M Mihaluta

P Martin

J Y Dantan

Manufacturing Process Modeling and Simulation

Keywords: Integrated Design, Process Modeling, Process Simulation

This paper presents a methodology to be employed in the whole process design phase including first and second processing. This methodology consists of a set of steps which are characterised by an independent model. This paper's objective is to analyse the coherence between the different models and the coherence between the model and the objectives of each step. The final stage is to develop the production plans. The casting process was the first one to be analyzed. Casting models were created using CAD software (Catia V5R17) and imported into the casting simulation environment (Magmasoft). Filling and solidifying processes have been simulated using different casting models in order to optimize the final configuration. The machining process was modeled using the machining features concept and it was simulated using Catia's Advanced Machining environment. Two machining strategies have been analyzed according to positioning strategies. Process engineering software was used to create the process plans and to analyze the resource allocation.

INTRODUCTION

It is well known that product development is constrained by the following demands from the part of the industrial actors: increase in production rate, cost and time to market reduction and part quality control. The product design department needs to rely on flexible and reactive solutions in order to efficiently respond to complex demands. These demands are most of the times translated into complex products and into an increase of part variants. Thus, the modeling of the product and of its manufacturing process becomes essential. Designers rely on models to materialize the clients' requirements and to validate specific design solutions taking into account manufacturing requirements. The rapid development of integrated modeling and simulation software has allowed the virtualisation of the product's manufacturing process. The methodology used to create these models should allow: integration with CAD environments; knowledge incorporation for collaborative design; easy access for rapid modification; reliability of results and coherence with the manufacturing processes. Previous work has been carried out on product modeling at separate stages of the designing process. Design methods have been proposed to create functional models that take into account features, parameters and tolerance specifications [1]. A certain degree of integration has been attained for the modeling and simulation of casting processes [2]. Machining models have been introduced to capture part functionality and processing requirements through feature-based modeling [3] and the integration of the available data has been studied [4]. No clear integration of the entire design phase is available. Product and process design consists of a set of steps which requires specific models. The aim of this study is to set up a formal methodology for manufacturing process modeling and its simulation in order to analyse the coherence between the various models. Each of these steps has stated objectives which require validation by analysing the coherence between the output models and the objectives. The aspects concerning manufacturing process modeling have been taken into account for primary and secondary processes only (Figure 1).

METHODOLOGY OUTLINES

The methodology used in the present work consists in creating, in an ascending manner, the manufacturing process models. Simulation is employed afterwards to asses and to optimise the casting and machining processes. The basic steps which lead to the creation of the manufacturing models and process simulations are shown in Figure 1. The designer creates an approximate functional model starting from the specified functions that the product must fulfil in a given volume in the desired assembly. The part's technical functions are materialized by creating solids around these surfaces. These elements do not need to be connected but only to provide functional and mechanical capabilities. Further design allows the refinement of the solid model which will resemble the final part. The final model issued from the primary design phase will serve as a reference for the following phases. It is identified with the starting point in the creation of the manufacturing models. Additional analysis can be performed to evaluate loading behaviour through finite element analysis after the application of an appropriate mesh. Only primary and secondary manufacturing processes are considered between the general manufacturing processes (Figure 2). After the product's detail design, the second phase consists of the modeling of the raw material's processing. In order to pass to the modeling of the primary manufacturing process the previous models require design modifications. They have to respond to process specifications (e.g. tapered faces to allow easy part extraction in the case of a casting process).

Figure 1: Main activities of the proposed method for manufacturing process modeling and simulation. Primary manufacturing process models are created mainly for use in simulation. Casting processes are closed mold processes and the part is only accessible at the end of the cycle, making it difficult to draw conclusions on the part's evolution at intermediary steps. In these cases process simulation is the only solution that provides easy interaction with the part. These simulations are performed on dedicated software which requires model transferring through normalized exchange standards (e.g. STEP, IGES). The available exchange standards do not have the capability to capture all the information and semantics contained in a model and the transferring procedure results in the loss of a certain amount of data. Thus, the model needs additional modifications and adaptation to the new environment. The designer and the manufacturing team obtain after simulation the models which will allow decision making for additional modifications and process validation. Most of the times primary processing isn't enough to obtain finished mechanical parts because of process limitations. Some part features and their dimensional tolerances are impossible to obtain through casting. In these cases full functionality is attained once secondary manufacturing processes have been employed. In the present case study machining operations have been selected and additional models need to be created. To this purpose machining features need to be identified and a machining model needs to be created. This model contains information related to operation order and tool accessibility. Simulation is then performed to assess the part's machinability and the validation of tool trajectories. Back loops between the designer experts' activities are necessary to indicate the fact that the models obtained through simulation serve as controls for process optimisation. These models will guide the ranking of available process solutions according to significant technological and socio-economical parameters.

CASE STUDY

This case study is intended to develop the previously presented methodology with the aid of a simple mechanical part as example (Figure 3). The part's design lifecycle steps, product's detail design along with its manufacturing process modeling and simulation, were analysed to assess the evolution of the required models and their coherence in an integrated design environment [6], [7]. The raw material needs processing by sand casting as primary manufacturing process. The cast part requires further machining by milling and drilling operations as secondary manufacturing process in order to obtain the finished part. Models have been created for simulation purposes following this ascending methodology. Solid modeling and machining simulations have been performed using the Catia V5R17 software. As for the casting simulations, they have been performed on Magmasoft which required the suppression of those features which did not correspond to the minimum dimensions demanded by the process. Auxiliary components such as the gating system and the feeders were designed (Figure 5(b)). The gating system's geometry was defined as a function of the number of parts in the mold and of the molten metal's flow speed. The feeder acts as a liquid metal reservoir and is essential for obtaining a porosity free part. It was designed as a function of the part's volume knowing that it has to solidify after the last volume in the part. The models of the rough casting, gating system and feeders were assembled to create the pattern model (Figure 5(c)) which was used for the design of the mold model. Since this phase was carried out in a different CAM environment, a transfer model was created which served as an input for the mold model definition and casting simulations. The pattern and mold assembly model was created using finite volumes. The strategy employed to apply the enmeshment depends on the desired results' accuracy and on the available time for calculations. Figure 5

Machining process modeling and simulation

The machining process was defined taking into account the part's final solid model, as it is the one that depicts the finished part, and the rough casting model (Figure 7(a). The latter was adapted to the machining process.

The modifications that it has undergone consisted in eliminating the gating system and the feeder. This was consequent to the hypothesis that the machining process started after these components were removed. the number of machining operations as a function of the feature's accuracy and finish degree; the geometrical conditions between features (used for operation grouping in order to minimise tool changes); A separate model was required for the assessment of the part's accessibility. This aspect was modelled with the help of a characteristic polyhedron materialised by the part's outermost faces that contain machining features. By relating this model to the tool direction model (Figure 8) it was possible to create the part's process equation through temporal reasoning. This equation allows a certain degree of freedom during the process selection phase as it contains all the possible tool directions for the machining of a feature. Where numbers represent the defined directions, ^ is the AND operator, W is the exclusive Or operator and the M (X) syntax fixes the precedence constraints. This mathematical model defining the machining process allowed the generation of the possible process plans. Refining the machining solutions needed additional constraints defined by: the tool's geometry;

the machining strategy; the part's positioning on the machining table and clamping system; the configuration of the machine tool the sequencing rules applied for machining operations.

Figure 8: Tools' directions. Simulations have been performed in Catia V5 Advanced Machining environment using the rough casting and finished part models. The job sequencing models that were previously created served as input for the definition of processing parameters. These simulations were performed taking into account different resource configurations to evaluate process capabilities and to validate the process models. Two part setups were considered which implied a variation of the number of required part positioning on the machine tool's clamping system. The simulations were performed according to two process plans: the processing on a 4-axis machining centre, which required only one positioning face;

DISCUSSIONS

This case study was intended to simulate a concurrent engineering environment and to point out the achievable degree of integration in manufacturing process modeling and simulation. The evolution of the product's model during the design phase is shown in Figure 10. This diagram represents an intermediary informational model acting as a framework for the integration between the product, the available manufacturing process knowledge and the associated resources. It has been used as a reference for the development of the required process plans in an integrated environment provided by Delmia's Process and Ressource Definition.

Figure 2 :

 2 Figure 2: General manufacturing processes [5].

Figure 3 :

 3 Figure 3: Mechanical part used for the case study (adapted from [8]).3.1 Product modelingThe modeling of the manufacturing processes required product models obtained after functional definition and solid modeling. Part specifications such as functions to be assured by the part and feature tolerances and constraints directed the creation of the functional model

Figure 4 :

 4 Figure 4: Designer models: (a) functional model; (b) intermediary solid model; (c) final solid model; (d) meshed model.

3. 2

 2 Casting process modeling and simulation The part's solid model was adapted (Figure 5(a)) to respond to the technological requirements of the casting process. This implied: the over sizing of the model, to overcome shrinkage after solidification; the definition of a parting line; the addition of machining allowances; the addition of a drafting angle for easy part extraction;

  (d) shows the pattern model obtained after enmeshment with approximately 2 million finite volumes.

