Armelle Guillou
email: armelle.guillou@math.unistra.fr

Marie Kratz
email: kratz@essec.edu.

Yann

Y Le Strat
email: y.lestrat@invs.sante.fr

An Extreme Value Theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella

Keywords: 62G32, Secondary: 62G05, 62G30 Extreme value theory, Return level, Return period, Outbreak detection, Salmonella, Surveillance

An extreme value theory approach for the early detection of time clusters. A simulation-based assessment and an illustration to the surveillance of Salmonella

Introduction

Since the pioneering work of Serfling ( [START_REF] Serfling | Methods for current statistical analysis of excess pneumonia-influenza deaths[END_REF]), several statistical models have been proposed to detect time clusters from surveillance data recorded as time series count in a given geographic area. A time cluster is defined as a time interval in which the observed number of events is significantly higher than the expected number of events. The term 'event' is generic enough to include any event of interest such as a case of illness, an admission to an emergency department, a death or any other health event.

The early prospective detection of time clusters represents a statistical challenge as the models must take the main features of the data into account such as secular trends, seasonality, past outbreaks but are also faced with idiosyncrasies in reporting, such as delays, incomplete or inaccurate reporting or other artefacts of the surveillance systems. Reporting delays are particularly problematic for surveillance systems that are not based on electronic reporting. Concerning syndromic surveillance systems, the same difficulties are encountered, excepted for the reporting delays because these surveillance systems are mostly based on electronic reporting.

The main statistical approaches introduced in the literature have been recently reviewed by Unkel et al [START_REF] Unkel | Statistical methods for the prospective detection of infectious disease outbreaks: a review[END_REF] with a discussion of statistical issues involved in evaluating and comparing methods. As mentioned by Unkel et al [START_REF] Unkel | Statistical methods for the prospective detection of infectious disease outbreaks: a review[END_REF], the published models are sometimes presented into broad classes such as regression techniques, time series methodology or statistical process control ( [START_REF] Farrington | Statistical aspects of detecting infectious disease outbreaks[END_REF]; [START_REF] Strat | Overview of temporal surveillance[END_REF]; [START_REF] Sonesson | A review and discussion of prospective statistical surveillance in public health[END_REF]) but this presentation is somewhat artificial as several methods can be classified into more than one of these classes.

However, in most cases they are based on two steps: (i) the calculation of an expected 0 This work was partially supported by the ESSEC Research Center, project #043.203.5.1.0.7.07. value of the event of interest for the current time unit (generally a week or a day); (ii) a statistical comparison between this expected value and the observed value. A statistical alarm is triggered if the observed value is noticeably different from the expected value.

The first step is based on the past counts, or more often on a sample of the past counts, that takes the seasonality pattern(s) into account. Thus, the current count is compared to counts that occurred in the past during the same time periods, e.g. 3 weeks around the current week from 5 previous years. Alternatively, sinusoidal seasonal components can be incorporated into regression models to deal with the seasonality and to easily take secular trends into account. More rarely, models try to reduce the influence of weeks coinciding with past outbreaks. One solution to avoid that such outbreaks reduce the sensitivity of the model is to associate low weights to these weeks in the estimation of the expectation (e.g. [START_REF] Farrington | A statistical algorithm for the early detection of outbreaks of infectious disease[END_REF]).

The intentional release of anthrax in the USA in October 2001 emphasized the need to develop new early warning surveillance systems ( [START_REF] Goldenberg | Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales[END_REF]; [START_REF] Reis | Using temporal context to improve biosurveillance[END_REF]). These surveillance systems treat an increasing number of data provided from multiple sources of information ( [START_REF] Centers For | Syndromic Surveillance: Reports from a National Conference 2003[END_REF]).

One logical consequence is to perform statistical analyses with a daily frequency.

Developing automated statistical prospective methods for the early detection of time clusters is thus essential. It is important for a public health surveillance agency to run several statistical methods concomitantly in order to compare the alarms generated by these methods.

To this aim, we propose in this paper a new approach based on Extreme Value Theory (EVT) (e.g. [START_REF] Embrechts | Modelling extremal events for Insurance and Finance[END_REF]) for the early detection of time clusters. EVT has not been used so far in the context of surveillance; adapting and combining in a specific way EVT tools introduces an innovative and interesting alternative to existing methods for surveillance data, not so numerous. One of the main advantages of the EVT approach is that we do not need any model to fit the time series and no assumption is required on the underlying distribution.

The main idea of our method is the following. First, we associate to each observation of the dataset a return period, which corresponds to the time that we expect to be able to see again such a level (and not earlier). Then, whenever we have a new observation, we look backward on its return period to see if already at least one observation exceeds it. If it does, we consider the new observation as "abnormal" (e.g. the potential start of an epidemic), in the sense of "not expected" as defined by our rule, and we set off an alarm.

To evaluate the method, we ran it on simulated data generated by an extensive simulation study based on recent work [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF]. Then to illustrate the method, we applied it to the detection of time clusters from weekly counts of Salmonella isolates reported to the national surveillance system in France. Salmonellosis is a major cause of bacterial enteric illness in both humans and animals, with bacteria called Salmonella. In France, Salmonella is the first cause of laboratory confirmed bacterial gastroenteritis, of hospitalization and of death. In 2005, a study estimated that between 92 and 535 deaths attributable to non typhoidal Salmonella occurred each year ( [START_REF] Vaillant | Burden of foodborne infections in France[END_REF]).

The paper is organized as follows. The surveillance system and the data are presented in Section 2. A description of our method to check if each new observation corresponds to an unusual/extremal event is given in Section 3. Applications to simulated data and real counts of Salmonella are developed in Section 4. A discussion follows in the last section.

Data

We will consider, in our paper, simulated datasets in order to test the performance of our method and real datasets in order to illustrate it. Concerning the latter, let us present in this section the real data, the counts of Salmonella, on which we will be working. The National Reference Center for Salmonella contributes to the surveillance of salmonellosis by performing serotyping of about 10000 clinical isolates each year.

Salmonella surveillance is based on a network of 1500 medical laboratories that voluntarily send their isolates. Salmonella enterica serotypes Typhimurium and Enteritidis represent 70 % of all Salmonella isolates in humans among many hundreds of serotypes; that is why we consider in this paper mainly these two serotypes. For illustrative purpose, four other less frequent serotypes (Manhattan, Derby, Agona and Virchow) are also considered; Figure 1 shows the weekly number of isolates for these six serotypes from January 1, 1995 to December 31, 2008. It highlights the great variability in terms of seasonality, secular trend and weekly number of reported isolates and frequencies of unusual events. The number of cases is recorded by week of reporting to the National Reference Center because some dates of typing are missing. Let Y = {Y t ; t = 1, . . . , T } be the univariate initial time series corresponding to the number of isolates at time point t for a given serotype. If the series Y exhibits a trend, we estimate it from a linear regression trend component, then we substract it.

Concerning the possible seasonality of Y , as mentioned by many authors (e.g. [START_REF] Goldenberg | Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales[END_REF]; [START_REF] Nobre | Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology[END_REF]), seasonal effects may have a strong impact in generating a statistical alarm. A common way to prepare the dataset is to select counts from comparable periods in past years, as described in the literature ( [START_REF] Farrington | A statistical algorithm for the early detection of outbreaks of infectious disease[END_REF]; [START_REF] Stroup | Detection of aberrations in the occurrence of notifiable diseases surveillance data[END_REF]). The dataset is restricted to the counts that occurred during the times within these comparable periods. For instance, if the current time is t of year y, then only the counts for the n = b(2w + 1) times from t -w to t + w of years from y -b to y -1 (b > 1, w > 1) are used. Another way to handle seasonality is to include seasonal factors in the statistical model running on a restricted dataset, as recently proposed by Noufaily et al. [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF].

As an illustration Figure 2 represents the restricted dataset for Salmonella Typhimurium, for a given current week with w = 3 and b = 5. In all the sequel, X = (X t ) will denote the transformed (i.e. the detrended subset) time series from (Y t ). To each new observation will correspond a new sample, indifferently named X or (X t ), to simplify. Each step of the method will apply on X itself.

An EVT approach

Suppose we have at our disposal n successive observations that we consider as realizations of a sample (X i ) of independent and identically distributed (i.i.d.) non-negative random variables defined on a probability space (Ω, A, P), from an unknown distribution function F . We do not make any assumption on F and do not need any to develop our method, which constitutes a great advantage of this approach.

We want to detect extreme events, which justifies an EVT framework. Extreme events have been extensively studied in the literature (e.g. [START_REF] Embrechts | Modelling extremal events for Insurance and Finance[END_REF]), with two main approaches developed in the i.i.d. setting, one based on the distributions of maxima (e.g. [START_REF] Fisher | Limiting forms of the frequency distributions of the largest or smallest member of a sample[END_REF]) and the other one based on exceedances above some threshold, known as the Peak-Over-Threshold (POT) method (e.g. [START_REF] Pickands | Statistical inference using extreme-order statistics[END_REF]).

