Estelle Martineau

Jean-Nicolas Dumez

Patrick Giraudeau
email: patrick.giraudeau@univ-nantes.fr

Fast quantitative 2D NMR for metabolomics and lipidomics: a tutorial

Keywords: 2D NMR spectroscopy, quantitative analysis, fast methods, metabolomics, lipidomics, targeted, untargeted Fast quantitative 2D NMR for metabolomics

Nuclear Magnetic Resonance (NMR) has a great potential for the quantitative analysis of complex biological samples. The acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. The experiment duration can be made compatible with high-throughput studies by relying on fast acquisition methods. This tutorial describes the general workflow to acquire fast quantitative 2D NMR data for metabolomics and lipidomics applications.

Introduction

Nuclear Magnetic Resonance (NMR) spectroscopy is a well-known and powerful analytical technique, which is widely used for structure elucidation. This technique, which is highly repeatable and non-destructive, has also proven its ability to quantify analytes in mixtures, most often with the use of 1D [START_REF] Rouger | Encyclopedia of Spectroscopy and Spectrometry[END_REF] H NMR. Unfortunately, in the case of complex mixtures, 1D [START_REF] Rouger | Encyclopedia of Spectroscopy and Spectrometry[END_REF] H NMR suffers from numerous peak overlaps that limit its usefulness as a quantitative tool in several applications: in fact, the quantification of compounds can be difficult or even impossible when no isolated peak can be found for the analyte of interest. Two-dimensional NMR is one of the solutions to overcome the overlap problem. 2D NMR offers a much better discrimination of resonances as the peaks are spread along an additional dimension. The availability of a broad variety of homonuclear and heteronuclear pulse sequences is a major advantage of 2D NMR experiments, since these have different levels of resolution and sensitivity. However, 2D NMR experiments suffer from two main drawbacks that make their use for quantitative analysis not straightforward. The first one is long experiment durations, which result from the need to repeat numerous 1D sub-experiments in order to obtain 2D spectra with sufficient resolution. Experiment durations can reach several hours per spectrum, which is incompatible with many applications such as metabolomics, which requires high-throughput methods to analyze large sample collections. In addition, long experiments are more sensitive to spectrometer instabilities, which affect the precision of quantitative measurements. Fortunately, numerous approaches have been suggested to reduce experiment durations [START_REF] Rouger | Encyclopedia of Spectroscopy and Spectrometry[END_REF] . Although these methods are associated with sensitivity losses and/or resolution penalties these limitations are well described and understood. Two of these methods (Non-Uniform Sampling or NUS and UltraFast (UF) NMR) have successfully been used in targeted and untargeted metabolomics and lipidomics workflows [START_REF] Dass | [END_REF], [3], [4], [5], [6] .

The second drawback of 2D NMR for quantitative analysis is linked to the multi-pulse nature of 2D NMR experiments, which results in a peak-dependent analytical response of the nuclei [7] . In other words, 2D NMR peak volumes are still proportional to analyte concentrations -as is the case in 1D NMR-but the coefficient of proportionality between the peak volume and the concentration is different for each peak in the spectrum. This coefficient of proportionality depends on peak-specific factors such as homo/hetero-nuclear J-coupling constants and transverse relaxation times. Consequently, several strategies have been developed to yield accurate analyte concentrations from 2D spectra (Figure 1). The development of specific pulse sequences has led to a peak-independent coefficient of proportionality between the 2D NMR signal and the concentration, hence to pulse sequences that are "intrinsically" quantitative such as HSQC0 [8] , Q HSQC [9], [10] or Q QUIPU HSQC [11] . These promising methods are still limited in terms of accuracy, yielding an average trueness of 10% [11] . Theoretical correction for relaxation during 2D pulse sequence has also been introduced by Rai et al. to quantify metabolites in urine samples [12] : this option is mentioned regularly but is not applied in practice in the literature, probably because of the practical impossibility to accurately determine correction factors in complex mixtures. Another strategy consists in calibrating the 2D peak response as a function of concentration for all the targeted analytes, relying on an analytical chemistry approach such as an external calibration [13], [14] or a standard addition procedure [15] . All the targeted analytes can be quantified simultaneously provided that standards of known purity are available -which is often the case for common metabolites detectable by NMR. This strategy yields a very high trueness and precision of a few percent.

Fast quantitative 2D NMR methods are particularly relevant to the fields of metabolomics and lipidomics. NMR spectroscopy has become a technique of choice for metabolomics over the past fifteen years, and it is used both as an untargeted method to record a fingerprint of complex biological samples (biofluids, extracts, food matrices, etc.) and as a targeted method to determine the concentration of multiple analytes in such complex samples. While the most widely used approach is 1D 1 H NMR, its applicability is often limited by the spectral crowding issues described above. That is why 2D NMR methods have been introduced in the field of metabolomics and lipidomics, and more precisely fast 2D NMR [4], [5], [6], [16] : indeed, the reduced experiment duration is compatible with high-throughput studies [19][17] , the experiments are more repeatable if they are shorter and they are compatible with a potential sample evolution over time.

This tutorial describes a general protocol to apply fast quantitative 2D NMR experiments in the "omics" context. The pulse sequences used here are well described in the literature, and we focus on their use to quantify analytes with calibration approaches [18] . We start with the presentation of some important technical aspects, as well as an initial evaluation describing how to decide which pulse sequence to use, including acquisition and processing parameters. Then, we detail how to apply this protocol for a metabolomics or lipidomics study. While the tutorial uses three specific sequences as illustrations, the corresponding protocol can be applied to other 2D NMR sequences (with and without fast acquisition strategies).

Equipment

Although the workflow described in this tutorial is not field-dependent, for applications to biological samples, we recommend the use of a high-field spectrometer, with a probe equipped with a z-axis magnetic-field gradient. For metabolomics applications, NMR spectrometers in the 500-800 MHz range offer a good performance in terms of sensitivity and separation of the signals of interest. Having a cryogenically cooled probe will be a plus if the concentration of samples is quite low (a few dozens µmol.L -1 ), but it can also be detrimental for samples with high salinity. If an automatic sample changer is available on the spectrometer, its use will be compatible with fast quantitative 2D NMR; a refrigerated high capacity one will be a plus to analyze big cohorts of samples and/or samples which could suffer from damages if they are kept for too long at ambient temperature. High quality tubes should be preferred. While samples are generally analyzed with 5 mm NMR tubes, if the quantity of samples is too small, the samples can be analyzed with 3 mm NMR tubes.

Sample preparation for NMR

The collection and the preparation of biological samples for NMR analysis have already been described in the literature in several protocols [19] , depending on the type of sample analyzed (urine, blood, extracts, tissues, etc.). Here, we will highlight some particular points of the sample preparation in the case of metabolomics studies:

• The appropriate extraction protocol should be chosen according to the aim of the study, depending if the entire or a specific fraction of the metabolome (lipids, polar metabolites, etc.) is targeted; • For biological extracts or samples sensitive to pH variation, a buffer can be used in order to avoid a deviation of chemical shifts due to pH variation; • Particular attention should be paid to the tubes used to take blood or serum, as they can contain EDTA, citrate or other added stabilizers which give additional signals on NMR spectra [20] ; • Some Quality Control samples (QCs) should be prepared when the design of the studies allows it: these samples consist of all the collected biological samples pooled in identical quantities mixed together. QCs can be used to regularly check the quality of the analysis; • The samples should be stored between -80°C and -20°C before NMR preparation [21] .

Before starting the NMR preparation, samples are thawed (15-30 minutes), then solvent(s) are added into the vial [19] :

• If the samples are in the solid state, between 600 µL and 700 µL of deuterated solvent (and/or buffer) should be added [5], [6] , or a mix of deuterated and non-deuterated solvents respecting the proportions of 80%/20% or 90%/10%. At least 20 mg of biological sample (wet mass) are necessary to acquire NMR spectra in a reasonable time -a mass of 100 mg of biological is the typical size for this kind of analysis [19] . However, particular attention must be paid to the sample solubility in the chosen solvent (preliminary tests can be necessary); • If the samples are in the liquid state and if possible, a ratio of 80%/20% for sample/deuterated solvent (and/or buffer) for a total volume of 700 µL should be used: in that case, 500 µL of biological sample is the best to acquire spectra in a reasonable time. The analyses are still possible with less sample, but they potentially will last longer; • The addition of a reference is recommended to calibrate the chemical shift scale for all the spectra [17] : this is particularly important for the untargeted approach to ensure that the spectra are stackable for the bucketing part, and this signal can also be used for the normalization part. The chemical shift of the chosen reference must not overlap with a signal of interest: for instance, the signal of TrimethylSilylPropanoic acid (TSP, which is soluble in D2O) or TetraMethylSilane (TMS, which is soluble in organic solvents such as CDCl3 but evaporates above 26°C) has the advantage to be at 0 ppm, where there is no other signal of interest. According to the targeted accuracy, the use of a silicon reference may be avoided because of the silicon satellites. In the case of lipidomics, DiMethyl SulfOne (DMSO2, which is soluble in organic solvents such as CDCl3) can be an appropriate reference as its signal is a singlet at a chemical shift of 3 ppm: prior to addition of DMSO2 inside each sample, it should be verified that there is no signal of interest close to this region. The concentration of the reference compound should be chosen to set its NMR signal intensity to a value smaller than or equal to that of the most intense signal of interest: indeed, as the receiver gain (RG) of the NMR experiments is set according to the most intense signal, if the concentration of the reference is too large, its NMR signal intensity determines the value of the receiver gain and the detection of the signals of interest is not optimal; • In the case of the targeted approach, and after identifying the metabolites of interest, a stock solution containing the targeted metabolites in known concentrations has to be prepared. In the specific case of standard additions, this stock solution should be concentrated in order to add a small volume (≤ 10 µL) to avoid diluting the analyzed samples.

