Brice Bouvier
email: bricebouvier.fr@gmail.com

Florence Gaillard
email: florence-gaillard@hotmail.fr

Guillaume Bouzillé
email: guillaume.bouzille@chu-rennes.fr

Thibault Pasquet
email: thibault.pasquet@chu-rennes.fr

Hélène Rauscent
email: helene.rauscent@chu-rennes.fr

Isabelle Bonan
email: isabelle.bonan@chu-rennes.fr

Armel Crétual
email: armel.cretual@univ-rennes2.fr

Proposal of a new 3D bimanual protocol for children with unilateral cerebral palsy: reliability in typically developing children

INTRODUCTION

Quantitative evaluation of upper limb (UL) kinematics in disabled children represents a key question for researchers and clinicians especially in the field of unilateral cerebral palsy (uCP) because of its high prevalence [START_REF] Himmelmann | The changing panorama of cerebral palsy in Sweden. IX. Prevalence and originin the birth-year period 1995-1998[END_REF]. This pathology induces impaired motor control of the UL that hinders the execution of their daily life activities [START_REF] Franco De Moura | Kinematic upper limb evaluation of children and adolescents with cerebral palsy: a systematic review of the literature[END_REF]. Although qualitative tools are used in clinical practice, such as the Assisting Hand Assessment (AHA) [START_REF] Krumlinde-Sundholm | The Assisting Hand Assessment: Current evidence of validity, reliability, and responsiveness to change[END_REF]], more precise information can be expected for a better understanding of motor limitations and a subsequent adaptation of therapeutics. Fine quantitative evaluation of UL kinematics in children with uCP, through three-dimensional movement analysis (3DMA) technologies, allows such benefits [START_REF] Brochard | The role of the scapulothoracic and gleno-humeral joints in upper-limb motion in children with hemiplegic cerebral palsy[END_REF][START_REF] Coluccini | Kinematic characterization of functional reach to grasp in normal and in motor disabled children[END_REF][START_REF] Gaillard | Kinematic motion abnormalities and bimanual performance in children with unilateral cerebral palsy[END_REF][START_REF] Kreulen | Movement patterns of the upper extremity and trunk associated with impaired forearm rotation in patients with hemiplegic cerebral palsy compared to healthy controls[END_REF][START_REF] Mackey | Reliability of upper and lower limb three-dimensional kinematics in children with hemiplegia[END_REF][START_REF] Reid | Repeatability of upper limb kinematics for children with and without cerebral palsy[END_REF] and may pretend to support clinical decisions by objectively assessing the efficiency of therapeutics, e.g. injection of botulinic toxin or surgery. Such analysis depends on the motor tasks performed by children, and therefore the choice of these tasks is a crucial question to solve prior to clinical evaluation. However, only a few studies have put their clinical protocol into place after investigating the reliability of the tasks. Jaspers et al. [Jaspers et al., 2011a;Jaspers et al., 2011b] used a very complete approach, with validations performed on typically developing children (TDC) and children with uCP. Their protocol includes three reach tasks, two reach-to-grasp tasks and three gross motor tasks, for a total of eight standardized unimanual UL tasks. Similarly, Butler et al. [START_REF] Butler | Temporal-spatial parameters of the upper limb during a Reach & Grasp Cycle for children[END_REF] designed a protocol based on a unique unimanual reach-to-grasp task divided in six sequential subtasks. Both of these studies consist of unimanual tasks for which children are asked to use only their paretic UL. Generally speaking, this methodological choice can be discussed for two reasons. First, it does not correspond to most daily life situations for a child with uCP since he/she would spontaneously use his/her non-paretic UL to execute a unimanual activity [START_REF] Rudisch | Kinematic parameters of hand movement during a disparate bimanual movement task in children with unilateral Cerebral Palsy[END_REF]. Second, it does not allow the exploration of the UL movements in bimanual situations, which represent a major issue of execution for this pathological population. The bimanual coordination implies many complex cerebral structures, with possible higher defaults of motor planning, selective motor control and/or mirror movements [Eliasson & Burtner, 2008;[START_REF] Jaspers | Review of quantitative measurements of upper limb movements in hemiplegic cerebral palsy[END_REF].

A few recent studies have explored UL kinematics of children with uCP during bimanual tasks such as decanting cups, moving a box on a desk [START_REF] Klotz | Motion analysis of the upper extremity in children with unilateral cerebral palsy-An assessment of six daily tasks[END_REF] and opening a box plus pressing a button inside [START_REF] Rudisch | Kinematic parameters of hand movement during a disparate bimanual movement task in children with unilateral Cerebral Palsy[END_REF]. Although these tasks are interesting to study due to their connections to activities of daily living and to their association with a playful setting masking the clinical surrounding [START_REF] Klotz | Motion analysis of the upper extremity in children with unilateral cerebral palsy-An assessment of six daily tasks[END_REF], they suffer from not covering a full spectrum of degrees of freedom (DoF) of the paretic UL. Furthermore, none of these studies has investigated the reliability of the kinematic parameters. Assessing the reliability of a protocol, in TDC and children with uCP, represents a compulsory step before its deployment as a clinical routine, for any follow-up of a therapeutic/treatment. Studying the nonpathological population before the pathological one often represents the first step of validation of a protocol [Jaspers et al., 2011].

The objectives of the present work are (1) to develop a 3DMA bimanual protocol designed for children with uCP and (2) to assess its reliability in TDC. This protocol allows the exploration of all degrees of freedom of the paretic UL, with standardized tasks integrated into a playful game situation of "Be an Airplane Pilot" (BE-API Protocol).

METHODS

Participants

Twenty TDC (11 boys, 9 girls) aged from 6 to 18 years (mean age 11.9±3.4 years old) voluntarily participated. Children had no history of UL complaints. Nineteen children were right-handed and one lefthanded. Ethical approval was granted by the Ethics Committee of the University Hospital Rennes (France). Parents and children gave their written informed consent for this experiment.

BE-API protocol

The 3DMA protocol consists of a game scenario "Be an Airplane Pilot" (BE-API protocol) which is divided into four bimanual tasks (Fig. 1). These tasks were designed based on discussions with Physical Medicine and Rehabilitation therapists. Each task is specifically designed to involve a unique and natural bimanual strategy, and to explore one, two or three DoF of UL known as limited in children with uCP ("primary DoF"). For each task, all DoF of the UL can be classified as "primary", "secondary" or "not explored" (Table 1), based on the a priori expected motion used to perform the task. The total set of four tasks allows the exploration of all the DoF of the UL: three DoF in the shoulder joint, two DoF in the elbow joint including the forearm prono-supination, two DoF in the wrist joint.

Prior to the start of the protocol, adjustments of the equipment were done according to the child anthropometry (Fig. 1). First, the seat is adjusted in order to obtain 90° hip and knee flexions. Second, the height of the table corresponds to the height of the elbow when the upper arm of the child is along the trunk. The table is placed in contact with the child's belly in order to limit trunk motion. According to the child's anthropometry, the position of all objects (i.e. joystick, turbo, buzzers 1 and 2), the height and the width of the joystick are adjusted.

Task 1, called "mountain passing", consists of four consecutive forward-backward movements of the control column, ranging from its vertical starting position to its horizontal upward position. This task was carried out in the child's sagittal plane, holding the joystick with the two hands. The prototype was designed to minimize the resistance or inertia effect from the control column. Primary DoF are elbow flexionextension (elbFE) and wrist abduction-adduction (wriAA).

Task 2, called "slaloming", consists of four consecutive rotations of the joystick of 180° with the nondominant UL, from top to bottom, while the turbo is continuously pressed using the dominant UL. Primary DoF are shoulder elevation (shoEl) and internal external rotations (shoIER), and wrist flexion-extension (wriFE).

Task 3, called "dropping parachutists", consists of four consecutive reach-and-press movements to buzzer 1 with the non-dominant UL, including a repositioning on the joystick after each pressure, while the joystick is maintained horizontal with the dominant UL. Primary DoF is the plane of elevation of the shoulder (shoPlE).

Task 4, called "refueling", starts with both forearms and hands flat on the table and consists of offering the non-dominant hand to receive a gas coin coming from the top, as soon as the buzzer 2 is pressed by the dominant UL. The coin was given by the operator at the level of the joystick. It is performed once. The primary DoF is forearm pronosupination (elbPS).

Details concerning the movements performed during the four tasks are available in the supplementary material.