Assuming independence might appear as a rough approximation to analyze epidemiological discrete time series; however we obtain quite reasonable and interesting results under this assumption, usual when introducing an EVT approach. Note also that in most of the existing methods for surveillance data the residuals from the models are assumed to be independent.

Moreover, the method we propose also allows handling the case of a discrete phenomenon, whereas the classic EVT method requires continuous distributions only.

We will face this issue of using extreme tools for discrete data in one step of our method, when having to estimate extreme quantiles (or return levels); instead of estimating them explicitly as we do in the continuous framework, we will estimate their upper bounds, using bounds introduced in [START_REF] Guillou | Return level bounds for discrete and continuous random variables[END_REF] which do not require any strong assumption (such as continuity) on the tail behavior of the distribution function. The practitioner, e.g. a biostatistician or an epidemiologist, is often ready to accept more alarms if he knows that the upper bound is well estimated and can provide a reasonable 'worst case scenario'. In that sense, our method, in particular the use of an upper bound for the return level, should be viewed as an alternative tool which provides additional information for the practitioner. Now let us present the fundamentals of our method. Associated with a given return period T which corresponds to T time units over the past, a return level z T is defined as the level expected to be exceeded on average once every T time units, i.e. such that

E T i=1 1l {X i >z T } = 1 (1)
where 1l {A} represents the indicator function that is equal to 1 if A is true and to 0 otherwise. The last equality can be rewritten as 1 -F (z T ) = 1/T . Hence, the return level z T corresponds simply to a p T -quantile with p

T = 1 -1/T , z T = F ← (1 -1/T ),
F ← denoting the generalized inverse function of F .

Notice that in (1), we may have chosen to replace the right-hand side of the equation by any small integer, say c. The value of c is related to the height of the threshold above which observations are considered as exceedances (above this threshold). The smaller c, the higher the threshold. If we want to select what we call extreme/unusual values, it is preferable to select a high threshold, which means a small value of c. We chose here to make it really extreme, not asking for more than one exceedance, in average, above the last observation. We could relax a bit this choice, by taking for instance c = 2, but we prefer to be on the safe side. As mentioned in the discussion of Section 5, an optimal value of c, or a prescribed value in order to match the performance of other methods already proposed in the literature is an interesting open question which will lead to further investigations.

The idea of the method is to associate with each observation x s a return period T s defined theoretically as (1 -F (x s )) -1 to be able to determine the return period T t associated to each new observation x t at time t. Then, we look backwards (and not forwards as in the 'standard' way) in the interval (t -T t ; t) for the existence of an observation that would exceed x t . If it exists, we set the rule to generate an alarm at time t based on the fact that, on average, we do not expect two or more exceedances

on (t -T t ; t].
Notice that in our discrete framework it will not be possible to estimate explicitly the return levels; instead estimated bounds will be considered.

Therefore, after a preliminary analysis of the data and definition of our sample, we will compute the estimated bounds of the return levels in order to obtain a graph of the return periods and levels. Then, we will allocate a return period to any new observation

x t to test if t corresponds to a warning time according to our definition.

Looking at extremal events leads us to the crucial problem of high quantile estimation, well-studied in the EVT literature (e.g. [START_REF] Embrechts | Modelling extremal events for Insurance and Finance[END_REF]), based on a sample of observations. However, EVT is only valid in the case where the underlying distribution function F of these observations is continuous. This is not the case in the surveillance context where the observations are counts and thus F is discrete. Therefore, we propose to use instead upper and lower bounds for the return level z t and estimate them as in [START_REF] Guillou | Return level bounds for discrete and continuous random variables[END_REF];

this approach has several advantages: the upper and lower bounds can be computed for any value of t (in particular it holds for large values), it does work for both small and large samples, and for F continuous or discrete. So it is well-adapted to our context, when assuming the random variables associated to the i.i.d. observations. Note that using bounds for a return level z t will imply that the return period defined theoretically by (1 -F (z t )) -1 cannot be explicitly estimated and we have

T ≤ (1 -F (z t )) -1 ≤ T b (2)
where T and T b denote the return periods of the lower and upper bounds t (u, w, q) and b t (u, v) respectively, defined in the on-line Web Appendix and estimated below.

Here u, v and w are suitable power functions and q > 1.

Now let us present our method to define an alarm system. For each new observation

x t i , i ≥ 1:
• First we check if the new observation is the largest on all the sample. If it is the case, we generate an alarm. This conservative condition may be weakened, applying it only when observing a local non decreasing trend.

• Otherwise, we proceed as follows.

Step 1: We draw the plot of the return period on the x-axis and the corresponding estimate of the upper bound of the return level (instead of the return level itself):

t, b t , where bt = bt α t , β t =   t θn α t , β t (1 -1/t) βt   1/ αt , with θn (α, β) = 1 n n i=1 x α i,n (i/n) β . (3)
Step 2: We allocate to each observation x t i , i ≥ 1, a return time T i using the previous plot. Namely, x t i corresponds to a value b T i of the y-axis of the plot from which we deduce the associated return level T i . Reading an observation as an upper bound of a return level means that T i is in fact the lower bound of the theoretical return period

(1 -F (x t i )) -1 that should be associated to the observation x t i , because of (2).

We justify our choice as follows. Considering T i instead of b T i in the above method would have led to underestimate the return period associated to the observation x t i , which could be a problem in the context of alarms (it is better to have more alarms than less), except if the plots t, t and t, b t were close enough, but it is generally not the case for our datasets where t appeared approximately as a constant function of time ( [START_REF] Borchani | Statistiques des valeurs extrêmes dans le cas de lois discrètes[END_REF]).

Step 3: We use the fact that if (X j ) are i.i.d. random variables, then we have for any

time interval I (T ) with length T E T i=1 1l {X i >z T } = 1 ⇔ E   i∈I(T ) 1l {X i >z T }   = 1. ( 4
)
This remark is important since we want to define for each new observation a warning time, which means to look backward in time.

Hence for each new observation x t i , to which a return time T i has been associated (via

Step 2), we will look in the interval (t i -T i ; t i ) to see if there exists an observation exceeding x t i , considered as the new exceedances threshold; if it does, we ring an alarm at this time t i .

• To finish this section, let us summarize our method as an iterative algorithm.

For each new observation x t i , i ≥ 1:

1. If x t i > max j<t i
x j , then generate an alarm time at time t i . Note that this conservative condition may be weakened, applying it only when observing a local non decreasing trend.

2. Otherwise, (a) associate the time T i to x t i , read from the plot t, b t when x t i is regarded as a return level b T i ;

(b) consider I (T i ) = (t i -T i , t i ];

(c) look for the existence of an observation x t ≥ b T i , for t ∈ (t i -T i , t i );

if there exists at least such an observation, then generate a warning time at

t i .
Remarks: in practice, if the return period of the new observation x t i is larger than the length of the sample (X t ), then we choose to be conservative by generating an alarm at time t i . Other choices may of course be considered. Also, in some sense, detecting outbreaks when two or more "extreme" observations in a period are observed, instead of one, might be considered as a "heuristic rule". But, as illustrated in the next section, our detections are in accordance with the ones obtained with other classical approaches already used in the literature. Nevertheless, a possible alternative, which would require further investigations, might consist in replacing the expectation by an estimation of the probability P j∈I(T i ) 1l {x j ≥ bT i } > 1 .

• Now to illustrate our method, let us consider the example of the number of Salmonella Virchow isolates. In Figure 3

Applications

Simulated data

We generated simulated data following the approach proposed by Noufaily et al. [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF].

Let us summarize briefly the procedure:

• First, simulated baseline data (i.e. time series of counts in the absence of outbreaks) were generated using a negative binomial model with a mean including trend and seasonality determined by Fourier terms. More precisely, these data were simulated from 42 different parameter combinations (called scenarios and presented in Table 1 in [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF]) with different trends, seasonalities, baseline frequencies of counts and dispersions. The simulations of the baseline data use 100 replicates from each scenario of size 624 weeks. The last 49 weeks were used as current weeks leading to 4900 replicates for each of the 42 scenarios.

• Secondly, outbreaks were simulated both in baseline and current weeks. Given a constant k, the size of a simulated outbreak, starting in week t i , follows a Poisson distribution with mean equal to k times the standard deviation of the count at t i . Then outbreak cases were randomly distributed according to a lognormal distribution with mean 0 and standard deviation 0.5. Finally, four outbreaks were generated in baseline weeks and one outbreak was generated in current weeks. Outbreaks start times were chosen randomly. We chose the values of k to be 3 and 5 in baseline weeks and from 1 to 10 in current weeks.