After solvent addition, the samples are vortexed and put inside NMR tubes. The samples should be stored at -20°C before analysis:

• If an automatic sample changer cannot be used, all samples must not be thawed at the same time, but only the analyzed one, usually 15 minutes before vortexing and insertion into the magnet; • If an automatic sample changer can be used, a preliminary evaluation of sample stability is necessary in order to be sure that the sample does not evolve in time at the temperature of the sample changer (refrigerated or not). In order to mimic a potential long stay of a sample prior to the insertion inside the magnet, stability studies at both ambient and refrigerated temperatures should be carried out, recording a 1D 1 H spectrum for each hypothesis tested. The choice to use or not an automatic sample changer will be made according to what can be observed on NMR spectra, and also depending on the number of samples.

NMR experiments

Before analyzing a cohort of samples, an initial evaluation is required to determine which NMR pulse sequence(s) are appropriate to answer the biological question of interest (Figure 2).

In this entire chapter, we assume that 2D spectra can be recorded under partial saturation conditions (i.e., with a recovery delay shorter than 5 times the longest longitudinal relaxation time T1) if the experiment duration is a critical factor. Indeed, unlike 1D NMR, where respecting this condition is critical to ensure that the coefficient of proportionality between peak areas and concentrations is the same for all the peaks in the spectrum, this condition is not required for 2D NMR spectra combined with calibration or standard addition approaches. In 2D NMR, the effect of T1 is one factor among others (T2, coupling constants) affecting the coefficient of proportionality between peak volumes and concentrations. Therefore, partial saturation conditions remain compatible with the approach described in this chapter, at the condition that T1 values do not vary significantly between samples in a study [7] . Note that partial saturation is not compatible with "intrinsically" quantitative 2D methods of the HSQC family, which are, however, out of the scope of this chapter.

Initial evaluation

Settings and acquisition parameters common to all pulse sequences

First, start by the analysis of a representative sample in the cohort (for example a control one or a QC). After thawing (an average 15-30 minutes) and vortexing, the sample can be introduced inside the magnet. The choice of the temperature of analysis must be done according to the nature of the analyzed sample and/or the chosen solvent: for instance, 298 K or 300 K are common temperatures in the literature [18][19] . After waiting typically 10-15 minutes in order to ensure thermal stability of the sample, the adjustment of the field-frequency lock and the magnetic field homogeneity (lock and shim) should be carefully made before NMR acquisition.

A 1D 1 H spectrum is acquired in order to verify the quality of shimming and to determine some important parameters needed before acquiring 2D homonuclear spectra on all the samples of the cohort:

• The spectral width and the frequency offset must be adapted according to the chemical shifts of the biological samples; • The saturation of the solvent signal can be necessary: applying a solvent saturation is generally required if the solvent signal is significantly more intense than the peaks of interest and/or overlaps with targeted peaks [22] . In that case, the parameters of the solvent suppression pulse sequence should be adapted to decrease the solvent signal as much as possible without disturbing the baseline and affecting nearby peaks. Thus, one should try to reduce the solvent signal intensity to a value smaller than the most intense signal of interest in order to set a receiver gain adapted to the sample. Moreover, the frequency offset of saturation must be adjusted to the exact position of the solvent peak: on cryogenically cooled probes, this parameter may require empirical optimization to improve solvent suppression. Note that several solvent suppression techniques are suitable for metabolite samples; an approach to evaluate the efficiency of the different methods for a given sample is described in Ref. [22]. Also note that solvent saturation can be avoided or reduced to a low power if exchangeable protons need to be observed; • A careful calibration of the hard power 90° pulse duration is necessary. Then, this value should be kept constant for all the experiments: in fact, the pulse duration is not expected to vary for a series of biological samples prepared in the same conditions.

Then, a collection of typical 2D spectra can be acquired: this is necessary to choose the most appropriate one for the entire study. As numerous pulse sequences are available, we will recommend to record at least one homonuclear 2D 1 H experiment, with or without solvent saturation, either an ultrafast COSY [23] (≤ 30 minutes -Figure 3 a andd) or a Z Filtered TOCSY (ZF-TOCSY -Figure 3 b and e) with 50% of Non-Uniform Sampling (NUS) [4], [24] , as well as an heteronuclear 1 H- 13 C HSQC experiment with 25% of NUS (Figure 3 c and f) [4] . We detail some specific parameters for each pulse sequence in paragraph 2 and focus here on those which are common to all pulse sequences.

More particularly, it is recommended to pay attention to:

• The receiver gain. It can be automatically calculated as for 1D experiments, on condition that a relaxation delay of at least 5 s is chosen for UF COSY to limit the demand on the gradient coil and amplifier. For NUS experiments, a 1D version of the 2D experiment should be used to set the receiver gain;

• The number of scans. Its value has to be a multiple of 2 for all the pulse sequences mentioned above and should be optimized in order to reach a signal-to-noise ratio (SNR) greater than or equal to 10 for the signals of interest (Limit Of Quantification or LOQ).

For UF COSY, this value must take into account a 30-minute maximum value for the experiment duration, in order to avoid damage of the gradient coil; • The recovery delay. Its value should be optimized according to the dedicated time allotted to the analysis of the whole cohort, remembering that partial saturation is in principle not an issue; • The acquisition time. Its value is calculated automatically for UF COSY experiments (see the online protocol for the implementation of ultrafast 2D NMR experiments [START_REF] Rouger | Implementing ultrafast 2D NMR experiments on a Bruker Avance Spectrometer[END_REF] ).

For ZF-TOCSY experiments, Thrippleton et al. recommend a value of 1 s [24] . For HSQC experiments, the acquisition time should be between 0.2 s and 0.4 s; • The use of dummy scans is recommended: the values suggested by Topspin can be used for HSQC and ZF-TOCSY experiments (DS = 16). For UF COSY experiments, the number of dummy scans is set at 2.

Thanks to this initial evaluation, the values of the receiver gain and the number of scans are now optimized for the untargeted approach. These values must be adjusted on the most concentrated sample (for the receiver gain) and the less concentrated one (for the number of scans) for the targeted approach (see below in the application part). Major acquisition parameters are described in Table 1.

Acquisition parameters specific to each pulse sequence 2.1. UF COSY

The acquisition and processing parameters to use for the UF COSY pulse sequence are described in a protocol available online [START_REF] Rouger | Implementing ultrafast 2D NMR experiments on a Bruker Avance Spectrometer[END_REF] . This protocol explains the different steps to follow in order to implement correctly ultrafast 2D NMR experiments; in particular, for UF COSY experiments, the main parameters which have to be optimized and/or used to acquire spectra are listed. It also underlines some traps to avoid.

Some particular attention should be paid to the dispersion induced by the gradients and the calibration of the encoding pulses. These preliminary calibrations consist in calibrating the gradients and the chirp pulse and are necessary to characterize hardware performances before acquiring UF COSY spectra.

ZF-TOCSY

The acquisition and processing parameters to use for the ZF-TOCSY with 50% of NUS are described in recent articles [4], [24] . The classical TOCSY pulse sequence is improved by the addition of a Z-Filter in order to dephase zero quantum coherence, and applying the spin-lock while the magnetization is along the z-axis ensures a better sensitivity. The corresponding pulse sequence is provided as supplementary information. Before acquiring ZF-TOCSY spectra, the parameters of this Z-filter, and more precisely the amplitude of the gradients, must be optimized with the protocol of Thrippleton and Keeler [24] . The power level to be used during the spin lock of the ZF-TOCSY pulse sequence should be calibrated to correspond to a 90° pulse duration of 30 µs. If possible (depending on time constraints), the number of increments t1 should be adjusted to obtain a resolution between 4 Hz and 8 Hz. The use of 50% of NUS allows faster acquisitions of NMR spectra without losing too much sensitivity (NB: NUS can also be used to record very high resolution spectra without compromising on the experiment time [START_REF] Guennec | [END_REF] ).

1 H-13 C HSQC

Before obtaining 2D HSQC spectra on all the samples of the cohort, 1D 13 C spectra (with 1 H decoupling) should be acquired in order to determine some important parameters needed:

• The spectral width and the frequency offset must be adapted according to the chemical shifts of the biological samples; • The duration of the hard power 90° pulse must be calibrated. Then, this value should be kept constant for all the experiments: no significant variation is expected for a series of biological samples prepared in the same conditions. Quaternary carbons can be ignored in this calibration procedure since they will not be observed in the HSQC spectrum; • The calibration of the power level corresponding to the duration of a 13 C decoupling pulse of 70 µs must be carried out. This duration will be used in a program adapted to the decoupling of 13 C nuclei (such as GARP or GARP4).

The approach is compatible with any of your favorite routine HSQC pulse sequence. The acquisition and processing parameters to use for the HSQC 1 H - 13 C pulse sequence with 25% of NUS are described in recent publications [4] . If the spectral width is too large, spectral aliasing can be applied in the indirect dimension ( 13 C chemical shift) [27], [28] . In this case, particular attention should be paid to the value of the corresponding frequency offset: a frequency change during the pulse sequence must be operated so that the frequency offset is in the middle of the aliased range during the evolution period (when t1 is incremented) and so that the frequency offset is in the middle of the entire 13 C range (without aliasing) during the rest of the experiment. If possible (depending on time constraints), the number of increments t1 must be adjusted to obtain a FID resolution between 20 Hz and 40 Hz. The delays of the INEPT blocks depend on the average constant value 1 JC-H: if this value cannot be determined a priori, we suggest using a typical value of 145 Hz. The classical HSQC pulse sequence is improved by the use of 25% of NUS (ie. only 25% of the points are actually recorded), which allows faster acquisitions of NMR spectra without losing too much sensitivity. Although much stronger NUS conditions have been suggested in the literature, the TD1 and AQ values suggested here do not allow setting the percentage of NUS below 25%.

Processing parameters

After acquisition of the different 2D spectra, the processing parameters must be adjusted to the analyzed samples. The time-domain data can be multiplied by the appropriate apodization functions before applying the Fourier transform operation. Several such functions exist (exponential, gaussian, sinusoidal, etc.), and the choice of the appropriate apodization functions depends on the pulse sequence, and should be made to obtain the best compromise between sensitivity and separation of the signals of interest. If needed, phase-correction parameters in each dimension can be adjusted manually. For UF COSY, a dedicated automation program is available to process the data with appropriate processing parameters (including apodization in the spatially-encoded dimension) and display the resulting spectra. For experiments using NUS, the use of the Iterative Reweighted Least-Square (IRLS) algorithm is recommended [29] (here, the experiments were processed with 20 iterations (mdd_csniter = 20). Moreover, a Hilbert transform must be applied in the direct dimension to be able to phase the indirect dimension.

