domain: spi info info.info-es

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Ahmad Hani

El Fawal

Mohamad Najem

Ali Mansour

Frédéric Le Roy

Denis Le Jeune

CTMC modelling for H2H/M2M coexistence in LTE-A/LTE-M networks

Machine-to-machine (M2M) devices with their expected exponential booming in the near future, will be one of the significant factors to influence all mobile networks. Inevitably, the expected huge number of M2M devices causes saturation problems, and leads to remarkable impacts on both M2M and human-to-human (H2H) traffics, services, and applications. The research free-space lack requires creating an appropriate model which describes the functionality of long-term evolutionadvanced (LTE-A) and long-term evolution for machine (LTE-M), through mathematical frameworks to evaluate relevant performance metrics. In this study, we bridge this gap by proposing a continuous-time Markov chain (CTMC) model as a stochastic process tool to characterise the H2H/M2M coexistence based on analytical equations. Afterwards, the authors validate the proposed model through extensive Monte Carlo simulations. Eventually, it becomes approachable to characterise the impact of H2H/M2M coexistence in one LTE-A/LTE-M radio resource allocation in dense areas and under disaster scenarios. The simulation results show that using a prioritise LTE-A system for both M2M and H2H traffics is convenient in dense area scenarios, while in emergency cases, it is more appropriate to use a non-prioritise traffic strategy to keep H2H and M2M traffics working properly at the same time.

Introduction

Traditionally, long-term evolution-advanced (LTE-A) network was developed to better serve human-to-human (H2H) services such as voice calls, video-streaming, and data traffics. However, with the novel paradigm so-called machine-to-machine (M2M) communications (e.g. actuators, sensors, smart meters etc.), an unprecedented innovation for the current LTE-A becomes a must [START_REF] Shi | Coexistence of delay-sensitive MTC/HTC traffic in large scale networks[END_REF]. Offering an attractive M2M services in a utopian autonomous internet of things (IoT) world seems tempting, but with massive connectivity, significant challenges arise on how to manage large number of devices, typically transmitting only small payloads, across wide range applications. In 2020, there will be around 50 billion connections with unavoidable coexistence among H2H and M2M traffics in one LTE-A network [START_REF] Mansour | New challenges in wireless and free space optical communications[END_REF]; consequently, an efficient radio access strategy becomes one of the most challenges for mobile operators, researchers and the third generation partnership project (3GPP) community [START_REF] Gudkova | Analyzing impacts of coexistence between M2M and H2H communication on 3GPP LTE system[END_REF]. This community sounds ultimately keen on conducting several studies and researches to identify the mutual impact among M2M and H2H communications. In this paper, we mathematically characterise the key performance characteristics of M2M and H2H communications, then we propose an analytical methodology using continuous-time Markov chain (CTMC) model with the following objectives:

• Studying the mutual impact of both M2M and H2H traffics.

• Measuring and analysing the M2M congestion solutions.

• Modelling a new framework called coexistence analyser and network architecture for long-term evolution (CANAL).

The CANAL framework optimises prioritise radio resource allocation procedures in a LTE-A/long-term evolution for machines (LTE-M) network and achieves understanding of the system performance to reach good balance between M2M and H2H communications. Then, we simulate different dense area and disaster scenarios, which can be studied, analysed, and measured. Finally, by comparing the calculated results with the simulated ones, we come to the conclusion that with a noticeable results' correlation we can validate our assumptions, models, and proposed architecture.

State of the art and motivations

Since the dawn of long-term evolution (LTE) in Rel-8 [START_REF] Gpp | Technical Specifications and Technical Reports for a UTRAN-based 3GPP system, Release 8[END_REF] till the recent Rel-15 [START_REF] Gpp | Technical Specifications and Technical Reports for a UTRAN-based 3GPP system[END_REF], 3GPP has made major enhancements on LTE perspectives in term of reducing latency, power consumption, improving measurement accuracy, improving random access reliability, and small cell support [START_REF] Ratasuk | Enhancements of narrowband IoT in 3GPP Rel-14 and Rel-15[END_REF].

LTE-A was coined initially to support H2H communications (e.g. web-browsing, phone calls, internet televisions etc.). M2M devices (e.g. smart cities, mobile health, smart meters etc.), with their sporadic small packet sizes, pour their payloads into the same LTE-A channel but with different aspects and specifications. Due to these differences, it would be an imperfect match for these two traffics to share the same LTE-A bandwidth. To solve this issue, the 3GPP introduces a licensed spectrum technology so-called LTE-M to serve M2M traffics, which occupies 1.4 MHz from the LTE-A bandwidth. However, with different perspectives and a diversity of applications of M2M and H2H traffics, many challenges are expected as a result of this coexistence. In [START_REF] Biral | The challenges of M2M massive access in wireless cellular networks[END_REF], the main challenges raised by the M2M vision are spotted and by focusing in particular on the problems related to the support of massive M2M access in current cellular communication systems. Then, the most common approaches proposed in the literature to enable the coexistence of H2H and M2M services in the current and next generation of cellular wireless systems are analysed. In [START_REF] Lévesque | Coexistence analysis of H2H and M2M traffic in FiWi smart grid communications infrastructures based on multi-tier business models[END_REF], the authors study the coexistence of H2H traffic and M2M traffic originating from wireless sensors. By using experimental measurements of realworld smart grid applications, the impact of variable H2H traffic loads on the sensor end-to-end delay performance is investigated. The obtained results show that a conventional Ethernet may cause a bottleneck and increase the delay for both H2H and M2M traffics. In [START_REF] Alqahtani | Analysis and modelling of power consumption-aware priority-based scheduling for M2M data aggregation over long-term-evolution networks[END_REF], the author addresses the high overhead signalling load problem of M2M devices using a priority-based data aggregation scheme at the M2M gateway to maintain a good tradeoff between the power consumption and delay requirement. According to this work, three types of priority are assigned to J. Eng. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) M2M devices based on their related applications. Therefore, the data from M2M devices with higher priorities are served faster than the ones with lower priorities. An analytical model considering the idle and busy states behavior of the aggregator using preemption queuing with priority disciplines is developed and analysed. In [START_REF] Hamdoun | A flexible M2M radio resource sharing scheme in LTE networks within an H2H/M2M coexistence scenario[END_REF], a shared channel resource allocation in an H2H/M2M coexistence scenario was considered in order to formulate the resource sharing problem among M2M and H2H communications. In [START_REF] Alqahtani | Performance analysis of cognitive-based radio resource allocation in multi-channel LTE-A networks with M2M/H2H coexistence[END_REF], the author proposes a cognitive-based radio access model with a priority queuing scheme. The model is applied for a LTE-A networks with M2M/H2H coexistence distinguishing M2M devices based on their traffic quality of service (QoS) requirements, in which M2M communications have real-time (M2M-RT) and non-real-time (M2M-NRT) traffic. Radio access gives the highest priority to H2H, while M2M-RT has higher priority than M2M-NRT. Although an analytical methodology is developed in normal scenarios, but the expected surge number of M2M devices which might have higher priority during disaster scenarios and how to deal with this sticky situation have not been discussed.

Additionally, Markov chains are also found in the literature to characterise M2M and/or H2H traffics in LTE-A/LTE-M networks, due to the specificity of LTE-A/LTE-M networks as time-variant networks. Thus many works can be spotted based on this stochastic method. In [START_REF] Alqahtani | Cognitive-based resource allocation scheme in LTE-A networks with M2M/H2H coexistence[END_REF], an efficient radio access strategy is proposed to manage an LTE network system where M2M devices and H2H users coexist. In this paper, a CTMC model is developed to evaluate the system performance in terms of service completion rate (SCR), blocking and forced termination probabilities, and mean queuing delay of the M2M traffic. Although the proposed model can be used to improve the system performance of M2M communication with different priorities, but the model design does not allow the analysis of the system behavior when facing different H2H applications with different priorities. Now, with a close look to many works found in the literature, many research flaws can be spotted: RACH (random access channel) congestion in a LTE-A/ LTE-M network, high and low priority for both M2M and H2H strategies, queuing for high and low priority M2M and H2H mechanisms, adaptive resource allocation, and emergency scenario solutions.

As for to the congestion and overload problems, which may occur when a large number of M2M devices attempt to access the LTE-A network using RACH, a serious degradation of performance for both M2M and H2H devices is more likely to happen [START_REF] Misic | Sharing It MyWay: efficient M2M access in LTE/LTE-A networks[END_REF]. In our previous work [START_REF] El Fawal | RACH overload congestion mechanism for M2M communication in LTE-A: issues and approaches[END_REF], we compare the most common mechanisms found in the literature that deal with the RACH procedure issues and challenges by analysing the existing solutions and approaches to avoid RACH overload congestion in the M2M communications. In [START_REF] Elattar | LTE dynamic scheduling scheme for massive M2M and H2H communication[END_REF], the overload of physical RACH caused by M2M delay sensitive was addressed. The authors proposed a non-preemptive queuing model in order to investigate the performance of different M2M and H2H traffic classes under dynamic access grant time interval controlled scheduling scheme. This was done by using a queuing model focusing on the resource utilisation and the QoS. The results show a delay reduction and a less percentage of packet loss.

Based on all previous work outcomes, we are motivated to propose a CTMC model to study the H2H and M2M coexistence through a mathematical framework. Additionally, a new architecture is proposed which can help in studying and analysing the mutual impact between M2M and H2H traffic coexistence while considering high and low priority traffics for both M2M and H2H devices. Moreover, an adaptive resource allocation is proposed also to scale the network bandwidth especially during disaster scenarios.

Coexistence analyser and network architecture for LTE

Our aim is to measure the mutual impact of M2M and H2H traffics in different scenarios while maintaining a sustainable level of services with minimal congestion during emergency events by allowing both M2M and H2H devices to access the network resources efficiently. In this context, we propose a new architecture so-called CANAL, which extends the classical functionality of the eNodeB and includes an adaptive control of bandwidth based on the M2M load status throughput, as shown in Fig. 1.