• Next, we used two measures, named FPR and POD, introduced in [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF] to assess the performance of the EVT method. For each scenario, we calculated the false positive rate (FPR) as the proportion of the current 49 weeks and 100 replicates, in which the method generated an alarm in the absence of outbreak. The second measure, called the probability that an outbreak is detected (POD) was also calculated. For each scenario and for each current week, if an alarm is generated at least once between the start and end times of the outbreak, the outbreak is considered as detected. The POD is the proportion of outbreaks detected in 100 runs. Results of this simulation study are presented in Figure 4 showing the FPRs and the POD when outbreaks of 3 (plots (a), (c)) and 5 (plots (b), (d)) standard deviations are included in baselines. We show the central estimates of the FPRs associated to their 95% confidence intervals. As noted in [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF], the FPRs are highest for scenarios 10-12 with low baseline and over-dispersed data. In our study, the FPR is also high for scenario 16. Concerning the POD, as expected it increased with k for each scenario. The PODs are not so different when outbreaks of 3 standard deviations and 5 standard deviations are present in the baselines.

• Finally, we compared the results obtained for each of the 42 simulated scenarios respectively with the EVT approach and with the Model 0 of Farrington's method (see [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF] run with α = 1%. We chose the Farrington method for two main reasons. First the aim of the Farrington algorithm was to develop a robust method for the routine monitoring of weekly reports on infections for many different pathogens at the Communicable Disease Surveillance Center in UK. This method was applied in particular to the detection of Salmonella outbreaks. Second, this method has been applied in France for the surveillance of human Salmonella since 2006 and for the surveillance of Salmonella isolated in the agro-food chain in France for the last two years ( [START_REF] Danan | Automated early warning system for the surveillance of Salmonella isolated in the agro-food chain in France[END_REF]) and has shown good performances. In 2008, performances of five methods were evaluated for the early detection of excess legionella cases in France ( [START_REF] Grandesso | Early detection of excess legionella cases in France: evaluation performance of five automated methods[END_REF]) concluding that the Farrington method had the best positive predictive value, equal to 67%. More specifically, we presented in Figure 5 the POD obtained from the 100 runs for each scenario with the Farrington method when outbreaks of 3 standard deviations are included in baselines. This plot can be compared to Figure 4(c) obtained with the EVT approach. As expected, this proportion increases with k for each scenario.

For completeness, we also represented on a same plot the false positive rates obtained respectively with the EVT approach (black circles) and with the Farrington method (white triangles). The means represented by the dots together with the 95% confidence intervals are based again on these 100 replicates. It is fairly clear that the performances of the methods depend both on the characteristics of the time series summarized by the different scenarios and the magnitude of past and current epidemics. However, both methods favor a good specificity rather than high sensitivity, and they have a close performance, with a slightly better one for the EVT. Figure 6: False positive rates obtained for outbreaks with the EVT approach (black circles) and with the Farrington method, Model 0 with α = 1%, (white triangles), for 42 scenarios.

Real data

For each week from January 2000 to December 2008, the EVT method was applied to two time series presented in Section 2 (Agona and Manhattan). For each time series Y and for a week t:

1. We note x t the observed number of cases at week t, that we will compare to selected counts of weeks from t -3 to t + 3 of years from y -5 to y -1 (to handle the seasonality as explained in Section 2). This restricted dataset corresponds to the resulting time series X on which we apply our EVT method.

2. We calculate the upper bound for x t regarded as the return level, using equation

.

3. We calculate a return period associated to the upper bound of the return level.

In practice we calculate for t = 2 to T max (e.g. T max = 100) the increasing upper bounds bt=2 , bt=3 , . . . , bt=Tmax . The return period is determined by the rank of the first upper bound greater than the observed number of cases x t .

4. We generate an alarm at time t if:

(a) the return period is larger than the length of the sample X, because we have not enough past data to claim that x t is not an unusual event;

(b) x t is larger than the maximum number of counts observed in the sample X;

(c) we observe, over the return period, a number of cases greater than x t .

Moreover, in order to reduce the probability that an alarm could be triggered for few sporadic cases, a standard rule, put as an option in the program because specific to Salmonella, has been adopted to keep an alarm at week t if at least 5 cases were observed during the 4 last weeks preceding t. This rule has already been applied at the former Communicable Disease Surveillance Center (CDSC, now Center for Infections of the Health Protection Agency) in the UK using the method developed by Farrington et al. ([6]). It is an empirical rule, which avoids too numerous alarms whenever the time series concerns a rare serotype only. In this specific case, an alternative could be to transform equation ( 1) into E T i=1 1l {X i >z T } = c with a suited small value c > 1 as previously discussed.

We applied the EVT method on the weekly number of isolates for serotypes Manhattan and Agona. We compared the alarms generated by the EVT method with those generated by the Farrington method (corresponding to Model 0 in [START_REF] Noufaily | An improved algorithm for outbreak detection in multiple surveillance systems[END_REF] with the threshold value at time t defined as the upper 99% prediction limit). The EVT method was implemented using R version 2.9 ( [START_REF]A Language and Environment for Statistical Computing[END_REF]). The R-code is available on request. The function called algo.farrington, implemented into the R-Package surveillance ( [START_REF] Höhle | Surveillance: An R package for the surveillance of infectious diseases[END_REF]) was used to apply the Farrington method.

Discussion

We believe that the EVT method meets a number of requirements, listed by Farrington et al. ( [START_REF] Farrington | Statistical aspects of detecting infectious disease outbreaks[END_REF]), for the outbreak detection algorithms implemented in surveillance systems. Indeed, this method is able to monitor a large number of time series which became an absolute necessity in modern computerized surveillance systems. It can deal with a wide range of events as it is the case for the Salmonella infections with the routinely analyses of several hundred serotypes per week. It can handle time series with great numbers of cases (such as Salmonella Enteritidis) or small numbers of cases (such as Salmonella Manhattan). Seasonality is taken into account by comparing counts over the same periods of time. Other methods propose a direct way to treat the past aberrations, for instance by associating low weights to the weeks coinciding with past outbreaks. There is no such need when using the EVT method since the return period is not a constant but depends on each observed count; alarms can then be generated even if past outbreaks exist. If a past outbreak is contained in the return period interval, then an alarm will be triggered at time t if the observation at t has been exceeded by this outbreak. Finally, the method is implemented in a function using the R language, allowing to run it in an automated procedure with minimal user intervention. This method could be easily included into the R surveillance package and used by public health surveillance practitioners. Recently, several papers have shown that R is routinely used for the early detection of outbreaks in Europe ( [START_REF] Cakici | CASE: a framework for computer supported outbreak detection[END_REF]; [START_REF] Danan | Automated early warning system for the surveillance of Salmonella isolated in the agro-food chain in France[END_REF]; [START_REF] Höhle | Aberration detection in R illustrated by Danish mortality monitoring[END_REF]; [START_REF] Robertson | Review of software for space-time disease surveillance[END_REF]; [START_REF] Hulth | Practical usage of computer-supported outbreak detection in five European countries[END_REF]).

Although the model developed by Farrington et al. ([6]) became a standard reference method, routinely used in France since many years and incorporated in several surveillance systems, the EVT method seems also to be a valuable and interesting tool for the recognition of time clusters. It could be integrated in the family of outbreak detection algorithms used by the public health surveillance agencies since developing effective computer-assisted outbreak detection systems still remains a necessity to en-sure timely public health intervention.

Obviously our approach does not pretend answering all the issues and could be improved. This paper must be viewed as an exploratory study and constitutes a first step for further developments already quite promising. The main two advantages of our approach is that it does not require any assumption on the underlying (unknown) distribution function and leads to a good specificity. Unfortunately, as the Farrington one, it is less sensitive than other methods. That is why it should not be used solely but combined with other more sensitive methods. Due to its competitiveness against standard approaches, it should help practitioners in the detection of epidemics.

The overall potential of our approach, theoretical as practical, together with a fuller comparison to other methods will be the subject of a forthcoming paper. In particular, an interesting open question is the choice of an optimal value c for the right-hand side in equation [START_REF] Serfling | Methods for current statistical analysis of excess pneumonia-influenza deaths[END_REF]. This question comes back to discuss the choice of the threshold. In this paper we fixed c = 1. As emphasized in the simulation study, this value leads to competitive results of our approach with respect to the Model 0 of Farrington's method in a large panel of situations, in terms of parameter combinations, trends, seasonalities, baseline frequencies of counts and dispersions, but this choice might be improved by an optimal selection. This leads to further investigations.

Figure 1 :

 1 Figure 1: Weekly counts of isolates reported to the National Reference Center for Salmonella in France, January 1, 1995 to December 31, 2008: (a) Salmonella Manhattan; (b) Salmonella Derby; (c) Salmonella Agona; (d) Salmonella Virchow; (e) Salmonella Typhimurium; (f) Salmonella Enteritidis.