The playful setting, including the speech of the operator, aims at surrounding the clinical environment [START_REF] Klotz | Motion analysis of the upper extremity in children with unilateral cerebral palsy-An assessment of six daily tasks[END_REF] and at leading to spontaneous movements of the child. All tasks were performed at selfselected speed. The chronological order of a protocol session was Task 1 -Task 4 -Task 2 -Task 4 -Task x Table 1: Primary (big dashed cross) and secondary (small cross) DoF of the non-dominant UL and the trunk explored through each task.

Motion analysis tracking

An optoelectronic system (Motion Analysis, Rohnert Park, USA, sampling frequency of 100 Hz) with 12 cameras was used to track the 3D position of 15 reflective markers placed on the non-dominant UL and the trunk of the child. The marker placement, the definition of the segment coordinate systems and the choice of Euler sequences followed the recommendations of the international society of biomechanics (ISB) [START_REF] Wu | ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand[END_REF]. The shoulder joint was defined as the "thoracohumeral joint" and its joint center was estimated using a functional method [START_REF] Gamage | New least squares solutions for estimating the average centre of rotation and the axis of rotation[END_REF][START_REF] Lempereur | In vivo estimation of the glenohumeral joint centre by functional methods: accuracy and repeatability assessment[END_REF]. Markers were also placed on the dominant side of the child but were not used in this study.

Data analysis

Tasks 1, 2, and 3 correspond to a series of 4 consecutive cycles. The second and the third cycles were selected for data processing, to avoid any bias of start/stop strategies [Jaspers et al., 2011]. Task 4 corresponds to a unique cycle performed after tasks 1, 2 and 3; these three cycles were retained for data processing. Considering that three consecutive sessions were performed by the child, a total of 6 movement cycles for tasks 1, 2 and 3, and 9 movement cycles for task 4 were retained for data processing (Fig. 2). The analysis was focused on the task execution (e.g. reaching the most anterior position in Task 1) which means we did not consider the "return to neutral position" movement in each cycle.

Fig. 2. The second and the third cycles of tasks 1, 2, 3 and all cycles of task 4 were selected for data processing, through the 3 sessions.

Statistical analysis

Reliability of the movement cycles was assessed through three kinematic parameters: the angular waveforms (WAVE), the maximum angle value (MAX) and the range of motion of the joint (RoM). It was applied for all DoF (primary and secondary) for all the four tasks. Reliability of WAVE was performed using the coefficient of multiple correlation (CMC) [START_REF] Kadaba | Measurement of lower extremity kinematics during level walking[END_REF]. The CMC index quantifies the similarity between joint angle waveforms by taking into account differences in shape, offset, correlation, and range of motion. CMC was calculated for each child and the mean value was reported with 95% confidence interval. Four mean CMC thresholds were considered: excellent (≥0.90), good (0.80-0.89), moderate (0.60-0.79), and poor (<0.60) [Jaspers et al., 2011]. Reliability of MAX and RoM was assessed with the intraclass correlation coefficient (ICC(2,k)) and the standard error of measurement (SEM). Mean values were reported with 95% confidence interval. Four ICC thresholds were considered: very high (≥0.80), moderately high (0.60-0.79), moderate (0.40-0.59), and low (<0.40) [Jaspers et al., 2011].

8

RESULTS

Angular waveforms

Within-day reliability of WAVE was excellent for all primary DoF during all tasks (0.93 ≤ mean CMC ≤ 0.98) (table 2). The same observation of excellence is done for all secondary DoF during all tasks (0.90 ≤ mean CMC ≤ 0.98), with the exception of the wrist flexion-extension during tasks 3 and 4 with moderate values (0.74 ≤ mean CMC ≤ 0.75).

RoM and MAX

Within-day reliability of RoM parameter was very high for all primary DoF during all tasks (0.86 ≤ mean ICC ≤ 0.96), as well as for all secondary DoF during all tasks (0.81 ≤ mean ICC ≤ 0.96) (table 3). All mean SEM values were between 1.7° and 4.3° for the primary DoF and between 1.7° and 5.0° for the secondary DoF. Concerning the MAX parameter, ICC values were very high for all primary DoF during all tasks tested (0.83 ≤ mean ICC ≤ 0.98), as well as for all secondary DoF during all tasks (0.81 ≤ mean ICC ≤ 0.98) (table 4). All mean SEM values were between 0.9° and 3.8° for the primary DoF and between 1.3° and 3.5° for the secondary DoF. The forward shoulder elevation angle is reported with positive values to ease the understanding, the negative sign involved by the YXY sequence used ( [START_REF] Wu | ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand[END_REF]) has been removed. The shoulder plane of elevation is negative for a posterior position, and positive for an anterior position. A maximal elbow extension is at 0° or slightly negative. The neutral forearm pronosupination is at 0°, a supination position has a negative value and a pronation position has a positive value."Dev uln" stands for "ulnar deviation".

DoF

DISCUSSION

Development of a new protocol

Any development of a new protocol requires a good definition of the scientific aim [START_REF] Kontaxis | A framework for the definition of standardized protocols for measuring upper-extremity kinematics[END_REF]. The final clinical outcome of this protocol is to be able to study the upper limb movements of TDC and children with uCP during bimanual situations in order to objectively assess the efficiency of clinical uCP-oriented therapeutics. To do so, all tasks were conceived in order to (1) be fully feasible by TDC, induce sufficient motion in DoF known as limited in children with uCP, (3) involve a unique and natural strategy of performance in bimanual condition. All the gaming elements were carefully positioned on the table to guarantee the main action of the asymmetric tasks was performed by the non-dominant limb (or the hemiplegic limb of the child with uCP) and not by the other limb. A special attention was put on the complementarity of tasks regarding the DoF explored. Various conditions of task execution were represented: with symmetrical and asymmetrical tasks, with guided and free trajectories, with and without object grasping. Tasks had also to be homogeneous to fit into a same environment of game (thematic of airplane) and to follow a plausible game scenario.

Although it is often disregarded in the literature [START_REF] Butler | Temporal-spatial parameters of the upper limb during a Reach & Grasp Cycle for children[END_REF]Jaspers et al., 2011;[START_REF] Kreulen | Movement patterns of the upper extremity and trunk associated with impaired forearm rotation in patients with hemiplegic cerebral palsy compared to healthy controls[END_REF], the presence of a playful environment is important to mask the surrounding clinical environment [START_REF] Klotz | Motion analysis of the upper extremity in children with unilateral cerebral palsy-An assessment of six daily tasks[END_REF] and to lead to spontaneous movements of the child. In the game scenario, the child had to pass four mountains, to perform four slaloms and to drop four parachutists during tasks 1, 2 and 3 respectively. The operator's speech, the elements used (including noisy buzzers) and the decoration of the table and the room were adapted to the thematic of airplane in our study. The set-up had also to be thought for left and right-handed children, without any restriction of height and weight.

3D data tracking and processing

Marker occlusions were minimized by having a capture area above the table and by using thin and small objects. Both upper limbs were equipped with reflective markers, for allowing future complementary investigations and for leaving the child in the dark concerning the purpose of the experiment. Alternative technologies such as inertial sensors may be used, with different advantages and drawbacks [START_REF] Bouvier | Upper Limb Kinematics Using Inertial and Magnetic Sensors: Comparison of Sensor-to-Segment Calibrations[END_REF]. The attribution for a DoF to be "primary" or "secondary" was established a priori considering the DoF targeted by the task. This attribution was confirmed a posteriori according to the RoM and MAX values actually calculated in the TDC group. Note: the shoulder elevation angle has been remained as "primary" and "secondary" DoF during tasks 2 and 1 respectively, although very convincing results were also obtained for task 1. Indeed, task 2 is a shoulder-oriented task which also involve motions of shoulder plane of elevation and rotations, whereas task 1 is not and may involve passive motion of the shoulder elevation due to high elbow and trunk contributions. These denominations are not strict and may evolve regarding future results on children with uCP.

Feasibility and adherence

This protocol was easily feasible by all children, aged from 6 to 18. Fifteen minutes for marker equipment and adjustments of the set-up, plus five minutes for briefing and familiarization were needed beforehand. One session lasted about 10 minutes. It is not recommended to use the protocol for children under the age of 6 due to lower ability of mental focus or a sequential temporal coupling of both hands [START_REF] Rudisch | Kinematic parameters of hand movement during a disparate bimanual movement task in children with unilateral Cerebral Palsy[END_REF]. The playful environment on the thematic of "Be an Airplane Pilot" was appreciated by all children. The adherence of children aged from 6 to 12 was very high. Children aged from 13 to 18 were slightly less attracted by the game scenario, the use of a video game or the virtual reality coupled with the set-up would possibly increase their adherence [START_REF] Kerem | Virtual Reality in Rehabilitation of Children with Cerebral Palsy[END_REF].

