Fouad Cherifi

Baptiste Hemery

Romain Giot

Marc Pasquet

Christophe Rosenberger

Performance Evaluation Of Behavioral Biometric Systems

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Performance Evaluation

Of Behavioral Biometric Systems

A B S T R A C T

We present in this chapter an overview of techniques for the performance evaluation of behavioral biometric systems. The BioAPI standard that defines the architecture of a biometric system is presented in the first part of the chapter... The general methodology for the evaluation of biometric systems is given including statistical metrics, definition of benchmark databases and subjective evaluation. These considerations rely with the ISO/IEC19795-1 standard describing the biometric performance testing and reporting. The specificity of behavioral biometric systems is detailed in the second part of the chapter in order to define some additional constraints for their evaluation. This chapter is dedicated to researchers and engineers who need to quantify the performance of such biometric systems.

K E Y W O R D S

Systems Evaluation, Biometrics, Authentication, Metrics, Behavioral biometrics

I N T R O D U C T I O N

Biometrics is now a technology that is present in our daily life. It is used as for example in airports (passport verification), offices (access control, biometric USB key...) and even in some places in the world for banking operations... Different biometric modalities can be used for the identification / verification of an individual (face recognition, keystroke dynamics recognition, DNA analysis...) [START_REF] Mahier | Biometric authentication[END_REF].

The characterization of an human by considering its behavior in its daily life operations (gait, signature dynamics, voice...) [START_REF] Han | Individual Recognition Using Gait Energy Image[END_REF][START_REF] Muramatsu | Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification[END_REF][START_REF] Petrovska-Delacretaz | Text -Independent Speaker Verification: State of the Art and Challenges[END_REF] or through its interactions with a computer (mouse dynamics, keystroke dynamics...) represents an interesting and open area in research [START_REF] Hwang | Improving Authentication Accuracy of Unfamiliar Passwords with Pauses and Cues for Keystroke Dynamics-Based Authentication[END_REF][START_REF] Orozco | Haptic-Based Biometrics: A Feasibility Study[END_REF].

The performance evaluation of such biometric systems is very important for many reasons:

 To be used in a real (that is to say in an industrial) context, the quality of a biometric system must be precisely quantified. The context of use, the efficiency, the robustness of the algorithm must be defined to determine if it fulfills the requirements of a particular industrial application (logical access, physical access, e-commerce...) ;

 The comparison of different biometric modalities is essential to qualify their relative advantages and drawbacks ;

 The performance evaluation is also necessary in order to facilitate the research in this field [START_REF] Hemery | Comparative study of invariant descriptors for face recognition[END_REF]. We need a reliable evaluation method in order to put into obviousness the benefit of a new biometric system.

The objective of this chapter is to make an overview on evaluation techniques that are used in the state of the art to quantify the performance of behavioral biometric systems.

An engineer or a researcher will find in the proposed chapter, the different criteria or methods he can use to validate a biometric system he intends to use in a real context. A behavioral biometric system can be evaluated by considering the general approach to evaluate a biometric system while taking into account the specificity of this type of modality.

The plan of the chapter is given below. In the section 1, we present the general approaches for the evaluation of a biometric system. It necessitates generally to use a benchmark database [START_REF] Hemery | The ENSIB database: a benchmark for face recognition[END_REF] and a set of criteria (computation time, FAR...). The benchmark database can be composed of real biometric templates or synthetic ones. We present different solutions from the state of the art. Section 3 focuses on specificities of behavioral biometric systems. We present their specificities that must be taken into account for their evaluation. Section 4 concerns the future trends that must be achieved in order to facilitate research progress in this domain. We conclude this chapter in section 5.

G E N E R A L E V A L U A T I O N M E T H O D O L O G I E S

Introduction

A biometric system is composed of different steps (see Figure 1). There are mainly two processes in the use of a biometric system. The enrollment phase has for objective to determine a model of an individual given the characteristics acquired by the selected biometric sensor. The identification / verification phase uses this model to make a decision an individual. The international standards committee for biometrics within ISO (ISO/IEC JTC1 SC37) developed a complete specification and reference implementation for a standardized API (BioAPI Consortium, 2005). The purpose of the BioAPI Specification is to define an open system standard application program interface (API) which allows software applications to communicate with a broad range of biometric technologies in a common way.

Figure 2 shows the interaction between the three BioAPI 2.0 components: applications, BioAPI Framework, and BSPs (Biometric Service Providers). The BioAPI 2.0 specification implements two APIs. The first one is the API which is the interface between the BioAPI Framework which supports the functions in the API specification and application. The second is the Service Provider Interface (SPI) that is the interface between the BioAPI Framework which invokes the functions in the SPI specification and to support the functions of the SPI specification and BSPs. The BioAPI Framework is responsible for the management of BSPs and for the mapping of function calls from an API function to an SPI function within the appropriate BSP. The performance evaluation of biometric systems is a crucial problem. It is generally realized within three contexts (ISO, 2006):

 Technology evaluation: It consists in testing an algorithm on a standardized corpus. The objective is to determine if the developed biometric system a priori meets the requirements. Testing is carried out using offline processing of the data and the results of technology tests are repeatable ;

 Scenario evaluation: The testing is carried out on a complete system in an environment that models a real-world target application of interest. Test results will be repeatable only to the extent that the modeled scenario can be carefully controlled ;

 Operational evaluation: It is an online test in real conditions. In general, operational test results will not be repeatable because of unknown and undocumented differences between operational environments.

The performance evaluation of a biometric system generally considers the quality of the input data and the output result. In order to evaluate them, we generally use an empirical process by considering the system as a black box [START_REF] Thacker | Performance characterization in computer vision: A guide to best practices[END_REF]. In this case, the internal working of the associated algorithms is not studied. The black box generates an output result given a biometric template as input and a set of parameters. We identified within this context different issues in the evaluation of biometric systems:

 Quality control of the biometric template: This quality is necessary to be quantified before the enrollment or verification/identification step ;

 Definition of benchmark databases: It is a challenge as it is used in the two first evaluation contexts ;

 Performance evaluation: The characterization of a biometric system uses many metrics such as error rates or average verification time ;

 Subjective evaluation: Many other aspects must be taken into account such as the user acceptability or its confidence.

We detail all these issues in the next sections.

Quality control of the biometric template

A biometric system is composed of two main components: a sensor that permits to acquire the biometric template and some algorithms for the enrollment and the verification/ identification steps. The quality of the biometric template is essential to guarantee a correct behavior of the biometric system. Many problems can alter this quality mainly because of three reasons (ISO, 2006):

 Problems due to the sensor: incorrect parameterization (volume for audio, focus for image based sensor...), dirty sensor (as for example, optical fingerprint sensor), transmission error...  Problems due to the user: incorrect use of the sensor (too far from the microphone, not in the field of the camera...), behavior (stress, tension, mood or distractions), personal characteristic (accent, handicap...), personal modifications (haircut change, keystroke...)...

 Problems due to the environment: conditions of acquisition (noise, light, humidity...)...

In the BioAPI standard, the quality of the biometric template can be evaluated by the BSP. If the quality (a score between 0 and 100) is considered as insufficient, the user is asked to acquire again the template.

Many specifications by the ISO organization defined some evaluation criteria for the quality of few biometric templates such as face, fingerprint or Iris. Other biometric modalities are currently studied such as the signature, voice or hand shape. If we consider as for example the face modality, the evaluation of the template takes into account the resolution of the image, the size of the face in terms of pixels in the image or the compression rate used to store the face image.

Definition of benchmark databases

In order to compare different biometric systems, we need generally to compute their performance following the same protocol (acquisition conditions, test database, metrics...). The testing database contains many samples specific to a biometric modality and each sample is associated to an individual. By comparing the performance of different systems on the same database and with the same experimental conditions (number of samples used in the enrollment step, thresholds), we can decide which system performs better. In this case, it provides us a relative evaluation of biometric systems.