Figure 2 :

 2 Figure 2: Weekly counts of isolates for Salmonella Typhimurium. The current week, represented by the arrow, is the last week of December 2008. The counts that occurred in comparable periods (± 3 weeks) in the five previous years, and used to generate or not an alarm, are represented by the striped bands.

  , the x-axis corresponds to the values of t from 2 to 100 weeks and the y-axis to b t , calculated for the last week of 2008; the two dashed lines indicate the 95% confidence interval bounds of b t .

Figure 3 :

 3 Figure 3: The return level/return period graph for Salmonella Virchow, calculated for the last week of 2008. The black curve represents the upper bound of the return level. Dashed curves represent the 95% confidence interval of this upper bound. To the observation y = 20 (respectively y = 15) does correspond on the x-axis b 91 (respectively b 7 ) from which we deduce the return period equals to T = 91 (respectively T = 7) weeks.

Figure 4 :

 4 Figure 4: False positive rates obtained for outbreaks of 3 (a) standard deviations and of 5 (b) standard deviations. Proportions of detected outbreaks of k standard deviations corresponding to the 42 scenarios. Outbreaks of 3 (c) and 5 (d) standard deviations are included in baselines.

Figure 5 :

 5 Figure 5: POD obtained with the Farrington method, Model 0 with α = 1%. Proportions of detected outbreaks of k standard deviations corresponding to the 42 scenarios. Outbreaks of 3 standard deviations are included in baselines.

Figures 7 and 8

 8 Figures 7 and 8 represent both the alarms and the weekly counts over time for the serotypes Manhattan and Agona. Each triangle represents a statistical alarm. Triangles on the first line represent the alarms generated by the EVT method, whereas the ones generated by the Farrington method are given on the second line.

Figure 7 :

 7 Figure 7: Salmonella Manhattan: Weekly counts from January 1, 2000 to December 31, 2008. Roman numerals refer to the quarters of the years. Alarms generated by the EVT method are represented by triangles on the first line. Alarms generated by the Farrington method are represented by triangles on the second line. The documented outbreak is delimited by the two dashed lines.

Figure 8 :

 8 Figure 8: Salmonella Agona: Weekly counts from January 1, 2000 to December 31, 2008. Roman numerals refer to the quarters of the years. Alarms generated by the EVT method are represented by triangles on the first line. Alarms generated by the Farrington method are represented by triangles on the second line. The documented outbreak is delimited by the two dashed lines.

Acknowledgements

The authors are grateful to an Associate Editor and two anonymous reviewers for their comments and suggestions that led to a marked improvement of the article. They would like to thank Angela Noufaily and Paddy Farrington for providing them simulated datasets and a R code used in Section 4.1 and Francois-Xavier Weill for providing us the Salmonella datasets. They also thank Anis Borchani for the implementation of the


Florence Brossier
email: florence.brossier@psl.aphp.fr

Wladimir Sougakoff

Christine Bernard

Matthieu Petrou

Karine Adeyema

Anne Pham

Diane Amy De La Breteque

Marine Vallet

Vincent Jarlier

Christophe Sola

Nicolas Veziris

Molecular analysis of the embCAB locus and embR gene involved in resistance to ethambutol in clinical isolates of Mycobacterium tuberculosis in France Running title: embCAB/embR in ethambutol-resistant M. tuberculosis

Modification of codon 306 in embB is regarded as the main mechanism leading to ethambutol (ETB) resistance in clinical isolates of Mycobacterium tuberculosis. However, numerous mutations elsewhere in the embCAB locus and embR, a putative transcriptional activator of this locus, have been reported to be involved in ETB resistance. Here, we investigated the diversity of nucleotide variations observed in embCAB and embR in M. tuberculosis complex isolates from France. These regions were sequenced in 71 ETB resistant (ETB-R) and 60 susceptible (ETB-S) clinical isolates of known phylogenetic lineage. The 131 isolates had 12 mutations corresponding to phylogenetic markers. Among the 60 ETB-S isolates, only 3 (5%) had nonsynonymous mutations that were not phylogenetic markers. Among the 71 ETB-R isolates, 98% had mutations in embCAB that likely contribute to ETB resistance; 70% located in embB codon 306, 406, or 497, 13% outside these three positions between codons 296 and 426, and 15% corresponding to mutations in the embC-embA intergenic region. We found a strong association between resistance to ETB and the presence of mutations in embB and the embC-embA intergenic region (P<0.001). In contrast, the mutations detected in embC and embA were not involved in ETB resistance, and no mutation was detected in embR. These results strongly suggest that the sensitivity of the diagnostic assays for detecting ETB resistance based on testing embB codon 306 can be increased by testing the embB region between codons 296 and 497, and by including the embC-embA intergenic region between positions -8 and -21.

INTRODUCTION

The emergence of multidrug-resistant tuberculosis (MDR-TB), which is resistant to at least rifampicin (RIF) and isoniazid (INH), and more recently extensively drug-resistant TB (XDR-TB), which is MDR-TB resistant to any fluoroquinolone and at least one of three injectable second-line drugs (i.e., amikacin, kanamycin, or capreomycin), is widely considered to be a serious threat to global TB control [START_REF]Multidrug-resistant tuberculosis (MDR-TB)[END_REF]. Rapid detection of drug resistance is essential to designing appropriate treatment regimens, preventing treatment failure, and reducing the spread of drug-resistant isolates. Molecular assays for the detection of mutations that confer resistance (e.g., based on DNA sequencing, real-time PCR, and strip technologies such as line-probe assay GenoType  MTBDR) have been increasingly used and have the potential to shorten the time to detection of resistance to one working day [START_REF] Brossier | Detection by GenoType MTBDRsl test of complex mechanisms of resistance to second-line drugs and ethambutol in multidrug-resistant Mycobacterium tuberculosis complex isolates[END_REF][START_REF] Feng | Rapid diagnosis of drug resistance to fluoroquinolones, amikacin, capreomycin, kanamycin and ethambutol using genotype MTBDRsl assay: a meta-analysis[END_REF][START_REF] Hillemann | Feasibility of the GenoType MTBDRsl assay for fluoroquinolone, amikacin-capreomycin, and ethambutol resistance testing of Mycobacterium tuberculosis strains and clinical specimens[END_REF][START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Parsons | Laboratory diagnostic aspects of drug resistant tuberculosis[END_REF]. However, these molecular assays require precise knowledge of the genetic variations involved in the development of resistance to particular anti-TB drugs.

Ethambutol (ETB) [dextro-2,29-(ethylenediimino)-di-1-butanol] was introduced in 1961 and is a first-line anti-TB agent used in drug combinations to prevent the emergence of drug resistance. ETB is also included in second-line regimens for MDR-TB when susceptibility is demonstrated [START_REF]Treatment of tuberculosis: guidelines -4th ed[END_REF]. ETB interferes with mycobacterial cell wall synthesis and integrity [START_REF] Alcaide | Role of embB in natural and acquired resistance to ethambutol in mycobacteria[END_REF][START_REF] Belanger | The embAB genes of Mycobacterium avium encode an arabinosyl transferase involved in cell wall arabinan biosynthesis that is the target for the antimycobacterial drug ethambutol[END_REF][START_REF] Mikusová | Biogenesis of the mycobacterial cell wall and the site of action of ethambutol[END_REF](11)[START_REF] Takayama | Inhibition of synthesis of arabinogalactan by ethambutol in Mycobacterium smegmatis[END_REF][START_REF] Zhang | Ethambutol resistance as determined by broth dilution method correlates better than sequencing results with embB mutations in multidrug-resistant Mycobacterium tuberculosis isolates[END_REF] by inhibiting arabinosyl transferases encoded by the embCAB locus (≈10 kb), which encompasses three contiguous genes: embC, embA, and embB [START_REF] Telenti | The emb operon, a gene cluster of Mycobacterium tuberculosis involved in resistance to ethambutol[END_REF]. These enzymes are essential for the synthesis of arabinogalactan (EmbA, EmbB) and lipoarabinomannan (EmbC) in the cell wall of the Mycobacterium tuberculosis complex (MTBC) [START_REF] Mikusová | Biogenesis of the mycobacterial cell wall and the site of action of ethambutol[END_REF][START_REF] Takayama | Inhibition of synthesis of arabinogalactan by ethambutol in Mycobacterium smegmatis[END_REF]. Although the embCAB locus is also called the embCAB operon, it is not a real operon because the promoter of embA and embB is thought to be in the embC-embA intergenic region (85 bp) [START_REF] Jaber | Minor contribution of mutations at iniA codon 501 and embC-embA intergenic region in ethambutol-resistant clinical Mycobacterium tuberculosis isolates in Kuwait[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF] and the promoter of embC is in the region upstream of embC, possibly in the Rv3792 gene (11,[START_REF] Sreevatsan | Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations[END_REF], while the embR gene of MTBC is located 2 Mb from the embCAB locus [START_REF] Cole | Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development[END_REF]40).