Figure 5 :

 5 Figure 5: Casting models: (a) rough casting model; (b) gating system and feeder model; (c) pattern model; (d)meshed pattern. The input data used to launch the casting process simulations on Magmasoft concerned the part's material (cast iron with the liquidus temperature of about 1168°C and the solidus temperature of about 1165°C), the molds material (green sand) and the temperature dependent heat transfer. A set of models was obtained after simulation (Figure6). Figure6(a) shows the flow of the molten metal through the different components of the pattern. It is used either to identify critical zones that favour premature solidification, with the assistance of a temperature scale, or to assess the correctitude of the gating system's design with the assistance of a speed scale. The

Figure 6 :

 6 Figure 5: Casting models: (a) rough casting model; (b) gating system and feeder model; (c) pattern model; (d)meshed pattern. The input data used to launch the casting process simulations on Magmasoft concerned the part's material (cast iron with the liquidus temperature of about 1168°C and the solidus temperature of about 1165°C), the molds material (green sand) and the temperature dependent heat transfer. A set of models was obtained after simulation (Figure6). Figure6(a) shows the flow of the molten metal through the different components of the pattern. It is used either to identify critical zones that favour premature solidification, with the assistance of a temperature scale, or to assess the correctitude of the gating system's design with the assistance of a speed scale. The

  Figure 7: a) Rough casting model overlapping the finished part model; b) machining model. The creation of machining models consisted in the identification of machining features. These features contain the knowledge related to the machining process and they serve as a frame for the future process models. The feature decomposition, together with the rough casting model, acted as a starting point for the definition of the machining model (Figure 7(b)). This last model takes into account machining allowances, which were previously defined, and the solid volumes to be removed from the rough casting. It represents the intersection between the two models shown in Figure 7(a). The feature-based model was used to identify the list of machining operations according to: the feature's geometry;

  Access=3^M(2W5)^M²{[5^M(2)^M²(4)W[2^M(5)^M²(4)]}(1)

Figure 9 :

 9 Figure 9: Machining process simulation models: (a) machined part model, (b) trajectory model. The simulations had as results a machined part model (Figure 9(a)) used to evaluate processing conditions. Another output of the simulations was the tool's trajectory model (Figure 9(b)) needed to assess process coherence. This coherence is translated into valid tool

(a) (b) (c) (d)

Identified constraints and manufacturing parameters

Different constraints appear during the model's evolution and they tend to direct the designer's steps towards a restrained number of choices. These constraints can appear from technological requirements. At the beginning of the functional design phase the designer is free to translate part requirements into a functional model. In order to design a robust final geometric model he needs to take into account mechanical constraints and topological limitations. The casting models take into account requirements which can be related to the part's geometry (minimal required dimensions) or the process (flow speed, heat transfer laws). Machining models are also constrained by technological parameters such as tool part interactions, clamping strategy (induced stress) and machining operational parameters. The simulation models provide expert knowledge to be used for process optimisation by modifying the geometrical models or the processing parameters. Other sources for design constraints are quality requirements introduced by the specified tolerances. They are assessed after obtaining the quality predictive models. Economical constraints are materialised through imposed production rates and resource availability. They act on the definition and choice of process plans. All these constraints are taken into account in order to limit processing solutions through multi-criteria analysis.

Model behaviour

The behaviour describes the different states that a model encounters during the product's integrated design and the way in which the transition between them is carried out. This behaviour is either defined by specifications from the part of the designer or relationships imposed by the CAD environment. Expert rules can be used to specify the model's behaviour through parametric tables which indicate the relationship between geometric elements. The previously presented constraints and manufacturing parameters are employed to guide the model's behaviour during the back loops that define the optimisation step.

Model coherence

The design methodology requires that the expert interacts with different instances of the model depicting the product at various design steps. Not only do these instances need to be coherent with each other but they also have to be coherent with external product specifications. This requires for a CAD environment to be able to assess this coherence by using an interface between the models and the external specifications. Unfortunately, the market available software environments do not offer this functionality. As there is no available solution to integrate all the design phases, transfer through exchange standards between different CAD/CAM environments is required. A loss in incorporated knowledge was noticed after these manipulations. A coherence analysis is required at this level through coherence indicators.

CONCLUSIONS

This research work presents a methodology for the integration of the design phase lifecycle. It aims at presenting the evolution of product models starting from functional design and continuing with the modeling of primary and secondary manufacturing processes. The simulation of these processes allowed the validation of the created models. Furthermore, the simulation models served as indicators for process optimisation from the point of view of design solutions and decision making rules for process selection. The recommendations for further work consist in the extension of this methodology to the entire manufacturing process. The coherence indicators, as design drivers, need further development to allow the precise evaluation of the elaborated models.


G Le Roux

F De Vleeschouwer

Preparation of peat samples for inorganic geochemistry used as palaeoenvironmental proxies

Keywords: acid digestion, freeze-drying, grinding, X-ray fluorescence. _______________________________________________________________________________________

This article provides a brief review of protocols used in peat inorganic geochemistry. We emphasise the key issues that could lead to inter-comparison problems. For each section (drying, grinding, non-destructive analyses, acid digestions and destructive analyses), recommendations are provided to guide the reader through an idealised protocol, which is the only workable approach for studies incorporating long-term comparisons.

INTRODUCTION

During recent decades, numerous techniques have been used to concentrate and analyse the inorganic components of peat samples (e.g. [START_REF] Kempter | Ti and Pb concentrations in rainwater-fed bogs in Europe as indicators of past anthropogenic activities[END_REF][START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14 C yr BP recorded in a peat bog profile[END_REF], Martinez-Cortizas et al. 1999[START_REF] Mighall | Geochemical evidence for atmospheric pollution derived from prehistoric copper mining at Copa Hill, Cwmystwyth, mid-Wales[END_REF]. These studies have generally aimed to reconstruct past human activities (e.g. [START_REF] Kempter | The impact of early mining and smelting on the local tropospheric aerosol detected in ombrotrophic peat bogs in the Harz, Germany[END_REF][START_REF] Mighall | Using peat bog archives to reconstruct paleopollution and vegetation change during the late Holocene[END_REF], atmospheric pollution (e.g. [START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14 C yr BP recorded in a peat bog profile[END_REF][START_REF] Novák | Origin of lead in eight central European peat bogs determined from isotope ratios, strengths, and operation times of regional pollution sources[END_REF], De Vleeschouwer et al. 2010b) or environmental changes (e.g. [START_REF] Shotyk | History of atmospheric lead deposition since 12,370 14 C yr BP recorded in a peat bog profile[END_REF][START_REF] Klaminder | Isotopic trends and background fluxes of atmospheric lead in northern Europe: analyses of three ombrotrophic bogs from south Sweden[END_REF], Roos-Barraclough et al. 2004[START_REF] De | Multiproxy evidence of 'Little Ice Age' palaeoenvironmental changes in a peat bog from northern Poland[END_REF], Kylander et al. 2010). However, while these studies contribute greatly to accurately retrieving and better understanding the inorganic geochemical signals contained in peat records, no comprehensive review of the various existing geochemical protocols has been undertaken. Such an idealised protocol is much needed because some fluctuations in geochemical records may be artefacts of the analytical procedures used to obtain the information. A protocol paper dedicated to peat geochemistry, which focuses on metal contamination and minimising peat compression during coring, has already been published by [START_REF] Givelet | Suggested protocol for collecting, handling and preparing peat cores and peat samples for physical, chemical, mineralogical and isotopic analyses[END_REF]. This paper also provides some methods for improving sampling resolution, and any researcher interested in the inorganic geochemistry of peat is encouraged to read it.

Since 2004, multidisciplinary studies, as opposed to investigations focusing solely on the geochemistry of major and trace elements in peat, have been achieved; and new projects combining biological and geochemical proxies have been launched. This article presents refinements of the protocol proposed by [START_REF] Givelet | Suggested protocol for collecting, handling and preparing peat cores and peat samples for physical, chemical, mineralogical and isotopic analyses[END_REF], but also different strategies depending on the inorganic geochemical proxies being studied.

PEAT DRYING

For all inorganic geochemical analyses, the samples must be dried before analysis. Usually, simple overnight oven drying at 105°C is performed. However, there is a major issue concerning such hot drying, as some elements are potentially volatile even at low temperatures. Therefore, we recommend freeze-drying following the procedure described below:

1. Freeze the sub-samples in their bags overnight at -20°C.

2. Also prepare ice cubes.

3. Place a series of bags upright in a plastic box that fits inside the freeze dryer.

4. Open the bags in such a way that they will remain slightly open during freeze-drying.

5. If some sub-samples are beginning to thaw after this operation, close the bags and place the whole box in the freezer for one hour, then retrieve the box and re-open the bags.

6. Start the freeze dryer.

7. Place a couple of ice cubes in visible positions around the sample bags within the box, then place the box in the freeze dryer. The purpose of the ice cubes is to monitor the completeness of freeze-drying.

8. When the ice cubes have totally disappeared, stop the freeze dryer, remove the box and close the bags.

9. Inspect the sub-samples whilst still inside their closed bags. They should be easily crushable. If any of them (especially large ones) still have solid cores, it may be that some ice is still present. In that case the sub-samples should be re-processed, starting from Step 1 above.

Note that it is possible to measure peat density in conjunction with freeze-drying. If this is required, before Step 1 of the freeze-drying process: Then, after Step 9 of the freeze-drying process: d) Weigh the freeze-dried sub-samples in their bags.

PEAT GRINDING

An environmental sample may be crushed in several different ways. The most widely used technique employs an automated planetary agate mortar ("ball mill"), which can crush rocks that have previously been reduced to a gravel grain-size. Each sample is placed in a separate agate jar, together with several small agate marbles. The jars are then rotated at high speed so that the movement of the marbles crushes the samples. This crushing method is highly efficient for non-fibrous peat, and in this case the crushing time can be reduced to ten minutes at 400 r.p.m. However, fibrous peat requires longer crushing times, sometimes up to one hour. The samples must be absolutely dry because even small quantities of residual water will cause the powder to clump and damage the device. After every crushing and before the next, each jar should be carefully cleaned with tap water and a synthetic brush, rinsed with ultra-pure water or acetone, and thoroughly dried using compressed air. Ideally, jars used for peat samples should not be used for other sample types such as rock, which may have very different elemental concentrations, in order to prevent any possibility of cross-contamination. An advantage of this crushing method is that one can control the theoretical grain size, which may be important for some non-destructive geochemical analyses such as X-ray fluorescence. A limitation is the amount of material needed; the pre-crushed sample should almost cover the agate marbles, which may be problematic depending on the size of the jars. For small samples, hand crushing using an agate mortar and pestle can be used as an alternative, but will not deliver the fine grain size obtained using the ball mill. Moreover, fibrous peat is difficult to crush by hand, and the overall procedure is time-consuming.

Because peat is composed mainly of plant remains, mills are good tools for pulverising peat samples. They can be crushed simply and quickly in an inexpensive coffee mill or herb grinder with an aluminium cup. However, possible contamination must be taken into account in a systematic way, especially for early Holocene peat with very low elemental contents. To our knowledge, the possibility of contamination from commercial mills has not been investigated.