These benchmark databases aim to be as close as possible as real use cases. By the way, a database must contain enough samples from an individual for the enrollment step. Moreover, a database is generally composed of two parts. The first one is used for the enrollment and the second one for the identification/verification task. A database must also contain a large number of individuals because the performance of biometric systems generally decreases as the number of user increases. Finally, the samples must represent most of different possible alterations that could be seen in a real use, as for example noisy or incomplete biometric data.

A benchmark database can contain real samples from individuals, which reflect the best the real use cases. Nevertheless, it is difficult to create such a database for several reasons. First of all, it can be difficult and costly to collect samples from a high number of individuals. Moreover, all samples must be acquired in the same conditions. This constraint can be very difficult to fulfill (as for example the guarantee to have the same lighting conditions for the face capture). Samples must then be acquired with some alterations to represent difficulties during the identification/verification task. Finally, each sample must be annotated by an human. A database can be specific to a modality, like the USF HumanID gait database [START_REF] Sarkar | The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis[END_REF], but can also be multimodal like the MCYT-100 database [START_REF] Ortega-Garcia | MCYT baseline corpus: a bimodal biometric database[END_REF] which contains samples of fingerprint and signature for the same individual.

A benchmark can also contain synthetic samples. The creation of such a database is easier but is less significant. The main advantage of synthetic database is that alterations on samples are fully controlled. This enables to verify the robustness of a biometric system face to a specific alteration. Such a database has been realized for fingerprints [START_REF] Cappelli | Synthetic Fingerprint -Database Generation[END_REF] as for example and used in the Fingerprint Verification competition in 2006 (FVC 2006). Figure 3 shows some examples of synthetic fingerprints. Alterations are simulated to make the fingerprint more realistic.

Figure 3: Some examples of synthetic fingerprints generated by SfinGe [START_REF] Maltoni | Generation of Synthetic Fingerprint Image Databases[END_REF].

Performance evaluation

The performance evaluation has for objective to provide some quantitative measures on the efficiency of biometric systems. The classical statistical metrics used to quantify the performance of a biometric system are:  Failure-to-enroll rate (FTE): proportion of the user population for whom the biometric system fails to capture or extract usable information from biometric sample. This failure may be caused due to behavioral or physical conditions pertaining to the subject which hinder its ability to present correctly the required biometric information ;


 Failure-to-acquire rate (FTA): proportion of verification or identification attempts for which a biometric system is unable to capture a sample or locate an image or signal of sufficient quality ;

 False match rate (FMR): The rate for incorrect positive matches by the matching algorithm for single template comparison attempts. FMR equals FAR when the biometric system uses one attempt by a user to match its own stored template ;

 False non-match rate (FNMR): The rate for incorrect negative matches by the matching algorithm for single template comparison attempts. FNMR equals FRR when the biometric system uses one attempt by a user to match its own stored template ;

 Identification rank: It is the smallest value k for which a user's correct identifier is in the top k identifiers returned by an identification system ;

 Receiver operating characteristic curve (ROC curve): The method most commonly used to assess the performance of a biometric system is the ROC curve. The aim is to plot a curve representing FAR according to the FRR. In order to plot this type of curve, we have to changes the value of the decision threshold. For each value of the threshold, we calculate the associated FRR and FAR that we plot on the curve. The advantage of this method is that it gives a compact representation of the performance of a biometric system through a single curve allowing the comparison of different biometric systems. In order to compare easily several biometric systems, we can then compute the area under the curve AUC and the equal error rate ERR where FAR = FRR. The optimal result is obtained if the AUC equals 1 and the ERR equals 0 ;

 Detection error trade-off curve (DET curve): DET curve [START_REF] Adler | Comparing Human and Automatic Face recognition Performance[END_REF] is a ROC curve which has its linear scale replaced by a scale based on a normal distribution, to make it more readable and usable. In this case, the curve flattens and tends towards the right. The benefits of the DET curves are the same as those of ROC curves, but they allow in addition to compare biometric systems that have similar performance. An example of a DET curve can be seen on Figure 4 ;

 Cumulative match characteristic curve (CMC curve): This curve plots the identification rank values on the x-axis and the probability of correct identification at or below that rank on the y-axis.

 Precision/recall curve (PR curve): This curve [START_REF] Muller | erformance evaluation in conten t-based image retrieval: Overview and proposals[END_REF]) has a similar behavior to ROC curves. In order to draw the PR curve, we plot the positive predictive value (PPR = TP /(TP+FP)), also known as the precision versus the recall. We can then compute AUC and ERR in a similar way as in ROC curves. One advantage is that we do not need the number of true negative in this method.

The most used methods are the DET curves, ROC curves and the PR curves.

Subjective evaluation

The performance evaluation is not the only thing to take into account when we have to consider a biometric system. To be accepted by users in real conditions (physical or logical access as for example), a biometric system must fulfill some other properties such as the acceptability, the easiness of use or the confidence in the system.

The acceptability denotes the way how users perceive the biometric system and interact with it. The acceptability is highly dependent of the culture of users. As for example, Asian users hardly accept to have a contact with a biometric sensor. This implies that biometric systems with contact less sensors, such as iris recognition, are better accepted by Asian users that biometric systems that need a contact, such as the fingerprint recognition. Another example, European users prefer fingerprint recognition to iris recognition.

The confidence in a biometric system is very close to its acceptability, as it is also highly dependent of the culture of users. It denotes how the reliability of a biometric system is perceived by users. Generally, users have a better confidence in biological biometric system than in behavioral biometric system. Fingerprint recognition or a DNA analysis is often considered, quite rightly, to be better than voice recognition. In the same time, the more the modality is efficient, such as the DNA analysis, the more it invades privacy and the less the acceptability is high.

The easiness of use depends on the quality of the sensor and the interface with users. It also depends on the time necessary for the identification: the system is not easy to use if several minutes are needed between the time the user gives his biometric data and the time the biometric system identifies the user. Another point that could be considered is the time necessary for the enrollment step and its easiness.

It is possible, especially during the operational evaluation, to ask the users to fill a form in order to have their opinion on these aspects. This permits to have complementary information on different biometric systems. A statistical analysis of the answers must be performed in order to keep only reliable users using the correlation factors or Chi square tests.

Finally, biometric systems are confronted to juridical problems concerning data corresponding to template of biometric system users. This depends mainly of the country where the biometric system is used. Each country has its own lawn concerning the protection of private data. As for example, in France, the use of computer devices to treat and save private data is regulated by the CNIL (French data protection authority). By the way, to use a biometric system, a French company must warn the CNIL and asks for their authorization before being able to collect samples and names used in biometric systems.

We detailed in this section the general scheme for the evaluation of any biometric system. We focus in the next section on behavioral ones and we put into obviousness their specificity.

T H E S P E C I F I C I T Y O F B E H A V I O R A L B I O M E T R I C S

Behavioral biometric systems are specific. Many characteristics make them difficult to define and to quantify their performance:

 The biometric template contains generally temporal information. As for example, for keystroke dynamics analysis, we generally use a template composed of a set N value couples {(Di,Fi) i=1..N} where N is the number of characters in the password, Di is the duration time the user presses a key and Fi is the time between this key and the next one in the password typing. For voice recognition systems, the biometric template is a sampled signal. Thus, the biometric template is generally quite important in size meaning that the parameters space is high ;

 The biometric template can change with time according to users. If we keep in mind the example of keystroke dynamics analysis, users with time learn how to type more efficiently their password. That means that the biometric template can be quite different compared to the one obtained after the enrollment step [START_REF] Hocquet | User Classification for Keystroke Dynamics Authentication[END_REF]. Another example, the dynamics of signature can also change a lot with time as it becomes a reflex for the user. This variability has multiple consequences. The first one concerns the number of templates for the enrollment step that is generally higher than other types of biometric systems. The second consequence is that the verification / identification algorithm should take into account this variability in order to make a correct decision. Another point concerns the testing of such biometric systems with biometric data that must embed this difficulty ;

 The behavior as biometric characteristic can be very different for an individual given its age, culture and experience. The evaluation of a behavioral biometric system is often realized considering a large diversity of users.