Resistance to ETB in MTBC has been associated with chromosomal mutations in the embCAB locus, but mainly embB [START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Telenti | The emb operon, a gene cluster of Mycobacterium tuberculosis involved in resistance to ethambutol[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20). The majority of detected mutations are concentrated in a 576-bp region of embB, called the ETB resistance-determining region (ERDR). This region includes codons 306, 406, and 497 and is predicted to be the recognition site of the enzyme EmbB [START_REF] Zhang | The Emb proteins of mycobacteria direct arabinosylation of lipoarabinomannan and arabinogalactan via an N-terminal recognition region and a Cterminal synthetic region[END_REF]. Mutations in this region cause structural changes in the enzyme and alterations in the ETB-binding site and drug-protein interactions [START_REF] Lety | A single point mutation in the embB gene is responsible for resistance to ethambutol in Mycobacterium smegmatis[END_REF][START_REF] Srivastava | emb nucleotide polymorphisms and the role of embB306 mutations in Mycobacterium tuberculosis resistance to ethambutol[END_REF], which result in the development of ETB resistance. Nucleotide changes in the ERDR of embB are found in approximately 50% to 70% of ETB-resistant isolates of MTBC, mainly in codon 306, with reports estimating that 18% to 78% of the isolates presenting with embB mutations have an EmbB 306 substitution (2-5, 13, 16, 17, 23-37). A meta-analysis of the line probe assay Genotype  MTBDRsl, which allows rapid diagnosis of ETB resistance by analyzing EmbB 306, showed that the sensitivity and specificity for ETB are 0.679 (0.652-0.706) and 0.799 (0.773-0.823), respectively (3). However, approximately 30% to 50% of ETB-resistant clinical isolates do not carry a mutation in EmbB 306 and are not detectable by molecular methods based only on the polymorphisms in EmbB 306 [START_REF] Brossier | Detection by GenoType MTBDRsl test of complex mechanisms of resistance to second-line drugs and ethambutol in multidrug-resistant Mycobacterium tuberculosis complex isolates[END_REF][START_REF] Feng | Rapid diagnosis of drug resistance to fluoroquinolones, amikacin, capreomycin, kanamycin and ethambutol using genotype MTBDRsl assay: a meta-analysis[END_REF][START_REF] Hillemann | Feasibility of the GenoType MTBDRsl assay for fluoroquinolone, amikacin-capreomycin, and ethambutol resistance testing of Mycobacterium tuberculosis strains and clinical specimens[END_REF][START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF]. Although other mutations in the embCAB locus have been suggested to confer resistance, limited data have been available until now, with most studies analyzing only a short fragment of the embB gene encompassing codon 306 (2-4, 24, 25, 28, 30, 34, 35, 38).

Our main objective was to study the mutations in the entire embCAB locus and embR that are involved in ETB resistance in clinical isolates of MTBC in France because the majority of previous studies have been based on partial sequencing of the 10-kb region containing the embCAB locus. The results were challenged with phenotypic drug susceptibility testing, phylogenetic analysis, data from the literature, and the PolyTB webbased tool. We also assessed the association between the presence of embB mutations and the number of first-line drugs resistance.

MATERIALS AND METHODS

Mycobacterium tuberculosis complex clinical isolates

A total of 131 MTBC clinical isolates collected throughout France and received by the French National Reference Center for Mycobacteria (NRC) between 2009 and 2014 were included in this study. Seventy-one isolates were resistant to ETB (ETB-R), among which 68 were MDR (including 7 XDR), 1 was resistant to RIF but susceptible to INH, and 2 were resistant to INH but susceptible to RIF. Among the 71 patients with ETB-R isolates, TB treatment history was positive for 31 (43%), negative for 36 (51%), and unknown for 4 (6%). Sixty-nine patients with ETB-R isolates had 26 different countries of birth and two had an unknown country of birth (Table S1). Sixty of the patients were susceptible to ETB (ETB-S), among which 16 were MDR, 5 were resistant to RIF, 9 were resistant to INH, 1 was resistant to streptomycin (STR), and 29 were susceptible to all anti-TB drugs. Among the 60 patients with ETB-S isolates, TB treatment history was positive for 13 (22%), negative for 26 (43%), and unknown for 21 (35%). Forty-nine of the patients with ETB-S isolates had 25 different countries of birth and 11 had an unknown country of birth (Table S1).

Phenotypic drug susceptibility testing

In vitro drug susceptibility testing for RIF, INH, STR, ofloxacin, amikacin, kanamycin, capreomycin, and ETB was performed on Löwenstein-Jensen medium following the proportion method with the following concentrations: 40 mg/l for RIF, 0.2 and 1 mg/l for INH, 4 mg/l for STR, 2 mg/l for ofloxacin, 20 mg/l for amikacin, 30 mg/l for kanamycin, 40 mg/l for capreomycin, and 2 mg/l for ETB (41).

DNA sequencing of ethambutol resistance-associated genes

Genomic DNA was isolated from bacteria grown on Löwenstein-Jensen medium. A loop of culture was suspended in water (500 µl) and heated at 95°C for 15 min. The DNA used for PCR amplification was obtained by heat shock extraction (1 min at 95°C and 1 min on ice, repeated five times). PCR amplification of the embCAB locus (9,949 bp) and embR (1,167 bp) was performed in a volume of 5 µl using the 15 oligonucleotide primer pairs in Table S2.

After amplification, unincorporated nucleotides and primers were removed by filtration with Microcon  100 microconcentrators (Amicon Inc., Beverly, MA) and the amplicons sequenced using the Big Dye  Terminator cycle sequencing-ready kit following the manufacturer's instructions.

Determination of phylogenetic lineage

The phylogenetic lineages of our 131 MTBC isolates were determined using the MIRU-VNTR molecular typing method for all isolates. Spoligotyping was performed for some isolates for which the MIRU-VNTR results were ambiguous. Standard 24-locus-based MIRU-VNTR typing was performed as described previously (42) with the MIRU-VNTR Typing Kit ® from GenoScreen. The amplified fragments were analyzed on a 16-capillary Applied Biosystems ® 3130 Genetic Analyzer. To determine the lineages of the isolates, the 24 numerical values generated by MIRU-VNTR were compared to those in the MIRU-VNTRplus database (http://www.miru-vntrplus.org). Spoligotyping was performed as described by Abadia et al. using the Luminex microbead-based approach (43). Spoligotypes in binary format were converted to an octal code based on the signatures given by the 43 spacer-spoligotyping patterns for comparisons with the international spoligotype database SITVIT/SpolDB4 (http://www.pasteur-guadeloupe.fr:8081/SITVITDemo), which contains all of the spoligotype international types (SIT) previously described in MTBC. The different phylogenetic lineages were described by Gagneux et al. (44).

PolyTB web-based tool

PolyTB is a web-based resource designed to explore MTBC genomic variation on a global scale [START_REF] Coll | PolyTB: a genomic variation map for Mycobacterium tuberculosis[END_REF][START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. Genomic polymorphisms and important meta-data, such as in silico inferred strain types and locations, are presented in a browser, geographic map, and phylogenetic views.

Statistical analysis

EmbCAB mutations and ETB resistance on one hand, and embB mutations and the number of first-line drugs resistance on the other hand, were compared using the chi-square test. Pvalues were two-tailed, and P≤0.05 was considered significant. For statistical analysis, 67/71 ETB-R and 60/60 ETB-S isolates were compared (4 related ETB-R isolates were excluded).

Nucleotide sequence accession numbers

The nucleotide sequences determined for the mutant genes included in the present report were deposited into the GenBank database under the following accession numbers: GU323395 to GU323398, KJ571490 to KJ571499, KJ571510 to KJ571513, KJ571515 to KJ571520, KM189805 and KR092803 for the EmbB nonsynonymous mutants M306V, M306I (atc/ata/att), G406C, G406S, G406A, G406D, Q497R, L402V, F330I, E378A, N296H, N399I, Y334H, D354A, M423T, A19D+N296H, S426N, and N13S and the EmbB synonymous mutants D534D (c1602t), T1027T (g3081a), L986L (c2956t), P965P (g2895a), T44T (g132t), V117V (c351t), and N760N (t2280c), respectively; KJ571500, KJ571509, KR092801, KJ571523 to KJ571525, and KR092802 for the EmbC nonsynonymous mutants A426T, V981L, and V987A and the EmbC synonymous mutants G559G (t1677c), L121L (g363t), R345R (c1035g), and T1036T (c3108t), respectively; KJ571501 to KJ571506 for the embC-embA intergenic region nucleotides -c8a, -c8t, -c11a (+EmbC V981L), -c12t, -c16t, and cg deleted at -21 and -20, respectively; and KJ571507, KJ571508, KJ571514, and KJ571521 for the EmbA nonsynonymous mutant G5V and the EmbA synonymous mutants C76C (c228t), Q38Q (a114g), and H764H (c1995t), respectively.