The proposed CANAL architecture manages automatically both H2H and M2M traffics. Based on the instantaneous requests of M2M, a part of the total bandwidth is dedicated to fulfill these sudden needs. To this end, the model operates at different conditions depending on the M2M load state throughout the network. The CANAL architecture consists of a single uplink cell in one eNodeB dedicated for a LTE-A/LTE-M network.

Let RB H2H be the maximum resource blocks reserved for LTE-A network and RB M2M be the one reserved for LTE-M network. There are two types of traffics: M2M and H2H traffics. Each traffic has LP and HP arrival rates (λ i ) assumed to be following Poisson's distributions [START_REF] Laner | Traffic models for machine type communications[END_REF]. Let (λ H1 /λ H2 ) be the average arrival rates for H2H HP/LP, respectively. Similarly, (λ L1 /λ L2 ) the average arrival rates for M2M LP/HP. The CANAL architecture contains also a queuing control unit (QCU) consisting of four different queues: two for H2H HP and LP traffics (H2H LP-Q, H2H HP-Q) with queue sizes n and o, respectively; similarly, two queues for M2M low and high priority (M2M LP-Q, M2M HP-Q) with queue sizes p and q, respectively.

Additionally, the CANAL architecture includes a resource allocation control (RAC) unit used to control, manage, and grant access the network requests based on the available resources. The RAC has a vital role when congestion occurs especially during emergency scenarios by dominating the resources adaptively in order to fulfill the excessive requests of M2M devices, while keeping the H2H requests within the acceptable norms. Finally, service rates (μ H1 , μ H2 , μ L1 , μ L2 ) will be accomplished for each traffic, respectively, H2H HP, H2H LP, M2M LP, and M2M HP. All notations used in our model architecture are summarised in Table 1.

Queuing control unit

The CANAL architecture contains four feedback queues for the four traffic types: H2H LP, H2H HP, M2M LP, and M2M HP. By applying a special priority strategy, we end up with an efficient radio management which dominates the H2H/M2M coexistence.

The system is considered in the 'full state' when the number of reserved resource blocks in a LTE-A network peaks to RB H2H and/or in a LTE-M network peaks to RB M2M . In Fig. 1, the queuing process is initiated when the system reaches its cut-off point (the full state) and a new M2M/H2H device request contends to access the system. The new arrival request might be either H2H/M2M HP or H2H/M2M LP. The contention resolution is the core of the QCU by differentiating between two cases:

• H2H/M2M LP new arrival: When a H2H/M2M LP requests an access to the system the QCU checks the RAC for available resources. If there is at least one resource available the RAC grants the request access to the network. If there is no available resources the request will be forwarded to the H2H/M2M LP-Q. However, if the queue is full the request is terminated, otherwise the request is enrolled in the appropriate queue, as shown by the flowchart presented in Fig. 2.

• H2H/M2M HP new arrival: When a H2H/M2M HP requests an access to the system, the QCU checks the RAC for available resources. If there is at least one resource available, then the RAC grants the request access to the network. If there is no available resources, the QCU verifies whether all resources are reserved by H2H/M2M HP or not. If the answer is 'yes', the request will be forwarded to the H2H/M2M HP-Q. However, if the queue is full, then the request is terminated, otherwise the request is enrolled in the appropriate queue. If the answer is 'No', an interruption command will be initiated asking for an immediate evacuation of a H2H/M2M LP resource reservation which will be pushed to the H2H/M2M LP-Q. Similarly, if the queue is full the request is terminated; otherwise, the request is enrolled in the appropriate queue, as shown by the flowchart presented in Fig. 2.

Resource allocation control unit

The CANAL architecture contains an important part so-called RAC unit, which plays a crucial role in granting access, interrupting services, and managing resources for both LTE-A and LTE-M networks. Especially when it comes to an increasing storm of requests during normal and emergency scenarios caused by the synchronisation behavior of M2M devices. A balancing act must be kept continuously; on one hand, the RAC might fulfill the excessive requests of M2M devices, but on the other hand any major QoS degradation on H2H traffic is not tolerated at all costs. Thus, we are in need of an efficient strategy to dominate network resources in an adaptive, simulative and rescued way which might help to master a new eNodeB that can go ahead of the curve, as shown in Fig. 3.

In normal situations, the CANAL architecture works similar to any legacy eNodeB in LTE-A/LTE-M networks as initial state (INI). An initial bandwidth-limited (BL0) dedicated for LTE-M traffic from the total bandwidth by reserving RB M2M for M2M devices. Meanwhile, the remaining resources are reserved for H2H devices denoted by RB H2H and connected to a LTE-A network.

In the case of an emergency EMG(1), a huge number of M2M connected devices (CD) saturate the initial LTE-M bandwidth (BL0) by their storm briefly. When the CD data rate reaches a certain initial threshold Th(0) the RAC increases the bandwidth from BL0 to BL1 = 2 × BL0 to allow more CD accessing the

BL(M) = (M + 1) × BL0.
Finally, if the CD data rate exceeds the last threshold Th(M) an overload problem occurs. Once the number of M2M connections starts dwindling, the RAC keeps receding the LTE-M bandwidth iteratively until reaching the initial phase at the end of the disastrous events, consequently, resumes operating similar to any legacy e-NodeB. Following this methodology, the CANAL architecture has the adaptability to lend progressively a temporary bandwidth up to BL(M) = (M + 1) × BL(0) of the total H2H bandwidth to M2M devices tentative use. By implementing this proposed solution for the limited bandwidth saturation in LTE-M networks, an adaptive reallocation of the bandwidth leads for an acceptable resolution for any presumable M2M storm.

Based on our previous work in [START_REF] El Fawal | Adaptive LTE-M eNodeB for emergency scenarios[END_REF], we found out that by leasing some folds of LTE-M classic bandwidth, we can achieve a significant gain in the M2M traffic with the same ratio.

CTMC analytical methodology

Our CTMC analytical methodology consists of four steps:

• First, we use CTMC as a stochastic method to describe the sequence of possible events M2M or H2H built on different states and various priorities. • Second, we generate the equilibrium equations which rule the probability of transitioning from one state to another.

• Third, we turn these probabilities and states into a linear system and by solving it we can calculate each state probability under certain conditions. • Fourth, using the aforementioned probabilities, we propose some metrics to characterise the performance of the network, with the aim to evaluate M2M and H2H traffics.

Representing the system as a set of states

In our system states, two variables (i,j) are considered to denote the number of two ongoing services one is high-priority traffic and the other is low-priority traffic, respectively. The system moves from one state to another if an event occurs (increase/decrease of i or j).

The two traffics have two average arrival rates (λ 1 , λ 2 ) assumed to follow Poisson distribution and two service rates (μ 1 , μ 2 ) assumed to follow an exponential distribution. The generic CTMC model is shown in Fig. 4. All notations used in the analytical methodology are summarised in Table 2.

The numbers of states (s t ) based on the number of resources C should satisfy the following constraints:

0 ⩽ i, j ⩽ C ( 1
)
0 ⩽ i + j ⩽ C ( 2
)
where i represents the number of ongoing services for high-priority traffic and j represents the number of ongoing services for lowpriority traffic.

Based on (1) and ( 2), s t can be derived as follows:

s t = ∑ 0 ⩽ i + j ⩽ C 1 = ∑ i = 0 C ∑ j = 0 C -i 1 = ∑ i = 0 C (C -i + 1) = ∑ i = 0 C (C + 1) -∑ i = 0 C i = (C + 1) 2 -∑ i = 1 C i = (C + 1) 2 - C(C + 1) 2 = (C + 1)(C + 2) 2 (3)
where 1 represents an indicator function defined on a set X that indicates membership of an element in a subset A of X, having the value 1 for all elements of A and the value 0 for all elements of X not in A. A numerical example can be found in [START_REF] El Fawal | CTMC modeling for M2M/H2H coexistence in a NB-IoT adaptive eNodeB[END_REF].

Generating the equilibrium equations

In the following, we assume that the observation time intervals are so small that at one time interval only one event (i + 1, i -1, j + 1, j -1) may occur. In this case, the system falls into one of the following three cases:

• Case 1: 'empty state', where i = j = 0, includes one state S(0, 0) and has the following equilibrium relationship:

(λ 1 + λ 2 )π (0, 0) = μ 1 π (1, 0) + μ 2 π (0, 1) (4)
where π (i, j) is the probability to be in the state S(i, j). • Case 2: 'occupied state', where 0 < i + j < c, includes ((c -1)(c + 2))/2 states and has the following equilibrium equation:

(λ 1 + λ 2 + iμ 1 + jμ 2 )π (i, j) = λ 1 π (i -1, j) + λ 2 π (i, j -1) +(i + 1)μ 1 π (i + 1, j) +( j + 1)μ 2 π (i, j + 1) (5)
• Case 3: 'full state', where i + j = c, includes (c + 1) states and has the following equilibrium equation:

(iμ 1 + jμ 2 )π (i, j) = λ 1 π (i -1, j) + λ 2 π (i, j -1) (6)
The above three equations can be summarised by the following generic equation:

(αλ 1 + αλ 2 + iβμ 1 + jβμ 2 )π (i, j) = βλ 1 π (i -1, j) + βλ 2 π (i, j -1)
+(i + 1)αμ 1 π (i + 1, j) + ( j + 1)αμ 2 π (i, j + 1) [START_REF] Biral | The challenges of M2M massive access in wireless cellular networks[END_REF] where α = 0 indicates the 'full state' (otherwise α = 1) and β = 0 represents the 'empty state' (otherwise β = 1).

