Mirco Theile

Harald Bayerlein

Richard Nai

David Gesbert

Marco Caccamo

UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) that can be applied to a wide range of mission scenarios. Specifically, we compare coverage path planning (CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor devices. By exploiting structured map information of the environment, we train double deep Q-networks (DDQNs) with identical architectures on both distinctly different mission scenarios to make movement decisions that balance the respective mission goal with navigation constraints. By introducing a novel approach exploiting a compressed global map of the environment combined with a cropped but uncompressed local map showing the vicinity of the UAV agent, we demonstrate that the proposed method can efficiently scale to large environments. We also extend previous results for generalizing control policies that require no retraining when scenario parameters change and offer a detailed analysis of crucial map processing parameters' effects on path planning performance.

I. INTRODUCTION

Autonomous unmanned aerial vehicles (UAVs) are envisioned for a multitude of applications that all require efficient and safe path planning methods, which necessitate the combination of a mission goal with navigation constraints, e.g., flying time and obstacle avoidance. Examples for these applications are area coverage path planning (CPP) [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF], and data harvesting (DH) from Internet of Things (IoT) sensor nodes [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]. As its name suggests, covering all points inside an area of interest with CPP is related to conventional path planning, where the goal is to find a path between start and goal positions. In general, CPP aims to cover as much of the target area as possible within given energy or path-length constraints while avoiding obstacles or no-fly zones.

In the DH scenario, the UAV's goal is to collect data from IoT devices distributed in an urban environment, which implies challenging radio channel conditions through alternating line-of-sight (LoS) and non-line-of-sight (NLoS) links between UAV and IoT devices through building obstruction. DH and CPP are very similar when described as an RL problem since the path planning problem's constraints are mostly identical, and only the goal function changes. In 1 Mirco Theile, Richard Nai, and Marco Caccamo are with the TUM School of Engineering and Design, Technical University of Munich, Germany {mirco.theile, richard.nai, mcaccamo}@tum.de {harald.bayerlein, david.gesbert}@eurecom.fr previous work, we have looked at CPP [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF] and DH [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF] separately. We show that both problems can be solved using the same deep reinforcement learning (DRL) approach based on feeding spatial map information directly to the DRL agent via convolutional network layers. This work's focus lies in proposing improvements to existing DRL approaches to generalized, large-scale UAV path planning problems with CPP and DH as examples.

Using maps as a direct input becomes problematic for large map sizes, as the network's size, trainable parameters, and training time increase equivalently. We introduce a global-local map scheme to address the scalability problems of the standard map-based input. In path planning, the intuition is that distant features lead to general direction decisions, while close features lead to immediate actions such as collision avoidance. Thus, the level of detail passed to the agent for distant objects can be less than for close objects. With the global map, a compressed version of the complete environment map centered on the agent's position, general information of all objects on the map is provided to the agent. In contrast, the local map, uncompressed but cropped to show only the UAV agent's immediate surroundings, provides detailed local information.

While numerous path planning algorithms for both problems exist, DRL offers the possibility to solve both distinctly different problems with the same approach. For each problem, DRL agents can learn control policies that generalize over a large scenario parameter space requiring no expensive retraining or recomputation when the scenario changes. However, previous work usually only focuses on finding optimal paths for one single scenario at a time. The DRL paradigm is popular in this context because of its flexibility regarding prior knowledge and assumptions about the environment, the computational efficiency of DRL inference, as well as the complexity of autonomous UAV control tasks, which are usually non-convex optimization problems and proven to be NP-hard in many instances [START_REF] Zeng | Accessing from the sky: A tutorial on UAV communications for 5G and beyond[END_REF], [START_REF] Shakeri | Design challenges of multi-UAV systems in cyber-physical applications: A comprehensive survey and future directions[END_REF]. A general summary of issues in using UAVs as part of communication networks, including IoT data harvesting, can be found in [START_REF] Zeng | Accessing from the sky: A tutorial on UAV communications for 5G and beyond[END_REF]. A survey of various applications for UAV systems from a cyberphysical perspective is offered in [START_REF] Shakeri | Design challenges of multi-UAV systems in cyber-physical applications: A comprehensive survey and future directions[END_REF]. Cabreira et al. [START_REF] Cabreira | Survey on coverage path planning with unmanned aerial vehicles[END_REF] provide a survey of UAV coverage path planning.

