Karim Arafah

Sébastien Nicolas Voisin

Victor Masson

Cédric Alaux

Yves Le Conte

Michel Bocquet

Philippe Bulet
email: philippe.bulet@univ-grenoble-alpes.fr

A MALDI-MS profiling to address honey bee health status under bacterial challenge through computational modelling

Keywords: Bee health, MALDI profiling, Bacterial infection, Apis mellifera, Computational model

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs.

In the last few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. MALDI mass spectrometry profiling has emerged within this decade as powerful to identify in routine micro-organisms and is currently used in real-time clinical diagnosis. We developed MALDI BeeTyping to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram-positive and -negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field was also considered. We individually recorded a series of hemolymph molecular mass fingerprints and built, to our knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees' systemic response to the bacteria. Hence, we challenged our model by classifying a training set of hemolymphs and obtained an overall recognition of 91.93%. Through this work, we aimed at introducing a novel, time and cost saving high-throughput strategy that addresses honey bee health and on an individual scale.

Introduction

Over several decades, an abnormal mortality of honey bees and other pollinators (bumblebees, solitary bees) has been observed in all industrialized countries [1][2][3][4] . This phenomenon has been particularly recorded in honey bees (Apis mellifera) [5] . The global loss of honey bee colonies has detrimental consequences for plant biodiversity, bee products, and negative economic and societal effects [6] . As a result, many scientific studies have been carried out to understand the mechanisms underlying phenomena such as colony weakening or collapse and colony mortality observed in most of the countries practicing intensive agriculture. Many reports concluded that biotic and abiotic factors are suspected to be involved in this phenomenon, either alone or in combination [2,5,[7][8][9][10] . Potential causes are exposure to (i) environmental and in-hive chemicals [11,12] , (ii) agricultural practices [13,14] , (iii) infection by microorganisms and predation by parasites [15][16][17] and (iv) nutritional factors [18][19][20] , among others, which lead to the transition from a health status qualified as normal to a health decline that would contribute to the colony collapse [7] . The expression of this pathological state may notably be linked to a decrease in the immune capacities of the bee and/or the colony [21][22][23][24][START_REF] Di Prisco | Proc Natl Acad Sci[END_REF] . The complex underlying mechanisms of stressors (biotic and abiotic) that impact honey bee health status remain still partially understood and solutions capable of rendering a prognosis, an early diagnosis or a diagnosis, need to be developed. Even if visual inspection of beehives, polymerase chain reaction and sensor-based analyses are available for surveillance of pathogen loads, prediction of the likely impact on the colony remains an issue not satisfactorily addressed [START_REF] Efsa | [END_REF][27][28][29] . Taking advantages of analytical improvements over the last 20 years, MALDI-MS has become increasingly popular to identify biological samples through mass fingerprinting of molecular changes that occur in fluid, cells or insects' appendices or simply as a robust microbiological technique in clinical diagnostics [30] . This approach may also play an essential role in the quest for innovative solutions in monitoring bee health.

Molecular mass fingerprinting (MFP) or signatures of biological matrices by MALDI-MS profiling is indeed a thriving approach, enabling the rapid detection of peptide/protein that can provide comparative information regarding immunity in Drosophila [31][32][33] , insects' appendices [34][35][36] and even from complex biological matrices. MALDI-MS based peptidomics/proteomics typing has found practical applications in fingerprinting honeys to determine specific molecular signatures that classify honeys according to their geographical origins [37] or to establish a robust proteomics mass fingerprint of sterile sperm chicken for diagnosis purpose [38] .

Relying on previously published studies [33,[39][40][41] , we first developed and validated an experimental model of challenged honey bees with Gram-positive and -negative bacteria (using notably a Serratia marcescens strain we have isolated from honey bees). Then, we assessed the usefulness of MALDI profiling to fingerprint the peptides/proteins within the molecular mass range 2-20 kDa from honey bee hemolymph. Finally, we generated a computational model that scored nine markers as a discriminant signature. These markers were successfully used to discriminate hemolymph molecular changes that occurred following bacterial infections. Through the present work, we are introducing this newly developed approach of MALDI profiling, referred as MALDI BeeTyping, to follow the health status of Apis mellifera under bacterial infections. While clinical application of biotyping is based on the identification of proteins derived from the micro-organisms themselves (for more about the biotyping approach, see [42][43][44] ), the BeeTyping approach we describe in this paper does not aim neither at identifying proteins from bacteria present in the hemolymph nor at isolating and characterizing bacteria from this fluid but rather at identifying the subsequent molecular changes they induced in the honey bee hemolymph. In addition, we determined the performance of this computational model using a training set of challenged bees. Through this work, we introduce BeeTyping as an effective method for monitoring peptide/protein changes within honey bees' hemolymph following bacterial challenges as a blood.

Material & Methods

The MALDI profiling strategy relies on a workflow divided into four major steps (see Supporting Information 1) and described in this section.

Biological models

Bacterial strains

To generate biological models of infection, we used the Gram-negative strains Pectobacterium carotovorum subsp. carotovorum 15 (formerly Erwinia carotovora carotovora 15 CFBP2141, generous gift from Bruno Lemaitre, EPFL Switzerland), Serratia entomophila (Institut Pasteur, CIP102919) and the Gram-positive Micrococcus luteus (ATCC 4698). We added in our infection model, a Serratia marcescens strain (SmBIOP160412, from our laboratory collection) isolated within the haemocoel from a naturally infected Apis mellifera collected in the field. This strain was identified using MALDI Biotyper® Compass (v4.1 from Bruker Daltonik GmbH) and according to the Bruker manufacturer protocol. Bacteria were cultured in Luria Bertani (LB) medium overnight at 32°C.

Experimental infection of the honey bees

Experimental infections were performed on newly-emerged honey bee workers (less than 12h old). To design the computational analyses, a training set of spectra was built using non experimentally infected (unpicked control) bees and bees infected with either Pectobacterium carotovorum subsp. carotovorum 15, M. luteus, S. entomophila or the isolated Serratia marcescens SmBIOP160412 strain. Infections were performed by pricking honey bees individually in the anterior lateral thorax (spiracle) using a fine needle (Fine Science Tools, Germany) dipped into a freshly concentrated culture pellet of live bacteria. All honey bees (experimentally infected and controls) were placed for 24h at room temperature in dedicated small cages and fed ad libitum with sugar syrup (Invertbee from SARL Isnard, France) containing fructose (36%), dextrose (30%), saccharose (31%), maltose (1.5%) and other sugars (1.5%). Hemolymph was collected from the dorsal side of the abdomen, using pulled glass capillaries (Sutter Instrument Corp, Novato, California). The collected hemolymph was immediately transferred into a chilled LoBind Protein microtube (Eppendorf, Germany) pre-coated with phenylthiourea and phenylmethylsulfonyl fluoride (both from Sigma Aldrich, France) to prevent melanization and proteolysis, respectively. The hemolymph samples were stored at -20°C until use.

Molecular mass fingerprints by MALDI MS

Data acquisition

Each individual hemolymph sample was analyzed with the Bruker AutoFlex™ III. The molecular mass fingerprints (MFP) were acquired following the Bruker Biotyper® recommendations (matrix, method of sample deposition and detection) with minor adjustments. Briefly, the hemolymph samples were 10-fold diluted in acidified water (0.1% trifluoroacetic acid, Sigma Aldrich, France) and 0.5µL of a given sample was mixed with 0.5µL of 4-HCCA (Sigma Aldrich, France) on a MALDI MTP 384 polished ground steel plate (Bruker Daltonik). Following co-crystallization of the hemolymph spots with the matrix droplet, MALDI MS spectra were recorded in a linear positive mode and in an automatic data acquisition using FlexControl 4.0 software (Bruker Daltonik).

The following instrument settings were used: 1.5kV of electric potential difference, dynamic range of detection of 600 to 18,000 Da, 69% of laser power, a global attenuator offset of 46% with 200Hz laser frequency, and 2,000 accumulated laser shots per hemolymph spectrum with a raster of random walk set to 50. The linear detector gain was setup at 1.82kV with a suppression mass gate up to m/z 600 to prevent detector saturation by clusters of the 4-HCCA matrix. The pseudo-molecular ions desorbed from the hemolymph were accelerated under 1.5kV. An external calibration of the mass spectrometer was performed using a standard mixture of peptides and proteins (Peptide Standard Calibration II and Protein Standard Calibration I, Bruker Daltonik) covering the dynamic range of analysis.

Data post-processing and statistical analyses

The MALDI-MS datasets were imported into the ClinProTools™ 2.2 Software (Bruker Daltonik) for post-processing and statistical analyses. All of the recorded spectra were processed with a baseline subtraction and spectral smoothing followed by an internal recalibration step with exclusion of null and/or "non-recalibratable" spectra. The total averaged spectra were calculated based on a signal over noise ratio equal to 5 for peakpicking and area calculations. The irrelevant spectra that did not pass the required signal intensity and resolution were excluded from any integration into the MALDI-MS computational model designed to match the biological models of honey bee infections. A post-processing step involving spectral normalization of all calculated peak area was performed with ClinProTools™ software prior to statistical analysis (95% confidence interval, standard deviation and Principal Component Analysis-PCA).

Hierarchical Clustering, heat maps and Receiver Operating Characteristic curves

The total number of spectra used to design the computational models were normalized and subjected to PCA and unsupervised hierarchical clustering analysis to measure distances between spectra. This analysis was used to determine Euclidean distances (based on PCA results with a reduced dimension limited to 70% and 95% of the total explained variance). The molecular correlation between four antimicrobial peptides with the ClinProTools™ software. The GA parameters were as follow: a maximum of 10 peaks harboring the greatest weight was selected and included in the model. A number of 50 generations (iterative algorithm searching) was chosen to achieve this maximum of peaks. The k-nearest neighbor parameter, which is a key parameter of artificial intelligence used in supervised machine learning, was set at 3.