A homogenous and fine preparation of the sample is normally necessary for geochemical analyses. However, first results on pre-industrial Belgian peat indicate quite homogenous trace element concentrations regardless of the method of preparation (Table 1). For investigation of volatile organic and metal pollutants, peat may be prepared using similar methods with the modification that liquid nitrogen is involved (Becker et al. 2006[START_REF] Pugh | Design and applications of the National Institute of Standards and Technology's (NIST's) environmental specimen banking programs[END_REF].

NON-DESTRUCTIVE CHEMICAL ANALYSES BY X-RAY FLUORESCENCE

X-ray fluorescence (XRF) is an inexpensive, rapid and non-destructive technique which is used widely in geology to characterise the bulk geochemical composition of a variety of materials. Samples may be analysed as compressed powder, pellets or fused glass discs (see [START_REF] Potts | A Handbook of Silicate Rock Analysis[END_REF] and references therein). In the case of peat, the last of these is excluded because the quantity of ash residue remaining after fusion would be too small to be measurable. The Table 1. Measured concentrations (μg g -1 ) of selected elements in three sets of replicate peat samples which were crushed using two different techniques. The variation between replicates is quite similar regardless of the crushing method used. )² ( 1

Sample Name

σ λ - = Z k (1)
where λ is the wavelength, Z is the atomic number of the element, k is the orbital (K,L,M) constant and σ is a shielding constant.

According to Beer-Lambert's law, the intensity of the secondary X-ray is given by:

µx e I I - = 0 (2)
where x is the finite thickness of the sample and µ is the mass attenuation coefficient. This coefficient is density dependent. In other words, the first limiting variable in XRF analyses of peat will be the density of the sample. A ~1.5 g dry pellet of a low-density material such as peat would require 3-5 g of powder for homogenisation reasons, and this in turn would be derived from 30-50 g of fresh peat on account of its high (~90 %) water content. This quantity of fresh material is difficult to obtain in conjunction with high-resolution sampling (i.e. 1 cm thick peat samples). However, [START_REF] Richardson | The use of X-ray fluorescence spectrometry for the analysis of plants, especially lichens, employed in biological monitoring[END_REF] noted that quantity is not a limiting factor for peat mosses, as 3 g of dry peat is relatively homogenous (owing to compaction during the burial process) compared to, for example, 3 g of single plant leaves. Therefore, it is possible to sub-sample at depth intervals of one centimetre and analyse the resulting dry powder. Depending on the set-up of the XRF device, it may be necessary to press the powder into a pellet. This may require addition of a coherence agent (e.g. lucite), but our experience has shown that most peat powders can be pressed successfully without any additive. Moreover, many modern XRF devices can provide measurements on both pellets and powder. For organic samples, measurements on powder are generally preferred, especially as the samples can then be re-used for other analyses.

Quantitative analyses of theoretically infinitely thick samples bring to the fore a second problem, namely the matrix effects caused by the mineralogy and thickness of the pellet or the powder layer. This is especially critical when analysing low-density materials (e.g. plant, food, lichen or peat samples). Amongst these effects are the attenuation of the primary X-ray beam, the attenuation of the fluorescence radiation, the secondary fluorescence, and the tertiary fluorescence [START_REF] Potts | A Handbook of Silicate Rock Analysis[END_REF]. In rock analyses, the secondary absorption effect is dominant, primary absorption is less significant, and the last two effects are often ignored in correction procedures [START_REF] Potts | A Handbook of Silicate Rock Analysis[END_REF]. Thus, there is a critical maximum depth below the sample surface where Xrays can no longer penetrate and be detected by the spectrometer. However, in peat samples (and indeed more generally in organic samples), an inverse effect can be expected because, being composed primarily of C, H, and O, their mass absorption coefficients are very low. In fact, bulk organic material is so transparent to X-rays that minor concentrations of elements other than C, H, and O could bias the absorbency of the pellet. Again, it must be emphasised that the powder weight and pellet thickness should be sufficient for the bulk analyses required, especially in the case of heavy metals.

To avoid transparency problems in plant leaves or lichen samples, [START_REF] Richardson | The use of X-ray fluorescence spectrometry for the analysis of plants, especially lichens, employed in biological monitoring[END_REF] recommend the addition of a moderately light element such as Al or Si in order to increase the absorbency of the pellet, which they postulate renders XRF suitable for determining low levels (i.e. a few μg g -1 ) of heavy metals such as Zn, Ni and Pb in plant material. Conversely, we do not recommend adding anything to the samples, as this amounts to contamination making them unusable for other purposes, and thus cancels out one of the main advantages of non-destructive XRF analyses. The most satisfactory method for circumventing the matrix effect is to calibrate the XRF properly by repeatedly measuring an adequate series of standards (i.e. with the same matrix as the samples). These standards are generally plant materials such as leaves, lichens, or sometimes coal. By calibrating the XRF using this series of standards, and by analysing the sample using the same procedure, the matrix effects can be prevented and good data can be acquired. Several environmental geochemists have now designed and optimised XRF devices for peat so that dry peat powder can be routinely analysed for major and trace elements (Cheburkin & Shotyk 1996[START_REF] Pusch | Experience from preparation and investigation of clay microstructure[END_REF], 2005). The detection limits and standard errors are variable but generally higher than for destructive techniques such as ICP-MS. They are suitable for a wide range of peat samples, and XRF has the great advantage of not requiring sample digestion. Also, new commercial XRF devices give promising detection limits which may possibly broaden their ranges to encompass low concentrations.

DESTRUCTIVE PEAT ANALYSES: PEAT DIGESTION

FOR ELEMENTAL AND ISOTOPIC GEOCHEMISTRY

Microwave system

The different protocols for peat digestion have been comparatively described and discussed in detail by [START_REF] Krachler | Digestion procedures for the determination of antimony and arsenic in small amounts of peat samples by hydride generation-atomic absorption spectrometry[END_REF]Krachler et al. ( , 2002aKrachler et al. ( , 2002b[START_REF] Krachler | Optimising accuracy and precision of lead isotope measurement ( 206 Pb, 207 Pb, 208 Pb) in acid digests of peat with ICP-SMS using individual mass discrimination correction[END_REF]) and others [START_REF] Weiss | Microwave digestion of ancient peat and determination of Pb by voltammetry[END_REF]. Recently developed microwave ovens and autoclaves enable the digestion of peat samples under high pressure and high temperature. The advantages of microwave systems are speed and the smaller volumes of acid required. Mixture recipes include ultrapure HNO 3 and HF or HBF 4 to digest minerals and HCl and/or H 2 O 2 to dissolve organic matter through oxidation. [START_REF] Krachler | Digestion procedures for the determination of antimony and arsenic in small amounts of peat samples by hydride generation-atomic absorption spectrometry[END_REF] show that HBF 4 can be used instead of the more harmful HF in microwave systems.

Mixed ashing-acid digestion procedure

When a microwave oven or autoclave is not available for digesting the peat samples, it is still possible to process them with some minor adaptations to the digestion protocol. Direct digestion of 200 mg samples of peat powder can be carried out in dedicated 15 ml vials made from Teflon ® or carbon glass using a mixture of HNO 3 -HCl and HF, possibly with H 2 O 2 , on a hotplate or in a warm waterbath. The main problem that arises in digesting peat in concentrated acid is that the high organic matter content causes a strongly exothermic reaction. Moreover, the reaction of bulk peat with acids is relatively 'dirty' and this often leads to incomplete digestion, reagent consumption, or a long time requirement. An easy way to circumvent these problems is to ash the peat before acid digestion. The ashing should ideally be performed in ceramic crucibles topped with ceramic caps, under oxidative environment, at 550°C for four hours. The ash residue needs to be handled with great care and its transfer to the Teflon ® vials should be performed in a room without air turbulence; firstly because the ash is very volatile, and secondly because only a small fraction of the initial volume of powder will remain as residue. Moreover, the reaction of organic matter with HNO 3 is strongly exothermic and causes a lot of degassing, leading to potential health hazards. A step-by-step procedure for digesting peat samples without an autoclave or microwave oven is given below:

1. Mark the bottom of each crucible using a graphite pen (graphite will survive the high ashing temperature).

2. Weigh the crucibles with their caps.

3. Weigh one crucible without its cap and reset the scale to zero.

4. Pour and weigh precisely 100 mg of dry peat powder into this crucible, and put the cap on it.

5. Alternatively, as 100 mg of dry peat powder will yield 1-10 mg of ash, and therefore a very small quantity of material to be transferred into the Teflon® vial, 1-2 g can be weighed and burned, and then an aliquot of the resulting ash can be weighed into the Teflon® vial.

6. Repeat the operation for all the samples.

7. Put the crucible in a muffle furnace and set the time to four hours and the temperature to 550°C.

8. Once the oven has cooled, place the crucibles in a desiccator or desiccator cabinet (this will remove any water adsorbed by the ash during cooling of the oven). 9. Re-weigh each closed crucible and calculate the loss on ignition by deducting the weight of the empty crucible; Chambers et al. (this volume) give a precise protocol for this measurement.

10. Mark all the Teflon ® vials, on both vials and caps, with waterproof pen (this operation may be repeated during the digestion process as the markings will progressively disappear).

11. Carefully transfer the ash into the Teflon ® vials.

12. The remaining steps should be performed in a high-quality clean laboratory (at least class 100) using high-purity reagents (bidistilled or commercial suprapur ® , or ultrapur ® ); 25. If the analytical session is not scheduled to immediately follow digestion, it is better to store the residues dry and dissolve them in diluted HNO 3 at the last moment before analysis.

26. The precise powder weight and the precise diluted HNO 3 weight will allow calculation of the dilution factor, which must be applied to the results obtained subsequently.