Previous works in UAV path planning have already made use of convolutional map processing for DRL agents. In the drone patrolling problem presented in [START_REF] Piciarelli | Drone patrolling with reinforcement learning[END_REF], a local relevance map of the patrolling area showing the agent's vicinity cropped to a fixed size is fed into a DDQN agent. No information of the physical environment is included, and there is no consideration for navigation constraints like obstacle avoidance or flying time. In [START_REF] Julian | Distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning[END_REF], fixed-wing UAVs are tasked with monitoring a wildfire propagating stochastically over time. Control decisions are based on either direct observations or belief maps fed into the DRL agents. The focus here is the inherent uncertainty of the problem, not balancing a mission goal and navigation constraints in large complex environments. Wildfire surveillance is also the mission of the quadcopter UAVs in [START_REF] Seraj | Coordinated control of UAVs for humancentered active sensing of wildfires[END_REF], which is set in a similar scenario without navigation constraints and makes use of uncertainty maps to guide path planning. Their approach is based on an extended Kalman filter and not the reinforcement learning (RL) paradigm. To monitor another natural disaster situation, Baldazo et al. [START_REF] Baldazo | Decentralized multi-agent deep reinforcement learning in swarms of drones for flood monitoring[END_REF] present a multi-agent DRL method for flood surveillance using the UAVs' local observations of the inundation map to make control decisions. All mentioned approaches focus on solving a single class of UAV missions in simple physical environments and do not consider combining local and global map information.

Missions, where UAVs provide communication services to ground users or devices, include the work in [START_REF] Esrafilian | Learning to communicate in UAV-aided wireless networks: Map-based approaches[END_REF] set in a complex urban environment where the UAV path planning is based on exploiting map information with a method combining dynamic and sequential convex programming. In [START_REF] Liu | Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning[END_REF], data is collected simultaneously with ground and aerial vehicles on a small map with obstacles. Due to the small map size, the full global map information can be fed into the DRL agents. Another scenario is investigated in [START_REF] Zhang | Radio map based path planning for cellular-connected UAV[END_REF], where a cellular-connected UAV has to navigate from a start to an end position maintaining connectivity with a ground network exploiting a radio map. The approach includes global radio map compression to reduce computational complexity but is not based on RL and includes no higher precision local map or hard navigation constraints. To the best of our knowledge, no dual global-local map processing method applicable to multiple mission types for autonomous UAVs has been suggested previously.

The main contributions of this paper are the following:

• Establishing the presented DRL approach as a general method for UAV path planning by demonstrating its applicability to two distinctly different mission scenarios: coverage path planning and path planning for wireless data harvesting; • Introducing a novel approach1 to exploit global-local map information that allows DRL for path planning to scale to large, realistic scenario environments efficiently, with an order of magnitude more grid cells compared to earlier works [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF], [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]; • Overcoming the limitation of fixed target zones in previous DRL CPP approaches [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF] by extending control policy generalization over scenario parameters to randomly generated target zones; • Analyzing and discussing the effects of key map processing parameters on the path learning performance.

II. PROBLEM FORMULATION

In the following, we show that a universal problem description of coverage path planning and path planning for data harvesting can be established through separation into two parts: the environment and the target.

A. Environment and UAV Model

We consider a square grid world of size M × M ∈ N 2 with cell size c, where N is the set of natural numbers. The environment contains designated start/landing positions, regulatory no-fly zones (NFZs), and obstacles. The map can be described through a tensor M ∈ B M ×M ×3 , where B = {0, 1} and with the start/landing zones in map-layer 1, the union of NFZs and obstacles in map-layer 2, and the obstacles alone in map-layer 3.

The UAV moves through this environment at a constant altitude h occupying one cell of the environment. Its position can thus be defined through p(t) ∈ N 2 . The movement of the UAV is constrained through collision avoidance with obstacles and not entering NFZs. Additionally, the UAV must start and end its mission in any cell belonging to the start and landing zones while staying within its maximum flying time determined by its initial battery level. The battery level of the UAV b(t) is set to b 0 ∈ N at time t = 0 and is decremented by 1 per action step.