External validation of the barcode model and classification of unknown spectra

In order to assess the capability of the GA classifier to recognize the infected bees from the control group, a new set of hemolymph MS spectra, never processed in the classifier model, was used to perform an external validation. This experimental set of honey bees included the three biological models; 26 controls, 37 honey bees infected with P. carotovorum subsp. carotovorum 15 and 10 with M. luteus. By submitting those hemolymph spectra to the classifier resulted in counting the correctly classified spectra, and also the mismatched and the invalid ones. In order to assess the performance of our classifier model, accuracy, sensitivity, specificity, informedness, specific-positive and negative likelihood ratios, false discovery rate (q-value) and false positive rate (p-value) were calculated. The accuracy, which informs on how efficient the model is, was calculated according to Wang et al. [45] . Sensitivity scores real positive cases that are correctly predicted positive by the model and the specificity scores the opposite i.e. the real negative cases that are correctly predicted negative. Informedness scores the probability that a prediction (e.g. result of a machine-learning model to classify one condition against the others) is informed regarding to the tested condition versus odds. Informedness helps to make diagnosis decision.

Sensitivity, specificity and informedness were determined as previously described [46] . The specificpositive and -negative likelihood ratios (abbreviated +LR and -LR) classically used in diagnostic testing with multiple classes informs on how likely the results from the classifier model will match the condition. +LR gives the change in the odds of satisfying the condition (fitting to the biological models), given a positive test result and -LR, the change in the odds of satisfying the condition when the test comes negative. +LR ranges from zero to infinity. With +LR values between zero and one, there is a weak probability that the test matches the condition. If the ratio equals to one, then the test lacks diagnostic value and if the ratio is >1, then the test increases the probability to match correctly with the condition. Regarding -LR, the closer to zero the value is, the more informative the test is [47] .

Results & Discussion

MALDI-MS profiling as a new approach to discriminate bacterial systemic infection through endogenous proteins from honey bees fingerprinting

In order to generate relevant biological models of honey bee infections, we used the bacteria Micrococcus luteus, Pectobacterium carotovorum subsp. carotovorum and Serratia entomophila. M. luteus is a Gram-positive bacterial strain frequently used when monitoring insect immunity [48] and has been shown to colonize bee hives and gastrointestinal tracts of honey bees [49] while P. carotovorum subsp. carotovorum [50] and S. entomophila (Institut Pasteur, CIP102919) are two bacterial strains that trigger a systemic immune response in insects. We also performed an additional model of infection using a Serratia marcescens strain (SmBIOP160412, Lab. collection) isolated from a naturally infected honey bee collected in the field and which is known to be a widespread pathogen of adult honey bees [51] , and a virulent opportunist, taking advantage of disturbed microbiota to develop in honey bee guts after exposure to the pesticide glyphosate [52] .

To certify the constructed biological models of infection, the four bacterial strains were classified by MALDI MS biotyping (Data not shown). A set of 64 MALDI MS spectra was recorded from individual hemolymph samples. These spectra were obtained from 22 control honey bees and from 23 and 19 honey bees individually infected with the Gram-positive M. luteus or the Gram-negative P. carotovorum subsp. carotovorum, respectively. An averaged spectrum, containing 110 MALDI MS ion peaks (MFP, Supporting Information 2), was built for each of the three biological models (Figure 1A). Statistical analysis based on Principal Component Analysis (PCA) and performed on these MFPs clearly segregated the three biological models (Figure 1B). As shown by the PCA plot score, the individual spectra were clustered in accordance with their corresponding models and were segregated based on their mass fingerprints. The unsupervised hierarchical clustering of hemolymph samples, classified almost all of the individual MFPs with respect to their corresponding biological models (Figure 1C).

Out of 64 normalized spectra used to build the clustering dendrogram, four and three recorded mismatched spectra were observed, corresponding to the lowest (70%) and highest (95%) limits of explained variance, respectively. The mismatched spectra were further identified within the representation of the PCA plot score of the hemolymph samples (Figure 1B, arrows). At the limit of explained variance of 70%, the mismatched four spectra included one spectrum from the control condition and one from the M. luteus infection model, both classified under the P. carotovorum subsp. carotovorum model, and two of this latter model, mismatched to the control model (see asterisks in Fig. 2C). Regarding the three mismatched spectra observed at 95% of the explained variance, one was from the M. luteus model and classified under the P. carotovorum subsp. carotovorum model and two, from the P. carotovorum subsp. carotovorum model, classified under the control model (see asterisks in Fig. 2C).

In order to assess the relationship between the MALDI-MS MFPs of the biological models and the honey bee's immune status, we correlated these MFPs with each of the four antimicrobial peptides (AMP) defined from Apis mellifera [53] : Apidaecin 1A (41) at m/z 2,107), Hymenoptaecin [54] at m/z 10,270, Abaecin [55] at m/z 3,878 and Defensin 1A [39] at m/z 5,519 (Figure 2). As shown, per-peak fingerprint correlations with the antimicrobial peptides (AMPs) were scored based on the molecular ion peak area and represented as heat maps through a colored scale intensity ranging from low (minimum score of -1, in red) to high correlation (maximum score of 1, in green). Reported in relation to the MFPs, four clades

Machine-learning as the first reported computational model to recognize and classify experimentally infected honey bees based on hemolymph MFPs

Because the proteomic mass spectra of hemolymph samples reflect the immune status of the honey bees, our next goal was to predict honey bee health status based on the bee MALDI-MS MFPs. For that purpose, we decided to build a molecular model based on the MFPs of hemolymph samples, by using a machine-learning algorithm, the Genetic Algorithm (GA). The GA classifier generated a set of discriminating peaks that recognized and classified hemolymph according to the biological model (honey bees challenged with P. carotovorum carotovorum or M. luteus and non-experimentally infected honey bees). These discriminating peaks form a barcode model and define the strength of this model through its recognition capability. The performance of the classifier barcode model was evaluated through internal cross validation by iterative reclassification of a set of spectra equal to half of the total number of spectra included in the model. For each of the ten iterations performed, a new set of spectra was chosen randomly through an automated internal process.

In an initial approach, we restricted our experimental infection to M. luteus as the Gram-positive strain and to P. carotovorum subsp. carotovorum. While this is, to our knowledge, the first time such a computational model has been applied to the classification of bacterial-infected honey bees, machinelearning algorithms have been used previously in other biological subjects. For example, MALDI MS has been successfully used to build a proteomic mass spectra database of different honeys and their MFPs to identify their geographical origin [37] . As another example of application, an experimental model of male chicken fertility was designed to perform on-cell direct proteomic fingerprinting by MALDI profiling and demonstrated the capability of the GA classifier to build a predictive model to classify chicken sperm fertility [38] .

In the present study, using GA, based on the individual hemolymph spectra of a cohort of 22 controls and 23 honey bees challenged with M. luteus or 20 honey bees challenged with P. carotovorum subsp. carotovorum, we identified a set of nine best m/z molecular ions based on their capability to discriminate the three biological models from each other (Figure 3). Further tests of recognition capability and cross validation of the GA model were assessed by using the MFPs from the same sample cohort. Considering the standard deviation and the 95% confidence interval of these nine molecular ions, weight indexes were calculated to rank the nine molecular signatures from the most discriminant molecular ion (m/z 3,348.17, weight of 6.24) to the least discriminant one (m/z 5,603.01, weight of 1.97). Moreover, we rated the accuracy of the GA classifier model following two distinct data processing. On the one hand, the classifier calculated the recognition capability by matching the MALDI MS spectra described above against their respective biological models. Therefore, we were able to re-assign hemolymph spectra derived from the control and the M. luteus biological models (score of recognition 100%) and for the P. carotovorum subsp. carotovorum model (score of 93.75%). Overall, performance recognition of the classifier reached 97.92%. On the other hand, internal cross validation scores were calculated for each biological model. To perform this cross validation, the same individual hemolymph spectra from each biological model were randomized and reassessed for successful matching in a batch mode of analysis by the classifier using solely the set of the nine molecular ion markers. The cross validations of the classifier were at 91.51%, 94.40% and 89.87% for the control, M. luteus and P. carotovorum subsp. carotovorum biological models, respectively, giving an overall validation of 91.93% (see Figure 3).

A new set made of 26, 10 and 35 MALDI MS spectra of hemolymph from control, M. luteus and P. carotovorum subsp. carotovorum biological models respectively was submitted for the to the GA classifier and classified individually (Table 1 and Supporting Information 3). Among the 26 control spectra, 16 were correctly classified, three were classified in P. carotovorum subsp. carotovorum and three in M. luteus models. Four spectra were found as invalid spectra because of the recalibration step. This result was caused by weaker intensities of the molecular fingerprints causing the ion mass recalibration to fail. Regarding the M. luteus infectious model, 10 spectra were subjected to the classifier.

Seven were correctly classified, two were considered as control and one as belonging to the P. carotovorum subsp. carotovorum model. No spectrum was deemed invalid. Considering the P. carotovorum subsp. carotovorum biological model of infection, from the 37 spectra, 13 were correctly classified, one matched to the control, three to the M. luteus biological models and 20 to the invalid spectra category. These 20 spectra were qualified as invalid due to noisy mass spectra (intensities of the nine peaks not sufficient to pass the classification) or to a failure in properly calibrating the mass spectra.

Given these results, we calculated the performance of the classifier for each of the biological model (see details in Supporting Information 4). The GA algorithm achieved 80 % to 90 % accuracy discriminating thus the three biological models. The sensitivity (true positive) and the specificity (true negative) of the GA classifier model were calculated for the three biological models. The model scored at least 70% of sensitivity and at least 84 % of specificity. As detailed in the Supporting Information 4, the highest sensitivity was observed for the P. carotovorum subsp. carotovorum model (76.47 %) and the highest specificity for the control model (95.16 %). Based on the sensitivity and the specificity, we calculated the informedness indexes and the positive-negative stratum-specific likelihood ratio (abbreviated +LR, -LR) which inform about how predictive the classifier model is and its performance as a diagnostic tool respectively. As reported in the Supporting Information 4, the three biological models scored indexes within the range [-1 ;1] with -1 as incorrect model predictions, 1 as maximum of correct predictions).

The calculated informedness indexes for the control, the P. carotovorum subsp. carotovorum scoring 0.68 and 0.64 respectively and for the M. luteus (0.55) demonstrated the model was a good predictor.