Exploration of DoF of the UL

One of the objectives of the BE-API protocol was to explore the DoF of the UL during functional tasks, in range of motion that correspond to activities of daily living (ADL).

Generally speaking, the BE-API protocol stimulates all the DoF of the non-dominant UL in ranges of motion and with maximum angle values that correspond to functional ranges of motion during ADL [START_REF] Gates | Range of motion requirements for upper-limb activities of daily living[END_REF]. The mean maximal shoulder elevation was measured as 88.9° and 71.8° during tasks 1 and 2 respectively and corresponds to the maximum needed to drink a glass, 62° and 87° as reported by [START_REF] Van Andel | Complete 3D kinematics of upper extremity functional tasks[END_REF] and [START_REF] References Aizawa | Three-dimensional motion of the upper extremity joints during various activities of daily living[END_REF] respectively. Mean maximal shoulder plane of elevation was reported as -19.7° during the task 3 and is in accordance with most of ADL such as fastening a button at navel level (-13° [Aizawa et al., 2010]), excepted very demanding backward tasks (i.e. "hand to pocket" with -65° [START_REF] Gates | Range of motion requirements for upper-limb activities of daily living[END_REF]). Maximal shoulder external rotation, measured during tasks 2 and 4 with values of 77.6° and 63.3° respectively are also compatible with various activities of daily living (32° and 55° as reported by [START_REF] Petuskey | Upper extremity kinematics during functional activities: Three-dimensional studies in a normal pediatric population[END_REF] and [START_REF] Gates | Range of motion requirements for upper-limb activities of daily living[END_REF] resp.). The elbow extension explored during task 1 is sub-maximal with a value of 32.5° and corresponds to most of ADL such as pouring from a pitcher (36°) or using a telephone (43°) [START_REF] Morrey | A biomechanical study of normal functional elbow motion[END_REF]. Task 1 may involve assistance from one UL to the other since both hands grasped the joystick, as it can occur in certain ADL of the child too. Task 4 succeeds in exploring the forearm supination in depth with mean maximum value of 63.8° higher than those reported by Jaspers et al. (15°) [Jaspers et al., 2011], Klotz et al. during bimanual tasks (28.4°) [16], and higher than the maximal value for five different ADL (53°) [24]. Concerning the wrist joint, maximal and RoM values are not wide due to the limited RoM of this joint and are similar to those reported in the literature [Jaspers et al., 2011]. Lastly, this protocol was not originally conceived to stimulate the trunk motion, the small motion measured on two DoF during tasks 1 and 3 has been reported to leave the door open to a future exploration of compensation movements from children with uCP.

Reliability on TDC

Results indicated a very high reliability of the three kinematic parameters (WAVE, RoM, MAX), for all DoF, for all tasks, through all statistical indicators (CMC, ICC, SEM). Concerning WAVE parameter, most of CMC values for primary and secondary DoF of shoulder and elbow joints were superior or equal to 0.90 and are higher than the corresponding literature. [Jaspers et al., 2011] and [START_REF] Vanezis | The reliability of the ELEPAP clinical protocol for the 3D kinematic evaluation of upper limb function[END_REF] reported CMC superior to 0.80 for shoulder and elbow joints during reaching tasks, with or without grasping, hand to mouth/head/shoulder tasks, and a throwing task, performed by TDC. Lower CMC values for the wrist flexion-extension during tasks 3 and 4 (0.74-0.75) are also consistent with these two studies, and may be explained by the smaller range of motion of this joint during these two tasks (17.5°-22.0°), in comparison with tasks 1 and 2 (32.9°-55.3°). It must be reminded that CMC index is sensitive to the range of motion, the number of subjects or even the sampling frequency used, therefore any comparison of CMC values from different studies has to be done with precaution [START_REF] Røislien | Evaluating the properties of the coefficient of multiple correlation (CMC) for kinematic gait data[END_REF].

Concerning RoM and MAX parameters, very high ICC values (ICC ≥ 0.81) presented in our study were superior to those reported by [Jaspers et al., 2011] for similar UL joint angles measured at a point of task achievement (ICC > 0.60). Similarly, SEM values (SEM ≤ 5.0°) were a bit smaller than those presented in the literature, (SEM ≤ 7.8° [START_REF] Vanezis | The reliability of the ELEPAP clinical protocol for the 3D kinematic evaluation of upper limb function[END_REF], SEM ≤ 8.5° [Jaspers et al., 2011] reported for movements a bit wider than in our study). These small values of SEM are optimistic for investigation on children with uCP, since they would guarantee a minimal detectable change inferior to 14° for any degree of freedom investigated.

These very high values of reliability can be partly explained by the high level of rigor of the protocol, including a strict placement of objects and some trajectories guided by objects (tasks 1 and 2). Ensuring excellent results on reliability for TDC is an important first validation step, since higher variability may be expected for children with uCP. The more reliable the BE-API protocol will be, the more efficient and useful clinical assessment may also be.

Kinematic parameters of this study have been narrowed to UL joint angles to draw a parallel with the methodology adopted for the lower limb during the quantitative gait analysis, already widely spread as a clinical routine. Other parameters such the movement duration, speed [Jaspers et al., 2011], movement efficiency [START_REF] Butler | Temporal-spatial parameters of the upper limb during a Reach & Grasp Cycle for children[END_REF] represent potential perspectives, by looking at the future results of children with uCP and at the comparison between the two populations. Crossing joint angle information from the two UL of the child may also be of interest to explore the bimanual coordination.

  Fig.1: A child with the set-up of the protocol BE-API. T is the turbo used for task 2, B1 is the buzzer 1 used for task 3 and B2 is the buzzer 2 used for task 4. Blue dash-arrow represents the movement of the control column during task 1, blue solid arrow represents the movement of the joystick during task 2.

Table 2

 2

	: Within-day reliability of the WAVE parameter through mean CMC values with [95% confidence
	interval] for primary DoF (bold) and secondary DoF (non-bold)

-Task

Three consecutive sessions were performed by the child (Fig.2), for a total duration of about 45 minutes, including a familiarization and briefing of 5 minutes performed beforehand.

ACKNOWLEDGEMENTS

The authors would like to thank all children and parents for participating in this study. Special thanks to Bertrand Elie for his help to design the protocol.

Conflict of Interest statement

The authors of the manuscript "Proposal of a new 3D bimanual protocol for children with unilateral cerebral palsy: reliability in typically developing children" have no conflicts of interest to declare.


Laura Quéro
email: laura.quero@univ-brest.fr

Priscillia Courault
email: priscillia.courault@biomerieux.com

Beatrice Cellière
email: beatrice.celliere@biomerieux.com

Sophie Lorber
email: sophie.lorber@inra.fr

Jean-Luc Jany
email: jean-luc.jany@univ-brest.fr

Olivier Puel
email: olivier.puel@inra.fr

Victoria Girard
email: victoria.girard@biomerieux.com

Valérie Vasseur
email: valerie.vasseur@univ-brest.fr

Patrice Nodet
email: patrice.nodet@univ-brest.fr

Jerome Mounier
email: jerome.mounier@univ-brest.fr

Jérôme Mounier
email: jerome.mounier@univ-brest.fr.

Jérôme Mounier

Jérôme Mounier

Application of MALDI-TOF MS to species complex differentiation and strain typing of food related fungi: Case studies with Aspergillus section Flavi species and Penicillium roqueforti isolates

Keywords: MALDI-TOF MS, filamentous fungi, species complex, strain typing

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

Introduction

Fungi are frequently involved in food spoilage and represent a major cause of food and economic losses. Indeed, among food losses and waste which represent 1 billion tons each year (FAO, 2011), it is estimated that 5 to 10% of them are due to fungal spoilage [START_REF] Filtenborg | Moulds in Food Spoilage[END_REF][START_REF] Pitt | Fungi and Food Spoilage[END_REF]. Fungi can spoil a large variety of feeds and foods, causing organoleptic properties deterioration such as visible growth on the product surface, off-flavor production, texture and color changes. Moreover, a large number of species such as Penicillium and Aspergillus spp. are potential mycotoxin producers, and may represent a great hazard for human health [START_REF] Waśkiewicz | MYCOTOXINS | Natural Occurrence of Mycotoxins in Food[END_REF]. Hence, rapid and reliable identification of filamentous fungi is a key step for a better management of food safety and quality.