B. Target and Mission Definitions

1) Coverage Path Planning: In coverage path planning, the mission is to cover a designated target area by flying above or near it, such that it is in the field of view of a camera-like sensor mounted underneath the UAV. The target area can be described through T(t) ∈ B M ×M , in which each element describes whether a cell has to be covered or not. The current field of view of the camera can be described with V(t) ∈ B M ×M indicating for each cell whether it is in the current field of view or not. In this work, the field of view is a square of 5 × 5 surrounding the current UAV position. Additionally, buildings can block line-of-sight, which is also incorporated in calculating V(t). This prohibits the UAV from seeing around the corner.

Consequently, the target area evolves according to

T(t + 1) = T(t) ∧ ¬V(t), (1)
in which ∧ and ¬ are the cell-wise logical and and negation operators, respectively. In our mission definition, obstacle cells in the environment cannot be a coverage target, while start and landing zones and no-fly-zones can be. The goal is to cover as much of the target area as possible within the maximum flying time constraint.

2) Data Harvesting: Conversely, the mission in path planning for wireless data harvesting is to collect data from K ∈ N stationary IoT devices spread throughout the environment at ground-level, with the position of device k ∈ [1, K] given through u k ∈ N 2 . Each device has an amount of data D k (t) ∈ R to be collected by the UAV. The data throughput C k (t) between the selected device k and the UAV is based on the standard log-distance path loss model with Gaussian shadow fading and whether they can establish a line-ofsight connection or are obstructed by obstacles. The UAV is communicating with one device at a time and selects the device with remaining data and the highest possible data rate. A detailed description of the link performance and multiple access protocol can be found in [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]. The data at each device evolves according to

D k (t + 1) = D k (t) -C k (t) (2)
Devices can be located in every cell except for the starting and landing zones or inside obstacles. The goal of the data harvesting problem is to collect as much of the devices' data as possible within the maximum flying time.

3) Unifying Map-Layer Description: Both problems can be described through a single target map-layer D(t) ∈ R M ×M . In CPP, the target map-layer is given through T(t) evolving according to [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF]. In DH, the target map-layer shows the amount of available data in each cell that one of the devices is occupying, i.e. the cell at position u k has value D k (t) and is evolving according to [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]. If a cell does not contain a device or the device data has been collected fully, the cell's value is 0. Since the two problems can be described with similar state representations, both can be solved through deep reinforcement learning with a neural network having the same structure.

III. METHODOLOGY

While a variety of methods exist to solve the CPP and DH problems separately, the approach presented in the following can be directly applied to both distinct path planning problems. In most classical CPP approaches, individual target zones are extracted through segmentation and then connected with distance costs into a graph, while each segment is covered with a boustrophedon path. This reduces the CPP problem to an instance of the travelling salesman problem (TSP), which is NP-hard and can be solved by classical methods, e.g. as demonstrated in [START_REF] Xie | An integrated traveling salesman and coverage path planning problem for unmanned aircraft systems[END_REF] at the price of an exponential increase in time complexity with the number of target zones.

In principle, the DH problem can also be converted into a TSP with the IoT devices as nodes in the graph and the distances between the devices as edge costs. However, the conversion neglects that communication with the device happens while traveling to and from it. In general, the optimal behavior in DH problems is not a sequential visit of all devices, as data can already be efficiently collected by establishing a LoS link from farther away, or a large amount of data waiting to be collected might require the drone to hover for an extended period of time near the device. These constraints in conjunction with stochastic communication channel models and the various possibilities for the choice of multiple access protocol are non-trivial to model and solve with classical approaches. For both problems, the UAV battery constraint adds another complication for classical approaches, as full coverage or full collection are not always feasible. The following DRL methodology allows us to combine all goals and constraints of the respective path planning problems directly without the need for additional approximations.