Regarding +LR and -LR, both parameters were calculated. The +LR, which required scores over 1 to be significant were found equal to 15.02; 4.55 and 6.12 for the control, the M. luteus and the P. carotovorum subsp. carotovorum models respectively (Supporting Information 4). This result demonstrated a good probability that our GA model classified positively the spectra against the biological models. The -LR, which required scores as close as possible to 0 to be significant were found equal to 0.28; 0.35 and 0.27 for the control, the M. luteus and the P. carotovorum subsp. carotovorum models respectively (Supporting Information 4). This result demonstrated the weak probability to misclassify the cohort of hemolymph spectra through the GA classifier. In addition, we determined the false discovery rate (qvalue) and the false positive rate (p-value) for each of the three biological models. The lowest q-value was of 0.158 and concerned the control model while P. carotovorum subsp. carotovorum and M. luteus models harbored highest values (0.235 and 0.461 respectively) denoting a better capability of the classifier to classify unknown spectra within the control model followed by the P. carotovorum subsp. carotovorum and M. luteus models respectively. Regarding the p-values, the control model harbored 0.0484 while P. carotovorum subsp. carotovorum and M. luteus models scored 0.125 and 0.154. Based on this statistical parameter, the classifier shares the same conclusion as obtained with the q-values regarding the algorithm's performance to classify properly the spectra.

To summarize, the computational model significantly discriminated control from infected honey bees and M. luteus from P. carotovorum subsp. carotovorum infection as well on the basis of the hemolymph molecular signatures.

MALDI-MS profiling of hemolymph as an effective molecular method to discriminate honey bees infected with different Serratia species

We assessed the performance of MALDI profiling to discriminate honey bee infections at the species level, in particular between two Serratia species: S. marcescens (SmBIOP160412, Lab. collection) isolated from a naturally infected honey bee and identified by MALDI Biotyper® Compass (data not shown), and a reference strain of S. entomophila. We found that the MFPs of the hemolymph samples collected from honey bees infected by S. entomophila and S. marcescens presented significant molecular differences (Figure 4A). Moreover, by using the two best discriminant molecular ions (m/z 12,752.8 and m/z 7,186.95), revealed by the PCA analysis, we could differentiate the hemolymph spectra of bees infected either with S. entomophila or S. marcescens SmBIOP160412. In contrast, the two weaker discriminant markers m/z 1,996.14 and m/z 6,113.68 failed to discriminate the two types of spectra resulting from the two Serratia species (Figure 4B).

To further evaluate and rank the measured ion markers within the MFPs based on their capability to discriminate the two Serratia species, we performed a receiver operating curve (ROC) analysis to highlight eight m/z ion markers (12,752.8; 7,186.95; 7,688.27; 10,269.8 (Hymenoptaecin); 5,057.69; 5,160.28) with AUC scores between 0.8 and 1 in sensitivity (Figure 4C). The Apidaecin, Abaecin and Defensin were also checked for their capability to discriminate the two Serratia species (Supporting Information 5). Based on the ROC test, Abaecin and Defensin were also capable to discriminate, to some extent, the two Serratia species (see Supporting Information 5) (AUC of 0.739 and 0.639, respectively). However, like the two markers m/z 1,996.14 and 6,613.68, Apidaecin (AUC=0.520) revealed to be a poor discriminant (Figure 4D). Hence, based on these results, it seems possible to discriminate infection by S. marcescens from S. entomophila in honey bees using computational modelling. To our knowledge, this is the first report on the feasibility of using MALDI MS as an MFP-based method capable of discriminating hemolymph molecular response to systemic infections induced by two different bacterial species of the same genus. S. marcescens is known to be a commensal bacterium present in low abundance in the gut of honey bees. By studying the pathogenicity of different strains of S. marcescens through two routes of in vivo exposure (oral and direct injection into the hemolymph), Raymann et al. (46) found that expression of the four honey bee AMPs Abaecin, Defensin, Hymenoptaecin and Apidaecin did not differ between infected and non-experimentally infected control honey bees. These results support the idea that markers other than AMPs need to be identified and monitored to efficiently discriminate bacterial infections in honey bee hemolymph. As we demonstrated, the correlation of the molecular fingerprints and the AMPs in hemolymph allowed us to discriminate the three different models. In order to determine how specific were the nine markers of the classifier to the control, M. luteus and P. carotovorum subsp. carotovorum biological models, we tested the classification of the hemolymph mass fingerprints of honey bees infected using the two Serratia strains. We submitted 13 MALDI mass fingerprints of hemolymph from infected bees with S. entomophila to the classifier. One was classified as control, seven as fitting with the P. carotovorum subsp. carotovorum model, and one fitting with the M. luteus model. The classifier excluded four hemolymphs' fingerprints because of noisy spectra signal or invalid mass recalibration. We also submitted 19 MALDI mass fingerprint of hemolymph from infected bees with S. marcescens to the classifier, which identified two as control, five as fitting with the P. carotovorum subsp. carotovorum model, and four fitting with the M. luteus model. The classifier excluded eight hemolymphs' fingerprints for the same reasons as above. Interestingly, the majority of the classified spectra from both, the S. entomophila and S. marcescens models matched with the P. carotovorum subsp. carotovorum model. It is particularly interesting as these three bacteria are Gram-negative. Nevertheless, some spectra matched against the control and the M. luteus models. Taking altogether, these results suggest that the MALDI profiling approach generates specific molecular barcodes/signatures defined accordingly to biological models. As we developed this approach for the monitoring of honey bee health, we define this approach of molecular signature by MALSI MS as MALDI BeeTyping.

Concluding remarks

Along with most relevant, technically feasible and primary observation-based health status indicators highlighted by EFSA's HEALTHY-B, MALDI-MS BeeTyping, a method derived from the biotyping approach used routinely in clinical microbiology, analyze the downstream responses to stressors through the matured effector molecules circulating in the hemolymph.

These effectors include the products of selected immune genes (i.e. genes coding for Apidaecin, Defensin, Hymenoptaecin, and Abaecin) and other molecular markers of stress that we intend to further characterize through an on-going proteomic study. Our approach of MFPs by MALDI-MS BeeTyping, is a cutting-edge analytic method that may complement and address some limitations issued of the HEALTHY-B toolbox by establishing robust, effective, sensitive and a comprehensive technology for profiling and deciphering, at the individual level, the honeybee health parameters including its immunity stage with regards to bacterial stressors. Moreover, MALDI BeeTyping has several advantages over other molecular biology techniques and visual observations, such as (i) the use of a drop of hemolymph allowing to keep the rest of the body for complementary molecular measurements such as PCR, (ii) a very simple and fast sample preparation, (iii) a short time for data acquisition and processing, (iv) low consumable costs, and (v) a user friendly workflow that can be standardized and automated for cost-effective high throughput use. We believe that future developments of MALDI BeeTyping could improve monitoring of honey bee health upon exposure to other biotic or abiotic stressors, the quality control and the origin traceability of apiary products based on molecular markers fingerprinting. Based on specific proteomics signatures, MALDI BeeTyping could bring out a novel analytical tool for early diagnosis of honey bees parasited with Nosema species, Varroa destructor and infected or not with deformed wing virus or acute bee paralysis virus. Fifty-seven hemolymph samples were collected individually from the biological models M. l., P. c. c. and control prior to being fingerprinted. The spectra were submitted to the GA-based computational model in order to assess classifier performance.

Supporting Information 1: MALDI profiling workflow for machine learning data-driven analysis of honey bee infections.

The methodological approach relied on four main steps addressing major tasks.

(

  AMPs) known from the honey bee [Apidaecin 1A at m/z 2,107 (Uniprot entry A0A088AIG0), Hymenoptaecin at m/z 10,270 (Uniprot entry Q10416), Abaecin at m/z 3,878 (Uniprot entry P15450) and Defensin 1A at m/z 5,519 (Uniprot entry P17722)]; and mass fingerprints (MFP) of the three biological models of infection were calculated and represented with a heat map. The ROC analyses were built using the ClinProTools™ program and the heat maps, using the OMICs add-on module provided by the XLSTAT program (interquartile threshold value of 0.25). Computational-based algorithm & machine learning model In the quest of delivering a barcode model capable of discriminating infected from control honey bees, a training set of spectra was established by fingerprinting the corresponding hemolymph samples using MALDI MS. Series of individual spectra were recorded from 22 controls, 23 honey bees challenged with M. luteus, 20 with P. carotovorum subsp. carotovorum 15, 15 with S. entomophila and 15 with S. marcescens SmBIOP160412. Data clustering (optimal spectral separation combined with the determination of a fixed number of peaks within the training set) was performed using the Genetic Algorithm (GA)

(

  Figure 2). Regarding the control condition, the four AMPs were found to be positively correlated with the molecular ion markers of the hemolymph MFPs of clades A, C and D and negatively correlated with markers of clade B. In the P. carotovorum subsp. carotovorum model, the same four AMPs were positively correlated with the MFPs of clades B and C and negatively with clade A and D markers, except for Hymenoptaecin, which exhibited positive and negative correlations with clade D. In the M. luteus model of infection, each of the AMPs was predominantly positively correlated with the molecular clades A and B, and negatively with clades C and D. These correlations show complementary molecular signatures in the three experimental models. Discrete dynamic molecular patterns are modulated and correlated to the immune status of the bees, allowing us to discriminate infected from non-infected bees and the type of infection.

Figure 1 :

 1 Figure 1: Differential PCA-based statistical analyses and hierarchical clustering of individual hemolymph samples from the biological models. Total averaged spectra were fingerprinted by MALDI MS from the infected and control individuals (A). The individual spectra were subjected to PCA analysis, which discriminated the hemolymph molecular mass fingerprints of Micrococcus luteus (M. l. in blue), Pectobacterium caratovorum subsp. carotovorum 15 (P. c. c. in green) and control (red) groups (B); arrows mark the mismatched outliers. An unsupervised hierarchical clustering based on the PCA results classified the individual spectra according to the lowest (70% left panel C) and highest (95%, right panel C) limits of explained variances.