We focus in the next sections on impacts of these remarks on the evaluation of behavioral biometric systems.

Benchmark definition

Benchmark definition is really important for the performance evaluation of biometric systems.

As mentioned previously in the chapter, a benchmark database can be composed of real biometric templates (from test users) or synthetic ones. The definition of synthetic templates is easier for behavioral biometric data. Indeed, many behavioral modalities can be synthesized rather easily such as keystroke dynamics, voice, lip movements, mouse dynamics, signature dynamics... For morphological biometric modalities, it is much more difficult to do. The ability to generate more easily synthetic biometric templates is an advantage for the evaluation of such systems.

Generally, a biometric model (generated after the enrollment phase) is computed for the same person given 2 or 3 capture sessions. As for example, the AR face database has been created considering two sessions with an interval between them of 15 days [START_REF] Phillips | The FERET Evaluation Methodology for Face-Recognition Algorithms[END_REF]. The difficulty of behavioral biometric systems is that the biometric template naturally changes with time. Indeed, a human is a nice machine who wants to do things quicker. As a consequence, a benchmark database for behavioral modalities needs more capture sessions in order to take into account this variation. As for example, Awad & Traore in the approach they proposed in 2005 for computer intrusion detection with behavioral biometrics has been validated with biometric data acquired during 9 sessions [START_REF] Awad | Detecting computer intrusions using behavioral biometrics[END_REF]. The number of capture sessions is important but also the period of time between them. This shows the difficulty and the cost of such benchmark definition for this type of biometric modality.

The variability of behavioral biometric templates is really important if we compare morphological ones. Indeed, the fingerprint of individuals from different cultures or age is not so different. If we consider now the behavioral biometric modalities such as the keystroke dynamics, voice or gait, the associated template can be very different from individuals at different ages. As a consequence, the benchmark database must embed all the variability of biometric templates to be representative of real applications. As for example, Janakiraman & Sim [START_REF] Janakiraman | Keystroke Dynamics in a General Setting[END_REF] tested their keystroke authentication method on users that were Chinese, Indian or European origin. This is so an additional constraint for the definition of benchmark databases.

Robustness analysis

The behavior of an individual is very dependent on many factors like his mood, emotion, tiredness or health... As for example, voice recognition systems are very sensitive to all these factors. In order to be used in a real context, one has to test the robustness of a biometric system face to all these modifications.

Behavioral biometric systems can be very sensitive according to the sensor (keystroke dynamics, mouse dynamics...). The behavior of an individual can be different for multiple sensors. As for example, the template generated during the enrollment based on keystroke dynamics for an individual and a given keyboard cannot easily be used for the verification on another keyboard [START_REF] Clarke | Advanced user authentication for mobile devices[END_REF]. Indeed, the performance in term of EER can be quite high (>10%) in this case.

Another point concerns the robustness of behavioral biometric systems face to attacks.

The main difficulty for these systems is that anybody can try to duplicate a biometric template. As for example, it is not very hard to launch the verification given its keystroke dynamics, voice or gait. That does not mean that the chance to be authenticated is necessary higher but it is very easy to make a try. For morphological biometric systems, it is much more difficult even if as for example, fingerprints can be duplicated with some effort to launch one verification.

Discussion

In order to evaluate a behavioral biometric system, one could use the general methodology described in the previous section by using benchmark databases and classical performance metrics. Nevertheless, several aspects must be taken into account to consider the specificity of such systems:  The number of sessions for the definition of biometric templates for testing such a biometric system must be high (≥ 3) ;

 The sessions must be relatively spaced in order to take into account the natural change of behaviors of individuals ;

 Behavioral biometric templates can be in general easily synthesized. This approach for the definition of a benchmark database is interesting. It allows to test a large number of biometric templates and to control their alterations to quantify the robustness of the system ;

 The benchmark database must contain some fake biometric templates to also test the robustness of the system ;

 A benchmark database must embed a large diversity of users (culture, age...);

 The performance evaluation of behavioral biometric systems must be realized using the same sensor during the enrollment and verification / identification steps.

E X P E R I M E N T A L R E S U L T S

We present the evaluation of a keystroke dynamics verification system [START_REF] Hocquet | User Classification for Keystroke Dynamics Authentication[END_REF] as illustration of the proposed methodology. We first detail the protocol of the experiment we realized.

Protocol

We asked 15 individuals to participate for this experiment. Figure 5 shows some information on these individuals considering their age. Three females and twelve males participated to this experiment. All the involved individuals use a computer in their daily life. We explained them before starting the objectives of the experiment and the acquisition process. Each user tried two times in the first session before we record the biometric data. The biometric template contains: time between two keys pressure, time between two keys release, time between one pressure and one release and time between one release and one pressure. We asked each individual for each session to type 5 times the same password "GREYC Laboratory". We measured many data such as the time necessary for each user to type all of them, the number of mistyping and of course, the data that will be used as biometric template. To quantify the objective evaluation performance of this biometric system, we used for each individual, 5 biometric templates for the enrollment step and the 10 last for the verification one. To complete this objective evaluation experiment, we also realized a subjective evaluation test by asking the users to answer the following questions:

Q1: Is the verification fast? Yes, no Q2: Is the system easy to use? Yes, no Q3: Are you ready to use this system in the future? Yes, no, do not know Q4: Do you feel confident in this system? Yes, no Q5: Do you feel embarrassed when using this system? Yes, no, do not know Q6: What is your general appreciation of the system? Very good, good, average, bad

Results

Figure 7 presents some statistics on the capture process. The FTA value is quite important for some individuals (the user wants to type the sentence too fast, users not enough concentrated…). The average FTA value equals 16% which is important. The resulting ROC curve is given in Figure 9. The computed EER value equals 20.5 %. The performance of this biometric system is not really important for this study as the main objective is to illustrate in this part, the different elements to take into account for its evaluation. If we want to obtain a FAR value equals to 0, the FRR value equals 50.16%. That means if we want none impostor, we have to set the value of the threshold having as consequence to reject genuine in 50.16% cases. In order to quantify the robustness of the selected biometric system, we made an experiment consisting in generating random synthetic keystroke dynamics. Given the 5 biometric templates used in the enrollment step, we compute an average biometric template denoted E[T]. We generated different biometric templates T i i=1:15 given E[T] and adding a random alteration by controlling its standard deviation. Figure 10 shows some examples of altered biometric templates given E[T] for an order value equals to 2. We can notice that a small alteration has a great impact on the EER value; this means that this biometric system is not very robust. We give now the results of the subjective evaluation experiment. We obtained for some questions similar answers for all users. As for example, all users found that the verification is fast. 93% of them considered the system is easy to use and is non intrusive for their privacy.

Figure 12 shows the results of the subjective evaluation. Even if the biometric system is not very efficient (EER = 20.7%), nearly 50% of users are ready to use it in the future. The general appreciation is good for 60% of users.

For a logical control access, this biometric system even with a very bad value of the EER, could be an interesting solution as it necessitates none additional sensor (all computer has a keyboard), it is very simple to use. This subjective study (even if realized with a low number of individuals) shows that the perception of users is interesting to take into account.

F U T U R E T R E N D S

How can we make progress for the evaluation of behavioral biometric systems?

The constraints and the cost for the evaluation of behavioral biometric systems are extremely prohibitive. High quality benchmark databases must be available for the research community taking into account the previous constraints. These databases would facilitate the testing and the development of new behavioral biometric systems. They also would able to compare different enrollment and identification / verification algorithms to increase the knowledge in the domain. Actually, a researcher in the domain generally creates its own database to validate the proposed system. It is generally difficult to say if the database is representative of real use cases and if the system achieves better than others in the state of the art.

The European BioSecure network of excellence (http://biosecure.it-sudparis.eu/) had for objective as for example to realize benchmark databases for different biometric modalities. If we consider behavioral modalities, only the speech was concerned. An organization should deliver for free to researchers some benchmark databases. It could be also a good thing to ask researchers to implement their biometric system following the BioAPI requirements. The cost of implementation is not so important and the benefit is high as a BSP is only a DLL file that can be transferred without giving the source code.