Then, a baseline correction can be applied in both dimensions: the degree of the polynomial baseline correction should be optimized depending on the baseline distortions observed on the row and column projections. A default value of 2 is recommended in both dimensions. Major processing parameters are described in Table 2.

Then, the spectra are calibrated on the chemical shift of the internal reference: this step is important in order to use the same bucketing or integration file for each spectrum (see below). Note that the calibration of UF COSY spectra requires two distinct peaks; typically the peak of the reference and another peak whose chemical shift is not varying throughout the samples. Ideally, all the spectra should be stackable after calibration of the chemical shift axis. In practice, when the samples are not buffered (e.g. urine or plasma) or do not have exactly the same pH, the calibration cannot lead to a perfect superposition of all the spectra. In this case, the position of the buckets or integration regions must be automatically or manually adjustedwithout changing their size.

After processing all the spectra, the bucketing or the integration can be applied to all the signals of interest with SNR ≥ 10, including, for homonuclear experiments, diagonal signals that are well separated from the other signals. Typically, integration boxes are drawn -for instance rectangles with AMIX (Aurelia/Bruker) or Topspin (Bruker), or ellipses with MestReNova (Mestrelab Research)-around the signals of interest. Particular attention must be paid to the size of the rectangles: they have to be large enough to include most of the signal but not too large, so as not to include another nearby signal or, in the case of magnitude spectra, additional noise. We recommend modifying the contour plot level to be sure that the integration box matches the signal of interest without integrating a nearby peak. If some doubt exists, it is better to draw a smaller box than a too large one. With the AMIX software, the boxes can be defined on all spectra at the same time whereas with Topspin or MestReNova software, they must be defined on one of the spectra, then applied to the others. In this case, the corresponding file must be named and saved, then recalled on each spectrum to pick up the values of integration areas. Once all the boxes are drawn, a spreadsheet file is created to collect all the "raw" integration values associated to each bucket or integration region, i.e., without applying any normalization at this stage.

Sometimes, the signals of some metabolites can be overlapped; applying a symmetrization on a homonuclear spectrum can help to identify the metabolites, but the symmetrized spectrum must not be used for the bucketing part or the quantification part.

Choice of the NMR pulse sequence(s)

The choice of the pulse sequence(s) depends on the biological question of interest and a compromise is often necessary to achieve the best separation of the signals of interest and the sensitivity needed to detect the metabolites of interest within the allotted time. This time has to be compatible with high-throughput metabolomic studies, i.e. analyzing a maximum of samples in the minimum of time. The person in charge of the experimental design should carefully define the goal of the study: does it aim at obtaining a maximum of information (= untargeted approach/profiling) or rather accurate information on a limited number of concentrated metabolites (= targeted approach)? In the case of untargeted approaches, the approach allowing the detection of the greatest number of metabolites in the minimum of time with a sufficient sensitivity will be preferred. In the case of targeted approach, one should focus on detecting, above the limit of quantification, the best separation of the signals in order to identify at least one signal well separated from the others for each metabolite of interest. These signals will be used for the quantification.

Table 3 summarizes the pros and cons for each NMR pulse sequence presented here. Moreover, with the upper part of Figure 2, one can easily select the most appropriate sequence pulse sequence to analyze a cohort of biological samples.

For instance:

• if spectra present an overlap in 1D NMR that can be solved by homonuclear 2D NMR, and if the concentration of the relevant metabolites is high enough, applying the UF COSY sequence will be a good compromise, especially if time is critical due to a high number of samples or to sample unstability; • if spectra present a strong overlap by 1D NMR and if the concentration of relevant metabolites is high enough, applying HSQC experiment with NUS will lead to an optimal separation of overlapping signals;

• if the concentration of relevant metabolites is low, applying ZF-TOCSY pulse sequence will be a good compromise to obtain separated signals with a reasonable sensitivity.

According to these recommendations, the most appropriate pulse sequence(s) must be selected. In order to be in the same experimental conditions for each sample, it is suggested to create a file containing the acquisition and processing parameters, which will be recalled for the analysis of the cohort, and to record also a shim file.

Application: untargeted and targeted approaches

As explained above, with 2D NMR, the coefficient or proportionality between peak volume and concentration is different for each signal in the spectrum. For the untargeted method, relative peak intensities are compared from one sample to another. Thus, 2D peak volumes can still be used for the intercomparison of samples, provided that all the samples are prepared in similar conditions [31] . For the targeted method, we will only focus here on the calibration approaches, i.e., standard addition procedure [15] and external calibration [13], [14] .

Untargeted approach

The different steps of the untargeted approach are presented in Figure 4.

NMR analysis

The first step consists of the acquisition of 2D spectra on all the samples of the cohort: they should be analyzed in a randomised order in order to limit effects on the final results. For this step, the samples must be thawed (15-30 minutes) and vortexed before insertion inside the magnet. If an automatic sample changer cannot be used, samples should be thawed only one at a time. If a refrigerated automatic sample changer is available, all or a part of the samples can be inserted inside at a temperature of 4° C. Moreover, and if allowed by the sample changer, the following sample must be automatically pre-heated at the analyzed temperature prior to the insertion inside the magnet (10 minutes on average). After waiting 10 minutes in order to ensure thermal stability of the sample, the system must be locked (automatic); the shim file recorded previously can be called on the first sample and the automatic setting of shims can be applied. When all the settings are done, a 2D experiment is run by calling the acquisition and processing parameter file recorded during the initial evaluation. The same procedure, from the insertion to the acquisition, must be applied to each sample of the cohort. For studies containing QC samples, they must be inserted regularly between series of samples, for instance between series of 5 or 10 analyzed samples.

Spectra processing

The acquired data must be processed as presented previously (step 2paragraph 3).

Bucketing

After processing all the spectra, the third step consists in bucketing all the signals of interest with SNR ≥ 10 by using an appropriate software (see aboveparagraph 3). Once all the buckets are drawn, a spreadsheet file is created to collect all the "raw" integration values associated to each bucket, i.e., without applying any normalization for the moment.

Data processing and statistics

The paragraph "Data processing and statistics" corresponds to the fourth step of the untargeted approach and is subdivided into three steps.

Normalization

In order to make comparisons between samples, a normalization can be applied to each integrated bucket. For instance, if there is an internal reference inside each sample, the data can be normalized on the bucket of the reference for each sample. If not, data normalization can be applied on the total sum of integrated buckets for each sample of the cohort, but this approach is only applicable when the global metabolite content does not vary significantly throughout the cohort. Other normalization methods exist and are described in the literature [19][17] .

Unsupervised analysis

After compiling all the data in an appropriate file, the processing goes on with statistical analyses in order to make comparisons between samples. Different software (free or not) are available: R, SIMCA-P, etc. First, a Principal Component Analysis (PCA) should be performed on all the samples in order to check the quality of the data. The percentage associated to each Principal Component (PC) on the score plot is of particular importance: the two main components should explain the majority of the variability of the data. If a separation trend appears between the samples (for instance between control samples and treated samples), one can try to identify which variables explain the discrimination between them. For studies containing QCs the PCA score plot must show clustered QCs to validate the quality of the data (repeatability and reproducibility). The QCs can be removed and the PCA performed again after the verification.

Supervised analysis

Then, the data can be analyzed with a supervised multivariate method such as two-component Partial Least Squares Discriminant Analysis (PLS-DA). The performances of the model have to be verified by checking the following values: R² -which must be as close as possible to 1and Q² -which must be the largest possible. Moreover, the robustness of the model has to be confirmed by applying a permutation test and a CV-ANOVA (p value ≤ 0.05). The buckets which can explain the discrimination between groups can be determined by drawing the loading plot or the VIP (variable importance in projection) plot. Thanks to a metabolomic database (MMCD/HMDB/ own database/ standard addition, etc.), each signal can ideally be assigned to the name of the metabolite of interest.

Targeted approach

As presented in the introduction, the targeted approach can be applied by using either an external calibration or a standard addition procedure. Each method has its advantages and drawbacks: the standard addition approach is more accurate than the external calibration strategy. Indeed, the first one is less sensitive to the matrix effects than the second one as all the procedure is applied on the same sample. On the other hand, the external calibration strategy is less time-consuming than the standard addition approach as the second one must be done on each sample of the cohort whereas the first one is only done once on the calibration range. Thus, the choice of the adequate procedure will depend on these criteria. The different steps of the targeted approach are detailed below and in Figure 5.

Preparation of standard samples for quantification

The first step consists in the preparation of standard samples. Thus, the mixture of standards that should be prepared to quantify the metabolites of interest has to be determined. From spectra acquired during the initial evaluation, the name of the metabolite of interest is assigned to each signal thanks to a metabolomic database. Each targeted metabolite should exhibit at least one isolated peak with SNR ≥ 10 in the 2D spectrum:

• For external calibration, a mixture of these metabolites must be prepared: this mixture will serve as a stock solution, therefore its concentration must be the highest one of the range. Then, three diluted solutions can be prepared from the stock solution. If possible, the concentration of the biomarkers inside the biological samples of interest should be within the range of concentrations covered by the samples used for external calibration; the concentration of the targeted metabolites can be estimated from the literature. When an external calibration approach is used in partial saturation conditions, the relaxation time T1 of each compound must not significantly vary between the most diluted and the most concentrated solution of the calibration range. Otherwise, the repetition time should be chosen as 3-5 times the longest T1 in the sample; • For standard additions, a stock solution containing the targeted metabolites in known concentrations is prepared. This solution must be concentrated in order to minimize the volume of standard additions (≤ 10 µL) to avoid diluting the analyzed samples. Moreover, the variation of concentration should be limited in order to avoid a change of relaxation times between samples without and with addition of stock solution.

Thus, an addition of 10 µL of stock solution at a concentration of 0.5 mmol/L is generally appropriate. This method is preferentially applied to small cohorts of samples, as explain previously.