Figure 2 :

 2 Figure 2: Heat-map of four antimicrobial peptides (AMPs) from Apis mellifera, Apidaecin 1A, Abaecin, Defensin 1A and Hymenoptaecin correlating with the MALDI MS fingerprints (102 m/z) of the hemolymph samples. Per-peak MALDI MS correlation in standard mode between the AMP molecular ions of Apidaecin 1A (m/z 2,107), Abaecin (m/z 3,878), Defensin 1A (m/z 5,519) and Hymenoptaecin (m/z 10,270), and the MALDI MS fingerprints of the biological model following an experimental infection with either Micrococcus luteus (M. l.) in blue, or Pectobacterium carotovorum subs. carotovorum (P. c. c.) in green, and the control experiment (non-experimentally infected bees, in red). Each rectangle in the heat map dendrogram represents the abundance level (scale from -1 to +1 from the lowest in red to the highest in green, respectively) of the area of each AMP cross-related with each molecular ion from the fingerprint.

Figure 3 :

 3 Figure 3: Genetic Algorithm-based classifier used to discriminate non-experimentally infected (Control, red) bees from experimentally infected ones with Micrococcus luteus (M. l., in blue) or Pectobacterium carotovorum subsp. carotovorum (P. c. c., in green). Nine molecular ion peaks determined by the computational model and ranked according to their weight indexes were found as the best discriminative features of the hemolymph samples on the basis of statistical criteria (Standard deviation determined for each curve representing the molecular ions of the model and the box plots showing the first and third interquartile range with line denoting the median and whiskers encompassing 95% of the individuals). The spectral gel view represents the intensity of each of the discriminative peaks represented by their weight index and the m/z values (Da) found within the individual spectra of the biological models.

Figure 4 :

 4 Figure 4: Differential PCA-based analysis of hemolymph fingerprints following infection with two Serratia strains, Serratia marcescens (S. m.) isolated from a naturally infected Apis mellifera and S. entomophila (S. e.) and statistical relevance of predictive markers.

Table 1 :

 1 External validation of the genetic algorithm classifier model using a new set of hemolymph spectra.

Step 1 :Supporting Information 2 :

 12 Sampling of unchallenged bees (controls) and experimental infection obtained by pricking honey bees with live strains of Pectobacterium caratovorum subsp. carotovorum 15 (P. c. c.) and Micrococcus luteus (M. l.). Step 2: Individual hemolymph collections followed by MALDI-MS molecular mass fingerprinting, and strain identification by MALDI biotyping. Step 3: Multi-stage processing of MALDI MS fingerprints including recalibration, peak picking, normalization and statistical calculation of individual MS spectra through Principal Component Analysis (PCA) for revealing differential molecular patterns across infection groups. Step 4: Genetic algorithm-based computational model for recognition and classification of honey bee infection using PCA discriminant analysis. Barcodes were built following the molecular fingerprints that discriminate control bees from bees infected either with M. l. or with P. c. c. Peak-to-peak correlation scores of the four antimicrobial peptides (Abaecin, Apidaecin, Defensin and Hymenoptaecin) with the mass fingerprint of hemolymph across the three biological models (non-experimentally infected/control, M. l. for Micrococcus luteus infection and P. c. c. for Pectobacterium caratovorum subsp. carotovorum 15 infection).

Acknowledgements

We would like to thank Dr. Bruno Lemaitre from the Ecole Polytechnique Fédérale de Lausanne -(EPFL, Switzerland) and the Institut Pasteur de Paris for their generous gifts (Micrococcus luteus strain and Serratia entomophila respectively). We are also thankful to Paul-Arthur Sol and Erwan Weber for their contribution during their Master 2 training. Financial support was provided by FEAGA (FranceAgriMer), N°14-04R and from the Association Plateforme BioPark of Archamps from its

Research & Development budget.

Conflict of interest

The authors have declared no conflict of interest.

Supporting Information 3:

Results for the external validation of the genetic algorithm-based classifier.

Supporting Information 4: Assessment of the Genetic Algorithm classifier performance.

Based on the result of the external validation, the performance of the GA-based classifier model was assessed for each of the biological models by calculating the accuracy, the sensitivity, the specificity, the specific-positive and -negative likelihood ratios (all five expressed as percentage), informedness, pvalue and q-value. Supporting Information 5: Assessment of ROC curves of Apidaecin, Abaecin and Defensin to discriminate S. marcescens-from S. entomophila-infected honey bees.


Cybèle Prigot-Maurice

Alexandra Cerqueira De Araujo
email: alexandra.cerqueiradearaujo@etu.univ-tours.fr

Sylvine Durand
email: sylvine.durand@univ-poitiers.fr

Tiffany Laverré
email: tiffany.lirand@univ-poitiers.fr

Romain Pigeault
email: pigeaultromain@gmail.com

Julien Verdon
email: julien.verdon@univ-poitiers.fr

Philippe Bulet
email: philippe.bulet@univ-grenoble-alpes.fr

Sophie Beltran-Bech
email: sophie.beltran.bech@univ-poitiers.fr

Christine Braquart-Varnier

Cybele Prigot-Maurice

Cybele Prigot-Maurice

Christine Braquart
email: christine.braquart@univ-poitiers.fr

Delphine Destoumieux-Garzon

Martin Zimmer

Survival capacity of the common woodlouse Armadillidium vulgare is improved with a second infection of Salmonella enterica

Keywords: Immune priming, Haemocytes, Invertebrates, Crustaceans, Armadillidium vulgare, Salmonella. Taxonomy Crustacean Disease, Salmonella, Animal Immunology, Cell-Mediated Immunity

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Thursday 22nd August 2019

Dear editorial board of Journal of Invertebrate Pathology, Enclosed you will find an electronic version of our manuscript: "The common woodlouse Armadillidium vulgare responds to a second infection of Salmonella enterica by improving its survival capacity" for submission as a research article in Journal of Invertebrate Pathology.

In this manuscript, we contribute to the characterization of immune priming in arthropods, a phenomenon defined as an enhanced protection from past infection with a pathogen.

We have for the first time brought to light the existence of this protection in the terrestrial crustacean Armadillidium vulgare, by demonstrating (i) survival improvement in response to a second infection with Salmonella enterica and (ii) differences in haemocyterelated parameters depending on the priming treatment. The originality of our study also lies in new elements about the delay required to observe a protective effect and the duration for which the host remains protected against living bacteria. By the way, the protection against Salmonella in A. vulgare follows a temporal dynamic over the time between the two infections. We used living bacteria, whereas experiments on crustacean immune priming generally used inactivated pathogens to prime animals. To date, only one published study was carried on a terrestrial crustacean while these animals display appropriated features for the evolution of immune priming.

We certify the findings have not been published elsewhere and are only being submitted to Journal of Invertebrate Pathology. All authors have read and approved the material being submitted. We truly hope the readers of Journal of Invertebrate Pathology will find the paper quite interesting and mostly helpful for researchers working on invertebrate immune priming.

Yours faithfully

Thursday 7 th of November 2019 Dear editorial board of Journal of Invertebrate Pathology, Enclosed you will find our revised manuscript: "Survival capacity of the common woodlouse Armadillidium vulgare is improved with a second infection of Salmonella enterica" for a second revision as a research article in Journal of Invertebrate Pathology.

We would like to express our thanks to the reviewer and editors for the positive feedback, constructive comments and the thorough checking of the text. We took into account all the suggested modifications, which had substantially improved the manuscript quality.

We are very grateful to know our work was approved for the Journal of Invertebrate Pathology.

Thank you again for your consideration of our work.

Yours faithfully

Highlights

Immune priming with Salmonella enterica improved survival of Armadillidium vulgare Survival of A. vulgare to an LD 50 dosage was higher when primed 7 days earlier The protection isn't observed when 1 or 15 days separate the two infections Primed animals display higher haemocyte viabilities but lower concentrations

Introduction

Because invertebrates rely solely on the innate immune system and are not known to possess a vertebrate-like adaptive immunity [START_REF] Kurtz | Specific memory within innate immune systems[END_REF]Milutinovic and Kurtz, 2016), they have long been considered unable to establish an immune memory [START_REF] Hoffmann | Phylogenetic perspectives in innate immunity[END_REF]. In the past 20 years, several studies have shaken this paradigm by providing evidence that the invertebrate immune system could be stimulated during a first infection to pathogens, leading to better protection upon a second exposure. Studying this phenomenon, called "immune priming" (Milutinovic and Kurtz, 2016;[START_REF] Gourbal | Innate immune memory: An evolutionary perspective[END_REF][START_REF] Pradeu | Immunological memory: What's in a name?[END_REF], is all the more relevant in that natural populations display variations in their immune priming [START_REF] Khan | Divergent immune priming responses across flour beetle life stages and populations 7847-7855[END_REF]. This variability may reflect adaptive responses to various environmental conditions independently from the evolution of resistance/tolerance strategies commonly assumed [START_REF] Khan | Experimental evolution of insect immune memory versus pathogen resistance[END_REF]Ayres & Schneider, 2008). Immune priming has been observed in more than 40 invertebrate species including arthropod, mollusk, cnidarian, annelid and nematode species (Milutinovic and Kurtz, 2016).