The statistical evaluation of biometric systems is important but is not sufficient. A biometric system to be used in real conditions must be easy to use, not reluctant to use... Subjective evaluation is a domain that needs a lot of research to take into account the user as the central element in the biometric system.

C O N C L U S I O N

We presented in this chapter several issues for the evaluation of behavioral biometric systems. We detailed the BioAPI standard that defines the architecture and the evaluation of biometric systems in a general context. Behavioral biometric modalities are currently under standardization.

Much specificity of behavioral biometric systems had been detailed in the second part of this chapter. These considerations must be taken into account for the evaluation of this kind of biometric systems by engineers or researchers in this field.

F

  . Cherifi, B. Hemery, R. Giot, M. Pasquet, C. Rosenberger G R E Y C L a b o r a t o r y E N S I C A E N -C N R S -U n i v e r s i t y o f C a e n , F r a n c e

Figure 1 :

 1 Figure 1: Diagram summarizing the various phases of a biometric system (Source: ISO/IEC JTC 1/SC 37 Part 1 Overview Standards Harmonization Document)

Figure 2 :

 2 Figure 2: BioAPI components (source BioApi, 2005).

Figure 4 :

 4 Figure 4: Example of DET curves used for the performance comparison of face recognition systems (source Adler et al., 2007).

Figure 5 :

 5 Figure 5: Repartition of users' ages for the experiment

Figure 6 :

 6 Figure 6: Session dates for the experiment

Figure 7 :

 7 Figure 7: FTA value for each individual for all sessions

Figure 8 :

 8 Figure 8: Plot of the biometric data for an individual for one session (5 acquisitions)

Figure 9 :

 9 Figure 9: (a) scores distribution and (b) Roc curve

Figure 10 :

 10 Figure 10: Three examples of random alteration (order 2) of the average biometric of an individual

Figure 11 :

 11 Figure 11: Evolution of the EER value given the amount of alterations.We give now the results of the subjective evaluation experiment. We obtained for some questions similar answers for all users. As for example, all users found that the

T E R M S A N D D E F I N I T I O N S

Behavioral biometric: A Behavioral Biometric is a measurable behavior trait that is acquired over time for the identification or identity verification of an individual.

Benchmark: A database composed of biometric templates supposed to represent real cases for the performance evaluation of biometric systems.

Biometric Application Programming Interface (BioAPI):

The BioAPI specification enables different biometric systems to be developed by the integration of modules from multiple independent companies.

Enrollment:

The process of collecting biometric samples from a person and the subsequent preparation and storage of biometric reference templates representing that person's identity.

False Acceptance Rate (FAR): Rate at which an impostor is accepted by an identification system.

False Rejection Rate (FRR): Rate at which the authorized user is rejected from the system.

Equal Error Rate (EER):

This error rate corresponds to the point at which the FAR and FRR cross (compromise between FAR and FRR).


Rozenn Josse

Ahmad Sharanek

Camille Savary

Professeur Andre Guillouzo
email: andre.guillouzo@univ-rennes1.fr

Impact of isomalathion on malathion cytotoxicity and genotoxicity in human HepaRG cells

Keywords: Pesticides, mixture, low doses, human hepatocytes, micronucleus assay

Isomalathion is a major impurity of technical grade malathion, one of the most abundantly applied insecticides; however little is known about its hepatotoxicity. In the present study, cytotoxicity and genotoxicity of malathion and isomalathion either individually or in combination, were assessed using the metabolically competent human liver HepaRG cell line. Isomalathion reduced cell viability starting at a 100µM concentration after a 24h exposure. It also significantly induced caspase-3 activity in a dose-dependent manner starting at 5 µM. On the contrary, even at concentrations as high as 500µM malathion affected neither cell viability nor caspase-3 activity.

Moreover, co-exposure of both compounds resulted in decreased toxicity of isomalathion. By contrast, malathion and isomalathion either separately or in combination, slightly induced micronuclei formation at low concentrations and had additive genotoxic effects when combined at 25µM. Individually or combined isomalathion directly inhibited activity of carboxyesterases which are involved in detoxication of malathion. In addition, transcripts of CYP2B6 and CYP3A4, two CYPs responsible for malathion phase I metabolism, were strongly induced by the mixture while isomalathion alone only moderately decreased CYP1A2 and increased CYP2B6 transcripts. However, these CYPs were not altered at the protein or activity levels. Taken altogether, our results showed that isomalathion was much more cytotoxic than malathion while both compounds had comparable genotoxic effects in HepaRG hepatocytes at low concentrations and brought further support to the importance of considering impurities and interactions during evaluation of health risks of pesticides.

INTRODUCTION

Extensive use of pesticides has caused both environmental and public health concerns. Because of its low mammalian toxicity and high selectivity toward insects [START_REF] Wauchope | The SCS/ARS/CES pesticide properties database for environmental decision-making[END_REF], malathion has become one of the most commonly used organophosphate insecticides for both domestic and commercial agricultural purposes; it has been employed in malaria eradication programs in Africa and Central America and in widescale pest-control in the United States. Exposure of workers who applied the insecticide or harvested the crops was estimated to be 1 to 3 mg/kg/day and 1 to 270 µg/kg/day, respectively which might give rise to a range of plasma concentrations between 0.03 and 80 µM [START_REF] Krieger | Malathion deposition, metabolite clearance, and cholinesterase status of date dusters and harvesters in California[END_REF]. Its rapid degradation by carboxylesterases (CEs) competes with the cytochrome P450 (CYP)-catalyzed formation of the toxic metabolite, malaoxon [START_REF] Sultatos | Mammalian toxicology of organophosphorus pesticides[END_REF].In human liver, malaoxon formation is mainly catalyzed by CYP1A2 and, to a lesser extent, CYP2B6 at low malathion concentrations (<50 µM), whereas at high levels the role of CYP3A4 becomes relevant [START_REF] Buratti | Malathion bioactivation in the human liver: the contribution of different cytochrome p450 isoforms[END_REF]. Alterations or individual variations in both CYP and CE activities could result in increased malaoxon formation, enhancing malathion toxicity. Technical grade malathion (90-95%) contains several impurities, such as isomalathion and various trimethyl phosphorothioate esters, formed during production and/or storage, that can potentiate malathion-induced toxicity up to 10-fold [START_REF] Pellegrini | Potentiation of toxicity of organophosphorus compounds containing carboxylic ester functions toward warm-blooded animals by some organophosphorus impurities[END_REF][START_REF] Aldridge | The toxicological properties of impurities in malathion[END_REF]. The presence of isomalathion, a toxic degradation product, in commercial formulations was implicated in the 1976 epidemic malathion poisoning of 2800 spraymen (including 5 deaths) among 7500 field workers, during the Pakistan malaria control program. The greatest toxicity was observed with the formulation containing the highest level of isomalathion [START_REF] Baker | Epidemic malathion poisoning in Pakistan malaria workers[END_REF]. Isomalathion has been found to inhibit CEs in a non-competitive manner, shifting the metabolic pathway toward the bioactivation reaction [START_REF] Talcott | Malathion carboxylesterase activity in human liver and its inactivation by isomalathion[END_REF][START_REF] Buratti | Malathion detoxification by human hepatic carboxylesterases and its inhibition by isomalathion and other pesticides[END_REF][START_REF] Hernandez | Toxic effects of pesticide mixtures at a molecular level: their relevance to human health[END_REF]. However, little is known about isomalathion toxicity.