For several years now, MALDI-TOF MS has been successfully applied to microorganism identification, from bacteria [START_REF] Basile | Pathogenic Bacteria: Their Detection and Differentiation by Rapid Lipid Profiling with Pyrolysis Mass Spectrometry[END_REF] to fungi [START_REF] Welham | Characterization of Fungal Spores by Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[END_REF] including food-related fungi (Quéro et al. 2018). In the latter study, a spectral database comprising 619 strains belonging to 136 species of food interest was built and 90 % correct identification at the species level were achieved after external validation. Several commercial instruments and databases are available [START_REF] Deak | Comparison of the Vitek MS and Bruker Microflex LT MALDI-TOF MS Platforms for Routine Identification of Commonly Isolated Bacteria and Yeast in the Clinical Microbiology Laboratory[END_REF] for routine identification. Besides identification at species level, MALDI-TOF MS has been shown as a powerful tool to discriminate fungal species complex and cryptic fungal species. As an example, [START_REF] Al-Hatmi | Rapid Identification of Clinical Members of Fusarium Fujikuroi Complex Using MALDI-TOF MS[END_REF] were able to correctly identify species of clinical interest belonging to the Fusarium fujikuroi complex, some of which being cryptic species. [START_REF] Allen | Bacterial Wilt Disease and the Ralstonia Solanacearum Species Complex[END_REF] defined species complex as a cluster of related isolates which individuals may represent more than one species while cryptic species are morphologically indiscernible biological/phylogenetic units that are only revealed using DNA-based molecular analysis [START_REF] Hawksworth | Pandora's Mycological Box: Molecular Sequences vs. Morphology in Understanding Fungal Relationships and Biodiversity[END_REF]. In most cases, the identification of such species requires the analysis of several specific genes and expertise in data analysis [START_REF] Balasundaram | How Many DNA Markers Are Needed to Reveal Cryptic Fungal Species?[END_REF]. Species complex are an issue not only in clinical context but also in the food context, particularly regarding mycotoxin production. For example, in the Aspergillus genera, and more particularly in the Flavi section which contains several cryptic species and currently comprises 33 phylogenetically distinct species [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF], species have different mycotoxin production abilities, some species being able to produce B1, B2, G1 and G2 aflatoxins (e.g. A. nomius, A. novoparasiticus, A. parasiticus) while others only produce B1 and B2 aflatoxins (A. flavus, A. pseudotamarii and A. togoensis) or no aflatoxins (A. caelatus, A. subflavus and A. tamarii) [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF]. Another challenge within this section is the discrimination between the toxigenic Aspergillus flavus and A. parasiticus and the non-toxigenic A. oryzae and A. sojae, the latter being used in the production of numerous fermented products like sake or soy sauce [START_REF] Gibbons | The Evolutionary Imprint of Domestication on Genome Variation and Function of the Filamentous Fungus Aspergillus oryzae[END_REF].

In order to discriminate Aspergillus species, MALDI-TOF MS could be a good alternative to molecular techniques, because of its accuracy, high-throughput and low cost per analysis.

Several studies already pointed out its use for different Aspergillus sections including the Flavi section. For example, [START_REF] Alanio | Matrix-assisted Laser Desorption Ionization Time-of-flight Mass Spectrometry for Fast and Accurate Identification of Clinically Relevant Aspergillus Species[END_REF] used MALDI-TOF MS for discriminating 10 species of Aspergillus section Fumigati while [START_REF] Hettick | Discrimination of Aspergillus Isolates at the Species and Strain Level by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Fingerprinting[END_REF] and De Carolis et al.

(2012) could differentiate A. flavus from A. parasiticus, and A. parasiticus, A. flavus and A. oryzae, respectively. In a more extensive study, [START_REF] Rodrigues | Species Identification of Aspergillus Section Flavi Isolates from Portuguese Almonds Using Phenotypic, Including MALDI-TOF ICMS, and Molecular Approaches[END_REF] could identify 9 species of Aspergillus section Flavi but could not distinguish aflatoxigenic and nonaflatoxigenic isolates of A. Flavus. More recently, [START_REF] Imbert | Multi-centric evaluation of the online MSI platform for the identification of cryptic and rare species of Aspergillus by MALDI-TOF[END_REF] assessed MALDI-TOF MS identification accuracy of Aspergillus cryptic species on a very large dataset of 1477 isolates using the freely available mass spectrometry identification (MSI) platform. After sequencing a subset of 245 cryptic species isolates for confirmation, they showed that, while very good identification (99.6 %) could be achieved with MALDI-TOF MS at the section level, only 66.1% of isolates were correctly assigned at the species level indicating that the database needed further improvement for cryptic species.

To another extent, MALDI-TOF MS could also be of great interest for differentiating strains from a same species, i.e. for strain typing. In the past 10 years, it has been applied to bacteria of different genera and species such as Salmonella enterica [START_REF] Kuhns | Rapid Discrimination of Salmonella Enterica Serovar Typhi from Other Serovars by MALDI-TOF Mass Spectrometry[END_REF], Staphylococcus aureus [START_REF] Ueda | Development of a Novel Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrum (MALDI-TOF-MS)-Based Typing Method to Identify Meticillin-Resistant Staphylococcus aureus Clones[END_REF], Legionella spp. [START_REF] Fujinami | Rapid Discrimination of Legionella by Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry[END_REF] or Arthrobacter spp. [START_REF] Vargha | Optimization of MALDI-TOF MS for Strain Level Differentiation of Arthrobacter Isolates[END_REF] and could be in some cases as effective as traditional typing methods such as multi-locus sequence typing (MLST) or Pulsed-Field Gel Electrophoresis (PFGE). In a recent study, [START_REF] Kern | Differentiation of Lactobacillus brevis Strains Using Matrix-Assisted-Laser-Desorption-Ionization-Time-of-Flight Mass Spectrometry with Respect to Their Beer Spoilage Potential[END_REF] showed the ability of MALDI TOF MS to differentiate Lactobacillus brevis isolates at the strain level, and correlations could also be made between spectra classification and strain physiological properties. MALDI-TOF MS typing was also applied to yeasts of clinical interest, for which it could be as powerful as microsatellite markers for monitoring the spread of nosocomial infections [START_REF] Pulcrano | MALDI-TOF Mass Spectrometry and Microsatellite Markers to Evaluate Candida Parapsilosis Transmission in Neonatal Intensive Care Units[END_REF]. This technique was also recently applied for the typing of brewing yeast strains allowing their classification into different major beer types [START_REF] Lauterbach | MALDI-TOF MS Typing Enables the Classification of Brewing Yeasts of the Genus Saccharomyces to Major Beer Styles[END_REF] as well as for the typing of 33 wine yeasts which could be sorted according to their genetic background [START_REF] Usbeck | Wine Yeast Typing by MALDI-TOF MS[END_REF]. One of the main challenges of fungal typing is that, as compared to bacteria, their phylogenetic relationships are more complex and species boundaries are not easily drawn [START_REF] Bader | MALDI-TOF-MS-Based Species Identification and Typing Approaches in Medical Mycology[END_REF]. Nevertheless, a rapid and reliable method for fungal typing would be of great interest in several contexts, e.g., to help understanding the domestication process and history of numerous species used in industry, for source-tracking of spoilage fungi in the food industry, to differentiate toxigenic and atoxigenic strains of a same species, to discriminate the different strains involved in natural fermentation processes for the selection of starter cultures and for deciphering the tenuous limits between contaminant and biotechnological isolates [START_REF] Flórez | Morphotypic and Molecular Identification of Filamentous Fungi from Spanish Blue-Veined Cabrales Cheese, and Typing of Penicillium Roqueforti and Geotrichum Candidum Isolates[END_REF]). In the past 15 years, several studies focused on strain-level classification of filamentous fungi with technological interest such as those used in cheese manufacture. [START_REF] Flórez | Morphotypic and Molecular Identification of Filamentous Fungi from Spanish Blue-Veined Cabrales Cheese, and Typing of Penicillium Roqueforti and Geotrichum Candidum Isolates[END_REF] and [START_REF] Fontaine | Influence of Intraspecific Variability and Abiotic Factors on Mycotoxin Production in Penicillium Roqueforti[END_REF], using randomly amplified polymorphic DNA (RAPD)-PCR, could discriminate, at the intraspecies level, P. roqueforti isolates from cheese and environmental origins.

Microsatellite markers were also used to investigate the genetic diversity within Penicillium roqueforti isolates [START_REF] Ropars | Induction of Sexual Reproduction and Genetic Diversity in the Cheese Fungus Penicillium Roqueforti[END_REF][START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], and these studies allowed the differentiation of isolates in several genetically divergent populations. For instance, [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], using 4 polymorphic microsatellite markers, distinguished 28 haplotypes among a worldwide collection of 164 P. roqueforti isolates from cheese and other environments.

Furthermore, these 28 haplotypes could be clustered into three well-defined genetically differentiated populations.