Among them, the ability to mount immune priming relies on several mechanisms and may occur against some pathogens but not others. It can be expressed against a broad repertoire of pathogens or be very specific to a single species of pathogen (Contreras-Guarduño et al., 2016;Milutinovic and Kurtz, 2016;Dhinaut et al., 2017), and may result in higher survival capacity when the primed individual faces a second infection (Milutinovic and Kurtz, 2016;Mellilo et al., 2018). This protective effect is often explained by immune changes involving either (i)

humoral response (e.g. production of anti-microbial peptides or AMPs, reactive oxygen species (ROS) and phenoloxydase activity), (ii) cellular response (e.g. haemocyte production, phagocytosis, encapsulation) or/and (iii) immune-related gene expression, depending on the species [START_REF] Wu | The specificity of immune priming in silkworm, Bombyx mori, is mediated by the phagocytic ability of granular cells[END_REF][START_REF] Söderhäll | Crustacean hematopoiesis[END_REF][START_REF] Pinaud | A shift from cellular to humoral responses contributes to innate immune memory in the vector snail Biomphalaria glabrata[END_REF]Kutzer et al., 2018;[START_REF] Fugmann | [END_REF], Mellilo et al., 2018). Although the invertebrate immune priming phenomenon has been widely described, the delay required to provide a protective effect remains partly characterized [START_REF] Little | Ecological and evolutionary implications of immunological priming in invertebrates[END_REF]. Few studies have addressed the time course of the immune response following the first infection by a pathogen [START_REF] Rosengaus | Immunity in a social insect[END_REF]Milutinovic and Kurtz, 2016;Browne et al., 2015). It has been suggested that mounting an immune priming response may be costly for organisms [START_REF] Moret | Survival for immunity: The price of immune system activation for bumblebee workers[END_REF][START_REF] Jacot | Juvenile immune system activation induces a costly upregulation of adult immunity in field crickets Gryllus campestris[END_REF][START_REF] Contreras-Garduño | Cost of immune priming within generations: Trade-off between infection and reproduction[END_REF]. Consequently, immune priming should preferably evolve in organisms for which probability of re-infection with the same pathogen(s) is high [START_REF] Schmid-Hempel | Evolutionary ecology of insect immune defenses[END_REF][START_REF] Best | The evolutionary dynamics of withingeneration immune priming in invertebrate hosts[END_REF] and is, thus, expected in species with long lifespan, low dispersion rate and groupliving behaviors [START_REF] Little | Ecological and evolutionary implications of immunological priming in invertebrates[END_REF][START_REF] Pigeault | Evolution of transgenerational immunity in invertebrates[END_REF][START_REF] Moret | Immune priming and transgenerational protection from parasites[END_REF].

Terrestrial isopods fulfill all the conditions assumed necessary for the evolution of immune priming. Most species can live up to 3 years and some have an estimated life expectancy of 5 years [START_REF] Warburg | The effect of climate on the distribution and abundance of isopods[END_REF]. These organisms live in moist soil, an environment suitable for micro-organisms [START_REF] Ranjard | Quantitative and qualitative microscale distribution of bacteria in soil[END_REF] where 10 9 bacteria per gram of soil can be found [START_REF] Torsvik | High diversity in DNA of soil bacteria[END_REF]. Isopods constitute the suborder of crustaceans best adapted to a terrestrial lifestyle with the evolution of various morphological, physiological and behavioral traits [START_REF] Hornung | Evolutionary adaptation of oniscidean isopods to terrestrial life: Structure, physiology and behavior[END_REF]. For example, they aggregate to fight efficiently against desiccation [START_REF] Broly | Benefits of aggregation in woodlice: A factor in the terrestrialization process?[END_REF][START_REF] Broly | Effects of group size on aggregation against desiccation in woodlice (Isopoda: Oniscidea)[END_REF] and display a low dispersion rate [START_REF] Beck | Genetic variation in the terrestrial isopod, Armadillidium vulgare[END_REF]. They are mostly promiscuous [START_REF] Zimmer | Nutrition in terrestrial isopods (Isopoda: Oniscidea): an evolutionaryecological approach[END_REF][START_REF] Durand | Paternity success depends on male genetic characteristics in the terrestrial isopod Armadillidium vulgare[END_REF], which increases the risk of pathogen transmission within populations. Considering these features, [START_REF] Roth | Strain-specific priming of resistance in the red flour beetle, Tribolium castaneum[END_REF] supported the plausibility of immune priming in the terrestrial isopod Porcellio scaber. In this species, individuals that were previously primed with heat-killed Bacillus thuringiensis had higher proportions of phagocyting haemocytes during the second encounter with the same bacteria compared to non-primed individuals. This immune response lasts at least 14 days after the first infection. No other studies have investigated immune priming in terrestrial isopods and it is not known whether the first immune stimulation provides a protection to the animal in term of survival against a subsequent infection.

In this work, we explored the hypothesis that a protective effect exists in Armadillidium vulgare. This terrestrial isopod is highly gregarious, lives more than 3 years and has a welldescribed immune system [START_REF] Braquart-Varnier | Wolbachia mediate variation of host immunocompetence[END_REF], 2015;[START_REF] Chevalier | The immune cellular effectors of terrestrial isopod Armadillidium vulgare: Meeting with their invaders, Wolbachia[END_REF]. The aim of our study was to examine whether a first infection provides protection in terms of improved survival against a second infection with the same pathogen, and to determine the time period between two infections needed to observe this protection. To further our understanding of this phenomenon, we also measured haemocyte concentration and viability immediately before the second inoculation. In crustaceans, these immune cells defend organisms against pathogens via phagocytosis, nodulation and encapsulation but are also support humoral immune responses such as coagulation, phenoloxidase activities or antimicrobial peptide storage [START_REF] Söderhäll | Crustacean immunity[END_REF][START_REF] Lee | Early events in crustacean innate immunity[END_REF][START_REF] Chevalier | The immune cellular effectors of terrestrial isopod Armadillidium vulgare: Meeting with their invaders, Wolbachia[END_REF]. Haemocytes are equally suspected to enhance their functions after a first infection, allowing a better immune response against subsequent pathogen infection [START_REF] Gourbal | Innate immune memory: An evolutionary perspective[END_REF]. To meet these aims, we first inoculated A. vulgare females with a non-lethal low dose of living Salmonella enterica bacteria (priming treatment), which have been shown to be pathogenic for this species (Braquart-Varnier et al., 2015). After 1 day, 7 days or 15 days postinfection, we investigated the cellular response by measuring the haemocyte concentration and viability. Then, the protective effect of the first injection of bacteria was explored by monitoring the survival rates of A. vulgare following a LD 50 injection of S. enterica occurring at the same time points (1, 7 or 15 days) after the first injection. Should a protective effect be triggered, we expected to observe higher survival rate in individuals previously primed with S. enterica compared to those not primed with S. enterica. We also expect to observe higher haemocyte concentrations and viabilities in response to the first infection as already observed in other biological models (Pope et al., 2001;[START_REF] Wu | Priming Galleria mellonella; (Lepidoptera: Pyralidae) Larvae with heat-killed bacterial cells induced an enhanced immune protection against Photorhabdus luminescens; TT01 and the role of innate immunity in the process[END_REF]Zang et al., 2014). These parameters may differ at each time point post-infection, which would provide insights about the temporal dynamics of the protective effect.

Materials and methods

Biological model

The Armadillidium vulgare laboratory line used descends from cross breeding of individuals sampled in 1982 at Helsingör (Denmark). Individuals were maintained in plastic boxes (10 x 30 cm, Ets Caubere) containing moistened potting mix and were supplied ad libitum with linden leaves and carrot slices. To carry out our experiments, we used 439 1-year-old (± 2 months) virgin females for survival monitoring and 153 additional females for the haemocyte analysis.

The pathogen used for infection was Salmonella enterica serovar Typhimurium J18 (Verdon et al., 2016), a Gram-negative bacterium which is found in soil and water [START_REF] Murray | Salmonellae in the environment[END_REF][START_REF] Andino | Salmonella enterica: Survival, colonization, and virulence differences among serovars, 2015[END_REF]. Cultures of S. enterica were produced as described in Braquart-

Priming procedure

Each individual was washed (3% NaClO then water) prior conducting three priming treatments: (1) non-injected ("non-primed" group) as a control, (2) injected with Luria-Bertani (LB) medium ("LB-primed" group), and (3) injected with a non-lethal dose of 10 3 ± 1.10 3 living S. enterica in 100 nL of LB medium ("S. enterica-primed" group). The sample sizes are summarized in Table I. All injections were performed under sterile conditions using a Drummond TM Nanoject (3-000-205A), in two 50 nL consecutive injections. The A. vulgare were injected dorsally in their posterior abdominal segment. Each individual was then isolated in a plastic box (5 cm x 8 cm Ø, Ets Caubere) containing a moist paper, under a natural photoperiod at 20°C and without food, to avoid any external contamination and variations in food intake. After each bacterial injection, the injected dose was controlled by spreading 100 µL of the solution diluted to 1 bacteria/µL onto LB agar plates. After an overnight culture at 37 °C, the number of S. enterica colonies was counted. The solutions were plated onto LB agar plates and incubated overnight at 37°C before counting the number of LB agar plates presenting at least one colony of S. enterica for each time point.

S. enterica was identified by using MALDI Biotyper® Compass (v4.1 from Bruker Daltonik GmbH), according to the Bruker manufacturer protocol. This technique is commonly use in clinical microbiology for strain identification because it is more precise than the morphological approach [START_REF] Scott | Diagnostic performance of matrix-assisted laser desorption ionisation time-of-flight mass spectrometry in blood bacterial infections : a systematic review and meta-analysis 4235[END_REF][START_REF] Kostrzewa | Application of the MALDI Biotyper to clinical microbiology: progress and potential[END_REF].

2.2.4. Survival monitoring All individuals (non-primed, LB-primed and S. enterica-primed) intended for survival monitoring were injected with a LD 50 (dosage to kill 50% of animals in 7 days) of 3.10 5 ± 1.10 5 living S. enterica in 100 nL (Braquart-Varnier et al., 2015; see sample size in Table I), either 1, 7 or 15 d after the priming procedure. Individuals were returned to their boxes and survival was monitored every 8 h for 7 d. Humidity was controlled and adjusted daily. The injected dosage was controlled using the same method as the priming procedure.

Statistical analysis

All statistical analyses were performed with R, version 3.4.1 (R Core Team, 2017).

For the haemocyte analysis, we constructed two generalized linear mixed-effect models:

one with concentration and the other with viability as response variables, both built using the lme4 package (Fox and Weisberg, 2011) and tested with Wald χ² using the car package (Bates et al., 2015). Haemocyte concentration (number of haemocytes per µL) was modeled with a

Poisson error distribution and haemocyte viability (percentage of living cells) was modeled with a Binomial error distribution. For these two models, the priming treatment (non-primed, LB-primed and S. enterica-primed) and the time point (1, 7, 15 d) were entered as categorical fixed effects with the interaction between these two terms. In addition, to correct the nonindependence of samples within the same series in a given replicate, we entered two categorical random factors: the experimental replicates and the series nested within each experimental replicate (Harrison et al., 2018). For the haemocyte concentration, we added an Observation Level Random Effect (OLRE). This variable corresponds to a unique level of a random factor for each data point, in order to cope with over dispersion (Harrison, 2014).