NMR analysis

The second step corresponds to the first one of the untargeted approach and has been described previously. We add here specific advice to each procedure:

• For external calibration, it is recommended to start by analyzing the most concentrated sample of the calibration range in order to calculate automatically the receiver gain corresponding to the selected 2D pulse sequence. Then, the most diluted sample can be analyzed in the same conditions as the most concentrated sample (i.e. applying the value of the receiver gain set for the most concentrated sample) in order to determine the number of scans necessary to detect the metabolites with a sufficient signal-to-noise ratio. If this value changes compared to the value used for the most concentrated sample, this sample must be analyzed again. Next, the two other samples of the calibration range as well as all the samples of the cohort can be analyzed in the same experimental conditions. For studies containing QCs, they must be inserted regularly between series of samples, for instance between series of 5 or 10 analyzed samples; • For standard additions, as all the experimental conditions have been optimized during the initial evaluation (see above), the first sample of the cohort is analyzed without any addition. Particular attention should be paid to the value of the receiver gain: as we will concentrate the sample after each addition, the receiver gain must not be too high in order to avoid saturation. We recommend dividing by a factor 2 the RG value determined by the automated routine. Then, a small volume of the stock solution can be added inside the NMR tube. The sample has to be vortexed before analyzing again. This step will be repeated three times and all the samples of the cohort must be analyzed in the same way.

Spectra processing

The acquired data must be processed as presented previously (step 3paragraph 3).

Integration

The fourth step consists in the integration of the signals of interest by using dedicated software (see 3 Processing parameters). Thanks to the previous identification, boxes can be drawn around the signals of interest (see above in the paragraph 3 concerning processing parameters). It is easier to define the integration areas on the spectrum of the most concentrated sample of the calibration range or on the spectrum corresponding to the sample after the third addition of stock solution in the case of standard additions. Once all the integration areas are drawn, the integration file is recorded and named in order to then recall and apply it on each spectrum of the calibration range and the samples of interest or on each spectrum of the samples of interest before and after standard additions. All the absolute integration values can be collected in a spreadsheet file in their "raw" form, i.e., without applying any normalization at this stage.

Determination of unknown concentration

The targeted approach ends with the determination of the unknown concentrations of targeted metabolites.

• In the case of external calibration, for each integrated signal of the calibration range, a table is created in which the concentration values Ci are listed in the first column and the corresponding integration values Vi in the second column. For each integrated signal, a linear regression of the type "y = ax+b" (y = integration value Vi, x = concentration Ci) is made and the values of a and b are gathered. The value of the coefficient of linear regression R² must be verified and close to 1. Normally, the linear Tables Table 1: Summary of how to determine, for each sequence, some important acquisition parameters.

UF COSY ZF-TOCSY HSQC Spectral width

Adapted according to the chemical shift ranges: typically, sw(

Figures

  Figures

Figure 1 :

 1 Figure 1 : Schematic of different approaches employed for quantification by 2D NMR.

Figure 2 :

 2 Figure 2 : Schematic of the targeted and untargeted metabolomic approach by fast 2D NMR.

Figure 3 :

 3 Figure 3 : Fast 2D NMR pulse sequences and their corresponding spectrum obtained on a model mixture of metabolites a) and d) UF COSY b) and e) ZF-TOCSY with 50% of NUS c) and f) 1H-13C HSQC with 25% of NUS.

Figure 4 :

 4 Figure 4: Schematic of the untargeted metabolomic approach by fast 2D NMR.

Figure 5 :

 5 Figure 5: Schematic of the determination of the concentration of a metabolite with the calibration range approach (left) andwith the standard addition approach (right).

Acknowledgments

The authors thank the Corsaire metabolomics facility.

regression curve is supposed to go through 0. In practice, we suggest not to force the model to cross this value in order to take into account a potential bias (e.g. coming from sample preparation, analytical measurements, etc.). The associated graph Vi = f(Ci) and the linear regression curve can be drawn to verify the model. For each integrated signal, y is then substituted by the integration value Vi of a sample of interest and the equation of the linear regression is used to calculate the unknown concentration Ci inside the NMR tube. If necessary, the dilution factor can be taken into account to determine the real concentration in each biological sample (i.e. before addition of deuterated solvent); • In the case of standard additions, for each integrated signal, a table is created in which the added concentration Ci is indicated in the first column and the corresponding integration values Vi in the second one. For each integrated signal, a linear regression of the type "y = ax+b" (y = integration value Vi, x = concentration Ci) is made and the values of a and b are gathered. The value of the coefficient of linear regression R² must be verified and close to 1. The associated graph Vi = f(Ci) and the linear curve regression can be drawn to verify the model: it should cross the y-axis at a value different from 0.

For each integrated signal, y is substituted by 0 and the equation of the linear regression is used to calculate the unknown concentration Ci inside the NMR tube. The extrapolation of the regression curve can also be made: thus, the unknown concentration will be determined by the intersection of the curve of linear regression with the x-axis. If necessary, the dilution factor can be taken into account to determine the real concentration in each biological sample (i.e. before addition of deuterated solvent).

LB

-170 Hz [30] --Parameters chosen according to the the apodization function

Special processing algorithm

Dedicated automation program

Iterative Reweighted Least-Square (IRLS) algorithm (Mdd_csniter = 20) Hilbert Transorm -Applied in the direct dimension after FT

Baseline correction

Automatic baseline correction in both dimensions with a polynomial of maximum degree 3 (default value 2)


Nathanael Jean Maillot

Pascale Infossi

Sébastien Dementin

Marie-Thérèse Giudici-Orticoni

Vincent Méjean

Olivier Genest
email: ogenest@imm.cnrs.fr

Modulation of the RNA polymerase activity by AtcB, a protein associated with a DnaK chaperone network in Shewanella oneidensis

Bacteria possess several molecular pathways to adapt to changing environments and to stress conditions. One of these pathways involves a complex network of chaperone proteins that together control proteostasis. In the aquatic bacterium Shewanella oneidensis, we have recently identified a previously unknown cochaperone of the DnaK/Hsp70 chaperone system, AtcJ, that is essential for adaptation to low temperatures. AtcJ is encoded in the atcJABC operon, whose products, together with DnaK, form a protein network allowing growth at low temperature. However, how these proteins allow cold adaptation is unknown. Here, we found that AtcB directly interacts with the RNA polymerase and decreases its activity. In addition, AtcB overproduction prevents bacterial growth due to RNA polymerase inhibition. Together, these results suggest that the Atc proteins could direct the DnaK chaperone to the RNA polymerase to sustain life at low temperatures.

Introduction

To colonize and survive in many ecological niches, bacteria have the astonishing ability to adapt to a large number of stress conditions and to changing environments.

For example, in response to stresses, bacteria can reorganize gene expression using complex regulatory mechanisms to adjust their metabolism to the disappearance of some nutrients [1]. In other conditions including heat or oxidative stresses that are known to alter protein folding and trigger protein aggregation, all living cells use a class of proteins called chaperones that prevent such deleterious effects on the proteins [2][3][START_REF] Finka | Experimental Milestones in the Discovery of Molecular Chaperones as Polypeptide Unfolding Enzymes[END_REF]. Together, chaperone networks ensure that protein homeostasis (proteostasis) is maintained.

DnaK (also called Hsp70 in eukaryotes) is an ATP-dependent chaperone that is present in most organisms [START_REF] Finka | Multi-layered molecular mechanisms of polypeptide holding, unfolding and disaggregation by HSP70/HSP110 chaperones[END_REF][START_REF] Genevaux | The Hsp70 chaperone machines of Escherichia coli: a paradigm for the repartition of chaperone functions[END_REF][START_REF] Mayer | Recent advances in the structural and mechanistic aspects of Hsp70 molecular chaperones[END_REF][START_REF] Rosenzweig | The Hsp70 chaperone network[END_REF][START_REF] Barriot | J-Domain Proteins in Bacteria and Their Viruses[END_REF]. It participates in the folding (and unfolding) of a large range of substrate proteins, and as a consequence, DnaK is virtually involved in all cellular functions, from metabolism to DNA replication and cell division. The mechanism of action of DnaK implies the contribution of two co-chaperones, one belonging to the J-domain protein (JDP) family which was named after the E. coli DnaJ protein, and a nucleotide exchange factor (NEF). The JDPs interact with substrate proteins, address them to ATP-bound DnaK and stimulate ATP hydrolysis by DnaK, inducing a conformational change of DnaK [START_REF] Craig | How Do J-Proteins Get Hsp70 to Do So Many Different Things?[END_REF][START_REF] Kampinga | The HSP70 chaperone machinery: J proteins as drivers of functional specificity[END_REF]. ADP-bound DnaK interacts tightly with the substrate, and substrate release depends on the NEF cochaperone that allows the exchange of ADP to ATP. In addition to DnaJ, many other JDPs are usually present in a given organism to enlarge the functions and the panel of substrate proteins that DnaK can take care of [START_REF] Barriot | J-Domain Proteins in Bacteria and Their Viruses[END_REF][START_REF] Craig | How Do J-Proteins Get Hsp70 to Do So Many Different Things?[END_REF][START_REF] Kampinga | The HSP70 chaperone machinery: J proteins as drivers of functional specificity[END_REF].

In the aquatic bacterium Shewanella oneidensis, a model for stress adaptation [START_REF] Lemaire | The Shewanella genus: ubiquitous organisms sustaining and preserving aquatic ecosystems[END_REF][START_REF] Honoré | Hsp90 Is Essential under Heat Stress in the Bacterium Shewanella oneidensis[END_REF][START_REF] Honoré | Interplay between the Hsp90 Chaperone and the HslVU Protease To Regulate the Level of an Essential Protein in Shewanella oneidensis[END_REF][START_REF] Hau | Ecology and biotechnology of the genus Shewanella[END_REF], we have recently identified a previously unknown JDP, AtcJ, that is essential for growth at low temperature (Fig. 1) [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF]. We have found that AtcJ is encoded in a fourgene operon, atcJABC. Interestingly, we have demonstrated that AtcC and AtcB are also essential at low temperatures. In addition to its interaction with DnaK, AtcJ interacts with AtcC, and AtcC binds AtcB (Fig. 1) [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF]. However, it is still unknown how these proteins together allow bacterial growth at low temperatures.