Linear system solution

To recall, in our system states, (i,j) denotes the number of ongoing services, i.e. H2H and M2M. The system moves from one state to another, when a service is achieved or a new request arrives (by increasing or decreasing i or j) with a steady-state probability π (i, j) :

∑ i = 0 c ∑ j = 0 c -i π (i, j) = 1 (8) 0 ≤ π (i, j) ≤ 1 (9)
The general equation in ( 7) can be written in linear form:

AΠ = 0 (10)
where the square matrix A represents the coefficients in the linear system, and Π represents the steady-state probability vector:

Π = π (0, 0) π (0, 1) ⋮ π (c, 0) (11)
By replacing the first row of A by the coefficients of (8), we obtain the following modified system:

BΠ = 1 0 ⋮ 0 (12)
Equation ( 7) can be solved while considering the numbers of states s t as dimension for the steady-state probability vector Π and (s t ) × (s t ) as dimensions for the square matrix B. Knowing that s t could be calculated using [START_REF] Gudkova | Analyzing impacts of coexistence between M2M and H2H communication on 3GPP LTE system[END_REF]. B is a full rank (s t ) × (s t ) matrix and linear system of ( 12) can be solved easily.

Performance and metrics

We use two performance metrics to validate our model: i. SCR: It gives the number of completed requests per time interval and it is based on the service rate μ and the number of ongoing requests for a certain traffic (e.g. SCR HP and SCR LP which represent the SCR for HP/LP traffics [START_REF] Alqahtani | Performance analysis of cognitive-based radio resource allocation in multi-channel LTE-A networks with M2M/H2H coexistence[END_REF]):

SCR = ∑ i j iμπ (i, j) ( 13
)
As we consider the service rate (μ) as constant in our model, [START_REF] Misic | Sharing It MyWay: efficient M2M access in LTE/LTE-A networks[END_REF] can be written as SCR = μ∑ i i∑ j π (i, j) [START_REF] El Fawal | RACH overload congestion mechanism for M2M communication in LTE-A: issues and approaches[END_REF] As the sum of conjoint probability leads to the marginal probability, the previous equations can be re-written as follows:

SCR = μ∑ i iπ (i)
= μE(i) [START_REF] Elattar | LTE dynamic scheduling scheme for massive M2M and H2H communication[END_REF] where E(i) represents the expected value of an ongoing service denoted by (i). ii. Resource utilisation (R u ): This metric gives the probability of the system to be busy serving the arrivals in terms of the number of utilised RBs in each state:

R u = ∑ i j i + j c π (i, j) = ∑ i j iπ (i, j) + ∑ i j jπ (i, j) c = E(i) + E( j) c ( 16
)

Simulations and result discussions

In this section, we present our developed simulation model which can generate both H2H and M2M traffics with full flexibility to add queuing or priority for any traffic in order to study the mutual impact for H2H and M2M traffics.

M/M/1 simulations

An M/M/1 queue made by having a single server, where arrivals are determined by a Poisson process and job service times have an exponential distribution. The proposed architecture is based on Matlab libraries presented in [START_REF]M/M/1 Queuing System[END_REF]. Many enhancements are made in order to match our CANAL architecture presented in Section 3:

• The architecture consists of a single-queue and single-server system with a double-traffic source. In order to omit the role of the queuing for the moment, we consider an infinite storage capacity (in this case the queue sizes n = o = p = q = 0 for the different traffics, see Table 1). • The architecture follows a CTMC with transition rate matrix similar to the one in Section 4. • Arrivals occur at rate λ according to a Poisson process and move the process from state (i) to (i + 1) and/or (j) to ( j + 1). • Service times have an exponential distribution with rate parameter μ in the M/M/1 queue, where 1/ μ is the mean service time. • A single server serves H2H and M2M traffics one at a time by allowing one event only (either i or j) in the smallest time interval. • The queue could be configured to work according to a FIFO (first input first output), LIFO (last input first output) or by priority discipline, with a flexible buffer size. • When the service is completed, the served request leaves the system and the number of ongoing services in the system reduces by one (i) to (i -1) and/or (j) to ( j -1). • The architecture makes it easy to compare empirical results with the corresponding theoretical results.

Our methodology to reach our final simulation model is to build the simplest model with one traffic first, then we develop the basic H2H and M2M traffic models in which we can fix a starting experimental point with some highlighted flaws. In the end, by working on the drawbacks of the basic model, many enhancements could be suggested as result of adding priority and queuing strategies and compare it with the basic model.

Single traffic simulations and results

As a first step, we start by experimenting the behavior of a single traffic system with the aim to prove the accuracy of the proposed CTMC model. Additionally, a comparison for both analytical and simulation results are conducted in order to validate the accuracy of the system by calculating the mean absolute error (MAE):

MAE = (1/n)∑ t = 1 n
x c(t) -x s(t) and the root mean squared error

(RMSE): RMSE = (1/n)∑ t = 1 n (x c(t) -x s(t) ) 2 .
The analytical and simulation results for the probability to be in a given state π (i) and with a number of resource blocks c = 6 RBs are shown in Fig. 5, which shows that the accuracy of the proposed system falls into the an error limit with a MAE max = 0.0592. Also, a slight mismatch between the analysis results and simulation results could be spotted especially in the full state P6 (where the arrival average peaks to the maximum system capacity 6). This mismatch is unavoidable because in our simulator, we added two random generators for two arrivals (i and j) following a Poisson distribution. In some cases, (i + j) exceeds the maximum number of available resources c. These cases should be removed in order to respect the constraint: (i + j ⩽ c) which affects our random variables supposed following the Poisson distribution. So, we solve this issue by introducing a saturation function in our simulations. Consequently, an acceptable approximation is considered for these cases (where i + j > c), similar to occurrences of (i + j = c), knowing that in our example (c = 6). In this case, the probability to be in the full state is more likely to happen with a noticed difference comparing to the analysis results. This non-linear behavior can explain the small mismatch.

The results obtained with (c = 6) drive us to do more exploration by increasing c to 25 RBs. The simulated results are shown in Fig. 6 in which we end up with MAE max = 0.0177. In Fig. 6a, we can realise a minor mismatch at the end of the curves. This mismatch is due to removing the unwanted values which do not respect the following constraint: i + j ⩽ C (as explained in Fig. 5). Similarly, in Fig. 6c, a minor mismatch at the beginning of the curves due to removing the unwanted values which do not respect the constraint: 0 ⩽ i + j. In Fig. 6b, the probability peaks when the number of resources reaches (C = 24) because of the high-arrival rate (λ 1 = 20) accompanied with a low-completion rate (μ 1 = 5) and only a limited number of resources (C max = 25) which results a fast network saturation.

To sum up, all previous exploration experiments are able to achieve a good accuracy with the aforementioned MAE and RMS, which might be considered as a valid starting point to take up our simulation model by upgrading the single traffic simulation to a basic simulation for H2H and M2M traffics.

Basic simulations and results

In order to study the mutual impact of H2H and M2M traffics in dense area or emergency scenarios, we conduct several simulations based on the proposed architecture in [START_REF]M/M/1 Queuing System[END_REF], with the following parameters:

• We assume to have one LTE-A network using a bandwidth of 5

MHz (c = 25) in order to stress H2H and M2M traffics to the maximum. • Each traffic has an average arrival rate (λ 1 , λ 2 ) with a service rate (μ 1 , μ 2 ). • H2H and M2M traffics have the same priority.

• A FIFO queue type is used with an infinite capacity size.

• Simulation duration = 1000 s.

Dense area scenario:

In urban and dense areas, a huge number of M2M and H2H devices may exchange their payloads exponentially. Consequently, we consider a fixed arrival rate of H2H requests (λ 1 = 5) and incremental arrival rate of M2M requests 2 < λ 2 ⩽ 20, while μ 1 = 0.5 and μ 2 = 1. From the results shown in Fig. 7a and by calculating the percentage of served requests comparing to the total arrivals, we can conclude the following:

• When λ 2 ⩽ 15: The system is able to serve all M2M (in red) and H2H (in blue) requests, because H2H arrivals, with λ 1 = 5 and a service rate μ 1 = 0.5, occupy an average of 5 resources from the 25 total resources for 2 consecutive time intervals, which means on the second time interval it occupies 10 resources (5 previous arrivals and 5 new arrivals). Consequently, it remains around 15 resources per time interval for M2M arrivals with a maximum λ 2 = 15 and a service rate μ 2 = 1. • When 15 < λ 2 ⩽ 20: A degradation on both M2M and H2H SCR can be realised, because in our assumption they have similar priority and the total number of requests are much more than the available resources c = 25. At the peak (λ 2 = 20), only 4K of 5K total H2H requests are served (80%) and 17K of 20K total M2M requests are served (85%). As an average of 4 from the 25 total resources will be occupied by H2H traffic for 2 consecutive time intervals (actually 4 previous arrivals and 4 new arrivals), and the remaining 17 resources will be used by M2M traffic. • The high utilisation of the system (R u = 100%) requires a queuing strategy to fulfill the excessive requests and to minimise the number of forced terminated services, as suggested in Section 3.1. From the results shown in Fig. 7b, when λ 2 = 50 a degradation on both M2M and H2H SCR can be realised, because they have similar priority and the total number of requests are much more than the available resources c = 25. Only 8K of 50K total H2H requests are served (16%) and 8 K of 50 K total M2M requests are served (16%), because an average of 8 from the 25 total resources will be occupied by H2H traffic for 2 consecutive time intervals, and the remaining 8 resources will be used by M2M traffic each time interval. By increasing λ 2 = 100, 150, 200, we end up with the following results as shown in Table 3.

M/M/1 results

In Section 5.3, two different scenarios (dense area and emergency) are conducted to measure the H2H and M2M mutual impact but without neither considering a prioritise system nor applying queuing strategy to ensure the QoS of different traffics. Now, it is time to consider two scenarios with similar parameters using a prioritise system as follows.