A. Partially Observable Markov Decision Process

To address the aforementioned problems we formulate them as a partially observable Markov decision process (POMDP) [START_REF] Kaelbling | Planning and acting in partially observable stochastic domains[END_REF] which is defined through the tuple (S, A, P, R, Ω, O, γ). In the POMDP, S describes the state space, A the action space, and P : S × A × S → R the transition probability function. R : S × A × S → R is the reward function mapping state, action, and next state to a real valued reward. The observation space is defined through Ω and O : S → Ω is the observation function. The discount factor γ ∈ [0, 1] varies the importance of long and short term rewards.

We unify the UAV path planning problems by describing their state space with

S = B M ×M ×3 Environment Map × R M ×M Target Map × N 2 Position × N Flying Time , (3)
in which the elements s(t) ∈ S are

s(t) = (M, D(t), p(t), b(t)). (4)
The four components of the tuple are

• M the environment map containing start and landing zones, no-fly zones, and obstacles; • D(t) the target map indicating remaining data at device locations or remaining cells to be uncovered at time t; • p(t) the UAV's position at time t; • b(t) the UAV's remaining movement budget at time t; Action a(t) ∈ A of the UAV at time t is given as one of the possible actions A = {north, east, south, west, hover, land}.

The generalized reward function R(s(t), a(t), s(t + 1)) consists of the following elements:

• r c (positive) the data collection or cell covering reward given by the collected data or the amount of newly covered target cells, comparing s(t + 1) and s(t); • r sc (negative) safety controller (SC) penalty in case the drone has to be prevented from colliding with a building or entering an NFZ; • r mov (negative) constant movement penalty that is applied for every action the UAV takes without completing the mission; • r crash (negative) penalty in case the drone's remaining flying time reaches zero without having landed safely in a landing zone.

B. Map Processing

To aid an agent in interpreting the large state space given in (3), two map processing steps are used. The first is centering the map around the agent's position, shown in [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF] to improve the agent's performance significantly. The downside of this approach is that it increases the representation size of the state space even further. Thus, the second map processing

B = f center (A, p, x pad ), (5)
with the centering function defined as

f center : R M ×M ×n × N 2 × R n → R Mc×Mc×n . (6)
The elements of B with respect to the elements of A are defined as

bi,j =      ai+p 0 -M +1,j+p 1 -M +1, M ≤ i + p0 + 1 < 2M ∧ M ≤ j + p1 + 1 < 2M xpad, otherwise, (7)
effectively padding the map layers of A with the padding value x pad . Note that a i,j , b i,j , and x pad are vector valued of dimension R n . For both problems, the map layers are padded with [0, 1, 1, 0] T , i.e. NFZs and obstacles. A qualitative description of centering with an example can be found in [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF].

2) Global-Local Mapping: The tensor B ∈ R Mc×Mc×n resulting from the map centering function is processed in two ways. The first is creating a local map according to

X = f local (B, l) (8)
with the local map function defined by

f local : R Mc×Mc×n × N → R l×l×n . (9)
The elements of X with respect to the elements of B are defined as

x i,j = b i+M -l 2 ,j+M -l 2 . ( 10
)
This operation is effectively a central crop of size l × l.

The global map is created according to

Y = f global (B, g) (11)
with the global map function defined by

f global : R Mc×Mc×n × N → R Mc g × Mc g ×n . ( 12
)
The elements of Y with respect to the elements of B are defined as

y i,j = 1 g 2 g-1 u=0 g-1 v=0 b gi+u,gj+v , (13)
which is an operation equal to average pooling. The functions f local and f global are parameterized through l and g, respectively. Increasing l increases the size of the local map, whereas increasing g increases the size of the average pooling cells, decreasing the size of the global map.

C. Observation Space

The observation space Ω, which is the input to the agent, is given as

Ω = Ω l × Ω g × N containing the local map Ω l = B l×l×3 × R l×l and the global map Ω g = R Mc g × Mc g ×3 × R Mc g × Mc g
. Note that the compression of the map layers through average pooling transforms the environment layers from boolean to real. The observations o(t) ∈ Ω are defined through the tuple

o(t) = (M l (t), D l (t), M g (t), D g (t), b(t)). (14)
In the observation, M l (t) and M g (t) are the local and global observations of the environment, and D l (t) and D g (t) are the local and global observations of the target, respectively. b(t) is the remaining flying time of the UAV and is equal to the one in the state space. Note that the local and global observations of the environment are time-dependent, as they are centered around the time-dependent position of the UAV.