While there is a strong body of evidence that MALDI-TOF MS can be applied to discriminate closely-related bacterial species and bacterial strains, only few studies have evaluated MALDI-TOF MS as a rapid tool to differentiate closely-related fungal species and strains or genetic populations from a same species. Therefore, the aim of this study was first to evaluate the potential of MALDI-TOF MS to accurately identify 23 closely-related species of Aspergillus section Flavi, and then to assess whether this technique could be used for discriminating Penicillium roqueforti isolates previously shown by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF] to belong to three distinct genetic populations.

Materials and methods

Fungal strains and cultivation

Sixty-eight strains belonging to 23 species from the Aspergillus section Flavi were obtained from different culture collections. They are listed in Table 1. Strain identification was performed by DNA sequencing in previous studies [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF][START_REF] Carvajal-Campos | Aspergillus korhogoensis, a Novel Aflatoxin Producing Species from the Côte d'Ivoire[END_REF]Quéro et al. 2018) or by the culture collection providing the isolates. Sixty-three Penicillium roqueforti isolates obtained from the Université de Bretagne Occidentale Culture Collection (UBOCC), Centraalbureau voor Schimmelcultures (CBS), Mycology laboratory of the Institute of Hygiene and Epidemiology (IHEM), Mycothèque de l'Université Catholique de Louvain (MUCL), Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ), and Laboratoire Cryptogamie Paris (LCP) collections belonging to 28 haplotypes and classified into three genetically distinct populations (1, 2 and 3) were chosen among the 164 isolates which were studied by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF]. Twenty-one, 22 and 20 isolates belonged to population 1, 2 and 3, respectively (Supplementary Table 1).

To assess viability and purity before spectra acquisition, all strains were first cultivated at 25°C on Malt Extract Agar (20 g.L -1 malt extract, 3 g.L -1 yeast extract, 15 g.L -1 agar) and Potato Dextrose Agar (PDA, bioMérieux, Marcy l'Etoile, France), for Aspergillus spp. and P. roqueforti, respectively. Then, they were cultivated on their respective media for 8 days at 25°C before spectra acquisition. Three culture replicates were made for each strain.

Sample preparation and spectra acquisition

After cultivation, isolates were processed following the manufacturer's instructions (bioMérieux, Marcy l'Etoile, France) as described previously (Quéro et al. 2018). Briefly, fungal biomass was suspended in 70% ethanol, before formic acid/acetonitrile protein extraction and centrifugation. After samples and matrix were deposited on target slides, spectra were acquired using the VITEK MS system (bioMérieux, Marcy l'Etoile, France) equipped with the Launchpad V2.8.4 acquisition software. Each sample was analyzed in duplicate.

All spectra were acquired in linear positive ion extraction mode in a mass range from 2000 to 20,000 Da. Individual spectra were accumulated from 500 laser shots (100 profiles with 5 shots per profile) with the 'Auto-Quality' option activated. The system was calibrated externally with fresh cells of Escherichia coli ATCC 8739. Raw spectra were automatically processed by smoothing and peak detection procedures implemented in the Launchpad acquisition software (bioMérieux, Marcy l'Etoile, France).

Raw spectra were then controlled for peak resolution, signal-to-noise ratio and absolute signal intensity as described previously [START_REF] Girard | Identification of Mycobacterium Spp. and Nocardia Spp. from Solid and Liquid Cultures by Matrix-Assisted Laser Desorption Ionization-time of Flight Mass Spectrometry (MALDI-TOF MS)[END_REF]. The spectra that did not reach the specified quality criteria were discarded.

Discrimination of Aspergillus section Flavi species complex

Two approaches were used to evaluate the power of MALDI-TOF MS to discriminate Aspergillus species from the Flavi section. First, a non-supervised approach was used in which the distances between all spectra were determined using the classical multidimensional scaling function (cmdscale) of Matlab (2014, The Mathworks Inc., USA). The calculated distances between spectra were based on the absence or presence of peaks and their intensities. These distances were then visualized using a multidimensional scaling (MDS) graphic. Secondly, a supervised approach was used after building a spectral database with 61 strains as previously described [START_REF] Girard | Identification of Mycobacterium Spp. and Nocardia Spp. from Solid and Liquid Cultures by Matrix-Assisted Laser Desorption Ionization-time of Flight Mass Spectrometry (MALDI-TOF MS)[END_REF]Quéro et al. 2018). Briefly, peak lists were binned and a predictive model was established using the Advanced Spectra Classifier (ASC) algorithm developed by bioMerieux (Marcy l'Etoile, France). Briefly, the ASC algorithm assigns weights to each bin depending on the absence or presence of a peak. For example, if a peak is always present in a bin for spectra of population 1 and absent in the same bin for the other groups, the algorithm will assign a high positive weight to that bin for classification of unknown spectra. On the contrary, if a peak is always absent for population 1 and present in the other populations, it will have a high negative weight. Moreover, if a peak is either present or absent in spectra of the same population, it will have a low weight as it is poorly discriminant. This decision algorithm was applied to only retain significant matches and a single choice identification was obtained when only one species was retained. When more than one species was retained, a low discrimination result was proposed. In case more than 4 species were retained or if no significant match was found, it was considered as a nonidentification result.

Spectra acquired from 7 species, i.e., A. bertholletius, A. coremiiformis, A. cerealis (ex A. korhogoensis), A. mottae, A. oryzae, A. pseudocaelatus and A. pseudonomius were not included in the database as the number of tested strains was not sufficient. Indeed, the ASC algorithm used for database construction requires a minimum of 2 strains per species.

Performances of this database were then evaluated using the same set of isolates by crossvalidation, i.e., internal validation, as described previously (Quero et al. 2018). The spectral data were randomly split into 5 subsets, 4 subsets used as learning phase and the last one used to validate the identification performances (or in this case, the attribution to the right group). This procedure was repeated 5 times, each subset being used once as a test. A correct identification was defined when the same identification occurred between cross-validation result and the reference identification. Low discrimination results were considered as correct if the expected identification was included in the matches. A misidentification result was defined as a discordant identification between the cross-validation result and the reference identification.

Discrimination of Penicillium roqueforti isolates

Prior to similarity comparison of spectra and database construction, a two-step calibration was applied on all spectra, involving a linear calibration step to correct any possible global mass shift and a quadratic calibration step on the spectra entire mass range.

For linear calibration, specific MS peaks of P. roqueforti spectra were used. To do so, MS spectra of P. roqueforti isolates recorded in the bioMérieux commercial database (version 3.2) were screened for specific MS peaks, which were then searched in the spectra acquired in the present study. Thirteen specific MS peaks with masses ranging from 3704.84 to 14431.15 Da were found. Then, only MS peaks present in more than 70% of isolates belonging to two of the three genetic populations were kept which led to the selection of 9 MS peaks with a mass of 3704.84, 3746.12, 4972.23, 6242.47, 6777.90, 6840.56, 7409.58, 7437.67 and 14431.14 Da. The selected theoretical MS peaks were then compared to those observed in the spectra acquired in the present study, allowing the application of a linear model to correct eventual mass deviations. After mass correction with linear calibration, a quadratic calibration was applied. For this calibration step, the entire mass range (3000-20000 Da) was targeted, in which masses ranging between 3000 and 6000 Da, 6000 and 10000 Da and 10000 and 20000 Da were screened using a 1-,2-and 3-Da interval, respectively. The masse range 2000-3000 Da was excluded for quadratic calibration as it contained a very high number of peaks which could bias the model used for calibration. For quadratic calibration, mass tolerance was more restrictive than previously and only MS peaks present in more than 83% of isolates belonging to two of the three genetic populations were kept. These selected masses which included 42 MS peaks (data not shown) were then used to build a quadratic model between theoretical and observed masses, allowing a good alignment of spectra.

A spectral database in which the spectra derived from P. roqueforti isolates were assigned to one of the three genetic populations identified by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF] was then implemented, using the ASC algorithm.

Performances of this database were first evaluated by cross-validation as described above and database performances were also evaluated by external validation using 21 strains not used to build the database. Spectra derived from these strains were used to challenge the database to assess whether they could be correctly assigned to the correct genetic population.