For the survival data (frequency of living animals), a global mixed-effects Cox proportional hazard regression model was built using coxme packages [START_REF] Therneau | Penalized survival models and frailty[END_REF].

We included the priming treatment, the time point and their interaction as fixed effects and the experimental replicate as well as the series nested within each replicate as random factors. We obtained the Hazard Ratios (HR) from this model as an estimate of the ratio between the instantaneous risks of death between control (non-primed) and the priming treatments (LBprimed, S. enterica-primed).

In all statistical models, our focal variable of interest was always the priming treatment.

The fixed effect "time point" was treated as a covariate and the inclusion of the interaction term between "time point" and "priming treatment" allowed us to compare the three priming treatments at each time point, independently of the two other time points. These comparisons were made by analyzing all relevant pairs of means with Tukey adjustment (lsmeans package;

Lenth, 2018). Using this approach, the interaction term and the main time point term were retained in the models even if not significant [START_REF] Agresti | Categorical data analysis[END_REF]. However, we did not use these models to statistically test for the effect of time point on haemocyte parameters and animal survival because of the correlation between the fixed effect "time point" and the random effect "experimental replicate", preventing the correct attribution of variance to each effect (see S2 in supplementary materials for more details).

Results

Haemolymph analysis 3.1.1 Variation in haemocyte parameters

Priming treatment had an effect on haemocyte concentration (χ 2 = 16.42, df = 2, p < 0.001), but there was no significant effect of time post-priming (χ 2 = 1.19, df = 2, p = 0.550) nor of the interaction between priming treatment and time post-priming (χ 2 = 4.20, df = 4, p = 0.379). At 1 and 7 d post-priming, the non-primed and LB-primed individuals had more haemocytes than S. enterica-primed ones (1 d: p = 0.004 and p = 0.023, respectively; 7 d: p = 0.043 and p = 0.023, respectively; Table II, Fig. 2). In contrast, no difference in haemocyte concentration between these priming treatments was observed at 15 d post priming (non-primed vs S. enterica-primed: p = 0.799; LB-primed vs S. enterica-primed: p = 0.966, Table II, Fig. 2).

For all time points, no significant difference in haemocyte concentration was observed between non-primed and LB-primed individuals (p > 0.05 for all comparisons, Table II, Fig. 2).

Priming treatment had an effect on haemocyte viability (percentage of living cells) (χ 2 = 139.49, df = 2, p < 0.001), but there was no significant effect of the time post-priming (χ 2 =1.50, df = 2, p = 0.470) nor of the interaction between priming treatment and time, even if a trend was observed (χ 2 =8.57, df = 4, p = 0.072). Whatever the time period after the priming procedure, haemocyte viability was higher for S. enterica-primed individuals than non-primed individuals (p < 0.001 for all comparisons, Table II, Fig. 3) and LB-primed individuals (1 d: p = 0.003; 7 d: p < 0.001; 15 d: p = 0.016, Table II, Fig. 3). Haemocyte viability was not significantly different between non-primed and LB-primed individuals at 1 d after priming (p = 0.20, Table II, Fig. 3), whereas non-primed individuals had a lower haemocyte viability compared to LB-primed one's at 7 d (p = 0.008, Table II, Fig. 3) and 15 d after the priming procedure (p < 0.001, Table II, Fig. 3).

Presence of living S. enterica in haemolymph

One day after the priming injections, 91% (20 out of 22 plates) of the LB agar plates spread with haemolymph from S. enterica-primed individuals contained S. enterica colonies.

At 7 d, 100% displayed colonies (10 plates) whereas only 59% (10 out of 17 plates) of the plates presented colonies 15 d after priming. This shows that S. enterica was still present in the haemolymph of some individuals when we injected the LD 50 (3.10 5 ± 1.10 5 living S. enterica).

No S. enterica was detected in the haemolymph from non-primed and LB-primed individuals.

Survival rate in response to a LD 50 injection of S. enterica

Priming treatment had an effect on the survival rate (χ 2 = 18.88, df = 2, p < 0.001, Fig. 4) as well as the interaction between priming treatment and time post-priming (χ 2 = 15.12, df = 4, p = 0.004). Therefore, the effect of the priming treatment depends on the time post priming.

No effect of the time point factor alone was observed on the survival rate (χ 2 = 2.58, df = 2, p = 0.274).

When the LD 50 injection occurred 1 d after the priming procedure, S. enterica-primed individuals did not survive significantly better than non-primed individuals (p = 0.411, Table III, Fig. 4A). LB-primed individuals displayed a significantly higher survival rate than nonprimed ones (p = 0.001, Table III, Fig. 4A) and S. enterica-primed individuals (p = 0.037, Table III, Fig. 4A). The risks of death of LB-primed and S. enterica-primed individuals were 78%

(HR = 0.22; 95% CI = [0.094 -0.52]) and 33% (HR = 0.67; 95% CI = [0.35 -1.26]) lower than that of non-primed controls.

When the LD 50 injection was performed 7 d after the priming procedure, survival rate was higher for S. enterica-primed individuals than LB-primed and non-primed ones (p = 0.003 and p = 0.0001 respectively, Table III, Fig. 4B). No significant difference between LB-primed and non-primed individuals was observed (p = 0.792, Table III, Fig. 4B). The risk of death of S. enterica-primed individuals was 52% lower than that of non-primed controls (HR = 0.48; 95% CI = [0.27 -0.84]), whereas that of LB-primed ones was 25% lower than that of nonprimed individuals (HR = 0.75; 95% CI = [0.44 -1.27]). At 15 d after priming procedure, no significant differences were observed between the three priming treatments (p > 0.05 for all comparisons, Table III, Fig. 4C). Nevertheless, S. enterica-primed individuals tended to have a higher survival rate than non-primed individuals (p = 0.095, Table III, Fig. 4C), with a risk of death 62% lower than that of non-primed (HR = 0.38; 95% CI = [0.17 -0.83]). The instantaneous risk of death of LB-primed individuals was 34% lower than that of non-primed controls (HR = 0.66; 95% CI = [0.34 -1.26]).

Discussion

Main results overview

Immune priming in invertebrates is defined as a phenomenon by which a first activation of the immune system (by killed/living pathogens or inactivated pathogen units) is associated with enhanced protection of the host against subsequent exposure to pathogens [START_REF] Kurtz | Specific memory within innate immune systems[END_REF][START_REF] Gourbal | Innate immune memory: An evolutionary perspective[END_REF]. In this study, we tested for enhanced protection in Armadillidium vulgare following a subsequent inoculation of the Gram-negative bacterium Salmonella enterica.

Our results demonstrated the existence of a protective effect in this species because A.

vulgare survival after a sublethal infection with S. enterica was higher when its immune system was activated during inoculation with the same bacteria. This protection is dynamic: immune primed individuals showed a higher survival rate than control individuals when the second infection occurred 7 d after a first infection with the same bacterial strain. There was a trend towards the persistence of this protection 15 d after the first infection (p-value = 0.09, Table III, Fig. 2), but this protection was not effective when the LD 50 injection was performed only 1 d after priming. We observed fluctuations in haemocyte concentration and viability, which also highlight a related dynamic immune response differing at each time following the infection.

When the first bacterial infection is too recent to trigger an immune protection, a simple stimulation without bacteria is enough

Firstly, we observed that a first activation of the immune system with Luria Bertani broth resulted in higher survival of an injection of bacteria at the LD 50 . In fact, LB-primed individuals survival rate was about twice that of non-primed ones when the LD 50 injection occurred 1 d after the LB-injection. Higher survival is likely the result of a protection phenomenon termed as "immune enhancement" by Contreras-Guarduño et al. (2016).

Because LB-primed individuals were not primed with the pathogen, we hypothesized that an immune response was triggered by the piercing in the integument during the priming procedure [START_REF] Cerenius | Coagulation in Invertebrates[END_REF]. When the lethal dosage of S. enterica arrived in the haemolymph 1 d after the LB broth injection, the bacteria could face an already "ready-to-fight" immune system, leading to better protection (by resistance and/or tolerance) and to a higher survival rate. As haemocyte concentration is known to rapidly decrease following an injury in crustaceans [START_REF] Söderhäll | Haemocyte production and maturation in an invertebrate animal; proliferation and gene expression in hematopoietic stem cells of Pacifastacus leniusculus[END_REF][START_REF] Söderhäll | Crustacean hematopoiesis[END_REF], we might expect to observe fewer haemocytes in LBprimed individuals compared to non-primed ones at 1 d post-priming. But LB-primed individuals had haemocyte concentration and viability similar to non-primed individuals. This suggests a highly effective immune system having already renewed haemocytes after the injury of the cuticle [START_REF] Söderhäll | Haemocyte production and maturation in an invertebrate animal; proliferation and gene expression in hematopoietic stem cells of Pacifastacus leniusculus[END_REF][START_REF] Söderhäll | Crustacean hematopoiesis[END_REF]. Furthermore, we observed an intermediate survival rate in S. enterica-primed individuals at 1 d. As the immune system of S. enterica-primed individuals should be activated as in LB-primed, the absence of immune protection compared to non-primed individuals could reflect either a saturation or a depletion event of A. vulgare immune system facing a high number of bacteria. Braquart-Varnier et al. (2015) showed that 24 hours post-injection with 10 4 living S. enterica, the bacterial load in the haemolymph was higher than the number of bacteria injected. The immune system of S. enterica-primed individuals might not have enough time, energy or reserve of immune effectors to cope with a new infection of S. enterica occurring too soon after the first infection. This hypothesis is supported by the lower haemocyte concentration observed in S. enterica-primed individuals, suggesting the immune cellular response was still fighting bacteria persisting in the haemolymph since the priming procedure. The loss of circulating haemocytes following an infection was previously observed in A. vulgare exposed to heat-killed Escherichia coli (Herbinière, 2005) and in other crustaceans [START_REF] Persson | The influence of haemocyte number on the resistance of the freshwater crayfish, Pacifastacus leniusculus Dana, to the parasitic fungus Aphanomyces astaci[END_REF][START_REF] Muñoz | Expression and distribution of penaeidin antimicrobial peptides are regulated by haemocyte reactions in microbial challenged shrimp[END_REF][START_REF] Söderhäll | Haemocyte production and maturation in an invertebrate animal; proliferation and gene expression in hematopoietic stem cells of Pacifastacus leniusculus[END_REF]. Following this loss of immune cells, new haemocytes are found to be released rapidly in the haemolymph to compensate for the loss [START_REF] Söderhäll | Haemocyte production and maturation in an invertebrate animal; proliferation and gene expression in hematopoietic stem cells of Pacifastacus leniusculus[END_REF][START_REF] Chevalier | The immune cellular effectors of terrestrial isopod Armadillidium vulgare: Meeting with their invaders, Wolbachia[END_REF]. We hypothesize the high viability of haemocytes in S. enterica-primed individuals reflects an important hematopoietic activity, but new haemocytes might be directly used in protection against bacteria without totally compensating previous cell loss, both at 1 and 7 d after the priming procedure. However, haemocyte concentration of S. enterica-primed individuals became similar to those of other groups at 15 d post-priming. This suggests the compensation was effective between 7 and 15 d following the first infection, either by even greater haemocyte production [START_REF] Pipe | Environmental contaminants influencing immune function in marine bivalve molluscs[END_REF][START_REF] Zhang | The specifically enhanced cellular immune responses in Pacific oyster (Crassostrea gigas) against secondary challenge with Vibrio splendidus[END_REF][START_REF] Söderhäll | Crustacean hematopoiesis[END_REF] or a smaller loss of new haemocytes.