Lead isotope analysis

Lead (Pb) isotopes are commonly measured in digested peat samples, either directly [START_REF] Krachler | Optimising accuracy and precision of lead isotope measurement ( 206 Pb, 207 Pb, 208 Pb) in acid digests of peat with ICP-SMS using individual mass discrimination correction[END_REF] or indirectly [START_REF] Weiss | Accurate and precise Pb isotope ratio measurements in environmental samples by MC-ICP-MS[END_REF]) after resin extraction (Table 2). Measurements using multicollection instruments (MC-ICP-MS, TIMS) need preliminary Pb purification [START_REF] Weiss | Accurate and precise Pb isotope ratio measurements in environmental samples by MC-ICP-MS[END_REF]. This purification can be affected if the digestion is not complete. Using digested peat samples directly, some problems have been encountered during resin extraction and filament loading for TIMS, perhaps because of remaining soluble organic material. Thus, in order to get better measurements on early Holocene peat, we suggest digesting the peat samples again using microwave systems or after ashing.

Table 2. Ion exchange column procedure for separating Pb from the sample matrix [START_REF] Weiss | Accurate and precise Pb isotope ratio measurements in environmental samples by MC-ICP-MS[END_REF]). The columns used here were made in-house from Teflon tubing and packed with 600 µl of EiChrom Sr-resin, resulting in a bed height of about 0.5 cm.

Stage

TECHNIQUES IN PROSPECT

Impregnated peat

Resin impregnation is classically used in micromorphology of soils (e.g. [START_REF] Murphy | Thin Section Preparation of Soils and Sediments[END_REF][START_REF] Tippkotter | Evaluation of polyester, epoxy and acrylic resins for suitability in preparation of soil thin sections for in situ biological studies[END_REF] and to stabilise lake sediments (e.g. [START_REF] Lamoureux | Embedding unfrozen lake sediments for thin section preparation[END_REF], Lotter & Lemke 1999, Boës et al. 2005) 1996), depending on the purpose of the analyses; the most widely used are epoxy polymers (e.g. [START_REF] Francus | An image-analysis technique to measure grain-size variation in thin-sections of soft clastic sediments[END_REF]).

The high water and organic contents of peat render it especially difficult to impregnate, and few laboratories have developed specific techniques (e.g. [START_REF] Mackenzie | The preparation and study of thin sections of wet organic soil materials[END_REF][START_REF] Takeda | A rapid method for preparing thin sections of soil organic layers[END_REF], De Vleeschouwer et al. 2008b). Water removal must be performed by freeze-drying because simple oven drying will lead to shrinkage; and full water-acetone exchange cannot be performed, as acetone causes physical stress within the peat by dissolving organic matter and weakening plant cells. As this technique is described prospectively here, we do not report the laboratory steps in full; instead, we direct the reader to De Vleeschouwer et al. (2008b), where the methodology is described step-by-step along with the main problems encountered (sample size, type of peat, freeze-drying of large sections, polymer viscosity, acetone content).

The impregnation of peat deposits can reveal information concerning their state of preservation, vegetation type, and the presence of charcoal or mineral particles. More specifically, their subsequent study in thin section using various techniques allows a broad range of observations which are useful for better understanding peat accumulation, compression and decomposition; and also to give a visual counterpart to geochemical analyses.

Continuous core scanning

X-ray fluorescence (XRF) core scanning has been widely used in studies of marine and lacustrine sediments [START_REF] Jansen | CORTEX, a shipboard XRFscanner for element analyses in split sediment cores[END_REF][START_REF] Haug | Southward migration of the intertropical convergence zone through the Holocene[END_REF], Brown et al. 2007[START_REF] Francus | The potential of high-resolution X-ray fluorescence core scanning: applications in paleolimnology[END_REF]) and provides rapid, non-destructive analyses of split sediment cores for a broad range of major, minor and trace elements [START_REF] Jansen | CORTEX, a shipboard XRFscanner for element analyses in split sediment cores[END_REF][START_REF] Richter | The Avaatech XRF core scanner: technical description and applications to NE Atlantic sediments[END_REF]. A mean spatial resolution of 1 mm can be achieved, potentially allowing for detection of fine structures and tackling of high-frequency signals. The XRF logging results are presented as number of XRF counts, tracing down-core geochemical variability in the sediment column. The first weakness of XRF core scanning of peat sections is that quantitative geochemical interpretation of bulk sediment data has not yet been attempted for this type of deposit. This is a major problem because further geochemical calculations, e.g. of elemental fluxes or dust concentrations, are often performed on the basis of elemental concentrations. The calibration of an XRF core scanner would require (1) corrections for sediment porosity, which is high in peat deposits and likely to be variable at a small spatial scale; (2) calibration based on standards with a similarly organic-rich matrix; and (3) validation by destructive quantitative analyses at selected depths. Attempts have been made to calibrate XRF core scanners [START_REF] Weltje | Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: theory and application[END_REF], but this is a time-consuming procedure requiring careful checks which is applicable to only one type of sediment and selected chemical elements, so that the calibration is more a case-by-case problem than an easily applicable procedure. It is hoped that further efforts will be made to solve this major difficulty. The second major problem of applying this technique to peat cores is that the split cores must be carefully flattened in order to reduce high noise:signal ratio arising from surface irregularities. Flattening is difficult in the case of peat, which is composed of various plant macrofossils, roots and fine material. Nonetheless, XRF scanning of fresh peat cores has sometimes been conducted successfully (e.g. Caseldine et al. 1999). Surface roughness problems may be avoided by stabilising the peat sections in resin (see Swindles et al. this volume) and polishing each impregnated block to obtain a perfectly smooth surface. However, the chemical composition of the resin must be critically assessed. The limited response depth of elements to incoming X-ray radiation should also be taken into account (Richter et al. 2006 and references therein), because the XRF scanning method will not 'detect' grains far below the surface of the impregnated core section. An example of successful XRF core scanning of impregnated peat sections is described in De Vleeschouwer et al. (2008a), who applied the technique to investigate geochemical trends in mineral-rich peat sections from Iceland and to attempt to detect cryptotephras. The major conclusions were that, although substantial tephra layers leave a clear signature in XRF logging records, cryptotephras cannot be detected; and that minor variability in elemental geochemical records can be interpreted only if microscopic observations of corresponding thin sections are carried out. Therefore, XRF core scanning of impregnated peat sections has potential for a wide range of chemical investigations, but there are problems regarding data acquisition.

Non-traditional stable isotopes and platinoids

Non-traditional stable isotopes (Hg, Cu, Zn, Sn etc.) are promising tracers of environmental processes and human influences, and of sources of metal in the environment [START_REF] Weiss | Atmospheric deposition and isotope biogeochemistry of zinc in ombrotrophic peat[END_REF]. Also, [START_REF] Rauch | Recent changes in platinum group element concentrations and osmium isotopic composition in sediments from an urban lake[END_REF][START_REF] Rauch | Anthropogenic forcings on the surficial osmium cycle[END_REF] have recently described a protocol specifically dedicated to the characterisation of platinoid and Os isotopes in peat. However, owing to the complexity of the peat matrix and because of possible volatility, further research is needed before a clear protocol can be provided for these measurements.

  a) Weigh the empty bags that will contain the subsamples. b) Precisely measure the volume of each subsample, following the instructions given by De Vleeschouwer et al. (2010a) and Chambers et al. (this volume). c) Put the fresh sub-samples inside their individual bags and weigh again.

  Becker, P.R., Gunter, E.W., Schlüter, C., Shibata, Y. & Wise, S.A. (2006) The U.S. national biomonitoring specimen bank and the marine environmental specimen bank. Journal of Environmental Monitoring, 8, 795-799. Boës, X. & Fagel, N. (2005) Impregnation method for detecting annual laminations in sediment cores: an overview. Sedimentary Geology, 179, 185-194. Boës X., Piotrowska N. & Fagel, N. (2005) Highresolution diatom/clay record in Lake Baikal from grey scale, and magnetic susceptibility over Holocene and Termination I. Global and Planetary Change, 46, 299-313. Brown, E.T., Johnson, T.C., Scholz, C.A., Cohen, A.S. & King, J.W. (2007) Abrupt change in tropical African climate linked to the bipolar seesaw over the past 55,000 years. Geophysical Research Letters, 34, L20702. Camuti, K.S. & McGuire, P.T. (1999) Preparation of polished thin sections from poorly consolidated regolith and sediment materials. Sedimentary Geology, 128, 171-178. Caseldine, C., Baker, A. & Barnes, W. (1999) A rapid, non-destructive scanning method for detecting distal tephra layers in peats. The Holocene, 9, 635-638. Chambers, F.M., Yu, Z. & Beilman, D. (this volume) Measurements of humification, bulk density, loss on ignition, total, organic and inorganic carbon in peat: a review. Mires and Peat, 7, in prep. Cheburkin, A.K. & Shotyk, W. (1996) An Energydispersive Miniprobe Multielement Analyzer (EMMA) for direct analysis of Pb and other trace elements in peats. Fresinus Journal of Analytical Chemistry, 354, 688-691. Cheburkin, A.K. & Shotyk, W. (1999) Highsensitivity XRF analyzer (OLIVIA) using a multi-crystal pyrographite assembly to reduce the continuous background. X-Ray Spectrometry, 28, 145-148. Cheburkin, A.K. & Shotyk, W. (2005) Energydispersive XRF spectrometer for


Colinet Hervé
email: herve.colinet@univ-rennes1.fr

Renault David

Hervé Colinet

Dietary live yeast alters metabolic profiles, protein biosynthesis and thermal stress tolerance of Drosophila melanogaster

Keywords: fruit fly, nutrition, live yeast, metabolic fingerprinting, cold stress 2

come

Dietary live yeast alters metabolic profiles, protein biosynthesis and thermal stress tolerance of Drosophila melanogaster