The mapping from state to observation space is given by O : S → Ω, with the elements o(t) ∈ O defined as:

M l (t) =f local (f center (M, p(t), [0, 1, 1] T ), l) (15a) D l (t) =f local (f center (D(t), p(t), 0), l) (15b) M g (t) =f global (f center (M, p(t), [0, 1, 1] T ), g) (15c) D g (t) =f global (f center (D(t), p(t), 0), g) (15d)
By feeding the observation space Ω into the agent instead of the state space S, the problem is artificially converted into a partially observable MDP. The partial observability results from the restricted size of the local map and the averaging in the global map. With the following results, we show that partial observability does not make the problem infeasible for memory-less agents and that the compression greatly reduces the size of the neural network, yielding significantly less training time.

D. Double Deep Reinforcement Learning -Neural Network

To solve the aforementioned POMDP, we use reinforcement learning, specifically double deep Q-networks (DDQNs) proposed by Van Hasselt et al. [START_REF] Van Hasselt | Deep reinforcement learning with double Q-learning[END_REF]. DDQNs approximate the Q-value of each state-action pair given as [START_REF] Zhang | A deeper look at experience replay[END_REF] describing the discounted cumulative reward of an agent following policy π. To converge to the optimal Q-value the agent explores the environment, collecting experiences (s(t), a(t), r(t), s(t + 1)) and storing them as (s, a, r, s ) in a replay memory D, omitting temporal information. Two Qnetworks parameterized through θ and θ are used, in which the first Q-network is updated by minimizing the loss

Q π (s(t), a(t)) = Eπ T k=t γ k-t R(s(k), a(k), s(k + 1)) ,
L(θ) = E s,a,s ∼D [(Q θ (s, a) -Y (s, a, s )) 2 ] ( 17
)
given by experiences in the replay memory. The target value is given by

Y (s, a, s ) = r(s, a) + γQθ(s , argmax a Q θ (s , a )). ( 18
)
The parameters of the second Q-network are updated as θ ← (1 -τ ) θ + τ θ with the soft update parameter τ ∈ (0, 1]. To address training sensitivity to the size of the replay memory we make use of combined experience replay proposed by Zhang and Sutton [START_REF] Zhang | A deeper look at experience replay[END_REF].

The neural network architecture used for both Q-networks is shown in Fig. 1. The environment map and target map are stacked and centered around the UAV position and then converted into global and local observation components. After being fed through two convolutional layers each, the resulting tensors are flattened and concatenated with the remaining flying time input and passed through three hidden layers with ReLU activation functions. The output layer with no activation function represents the Q-values directly, passed through a softmax function to create an action distribution for exploration or an argmax function for exploitation. The relevant parameter for scalability is the size of the flatten layer. It can be calculated through

N = n k l -nc s k 2 2 + Mc g -nc s k 2 2 + 1 (19)
with n k being the number of kernels, n c the number of convolutional layers, and s k being the kernel size. Setting the global map scaling parameter to g = 1 and the local map size to l = 0 deactivates global-local map processing, i.e., no downsampling and no extra local map. The parameters used in evaluation are listed in Table I.

IV. SIMULATIONS

A. Simulation Setup

The UAV is flying in two different grid worlds. The 'Manhattan32' scenario (Fig. 2a and2c) with 32 × 32 cells with two starting and landing zones in the top left and bottom right corners. Besides regular building patterns, some irregularly shaped buildings and additional NFZs are present. The 'Urban50' scenario (Fig. 2b and2d) contains 50 × 50 cells and one starting and landing area around the center building. Buildings are generally larger and spaced out, and an additional large NFZ is present on the bottom of the map. Note that the number of cells in the 'Urban50' map is roughly one magnitude larger than in the previous works [START_REF] Theile | UAV coverage path planning under varying power constraints using deep reinforcement learning[END_REF] and [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]. The cell size for the scenarios is 10m × 10m with Table II providing a legend for the plots.