Prioritise dense area scenario:

In this simulation, we emphasis similar case to the one considered in Section 5.3.1; While assigning a higher priority for one traffic, we study the trade-off between cost or gain on H2H and M2M traffics.

From results shown in Fig. 7a, we realise that the system is able to serve all M2M and H2H requests as long as λ 2 ⩽ 15. After this point, the system maintains the higher priority traffic towards a degradation on the lower priority traffic.

Prioritise emergency scenario:

Similar to the case in Section 5.3.2 but with different M2M and H2H priorities, the results shown in Fig. 7b spot that the system maintains the higher priority traffic with a total elimination of the lower priority traffic.

To sum up, the simulation results show that using a prioritise LTE-A system for both M2M and H2H traffics could handle more requests in dense area cases by maintaining the higher priority traffic without any degradation while reducing the completion rate of the lower priority traffic. Meanwhile, in emergency cases, the higher priority traffic takes precedence over the lower priority traffic with an improvement on its completion rate while totally eliminating the low priority traffic; in such cases, the non-priority traffic strategy has a competitive advantage because it keeps H2H and M2M traffics working at the same time.

Conclusion and perspectives

In this paper, we address the saturation problem caused by the expected huge number of M2M devices which leads to remarkable impacts on both M2M and H2H traffics. We have proposed an enhanced architecture designed for LTE-A/LTE-M networks in order to fulfill H2H/M2M traffic coexistence supported with various priority strategies to satisfy the QoS for each traffic. A queuing strategy is enforced by a QCU, which rule four queues allocated for different traffic types. A CTMC model is proposed as a stochastic process tool to characterise the H2H/M2M coexistence based on analytical equations. Our simulation results matched with the CTMC analytical model which corroborates the proposed model. Moreover, they show that using a prioritise LTE-A system for both M2M and H2H traffics is more convenient in dense area scenarios; while in emergency cases, it is more appropriate to use a non-prioritise traffic strategy to keep both H2H and M2M traffics working properly at the same time.

In this work, a homogeneous number of resources has been considered for all applications; in our future work, the authors aim to study the heterogeneity in different applications which reflects the actual behavior of various IoT applications.

Fig. 1

 1 Fig. 1 CANAL architecture

Fig. 2 Fig. 3

 23 Fig. 2 H2H/M2M HP and LP queuing

Fig. 4

 4 Fig. 4 Generic CTMC model, where i: number of ongoing services for HP requests, j: number of ongoing services for LP requests, c: maximum number of resource blocks, S(i, j): a certain state denoted by i and j

Fig. 5 π

 5 Fig. 5 π (i) result comparison for c = 6, where π (i) : the probability to be in a given state S(i, j), c: the maximum number of resource blocks, λ 1 : the average arrival rate, μ 1 : completed service rate (a) λ 1 = 4; μ 1 = 1, (b) λ 1 = 5; μ 1 = 4, (c) λ 1 = 2; μ 1 = 4

Fig. 6 πFig. 7

 67 Fig. 6 π (i) result comparison for c = 25, where π (i) : the probability to be in a given state S(i, j), c: the maximum number of resource blocks, λ 1 : the average arrival rate, μ 1 : completed service rate (a) λ 1 = 15; μ 1 = 12, (b) λ 1 = 20; μ 1 = 5, (c) λ 1 = 23; μ 1 = 25

Table 1

 1 Description of notations used in CANAL architecture

	Notation

J. Eng.

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) network. Similarly, if the CD data rate reaches the next threshold Th(1) again caused by an additional M2M storm, the CANAL

Table 2 Description

 2

	c	number of resource blocks used in the network
	s t	numbers of states
	π (i, j)	the probability to be in the state S(i, j)
	A	the coefficient matrix in a linear system
	Π SCR HP	the steady-state probability vector SCR for HP traffic
	SCR LP	SCR for LP traffic
	R u E(i)	resource utilisation the expected value of ongoing services for HP traffic
	E( j)	the expected value of ongoing services for LP traffic

of notations used in the analytical methodology Notation Description i number of ongoing services for HP traffic j number of ongoing services for LP traffic λ 1 average arrival rate for HP traffic λ 2 average arrival rate for LP traffic μ 1 completed service rate for HP traffic μ 2 completed service rate for LP traffic J. Eng. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Table 3

 3 Emergency scenario results λ 2 SCR H2H/M2M % This is an open access article published by the IET under the Creative Commons Attribution License

			Completed H2H	Completed M2M
	100	12	6K	12K
	150	10	5K	15K
	200	8	4K	16K

(http://creativecommons.org/licenses/by/3.0/)

J. Eng.This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)


Ziad Rizk

Youssef El

Chantal Ghanem

Florence Mathieu

Patricia Taillandier

Nancy Nehme
email: nehmenancy@hotmail.com

Isak Pretorius

Identification of multiple-derived peptides produced by Saccharomyces cerevisiae involved in malolactic fermentation inhibition

Keywords: malolactic fermentation (MLF), anti-MLF yeast peptides, Wtm2p, Utr2p, glyceraldehyde-3-phosphate dehydrogenase (GAPDH)

An oenological strain of Saccharomyces cerevisiae was previously shown to produce a 5-10 kDa peptidic fraction responsible for the inhibition of malolactic fermentation (MLF). In the present study, we aim to further purify the anti-MLF peptides of this fraction. The yeast fermented synthetic grape juice medium was fractionated by ammonium sulfate precipitation combined with ultrafiltration. The 5-10 kDa fraction recovered at a saturation degree of 60%-80% was the only fraction that inhibited both the bacterial growth and the malate consumption in vivo. It also inhibited the malolactic enzyme activity in vitro at a pH range between 3.5 and 6.7. Therefore, it was purified by both anion and cation exchange chromatography. The eluates that inhibited the malolactic enzyme activity in vitro were migrated on Tricine SDS-PAGE and the protein bands were excised and sequenced by LC-MS/MS. The sequencing revealed nine peptides originating from eight proteins of S. cerevisiae. Two GAPDH cationic fragments of 0.9 and 1.373 kDa having a pI of 10.5 and 11 respectively, Wtm2p and Utr2p anionic fragments of 2.42 kDa with a pI of 3.5 and 4 respectively were thought to contribute the most to the MLF inhibition.

INTRODUCTION

Antimicrobial peptides (AMPs) play an important role in the innate immune system of eukaryotes against virulent bacteria, viruses and fungi [START_REF] Wong | A review of defensins of diverse origins[END_REF][START_REF] Sang | Porcine host defense peptides: expanding repertoire and functions[END_REF]. The majority of AMPs are cationic in nature and kill microbes by interacting with the negatively charged components of the cell-wall and/or plasma membrane [START_REF] Brogden | Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria?[END_REF]. However, many anionic AMPs have also been found to be involved in the defense mechanisms [START_REF] Harris | Anionic antimicrobial peptides from eukaryotic organisms[END_REF]. The activity of anionic AMPs can be enhanced either by divalent metal cations such as Fe ++ , Mn ++ and Mg ++ that facilitate their binding to anionic membrane components [START_REF] Dashper | Divalent metal cations increase the activity of the antimicrobial peptide kappacin[END_REF], or by the aggregation of several peptides [START_REF] Nissen-Meyer | A novel lactococcal bacteriocin whose activity depends on the complementary action of two peptides[END_REF]. Some AMPs are able to translocate across the membrane of sensitive cells or increase the membrane permeability by forming pores [START_REF] Harris | Anionic antimicrobial peptides from eukaryotic organisms[END_REF], while others have been found to induce apoptosis in sensitive cells. For example, killer strains of Saccharomyces cerevisiae that produce killer toxins K1 and K28, induce apoptosis in sensitive yeast cells [START_REF] Reiter | Viral killer toxins induce caspase-mediated apoptosis in yeast[END_REF]. Besides, bacteriocins produced by some strains of Gram positive bacteria are either bacteriostatic or bactericidal, against a wide range of pathogenic and spoilage bacteria. Some of them like nisin are already used as biopreservatives in different food sectors such as the dairy industry; however, their use in the wine sector is still prohibited. Pediocin PD-1 produced by Pediococcus damnosus NCFB 1832, was able to kill Oenococcus oeni and outcompete nisin [START_REF] Nel | Effect of bacteriocin pediocin PD-1, plantaricin 423 and nisin on biofilms of Oenococcus oeni on a stainless steel surface[END_REF].

In winemaking, several AMPs were found to be responsible for the inhibition of malolactic fermentation (MLF). These AMPs, produced by different strains of S. cerevisiae, were partially purified, and assumptions about their mechanism of action were given but the results were not conclusive. Most of the time, the peptides were considered to target the plasma membrane of O. oeni, resulting in the inhibition of the bacterial growth and consequently the malate consumption [START_REF] Comitini | Interactions between Saccharomyces cerevisiae and malolactic bacteria: preliminary characterization of a yeast proteinaceous compound(s) active against Oenococcus oeni[END_REF][START_REF] Osborne | Inhibition of malolactic fermentation by a peptide produced by Saccharomyces cerevisiae during alcoholic fermentation[END_REF]. The AMPs found by [START_REF] Mendoza | Antagonistic interaction between yeasts and lactic acid bacteria of oenological relevance[END_REF] inhibited the bacterial growth only. [START_REF] Branco | Identification of novel GAPDH-derived antimicrobial peptides secreted by Saccharomyces cerevisiae and involved in wine microbial interactions[END_REF] were the first to identify the nature of the AMPs produced by S. cerevisiae CCMI 885. These AMPs were active against several non-Saccharomyces yeasts and one strain of O. oeni. They corresponded to two fragments of the C-terminal amino acid sequence of the S. cerevisiae GAPDH isoenzymes (GAPDH 2/3 of 1.638 kDa and GAPDH1 of 1.622 kDa, pI of 4.37). Recently, [START_REF] Branco | Antimicrobial properties and death-inducing mechanisms of saccharomycin, a biocide secreted by Saccharomyces cerevisiae[END_REF] found that these AMPs were also produced by other strains of S. cerevisiae but at different concentrations. The GAPDH peptidic fragments disturbed the plasma membranes of sensitive yeast cells and increased their permeability. They were able to enter the cytoplasm and trigger apoptosis. However, the experiments were only conducted on yeasts and not on O. oeni. [START_REF] Nehme | Impact of the co-culture of Saccharomyces cerevisiae-Oenococcus oeni on malolactic fermentation and partial characterization of a yeastderived inhibitory peptidic fraction[END_REF] demonstrated that S. cerevisiae Uvaferm BDX released a 5-10 kDa peptidic fraction that strongly inhibited the growth and the malate consumption during the MLF performed by O. oeni Vitilactic F. Later on, [START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF] showed that this fraction was gradually released during alcoholic fermentation (AF) and reached its highest concentration at late stages of the stationary phase (120 h). It was able to directly inhibit the malolactic enzyme activity in vitro at a pH range between 3.5 and 6.7. [START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF] were the first researchers to demonstrate that AMPs can inhibit the malate consumption by directly inhibiting the malolactic reaction. These results suggest that in vivo the peptides would enter the cells by mechanisms yet to be identified in order to inhibit MLF. Since MLF is one of the main energy sources during winemaking, the inhibition of the bacterial growth can be a consequence of the MLF inhibition. [START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF] also showed that the same pairing (S. cerevisiae Uvaferm BDX/O. oeni Vitilactic F) maintained the MLF inhibition in Cabernet Sauvignon and Syrah wines.