Introduction

A number of studies have examined how nutrition affects various traits in insects, with special emphasis on the effects of dietary intake on reproduction and longevity (Le [START_REF] Bourg | Food restriction and longevity in Drosophila melanogaster[END_REF][START_REF] Chippindale | Phenotypic plasticity and selection in Drosophila life history evolution. I. Nutrition and the cost of reproduction[END_REF][START_REF] Leroi | Long term evolution of a life history trade-off in Drosophila melanogaster. 1. The role of genotype by environment interaction[END_REF][START_REF] Anagnostou | Influence of dietary yeasts on Drosophila melanogaster life-history traits[END_REF]. More specifically, the trade-off between reproduction and lifespan resulting from the qualitative and quantitative manipulations of diet has been studied extensively [START_REF] Carey | Longevity-fertility trade-offs in the tephritid fruit fly, Anastrepha ludens, across dietary restriction gradients[END_REF][START_REF] Ellers | Discriminating between energetic content and dietary composition as an explanation for dietary restriction effects[END_REF][START_REF] Moore | Oosorption in response to poor food: complexity in the tradeoff between reproduction and survival[END_REF]. Drosophila melanogaster is a very popular model used in the dietary restriction (DR) literature of gerontology because of its relatively short generation time and ease of handling for demographic analysis [START_REF] Partridge | Dietary restriction in Drosophila[END_REF]. The composition of Drosophila sp. food recipes often varies among laboratories and medium types, but the standard ingredients usually include water, agar, sugar, killed yeast, and fungicides. In addition, the medium can be supplemented with live yeast. Often adding live yeast on the surface of the diet strongly stimulates females to lay eggs [START_REF] Markow | Drosophila: A guide to species identification and use[END_REF][START_REF] Stocker | Getting started: an overview on raising and handling Drosophila[END_REF]. Dietary yeast is a major source of nutrition for the adults and larvae of most saprophagous Drosophila (Diptera: Drosophilidae) [START_REF] Begon | Yeasts and Drosophila[END_REF]. It provides essential nutrients for the developmental and reproductive processes such as amino acids, sterols, vitamins, and fatty acids [START_REF] Davis | Essential dietary amino acids for growth of larvae of the yellow mealworm, Tenebrio molitor[END_REF][START_REF] Anagnostou | Influence of dietary yeasts on Drosophila melanogaster life-history traits[END_REF]). As a result, the concentration of yeast in the artificial diet is the primary determinant of egg production in D.

melanogaster [START_REF] Sang | Nutritional requirements of axenically cultured Drosophila melanogaster adults[END_REF][START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF], and the formation of yolk proteins can thus be strongly curtailed by depriving flies of nutritional proteins or essential amino acid present in yeasts [START_REF] Sang | Nutritional requirements of axenically cultured Drosophila melanogaster adults[END_REF][START_REF] Bownes | Dietary components modulate yolk protein gene transcription in Drosophila melanogaster[END_REF][START_REF] Chippindale | Phenotypic plasticity and selection in Drosophila life history evolution. I. Nutrition and the cost of reproduction[END_REF]. Hence, the incorporation of live yeast in mediums highly stimulates vitellogenesis because it provides essential nutrients that are not necessarily present in large amounts in the usual adult food [START_REF] Sang | Nutritional requirements of axenically cultured Drosophila melanogaster adults[END_REF][START_REF] Simmons | An analysis of resource allocation in response to dietary yeast in Drosophila melanogaster[END_REF].

Owing to the major importance of the nutritional status on physiological and biochemical processes of insects, any alteration of nutritional regime is likely to affect all aspects of their life, including not only reproduction but also stress tolerance (such as thermal tolerance) [START_REF] Hallman | Temperature sensitivity in insects and applications in integrated pest management[END_REF][START_REF] Chown | Insect Physiological Ecology: Mechanisms and Patterns[END_REF][START_REF] Nyamukondiwa | Thermal tolerance in adult Mediterranean and Natal fruit flies (Ceratitis capitata and Ceratitis rosa): Effects of age, gender and feeding status[END_REF][START_REF] Andersen | Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster[END_REF][START_REF] Colinet | Insect parasitoids cold storage: A comprehensive review of factors of variability and consequences[END_REF][START_REF] Sisodia | Experimental evidence for nutrition regulated stress resistance in Drosophila ananassae[END_REF]).

Yet, a limited number of studies have considered the impact of nutritional resources on environmental stress tolerance, and more particularly on thermal tolerance [START_REF] Andersen | Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster[END_REF]. In this particular respect, no clear-cut response has been observed as nutritional effects on thermal tolerance seem to be rather complex and involve many interacting factors.

Carbohydrate-rich diets tend to increase drosophilids' cold tolerance compared to protein-rich diets, and the opposite effect is observed on measures of heat resistance [START_REF] Andersen | Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster[END_REF][START_REF] Sisodia | Experimental evidence for nutrition regulated stress resistance in Drosophila ananassae[END_REF]. However, when supplemented at high levels, dietary sugars induce a severe nutritional imbalance and a pathological state in D. melanogaster [START_REF] Wang | Physiological genetics of the response to a high-sucrose diet by Drosophila melanogaster[END_REF][START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF][START_REF] Musselman | A high-sugar diet produces obesity and insulin resistance in wild-type Drosophila[END_REF]Colinet et al., 2013a) and these high sugar doses negatively affect cold tolerance (Colinet et al., 2013a). Quantitative manipulation of food supply via dietary restriction (i.e. dilution.) has no detectable effect on cold tolerance (chill-coma recovery, CCR) of young flies and only marginally reduces cold tolerance later in adult life [START_REF] Burger | The functional costs and benefits of dietary restriction in Drosophila[END_REF]. Removing (or adding) live yeast from D. melanogaster food also impacts on thermal traits in a rather complex manner. Le Rohellec and Le [START_REF] Le Rohellec | Contrasted effects of suppressing live yeast from food on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF] found that removing live yeast only weakly decreased cold survival of females subjected to a 16 h coldshock (0 °C), but only when these were mated. In another study, absence of live yeast in food killed nearly 100% of flies (males and females) subjected to the same cold treatment, whereas access to live yeast resulted in medium to high survival rate depending on age (Le [START_REF] Bourg | Combined effects of suppressing live yeast and of a cold pretreatment on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF]. Tolerance to heat (37 °C) was either unaffected (Le [START_REF] Bourg | Combined effects of suppressing live yeast and of a cold pretreatment on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF] or improved by removal of live yeast (but in young females only) (Le Rohellec and Le [START_REF] Le Rohellec | Contrasted effects of suppressing live yeast from food on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF]. From the above examples, it seems clear that nutritional status can be a significant component of thermal tolerance of insects, affecting both heat-and cold-related traits. It also appears that nutritional effects on thermal tolerance depend on several interacting factors including gender, mating status, and age. Although the physiological and biochemical basis of thermal responses is becoming clearer through metabolic and physiological studies [START_REF] Overgaard | Metabolomic profiling of rapid cold hardening and cold shock in Drosophila melanogaster[END_REF][START_REF] Doucet | The bugs that came in from the cold: molecular adaptations to low temperatures in insects[END_REF]Colinet et al., 2012a;[START_REF] Kostál | Conversion of the chill susceptible fruit fly larva (Drosophila melanogaster) to a freeze tolerant organism[END_REF][START_REF] Storey | Insect cold hardiness: metabolic, gene, and protein adaptation[END_REF][START_REF] Teets | Physiological mechanisms of seasonal and rapid coldhardening in insects[END_REF], there remains limited information on the physiology of nutrition-mediated variation in thermal tolerance. A way in which insects deal with nutrient variations is through altered physiology, namely by affecting developmental and metabolic processes [START_REF] Markow | Elemental stoichiometry of Drosophila and their hosts[END_REF]. Therefore, it can be assumed that manipulating the source of essential nutrients found in live yeast, such as amino acids and proteins, could alter the physiology and also the general stress tolerance.

In the present study, we completed a comprehensive assessment of the impact of dietary live yeast supplementation on body mass characteristics, proteins, metabolic profiles and basal cold tolerance (to acute and chronic exposures) in D. melanogaster females. We hypothesized that the absence of the source of proteins (i.e. live yeast) from adult food would be associated with deep physiological alterations; therefore, we expected contrasted metabolic profiles (i.e. metabotype) between yeast-deprived and yeast-fed females. Because live yeast is a rich source of proteins and amino acids, we hypothesized that pathways related to protein biosynthesis would be particularly targeted by dietary live yeast supplementation. In addition, we expected body mass parameters to be strongly curtailed by depriving females of live yeast.

Finally, we hypothesized that the nutritional and the metabolic variations caused by manipulating dietary live yeast will translate into altered thermal stress tolerance.

Materials and methods

Fly culture and diets

We conducted the experiments on a mass-bred D. melanogaster line derived from the mix of two wild populations collected in October 2010 and September 2011 at Plancoët (Brittany, France). Prior to the experiment, flies were maintained in laboratory in 200 mL bottles at 25 ± 1 °C (16L:8D) on standard fly medium consisting of deactivated brewer yeast (80 g/L) (MP Biochemicals, Illkirch, France), sucrose (50 g/L), agar (15 g/L), kalmus (9 g/L)

and Nipagin® (8 mL/L) as described previously (Colinet et al., 2013a). To generate flies for the experiments, groups of 15 mated females were allowed to lay eggs during a restricted period of 6 h in bottles (200 mL) containing 25 mL of standard fly medium. This controlled procedure allowed larvae to develop under uncrowded conditions at 25 ± 1 °C (16L:8D). At emergence, adult flies were allowed to age for six days on different diets and controls. The diets were changed every day for six consecutive days. Two different experiments were used to assess the effect of adult dietary live yeast supplementation (see Fig. 1 for experimental design).

- In the first experiment, emerging flies did not have any nutrient supply except from sugar. It is thus conceivable that these flies could suffer from malnutrition. Therefore, a second experiment was designed with a standard diet as control that contains protein supply [SAY(-)] rather than a minimal diet [SA], in order to assess the effect of dietary live yeast supplementation without any putative malnutrition. In both experiments, the amounts of sugar, agar and killed yeast when supplied were 50 g/L, 15 g/L and 80 g/L respectively. When supplemented, the live yeast was provided with ad libitum paste placed on the surface of the food [i.e. for SAY(+) and SAY(±)]. We used synchronized six day-old adults for all assays to avoid the uncontrolled variation of stress tolerance during the first days of age (Colinet et al., 2013b). Adults were sexed visually (with an aspirator) without CO 2 to avoid any confusing metabolic effects due to anaesthesia (Colinet and Renault, 2012), and only females were kept.

Six day-old females from each nutritional group were either directly used for the cold assays or snap-frozen in liquid nitrogen and stored at -80 °C for the other assays.

Body mass and protein levels

We assessed total protein content using the Bradford procedure [START_REF] Bradford | A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding[END_REF].