Malathion has been found to be genotoxic in several in vivo and in vitro studies. However, data were usually obtained with the technical grade and high doses of the compound [START_REF] Flessel | Genetic toxicity of malathion: a review[END_REF][START_REF] Pluth | Increased frequency of specific genomic deletions resulting from in vitro malathion exposure[END_REF]. No increase in the frequency of micronucleated cells and no inhibition of proliferation in lymphocyte cultures from two cohorts of applicators intermittently exposed to relatively low doses of malathion were observed [START_REF] Titenko-Holland | Genotoxicity of malathion in human lymphocytes assessed using the micronucleus assay in vitro and in vivo: a study of malathion-exposed workers[END_REF].On occupational settings, pesticide applicators exposed to technical grade malathion and other insecticides were reported to exhibit higher levels of chromosome aberrations and sister chromatid exchanges [START_REF] Rupa | Clastogenic effect of pesticides in peripheral lymphocytes of cotton-field workers[END_REF]. Weak or negative results have been reported with pure-grade malathion [START_REF] Blasiak | In vitro studies on the genotoxicity of the organophosphorus insecticide malathion and its two analogues[END_REF]. Noticeably, most genotoxic studies have been performed in the absence of a bioactivation system.

In the present study, mixtures containing both malathion and its major impurity, isomalathion, were used to better understand their toxic and genotoxic effects in the metabolically competent human liver HepaRG cell line [START_REF] Guillouzo | The human hepatoma HepaRG cells: a highly differentiated model for studies of liver metabolism and toxicity of xenobiotics[END_REF]. Since in vivo hepatic concentrations are usually higher than circulating ones, malathion concentrations around 50 µM could be easily achieved in exposed individuals, and therefore, a 50 µM maximal concentration was used in vitro to be representative of the in vivo situation. Moreover, the presence of isomalathion levels as high as 0.2% is documented in actual commercial formulations, and it is known that additional amounts can be formed during storage [START_REF] Aldridge | The toxicological properties of impurities in malathion[END_REF]. This study showed for the first time, the potent toxic and genotoxic effects of isomalathion in an in vitro metabolically competent cell model and highlighted the necessity to take into consideration the impurities present in technical pesticide grade.

Materials and methods

Chemicals

Malathion, isomalathion, methylthiazoletetrazolium (MTT), 6β-hydroxytestosterone, 16β-hydroxytestosterone and p-nitrophenyl acetate (PNPA) were purchased from Sigma (St. Quentin Fallavier, France). 2',7'-dichlorodihydrofluorescein (H2DCFDA) was from Invitrogen-Molecular Probe (Cergy-Pontoise, France).

Cell cultures

HepaRG cells are derived from a human cholangiohepatocarcinoma; they are untransformed cells and were used before passage 18. Briefly, they were seeded at a density of 2.6×10 4 cells/cm² as described previously [START_REF] Aninat | Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells[END_REF][START_REF] Gripon | Infection of a human hepatoma cell line by hepatitis B virus[END_REF]. They were first incubated in the Williams' E medium supplemented with 10% fetal calf serum (FCS), 100 units/ml penicillin, 100 µg/ml streptomycin, 5 µg/ml insulin, 2 mM glutamine, and 5 × 10 -7 M hydrocortisone hemisuccinate for 2 weeks. Then, HepaRG cells were shifted to the same medium supplemented with 2% dimethylsulfoxide (DMSO) for two further weeks in order to obtain confluent differentiated cultures with maximum functional activities. At this time, these cultures contained hepatocyte-like and progenitors/primitive biliary-like cells at around 50% each [START_REF] Cerec | Transdifferentiation of hepatocyte-like cells from the human hepatoma HepaRG cell line through bipotent progenitor[END_REF]. The medium was renewed every 2 or 3 days. During treatment, the cells were exposed to the compounds in a medium supplemented by 2%FCS and 1% DMSO.

Preparation of microsomal fractions

Human liver tissue samples were homogenized in 50mM Tris-HCl buffer (pH 7.4) containing 0.25M sucrose and 1mM EDTA. Microsomal fractions were the sediment and supernatant, respectively, from the last of three successive centrifugations at 4 °C (3000 g, 10 min; 8000 g, 20 min; and 30000 g, 60 min).

Cell viability

Cytotoxicity of malathion and isomalathion, was evaluated by the MTT spectrometry assay [START_REF] Aninat | Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells[END_REF]. Briefly, after treatment, medium was removed and serum-free medium containing MTT (0.5 mg/ml) was added to each well and incubated for 2 h at 37°C. After removal of the incubation solution, water-insoluble formazan was dissolved in DMSO and absorbance was measured at 550 nm. Data were expressed as the mean of three independent experiments.

Caspase-3 activity

After treatment with pesticides, differentiated HepaRG cells were harvested and stored as pellets at -80°C. After cell lysis, 40µg of protein were incubated with 80µM Ac-DEVD-AMC in caspase-3 activity buffer (20 mM PIPES pH 7.2, 100 mM NaCl, 10mM dithiotreitol, 1mM EDTA, 0.1% CHAPS and 10% sucrose) at 37°C for 1 h. Caspase 3-mediated cleavage of Ac-DEVD-AMC peptide was continuously measured by spectrofluorimetry using excitation/emission wavelengths of 380/440 nm [START_REF] Dumont | Differential toxicity of heterocyclic aromatic amines and their mixture in metabolically competent HepaRG cells[END_REF].

Evaluation of oxidative stress

Cells were incubated for 2 hours at 37°C with 5 µM H2DCFDA; then they were washed with cold phosphate buffered saline (PBS), and scraped in a solution containing an equal volume of potassium buffer (10 mM, pH 7.4) and methanol (v/v), supplemented with 0.1 % Triton X-100. Fluorescence intensity of cell extracts was determined by spectrofluorimetry using excitation/emission wavelengths of 498/520 nm.

Real time -quantitative polymerase chain reaction (RT-qPCR) analysis

Total RNA was extracted from HepaRG cells with the SV total RNA isolation system (Promega, Madison, WI). Five hundred nanograms of total RNA were reversedtranscribed into cDNA using the High-Capacity cDNA Archive kit (Applied Biosystems, Foster City, CA). RT-qPCR was performed by the fluorescent dye SYBR Green methodology using the SYBR Green PCR Master Mix and the ABI Prism 7000 (Applied Biosystems). Primers pairs for each transcript are described in Table 1.

Amplification curves were read with the ABI Prism 7000 SDS Software using the comparative cycle threshold method and the relative quantification of the steady-state mRNA levels was normalized against 18S RNA.

Micronucleus assay

Genotoxic effects of malathion and isomalathion either individually or combined, were evaluated by the micronucleus assay as previously described [START_REF] Josse | An adaptation of the human HepaRG cells to the in vitro micronucleus assay[END_REF]. Briefly, after 24 h of treatment with pesticides, followed by a 72h epidermal growth factor (EGF) stimulation corresponding to the recovery time, HepaRG cells were fixed for 10 min in 4% paraformaldehyde at 4°C and then permeabilized for 45 min in 1X phosphatebuffered saline containing 10% donkey serum and 0.1% saponin. F-actin, a cytoskeletal component, was labelled with 1/400 phalloidin conjugated to sulforhodamine for 20 min and nuclei were stained with 1/5000 Hoechst for 5 min.

Images were taken with an upright microscope (Axioimager M1) and were a mosaic of six pictures per condition. All experiments were performed in triplicate. Monomicronucleated, multi-micronucleated, apoptotic and mitotic cells were scored manually and blindly with Image Tool software from micrographs obtained with a Axioimager microscope. Criteria used for identifying micronuclei were as follows: area less than one-third the main nucleus area, no overlap with the nucleus (distinct borders) and same aspect as the chromatin. Cells containing two to five and more than five micronuclei were scored as multi-micronucleated and apoptotic cells, respectively. Cytotoxicity was also evaluated by calculation of relative population doubling (RPD) as follows: RPD = 100 × number of population doublings in treated cultures/number of population doublings in control cultures; where population doubling = [log (post-treatment cell number)/initial cell number)]/log2.