In the present work, we intend to further purify the AMPs of the 5-10 kDa peptidic fraction in order to identify their nature and to better understand their mechanism of action. The synthetic grape juice (SGJ) medium fermented by S. cerevisiae Uvaferm BDX is fractionated by ammonium sulfate precipitation combined with ultrafiltration. Then, the peptidic fraction responsible for the MLF inhibition in vivo and in vitro experi-ments is purified by ion exchange chromatography (both anion and cation) followed by SDS-PAGE. Finally, the peptides of interest are sequenced by LC-MS/MS and characterized regarding their amino acid sequence, molecular weight, theoretical pI, the yeast proteins from which they derive and their putative mechanisms of action.

MATERIALS AND METHODS

All culture media components were supplied by Sigma-Aldrich (Taufkirchen-Germany) except for the yeast extract and the peptone that were supplied by Oxoid (Hampshire-England). The Aurum ion exchange mini kits and columns and the chemicals used for SDS-PAGE were purchased from Bio-Rad (California, USA).

Strains and storage conditions

Saccharomyces cerevisiae Uvaferm BDX, Lalvin CY3079 and O. oeni Vitilactic F used in this work were kindly provided by Lallemand Inc. (Blagnac, France). Yeast stock cultures were kept at 4 • C in YEPD (Yeast Extract Peptone Dextrose) agar slants composed of 10 g L -1 Yeast Extract, 20 g L -1 peptone, 20 g L -1 D-glucose and 20 g L -1 agar. The bacterial strain was kept frozen at -20 • C in MRS (De Man, Rogosa and Sharpe) broth containing 20% glycerol (v/v).

Growth media

SGJ medium

The SGJ medium that simulated the natural grape juice consisted of: D-glucose 100 g L -1 , D-fructose 100 g L -1 , Yeast Extract 1 g L -1 , (NH 4 ) 2 SO 4 2 g L -1 , citric acid 0.3 g L -1 , L-malic acid 3 g L -1 , L-tartaric acid 5 g L -1 , MgSO 4 0.4 g L -1 and KH 2 PO 4 5 g L -1 , pH adjusted to 3.5 using a 10 M NaOH solution. The medium was autoclaved before use (121 • C, 20 min).

Modified MRS medium

The MRS medium was supplemented with L-malic acid (3 g L -1 ) and Tween 80 (1 mL). After autoclaving (121 • C, 15 min), 10% ethanol (v/v) were aseptically added through sterile filter membranes of 0.22 μm cut-off (Elvetec services, Meyzieu-France) and the pH was adjusted to 3.5 using an 85% orthophosphoric acid solution.

Alcoholic fermentation

The SGJ media were inoculated either with S. cerevisiae Uvaferm BDX or with S. cerevisiae Lalvin CY3079 at an initial concentration of 3 × 10 6 cells mL -1 (Thoma counting chamber). The AF was followed until total or cessation of sugar consumption (<2 g L -1 ). The yeast inoculum was beforehand prepared in two steps. First, a pre-culture of the yeast strain was obtained by reactivating the stock culture in YEPD broth for 24 h. Second, the pre-culture was used to inoculate a low sugar concentration SGJ medium: D-Glucose 50 g L -1 and absence of D-Fructose. This step was carried out for 24 h and provided the yeast inoculum. All fermentation steps were carried out at 22 • C with stirring at 150 rpm in Erlenmeyer flasks.

AFs were performed in three independent replicates.

Fractionation of yeast fermented media by ammonium sulfate precipitation combined with ultrafiltration

After completion of the AF (120 h), yeast cells were removed by centrifugation (3500 rpm, 20 min at 4 • C) and 500 mL of the supernatant were recuperated. The amounts of solid ammonium sulfate added to the supernatant were calculated according to the program [www.encorbio.com/protocols/AM SO4.htm] in order to reach the following saturation percentages: A: 0%-20%, B: 20%-40%, C: 40%-60%, D: 60%-80% and E: 80%-100%.

For each saturation level, the salt was added gradually to the supernatant for 1 h while shaking at 4 • C. 90 min later, the medium was centrifuged at 5000 rpm for 30 min at 4 • C. After centrifugation, the protein pellet was recovered and suspended in 10 mL of a phosphate buffer (0.1 M, pH 6.7). This resulted in samples that were 50 times more concentrated (50×). Afterwards, the sample was fractionated through a centrifugal filter unit presenting a cut-off of 10 kDa (Amicon R Ultra-15 with ultracel-10 membrane) at 3500 g for 45 min at 4 • C. The filtrate with a MW ≤10 kDa was recovered and ultrafiltered again using a centrifugal filter unit of 5 kDa cut-off (Corning R Spin-X UF 20 Sigma-Aldrich). A desalted protein retentate of 0.2 mL with a MW between 5 and 10 kDa was then recovered. The ultrafiltration added a new concentration factor of 50×. The protein retentate (2500×) was added to 20 mL of a modified MRS medium in order to evaluate any potential effect on MLF. It was 100 times diluted and was finally 25 times concentrated in the MRS broth.

The remaining supernatant was used again to reach a new saturation level and the same procedure was applied. The impact of the five precipitated fractions (A, B, C, D and E) on MLF was evaluated separately in modified MRS media, inoculated with 2 × 10 6 cells mL -1 (Petroff-Hausser counting chamber) of O. oeni Vitilactic F corresponding to an initial bacterial biomass of 0.0016 g L -1 . The bacterial inoculum was beforehand prepared in two steps. A pre-culture was obtained by reactivating the stock culture in MRS broth, with 3% ethanol (v/v) added. After 24 h, the pre-culture was used to inoculate another MRS broth with 6% ethanol (v/v) added. The latter provided the bacterial inoculum after an incubation period of 24 h. All the fermentation steps were performed at 22 • C with stirring at 150 rpm.

Both bacterial growth and malate consumption were followed by regular sampling until cessation of malate consumption and were compared to those of a modified MRS control.

All the experiments were performed in three independent replicates.

Protein purification by Aurum ion exchange mini kits and columns

Aurum anion exchange (AEX) and cation exchange (CEX) columns allowed rapid fractionation of complex protein mixtures in only a few steps using the common technique of ion exchange chromatography. Each Aurum AEX (732-6706) or CEX (732-6703) column contained 0.2 mL of UNOsphere Q [-N + (CH 3 ) 3 ] or S [-SO 3 -] support. The buffers selected were the following:

-Binding buffers: 20 mM phosphate pH 6.7 for CEX and 20 mM Tris pH 8.3 for AEX -Elution buffers: binding buffer +0.5 M NaCl or 1 M NaCl For each analysis, 1 mL (5 × 0.2 mL) of the peptidic fraction (2500×), collected in its appropriate binding buffer, was filtered through a 0.45 μm syringe filter, loaded onto the column, and gravity filtered. The column placed in a collection tube, was then washed with 0.9 mL of the appropriate Aurum AEX or CEX binding buffer, and was centrifuged for 20 sec at 1000 g in a microcentrifuge. The aim of this step was to properly bind the sample. The eluate obtained, also called 'unbound fraction' was recovered. Next, the column was placed in a new 2 mL collection tube, and 0.3 mL of the appropriate Aurum AEX or CEX elution buffer containing 0.5 M NaCl were added to the top of the column before centrifugation at 1000 g for 10 sec. The elution step Table 1. Reaction mixture for the monitoring of the L-malic acid consumption in vitro at different pH values with or without the peptidic fractions.

Aliquot 1 (Control)

Aliquot 2 (Test) Amounts

Cell-free enzymatic extract

Cell-free enzymatic extract 1 mL

Mn ++ Mn ++ 0.1 mg NAD + (35 g L -1 ) N A D + (35 g L -1 ) 0 . 1 m L a buffer b AMPs 0.14 mL L-malic acid (1 g L -1 ) L-malic acid (1 g L -1
) 1 m g a buffer: 0.1 M of citrate buffer pH 3.5, phosphate buffer pH 6 or phosphate buffer pH 6.7. b AMPs: antimicrobial peptides of the peptidic fractions tested.

was repeated twice and the eluate was recovered (eluate 1). Finally, the column was placed in a new 2 mL collection tube and washed with 0.3 mL of the Aurum AEX or CEX elution buffer containing 1 M NaCl. It was then centrifuged for 10 sec at 1000 g and the eluate was recovered (eluate 2).