Results and discussion

Evaluation of MALDI-TOF MS for discriminating Aspergillus section Flavi species

The distances between spectra of all tested species can be visualized in Figure 1, which shows the results of MDS analysis for all spectra (Figure 1A), spectra of the A. flavus-clade (Figure 1B) and spectra from the other clades (Figure 1C). Overall, most species were quite well separated from the others while some did not (Figure 1A). Based on spectra similarity, 3 groups of species could be distinguished except for spectra of A. pseudotamarii which stood apart from these groups (Figure 1A). The first two groups (groups A and B, Figure 1A) contained spectra of all species of the A. flavus-clade that gathers the A. flavus and A. parasiticus related species (Varga et al, 2011) with the exception of A. mottae and A. oryzae as well as spectra from A. pseudonomius and A. avenaceus from the nomius-and avenaceusclade. Although A. mottae is considered to belong to A. flavus-clade, this latter species is considered as an ancestral taxon of the group including the A. parasiticus-and A. flavussubclades [START_REF] Soares | Three new species of Aspergillus section Flavi isolated from almonds and maize in Portugal[END_REF][START_REF] Carvajal-Campos | Aspergillus korhogoensis, a Novel Aflatoxin Producing Species from the Côte d'Ivoire[END_REF]. Interestingly, Group B contained all species that are closely phylogenetically related to A. parasiticus, namely A. arachidicola, A. novoparasiticus, A. transmontanensis, A. sojae and A. sergii. All of these species are considered in the literature as pertaining to the A. parasiticus-clade [START_REF] Rodrigues | Species Identification of Aspergillus Section Flavi Isolates from Portuguese Almonds Using Phenotypic, Including MALDI-TOF ICMS, and Molecular Approaches[END_REF][START_REF] Carvajal-Campos | Aspergillus korhogoensis, a Novel Aflatoxin Producing Species from the Côte d'Ivoire[END_REF]. It is worth mentioning that the separate grouping of A. parasiticus-clade MALDI-TOF MS spectra from other A. flavus-clade species was also reported by [START_REF] Rodrigues | Species Identification of Aspergillus Section Flavi Isolates from Portuguese Almonds Using Phenotypic, Including MALDI-TOF ICMS, and Molecular Approaches[END_REF] but on a smaller number of species. Group C contained all the species from the other clades, with the exception of A. oryzae and A. mottae from the A.

flavus-clade.

When only representing spectra from species of the A. flavus-clade (Figure 1B), it can be observed that most of these species were clearly separated. Interestingly, spectra of A. flavus and A. oryzae (the likely domesticated form of A. flavus) clearly stood apart despite their very close phylogenetic relatedness [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF], reflecting the fact that A. oryzae expressed many proteins that differed from those of A. flavus. Indeed, it is well established that domestication of A. flavus has led to important genetic and functional changes in its metabolism [START_REF] Gibbons | The Evolutionary Imprint of Domestication on Genome Variation and Function of the Filamentous Fungus Aspergillus oryzae[END_REF]. Instead, spectra of A. oryzae were more similar to those of A. minisclerotigenes which is also closely phylogenetically related to A. oryzae (Frisvad et al., 2019). In contrast to A. flavus and A. oryzae, spectra profiles of A. parasiticus and its likely domesticated form, A. sojae, shared much more common characteristics. Figure 1C shows the distance between spectra of the 11 other tested species belonging to the other clades of Aspergillus section Flavi. Spectra of species from the A. tamarii-clade were completely separated from the other ones but not grouped together. Indeed, spectra of A. tamarii and A. pseudotamarii were clearly separated from each other while those of A. caelatus and A. pseudocaelatus were more similar. Though they belonged to different clades, spectra of the remaining species were grouped together except those of A. pseudonomius and A. avenaceus isolates which were grouped together. Overall, spectra of the tested species were quite well separated, indicating that it should be possible to differentiate and identify them using a supervised approach. Spectra of the 16 species which were represented by at least two strains were integrated into the bioMérieux spectral database and identification performances were assessed by crossvalidation (Table 2). Overall, more than 99% of spectra (271/272) were assigned to the correct species. Only one spectrum of A. novoparasiticus was not identified, and one spectrum of A. avenaceus was attributed to both A. avenaceus and A. flavus. These results show that, even if the species of Aspergillus section Flavi tested here are closely related, their spectra are sufficiently different to yield correct identification when implemented into a spectral database and analyzed with the ASC algorithm. Results obtained here are of particular interest, especially for A. parasiticus and A. sojae whose spectra were perfectly separated and identified during cross-validation. It should be noted that these species are very similar from a morphological point of view; cannot be distinguished from each other based only on gene sequencing of taxonomic markers such as β-tubulin, calmodulin and RPB2 genes and extrolite analysis is necessary to differentiate them (i.e., in contrast to A. parasiticus, A. sojae does not produce aflatoxins and aflatrem [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF]). In the same way, A. parasiticus and A. novoparasiticus were shown to share similar β-tubulin sequences, requiring the sequencing of other specific genes for identification [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF], whereas the analysis of their MS spectra was enough to discriminate them. This analysis also allowed separation of aflatoxin-producing species (A. aflatoxiformans, A.

arachidicola, A. flavus, A. luteovirescens (ex A. bombycis), A. minisclerotigenes, A. nomius, A. novoparasiticus, A. parasiticus, A. pseudotamarii, A. sergii, A. transmontanensis) from non-aflatoxin producing ones (A. avenaceus, A. caelatus, A. leporis, A. sojae, A. tamarii).

To our best knowledge, this is the first time that as many species of the Flavi section are studied and correctly identified by MALDI-TOF MS analysis. Indeed, several papers focused on the identification of Aspergillus section Flavi species of clinical interest such as A. flavus, A. oryzae, A. nomius or A. tamarii and though a separate clustering of species was obtained using hierarchical analysis, no commercial databases were able to accurately identify them [START_REF] De Carolis | Use of Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for Caspofungin Susceptibility Testing of Candida and Aspergillus Species[END_REF][START_REF] Li | Evaluation of the Bruker Biotyper Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry System for Identification of Aspergillus Species Directly from Growth on Solid Agar Media[END_REF][START_REF] Masih | Identification by Molecular Methods and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Antifungal Susceptibility Profiles of Clinically Significant Rare Aspergillus Species in a Referral Chest Hospital in Delhi, India[END_REF][START_REF] Park | Evaluation of Matrix-Assisted Laser Desorption/Ionization Time-of-Fight Mass Spectrometry for Identification of 345 Clinical Isolates of Aspergillus Species from 11 Korean Hospitals: Comparison with Molecular Identification[END_REF][START_REF] Rodrigues | Species Identification of Aspergillus Section Flavi Isolates from Portuguese Almonds Using Phenotypic, Including MALDI-TOF ICMS, and Molecular Approaches[END_REF][START_REF] Tam | Misidentification of Aspergillus nomius and Aspergillus tamarii as Aspergillus flavus: Characterization by Internal Transcribed Spacer, β-Tubulin, and Calmodulin Gene Sequencing, Metabolic Fingerprinting, and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry[END_REF]. This underlines the need of expanding spectral databases to reach good identification performances.

Regarding the results obtained in the present study, MALDI-TOF MS spectra analysis could be as powerful as the polyphasic approach commonly used to identify Aspergillus species of this section which may include morphological, physiological, molecular and/or extrolite data analysis [START_REF] Frisvad | Taxonomy of Aspergillus Section Flavi and Their Production of Aflatoxins, Ochratoxins and Other Mycotoxins[END_REF], Carvajal-Campos et al, 2018). These results are very promising even though they are based on a relatively low number of isolates and spectra and that only 16 species among the 33 currently recognized species were included in the database for validation. The next step would be to integrate more species and isolates to the database as it can influence identification performances [START_REF] Normand | Assessment of various parameters to improve MALDI-TOF MS reference spectra libraries constructed for the routine identification of filamentous fungi[END_REF] and also to challenge the database with external isolates, in order to assess if spectra and isolates that were not used to build the database can be correctly identified.

Evaluation of MALDI-TOF MS for typing of P. roqueforti isolates

Similarity comparison and effect of calibration on the grouping of P. roqueforti isolates

The distances between all spectra according to their genetic populations and the impact of calibration on the separation of these three populations can be visualized in Figure 2 which shows the results of MDS analysis before (Figure 2A) and after calibration (Figure 2B).

Overall, there was a good separation of P. roqueforti MS spectra according to their respective genetic population and the two-step calibration approach applied in the present study improved separation of these spectra, thus confirming the potential of MALDI-TOF MS to discriminate filamentous fungi at the intraspecific level when combined with recalibration. Indeed, P. roqueforti isolates from population 2 were clearly separated from those of populations 3 and 1 (Figure 2B), even though, for the latter population, spectra from few strains, e.g., strain F77-1 which belonged to population 1, showed a high similarity with other spectra from population 2. It is also worth mentioning that spectra derived from population 2 isolates, in contrast to population 1 and 3 spectra, showed a high heterogeneity, as underlined by the distances between spectra of population 2 isolates. It was especially true for spectra derived from strain F41-4 that formed a separated group from other population 2 isolates. A higher genetic diversity was also reported in isolates from this population as compared to other populations in the study of [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], which confirms the results obtained here. Indeed, population 2 contained almost all non-cheese isolates which harbored a larger number of allelic profiles to that of cheese isolates [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF]. Moreover, cheese isolates from this population were systematically retrieved from a given Protected Designation of Origin (PDO) or Protected Geographical Indication (PGI) cheese type (i.e.