Here, we found that AtcB interacts with the RNA polymerase and modulates its activity. These results represent an important step forward to understand the physiological functions of the Atc proteins and understand how they can allow cold adaptation.

Materials and Methods

Strains and growth conditions. Strains were grown aerobically with shaking in rich LB medium at the temperatures indicated in the figures. When necessary, chloramphenicol (25 µg/mL), kanamycin (25 µg/mL), or ampicillin (50 µg/mL) was added. The wild-type S. oneidensis used in this study is MR1-R [START_REF] Bordi | Effects of ISSo2 insertions in structural and regulatory genes of the trimethylamine oxide reductase of Shewanella oneidensis[END_REF], and the E. coli strains used in this study are MG1655 [START_REF] Blattner | The complete genome sequence of Escherichia coli K-12[END_REF] and BL21 (DE3) (Novagen). The ΔatcB strain was constructed as described earlier [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF]. Growth of S. oneidensis and E. coli strains was measured in liquid cultures or on LB-agar plates supplemented with chloramphenicol and with 0.2% (w/v) L-arabinose when indicated, as described [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF].

Plasmids. Plasmids pET-RpoASo, pET-RpoBSo and pET-RpoCSo, allowing the overproduction of RpoA6His, RpoB6His, and RpoC6His, and the plasmid pBad24-CBP-AtcC used in co-purification experiments were constructed as described in Supplementary Materials and Methods. The plasmids placZ, pBad33-AtcB, pBad33-AtcC, pBad24-CBP-AtcB, pET-AtcB, pEThsp90So, pET52-AtcB, and pET52-HtpGSo were constructed as described [START_REF] Honoré | Hsp90 Is Essential under Heat Stress in the Bacterium Shewanella oneidensis[END_REF][START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF].

Protein purification. To product proteins, BL21 strains containing pET-RpoASo, pET-RpoBSo, pET-RpoCSo, pET-AtcB, pEThsp90So, pET52-AtcB, and pET52-HtpGSo were grown at 37°C until OD600 reached 0.8, then 1 mM IPTG was added for 3 hours at 28°C. To purify the RpoA6His, AtcB6His, HtpG6His, StrepAtcB and StrepHtpG proteins, the same procedures as described earlier were performed [START_REF] Honoré | Hsp90 Is Essential under Heat Stress in the Bacterium Shewanella oneidensis[END_REF][START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF].

Polymerase complex reconstitution. To purify and reconstitute the core RNA polymerase composed of α2ββ' (RpoA2, RpoB, RpoC), cells that produced RpoB6His and RpoC6His were collected, resuspended in buffer A (40 mM sodium phosphate buffer pH 7.4, 500 mM NaCl, 10% glycerol), and lysed by French Press. Following the protocol described by Fujita and Ishihama [START_REF] Fujita | Reconstitution of RNA polymerase[END_REF], lysates were centrifuged 10 min at 10 000 rpm, and the pellets were washed 2 times with buffer B (50 mM Tris-HCl pH 8.0, 10 mM EDTA, 100 mM NaCl, 0.5% Triton X-100). Then, the pellets were resuspended in buffer C (50 mM Tris-HCl pH 8.0, 1 mM EDTA, 10 mM DTT, 200 mM KCl, 10 mM MgCl2, 20% glycerol, 6 M urea). After 30 min on ice, resuspensions containing RpoB6His and RpoC6His were mixed with purified RpoA6His, according to a molar ratio of 4RpoA:1RpoB:1RpoC. This mixture was dialyzed overnight at 4°C in buffer D (50 mM Tris-HCl pH 8.0, 0.1 mM EDTA, 1 mM DTT, 0.3 M KCl, 10% glycerol), and centrifuged 20 min at 14 000 rpm. Protein concentration of the supernatant corresponding to the core RNA polymerase was measured by Bradford assay and stored at -80°C.

In vitro transcription-translation.

The "E. coli S30 Extract System for Linear Templates" kit (Promega) was used to measure the efficiency of transcriptiontranslation in vitro. Assays were performed according to the manufacturer instruction with slight modification as follows. Reaction mixtures (50 µL) contained 10 ng/µL of pBest-Luc vector, the S30 extract, and purified proteins or buffer as indicated in the figure. After 3 hours at 37°C, luciferase activity was measured from 20 µL, using buffer F (25 mM HEPES pH 7.5, 0.5 mM ATP, 10 mM MgCl2, 200 µM D(-)-Luciferin) and a Tecan Spark Microplate reader. Luciferase activity was expressed as percent of the level measured in the absence of AtcB.

Co-purification assays. In vivo co-purifications were performed from MR1 ΔatcB strain containing the pbad33 vector or pBad33-AtcB (coding for AtcB6His). Cells were grown at 28°C until OD600 reached 0.8 and then expression from the vector was induced with the addition of 0.2% (w/v) of L-arabinose during 2 hours. Cells were collected by centrifugation 10 min at 10 000 rpm, resuspended in buffer A supplemented with 5 mM imidazole, and lysed by French Press. Lysates were centrifuged 10 min at 14 000 rpm, and supernatants were loaded on HisPur™ Ni-NTA resin (Thermo) equilibrated in buffer A. After 1 hour at 4°C, beads were washed successively with buffer A supplemented with increasing concentration of imidazole (20 mM to 100 mM). Proteins were eluted with buffer A supplemented with 500 mM imidazole, samples were heat denatured and separated by SDS-PAGE subsequently stained with Coomassie Blue. When indicated, proteins were cut out from the gel for identification by mass spectrometry analysis.

Co-purification experiments from E. coli were performed with the MG1655 strain containing either the pBad24-CBP vector or the pBad24-CBP-AtcB plasmid. Strains were grown at 37°C. At OD600=0.5, 0.05% arabinose was added and cells were collected 1 hour later. Equivalent amount of cells were resuspended in buffer G (10mM Tris-Hcl pH = 8, 150mM NaCl, 1 mM Mg acetate, 1mM imidazole, 2mM CaCl2, 0.1% Triton X-100, 0.1% Tween-20, 20mM β-mercaptoethanol), lysed with French press and centrifuged 10 min at 8000 rpm. Co-purification was performed as described [START_REF] Honoré | Hsp90 Is Essential under Heat Stress in the Bacterium Shewanella oneidensis[END_REF] For in vitro co-purifications using purified proteins, the core RNA polymerase (36 µg), AtcB-strep (20 µg) and HtpG-strep (20 µg) were mixed as indicated in the figure in buffer H (100 mM Tris-HCl pH = 8.0, 150 mM NaCl, 1 mM EDTA, 0.05% Triton X-100) before the addition of 50 µL Strep-Tactin beads (IBA Lifesciences). Samples were then treated and analyzed as described [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF].

Blue Native PAGE. After an overnight culture at 28°C, strains as indicated in the figure were diluted to OD600=0.05, grown at 28°C, and 0.2 % arabinose was added at OD600=0.8 to induce expression from the plasmids. Cells were collected two hours later. Pellets were resuspended in 2 mL of 50 mM Tris-HCl pH 7.5, 10% glycerol, 1 mM DTT and lysed with French press. Extracts were centrifuged 10' at 10 000 rpm, and supernatants were centrifuged again 20' at 14 000 rpm. Protein concentrations of the soluble extracts were determined by Bradford assays and equivalent amount of proteins were separated on BN-PAGE (3-12%) as described earlier [START_REF] Guiral | New insights into the respiratory chains of the chemolithoautotrophic and hyperthermophilic bacterium Aquifex aeolicus[END_REF]. Gels were stained with 0.2% Coomassie blue R250, 0.05% Coomassie blue G250, 45% ethanol, 10% acetic acid. For Western blot analysis, proteins were transferred onto PVDF membranes.

Mass spectrometry analyses. To identify proteins, LC-MS/MS experiments were

performed by the Proteomics core facility of the IMM as described [START_REF] Santin | In vivo TssA proximity labelling during type VI secretion biogenesis reveals TagA as a protein that stops and holds the sheath[END_REF].

β-galactosidase activity. MG1655/placZ E. coli strains containing either pBad24-CBP, pBad24-CBP-AtcB, or pBad24-CBP-AtcC as indicated in the figure were grown at 28°C. To follow growth, OD600 was measured every 40 min, and during exponential growth phase, expression from vector was induced with the addition of 0.2% (w/v) L-arabinose. For β-galactosidase activity measurement, same amount of cells were harvested and stored at -20°C for subsequent β-galactosidase assay following the same procedure as described [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF].

Western Blot. The level of the AtcB6His and AtcC6His proteins were observed as described in Supplementary Materials and Methods.

Results and discussion

AtcB overproduction significantly reduces bacterial growth. We have previously shown that deletion of the genes coding for the AtcJ, AtcB and AtcC proteins strongly reduces S. oneidensis growth at low temperature (7°C), but not at permissive temperature (28°C) [START_REF] Maillot | Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network[END_REF]. As expected, we have demonstrated that these growth defects were complemented by producing the proteins in trans from a plasmid. Surprisingly, we found that overproduction of AtcB in a wild type S. oneidensis strain significantly reduces growth on plates and in liquid cultures at 28°C (Fig. 2A andS1).

This result is specific of the overproduction of AtcB since overproduction of AtcC did not affect bacterial growth (Fig. 2A). To make sure that the toxicity observed with AtcB compared to AtcC overproduction was not due to a difference in protein level, we confirmed that similar levels of AtcB and AtcC were produced in the two strains by Western blot on total protein extracts (Fig. 2B).

Strikingly, overproduction of AtcBbut not of AtcCwas also toxic in an E. coli wildtype strain (Fig. 2C). Note that E. coli does not possess any homolog of the Atc proteins. Therefore, these results suggest that overproduction of AtcB could target and impact a general and essential pathway that is conserved between S. oneidensis and E. coli.