All the experiments were performed in three independent replicates.

In vitro evaluation of the peptidic fractions effect on the malolactic enzyme activity

This method was implemented and fully described by [START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF]. It consisted of preparing a cell-free enzymatic extract containing the malolactic enzyme of O. oeni Vitilactic F. The enzymatic extract was recovered in 2 mL of a buffer (0.1 M of citrate buffer pH 3.5, phosphate buffer pH 6 or phosphate buffer pH 6.7). 2 mL buffer was divided into two aliquots of 1 mL each. One was used as a control and the other as a test medium to which the peptidic fractions were added. The reaction mixture is given in Table 1. The reagents were added to each aliquot in the order listed in Table 1. The enzymatic reaction was performed at 30 • C for 70 min at pH 3.5, 30 min at pH 6 and 40 min at pH 6.7.

At the end of each assay, the concentrations of the L-malic acid consumed and the L-lactic acid produced were measured in both aliquots (control and test) and compared.

The effect of each peptidic fraction on the malolactic enzyme activity was tested in three independent replicates.

Protein analysis by Tricine SDS-PAGE

The peptidic fractions were analyzed by Tricine SDS-PAGE as described by Sch ägger and von Jagow (1987) and Schägger ( 2006), to separate proteins on the basis of their molecular sizes. A trichloroacetic acid solution or TCA solution (20% TCA, 0.2% DTT in ice-cold acetone -20 • C, Bio-Rad) was used for protein precipitation. For each sample of 1 mL, 250 μL of TCA solution were added and the samples were dried in a dry heat block (Major Science, USA) for 15 min. Proteins were then pelleted (14 000 g, 4 • C, 15 min), washed twice in 200 μL of ice-cold acetone (14 000 g, 4 • C, 5 min) and suspended in 100 μL of Triton X-100 buffer (2% Triton X-100, 1% DTT, Sigma-Aldrich). Electrophoresis was carried out using ready-to-use [8%-16%] Mini-PROTEAN R TGX precast gels composed of 12 wells in a Mini-PROTEAN R Tetracell (Bio-Rad). 20 μL of concentrated samples (10×) were run against 10 μL precision plus protein Dual-Xtra standard (2-250 kDa, criterion 10%-20% Tris-Tricine) composed of 12 recombinant proteins (Bio-Rad) at 100 V for 15 min. Gels were stained using either Bio-safe Coomassie G-250 stain or Silver stain kit (Bio-Rad). Imaging and documentation were done by using the GelDoc XR + system, controlled by Image Lab software (Bio-Rad).

Peptides sequencing by LC-MS/MS (LC1D-nanoESI-LTQ-Velos Orbitrap)

The sequencing was contracted out to PAIB2 (Plate-forme d'Analyse Int égrative des Biomol écules Ph énomiques des Animaux d'Int ér êt Bio-agronomique), Laboratoire de Spectrom étrie de Masse, Centre de Recherches INRA Val de Loire-Tours, France. The protein bands were first excised from SDS-PAGE and submitted to in-gel tryptic digestion. The peptidic extract was then dehydrated and analyzed by using high resolution mass spectrometry. The peptides derived from the digestion of the bands were analyzed by a nano UHPLC Ultimate 3000 RSLC system (Thermo Electron, Waltham-Massachusetts, USA) coupled with a high resolution mass spectrometer LTQ Orbitrap Velos ETD (Thermo Electron). The samples were desalted and concentrated in line by a precolumn Acclaim PepMap100 C18 trap (3 μm, 100 μm × 2 cm) (Dionex, California, USA), then separated on an analytical reverse phase column Acclaim PepMap 100 C18 (2 μm, 75μm × 50 cm) (Dionex) with a flow rate of 300 μL min -1 . The column was equilibrated with 96% of solvent A (0.1% formic acid, 2% acetonitrile, 97.9% water) and 4% of solvent B (0.1% formic acid, 80% acetonitrile, 19.9% water) with a gradient of 4% to 55% B for 90 min, followed by 99% B for 10 min.

The data acquisition was performed automatically between high resolution MS modes (R = 60 000) and low resolution fragmentation MS/MS. An enhanced centroid full scan (m/z 400-1800) was followed by MS/MS centroid scans of the 20 most intense peaks detected (q z 0.25, activation time 10 ms, collision energy 35%). The dynamic exclusion was turned on for 30 sec with a repeat count of 1.

The data were converted into Mascot Generic file format (mgf) using Proteome discoverer 1.3 software. It was then compared to the database through Mascot/PD Deamon 2.3 software by taking into consideration the following criteria: NCBI nr database; trypsin and no enzyme; 2 miscleavages; 5 ppm mass accuracy for MS and 0.8 Da for MS/MS; peptide load 2 and 3+; carbamidomethylation of cysteine; N-terminal acetylation; oxidation of methionine; data format: mgf; decoy database; instrument: ESI-FTICR; taxonomy: Saccharomyces cerevisiae .

The peptides and proteins identified by the Mascot search engine were validated by the Peptide Prophet and Protein Prophet algorithms using Scaffold 4 software.

Determination of the protein concentration by the Lowry method

Protein concentration was determined using the Lowry method as described by [START_REF] Frolund | Enzymatic activity in the activated-sludge floc matrix[END_REF]. Results were determined according to a BSA standard curve and expressed in mg L -1 .

Determination of the L-malic acid concentration

The L-malic acid concentration was determined using an enzymatic assay (Boehringer Mannheim/R-Biopharm, kit. No 10 139 068 035, Darmstadt-Germany) and the results were expressed in g L -1 .

Determination of the L-lactic acid concentration

The L-lactic acid concentration was determined using an enzymatic assay (Boehringer Mannheim/R-Biopharm, kit. No 10 139 084 035, Darmstadt-Germany) and the results were expressed in g L -1 .

Determination of the bacterial biomass

The biomass (dry weight) was determined using a thermobalance or moisture analyzer (Ohaus-UK). A correlation between the bacterial cell concentration (cells mL -1 ) and the bacterial biomass (g L -1 ) was established using high bacterial concentrations in modified MRS media. The correlation obtained was the following: y (gL -1 ) = 8.10 -10 x (cells mL -1 ) .

It was then used to determine the biomass of low concentrated samples.

Statistical analyses

Means and standard deviations of the assays were calculated using conventional statistical methods. Each experiment was performed in three replicates. The statistical analysis was carried out using XLSTAT 2014.5 for MS Excel.

RESULTS

Fractionation of the SGJ medium fermented by S. cerevisiae Uvaferm BDX by ammonium sulfate precipitation combined with ultrafiltration

The SGJ medium fermented by S. cerevisiae Uvaferm BDX was fractionated by ammonium sulfate precipitation combined with ultrafiltration. A 5-10 kDa peptidic fraction was recovered from each ammonium sulfate precipitate (A: 0%-20%, B: 20%-40%, C: 40%-60%, D: 60%-80% and E: 80%-100%). The effect of each peptidic fraction on MLF was evaluated separately in a modified MRS medium inoculated with O. oeni Vitilactic F. This choice was made because it was previously shown that S. cerevisiae Uvaferm BDX was able to produce and release a peptidic fraction with an apparent MW between 5 and 10 kDa that strongly inhibited O. oeni Vitilactic F [START_REF] Nehme | Impact of the co-culture of Saccharomyces cerevisiae-Oenococcus oeni on malolactic fermentation and partial characterization of a yeastderived inhibitory peptidic fraction[END_REF][START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF]). In the previous works, the peptidic fraction was recovered by ultrafiltration whereas in this paper, the ammonium sulfate precipitation was combined with ultrafiltration in order to further reduce the amount of peptides and easily select the anti-MLF ones.

Fig. 1 shows that neither the growth nor the malate consumption were affected by the 80%-100% precipitate and the kinetics were similar to those of the control. However, the 0%-20%, 20%-40% and 40%-60% precipitates strongly inhibited the bacterial growth with no effect on the malate consumption. The 60%-80% precipitate was the only one that strongly inhibited both the bacterial growth and the malate consumption. Its protein concentration was the highest (650 mg L -1 ); 2.54 times higher than that of the other precipitates (data not shown).

The same work was carried out with the SGJ medium fermented by S. cerevisiae Lalvin CY3079 that is a non-inhibitory strain [START_REF] Nehme | Quantitative study of interactions between Saccharomyces cerevisiae and Oenococcus oeni strains[END_REF]. The results obtained with all the precipitates were similar to those of the modified MRS control, showing no inhibition, neither on the bacterial growth nor on the malate consumption (data not shown). Therefore, S. cerevisiae Lalvin CY3079 was used as a reference strain in this study.

In vitro evaluation of the malolactic enzyme inhibition by the 5-10 kDa peptidic fraction of the 60%-80% ammonium sulfate precipitate

In this experiment, the malolactic enzyme of the cell-free bacterial enzymatic extract was directly exposed to the peptidic fractions. The protein concentration of all the enzymatic extracts prepared was measured before addition of the peptidic fractions and was approximately the same with an average value of 302.2 (± 20) mg L -1 . pH 3.5 was chosen because all the MLFs of this study were carried out at this pH value. pH 6 was used because it is the optimum pH for the malolactic enzyme activity of O. oeni and pH 6.7 is around the cytoplasmic pH of the majority of lactic acid bacteria. Table 2 shows that the 5-10 kDa peptidic fraction of the 60%-80% ammonium sulfate precipitate obtained from S. cerevisiae Uvaferm BDX was able to inhibit the malate consumption by directly targeting the malolactic enzyme activity at all the pH values tested. For instance, at pH 6.7, the malate consumption was reduced by 99% in comparison to the control. The 0%-20% precipitate was used as an additional control since it only inhibited the bacterial growth in the modified MRS medium and not the malate consumption (Fig. 1). Table 2 also shows that this precipitate did not affect the malolactic enzyme activity in vitro. Additionally, the 5-10 kDa fraction of the 60%-80% precipitate isolated from the SGJ medium fermented by S. cerevisiae Lalvin CY3079 (reference strain or non-inhibitory strain), was tested and gave results identical to the control. In terms of specific malolactic activities, the highest value (0.1 g L -1 of L-malic acid consumed/min/mg of proteins) was obtained at pH 6 in the absence of inhibition. It was 1.37 and 2.33 times higher than those obtained at pH 6.7 and 3.5 respectively. However, in the presence of the inhibitory fraction (5-10 kDa of the 60%-80% precipitate from Uvaferm BDX), the specific activities were almost null at all the pH values tested (Table 3).