Evidence of a protective effect against the same pathogen: a minimal delay required between successive infections

We demonstrated increased survival when A. vulgare had encountered S. enterica 7 d before receiving a LD 50 of the same pathogen, with boosted survival rates -approximately 50% higher than non-primed individuals. This protection tended to be effective until 15 d postpriming procedure (30% increase in survival), which is consistent with previous studies suggesting immune priming in terrestrial and aquatic crustaceans may last for 7 to 30 d [START_REF] Roth | Strain-specific priming of resistance in the red flour beetle, Tribolium castaneum[END_REF][START_REF] Chang | What vaccination studies tell us about immunological memory within the innate immune system of cultured shrimp and crayfish[END_REF]. Because the improved survival for LB-primed individuals observed at 1 d disappeared for individuals treated at 7 d and 15 d, we suppose the immune enhancement against S. enterica induced by the piercing disappeared between 1 day and 7 d post-priming procedure. By contrast, the first infection with S. enterica protected individuals from the LD 50 infection, supporting that the protective effect is established between 1 and 7 d following the first infection. With regards to the duration of the protective effect, we cannot determine whether the trend of better protection observed 15 d after the priming procedure is related to a decrease in the protective effect over time or an analytical limit. This absence of significant differences between S.

enterica-primed and non-primed individuals at 15 d could also be influenced by the animal physiological states. Individuals displayed different levels of nutritional stress at 1, 7 or 15 d post-priming, which may affect the resistance/tolerance ability of animals facing a second infection [START_REF] Siva-Jothy | Short-term nutrient deprivation affects immune function[END_REF][START_REF] Akoda | Nutritional stress affects the tsetse fly's immune gene expression[END_REF][START_REF] Ayres | The role of anorexia in resistance and tolerance to infections in Drosophila[END_REF][START_REF] Ponton | Integrating nutrition and immunology: A new frontier[END_REF][START_REF] Adamo | Stress responses sculpt the insect immune system, optimizing defense in an ever-changing world[END_REF]. Interestingly, we showed that living S. enterica bacteria from the first injection were still present in haemolymph of S. enterica-primed individuals until 15 d post-priming procedure. While we would expect to observe a higher mortality in such individuals because they should be weak due to the constant fight against bacteria from the first infection, A. vulgare still presented higher survival rates when receiving a LD 50 of bacteria 7 (and potentially 15) d later. We thus highlight that the enhanced protection against a second infection with S. enterica in A. vulgare is possible despite the lack of clearance of the same pathogenic bacteria in the haemolymph. This species is thus able to better survive inoculation with a large quantity of living pathogen, provided the successive infections are separated enough in time. Since the natural habitat of A. vulgare is highly contaminated by several species of bacteria [START_REF] Ranjard | Quantitative and qualitative microscale distribution of bacteria in soil[END_REF][START_REF] Karimi | Atlas français des bactéries du sol[END_REF], it would not be surprising that during the immune system evolution, A. vulgare developed strong resistance and/or tolerance to repeated pathogenic infections. Most of the studies focusing on immune priming used inactivated pathogens or purified molecules to trigger this phenomenon (Milutinovic and Kurtz, 2016;[START_REF] Chang | What vaccination studies tell us about immunological memory within the innate immune system of cultured shrimp and crayfish[END_REF], but the use of living pathogens seems more appropriate to describe the immunological capacities occurring under natural conditions.

Going inside the possible underlying mechanism(s)

Three main mechanisms are currently proposed to explain the protection conferred by immune priming (i) the "immune memory", a recalled biphasic immune response implying a specific pathogen recognition, (ii) the "immune shift" consisting in a shift from one immune effector to another between successive infections and (iii) the "sustained immune boost", an up-regulated and long lasting immune response lasting from the first to the second infection events [START_REF] Contreras-Garduño | Plasmodium berghei induced priming in Anopheles albimanus independently of bacterial co-infection[END_REF][START_REF] Coustau | A novel mechanism of immune memory unveiled at the invertebrate-parasite interface[END_REF][START_REF] Pinaud | A shift from cellular to humoral responses contributes to innate immune memory in the vector snail Biomphalaria glabrata[END_REF][START_REF] Greenwood | Oral immune priming with Bacillus thuringiensis induces a shift in the gene expression of Tribolium castaneum larvae[END_REF]Lanz-Medosa and Garduño, 2018;[START_REF] Gourbal | Innate immune memory: An evolutionary perspective[END_REF][START_REF] Melillo | Innate immune memory in invertebrate metazoans: a critical appraisal[END_REF][START_REF] Moret | Immune priming and transgenerational protection from parasites[END_REF]. In this study, as S. enterica persists in the haemolymph until 15 d after the first sublethal infection, the more plausible mechanism explaining such protection could be a sustained immune boost in A. vulgare individuals primed with the bacteria. As an example, the butterflies Parasemia plantaginis and Galleria mellonella display a sustained immune response to some pathogens, conferring a protective effect and with highly maintained ROS levels and haemocyte density following a first exposure [START_REF] Mikonranta | Insect immunity: Oral exposure to a bacterial pathogen elicits free radical response and protects from a recurring infection[END_REF][START_REF] Wu | Priming Galleria mellonella; (Lepidoptera: Pyralidae) Larvae with heat-killed bacterial cells induced an enhanced immune protection against Photorhabdus luminescens; TT01 and the role of innate immunity in the process[END_REF].

Sustained immune response in our study was illustrated by the high haemocyte viability at any time for S. enterica-primed individuals. However, this high viability of haemocytes could also reveal the production and/or differentiation of a competent cell subpopulation [START_REF] Sokolova | Cadmium-induced apoptosis in oyster haemocytes involves disturbance of cellular energy balance but no mitochondrial permeability transition[END_REF][START_REF] Snyman | Effect of cadmium on haemocyte viability of the woodlouse Porcellio laevis (Isopoda, Crustacea)[END_REF]. As stated by [START_REF] Gourbal | Innate immune memory: An evolutionary perspective[END_REF], "insects are able to select and activate a competent subpopulation of cells with enhanced capacities of lysis or phagocytosis". For example, [START_REF] Rodrigues | Hemocyte differentiation mediates innate immune memory in Anopheles gambiae mosquitoes[END_REF] demonstrated that the immune priming protection in mosquitoes against Plasmodium is not linked to a variation in haemocyte concentration but to an increased proportion of circulating granulocytes. Similarly, the survival improvement of S. enterica-primed individuals could be due to the selection and/or production of a more efficient subset of haemocytes, resulting in a protective effect against the second infection of S. enterica. We noticed that LB-primed individuals also displayed higher haemocyte viability than non-primed ones at 7 and 15 d post-priming, suggesting LB broth injection could also induce a cellular immune response (e.g Markus et al., 2005). The protective effect of immune priming in invertebrates is not based on a universal system but mobilizes a diversity of immune effectors [START_REF] Brehélin | Specificity, learning and memory in the innate immune response[END_REF][START_REF] Pradeu | Immunological memory: What's in a name?[END_REF]Pasquier, 2018, Melillo et al., 2018). We thus assume that haemocyte concentration and viability in A. vulgare cannot fully explain the increase in survival. The next step to further investigate the immune priming mechanism would be to analyze the dynamics of other immune parameters, for example, haemocyte types, ROS and AMP production. We also suggest studying the immune response over a longer period of time and testing the specificity of this response against different pathogenic species. In addition, inactivated pathogens could be used for the priming injection in order to determine the end of the first immune response if there is one (Milutinovic and Kurtz, 2016).

Concluding remarks

1

 1 Survival capacity of the common woodlouse Armadillidium vulgare is improved with a second infection of Salmonella entericaPrigot-Maurice Cybèle #, 1,* , Cerqueira de Araujo Alexandra *, 2 , Durand Sylvine 1 , Laverré Tiffany 1 , Pigeault Romain 3 , Verdon Julien 1 , Bulet Philippe 4,5 , Beltran-Bech Sophie 1, ¤ , Braquart-Varnier Christine1, ¤

  Ours results contribute to the understanding of immune priming, by providing additional details about the protective effect (in terms of survival) and the haemocyte responses in another example of a long-lived non-model arthropod. Since cell proliferation is not often monitored during the priming response(Contreras-Guarduño et al. 2016), this work also constitutes a baseline for further studies on cellular responses and the temporal dynamics of immune priming in invertebrates.