AtcB interacts with the RpoB/RpoC subunits of the RNA polymerase. In an attempt to understand why AtcB overproduction is toxic, we aimed to identify its partner proteins. To do that, we set up a pull-down approach. In these experiments, a 6His-tagged AtcB was produced in S. oneidensis grown at 28°C and purified from the cell lysate on Ni-beads. As a control, the same protocol was performed with a strain that did not produce AtcB. As expected, we observed on SDS-PAGE a band corresponding to AtcB that migrates with a relative molecular weight (MW) of about 30 kDa (Fig. 3A). Western blot analysis with an anti-His antibody confirmed that this band corresponds to AtcB (Fig. 3A). Interestingly, an intense additional band of high MW was observed when AtcB was purified. Mass spectrometry analysis revealed that this band contains the proteins RpoB (150 kDa) and RpoC (155 kDa), the β and β' subunits of the RNA polymerase, respectively (Fig. S2). This result was also confirmed by Western blot analysis with an anti-RpoB antibody (Fig. 3A). Bacterial RNA polymerase (RNAP), a large protein complex, is the key enzyme for transcription [START_REF] Borukhov | RNA polymerase: the vehicle of transcription[END_REF]. It is composed of a core enzyme (α2ββ'ω) made of 2 α subunits (RpoA), a β (RpoB) and β' (RpoC) subunits, and a small ω subunit (RpoZ). To initiate transcription, a sigma factor transiently interacts with the core enzyme forming the holoenzyme [START_REF] Feklístov | Bacterial sigma factors: a historical, structural, and genomic perspective[END_REF].

Since the RpoB and RpoC proteins are highly conserved between E. coli and S. oneidensis with more than 80% sequence identity, we hypothesized that AtcB could also interact with RpoB/RpoC in E. coli. Therefore, AtcB with a CBP-tag was heterologously produced in E. coli, and a pull-down experiment was performed.

When AtcB was purified on calmodulin resin, we observed a doublet of bands corresponding to high MW proteins identified by mass spectrometry as RpoB and RpoC, as well as a band just above the AtcB band corresponding to the α subunit of the RNA polymerase, RpoA (Fig. 3B and Fig. S3).

To confirm that AtcB interacts with the RNA polymerase complex and to show that the interaction is direct, we performed a pull-down experiment with purified proteins.

As already described, the RpoB and RpoC proteins are not stable when purified alone. However, they are stabilized by the RpoA protein [START_REF] Fujita | Reconstitution of RNA polymerase[END_REF]. Therefore, the RpoA, RpoB and RpoC proteins were purified as a complex following a previously published procedure [START_REF] Fujita | Reconstitution of RNA polymerase[END_REF] (Fig. S4). AtcB with a Strep-tag was incubated with or without the RpoA-RpoB-RpoC complex, and AtcB was pulled-down on a Strep-Tactin resin. We observed that the RpoB and RpoC proteins were co-purified with AtcB (Fig. 3C). As a control, we checked that the RNA polymerase subunits were not retained on the resin in the absence of AtcB or in the presence of a non-related protein, HtpG with a Streptag (Fig. 3C).

Finally, we wanted to directly detect the native complex made of the RNA polymerase and AtcB without any step of purification. To do that, wild-type and ΔatcB S. oneidensis strains containing an empty vector or a plasmid with the atcB gene were grown at 28°C, and AtcB production was induced by adding arabinose for 2 hours.

Soluble protein extracts from these strains were separated on blue native PAGE (BN-PAGE), a technic that allows the separation of protein complexes in native conditions. Proteins were directly visualized after Coomassie blue staining (Fig. 3D, upper panel) or by Western blot with an anti-RpoB antibody (Fig. 3D, lower panel).

No difference was noticed between the soluble extracts of the WT and ΔatcB strains.

Western blot directed against RpoB and a subsequent mass spectrometry analysis revealed that the core RNA polymerase complex corresponds to the intense band that migrates slightly above the 480 kDa marker (Fig. 3D and Fig. S5). As already described, the core RNA polymerase migrates on BN-PAGE with an apparent MW around 480 kDa, slightly above its expected MW of about 400 kDa [START_REF] Mehandziska | Isolation and Analysis of RNA Polymerase Supramolecular Complex with Associated Proteins[END_REF]. However, when AtcB was overproduced, we observed an additional band after Coomassie blue staining that was also detected with the anti-RpoB antibody, indicating that AtcB overproduction modifies RNAP migration. Surprisingly, Western blot and mass spectrometry analysis indicated that AtcB was present in the two bands (Fig. 3D andS5). This experiment confirms that AtcB interacts with the core RNA polymerase.

AtcB overproduction reduces the level of transcription. Since AtcB interacts with the RNA polymerase, we hypothesized that the toxicity observed upon AtcB overproduction could be due to an inhibition of the transcription. To test this hypothesis, a plasmid coding for AtcB or AtcC was introduced into an E. coli strain containing a lacZ reporter gene encoded on another plasmid. Note that the expression of lacZ and atcB or atcC are under the control of a pBad promoter, inducible by arabinose. We assumed that a decrease in transcription would result in a decrease in the level of β-galactosidase activity. In our experiments, strains were first grown without inducer for three hours. Then, arabinose was added to induce gene expression from the plasmids. Bacterial growth and β-galactosidase activity were measured with time (Fig. 4A). Interestingly, the level of β-galactosidase activitythat was normalized by the number of bacteriawas significantly reduced in the strain producing AtcB, reaching 54 % of the level measured with the wild-type strain at 200 minutes after induction. This result suggests that an excess of AtcB leads to a reduction of transcription in vivo.

To confirm this result, we set up an in vitro assay to test the effect of AtcB on transcription. We used a commercially available semi-purified S30 extract containing all the proteins needed for the transcription and the translation of a DNA matrix (coding for the luciferase), including the RNA polymerase. When the DNA matrix was incubated with the S30 extract, a basal level of luciferase activity was measured (set to 100%) (Fig. 4B). Interestingly, we observed that when the S30 extract was incubated with the DNA matrix and increasing concentrations of purified AtcB (from 0.2 to 1 µM), luciferase activity was strongly decreased. Indeed, 1µM AtcB led to a reduction of more than 75% of the level of activity measured in the absence of AtcB.

We show that this result is specific of AtcB since adding 1µM of purified HtpG did not affect the level of luciferase activity. Altogether, these experiments strongly suggest that AtcB interacts with the RNA polymerase and inhibits its activity.

In summary, our experiments demonstrate that AtcB from S. oneidensis directly interacts both in vivo and in vitro with the RNA polymerase, and most probably with the RpoB and/or RpoC catalytic subunits. In addition, an excess of AtcB inhibits the activity of the RNA polymerase. Although the phenotype of toxicity observed upon AtcB overproduction both in E. coli and in S. oneidensis is not physiologically relevant, it could reveal an unanticipated role of the Atc proteins to explain their essentiality at low temperatures. Indeed, low temperature is known to alter transcription by decreasing the processivity of the RNA polymerase, which can lead to early transcription termination [START_REF] Phadtare | RNA remodeling and gene regulation by cold shock proteins[END_REF][START_REF] Weber | Bacterial cold shock responses[END_REF]. In addition, low temperature could also alter the folding and stability of the RNA polymerase. Interestingly, DnaK was shown to protect the RNA polymerase from heat denaturation and participate to its reactivation [START_REF] Skowyra | coli dnaK gene product, the hsp70 homolog, can reactivate heat-inactivated RNA polymerase in an ATP hydrolysis-dependent manner[END_REF]. Therefore, we hypothesize that AtcB, through its interaction with AtcC that itself contacts AtcJ, could target the DnaK chaperone to the RNA polymerase. Together, the Atc proteins and DnaK could collaborate by a still unknown mechanism to remodel the RNA polymerase, allowing efficient transcription at low temperature. were incubated with a plasmid coding for the luciferase and with increasing concentrations (0 to 1 µM) of purified AtcB or with 1 µM HtpG. After three hours of incubation, luciferase activity was measured using a Tecan Spark microplate reader.

In A and B, data from at least three replicates are presented as mean +/-SD.

Figure 1 .

 1 Figure 1. The Atc proteins from Shewanella oneidensis allow cold adaptation

Figure 2 .

 2 Figure 2. AtcB overproduction is toxic both in S. oneidensis and in E. coli. (A)

Figure 3 .

 3 Figure 3. AtcB interacts with the RNA polymerase. (A) In vivo co-purification of

Figure 4 .

 4 Figure 4. AtcB inhibits the activity of the RNA polymerase. (A) AtcB

  Figure 1

Acknowledgments

We thank Flora Honoré, Paramita Sarkar, Pierre Genevaux, Yann Denis, Romain Barré, and members of our group for help and fruitful discussions. We also thank members from the Proteomics Core facility of the IMM, one node of the network « Marseille Proteomique », IBiSA and Aix Marseille Univ labelled. This work was supported by the Centre National de la Recherche Scientifique and Aix Marseille Université. O.G. was supported by a grant from the Agence Nationale de la Recherche (ANR-16-CE11-0002-01). N.J.M was supported by a MESR fellowship.

Supplementary material

Supplementary Materials and Methods

Plasmid construction. To construct pET-RpoASo, pET-RpoBSo and pET-RpoCSo, allowing the overproduction of RpoA6His, RpoB6His and RpoC6His, respectively, genes were PCR-amplified from the chromosome of MR1 [1] using forward oligonucleotides with the NdeI restriction site, and reverse nucleotides with the SacI restriction site and a sequence coding for a 6His tag (6 x CAC). After digestion, the DNA fragments were inserted by ligation into the pET24b vector (Novagen) digested with the corresponding restriction enzymes. The pBad24-CBP-AtcC plasmid was constructed by digesting pT25-AtcC [2] with XbaI and XhoI enzymes, and the digestion product was cloned into the pBad24-CBP vector [3] previously digested with the same enzymes.