The amounts of L-lactic acid produced were measured and found to be stoichiometrically equivalent to the amounts of Lmalic acid consumed. This is an additional proof that the inhibitory peptidic fraction targeted specifically the malolactic enzyme of the enzymatic extract.

Altogether, it can be concluded that the inhibition of the malolactic enzyme by the 5-10 kDa peptidic fraction of the 60%-80% ammonium sulfate precipitate of S. cerevisiae Uvaferm BDX was highly specific. Therefore, it was retained for further purification.

Comparison of the protein profiles of S. cerevisiae Uvaferm BDX and Lalvin CY3079 by Tricine SDS-PAGE

The 5-10 kDa peptidic fractions of the 60%-80% ammonium sulfate precipitates obtained from the SGJ media fermented by both strains of S. cerevisiae were analyzed and compared by Tricine SDS-PAGE. The results are given in Fig. 2. Tricine SDS-PAGE analysis of the inhibitory fraction released by S. cerevisiae Uvaferm BDX, revealed three protein bands with an apparent MW of Table 2. Consumed L-malic acid (g L -1 ) and produced L-lactic acid (g L -1 ) during the enzymatic reaction in vitro performed at different pH values in the presence of peptidic fractions isolated from the SGJ media fermented by S. cerevisiae Uvaferm BDX and Lalvin CY3079. Table 3. Specific malolactic activities of the enzymatic reactions in vitro calculated in the controls and in the presence of different yeast peptidic fractions.

(g L -1 ) Produced L-lactic acid (g L -1 ) Consumed L-malic acid (g L -1 ) Produced L-lactic acid (g L -1 ) Consumed L-malic acid (g L -1 ) Produced L-lactic acid (g L -1 ) Control (
b Specific malolactic activities (g L -1 of L-malic acid consumed/min/mg of proteins) pH 6.7 pH 6 pH 3.5

Control (absence of a AMPs) 0.08 0.1 0.05 5-10 kDa fraction of the 0%-20% precipitate from Uvaferm BDX 0.07 0.09 0.04 5-10 kDa fraction of the 60%-80% precipitate from Lalvin CY3079 0.07 0.1 0.04 Average specific malolactic activities in the absence of inhibition 0.073 (±0.006) 0.1 (±0.006) 0.043 (±0.006) 5-10 kDa fraction of the 60%-80% precipitate from Uvaferm BDX 0.0005 0.0001 0.0003 a AMPs: antimicrobial peptides of the peptidic fractions tested.

b Specific malolactic activities are calculated by dividing the amount of L-malic acid consumed (g L -1 ) by the duration of the enzymatic reaction (min) and by the amount of proteins present in the enzymatic mixture (mg).

The different amounts of proteins are: 0.302 mg in the controls; 0.338 mg in the presence of the 0%-20% precipitate from Uvaferm BDX, 0.349 mg in the presence of the 60%-80% precipitate from Lalvin CY3079 and 0.393 mg in the presence of the 60%-80% precipitate from Uvaferm BDX. approximately 5.1 kDa (1), 7.2 kDa (2) and 8.4 kDa (3) as shown in lane B. Two of these protein bands, ( 1) and ( 2), were not present in the SGJ medium fermented by Lalvin CY3079 (lane C). Besides, band (3) of approximately 8.4 kDa was released by both strains, but was more concentrated in the SGJ medium fermented by Uvaferm BDX. Therefore, it is likely that the peptides of these bands were involved in the MLF inhibition. The use of Lalvin CY3079 strain as a reference strain was helpful since the comparison of the protein profiles proved that strain Uvaferm BDX was able to produce additional peptides.

Fractionation of the 5-10 kDa peptidic fractions of the 60%-80% ammonium sulfate precipitates by anion and cation exchange chromatography and evaluation of the inhibitory effect of the eluates on the malolactic enzyme activity in vitro

In order to further reduce the amount of peptides and specifically target the anti-MLF ones, the 5-10 kDa peptidic fraction a Control 1: 0.14 mL of phosphate buffer 0.1 M pH 6.7 were added instead of the peptidic eluate. b Control 2: 0.14 mL of NaCl 0.5 M were added instead of the peptidic eluate. c Control 3: 0.14 mL of NaCl 1 M were added instead of the peptidic eluate. d Unbound fraction: 0.14 mL of the fraction that was not retained on the column after sample binding in AEXC or CEXC.

of the 60%-80% ammonium sulfate precipitate of S. cerevisiae Uvaferm BDX was analyzed by ion exchange chromatography. The eluates from both anion and cation exchange chromatography (AEXC and CEXC) were tested in vitro in the presence of the malolactic enzyme of the cell-free enzymatic extract. Control 1 in Table 4 is the usual control used in all the in vitro experiments. It enables us to monitor the malolactic enzyme activity in vitro. Controls 2 and 3 were added to this experiment to evaluate the effect of NaCl on the malolactic enzyme activity. In fact, the elution of peptides in both AEXC and CEXC was performed using increasing concentrations of NaCl (0.5 M then 1 M of NaCl) in the elution buffers. We wanted to make sure that the inhibition observed in the presence of the eluates was only due to the peptides and not to the NaCl. The results show that the presence of NaCl did not affect the malolactic enzyme activity since the 1 g L -1 of L-malic acid initially present in the control aliquots (controls 2 and 3) was totally consumed. The unbound fraction corresponds to the fraction that was not retained on the column after loading and binding the sample. It did not inhibit the malolactic enzyme activity as shown in Table 4. Therefore, it did not contain any inhibitory peptide. Its analysis underlines the effectiveness of this purification step. Eluates obtained after elution with 0.5 M NaCl from both AEXC and CEXC (eluates 1 of Table 4) strongly inhibited the malolactic enzyme activity since only 0.2% of the malate present were consumed. However, those eluted with 1M NaCl did not affect the enzyme's activity and the malate was entirely consumed. Therefore the peptides of interest were recuperated in eluates 1 of both AEXC and CEXC. The same procedure was performed with strain Lalvin CY3079 and none of the eluates showed any inhibition (data not shown).

Tricine SDS-PAGE analysis of the inhibitory eluates followed by sequence analysis of the protein bands by LC-MS/MS

Fig. 3 shows the results obtained after migration of the inhibitory eluates on Tricine SDS-PAGE (eluates one from both AEXC and CEXC). The cationic eluate gave two protein bands of approximately 5.1 and 7.2 kDa whereas the anionic eluate gave one protein band of 5.1 kDa. The three protein bands from both eluates were excised and sequenced in order to identify the nature of the putative antibacterial peptides responsible for the MLF inhibition.

Tables 5 and6 give the nature of the peptides identified by LC-MS/MS (LC1D-nanoESI-LTQ-Orbitrap) as well as their amino acid sequence, molecular weight and theoretical isoelectric point (pI). The cationic eluate revealed 7 protein fragments corresponding to 6 proteins of S. cerevisiae whereas the anionic eluate revealed 2 protein fragments corresponding to 2 yeast proteins.

DISCUSSION

The current study aims to further purify and identify the putative anti-MLF peptides of the 5-10 kDa peptidic fraction produced by S. cerevisiae Uvaferm BDX that inhibited the growth of O. oeni Vitilactic F and its ability to consume L-malic acid during sequential fermentations [START_REF] Nehme | Impact of the co-culture of Saccharomyces cerevisiae-Oenococcus oeni on malolactic fermentation and partial characterization of a yeastderived inhibitory peptidic fraction[END_REF][START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF]. First, the SGJ medium, fermented by S. cerevisiae Uvaferm BDX, was fractionated by ammonium sulfate precipi-tation combined with ultrafiltration. The results obtained suggested the presence of two categories of inhibitory proteinaceous compounds (Fig. 1). The first one inhibited the growth of O. oeni Vitilactic F only (5-10 kDa peptidic fractions of the 0%-60% precipitates), while the second one inhibited both the bacterial growth and the malate consumption (5-10 kDa peptidic fraction of the 60%-80% precipitate). With the latter, the inhibition of the bacterial growth could be a consequence of the malate consumption inhibition or it could be due to the presence of both categories of protein compounds. In winemaking conditions, MLF is one of the main energy sources for bacterial survival and growth [START_REF] Cox | Proton motive force and ATP generation during malolactic fermentation[END_REF][START_REF] Bouix | Rapid assessment of Oenococcus oeni activity by measuring intracellular pH and membrane potential by flow cytometry, and its application to the more effective control of malolactic fermentation[END_REF], thus, an inhibition of the malate consumption can also cause an inhibition of the bacterial growth.

Second, the 5-10 kDa peptidic fraction of the 60%-80% ammonium sulfate precipitate was tested in vitro in a cell-free cytosolic extract of O. oeni Vitilactic F containing the malolactic enzyme. The results of Table 2 show that the peptidic fraction strongly inhibited the malolactic enzyme activity in vitro at a pH range between 3.5 and 6.7. Therefore, this fraction is believed to enter the bacterial cytoplasm in vivo by mechanisms yet to be identified in order to directly target and inhibit the malolactic enzyme. These results are in accordance with those obtained by [START_REF] Rizk | Impact of inhibitory peptides released by Saccharomyces cerevisiae BDX on the malolactic fermentation performed by Oenococcus oeni Vitilactic F[END_REF] who were the first to show that yeast proteinaceous compounds can inhibit MLF by directly targeting the malolactic enzyme activity.