Determination of xenobiotic-metabolizing enzyme activities

For the determination of the effects of pesticides on CYP3A4-, CYP2B6-and CYP1A2-related activities HepaRG cells were incubated with 200 µM testosterone for 2h for CYP3A4 and CYP2B6 analysis or with 200 µM phenacetin for 20h for measurement of CYP1A2 activity in phenol red-free medium deprived of both FCS and DMSO. Rifampicin at 50 µM and 3-methylcholanthrene at 5 µM were used as positive controls and added to the cell cultures for 48 h before incubation with the specific substrates. Culture media were then collected and centrifuged for 2 min at 10,000 rpm; 50µl of sample was injected in the high-performance liquid chromatography system for quantification of the specific metabolites, 6β-and 16β- Carboxylesterase activity was determined by measuring hydrolysis of PNPA, an esterase substrate, to p-nitrophenol in HepaRG cells and for comparison in human liver microsomes as previously described [START_REF] Morgan | Purification and characterization of two rat liver microsomal carboxylesterases (hydrolase A and B)[END_REF]. One, 4, 24h and 48h after pesticide treatment, HepaRG cells were incubated with 5mM PNPA in phenol red-free medium deprived of both FCS and DMSO during 15 min. In microsomes, after a 2-min preincubation at 37°C with either pesticides or DMSO (vehicle) the reaction was initiated by addition of PNPA 1 mM (final concentration) to 200 µl solution containing100 mM K 2 HPO 4 buffer, pH 7.4, containing 5 mM MgCl 2 and 20 µg of microsomal proteins in 96 multi-well plates as final concentrations.

Hydrolysis of PNPA to p-nitrophenol was determined spectrophotometrically by measuring increase in absorbance determined at 405 nm, after a 15 min exposure.

Because p-nitrophenol exists as a contaminant in commercially available PNPA, its content in the mixture incubated without cells or without microsomes was substracted from that measured in the cells or microsomes respectively to correct the activity level. Data are expressed relative to absorbance found in untreated cells arbitrarily set at 1.

Western blot analysis

HepaRG cells were treated for 48 hours with malathion and isomalathion either individually or with various combinations, then they were scrapped with CytoBuster Protein Extraction Reagent (Novagen, USA) supplemented with 1X complete protease inhibitors and 1X Phosphatase Inhibitor (Roche, Mannheim, Germany).

Twenty micrograms of protein were separated by electrophoresis on 4 to 12% gradient Bis-Tris gels (Invitrogen), transferred to Hybond ECL nitrocellulose membranes (GE Healthcare, Chalfont St. Giles, Buckinghamshire, UK) and immunoblotted with antibodies against CYP3A4 or CYP2B6 (Santa Cruz Biotechnology, Inc., Santa Cruz, CA) and heat shock cognate protein 70 (HSC70) (Tebu-bio, Le Perray en Yvelines, France). Blots were then incubated with appropriate secondary antibodies, and protein bands were revealed by enhanced chemiluminescence in a Chemi-smart imager (Thermo Fisher Scientific, Ilkirch, France). HSC70 was used to normalize protein loadings, and quantification was performed with BIO-1D software (Vilber Lourmat, Marne la Vallée, France).

Statistical analysis

The Mann-Whitney U test was applied to compare data between treated cells and corresponding control cultures. For the micronucleus assay, a chi-square one-tailed test associated with a Yates correction was performed. Data were considered significantly different when P <0.05.

RESULTS

Cytotoxicity of malathion and isomalathion individually and in combination

First, cytotoxicity induced by malathion (M), isomalathion (IM) and an equimolar mixture (M+IM) was assessed in HepaRG cells after a 24 h exposure to varying concentrations up to 500 µM using the MTT assay. Similar results were obtained with the ATP assay (not shown). Malathion did not affect cell viability contrary to isomalathion which reduced cell viability at 100 µM (Figure 1A). A decrease in cell viability occurred with the (M + IM) mixture from 200 µM. Importantly, as evidenced by light microscopic examination, morphological alterations of HepaRG cells were restricted to hepatocyte-like cells (Figure 1B). No change in cell viability was observed in HepG2 cells using the same range of concentrations (data not shown).

Cytotoxic effects of isomalathion were confirmed by measuring caspase-3 activity.

Isomalathion significantly induced caspase-3activity in a dose-dependent manner at concentrations that did not affect cell viability (Figure 2). A slight production of ROS was observed after treatment with 50 µM isomalathion and the equimolar (M25 + IM 25) mixture (Figure 3A). In order to evaluate modulation of the antioxidant system in response to the compounds, mRNA expression levels of the transcription factor Nrf2, hemeoxygenase 1 (HO-1) and superoxide dismutase (MnSOD) were measured. In parallel to the oxidative stress induced by isomalathion, a two-fold increase of HO-1 mRNA expression was observed but no effect was noticed on Nrf2 and MnSOD level expression after a 24h exposure to either malathion or isomalathion (Figure 3B).

Genotoxicity of malathion and isomalathion individually and in combination

In order to evaluate malathion and isomalathion genotoxic effects, the micronucleus assay was performed in HepaRG cells as previously described [START_REF] Josse | An adaptation of the human HepaRG cells to the in vitro micronucleus assay[END_REF]. Two positive controls were used: MMS, a classical DNA damaging agent and AFB1, a metabolism-requiring genotoxic compound (Table 2). At the tested concentrations, none of the compounds alone or in combination induced toxicity as shown by the calculation of the RPD. Both malathion and isomalathion slightly increased the number of mono-micronucleated cells compared to untreated cells but this number did not vary with concentrations up to 25 µM and no variation was observed in the number of multi-micronucleated or apoptotic cells. Similarly, the number of monomicronucleated cells induced by the (M + IM) mixture was not significantly different from that induced by either malathion or isomalathion, except for the equimolar (M 25 + IM 25) mixture which had additive effects compared to either compound at 25 µM.

Effect of malathion and isomalathion on their metabolic-required enzymes

To better understand malathion and isomalathion effects in HepaRG cells, we analyzed gene expression and activities of the main CYPs as well as the two detoxifying enzymes CE1 and CE2 (Figures4 and 5). Malathion alone did not modify expression of any of the main CYPs while isomalathion decreased CYP1A2 mRNA and increased CYP2B6 mRNA. All the mixture ratios except the (M25 + IM 0.05) ratio, strongly induced CYP2B6 and CYP3A4 expression (Figure 4A). Despite a change in CYP expression levels caused by isomalathion alone or in mixture, CYP activities and protein levels were not altered (Figure 4B-D). Nevertheless, neither alone nor in mixture did malathion or isomalathion modify the transcript levels of the two CEs (CE1 and CE2) (Figure 5A) except IM at 50 µM which slightly downregulated CE1 and CE2. However, isomalathion was found to strongly inhibit CEs activity in HepaRG cells. As early as one hour of pesticide treatment, this noncompetitive inhibition was nearly complete with 5 µM isomalathion and persisted after 48h (Figure 5B-C). Malathional one had no any modulatory effect on CEs activity even at high concentration. In agreement, all the mixture ratios had the same inhibitory effect as isomalathion alone. Similar results were obtained with human liver microsomes (Figure 5D). Noticeably, transcripts of other genes related to drug metabolism (UGT1A1, MRP2), the p53 pathway (p21,FDXR, BTG2)or previously found to be deregulated by genotoxic compounds (FHIT, BCAS3, SMYD3) [START_REF] Josse | Identification of early target genes of aflatoxin B1 in human hepatocytes, inter-individual variability and comparison with other genotoxic compounds[END_REF], were also measured; none of them was found to be significantly deregulated after exposure to malathion and isomalathion individually or in combination (not shown).