Twelve biological replicates, each consisting of a pool of three females, were used for each experimental condition. Each sample was vacuum-dried (GENEVAC, model DNA-23050-B00) set at 30 °C for 24 h and then weighed (dry mass, Mettler Toledo UMX2, accurate to 1 µg) before proteins were extracted in a phosphate buffer (100 mM KH 2 PO 4 , 1 mM DTT and 1 mM EDTA, pH 7.4, [START_REF] Foray | A handbook for uncovering the complete energetic budget in insects: the van Handel's method (1985) revisited[END_REF] and homogenized using bead-beating at 25 Hz for 1.5 min. The concentration of total proteins was then measured in the whole body extracts using Bio-Rad Protein Assay (catalog number 500-0006) following manufacturer's instructions.

For each nutritional treatment, 15 females were subjected to individual fresh mass (FM) measurements (Mettler Toledo UMX2, accurate to 1 µg). Then, individual females were dried at 60 °C for two days, and reweighed to measure dry mass (DM). Water mass (WM) was determined by subtracting DM from FM. Water content (WC) represents the ratio WM/FM. Lean dry mass (LDM) was measured by extracting total lipids in a chloroform/methanol solution (Folch reagent 2:1, v:v) for one week under continuous agitation. The samples were then dried at 60 °C to eliminate residues of the extracting solution before measurement of LDM. Body lipid mass (LM), corresponding to DM-LDM, was calculated (see [START_REF] Colinet | Water relations, fat reserves, survival, and longevity of a cold-exposed parasitic wasp Aphidius colemani (Hymenoptera : Aphidiinae)[END_REF]. Folch reagent may extract a small fraction of other compounds than lipids, but measurements obtained with this method are still considered as a good index of lipid content for comparative studies [START_REF] Williams | Triacylglyceride measurement in small quantities of homogenised insect tissue: comparisons and caveats[END_REF].

2.3.

Cold tolerance assays

Different metrics were used to assess cold tolerance. First, recovery time following a non-lethal chronic cold stress was measured (i.e. chill-coma recovery, CCR). Fifty females were exposed to 0 °C for various durations: 8, 10 and 12 h for the flies of the experiment 1

[i.e. SA vs. SAY(+)], and 10 and 12 h for the flies of the experiment 2 [i.e. SAY(-) vs.

SAY(±)

]. Cold-exposed flies were then allowed to recover at 25 ± 1 °C (16L:8D) and recovery times were individually recorded; flies were considered recovered when they stood up. A cold incubator (Model MIR-153, SANYO Electric Co. Ltd, Japan) was used for the assays. After scoring the recovery times, the same females were returned to 25 ± 1 °C (16L:8D) on their respective diet and the mortality was scored after 24 h (i.e. latent damage assessment).

Second, tolerance to acute cold stress was measured. A total of 100 females (5 replicates, 20 females per replicate) were placed in 42 mL glass vials immersed in a glycol solution cooled to -3.5 °C for different durations: 90, 120 and 135 min for the flies of the experiment 1 [i.e.

SA vs. SAY(+)], and 90 and 120 min for the flies of the experiment 2 [i.e. SAY(-) vs.

SAY(±)

]. After the acute cold stress, the flies were returned to 25 °C on their respective diet, and the mortality was scored after 24 h. Most mortality in D. melanogaster adults happens within 24 h after the cold stress [START_REF] Rako | Complexity of the cold acclimation response in Drosophila melanogaster[END_REF], and we therefore did not consider a longer period.

Metabolic fingerprinting

The metabolic effect of dietary live yeast supplementation was assessed by comparing the metabotypes of SA vs. SAY(+) (experiment 1) and SAY(-) vs. SAY(±) (i.e. experiment 2). To ensure that the differences observed were not only related to presence/absence of live yeast in the gut content, we included an additional treatment where flies were starved before sampling.

In this experiment 3 (conducted in 2013), the same flies as in the experiment 2 were starved for 8 h on agar before their metabolic profiles were compared. Hence, we compared the following conditions: sugar, agar, killed yeast, plus 8h starvation (St-SAY-) versus sugar, agar, killed yeast and live yeast, plus 8h starvation (St-SAY±) (see Fig. 1).

For each nutritional group, six biological replicates, each consisting of a pool of 15 females, were used for metabolic fingerprinting. Each sample was weighed (Mettler Toledo UMX2, accurate to 1 µg) before metabolite extractions. Sample preparation and derivatization were performed as previously described (Colinet et al., 2012b), with minor modifications.

Briefly, after homogenisation in methanol-chloroform solution (2:1, v:v) and phase separation with 400 µL of ultrapure water, an 120 µL aliquot of the upper phase, which contained polar metabolites, was vacuum-dried. The dry residue was resuspended in 30 µL of 20 mg mL -1 methoxyamine hydrochloride in pyridine before incubation under automatic orbital shaking at 40 °C for 60 min. Then, 30 µL of MSTFA were added and the derivatization was conducted at 40 °C for 60 min under agitation (see Colinet et al., 2012b). A CTC CombiPal autosampler (GERSTEL GmbH and Co.KG, Mülheim an der Ruhr, Germany) was used, ensuring standardized sample preparation and timing. Metabolites were separated, identified and quantified using a GC/MS platform consisting of a Trace GC Ultra chromatograph and a

Trace DSQII quadrupole mass spectrometer (Thermo Fischer Scientific Inc, Waltham, MA, USA). The oven temperature ranged from 70 to 170 °C at 5 °C min -1 , from 170 to 280 °C at 7 °C min -1 , from 280 to 320 °C at 15 °C min -1 , and then, the oven remained at 320 °C for 4 min.

We completely randomized the injection order of the samples. All samples were run under the SIM mode rather than the full-scan mode. We therefore only screened for the 63 pure reference compounds included in our custom spectral database. Calibration curves for 60 pure reference compounds at 5, 10, 20, 50, 100, 200, 500, 750, 1000, 1500 and 2000 M concentrations were run concurrently. Chromatograms were deconvoluted using XCalibur 2.0.7, and metabolite levels were quantified using the quadratic calibration curve for each reference compound and concentration. Arabinose was used as the internal standard (see Colinet et al., 2012b). Among the 63 metabolites included in our spectral library, 37, 34 and 34 compounds were detected in the samples from experiment 1, 2 and 3 respectively (see Table 1 for compounds' list and abbreviations).

Statistics

Since allometric relationship may exist between the body mass parameters and size, we first determined if the variables were linearly related to LDM (with least-squares regressions) [START_REF] Packard | The use of percentages and size-specific indices to normalize physiological data for variation in body size: wasted time, wasted effort?[END_REF]. Analysis of covariance (ANCOVA) was then used if linear relationships was established, using LDM as co-variable, whereas analysis of variance (ANOVA1) was used with nutritional treatment as factor when the allometric relationship was not found. The same approach was used for analysing the protein content but with DM as covariable. Comprehensive details on regression statistics and individual plots are shown in supplementary file S1. For cold tolerance, Chi-square contingency tests were used to compare mortality rates between nutritional groups (with Yates' correction to prevent overestimation of statistical significance). For CCR, the data were used to generate temporal recovery curves which were compared with Mantel-Cox (Log rank) test. This non parametric method tests the null hypothesis that there is no difference between the populations in the probability of an event at any time point (i.e. a curve comparison test). Analyses were performed using Prism v.

5.01 (GraphPad Software, Inc., San Diego, CA, USA, 2007) or the statistical software 'R 2.13.0' (R Development Core Team, 2008). For metabolic data, a principal component analysis (PCA) was performed on the whole dataset to detect the compounds contributing the most to the separation between the nutritional groups. The inertia calculated in the PCA represents the part of the total variance that is due to the difference between modalities.

Scaled data (i.e. mean-centered and divided by SD) were used in the multivariate analyses to prevent the effects of the metabolite concentration means and ranges of variability on the correlations with the principal components (PCs). This analysis was performed using the 'ade4' library in the statistical software 'R 2.13.0'. In addition, to look for evidence of enriched metabolic pathways in response to dietary live yeast supplementation, metabolite pathway enrichment analysis (MPEA) was conducted using MetPA online package, with D.

melanogaster specific library [START_REF] Xia | MetPA: a web-based metabolomics tool for pathway analysis and visualization[END_REF], as previously described (Colinet et al., 2013a).

Results

Body mass and protein levels

The Figure 2 summarizes the variations in mass parameters according to nutritional treatments. The DM corresponds to the sum of LM and LDM, and FM corresponds to the sum of LM, LDM and WM (Fig. 2). Females fed with live yeast [SAY(+) and SAY(±)] were heavier in terms of FM and DM than their counterparts fed without live yeast. Since FM and DM were linearly related to LDM (P < 0.05; see supplementary file S1), we used ANCOVA to assess the effect of nutritional treatment with LDM as co-variable. The effect of the treatment remained significant even when the allometric effect of size was removed (FM: F = 30.69, df = 3, P < 0.001; DM: F = 8.27, df = 3, P < 0.001; n = 15). Multiple comparisons revealed that SA < SAY(-) < SAY(+) = SAY(±) for FM, and SA < SAY(-) = SAY(+) = SAY(±) for DM. The WM was also correlated to LDM (P < 0.05; see supplementary file S1).

The ANCOVA revealed a significant effect of the treatment (F = 29.94, df = 3, P < 0.001; n = 15). Multiple comparisons revealed that SA < SAY(-) < SAY(+) = SAY(±) for WM. Contrary to WM, the WC was unrelated to LDM (P > 0.05; see supplementary file S1) and the ANOVA did not detect variation according to the diet treatment (F = 0.197, df = 3, P = 1.61; n = 15). The LM was unrelated to LDM (P > 0.05; see supplementary file S1), and ANOVA detected a significant effect of nutritional treatment, with a lower total lipid content for the treatment SA (F = 10.4, df = 3, P < 0.001; n = 15). The LDM varied according to nutritional treatment (F = 76.14, df = 3, P < 0.001; n = 15), with the following rank order: SA < SAY(-)

< SAY(+) < SAY(±). Finally, the total protein content was not related to DM (P > 0.05; see supplementary file S1), and ANOVA revealed a significant effect of nutritional treatment (F = 155.3, df = 3, P < 0.001; n = 12), with higher protein contents in females fed with live yeast [i.e. SA < SAY(-) < SAY(+) < SAY(±)].