Western Blot. To check in the wild-type MR1-R strain the production of AtcB6His and AtcC6His from pBad33-AtcB and pBad33-AtcC, respectively, cells were grown at 28°C, expression was induced with 0.2% (w/v) L-arabinose at OD600=0.8, and after 2 hours cells were collected. Total protein extracts were heat-denatured, separated by SDS-PAGE, and transferred to nitrocellulose membrane. Proteins were probed using anti-RpoB (Biolegend) or anti-6His (Thermo) antibodies, or with HisProbe-HRP (Thermo). 3D). Bands that were cut out of the gel as indicated were analyzed by mass spectrometry. Indicated are the most abundant proteins that were identified. Coverage: percentage of the protein sequence covered by identified peptides. #PSMs: number of peptide spectrum matches. # Peptides: total number of distinct peptide sequences identified in the protein group. # Unique Peptides: number of peptide sequences that are unique to a protein group. MW: molecular weight (kDa). The β and β' subunits of the RNA polymerase are written in red, the α subunit in green, and AtcB in blue.

Supplementary References Supplementary


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MD Marco Zappa

Metastatic Prostate Cancer incidence and prostate-specific antigen testing: new insights from the European Randomized Study of Screening for Prostate Cancer

Background-The European Randomized study of Screening for Prostate Cancer (ERSPC) has shown a 21% reduction in prostate cancer (PC) mortality and a 1•6-fold increase in PC incidence with PSA-based screening (at 13 years of follow-up). We evaluated PC incidence by risk category at diagnosis across the arms in order to assess the potential impact on PC mortality. Design, setting, and participants-Information on arm, centre, T and M stage, Gleason score, serum PSA at diagnosis, age at randomization, follow-up time and vital status were extracted from the ERSPC database. Four risk categories at diagnosis were defined: 1-low, 2intermediate, 3-high, 4-metastatic disease. PSA (<=100 / >100) was used as the indicator of metastasis.

Outcome measurements and statistical analysis-Incidence rate ratios (RR) for screening versus control arm by risk category at diagnosis and follow-up time were calculated using Poisson regression analysis for 7 centres. Follow-up was truncated at 13 years.

Introduction

An update to the European Randomized study of Screening for Prostate Cancer (ERSPC) has recently confirmed the reduction of prostate cancer (PC) mortality within the ERSPC screening arm invited to prostate-specific antigen (PSA) testing. [START_REF] Schröder | Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up[END_REF] The validity of the result has been questioned [START_REF] Miller | New Data on Prostate-Cancer Mortality after PSA Screening[END_REF][START_REF] Haines | Prostate-specific antigen screening trials and prostate cancer deaths: the androgen deprivation connection[END_REF] based mainly on the differences in treatment between the arms, ignoring the difference in disease characteristics at presentation. [START_REF] Carlsson | RE: Prostate-specific antigen screening trials and prostate cancer deaths: the androgen deprivation connection[END_REF] Previous evaluations of treatment and different methods of mortality analysis, [START_REF] Wolters | The effect of study arm on prostate cancer treatment in the large screening trial ERSPC[END_REF][START_REF] Van Leeuwen | Impacts of a population based prostate cancer screening programme on excess all-mortality rates in men with prostate cancer: a randomized controlled trial[END_REF][START_REF] Kranse | Excess all-cause mortality in the evaluation of a screening trial to account for selective participation[END_REF][START_REF] Zappa | A Different Method of Evaluation of the ERSPC Trial Confirms That Prostate-specific Antigen Testing Has a Significant Impact on Prostate Cancer Mortality[END_REF] however, have shown that differences in treatment are unlikely to explain the impact of screening on mortality. Nevertheless, detailed analysis of key prognostic features of PC across the study arms is worthwhile to better understand how the mortality difference has emerged.

In principle, [START_REF] Autier | Advanced breast cancer and breast cancer mortality in randomized controlled trials on mammography screening[END_REF] assuming that the cancer staging system is consistent over the compared groups, the requirements of an effective screening programme are:

a)

an ability to produce a stage migration ('down-staging') in the screening arm; b) a stage migration accompanied by a decrease in the incidence of advanced cancers; c) a decrease in advanced stage incidence occurring before a mortality reduction in the screening arm.

Meeting these requirements would not mean that early diagnosis had been the cause (or the only cause) of any observed reduction in mortality; yet, if these requirements were not met, it would be unlikely that screening could explain any observed mortality reduction.

A reduction in the risk of diagnosis of metastatic disease in the screening arm of four ERSPC centres was shown in a previous work. [START_REF] Schröder | Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC)[END_REF] That paper addressed the occurrence of metastatic disease both at diagnosis and in the post-treatment follow-up.

In the current study, we present up-dated results on the stage distribution by arm in 7 ERSPC centres. We evaluate whether the above mentioned requirements were met and explore the association of the decrease of advanced cancer incidence rates (if any) with the reduction in PC mortality.

Materials and Methods

The ERSPC is a multi-centre, randomized screening trial comparing an intervention arm of men to whom regular PSA screening is offered with a control arm to which such screening is not offered. The trial started in Belgium, the Netherlands, Finland, Sweden, Spain, Italy, Switzerland and France during the period 1993-1998. [START_REF] Schröder | The story of the European Randomized Study of Screening for Prostate Cancer[END_REF] The study methodology has been described in detail previously. [START_REF] Schröder | Screening and prostate-cancer mortality in a randomized European study[END_REF] Data were obtained by linkage to local cancer registries for PC incidence and national registries for overall mortality. PC deaths were ascertained by local, independent cause-of-death committees. [START_REF] Koning | Determining the cause of death in randomized screening trial(s) for prostate cancer[END_REF] Data on trial arm, centre, T and M stage, [START_REF]The AJCC Cancer Staging Atlas[END_REF] Gleason score, serum PSA at diagnosis, age at randomization, follow-up time (until December 31, 2010) and vital status were extracted from the trial database.

We selected men aged 55 to 69 years (the core age group), excluding those who died, were diagnosed with PC or emigrated between randomization and screening invitation (N=145). The median follow-up for the two French centres, Herault and Tarn, was 6 to 7 years and, therefore, too short for their data to be included in this analysis.

The risk classification adopted in the last ERSPC analysis 1 was used. Four risk categories at diagnosis were defined using information available at diagnosis: low risk (category 1) as clinical stage T1-2 and Gleason score <= 6; intermediate risk (2) as T1-2 and Gleason score = 7 or T3 and Gleason score <= 7; high risk (3) as T1-3 and Gleason score = 8-10 or T4 and any Gleason score; and the metastatic disease category (4) as M1 or PSA >100 ng/mL. Data on disease progression and metastasis status during the entire period of post diagnosis follow-up have already been published for those centres for which information was available. [START_REF] Schröder | Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC)[END_REF] In the present analysis only information on risk category at diagnosis, available for all centres, is considered.

The accuracy of risk category definition was evaluated by a sensitivity analysis, restricting the evaluation of metastasis to M1 cases only.

We imputed missing data for the variables used in defining the risk categories at diagnosis using multiple imputations by the technique of chained equations (MICE). [START_REF] Van Buuren | Multivariate Imputation by Chained Equations: MICE V1.0 User's manual[END_REF] Briefly, the MICE uses the distribution of observed data to identify a set of plausible values for the missing data, by incorporating a random component to reflect the uncertainty of the estimate. Several data-sets are generated and the same analysis is conducted on each data-set with the aim of obtaining a set of parameter estimates that are combined in a single overall estimate (computed as the average of the estimates in each simulated dataset) with relative standard errors (computed including the between and within imputation variance) and confidence intervals. [START_REF] Rubin | Inference and missing data[END_REF] For the incomplete variables (T and M stage, Gleason score, and PSA level), we derived imputation models that included the complete variables -arm, centre, vital status and follow-up time, age at randomization and interactions between each variable and trial arm. The interactions between each variable and trial arm were included in order to incorporate potential differences among centres in the mechanism causing the missing data. Ten final data-sets were generated after the algorithm converged. In the literature on multiple imputation, between 3 and 5 imputed data-sets is considered adequate. [START_REF] White | Multiple imputation using chained equations: Issues and guidance for practice[END_REF] The estimates were combined using Rubin's rules. [START_REF] Rubin | Multiple Imputation for Nonresponse in Surveys[END_REF] We conducted a sensitivity analysis by imputing directly the risk categories at diagnosis. We used version 12 of the STATA software, StataCorp, College Station, TX, USA. PC incidence rate ratios (RR) by centre and risk categories at diagnosis were calculated for the screening versus control arms using Poisson regression analysis. Person-years were calculated as time from randomization to PC diagnosis, death or end of follow-up, whichever came first. We adjusted all the analyses for the randomisation ratio 1:1•5 screening versus control arm in Finland. Primary analyses were carried out on an intention-to-treat (intention-to-screen) basis, i.e. men were included in the analysis in the arm they were randomly assigned to, regardless of compliance. Moreover, to provide a sensitivity analysis, we split the cumulative occurrence within the screening arm according to screening status defining subjects who underwent at least the first screening, after first invitation, test as attendees.

Complete case analysis (before missing data imputation) and all-case analysis (after data imputation) are presented.

Results

In the core age group, 162,338 men were randomized. The median age at randomization was 60•2 years.

With data truncated at 13•0 years of follow-up 7,408 and 6,107 PC cases were diagnosed in the screening and control arms, respectively. Overall, the distribution of missing values was rather similar in the two arms (8% in the screening and 10% in the control arm), but in some centres (Belgium, Italy, and Spain) data were missing for between 38% and 77% of cases and there was an imbalance between the arms (of at least 10 percentage points) (Table 1).

Imputation did not materially affect the results (Table 2) and therefore the results based on imputed data are shown henceforth. The RRs (screening versus control arm) for the low and intermediate risk categories dropped strongly and consistently with follow-up. The high RRs in the first 3 years were associated with the prevalence round and overdiagnosis. There was a sharp decrease of RR in the high risk category within the first six years, and a further reduction thereafter as the risk converged with the control arm. A slight decrease of RR was evident until the 9 th year for the metastatic disease category but afterwards it remained constant (Table 3).

A sharper contrast was seen between the arms for the entire 13 year period when only the attendees were used to calculate the RRs - The reduction in cumulative (0-13 years) RRs (screening versus control arm) for metastatic disease at diagnosis (RR = 0•60) is clear and evident within the first six years of follow-up, while in mortality the reduction is smaller (RR=0•79) and emerges roughly three years later.

(Figure 1).

Discussion

In the present study we have examined the incidence rates of PC by risk category at diagnosis between the two arms of the ERSPC trial.