DISCUSSION

In the present study, we investigated the effects of malathion and its impurity, isomalathion, individually or in combination, in HepaRG cells, an in vitro metabolically competent liver cell model [START_REF] Guillouzo | The human hepatoma HepaRG cells: a highly differentiated model for studies of liver metabolism and toxicity of xenobiotics[END_REF]. Up to now, most of the literature concerns malathion toxicity and only few studies have been performed on human liver cells. We found that malathion alone was much less cytotoxic than isomalathion and exerted cytotoxic effects at similar concentrations as in primary human hepatocytes [START_REF] Sousa | Insecticide cytotoxicity and CYP1A1/2 induction in primary human and rat hepatocyte cultures[END_REF]. It was also previously reported to significantly decrease cell viability in HepG2 cells after a 48 h exposure only at very high concentrations close to the insolubility, i.e. at 18 mM [START_REF] Moore | Malathion-induced oxidative stress, cytotoxicity, and genotoxicity in human liver carcinoma (HepG2) cells[END_REF]. In agreement, we observed that malathion and isomalathion did not cause any cell loss in HepG2 cells when using the same range of concentrationsas those used for HepaRG cells. These results support our previous data showing that HepaRG cells are much more sensitive to compounds requiring liver metabolism to exert their toxic effects than HepG2 cells that did not express most of the major cytochromes P450 [START_REF] Josse | An adaptation of the human HepaRG cells to the in vitro micronucleus assay[END_REF][START_REF] Josse | Long-term functional stability of human HepaRG hepatocytes and use for chronic toxicity and genotoxicity studies, Drug metabolism and disposition: the biological fate of chemicals[END_REF].

Malathion concentrations used in the present study were relevant to an human in vivo exposure [START_REF] Krieger | Malathion deposition, metabolite clearance, and cholinesterase status of date dusters and harvesters in California[END_REF]. Indeed, malathion concentrations ≤50 µM could be very likely present in the blood of exposed individuals. Even higher concentrations can be reached in the liver, where most of malathion bioactivation takes place [START_REF] Kutz | Selected pesticide residues and metabolites in urine from a survey of the U.S. general population[END_REF][START_REF] Adgate | Measurement of children's exposure to pesticides: analysis of urinary metabolite levels in a probability-based sample[END_REF]. For agricultural workers, exposure to malathion was estimated to be 1-270 µg/kg/day [START_REF] Krieger | Malathion deposition, metabolite clearance, and cholinesterase status of date dusters and harvesters in California[END_REF], which may give rise to plasma concentrations up to 80 µM, considering an almost complete absorption. It has been reported that malathion induced complex structural chromosomal aberrations in whole human blood cells in the presence of S9 mix [START_REF] Garry | Preparation for human study of pesticide applicators: sister chromatid exchanges and chromosome aberrations in cultured human lymphocytes exposed to selected fumigants[END_REF].

In a review, Flessel et al. [START_REF] Flessel | Genetic toxicity of malathion: a review[END_REF] concluded that technical grade malathion and malaoxon caused an increase in sister chromatid exchange frequency, indicating that this pesticide, its impurities or metabolites, can affect DNA. Moreover, human lymphocytes exposed to malaoxon and isomalathion showed a positive comet assay from 25 µM and 75 µM, respectively. DNA damage induced by isomalathion was efficiently and quickly repaired as soon as 15 min while malaoxon induced more persistent DNA damage [START_REF] Blasiak | In vitro studies on the genotoxicity of the organophosphorus insecticide malathion and its two analogues[END_REF] but these data were obtained in the absence of a bioactivation system. To our knowledge, no report is available in the literature regarding the level of cytotoxicity and DNA damage when human liver cells are exposed to a combination of both compounds. According to the degree of contamination of technical malathion by isomalathion, different toxic effects could be expected. In the metabolically competent HepaRG liver cell model, malathion and isomalathion were able to slightly increase micronuclei at a concentration as low as 10 µM or less but no further increase was evidenced with concentrations up to 25µM and, in combination they showed a limited additive effect only at the equimolar concentration of 25 µM. An in vivo study has also shown that three common organosphosphate pesticides, i.e. malathion, chlorpyrifos and methyl parathion, given in mixture to rats were not able to potentiate the toxicity of each other [START_REF] Ojha | Evaluation of DNA damage and cytotoxicity induced by three commonly used organophosphate pesticides individually and in mixture, in rat tissues[END_REF].

In our study, the interaction between malathion and isomalathion was shown to be dependent on the endpoint measured and their concentration. Noticeably, malathion antagonized cytotoxic and apoptotic effects of isomalathion. A concentrationdependent induction of caspase-3 activity by isomalathion was evidenced at concentrations that did not alter cell viability. To our knowledge, these results represent the first demonstration that isomalathion is directly hepatotoxic. No synergistic toxic effects between malathion and isomalathion were observed, suggesting occurrence of a more complex balance between the bioactivation and detoxication/DNA repair pathways in a metabolically competent system such as the HepaRG cell line. In order to better understand the underlying toxic mechanisms, we focused on the effects of malathion and isomalathion on the phase I metabolism enzymes. Formation of metabolites could be at least partially responsible for genotoxicity of the compounds in HepaRG cells as shown by using the micronucleus assay. As previously described by Buratti et al. [START_REF] Buratti | Malathion bioactivation in the human liver: the contribution of different cytochrome p450 isoforms[END_REF], CYP1A2>CYP2B6>CYP3A4 play a pivotal role in malaoxon production while CE, mainly CE1 in liver, also detoxifies malathion. However, as previously reported [START_REF] Buratti | Malathion detoxification by human hepatic carboxylesterases and its inhibition by isomalathion and other pesticides[END_REF] we found that isomalathion inhibited CEs in human microsomes and similarly in HepaRG cells in a non-competitive manner, suggesting that malathion in the presence of isomalathion, might shift its metabolic pathway toward a bioactivation reaction and be more toxic [START_REF] Hernandez | Toxic effects of pesticide mixtures at a molecular level: their relevance to human health[END_REF]. Noticeably, even at high concentration malathion did not alter CEs activity. In addition, while malathion and isomalathion combination did not change CYP1A2 expression, it strongly induced expression of CYP2B6 and CYP3A4 without any induction of corresponding protein or activity levels. In agreement, in a recent study using the HepaRG cell line but in different treatment conditions, malathion was found to activate the nuclear receptor constitutive androstane receptor (CAR) and some of its target genes, including CYP2B6 and CYP3A4, without induction of their corresponding activities [START_REF] Abass | Characterization of human cytochrome P450 induction by pesticides[END_REF]. Using bupropion as another specific CYP2B6 substrate, the same lack of induction was observed after malathion exposure (data not shown).

Presently, such differences between transcripts and protein activities response remain unclear and require further investigation.

In conclusion, our findings show that although chemically similar, malathion and its impurity isomalathion, either individually or in combination, might exert different toxic effects in the metabolically competent HepaRG cells. We clearly evidence that isomalathion by itself induced caspase-3 activation and can be a DNA-damaging agent. The combination of malathion with isomalathion had additive effects on genotoxicity. However, isomalathion-induced apoptosis was abolished by a coexposure with malathion. This study highlights the importance of considering possible impurities and interactions during evaluation of health risks of pesticides.

Legends to Figures

  hydroxytestosterone and acetaminophen generated by CYP3A4, CYP2B6 and CYP1A2 respectively, resulting from 6β-and 16β-hydroxylation of testosterone and phenacetin deethylation activities, respectively. A nucleosil C18 column (150 × 4.6 mm, 5 µm) was used for chromatographic separation of testosterone metabolites at 254 nm. The mobile phase consisted of two solvents, A (0.1% acetic acid) and B (acetonitrile), with the following gradient: 0 min, 20% B; and 20 min, 33% B. Chromatographic separation of phenacetin metabolites was carried out with an Interchrom C18 column (250 × 4.0 mm, 5 µm) at 250 nm. The mobile phase consisted of two solvents, A (2 mM ammonium acetate, pH 2.6) and B (acetonitrile) with a linear gradient (20 min, 3% B).The high-performance liquid chromatography apparatus consisted of an Agilent 1100 Series high-performance liquid chromatograph equipped with an autosampler and Agilent 1100 Series fluorescence and UV detectors (Agilent Technologies, Palo Alto, CA). A computer running Agilent 1100 software (ChemStation) was used to integrate and calculate the separated peak area and to plot metabolite patterns. Metabolites were identified by comparison of retention times, and quantifications were estimated from calibration.

Figure 1 .

