in MCF-7 cells by phytoestrogen-rich soy products. A distinctive phytoestrogen signature, different from that elicited by the endogenous hormone 17β-estradiol, could be detected in target cells overexpressing the estrogen receptor subtype β

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(Dip et al. 2008b). This finding is consistent with the higher binding affinity of soy isoflavones like genistein for the estrogen receptor β compared with the α subtype (Kuiper et al. 1998), and suggests that the supposed health benefits of phytoestrogens in preventing cancer or other diseases may depend on the age-, tissue- and tumor cell-dependent repertoire of distinct estrogen receptors. In view of a possible health risk due to an exaggerated exposure to estrogenic chemicals during early life, the estrogenic activity of soy-based infant formula as well as vegetable- and/or cereal-based formula has been examined in quantitative terms using the aforementioned DNA microchips. Surprisingly, a direct comparison with the effects of a pure genistein standard revealed that the soy-based formula, which has a particularly high phytoestrogen content, displays an estrogenic potency that is, depending on the target transcript, 10- to 100-fold lower than predicted from the concentration of its major isoflavones, genistein and daidzein. This lower-than-expected induction of estrogen-responsive genes is consistent with the notion that mixtures of phytochemicals contain both estrogen receptor agonists and antagonists (Moutsatsou 2007). Thus, the fingerprinting approach discloses interactive effects between soy components that are likely to mitigate the overall biological impact of phytoestrogens ingested as part of natural dietary mixtures.

4.6

Conclusion and future trends

There are several potential problems associated with the use of a cytosensor strategy in the screening for food-borne toxicants. First, cytosensors lack specificity for the identification of individual analytes. Nevertheless, these effectbased assays have an advantage over standard analytical methods because they are able to integrate the effects of multiple analytes, either known or unknown, that trigger a particular cellular response. Thus, they provide effective first responders that are able to identify the additive outcome of emerging or unexpected threats. Second, an intrinsic problem of cytosensors is that they are sensitive to changes in their culture environment. Therefore, to achieve reliability and assay robustness, it is imperative that cells are maintained under constant and possibly optimal conditions. Third, the maintenance of mammalian cells requires appropriate facilities, for example for the provision of controlled ambient culture conditions or for the cryogenic storage of cell stocks in liquid nitrogen. Fourth, mammalian cell culture is expensive, and cytosensor assays will fall within the higher price range of currently available screening methods. In view of the increasing advent of rapid screening tests, which are not related to any toxicological endpoint, effect-based assays will generally gain importance in order to support risk assessment and risk management procedures in the food industry. Currently, the majority of existing screening bioassays in the food safety area take advantage of specific antibodies that recognize single analytes or a narrow range of structurally related targets. In a few cases, effect-driven assays are employed, whereby chemical stimuli act upon a cytosensor leading to the

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induction of a single reporter gene. However, the dynamically changing scenarios of hazards and risks in food safety necessitate not only sensitive and rapid detection tools but also the deployment of a broad-spectrum screening strategy that can be used as a first line of defense against emerging or unexpected threats. In this respect, cytosensor assays provide more comprehensive and complex risk-based information involving changes of gene expression, apoptosis, cell death or cellular proliferation, changes in metabolism and cellular composition, than currently used immunochemical assays (Banerjee and Bhunia, 2009). Here, taking the example of transcriptomics, we propose a multi-endpoint alternative exploiting the ability of natural or anthropogenic chemicals to induce distinguishable expression fingerprints in easy-to-culture human cells. Such a transcriptomics fingerprinting method requires trained personnel and considerably more time and resources than standard rapid tests. However, a key advantage of the proposed multi-endpoint strategy is that it delivers a proof of principle for novel effect-driven assays that exploit a biologically relevant response (reprogramming of the genome) in a toxicologically significant target system (human cells). The same principle will certainly be applied to monitor changes in the respective proteome, metabolome or lipidome of selected cytosensors. Expected applications of multi-endpoint fingerprinting assays (based on transcriptomics, proteomics, metabolomics, lipidomics or other ‘-omics’) include, for example, the evaluation of mixture effects, the functional analysis of food additives or supplements, the analysis of risk/benefits of novel foods, the monitoring of hazards associated with food storage or processing and, as mentioned before, the detection of emerging or unexpected contaminants. The proposed fingerprinting method offers the added benefit that, by increasing the number of probes on microchips or other appropriate platforms, it will allow the simultaneous detection of multiple groups of contaminants (or ‘cocktails’) that alter gene expression in the same cell line. Such risk-based cytosensor approaches may also become relevant in the developing world, where they could be used as a prime warning system to detect high-priority contaminants that pose a particular threat to consumers’ health. Future work will be devoted to increasing the responsiveness of cytosensors of choice by integrating genetic constructs coding for additional receptors (for example, androgen receptors in MCF-7 cells). In parallel, it will be necessary to further adapt miniaturized cell culture formats towards a multi-contaminant screening platform and to validate this procedure for the detection of particular groups of compounds. Another critical issue, common to all cytosensor assays, is the future choice of cell type: cancer cells are easy to grow but should perhaps be replaced with immortalized primary cells that represent more faithfully the biologic response of whole organisms. The pharmaceutical industry is already investing large sums in the automation, miniaturization and increased robustness of cell-based bioassays. In particular, tissue culture systems in three dimensions (3-D systems) are thought to facilitate important physiologic processes such as communication between cells and interactions with the extracellular matrix. Therefore, 3-D systems mimic in vivo responses more efficiently and can be

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adopted to enhance the performance of cytosensor assays (Lee et al. 2008). Finally, another expected development is the use of induced pluripotent stem cells as cytosensors (Laustriat et al. 2010). In fact, human pluripotent stem cells provide a rich biological source, as they display different useful advantages for the development of cell-based assays. First, they are amenable to genetic engineering to amplify their range of sensitivity. Second, they have an unlimited self-renewal capacity and, as a consequence, provide a steady source of living cells. Third, they represent a versatile cytosensor tool as they can be differentiated into many different cell types or complex tissues.

4.7 Acknowledgements We would like to thank Dr T. Ellinger (Clondiag) and Dr H. Gmuender (Genedata) for their continuous support and helpful discussions. Research in the authors’ laboratories is supported by the European Commission (FOOD-CT-2004-06988).

4.8

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4.9 Appendix: Abbreviations cDNA cRNA DON GAPDH GSH MCF-7 MRL mRNA NIV PCA PCBs PCR RT-PCR SAGE

complementary DNA complementary RNA deoxynivalenol glyceraldehyde-3-phosphate dehydrogenase glutathione human breast carcinoma cell line maximum residue limit messenger RNA nivalenol principal component analysis polychlorinated biphenyls polymerase chain reaction reverse-transcriptase polymerase chain reaction serial analysis of gene expression

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