Cold tolerance

Concerning cold tolerance, we found that CCR significantly varied between the two nutritional groups of the experiment 1, with females fed on SAY(+) diet recovering faster than females fed on SA diet (Fig. 3). This difference manifested for all the durations of cold stress that were tested in the experiment 1 (8h: Chi² = 19.17, df = 1, P < 0.001; 10 h: Chi² = 16.29, df = 1, P < 0.001; 12 h: Chi² = 14.65, df = 1, P < 0.001; n = 50). Survival after chronic cold stress was also affected by nutritional regime. For all the durations of cold stress at 0 °C (8, 10 and 12 h), the post-stress mortality was significantly lower when females fed on SAY(+) diet compared to SA diet (8 h: Chi² = 21.23, df = 1, P < 0.001; 10 h: Chi² = 19.10, df = 1, P < 0.001; 12 h: Chi² = 21.23, df = 1, P < 0.001; n = 50) (Fig. 3). Finally, the acute cold tolerance also varied with the nutritional regimes of the experiment 1 (Fig. 3). For all the durations of cold stress at -3.5 °C (90, 120 and 135 h), the post-stress mortality was significantly lower when females fed on SAY(+) diet compared to SA diet (90 min: Chi² =39.61, df = 1, P < 0.001; 120 min: Chi² =35.57, df = 1, P < 0.001; 135 min: Chi² =17.56, df = 1, P < 0.001; n = 100).

The cold tolerance of the flies from the experiment 2 was also affected by the nutritional treatments; however, this was not manifested on CCR. Females feeding on SAY (-) and SAY(±) recovered from chronic cold stress with similar temporal dynamics (10 h: Chi² = 2.55, df = 1, P = 0.10; 12 h: Chi² = 1.01, df = 1, P = 0.31; n = 50) (Fig. 4). On the other hand, the survival after the chronic cold stress was affected by the nutritional regimes. For both durations of chronic cold stress (10 and 12 h), the post-stress mortality was significantly lower when females fed on SAY(±) diet compared to SAY(-) diet (10 h: Chi² = 5.02, df = 1, P = 0.025; 12 h: Chi² = 16.94, df = 1, P < 0.001; n = 50) (Fig. 4). Finally, the acute cold tolerance also varied with the nutritional regimes of the experiment 2. For both durations of acute cold stress at -3.5 °C (90 and 120 h), the post-stress mortality was significantly lower when females fed on SAY(±) diet compared to SAY(-) diet (90 min: Chi² =69.01, df = 1, P < 0.001; 120 min: Chi² =66.66, df = 1, P < 0.001; n = 100) (Fig. 4).

Metabolic fingerprinting

The metabolic profiles of flies from experiment 1 showed that a number of metabolites had their concentrations affected by dietary live yeast, which resulted in contrasted metabotypes between the two nutritional groups (Fig. 5). A clear-cut separation was observed along the first principal component (PC1) of the PCA, which accounted for 43.9% of the total inertia (Fig. 5). GDL, Fru, Glc, Tre and sorbitol were the molecules the most positively correlated to PC1 (i.e. accumulated in SA flies), whereas on the opposite side, the amino acids Val, Ile, Leu, Thr, Gly, Phe and Glu were the molecules the most negatively correlated to PC1 (i.e. accumulated in SAY(+) flies) (Fig. 5). The other principal components accounted for 28.4% (PC2) and 11.3% (PC3) of the total inertia and mainly represented within-treatment variations. MPEA based on the metabolites that were positively correlated to PC1 revealed three enriched metabolic pathways (Holm adjust P < 0.05), and all were directly involved in carbohydrate metabolism. MPEA based on all the metabolites that were negatively correlated to PC1 revealed three enriched metabolic pathways; all were directly involved in amino acids and protein biosynthesis (see Dataset S1 for detailed concentrations and fold changes).

Similar results were obtained with the flies from experiment 2. A clear-cut separation was observed along the PC1 of the PCA, which accounted for 47.1% of the total inertia (Fig. 6). Fru, Tre, xylitol, Glc and GDL were the most positively correlated to PC1 (i.e. accumulated in SAY(-) flies), whereas on the opposite side, Glu, inositol, Leu, Phe and Val were the most negatively correlated metabolites to PC1 (i.e. accumulated in SAY(±) flies) (Fig. 6). The other principal components accounted for 31.6% (PC2) and 6.4% (PC3) of the total inertia and mainly represented within-treatment variations. MPEA also revealed that that carbohydrate metabolism was enriched in the SAY(-) flies, while amino acids and protein biosynthesis were enriched in the SAY(±) flies (see Dataset S2 for detailed concentrations and fold changes).

Finally, the addition of a starvation period to empty the gut content of the flies before assessing the flies (i.e. experiment 3) resulted in a similar metabolic response. Again, a clearcut separation was observed along the PC1, which accounted for 57.3% of the total inertia (Fig. 7). Xylitol, Man, Ala, Fru, Tre and Glc were the most positively correlated metabolites to PC1 (i.e. accumulated in St-SAY(-) flies), whereas Glu, Thr, Ile, Phe, inositol and Leu were the most negatively correlated to PC1 (i.e. accumulated in SAY(±) flies) (Fig. 7). The other principal components accounted for 21.2% (PC2) and 7.15% (PC3) of the total inertia.

MPEA also revealed that that carbohydrate metabolism was enriched in the St-SAY(-) flies, while amino acids and protein biosynthesis were enriched in the St-SAY(±) flies (see Dataset S3 for detailed concentrations and fold changes).

Discussion

Dietary yeast is a major source of nutrition for the adults and larvae of most saprophagous Drosophila sp. (Diptera: Drosophilidae) [START_REF] Begon | Yeasts and Drosophila[END_REF], and as a consequence, yeast is typically incorporated into artificial diets [START_REF] Markow | Drosophila: A guide to species identification and use[END_REF][START_REF] Stocker | Getting started: an overview on raising and handling Drosophila[END_REF]. Dietary yeast provides essential nutrients such as amino acids, sterols, vitamins, and fatty acids [START_REF] Davis | Essential dietary amino acids for growth of larvae of the yellow mealworm, Tenebrio molitor[END_REF][START_REF] Anagnostou | Influence of dietary yeasts on Drosophila melanogaster life-history traits[END_REF]. We assumed that removing or adding live yeast from adult food at eclosion would be associated with physiological remodelling that would subsequently affect fitness-related traits such as body size and stress tolerance. In the present study, we completed a comprehensive assessment of the impact of dietary live yeast supplementation on body mass characteristics, stored proteins, metabolic profiles and basal cold tolerance (to acute and chronic exposures) in D. melanogaster females.

We expected body mass parameters to be affected by dietary live yeast supplementation. Indeed, the body mass of the flies is known to reflect protein level in food, with high levels of dietary yeast leading to heavier flies [START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF]. We have conclusively shown that body mass parameters (FM, DM, WM, LM and LDM) increased when females were fed with live yeast, which is consistent with previous studies [START_REF] Simmons | An analysis of resource allocation in response to dietary yeast in Drosophila melanogaster[END_REF][START_REF] Le Rohellec | Contrasted effects of suppressing live yeast from food on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF]. For all the considered mass parameters, the SA flies had significantly smaller values than the SAY(-) flies which shows that SA flies disproportionally suffered from the complete lack of dietary protein and suggests a malnutrition in this group. Concerning the fat (i.e. LM), we found that the SA flies had lower stored fat than the live yeast-fed flies [i.e. SAY(+), SAY(±)], but this reduction was not observed in SAY(-) flies. Hence, the reduction of fat was not related to the suppression of live yeast per se, but to the complete suppression of proteins supply from the diet (i.e. SA). It was previously reported that the increase in body mass with dietary live yeast is almost exclusively due to increased ovary size [START_REF] Simmons | An analysis of resource allocation in response to dietary yeast in Drosophila melanogaster[END_REF], and ovaries comprise approximately 15% of the body lipids of insects [START_REF] Lease | Lipid content of terrestrial arthropods in relation to body size, phylogeny, ontogeny and sex[END_REF]. The lower LM of the flies on SA diet is not surprising as these flies had small ovaries and hardly produced eggs (data not shown). The LDM was different among all treatments and a corresponding pattern was observed for the protein content. This suggests that feeding on a diet that contains killed yeast [SAY(-)] provides proteins to the flies, but feeding on a diet that also contains live yeast provides additional amounts of proteins. Storage of proteins is largely independent of dietary carbohydrates but is almost exclusively determined by the presence and concentration of yeast in the medium [START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF]. Our data corroborate this idea.

A way in which insects deal with nutrient variations is through altered physiology, namely by affecting developmental and metabolic processes [START_REF] Markow | Elemental stoichiometry of Drosophila and their hosts[END_REF]. Therefore, we assumed that manipulating the adult food (via live yeast supplementation) would be associated with physiological changes that would translate into contrasted metabolic profiles between nutritional groups. We have conclusively shown that a number of metabolites had their concentrations affected by the nutritional treatments, which resulted in contrasted metabotypes between live yeast-supplemented flies [SAY(+) and SAY(±)] and the control flies. Whatever the control used [SA or SAY(-) or St-SAY(-)], a similar response was repeatedly observed : sugars (Fru, Glc, and Tre) exhibited elevated amounts in the control whereas amino acid amounts (Val, Ile, Leu, Thr, Gly, Phe and Glu) were more abundant in the live yeast-supplemented groups. The fact that the relative abundance of sugars was higher in SA metabotype is not surprising, as these flies were fed on a minimal diet with no access to any source of proteins from adult eclosion. For the flies fed on SAY(-) and St-SAY(-) diets, the increased levels of sugars likely translates that these diets were proportionally richer in sugar than the corresponding live yeast-supplemented diets. We also found that GDL, sorbitol and xylitol contributed to the control metabotypes. GDL is a metabolite (a lactone) resulting from the degradation of Glc through the pentose phosphate pathway [START_REF] Garrett | Biochemistry. Second Edition[END_REF]. Polyols such as sorbitol are derived from hexose monophosphates and can be produced from both Glc and Fru [START_REF] Storey | Metabolism and bound water in overwintering insects[END_REF][START_REF] Wolfe | A thermoprotective role for sorbitol in the silverleaf whitefly, Bemisia argentifolii[END_REF]. The higher relative abundance of these sugar-related compounds is thus congruent with the nutritional regime of the flies.