Overall, the net effect in the screening arm was a marked increase of the cumulative incidence of low risk PC, along with a significant reduction of cumulative incidence of metastatic disease at diagnosis. A slight increase in the intermediate risk category and no difference in the high risk category (with an early excess disappearing by year 10) were observed. When interpreting these results, we must keep in mind that early detection resulting in a stage migration is expected to increase early disease with corresponding decreases in advanced disease.

In this study, the reduction found for metastatic disease at diagnosis was not reflected as an excess in the high risk category, but increased risk was found only in the intermediate and, above all, the low risk categories. The absolute excess in the low risk category was substantially larger than the deficit of advanced disease: the difference reflects the excess of incidence in the screening arm which was reported in the aforementioned update to the ERSPC trial along with the conclusion that one PC death is averted for 27 excess cases detected in 13 years of follow-up. [START_REF] Schröder | Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up[END_REF] In order to avoid potential biases due to the imbalance of the proportions of missing data in the two arms and among centres, we used multiple imputation. As the results did not change considerably with imputation, results with imputed data are shown because they are more comprehensive and precise.

The RRs for the screening versus control arms decreased with follow-up time both overall and for each risk category at-diagnosis, though not in a similar fashion. Cumulative incidence for low-risk PC was 3 times higher in the screening arm than in the control arm during the first 3 years of follow-up, and then fell off steadily to an incidence twice as high in the screening arm in the overall period of 13 years. The RRs for intermediate and high risk categories also decreased over time. In contrast, a decrease in the cumulative incidence for the metastatic disease category was not evident in the first 3 years of follow-up (RR=0•94) but thereafter it decreased until the 9th year (RR=0•63) and then remained stable.

The pattern described above was reinforced for those who were attendees. For example, the RR (screening versus control arm) for metastatic disease at diagnosis was 0•45. Obviously, a comparison of attendees with the entire control arm could be biased because of a difference in underlying risk due to selection bias. Nevertheless, such a comparison confirms that PSAbased early diagnosis determined a decrease of metastatic disease at diagnosis.

The current result is consistent with an earlier analysis [START_REF] Schröder | Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC)[END_REF] in showing a reduction in metastatic disease at diagnosis, although the magnitude of effect is slightly smaller in the current analysis with data from a larger number of centres and longer follow-up and data imputation. In the previous paper, a relative reduction of 50% of metastatic disease at diagnosis was reported (RR=0•50, 95%C I 0•40; 0•62).

In the present paper, we had no information on disease progression after diagnosis. It is not sufficient for screening to increase detection of early disease, but it should affect the course of the disease so that deaths are averted or postponed by early treatment. This requires that there is no compensatory increase in disease progression after diagnosis. In the earlier paper, no differences were reported in metastatic disease emerging during the follow-up period (after diagnosis) although a slight, but statistically non-significant, increase was seen in the post-diagnosis incidence of metastasis in the screening arm (RR=1•16, 95% CI 0•9-1•47). [START_REF] Schröder | Screening for prostate cancer decreases the risk of developing metastatic disease: findings from the European Randomized Study of Screening for Prostate Cancer (ERSPC)[END_REF] The lack of further reduction in development of metastatic disease after diagnosis is not surprising given the higher incidence of low and intermediate risk PC in the screening arm and the lack of any difference in high risk PC between the two arms. Hence, a similar occurrence of metastatic disease after diagnosis seems plausible, likely indicating that early diagnosis and treatment could not stop disease progression in all cases. The lack of reduction in incidence of metastasis after diagnosis is in agreement with the smaller reduction of mortality (RR=0•79) than in incidence of metastatic PC (RR=0•60).

Limitations

Two potential limitations regarding the risk categories at diagnosis adopted in the present analysis should be considered.

First, we use a rather crude classification, based only on Gleason and clinical stage, including PSA > 100 ng / mL just as a criterion for metastasis. The choice was driven by a need to use the classification utilized in the last ERSPC analyses. [START_REF] Schröder | Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up[END_REF][START_REF] Schröder | Prostate-cancer mortality at 11 years of follow-up[END_REF] Moreover PSA data for the control arm are incomplete and therefore unusable for finer groups' definition. The accuracy of such a definition was evaluated by a sensitivity analysis and when restricting the evaluation to M1 cases only the reduction was even higher. The lower value is probably associated with the higher completeness of PSA information in the screening arm. The assumption of consistency in the cancer staging system over the compared groups is plausible in a randomized trial because a similar distribution by stages in the two arms would be expected in the absence of screening. We used the staging assigned by populationbased cancer registries. Further information on staging and treatment were recovered from medical records in a non differential way for all cases in both arms.

Second, consistency in cancer staging presumably changed over time. Modifications are due to trends in pathology reporting but we have assumed that this phenomenon doesn't affect the comparability between arms because there is no reason to suspect it would be differential by study assignment.

Conclusion

The present results confirm a stage migration in the screening arm with a 40% reduction in metastatic disease at diagnosis, which preceded a mortality reduction by almost three years.

These results strongly suggest that a decrease of metastatic disease at diagnosis is a major determinant of the reduction of PC mortality in the ERSPC trial, although we cannot exclude additional contributions from other factors. Cumulative incidence rate ratios for risk category 4 (after data imputation) and prostate cancer mortality rate ratio by time since randomization, 95% confidence interval. IRD = Incidence Rate difference per 1000 randomized men, MRD = Mortality Rate difference per 1000 randomized men.

Figure 1 .

 1 Figure 1.

Table 1

 1 Numbers of randomized subjects and prostate cancer cases (total and by risk category) by centre.

SCREENING ARM (

Rand. age: 55-69 y.) CONTROL ARM (Rand. age: 55-69 y.) Risk category Risk category

	Missing	31 (3%)	272 (85%)	35 (7%)	45 (1%)	177 (61%)	28 (54%)	12 (4%)	396 (36%)	21 (4%)	1017 (13%)	600 (10%)
	4	(Metastatic)	125 (12%)	11 (3%)	59 (13%)	361 (10%)	12 (4%)	3 (6%)	15 (5%)	47 (4%)	14 (3%)	647 (8%)	586 (10%)
	3	(High)	143 (13%)	8 (2%)	35 (7%)	419 (12%)	27 (9%)	3 (6%)	32 (11%)	33 (3%)	71 (14%)	771 (10%)	667 (11%)
	2	(Intermediate)	290 (27%)	5 (2%)	132 (28%)	1165 (32%)	32 (11%)	9 (17%)	78 (26%)	241 (22%)	193 (38%)	2145 (28%)	1711 (28%)
	Missing mized PC cases 1 Rando-(Low) men	96 (4%) 17,390 1070 (100%) 481 (45%)	280 (67%) 4,255 321 (100%) 25 (8%)	15 (2%) 5,951 469 (100%) 208 (44%)	17 (1%) 48,409 3609 (100%) 1619 (45%)	125 (32%) 7,251 289 (100%) 41 (14%)	26 (30%) 1,141 52 (100%) 9 (17%)	11 (2%) 4,955 297 (100%) 160 (54%)	403 (34%) 28,869 1094 (100%) 377 (34%)	24 (4%) 10,470 506 (100%) 207 (41%)	997 (11%) 128,691 7707 (100%) 3127 (41%)	570 (8%) 89,352 6107 (100%) 2543 (42%)
	4	(Metastatic)	39 (2%)	14 (3%)	32 (4%)	149 (5%)	10 (3%)	2 (2%)	6 (1%)	51 (4%)	21 (4%)	324 (4%)	252 (3%)
	3	(High)	126 (6%)	10 (2%)	27 (4%)	269 (9%)	42 (11%)	4 (5%)	41 (7%)	40 (3%)	74 (13%)	633 (7%)	519 (7%)
	2	(Intermediate)	472 (22%)	28 (7%)	139 (19%)	761 (25%)	77 (19%)	15 (17%)	133 (23%)	269 (22%)	192 (34%)	2086 (23%)	1625 (22%)
	Rando-1 mized PC cases (Low) men	17,443 2180 (100%) 1447 (66%)	4,307 417 (100%) 85 (20%)	5,901 738 (100%) 525 (71%)	31,970 3018 (100%) 1822 (60%)	7266 396 (100%) 142 (36%)	1,056 87 (100%) 40 (46%)	4,948 572 (100%) 381 (67%)	28,793 1196 (100%) 433 (36%)	10,879 559 (100%) 248 (44%)	112,563 9163 (100%) 5126 (56%)	72,891 7408 (100%) 4442 (60%)
	Centre	Netherlands	Belgium	Sweden	Finland	Italy	Spain	Switzerland	Herault	Tarn	Total	Total (ex.	Fr * )

Table 2

 2 Cumulative incidence rate ratio (RR) by risk category for the original data and after data imputation

	After data imputation	Screening Control RR (95%CI)	65% 47% 2•14 (2•03 ; 2•25)	24% 30% 1•24 (1•16 ; 1•34)	8% 12% 1•00 (0•89 ; 1•13)	4% 11% 0•60 (0•52 ; 0•70)	--	100% 100% 1•56 (1•51 ; 1•62)
	Original data	Screening Control RR (95%CI)	4,442 60% 2,543 42% 2•29 (2•18 ; 2•42)	1625 22% 1711 28% 1•27 (1•18 ; 1•37)	519 7% 667 11% 1•02 (0•90 ; 1•15)	252 3% 586 10% 0•56 (0•48 ; 0•65)	570 8% 600 10% 1•01 (0•90 ; 1•13)	7,408 100% 6,107 100% 1•56 (1•50 ; 1•62)
		Risk Category	1 LOW Risk	2 INTERMEDIATE Risk	3 HIGH Risk	4 M1 And/Or Psa 100+ Risk	5 Missing Values	Total

Eur Urol. Author manuscript; available in PMC 2016 November 01.

Herault and Tarn (French centres) excluded.Eur Urol. Author manuscript; available in PMC 2016 November 01.

Funding-Each centre had its own funding

Author Manuscript

Buzzoni et al.

Table 3 Cumulative incidence rate ratios (RR) by risk category and periods from randomization after data imputation. Incidence Rate difference (IRD) per 1000 randomized men.