RESULTS

Ethambutol-susceptible isolates

The 60 ETB-S strains had different spoligotypes and MIRU-VNTR codes, representing 60 unrelated clinical isolates. Twenty isolates had no mutation in the embCAB locus (Table 1). Among the other 40 isolates, one of the two type S isolates and one of the eight LAM isolates had undescribed synonymous mutations: embC g363t (L121L) and embB t2280c (N760N), respectively (Table 1). Eleven Beijing isolates harbored the synonymous mutation embA c228t (C76C), which is known to be associated with the Beijing lineage (20, [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF], and one of the Beijing isolates carried an undescribed mutation, EmbC V987A (Table 1).

Twenty-two ETB-S isolates with the nonsynonymous mutation V981L in EmbC belonged to the Haarlem (n=15), Ghana (n=2), T3 variant (n=3), T2 (n=1), or X (n=1) phylogenetic lineages (Table 1). This mutation was previously reported in the Haarlem lineage [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20) in all H1, X, and ambiguous T2T3 and T2X1 isolates, as well as one Manu2, one T5, some T1, and some T2 isolates [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. Among the ETB-S isolates with V981L, one T3 variant isolate carried a not yet described synonymous mutation, embB g132t (T44T), whereas an X isolate carried two mutations: g2895a (P965P) in embB and c1035g (R345R) in embC, which were previously reported in the X and X2 lineages, respectively [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. The last isolate carrying the EmbC V981L polymorphism belonged to the Haarlem family and harbored an uncharacterized mutation, EmbC A426T (Table 1). Among the five remaining ETB-S isolates, two Delhi/CAS isolates carried the mutation EmbC R738Q reported in the CAS lineage (20, 46); a Cameroon strain had a mutation in EmbB associated with ETB resistance, M306I; and a West africanum 2 strain and a M. bovis strain had both EmbB E378A and EmbC T270I, which were previously reported as phylogenetic markers of ancestral MTBC (in M. bovis and lineages 1, 5, and 6 of MTBC) [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF][START_REF] Köser | Thr270Ile in embC (Rv3793) is not a marker for ethambutol resistance in the Mycobacterium tuberculosis complex[END_REF][START_REF] Köser | Comment on: Characterization of the embB gene in Mycobacterium tuberculosis isolates from Barcelona and rapid detection of main mutations related to ethambutol resistance using a low-density DNA array[END_REF]. The M. bovis strain also carried mutations EmbB N13S and embC c3108t (T1036T) previously reported in M. bovis [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF], and embB c351t (V117V) previously reported in M. bovis and AFRI-1 strains [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. Finally, no embR mutation was detected in ETB-S isolates.

Ethambutol-resistant isolates

Based on spoligotyping and MIRU-VTNR typing, the 71 ETB-R isolates represented 67 unrelated strains; in the Beijing family, two distinct subsets each included three isolates sharing the same MIRU code or spoligotype, respectively.

Regarding the mutations corresponding to phylogenetic markers, the 41 ETB-R isolates carrying synonymous mutation embA c228t (C76C) belonged to the Beijing phylogenetic lineage as expected. Among these isolates, three also harbored the embA a114g (Q38Q) mutation reported as a phylogenetic marker of a subgroup of Beijing strains (20, 46).

The ETB-R isolates with the EmbC V981L mutation previously reported in several phylogenetic lineages [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20,[START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF] belonged to the Haarlem (n=5), T1 (n=1), Ghana (n=1), or X (n=1) strain families, with the latter also harboring the phylogenetic marker embB g2895a (P965P) that is specifically present in the X lineage [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. Furthermore, one LAM ETB-R isolate obtained from a patient born in Portugal had the EmbB M423T mutation commonly reported in LAM strains from Portugal [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF], and one Delhi/CAS ETB-R isolate carried the EmbC R738Q mutation reported in the CAS lineage (20, 46) (Table 1).

Regarding mutations potentially associated with ETB resistance, 40/71 (56%) ETB-R isolates had mutations in codon M306 of EmbB, including 24 with M306V (34%) and 16 with M306I (22%). For the remaining ETB-R isolates, 30 (42%) had mutations in the embCAB locus outside EmbB 306 and one belonging to the NEW1 lineage had no mutation (Table 1).

Among the 40 isolates with a mutation in codon M306 of EmbB, five had additional mutations in the embC-embA intergenic region (-c12t, n=3; -c16t, n=1; and -c11a, n=1), whereas two had undescribed synonymous mutations in embB: c1602t (D534D) and g3081a (T1027T) (Table 1).

Among the 30 isolates with mutations in the embCAB locus outside codon 306 of embB, 18 harbored nonsynonymous mutations in EmbB exclusively, two of which were the undescribed synonymous mutations c2956t (L986L) in embB and c1995t (H764H) in embA (Table 1). The repartition of EmbB mutations into 18 isolates was as follows: 8 G406A/C/D/S, 2 Q497R, 1 F330I, 1 D354A, 1 N399I, 1 L402V, 1 S426N, and 3 N296H. For the remaining 12 isolates with mutations in the embCAB locus, seven had mutations in the embC-embA intergenic region exclusively (-c8a, n=1; -c8t, n=1; -c12t, n=2; -c16t, n=2; and deletion cg -21/-20, n=1), two of them with the X and CAS phylogenetic markers embB g2895a (P965P) and EmbC R738Q, respectively. Four isolates had a -c12t mutation in the embC-embA intergenic region in association with nonsynonymous mutations in EmbB outside codon 306: Y334H (n=3) and D354A (n=1), with the latter also carrying the undescribed silent mutation t1677c (G559G) in embC. Finally, one Uganda isolate harbored three nonsynonymous mutations in EmbA (G5V) and EmbB (A19D and N296H) (Table 1). No embR mutation was detected in the 71 ETB-R isolates.

Notably, the synonymous mutation embC c2781t (R927R) was present in all ETB-S and ETB-R isolates included in the present study. This polymorphism has also been reported in all isolates in the PolyTB web-based tool [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. This single nucleotide polymorphism (SNP) could be the result of a sequencing discrepancy in the M. tuberculosis reference strain H37Rv deposited in GenBank (accession number AL123456.3).

DISCUSSION

Few publications have reported sequencing the entire embCAB locus [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20,[START_REF] Zhao | Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China[END_REF], most previous studies investigated only part of the embCAB locus [START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Guerrero | Association between embB mutations and ethambutol resistance in Mycobacterium tuberculosis isolates from Cuba and the Dominican Republic: reproducible patterns and problems[END_REF], part of embC/embB and embR almost entirely [START_REF] Srivastava | emb nucleotide polymorphisms and the role of embB306 mutations in Mycobacterium tuberculosis resistance to ethambutol[END_REF], or the embC-embA intergenic region entirely and part of embB [START_REF] Jaber | Minor contribution of mutations at iniA codon 501 and embC-embA intergenic region in ethambutol-resistant clinical Mycobacterium tuberculosis isolates in Kuwait[END_REF]. In these studies, the percentage of ETB-R isolates with mutations in the embCAB locus ranged from 65% (16) to 96% [START_REF] Zhao | Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China[END_REF]. In our study, 70/71 (98%) of ETB-R isolates harbored mutations in the embCAB locus that likely contribute to ETB resistance.