 1 Figure 1. Cytotoxic effects of malathion (M), isomalathion (IM) and their mixture (M + IM) in HepaRG cells (A). Cells were exposed to M, IM or the mixture (M + IM) for 24 h. Cytotoxicity was assayed using the MTT assay. Results are expressed as % of the value found in control cells arbitrarily set at 100%. Data are means ± SD of three independent experiments. (B) Morphological changes after exposure of HepaRG cells to 0.2 % DMSO (UNT), M, IM or their mixture (M + IM) for 24 h.

Figure 2 .

 2 Figure 2. Pro-apoptotic effects of malathion (M), isomalathion (IM) and their mixture (M+IM) in HepaRG cells. Cells were exposed to 0.2 % DMSO (UNT), M, IM, the mixture (M + IM) or 2 µM staurosporine (positive control of apoptosis induction) for 24 h. Apoptosis was assayed by measuring caspase-3 activation. Results are expressed as % of the value found in control cells arbitrarily set at 100%. Data are means ± SD of three independent experiments. *p<0.05 compared to untreated cells.

Figure 3 .

 3 Figure 3. Oxidative stress induced by malathion (M), isomalathion (IM) and their mixture (M + IM) in HepaRG cells. (A) Oxidative stress was assessed by ROS formation. Cells were exposed to 0.2 % DMSO (UNT), M, IM or the mixture (M + IM) and 100 mM H 2 O 2 (positive control) for 24 h. Data are means ± SD of three independent experiments. *p<0.05 vs control cells. (B) Nrf2, HO-1 and MnSOD mRNA levels were estimated by RT-qPCR. Data were expressed relative to mRNA levels found in untreated cells arbitrarily set at 1.

Figure 4 .

 4 Figure 4. Effect of malathion (M), isomalathion (IM) and their mixture (M + IM) on expression and activities of CYPs involved in malathion metabolism. (A) CYP mRNAs were analyzed by RT-qPCR. HepaRG cells were exposed to 0.2 % DMSO (UNT) or incubated with different concentrations of M, IM, or their mixture (M + IM) for 24 h. Results are expressed as fold of the value found in untreated cells arbitrarily set at 1. Data are means ± SD of three independent experiments. *p<0.05 compared to untreated cells. Activity of CYP3A4, CYP2B6 (B) and CYP1A2(C) and protein levels (D) after treatment of HepaRG cells with malathion (M), isomalathion (IM) and their mixture (M + IM).(B-C) Cells were exposed to 0.2 % DMSO (UNT), M, IM, the mixture (M + IM) or positive control (25 µM rifampicin or 5 µM 3methylcholanthrene) for 48 h. CYP activities were determined directly into the cells as pmoles/min/mg protein. Data are expressed as fold induction of the value found in untreated cells arbitrarily set at 1; they are means ± SD of three independent experiments. *p<0.05 compared to untreated cells. (D) Western blotting of CYP3A4 and CYP2B6 proteins after a 48 h exposure.

Figure 5 .

 5 Figure 5. Effect of malathion (M), isomalathion (IM) and their mixture (M + IM) on expression and activities of CEs involved in malathion metabolism. (A) CE mRNAs were analyzed by RT-qPCR. HepaRG cells were exposed to 0.2 % DMSO (UNT) or incubated with different concentrations of M, IM, or their mixture (M + IM) for 24 h. Results are expressed as fold of the value found in untreated cells arbitrarily set at 1. Data are means ± SD of three independent experiments. *p<0.05 compared to untreated cells. CE activities in HepaRG cells after 1 (B) and 48 h (C) and in human liver microsomes (D) after treatment with malathion (M), isomalathion (IM) and their mixture (M + IM). Data are expressed as fold induction of the value found in untreated cells arbitrarily set at 1; they are means ± SD of three independent experiments. *p<0.05 compared to untreated cells.

Table 1 .

 1 List of primer sequences used in RT-qPCR

	Gene	Name	Forward Primer	Reverse Primer
	CE1	carboxylesterase 1	AGGTCCTGGGGAAGTATGCC	TGCATCTTGGGAGCACATAGG
	CE2	carboxylesterase 2	GGAGTGGTGTGAGAGATGCG	CAGGTTAGAGCCCTCACGG
	CYP1A2 cytochrome P450, family 1,	TGGAGACCTTCCGACACTCCT	CGTTGTGTCCTTTGTTGTGC
		subfamily A, polypeptide 2
	CYP2B6 cytochrome P450, family 2,	TTCCTACTGCTTCCGTCTATCAAA GTGCAGATTCCCACAGCTCA
		subfamily B, polypeptide 6
	CYP3A4 cytochrome P450, family 3,	CTTCATCCAATGGACTGCATA	TCCCAAGTATAACAGCACTCTA
		subfamily A, polypeptide 4		CACAGAC
	HO1	hemeoxygenase 1	ACTTTCAGAAGGGCCAGGT	TTGTTGCGCTCAATCTCCT
	MnSOD	manganese superoxide dismutase	GGGTTGGCTTGGTTTCAATA	CTGATTTGGACAAGCAGCAA
	Nrf2	NF-E2-related factor 2	TCAGCATGCTACGTGATGAAG	TTTGCTGCAGGGAGTATTCA

Table 2 .

 2 Numbers of mono-micro-nucleated , multi-micronucleated, apoptotic and metaphasic cells induced by malathion (M) and isomalathion (IM) separately or in combination (M + IM) in HepaRG cells after a 24 h exposure.

	Results are expressed as mean number per 1000 cells ± SD of three independent
	cultures. Chi-square test associated with a Yates correction: *P< 0.05; **P< 0.01;
	***P< 0.001 from untreated (UNT) cells. AFB1 (aflatoxinB1) and MMS (methyl
	methanesulfonate) were used as positive controls. RPD (Relative population
	doubling) has been calculated.
	Compound (µM)	Mono-micro-nucleated	Multi-micro-nucleated	Apoptotic Metaphasic	RPD (%)
	UNT	7 ± 1	0.7 ± 1.2	1.7 ± 0.6	19.7 ± 5.7 100
	AFB1
	0.5	49 ± 2.6***	11 ± 2.6	10 ± 4	28 ± 5.3	88.2
	MMS
	270	50 ± 4.4***	9.3 ± 2.5	5.3 ± 2.1	33.7 ± 8.3 95.4
	Malathion
	10	17.7 ± 3.1*	1 ± 0	2 ± 1	24 ± 6.2	99.7
	25	17.3 ± 3.2*	2.7 ± 2.1	3 ± 2.6	22.7 ± 4.5 100.2
	50	21 ± 4.6**	0.7 ± 0.6	3.7 ± 1.2	22.7 ± 6.7 100.2
	Isomalathion
	0.05	13.7 ± 5.5	1.7 ± 1.5	2.7 ± 1.5	21.7 ± 3.1 99.6
	5	11.3 ± 2.1	1 ± 1	1.3 ± 0.6	18.7 ± 1.2 100.6
	10	16 ± 1.7	1 ± 1	1 ± 0	20.7 ± 7	99.8
	25	17.7 ± 5.5*	2.3 ± 2.5	2.3 ± 1.2	22.7 ± 6.4 99.7
	50	26.7 ± 2.3***	2 ± 1.0	1.3 ± 0.6	23.7 ± 5.7 100.5
	Mixture
	M25 + IM 0.05 15.3 ± 2.1	1 ± 1	2.3 ± 0.6	19.3 ± 3.8 99.6
	M25 + IM 5	15.7 ± 2.3	0.7 ± 0.6	2.7 ± 2.1	21.7 ± 5.7 99.5
	M10 + IM 10	18.7 ± 4*	2 ± 1	3.3 ± 2.1	23 ± 7.8	99.6
	M25 + IM 25	27 ± 8.9***	1.7 ± 0.6	2 ± 2	23.3 ± 4	100.3

Funding

This work was supported by grants from the Ligue 35 contre le Cancer and ANR (contract NISTEC 09-CESA-003-002).