  Fig.2

  Experimental design We performed two experiments on individuals that received a first injection (i.e. priming procedure, see part 2.2.2). The first experiment was conducted to measure haemocyte concentration and viability after 1, 7 or 15 d post priming procedure (see part 2.2.3.1) but also the persistence of living S. enterica in haemolymph (see part 2.2.3.2). We selected these time points based on results of previous studies on crustacean immune priming[START_REF] Roth | Strain-specific priming of resistance in the red flour beetle, Tribolium castaneum[END_REF][START_REF] Chang | What vaccination studies tell us about immunological memory within the innate immune system of cultured shrimp and crayfish[END_REF]. The second experiment was performed to monitor individual survival rate after receiving a LD 50 of bacteria injected 1, 7 or 15 d after the priming procedure (see part 2.2.4 and Fig.1). Different individuals were used for each experiment and time point. Each experiment was replicated several times (three independent experimental replicates for haemolymph analysis and seven for survival monitoring). Within each experimental replicate, we performed several series of the priming procedure (see S2 in supplementary materials).

Varnier et al. (2015) (see S1 in supplementary materials for detailed method). 2.2 Experiments 2.2.1 Datasets are available on the open access repository Mendeley Data (Prigot-Maurice et al., 2019).

  Haemocyte parameters Haemocyte concentration and viability were generally measured on the same individual (see sample size in TableI). The haemolymph sampling procedure as well as the measure of the number of immune cells per µL of haemolymph (i.e. concentration) and the percentage of living cells (i.e. viability) were performed as described by[START_REF] Sicard | Variations of immune parameters in terrestrial isopods: A matter of gender, aging and Wolbachia[END_REF]. Briefly, each Evaluation of S. enterica persistency To test whether living S. enterica persist in the haemolymph of individuals primed with the bacteria at the three time points tested(1, 7, 15 d), 5 additional µL of haemolymph collected from individuals used to measure haemocyte parameters were added to 95 µL of LB medium.

	All animals remained in their individual box for either 1, 7 or 15 d until haemolymph analysis (first experiment, see part 2.2.3) or LD 50 injection for survival monitoring (second experiment, see part 2.2.4). 2.2.3 Haemolymph analysis dorsally on the sixth tergite with a sterile needle, and the resulting drop of haemolymph was collected with a pipette. Three µL of haemolymph were diluted in 15 µL of MAS (27 mM sodium citrate: 336 mM NaCl, 115 mM glucose, 9 mM EDTA, pH 7; Herbinière et al. 2005) and 6 µL of 0.4 % Trypan blue. Then, 10 µL of each sample were deposited in a counting chamber and analyzed in an automated cell counter (Countess TM Version B, Invitrogen). 2.2.3.1 individual was washed a second time (3% NaClO) then water). The integument was pierced 2.2.3.2

Table I

 I Sample size details of A. vulgare females used for haemocyte parameters and survival monitoring, for each priming treatment (Non-primed: non-injected; LB-primed: injected with LB; S. enterica-primed: injected with 10 3 S. enterica during the priming procedure) and each time point after priming procedure. For the haemocyte concentration and viability, measurements were mostly made on same individuals. As a result, sample sizes were the same for the haemocyte concentration and viability, except for sample sizes with exponent letter. a :

	First number is haemocyte concentration sample size; second (in parentheses) is haemocyte
	viability sample size.
		HAEMOCYTE PARAMETERS
	Time points				Total animals per
		Non-primed	LB-primed	S. enterica-primed
	(days)				time point
	1	15 a (17)	18 b	19 a (14)	52 (49)
	7	17	16	15	48
	15	19	14	12 a (14)	45 (47)
			SURVIVAL MONITORING
	Time points				Total animals per
		Non-primed	LB-primed	S. enterica-primed
	(days)				time point
	1	35	35	36	106

Table II

 II Pairwise comparisons (Tukey adjustment) of haemocyte parameters for the priming treatments (Non-primed: non-injected; LB-primed: injected with LB; S. enterica-primed: injected with 10 3 S. enterica during priming procedure) at time points between priming and haemolymph sampling, 1, 7 and 15 d.

	HAEMOCYTE CONCENTRATION (number of cells per µL of haemolymph)
	Days	estimate	std error	p-value
	1
	S. enterica-primed / Non-primed	0.600	0.188	0.004 **
	S. enterica-primed / LB-primed	0.471	0.179	0.023 *
	LB-primed / Non-primed	0.128	0.191	0.778
	7
	S. enterica-primed / Non-primed	0.470	0.193	0.043 *
	S. enterica-primed / LB-primed	0.515	0.196	0.023 *
	LB-primed / Non-primed	-0.045	0.190	0.968
	15
	S. enterica-primed / Non-primed	0.128	0.201	0.799
	S. enterica-primed / LB-primed	0.053	0.215	0.966
	LB-primed / Non-primed	0.075	0.192	0.919

HAEMOCYTE VIABILITY (frequency of living cells) Days 1

  Values where p ≤ 0.05 are given in bold. Stars indicate statistical differences between priming treatments with * = 0.05 < p < 0.06, ** = p ≤ 0.05, p ≤ 0.01, *** = p ≤ 0.001. Pairwise comparisons (Tukey adjustment) of survival rate for the priming treatments (Nonprimed: non-injected; LB-primed: injected with LB; S. enterica-primed: injected with 10 3 S. enterica during the priming procedure) under the time points (time elapsed between priming procedure and LD 50 injection. Values where p ≤ 0.05 are given in bold. Stars indicate statistical differences between priming treatments with ** = p ≤ 0.01 and *** = p ≤ 0.001.

	Table III	S. enterica-primed / LB-primed	-0.158	0.057	0.016**
		LB-primed / Non-primed	-0.269	0.057	< 0.001***
	SURVIVAL RATE (frequency of living animals)
	Days Non-primed	estimate	std error	p-value
	1	LB-primed
		S. enterica-primed / Non-primed	0.412	0.324	0.411
		S. enterica-primed / LB-primed	-1.103	0.450	0.037*
		LB-primed / Non-primed	1.515	0.435	0.001**
	7
		S. enterica-primed / Non-primed	1.208	0.296	< 0.001***
		S. enterica-primed / LB-primed	1.030	0.316	0.003**
		LB-primed / Non-primed	0.178	0.274	0.792
	15
		S. enterica-primed / Non-primed	0.858	0.413	0.095
		S. enterica-primed / LB-primed	estimate 0.488	std error 0.430	p-value 0.492
		LB-primed / Non-primed	0.369	0.332	0.506
	S. enterica-primed / Non-primed	-0.269	0.056	< 0.001***
		S. enterica-primed / LB-primed	-0.177	0.054	0.003**
		LB-primed / Non-primed	-0.091	0.054	0.207
	7
	S. enterica-primed / Non-primed	-0.434	0.055	< 0.001***
		S. enterica-primed / LB-primed	-0.267	0.054	< 0.001***
		LB-primed / Non-primed	-0.167	0.056	0.008**
	15
	S. enterica-primed / Non-primed	-0.427	0.055	< 0.001***

Acknowledgements

We thank Maryline Raimond and Clémentine Bernier for their help in the laboratory and Charlotte Depeux for discussions on the subject.

Declaration of interest

None

Funding

This work was supported by the grant Vivipary and Immune Priming (PEPS EXOMOD CNRS); the State-Region Planning Contracts (CPER); the European Regional Development Fund (FEDER); the French Centre National de la Recherche Scientifique; the French Ministère de l'Enseignement Supérieur et de la Recherche and the University of Poitiers.

Data set

https://data.mendeley.com/datasets/r858zgchp3/draft?

Hours post lethal injection

Survival capacity of the common woodlouse

SUPPLEMENTARY MATERIALS

S1: detailed description of the bacterial culture methods Salmonella enterica is a non-spore-forming facultative aerobic Gram-negative strain [START_REF] Velge | Multiplicity of Salmonella entry mechanisms, a new paradigm for Salmonella pathogenesis[END_REF]. S. enterica serovar Typhimurium J18 [2] cultures were performed either in Luria-Bertani (LB) broth composed of 25 g.L -1 of LB base (Invitrogen 12795-027), or on LB agar plates (LB broth supplemented with 15 g.L -1 of agar-agar, Fisher BioReagentsTM BP1423-2). All inocula used for injections came from a same frozen glycerol stock streaked on LB agar plate incubated at 37 °C overnight. Before each injection experiment, the bacterial solution was prepared as described in Braquart-Varnier et al. (2015) [START_REF] Braquart-Varnier | The mutualistic side of Wolbachia-isopod interactions: Wolbachia mediated protection against pathogenic intracellular bacteria[END_REF]. Briefly, one colony of S. enterica was cultured overnight in 5 mL of LB broth at 37 °C, 180 rpm. The next day, 100 μL of this culture were added to 3 mL of fresh LB broth and incubated at 37 °C, 180 rpm to reach a 0.7 optical density (600 nm), which corresponds to a concentration around 3.10 5 bacteria/μL. After centrifugation of 1 mL of this solution (2 min, 4 °C, 13.000 g), the bacterial pellet was resuspended in 300 μL of fresh LB broth, to obtain a concentration of 10 6 bacteria/μL (the lethal dose being around 10 5 bacteria/100 nL). Then we performed a serial dilution, to obtain the concentration for the priming injection (10 3 bacteria/100 nL) and a sufficient low concentration of S. enterica (1 bacteria/µL) to control the quantity of bacteria injected. To do so, we spread 100 µL of the 2 diluted solution onto a LB agar plate. After an overnight culture at 37°C, we counted the number of colonies, expected to be around 100.

Supplementary materials references

S2: Experimental design

Two experiments were design (haemolymph analysis and survival monitoring; see diagram below). Each experiment was replicated in several independent experimental replicates (Bloc 1, Bloc 2, Bloc 3, Bloc 4 etc.) themselves divided into independent experimental series (S1, S2, S3, S4 etc.) Each series correspond to a single priming procedure performed on the three treatments (non-primed, LB-primed and S. enterica-primed) for one time point (1 d, 7 d or 15 d, the time elapsed between priming procedure and sampling or second infection). For the haemolymph analysis, all time points are distributed equally in a same bloc. For the survival monitoring, each bloc corresponds to a single replicated time point, preventing to distinguish correctly the effect of "bloc" to the effect of "time point" in our statistical models. Because of the correlation between blocs and time points, we added and kept the "time point" factor as fixed effect, in order to adjust the attributed part of the variance and separate the comparisons of priming treatments per time point (1 d, 7 d or 15 d). We also corrected the non-independence of the samples within a same series by adding a random factor "series" nested in "blocs".