Regarding mutations that do not generate ETB resistance, we identified 12 mutations that are associated with phylogenetic lineages of MTBC (Table 2), four of which are well described in the literature: EmbC T270I and EmbB E378A in ancestral MTBC lineages 1, 5, and 6 and M. bovis [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF][START_REF] Köser | Thr270Ile in embC (Rv3793) is not a marker for ethambutol resistance in the Mycobacterium tuberculosis complex[END_REF][START_REF] Köser | Comment on: Characterization of the embB gene in Mycobacterium tuberculosis isolates from Barcelona and rapid detection of main mutations related to ethambutol resistance using a low-density DNA array[END_REF]; embA c228t (C76C) in the Beijing lineage (20, 46); and EmbC V981L in the Haarlem lineage [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20). We found EmbC V981L not only in Haarlem strains, but also type X, T1, T3 variant, and Ghana isolates. In the PolyTB database, this mutation has been reported in all H1, all X, all ambiguous T2T3 and T2X1, in some T1, T2, and T5, and in one Manu2 (but not Manu1) strain. Taken together, these data suggest that EmbC V981L is not a phylogenetic marker restricted to Haarlem strains, but it could correspond to a SNP that occurred earlier in the evolutionary pathway of MTBC lineages at the level of a branch leading to a group encompassing the Haarlem, X, Ghana, and some T strains. Five other mutations found in our study are also associated with phylogenetic lineages of MTBC according to the PolyTB database. Several of the mutations were found in ETB-S strains, including embC c1035g (R345R) and embB g2895a (P965P) in the X lineage [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF], embB c351t (V117V) in all AFRI-1 isolates and M. bovis, and embC c3108t (T1036T) and EmbB N13S in M. bovis [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF], whereas others were detected in ETB-R isolates: embA a114g (Q38Q), specific to a subgroup of Beijing lineage and associated with EmbB N296H in three Beijing isolates in our study (20,[START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]; the CAS-specific EmbC R738Q polymorphism associated with embCA -c16t (20, 46); and the EmbB M423T polymorphism, which characterizes the LAM4 lineage from Portugal and was associated with EmbB M306V and embCA -c16t in our study (Table 2). Finally, eight SNPs that are probably not involved in ETB resistance were also identified from our strains, but they are unlikely to represent phylogenetic markers because they correspond mostly to synonymous SNPs found in single isolates within a given phylogenetic group. Four of the SNPs were found in ETB-S isolates: embB g132t (T44T) and t2280c (N760N), embC g363t (L121L), and the nonsynonymous mutation EmbC A426T (Table 1). Five sporadic synonymous mutations, embB c1602t (D534D), g3081a (T1027T) and c2956t (L986L), embC t1677c (G559G), and embA c1995t (H764H), were also detected in ETB-R isolates in association with mutations known to confer ETB resistance (Table 1). These synonymous mutations are unlikely to participate in drug resistance.

In the literature, between 38 and 94% of ETB-R isolates are reported to have mutations in EmbB (5, 13, 15-17, 20, 23, 26, 28, 29, 31, 32, 34, 36, 49). In our study, 90% of ETB-R isolates had mutations in 18 EmbB codons, corresponding to 22 mutations, including the aforementioned phylogenetic markers (n=3) and synonymous mutations (n=4). Among the 15 remaining nonsynonymous EmbB mutations likely involved in ETB resistance, 7 (M306V/I, G406D/A/S/C, and Q497R) have been unequivocally proven by site-directed mutagenesis or allelic exchange to increase minimum inhibitory concentrations (MICs) and can be considered canonical EmbB mutations in ETB resistance [START_REF] Starks | Mutations at embB codon 306 are an important molecular indicator of ethambutol resistance in Mycobacterium tuberculosis[END_REF][START_REF] Nebenzahl-Guimaraes | Systematic review of allelic exchange experiments aimed at identifying mutations that confer drug resistance in Mycobacterium tuberculosis[END_REF][START_REF] Plinke | Mycobacterium tuberculosis embB codon 306 mutations confer moderately increased resistance to ethambutol in vitro and in vivo[END_REF][START_REF] Safi | Allelic exchange and mutant selection demonstrate that common clinical embCAB gene mutations only modestly increase resistance to ethambutol in Mycobacterium tuberculosis[END_REF][START_REF] Safi | Transfer of embB codon 306 mutations into clinical Mycobacterium tuberculosis strains alters susceptibility to ethambutol, isoniazid, and rifampin[END_REF]. Accordingly, in our study, only one isolate phenotypically susceptible to ETB harbored the M306I mutation, and a strong statistical association was found between ETB resistance and mutation 306 alone (P<0.001) or EmbB mutations 306-406-497 (P<0.001). Some authors [START_REF] Johnson | Ethambutol resistance testing by mutation detection[END_REF][START_REF] Safi | Transfer of embB codon 306 mutations into clinical Mycobacterium tuberculosis strains alters susceptibility to ethambutol, isoniazid, and rifampin[END_REF][START_REF] Hazbón | Role of embB codon 306 mutations in Mycobacterium tuberculosis revisited: a novel association with broad drug resistance and IS6110 clustering rather than ethambutol resistance[END_REF]57) have argued that mutations at 306 in EmbB do not cause ETB resistance, but predispose M. tuberculosis isolates to the development of drug resistance and increase the capacity of resistant isolates to be transmitted [START_REF] Johnson | Ethambutol resistance testing by mutation detection[END_REF][START_REF] Safi | Transfer of embB codon 306 mutations into clinical Mycobacterium tuberculosis strains alters susceptibility to ethambutol, isoniazid, and rifampin[END_REF][START_REF] Hazbón | Role of embB codon 306 mutations in Mycobacterium tuberculosis revisited: a novel association with broad drug resistance and IS6110 clustering rather than ethambutol resistance[END_REF]57). Accordingly, several reports have described isolates with mutations at codon 306 of EmbB that remain susceptible to ETB [START_REF] Brossier | Detection by GenoType MTBDRsl test of complex mechanisms of resistance to second-line drugs and ethambutol in multidrug-resistant Mycobacterium tuberculosis complex isolates[END_REF][START_REF] Hillemann | Feasibility of the GenoType MTBDRsl assay for fluoroquinolone, amikacin-capreomycin, and ethambutol resistance testing of Mycobacterium tuberculosis strains and clinical specimens[END_REF][START_REF] Alcaide | Role of embB in natural and acquired resistance to ethambutol in mycobacteria[END_REF][START_REF] Zhang | Ethambutol resistance as determined by broth dilution method correlates better than sequencing results with embB mutations in multidrug-resistant Mycobacterium tuberculosis isolates[END_REF][START_REF] Sreevatsan | Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations[END_REF][START_REF] Ahmas | Frequency of embB codon 306 mutations in ethambutol-susceptible and -resistant clinical Mycobacterium tuberculosis isolates in Kuwait[END_REF][START_REF] Lee | Novel mutations within the embB gene in ethambutol-susceptible clinical isolates of Mycobacterium tuberculosis[END_REF][START_REF] Mokrousov | Detection of embB306 mutations in ethambutol-susceptible clinical isolates of Mycobacterium tuberculosis from Northwestern Russia: implications for genotypic resistance testing[END_REF][START_REF] Perdigão | Genetic characterisation of the ethambutol resistance-determining region in Mycobacterium tuberculosis: prevalence and significance of embB306 mutations[END_REF][START_REF] Shi | Characteristics of embB mutations in multidrug-resistant Mycobacterium tuberculosis isolates in Henan, China[END_REF][START_REF] Zhao | Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China[END_REF][START_REF] Hazbón | Role of embB codon 306 mutations in Mycobacterium tuberculosis revisited: a novel association with broad drug resistance and IS6110 clustering rather than ethambutol resistance[END_REF]57). There are two explanations for the presence of a mutation at codon 306 in EmbB in an ETB-S isolate: the ETB concentration recommended for drug susceptibility testing varies from 2 to 7.5 mg/l depending on the phenotypic method used for susceptibility testing (41) and the ETB MICs induced by mutations in codon 306 could be close to the breakpoint defining ETB resistance [START_REF] Starks | Mutations at embB codon 306 are an important molecular indicator of ethambutol resistance in Mycobacterium tuberculosis[END_REF][START_REF] Safi | Transfer of embB codon 306 mutations into clinical Mycobacterium tuberculosis strains alters susceptibility to ethambutol, isoniazid, and rifampin[END_REF].

Other mutations in EmbB that are likely responsible for ETB resistance were identified in our study: D354A was previously reported in ETB-R strains selected in vitro [START_REF] Starks | Mutations at embB codon 306 are an important molecular indicator of ethambutol resistance in Mycobacterium tuberculosis[END_REF]. For F330I and N399I, other mutations (F330V/S/L and N399T/H/D) were reported in the same codons as ETB-R strains (n=9) in five studies [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Sreevatsan | Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations[END_REF][START_REF] Srivastava | emb nucleotide polymorphisms and the role of embB306 mutations in Mycobacterium tuberculosis resistance to ethambutol[END_REF][START_REF] Shi | Characteristics of embB mutations in multidrug-resistant Mycobacterium tuberculosis isolates in Henan, China[END_REF][START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF]) and a single ETB-S strain in one study [START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF]. Similarly, mutations L402V and Y334H were previously described in respectively ETB-R (n=3) strains in three studies [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20,[START_REF] Plinke | Significance of mutations in embB codon 306 for prediction of ethambutol resistance in clinical Mycobacterium tuberculosis isolates[END_REF] and ETB-S (n=1) strain in one study [START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF]. A total of three new EmbB mutations were identified in our study: S426N in an Ural isolate harboring no other mutation in embCAB, N296H in a Beijing isolate with three additional synonymous mutations in EmbA (C76C, Q38Q, and H764H), and A19D in an Uganda strain harboring N296H in EmbB and G5V in EmbA. Causal relationships between these three mutations and ETB resistance are yet to be experimentally verified. Overall, a strong statistical association was found between ETB resistance and all the embB mutations reported in our study (P<0.001).