Roquefort, Bleu d'Auvergne and Bleu de Gex). The two-step calibration approach used in the present study also improved separation of spectra derived from populations 1 and 3 isolates which mainly corresponded to cheese isolates from different PDO or PGI, with population 3 and 1 isolates originating mainly from Gorgonzola-Type cheeses and other cheeses (i.e.

Stilton, Cabrales, Danablu, Fourme d'ambert Bleu de Gex and Jihoceska Niva), respectively.

It is also worth mentioning that, as previously reported by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], a common macroscopic aspect was observed within population 3 isolates, with both a velvety to weakly floccose texture and a light greenish gray to pale green color. As shown in Figure 2B, spectra of several isolates from these two populations could not be clearly distinguished using MDS analysis underlining the fact that they shared highly similar MS profiles and therefore expressed features. The fact that spectra derived from populations 1 and 3 isolates shared quite similar MS profiles may be explained by the fact that most isolates from these 2 populations were cheese isolates, which have been selected by cheese producers and/or by the natural conditions prevailing during cheese ripening [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF]. Similar results were also obtained after investigating the relationship between the intraspecific variability of the biological response to temperature and aw and the different genetic populations within the selected P. roqueforti strains (Nguyen et al. submitted).

Database construction and validation

To go further in the assessment of the MALDI-TOF MS discriminative power at the intraspecific level, a database was constructed with 252 spectra, representing 42 strains, with 14, 15 and 13 isolates belonging to populations 1, 2 and 3, respectively. Cross-validation results are shown in Table 3. Overall, the results of cross-validation showed that 94.1% of all spectra (235 out of 252 spectra) were correctly assigned to their corresponding genetic population. All spectra derived from population 2 and 3 isolates were either correctly assigned to the right population (96.67-98.72 % of all spectra) or not identified at all (3.33-1.28 % of all spectra corresponding to 3 and 1 spectra, respectively). These non-identified spectra were derived from strain F41-4 from population 2 and strain F34-1 from population 3.

Other spectra derived from these strains were correctly assigned to their respective genetic population. Identification performances of spectra derived from population 1 isolates were lower than those obtained with the other two populations with 84.52% of spectra (71 out of 84 spectra) correctly assigned. The two spectra which yielded low discrimination results corresponded to two spectra of strain F84, that were either attributed to population 1 and 3 while other spectra of this strain were correctly identified. One of the two unidentified spectra with no identification results corresponded to one spectrum of strain F21-1, for which the other five spectra were correctly assigned. The second one corresponded to one spectrum of strain F81, for which three other spectra yielded discordant results as they were attributed to population 3, while the last two were correctly assigned to population 1. Finally, the six incorrectly assigned spectra belonged to strain F77-1 for which all spectra were incorrectly assigned to population 2. This result could be explained by the fact that this isolate had MS spectra which were more similar to those of several population 2 isolates (Figure 2B).

For external validation, 126 spectra corresponding to 21 independent isolates including 7 isolates from each genetic population, were used to challenge the database. The results are shown in Table 4. 95.24 % of all spectra (110 out of 126 spectra) were correctly assigned to their corresponding population. Spectra acquired from external isolates from populations 1 and 2 were all correctly assigned, with the exception of one spectrum from the strain IHEM3196 and F53 from population 1 and two spectra from strain LCP03969 from population 2. Finally, for external isolates from population 3, 71.43% of spectra (30 out of 42 spectra) were accurately assigned to their respective population. All incorrectly assigned spectra (either yielding no or discordant identification) belonged to strains F28-3 and UBOCC-A-101449. For strain F28-3, five out of six spectra were not identified, and the last one was incorrectly attributed to population 2 while for strain UBOCC-A-101449, three out of six spectra were incorrectly identified to population 1 and the other three were assigned to both populations 1 and 2 by the algorithm. Interestingly, when looking in details at the allelic profiles of strains F28-3 and UBOCC-A-101449, we observed that the 2 strains, despite being classified in population 3 by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], shared 1 similar allele with that of isolates from population 2 and 1, respectively. Hence, it could explain why their assignment to the right population was problematic.

Noteworthy, several strains from populations 1 and 2 that were chosen for external validation also shared common alleles with isolates from other populations [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF]. Indeed, strains IHEM3196 and F53, that were assigned to population 1 by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF], also shared 1 common allele with isolates from population 2, while strains UBOCC-A-111178, DSMZ1999 and UBOCC-A-111170, assigned to population 2 by [START_REF] Gillot | Insights into Penicillium roqueforti Morphological and Genetic Diversity[END_REF] shared 2 common alleles with population 2 and for the two other alleles, presented alleles that were different from those of other isolates from population 3. Nevertheless, spectra of these isolates were correctly assigned after external validation suggesting that their MS peak profiles were much more closely related to that of the isolates which were used to build the database.

As indicated in the 'Materials and methods' section, for database construction, peak lists of spectra from each population were binned and a predictive model was established. In order to understand what were the main differences in the MS spectra of the three P. roqueforti populations, we investigated which bins (or mass-intervals) had a high impact (positive or negative) for population assignment. The two bins that had the higher and lower impact on population assignment are shown in Table 5. All these bins had masses ranging from 3000 to 7716.1 Da. In comparison, [START_REF] Hettick | Discrimination of Aspergillus Isolates at the Species and Strain Level by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Fingerprinting[END_REF] found differences between spectra of Aspergillus flavus isolates in a mass range from 7000 to 10000 Da, but in contrast to the present study in which the ASC algorithm was used, such comparison was performed on whole spectra based on peak lists. It is also interesting to note that high-impact weights for population 2 were slightly higher than those of the two other populations and that overall, high-impact bins were different depending on the population, except for the [3000.0;3004.0] bin which allowed to discriminate population 3 isolates from population 1 and 2 isolates (Table 5). Thus, it would be of great interest to look closer into the different bins that allowed population separation during MALDI-TOF MS analysis, especially those which were identified by the algorithm as having a high-impact weight. The application of MALDI-TOF MS/MS could help to determine the exact m/z of proteins which allowed to discriminate P. roqueforti isolates at the population level as performed previously by [START_REF] Freimoser | Direct Identification of Monilinia Brown Rot Fungi on Infected Fruits by Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry[END_REF] on different Monilinia species. Then, after comparison with theoretical masses of proteins predicted from available sequenced genomes of P. roqueforti, it may be possible to identify which are the proteins (and their functions) that permit to discriminate one population from another. However, such comparison can lead to the identification of several different proteins for one single peak because different proteins can share the same m/z value [START_REF] Spinali | Microbial Typing by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry: Do We Need Guidance for Data Interpretation?[END_REF].

Altogether, these results suggest that there is a very good agreement between genetic data and MS peak profiles and that MALDI-TOF MS could be used as a rapid tool for typing of P. roqueforti isolates using a dataset of well characterized isolates at the intraspecific level. Indeed, MALDI-TOF MS was able to highlight intra-specific polymorphism and yielded similar results to those obtained from genetic analysis based on the study of three microsatellites markers of Penicillium roqueforti isolates. In addition, it was even possible to assign external isolates to the right populations by constructing a spectral database. However, two strains which possessed allelic profiles found in other genetic populations, could not be classified accurately with the approach used in the present study. Despite a very good agreement between genetic data and MS profiles, it must be noted that differentiation between the three populations is not made on the same markers in each analysis. Indeed, genetic analysis is based on microsatellite markers, short and repeated DNA sequences [START_REF] Mathimaran | Microsatellites for Disentangling Underground Networks: Strain-Specific Identification of Glomus Intraradices, an Arbuscular Mycorrhizal Fungus[END_REF], while MALDI-TOF MS detects predominantly ribosomal proteins and other proteins, that are constantly expressed and highly abundant [START_REF] Santos | Filamentous Fungal Characterizations by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[END_REF].

In conclusion, the results obtained in this study, together with those previously published by Quero et al. (2018) confirm that MALDI-TOF MS can be a powerful tool to differentiate and identify food-related filamentous fungi not only at the species level but can also be applied to differentiate species complex and cryptic species as well populations from a same species. This method could be of great interest for the management of mycological safety and quality of foods. Indeed, correct species identification of moulds is of high importance and the possibility of going beyond species identification could also be a valuable asset for source tracking of fungi in the food chain. (B) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of P. roqueforti isolates based on 6 replicates per strain (3 biological replicates and 2 technical replicates), after calibration of the spectra. Spectra are colored according to the respective genetic populations to which the isolates belong. Spectra circled in dashed-green and -blue correspond to spectra from P. roqueforti F41-4 and F77-1, respectively.