The 5-10 kDa peptidic fraction of the 60%-80% ammonium sulfate precipitate was further purified by ion exchange chromatography. The anionic and cationic eluates that inhibited the malolactic enzyme activity in vitro (eluates 1 of Table 4) were migrated by SDS-PAGE and the bands of interest were excised and sequenced by LC-MS/MS. The cationic eluate revealed 7 protein fragments corresponding to 6 proteins of S. cerevisiae whereas the anionic eluate revealed 2 protein fragments corresponding to 2 yeast proteins. The following identified proteins play diverse vital roles in S. cerevisiae cells: the short-chain dehydrogenase/reductase protein binds and stabilizes the pre-tRNAs and RNA polymerase III transcripts [START_REF] Schenk | La-motif-dependent mRNA association with Slf1 promotes copper detoxification in yeast[END_REF]. Yeast actin is a structural protein involved in cell polarization, endocytosis, cytoskeleton functions and histone acetyltransferase activity [START_REF] Ch | Actin from Saccharomyces cerevisiae[END_REF][START_REF] Moseley | The yeast actin cytoskeleton: from cellular function to biochemical mechanism[END_REF]. YDL025C-like protein is a major protein kinase involved in protein phosphorylation [START_REF] Ptacek | Global analysis of protein phosphorylation in yeast[END_REF]. Petite colonies protein or Pet127p is a membrane-associated protein involved in stability and processing of S. cerevisiae mitochondrial RNAs [START_REF] Wiesenberger | Pet127p, a membrane-associated protein involved in stability and processing of Saccharomyces cerevisiae mitochondrial RNAs[END_REF]. Utr2p has a hydrolase activity, hydrolyzing O-glycosyl compounds [START_REF] Novo | Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast Saccharomyces cerevisiae EC1118[END_REF]. Wtm1p (WD repeatcontaining transcriptional modulator) is a protein present in a large nuclear complex and presenting two homologs, Wtm2p and Wtm3p, which probably arose by gene duplications. These proteins are transcriptional modulators with roles in meiotic regulation and silencing and RNR genes expression. They are also involved in response to replication stress and are relocalized in the cytosol in response to hypoxia [START_REF] Pemberton | Characterization of the Wtm proteins, a novel family of Saccharomyces cerevisiae transcriptional modulators with roles in meiotic regulation and silencing[END_REF]. Therefore, peptidic fragments of Wtmp are produced and released by yeasts in response to stress.

There are no previous studies that describe the antimicrobial activities of the above mentioned proteins and/or their corresponding fragments against bacteria or against fungi.

The sequencing also revealed two peptidic fractions of GAPDH in the cationic eluate, one having a MW of 0.9 kDa and a pI of 10.5 while the other presented a MW of 1.373 kDa and a pI of 11 (Table 5). [START_REF] Branco | Identification of novel GAPDH-derived antimicrobial peptides secreted by Saccharomyces cerevisiae and involved in wine microbial interactions[END_REF] previously demonstrated that GAPDH fragments were involved in the growth In the present study, the cationic eluate containing the GAPDH fragments strongly inhibits the malate consumption in vitro at pH 6.7 (pH of the enzymatic reaction) (Table 4), which leads us to suggest that the GAPDH peptides may have inhibited the malolactic enzyme activity by competing for the binding site of NAD + . In fact, NAD + is essential for the activation of the enzyme. By depriving the malolactic enzyme from NAD + , it will become dysfunctional. Moreover, the GAPDH fragments may bind to other parts of the enzyme, thus modifying its conformation and affecting its function. In vivo, they should enter the bacterial cytoplasm by mechanisms yet to be identified in order to exert their inhibitory effect.

Although there is no previous study on the antimicrobial role of Wtm2p and Utr2p, their fragments seem to play a major role in the inhibition since the corresponding anionic eluate strongly inhibited the malolactic enzyme activity in vitro, as previously shown in Table 4. Giving their pI (3.5 and 4), the peptidic fragments are anionic at pH 6.7 that is the pH of this in vitro experiment. Therefore, they may deprive the malolactic enzyme of its Mn ++ cofactors by chelating them or of NAD + by binding to it, thus inhibiting the malolactic enzyme activity and the malate consumption. Since pH 6.7 is around the cytoplasmic pH of most lactic acid bacteria, we can assume that when Wtm2p and Utr2p peptidic fragments enter the cytoplasm of O. oeni Vitilactic F in vivo, they will be able to work similarly, leading to an inhibition of the MLF.

Moreover, it was previously demonstrated that some AMPs are able to translocate across membranes and target cytoplasmic constituents without disrupting plasma membranes [START_REF] Park | Structure-activity analysis of buforin II, a histone H2A-derived antimicrobial peptide: the proline hinge is responsible for the cell-penetrating ability of buforin II[END_REF][START_REF] Powers | The relationship between peptide structure and antibacterial activity[END_REF][START_REF] Brown | Cationic host defense (antimicrobial) peptides[END_REF]. Therefore, it would be interesting to see if the peptides obtained in the current study are able to disrupt the bacterial plasma membrane, or translocate inside the cell without membrane disruption. The entrance of anionic peptides like Wtm2p and Utr2p may require some divalent metal cations like Fe ++ , Mn ++ , Mg ++ , or others. In fact, several studies have shown that the antimicrobial activity of anionic peptides is enhanced by metal cations that promote their binding to the negatively charged components of cell-walls and/or plasma membranes [START_REF] Dashper | Divalent metal cations increase the activity of the antimicrobial peptide kappacin[END_REF][START_REF] Dashper | Antimicrobial peptides and their potential as oral therapeutic agents[END_REF]. Some of them are able to translocate across membranes or increase membrane permeability by forming pores [START_REF] Harris | Anionic antimicrobial peptides from eukaryotic organisms[END_REF].

CONCLUSIONS

Saccharomyces cerevisiae Uvaferm BDX was able to release nine anti-MLF peptides with a MW range between 0.8 and 2.42 kDa. These peptides derived from eight proteins that play diverse vital roles in yeast cells. Two GAPDH fragments of 0.9 and 1.373 kDa having a pI of 10.5 and 11 respectively, and fragments of Wtm2p and Utr2p of 2.42 kDa with a pI of 3.5 and 4 respectively were thought to contribute the most to the MLF inhibition. However, it is likely that one or more of the nine peptides worked synergistically to inhibit MLF. The peptides were able to inhibit the malolactic enzyme activity in vitro at a pH range between 3.5 and 6.7. In vivo, they are supposed to enter the bacterial cytoplasm by mechanisms yet to be identified in order to reach the malolactic enzyme. It would be interesting for future studies to synthesize these peptides in order to test them individually in vivo and in vitro experiments and to evaluate the contribution of each in the MLF inhibition. It would be also important to evaluate their spectrum of activity against other O. oeni strains, wine spoilage and pathogenic microorganisms. Studying their interaction with the plasma membrane, the mechanisms by which they enter inside the bacterial cytoplasm and inhibit the malolactic enzyme activity would also be beneficial.

Figure 1 .

 1 Figure 1. Growth (A) and malic acid consumption (B) kinetics of O. oeni Vitilactic F in the presence of the 5-10 kDa peptidic fractions of the ammonium sulfate precipitates obtained from the SGJ medium fermented by S. cerevisiae Uvaferm BDX. ( ) Control: modified MRS medium without peptidic fractions; modified MRS medium with 5-10 kDa fractions collected at different saturation degrees: (x) 0%-20%, ( ) 20%-40%, (•) 40%-60%, ( ) 60%-80%, ( ) 80%-100%. Each value is the mean of triplicate experiments ± SD.

a

  AMPs: antimicrobial peptides of the peptidic fractions tested. b t = 40 min, t = 30 min and t = 70 min: incubation time required for the total conversion of L-malic acid (1 g L -1 ) into L-lactic acid and CO2 during the malolactic reaction in vitro in the control aliquot at the different pH values tested.

Figure 2 .

 2 Figure 2. Silver stained Tricine SDS-PAGE gel. Lane A corresponds to the dual-xtra MW standard 2-250 kDa (Bio-Rad). Lane B corresponds to the 5-10 kDa fraction of the 60%-80% precipitate of S. cerevisiae Uvaferm BDX. Lane C corresponds to the 5-10 kDa fraction of the 60%-80% precipitate of S. cerevisiae Lalvin CY3079 (reference strain).

Figure 3 .

 3 Figure 3. Coomassie G-250 stained Tricine SDS-PAGE gel. Lane A contains dualxtra MW standard 2-250 kDa (Bio-Rad). Lane B corresponds to eluate one from CEXC. Lane C corresponds to eluate one from AEXC.

Table 4 .

 4 Consumed L-malic acid (g L -1 ) and produced L-lactic acid (g L -1 ) in the controls and in the presence of the eluates obtained from AEXC and CEXC.

	Consumed	Produced
	L-malic acid	L-lactic acid
	(g L -1 )	(g L -1 )

pH = 6.7; t = 40 min

Table 5 .

 5 Sequence analysis by LC1D-nanoESI-LTQ-Orbitrap of the peptides corresponding to the 5.1 and 7.2 kDa protein bands obtained from the cationic eluate.

	Cationic peptides/proteins

Table 6 .

 6 Sequence analysis by LC1D-nanoESI-LTQ-Orbitrap of the peptides corresponding to the 5.1 kDa protein band obtained from the anionic eluate.

	Anionic peptides/proteins

ACKNOWLEDGEMENTS

The authors would like to thank LARI (Lebanese Agricultural Research Institute) for their financial support; Lallemand Inc. for providing the strains and the Laboratoire de Spectrom étrie de Masse, Centre de Recherches INRA Val de Loire-Tours for the protein sequencing.

Conflicts of interest.

None declare.