It is worth to highlight here that ETB-R conferring mutations in embB could be regarded as sensitive markers for the prediction of MDR-TB. By performing trend analysis correlating any ETB-R conferring mutations in embB to the number of first-line drugs resistance, we found a statistically significant association between these mutations and resistance to INH plus RIF (P<0.001). However, among the 23 strains showing resistance to 2 to 3 first-line anti-TB drugs excluding ETB, of which 16 were MDR, only 1 was found to have an embB mutation, supporting the idea that resistance to ETB is not a prerequisite for the development of multidrug resistance.

According to the literature, the proportion of ETB-R isolates carrying a mutation in the embC-embA intergenic region varies from 0 to 27% [START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Jaber | Minor contribution of mutations at iniA codon 501 and embC-embA intergenic region in ethambutol-resistant clinical Mycobacterium tuberculosis isolates in Kuwait[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20,[START_REF] Zhao | Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China[END_REF][START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF]. In our study, 23% of ETB-R isolates had mutations at five positions in the embC-embA intergenic region, with a total of six distinct mutations: -c8a/t, -c11a, -c12t, -c16t, and a deletion of two nucleotides at positions -21 and -20. None of these mutations was observed in ETB-S isolates and we found a strong statistical association between ETB resistance and mutations in the embC-embA intergenic region (P<0.001). Excluding -c11a, the mutations at positions -8, -12, and -16 and the deletion of cg in positions -21 and -20, which are located within/adjacent to a predicted TATA box [START_REF] Jaber | Minor contribution of mutations at iniA codon 501 and embC-embA intergenic region in ethambutol-resistant clinical Mycobacterium tuberculosis isolates in Kuwait[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF], were found in ETB-R strains carrying no other mutations involved in ETB resistance, confirming their involvement in ETB resistance. The role of -c11a is less clear because this mutation was identified in a strain that also has the EmbB M306I mutation, but Cui et al demonstrated that mutations in the embC-embA intergenic region (including -c11a) increase ETB resistance by enhancing the transcription of embA and embB, the MICs of ETB for strains with both embC-embA intergenic region mutations and embB mutations being much higher than strains with only an embB mutation [START_REF] Cui | Mutations in the embC-embA intergenic region contribute to Mycobacterium tuberculosis resistance to ethambutol[END_REF].

Notably, we detected no mutation that is clearly associated with ETB resistance in embC and embA, despite the percentage of ETB-R isolates harboring a mutation in these two genes varying in the literature from 0.6 to 77% and 0 to 12%, respectively [START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF]20,[START_REF] Srivastava | emb nucleotide polymorphisms and the role of embB306 mutations in Mycobacterium tuberculosis resistance to ethambutol[END_REF][START_REF] Zhao | Analysis of embCAB mutations associated with ethambutol resistance in multidrug-resistant Mycobacterium tuberculosis isolates from China[END_REF].

In our study, 13% (9/71) of ETB-R isolates and 47% (28/60) of ETB-S isolates had mutations in embC, affecting eight codons, but six of them corresponded to synonymous mutations (n=3) or markers of phylogenetic lineages (V981L, T270I, R738Q). Among these mutations, the absence of a role of EmbC T270I in ETB resistance was experimentally confirmed by sitedirected mutagenesis [START_REF] Goude | The arabinosyltransferase EmbC is inhibited by ethambutol in Mycobacterium tuberculosis[END_REF]. Regarding the last two mutations EmbC (A426T and V987A), a previous publication described a similar mutation in codon 426 but not with the same substitution (A426P) [START_REF] Polytb | a web-based tool to display Mycobacterium tuberculosis genetic polymorphisms derived from publicly available WGS datasets[END_REF]. In our study, A426T and V987A were found in clinical isolates susceptible to ETB, suggesting that these mutations play no significant role in ETB resistance.

With respect to EmbA, 58% of ETB-R and 18% of ETB-S isolates included in our study had mutations in embA in four codons, but all were synonymous mutations, except G5V. Codon 5 in embA was previously reported by two different groups to have a G5S mutation in ETB-R isolates [START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Sreevatsan | Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations[END_REF]. In our study, this mutation was detected in association with two EmbB mutations, A19D and N296H, the latter being detected alone in another of our ETB-R isolates. Therefore, we cannot definitively state that ETB resistance is associated with the occurrence of G5V in EmbA.

Finally, we found no isolate with a mutation in embR, despite their presence in 1% to 36% of ETB-R isolates in the literature [START_REF] Huang | Performance assessment of the GenoType MTBDRsl test and DNA sequencing for detection of second-line and ethambutol drug resistance among patients infected with multidrug-resistant Mycobacterium tuberculosis[END_REF][START_REF] Ramaswamy | Molecular genetic analysis of nucleotide polymorphisms associated with ethambutol resistance in human isolates of Mycobacterium tuberculosis[END_REF][START_REF] Srivastava | emb nucleotide polymorphisms and the role of embB306 mutations in Mycobacterium tuberculosis resistance to ethambutol[END_REF]. Based on the data generated from the panel of clinical isolates included in the present study, embR plays no significant role in the development of ETB resistance in M. tuberculosis.

In conclusion, our results strongly suggest that molecular analysis of the embCAB locus should prioritize the search for mutations at the level of two "hot spots" for mutations in order to efficiently detect ETB resistance in clinical isolates of M. tuberculosis. These two regions correspond to (i) the ERDR in EmbB that could be tentatively extended to a 606 bp region spanning residues 296 to 497 and encompassing the canonical mutations at the level of M306, G406, and Q497, and (ii) the DNA segment extending from position -8 to -21 in the embC-embA intergenic region. According to our data and excluding synonymous mutations and those corresponding to phylogenetic markers, 70% of ETB-R isolates had amino acid substitutions affecting codons M306 (56%), G406 (11%), or Q497 (3%) of EmbB, and 13% EmbB mutations outside positions 306, 406, and 497 that are very likely involved in ETB resistance (i.e., N296H, F330I, D354A, N399I, L402V, and S426N), and 15% had nucleotide substitutions in the embC-embA intergenic region between positions -8 and -21 (without taking into account the isolates harboring a mutation in codons 306, 406, or 497). Importantly, the commercial DNA strip assay Genotype® MTBDRsl, which is widely used in routine laboratories to detect ETB resistance in clinical M. tuberculosis isolates, evaluates only the EmbB M306 codon, resulting in its limited sensitivity (68%) in the detection of ETB resistance [START_REF] Feng | Rapid diagnosis of drug resistance to fluoroquinolones, amikacin, capreomycin, kanamycin and ethambutol using genotype MTBDRsl assay: a meta-analysis[END_REF]. Taken together the molecular results from testing the embB ERDR region and

  embC-embA intergenic region and the results obtained from the MTBDRsl line probe assay should markedly increase the percentage of ETB-R clinical isolates of M. tuberculosis detected by molecular testing: by 85% when testing codons 306, 406, and 497 and nucleotide positions -8, -12, and -16, and up to 98% by testing the entire embB gene and the embC-embA intergenic region from -8 to -21. Safi H, Lingaraju S, Amin A, Kim S, Jones M, Holmes M, McNeil M, Peterson SN, Chatterjee D, Fleischmann R, Alland D. 2013. Evolution of high-level ethambutol-resistant tuberculosis through interacting mutations in Park YK, Ryoo SW, Lee SH, Jnawali HN, Kim CK, Kim HJ, Kim SJ. 2012. Correlation of the phenotypic ethambutol susceptibility of Mycobacterium tuberculosis with embB gene mutations in Korea. J. Med. Microbiol. 61:529-534. Abadia E, Zhang J, Ritacco V, Kremer K, Ruimy R, Rigouts L, Gomes HM, Elias AR, Fauville-Dufaux M, Stoffels K, Rasolofo-Razanamparany V, Garcia de Viedma D, Herranz M, Al-Hajoj S, Rastogi N, Garzelli C, Tortoli E, Suffys PN, van Soolingen D, Refrégier G, Sola C. 2011. The use of microbead-based spoligotyping for Mycobacterium tuberculosis complex to evaluate the quality of the conventional method: providing guidelines for Quality Assurance when working on membranes. BMC Infect. Dis. 11:110.

ACKNOWLEDGMENTS

This work was supported by grants from the Ministère de la Recherche (grant UPRES EA 1541) and by an annual grant from the Institute National de la Santé et de la Recherche Médicale (INSERM) and the Université Pierre et Marie Curie (UPMC).

We gratefully acknowledge Gérald Millot for his technical assistance.

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Shen X, Shen GM, Wu J, Gui XH, Li X, Mei J, DeRiemer K, Gao Q. 2007.

Association between embB codon 306 mutations and drug resistance in

Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 51:2618-2620.

Oliver K, Osborne J, Quail MA, Rajandream MA, Rogers J, Rutter S, Seeger K, Skelton J, Squares R, Squares S, Sulston JE, Taylor K, Whitehead S, Barrell BG.

Deciphering the biology of