  Figure legends.

Figure 1 .

 1 Figure 1. (A) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of 23 species of Aspergillus belonging to the Flavi section. Spectra are colored according to the respective species to which they belong. Spectra circled in dashedred, dashed-blue and dashed green correspond to spectra of groups A, B and C, respectively. (B) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of 12 species of Aspergillus belonging to the A. flavus clade of Flavi section. Spectra are colored according to the respective species to which they belong. (C) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of 11 species of Aspergillus belonging to the A. tamarii, A. bertholletius, A.nomius, A. coremiiformis, A. leporis and A. avenaceus clades of Flavi section. Spectra are colored according to the respective species to which they belong.

Figure 2 .

 2 Figure 2. (A) Multidimensional scaling (MDS) of the spectra dataset obtained after MALDI-TOF MS analysis of P. roqueforti isolates based on 6 replicates per strain (3 biological replicates and 2 technical replicates), before calibration of the spectra. Spectra are colored according to the respective genetic populations to which the isolates belong.

  Figure 1.

Table 1 .

 1 Aspergillus spp. isolates from Aspergillus section Flavi analyzed by MALDI-TOF MS in the 708

	709	present study.
		Clade	Species	Strain number	Origin substrate
		Avenaceus	Aspergillus avenaceus	NRRL 517 T	Green pea
				NRRL 4517	Unknown
		Bertholletius	Aspergillus bertholletius	CCT 7615 T	Rain forest soil
		Coremiiformis	Aspergillus coremiiformis	NRRL 13603 T	Soil
		Flavus	Aspergillus aflatoxiformans	CBS 121.62	Arachis hypogea
			(ex A. parvisclerotigenus)	SF1	Rain forest soil
				SF6	Rain forest soil
		Flavus	Aspergillus arachidicola	UBOCC-A-117374 = CBS 117612	Arachis glabrata leaf
				UBOCC-A-117375 = CBS 117611	Arachis glabrata leaf
				UBOCC-A-117376 = CBS 117614	Arachis glabrata leaf
				UBOCC-A-117377 = CBS 117615	Arachis glabrata leaf
				UBOCC-A-117373 = CBS 117610 T	Arachis glabrata leaf
		Flavus	Aspergillus cerealis	NRRL 66708 = MACI46	Peanut pods
			(ex A. korhogoensis)
		Flavus	Aspergillus flavus	UBOCC-A-108068 = LCP 89.4253	Molten cheese
				UBOCC-A-108067 = CBS 100927 T	Cellophane
				UBOCC-A-101061	Pig feed
				UBOCC-A-101063	Cotton oil cake
				UBOCC-A-106028	Wheat
				UBOCC-A-106029	Pig feed
				UBOCC-A-106030	Wheat
				UBOCC-A-106031	Maize
				UBOCC-A-106032	Barley
				UBOCC-A-106033	Poultry feed
		Flavus	Aspergillus minisclerotigenes	UBOCC-A-117303 = CBS 117635 T	Arachis hypogea seed
				UBOCC-A-117304 = CBS 117633	Arachis hypogea seed
				UBOCC-A-117305 = CBS 117634	Arachis hypogea seed
				UBOCC-A-117306 = CBS 117620	Arachis hypogea seed
				UBOCC-A-117307 = CBS 117639	Arachis hypogea seed
				NRRL 29000	Peanut soil
		Flavus	Aspergillus mottae	MUM 10.231 T	Maize kernel
		Flavus	Aspergillus novoparasiticus	AFc32 = NRRL 62795	Cassava
				UBOCC-A-117379 = CBS 126850	Air sample
				UBOCC-A-117378 = CBS 126849 T	Human sputum
				LEMI 267	Human sputum
		Flavus	Aspergillus oryzae	CBS 100925 T	Unknown
		Flavus	Aspergillus parasiticus	UBOCC-A-111038 = CBS 100308	Unknown
				UBOCC-A-111039	Unknown
				UBOCC-A-111041 = CBS 971.97	Indian sweets
				UBOCC-A-110223 = CBS 100926 T	Sugar cane mealy bug
				NRRL 492	Unknown

Table 2 .

 2 Identification performances of Aspergillus section Flavi species after crossvalidation. number of spectra out of the total number of spectra acquired

	Species	Overall	Single	Low	No	Discordant
		correct	choice	discrimination	identification
	A. aflatoxiformans	100%	100%	0%	0%	0%
		(11/11)*	(11/11)	(0/11)	(0/11)	(0/11)
	A. arachidicola	100%	100%	0%	0%	0%
		(18/18)	(18/18)	(0/18)	(0/18)	(0/18)
	A. avenaceus	100%	90%	10%	0%	0%
		(10/10)	(9/10)	(1/10)	(0/10)	(0/10)
	A. luteovirescens	100%	100%	0%	0%	0%
		(12/12)	(12/12)	(0/12)	(0/12)	(0/12)
	A. caelatus	100%	100%	0%	0%	0%
		(12/12)	(12/12)	(0/12)	(0/12)	(0/12)
	A. flavus	100%	100%	0%	0%	0%
		(38/38)	(38/38)	(0/38)	(0/38)	(0/38)
	A. leporis	100%	100%	0%	0%	0%
		(12/12)	(12/12)	(0/12)	(0/12)	(0/12)
	A. minisclerotigenes	100%	100%	0%	0%	0%
		(25/25)	(25/25)	(0/25)	(0/25)	(0/25)
	A. nomius	100%	100%	0%	0%	0%
		(12/12)	(12/12)	(0/12)	(0/12)	(0/12)
	A. novoparasiticus	95%	95%	0%	5%	0%
		(19/20)	(19/20)	(0/20)	(1/20)	(0/20)
	A. parasiticus	100%	100%	0%	0%	0%
		(20/20)	(20/20)	(20/20)	(20/20)	(20/20)
	A. pseudotamarii	100%	100%	0%	0%	0%
		(24/24)	(24/24)	(0/24)	(0/24)	(0/24)
	A. sergii	100%	100%	0%	0%	0%
		(11/11)	(11/11)	(0/11)	(0/11)	(0/11)
	A. sojae	100%	100%	0%	0%	0%
		(12/12)	(12/12)	(0/12)	(0/12)	(0/12)
	A. tamarii	100%	100%	0%	0%	0%
		(26/26)	(26/26)	(0/26)	(0/26)	(0/26)
	A. transmontanensis	100%	100%	0%	0%	0%
		(9/9)	(9/9)	(0/9)	(0/9)	(0/9)
	Global performances	99.65%	99.3%	0. 35%	0.35%	0%
		(271/272)	(270/272)	(1/272)	(1/272)	(0/272)

*

Table 3 .

 3 Classification performance of P. roqueforti isolates into three genetic populations after cross-validation. number of spectra out of the total number of spectra acquired

	Population	Overall	Single	Low	No	Discordant
		correct	choice	discrimination	identification
	1	86.9 %	84.52%	2.38%	2.38%	10.71%
		(73/84)*	(71/84)	(2/84)	(2/84)	(9/84)
	2	96.67 %	96.67%	0%	3.33%	0%
		(87/90)	(87/90)	(0/90)	(3/90)	(0/90)
	3	98.72 %	98.72%	0%	1.28%	0%
		(77/78)	(77/78)	(0/78)	(1/78)	(0/78)
	Total	94.1 %	93.3%	0.79%	2.33%	3.57%
		(237/252)	(235/252)	(2/252)	(6/252)	(9/252)

*

Table 4 .

 4 Classification performance of P. roqueforti isolates into three genetic populations after external validation. number of spectra out of the total number of spectra acquired

	Population	Correct identification	No identification	Discordant
	1	95.24%	4.76%	0%
		(40/42)*	(2/42)	(0/42)
	2	95.24%	2.38%	2.38%
		(40/42)	(1/42)	(1/42)
	3	71.43%	11.90%	16.67%
		(30/42)	(5/42)	(7/42)
	Total	87.3%	6.35%	6.35%
		(110/126)	(8/126)	(8/126)

*

Acknowledgements

This work was done as part of a CIFRE PhD funded by bioMérieux and the French Association for Research and Technology (ANRT) [Convention #2015/0821] in collaboration with the LUBEM laboratory.

Conflict of interest

LQ, PC, BC and VG are employees of bioMérieux, a company developing and selling in vitro diagnostic assays including the VITEK MS used in this study.