Moreover, MPEA revealed several enriched metabolic pathways associated with the control metabotypes [SA or SAY(-) or St-SAY(-)], and all of them were directly involved in the carbohydrate metabolism. This further confirmed the relative higher impact of sugars in shaping the metabotype of these nutritional groups. Concerning the live yeast-fed flies, we found a higher relative abundance of amino acids (e.g. Val, Ile, Leu, Thr, Gly, Phe and Glu) associated with these nutritional groups. This response was observed whatever the treatment used [SAY(+) or SAY(±), or St-SAY(±)]. This is congruent with the nutritional regime of these flies. Live yeast is known to provide essential nutrients such as proteins and amino acids [START_REF] Davis | Essential dietary amino acids for growth of larvae of the yellow mealworm, Tenebrio molitor[END_REF][START_REF] Anagnostou | Influence of dietary yeasts on Drosophila melanogaster life-history traits[END_REF]. This most likely explains why MPEA revealed several enriched metabolic pathways related to amino acids and protein biosynthesis in these nutritional groups. This biological interpretation also coincides with the larger body protein content detected in these nutritional groups. The differences observed in the metabolic profiles between the live yeast-supplemented and the control groups may also be partly due to different food intake and thus incorporation of nutrients. Indeed, food intake increases with concentration of dietary yeast in D. melanogaster (Min and Tara, 2006). The fact that metabolic patterns were consistent among experiments suggest that (i) live yeast promotes amino acids biosynthesis even when the flies are already fed with killed yeast, and (ii) that differences observed were not related to presence/absence of live yeast in the gut content.

Many insect species feed on yeasts and the effects of this nutritional resource on the growth, fecundity and survival has been demonstrated in a wide range of species (e.g. [START_REF] Starmer | Coadaptation of Drosophila and yeasts in their natural habitat[END_REF][START_REF] Ganter | Yeast and Invertebrate Associations[END_REF][START_REF] Anagnostou | Influence of dietary yeasts on Drosophila melanogaster life-history traits[END_REF]. In spite of this, there is limited information on nutrition-mediated variations in stress tolerance in insects, and more particularly regarding thermal tolerance [START_REF] Andersen | Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster[END_REF]. Here, we report convincing evidence that supplementing adult flies with sources of dietary proteins and amino acids (live yeast) promoted their subsequent cold tolerance. This positive effect of live yeast was repeatedly found in almost all of the metrics used to assess their cold tolerance (acute and chronic tolerance), and for all the stress intensities applied in the first experiment. In the second experiment, CCR was not affected by live yeast supplementation, but all the other assays (post-stress survival) supported a positive effect of live yeast on cold tolerance.

Previous works reported an effect of dietary yeast on Drosophila cold tolerance, but the effects ranged from weak to very intense, and were thus difficult to interpret. For instance, Le Rohellec and Le [START_REF] Le Rohellec | Contrasted effects of suppressing live yeast from food on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF] found that removing live yeast weakly decreased cold survival of females subjected to a 16h cold-shock (0 °C), but only when these were mated.

In another study, the absence of live yeast in food killed nearly all flies (males and females)

subjected to the same cold treatment, whereas access to dietary yeast resulted in medium to high survival rates, depending on the age of the specimens (Le Bourg, 2010). These incongruities likely arise from the fact that nutrition-related variation in thermal tolerance involves interacting factors such as age, mating and gender. It remains unclear why in our study the CCR was affected by the nutritional treatment in the experiment 1 but not in the experiment 2. Longer temporal recovery dynamics of the SA flies (experiment 1) could result from the lack of essential nutrients necessary for an optimal functioning of the wholesystem physiology, or from an excessive amount of consumed sugars (as sugar was the sole source of food in this specific group). With regard to sugars, it appears that carbohydrateenriched diets tend to increase Drosophila cold tolerance [START_REF] Andersen | Protein and carbohydrate composition of larval food affects tolerance to thermal stress and desiccation in adult Drosophila melanogaster[END_REF]Sisoda and Singh, 2012). However, when provided at too high levels, dietary sugars induce a severe nutritional imbalance and a pathological state in D. melanogaster [START_REF] Wang | Physiological genetics of the response to a high-sucrose diet by Drosophila melanogaster[END_REF][START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF][START_REF] Musselman | A high-sugar diet produces obesity and insulin resistance in wild-type Drosophila[END_REF]Colinet et al., 2013a), which in turn negatively affects cold tolerance including CCR (Colinet et al., 2013a). In spite of this, our data and earlier observations (Le Rohellec and Le [START_REF] Le Rohellec | Contrasted effects of suppressing live yeast from food on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF][START_REF] Bourg | Combined effects of suppressing live yeast and of a cold pretreatment on longevity, aging and resistance to several stresses in Drosophila melanogaster[END_REF] all converge towards the same conclusion that cold tolerance of the females of D. melanogaster is generally promoted by dietary live yeast. Females fed with live yeast had increased body mass and contained more lipids and proteins, and MPEA corroborated that live yeast supplementation was associated with amino acids and protein biosyntheses. Interestingly, it was previously found in D. melanogaster that the level of glycogen, triglycerides, and total proteins was higher in cold-selected than in control lines [START_REF] Chen | Cold-shock and chilling tolerance in Drosophila[END_REF]. The same authors also noted that these levels quickly decreased 24 h after a cold stress and suggested that higher storage of energy reserves entails increased cold tolerance of coldselected lines. Thus, the higher energy reserves of the live yeast-supplemented flies may explain why cold survival (assessed after 24 h) was higher in this nutritional group.

Stressful conditions are known to critically increase energy expenditure because the repairing mechanisms require excess of energy [START_REF] Parsons | Evolutionary rates: Stress and species boundaries[END_REF]. We suggest that in nutrientunbalanced conditions (e.g. SA), individuals might disproportionately suffer from stressful conditions because the metabolically available energy is already constrained. The ability to synthesize essential stress-related proteins, due to dietary depletion of amino acids and protein building blocks could be an alternate explanation for the reduced cold tolerance.

Hence, dietary balance is likely to be a key point of environmental stress physiology. Stress tolerance is probably compromised under conditions of excessive nutritional imbalance, as for life-history traits [START_REF] Skorupa | Dietary composition specifies consumption, obesity, and lifespan in Drosophila melanogaster[END_REF]. In the natural environment, larvae may occasionally face nutritional stress and this might further affect the stress tolerance of the adults (carry-over effect), however, this question has not been examined. This study conclusively demonstrates an interaction between dietary live yeast and thermal stress tolerance of D. melanogaster females. Whether dietary live yeast positively affects the tolerance to other stressors remains to be further examined. Moreover, the mechanistic link between thermal tolerance and dietary live yeast remains an open question. Understanding the link between thermal stress tolerance and nutrient quality represents an important step in physiological ecology that may further add to our understanding of thermal biology of ectotherms.

Acknowledgments

This study was supported by Observatoire des Sciences Universelles Rennais (OSUR). We are grateful to Raphael Bical for technical help and to anonymous referees for helping improving the manuscript SAY(+) for experiment 1 and SAY(-) vs. SAY(±) for experiment 2. In the experiment 3, females were fed on the same experimental conditions as in experiment 2, but they were starved for 8 h before sampling, St-SAY(-) vs. St-SAY(±). Symbols S, A and Y for sugar, agar and yeast, respectively. Sign (+), (-) and (±) for live yeast only, killed yeast only, and both live and killed yeast, respectively.

Figure 1 :

 1 Figure 1: Schematic diagram of the experimental design used to investigate the effect of dietary live yeast supplementation on mass parameters, cold tolerance and GC-MS metabolic profiles of D. melanogaster. In all experiments, the flies developed from egg to adult on a standard diet [SAY(-)]. Emerging females were then fed on different diets for 6 days: SA vs.

Figure 2 :

 2 Figure 2: (A) Body mass parameters showing changes in lipid mass (LM), lean dry mass (LDM), and water mass (WM) according to nutritional treatment [SA, SAY(+), SAY(-), SAY(±)] (n = 15). (B) Total protein content of female D. melanogaster (n = 12).

Figure 3 :Figure 4 :Figure 5 :

 345 Figure 3: Composite panel summarizing all the cold tolerance assays of the experiment 1. Temporal recovery curves of live yeast-fed females [blue line, SAY(+)] and yeast-deprived females [red line, SA] exposed to chronic cold stress (0 °C) for various durations: 8, 10 and 12 h in figures A, D, and G, respectively. Each line represents the mean proportion (± 95% confidence interval) of recovering flies in relation to time after cold stress (n = 50). Mortality rates, assessed 24 h after the chronic cold stresses, are shown in figures B, E and H for each nutritional treatment [SA vs. SAY(+)] (n = 50). Mortality rates assessed 24 h after an acute cold stress (-3.5 °C) for various durations: 90, 120, 135 min are shown in figures C, F and I, respectively (n = 100). The black part of the bars represents the percent mortality and grey part is percent survival.

Figure 6 :

 6 Figure 6: (A) Multivariate analysis (PCA) based on the GC/MS metabolomic data of the experiment 2 illustrating the plotting of PC1 against PC2. The unit "d" (top right of the plot) represents the side-length of a square in the grid. A clear separation was observed between live yeast-fed [blue ellipse, SAY(±)] and live yeast-deprived metabotypes [red ellipse, SAY(-)]. Lines link replicates to their respective centroids (n = 6). (B) Correlation values of the different metabolite concentrations to the principal components PC1 in the principal component analysis. Correlations are ranked on Y-axis according to their values. Blue bars for negative correlations (i.e. accumulated in SAY(±) flies) and red bars for positive correlations (i.e. accumulated in SAY(-) flies). See Dataset S2 for detailed concentrations and fold changes.