1) Coverage Path Planning: For the CPP problem, the UAV is flying at a constant altitude of 25m with a camera mounted underneath that has a field of view angle of 90 • . Consequently, the UAV can cover an area of 5×5 cells simultaneously, as long as obstacles do not block line of sight. The target areas are generated by randomly sampling geometric shapes of different sizes and types and overlaying them, creating partially connected target zones. For evaluation, a traditional metric for the CPP problem is the path length. However, this metric only offers meaningful comparison when full coverage is possible. In this work, we investigate flight time constrained CPP, in which full coverage is often impossible. Therefore, the evaluation metrics used are the coverage ratio (CR), i.e. the ratio of covered target cells to the initial target cells at the end of the episode, and coverage ratio and landed (CRAL), which is zero if the UAV did not land successfully and equal to CR if it did. The benefits of the CRAL metric are that it combines the two goals, achieving high coverage and returning to the landing zone within the flight time constraint. By normalizing performance to a value in [0, 1], it enables performance comparisons over the changing scenarios with randomly generated target zones.

2) Data Harvesting: In the DH problem, the UAV is flying at a constant altitude of 10m communicating with devices on ground level. The achievable data rate is calculated based on distance, random shadow fading, and lineof-sight condition with the same communication channel parameters used in [START_REF] Bayerlein | UAV path planning for wireless data harvesting: A deep reinforcement learning approach[END_REF]. As in CPP, the path length is not an applicable metric. It is impossible to collect all data in all scenarios depending on the randomly changing locations of IoT devices, data amount, and maximum flying time. Therefore, the evaluation metric used is the collection ratio (CR), describing the ratio of collected data from all devices to the initially available data summed over all devices. Like in CPP, we also use collection ratio and landed (CRAL) in this context, showing the full data collection and landing performance in one normalized metric.

B. General Evaluation

The CPP agents were trained on target zones containing 3-8 shapes covering 20-50% of the available area. The movement range was set to 50-150 steps for the 'Manhattan32' scenario and 150-250 for the 'Urban50' scenario. For the DH scenarios, 3-10 devices are placed randomly in free cells and In the CPP scenarios, the agents in Fig. 2a and 2b show that they can find trajectories to cover most of the target area. Even the area in the NFZs is mostly covered. It can be seen that small areas that would require a detour are ignored, leading to incomplete coverage. However, most of the target area is covered efficiently.

The agents in the DH scenarios in Fig. 2c and 2d perform very well. In the 'Manhattan32' scenario, the agent leaves small amounts of data at the orange and purple devices totaling a collection ratio of 99.1%. However, the agent finds a concise path, using only 92 of the allowed 150 movement steps. In the 'Urban50' scenario, the agent manages to collect all the data and return with some movement steps in spare.

All four agents were trained for 2 million steps. When analyzing their performance in all four missions using 1000 Monte Carlo generated scenarios (see Table III), it can be seen that all agents' landing performances are good, with the 'Urban50' DH agent being slightly better.

C. Global-Local Parameter Evaluation

To establish the performance sensitivity to the new hyperparameters, global map scaling g, and local map size l, we trained multiple agents with different parameters on the CPP and DH problems. We chose four values for l and four for g and trained three agents for each possible combination. Additionally, we trained three agents without the usage of global and local map processing, which is equivalent to setting g = 1 and l = 0. The resulting 51 agents for the CPP and DH problems were trained for 500k steps each and evaluated on 200 Monte Carlo generated scenarios. The difference to the previous evaluation is that the movement budget range was set to 150 -300.

Table IV shows the selected parameters and the resulting flatten layer size according to (19). A significant speedup of the training process compared to agents without global and local map processing can be observed in Table V.

The resulting CRAL values from the Monte Carlo simulations for each agent with respect to the agent's flatten layer size are shown in Fig. 3a and 3b for the CPP and DH problem, respectively. It can be seen that the DH problem is more sensitive to the parameters than the CPP problem. Generally, a larger flatten layer yields better performance up to a point. For both problems, it can be seen that a large flatten layer can cause the learning to get unstable, resulting in a CRAL of zero for some runs. This is caused by the agent's failure to learn how to land. The DH agents, which are not using the global-local map approach, never learn how to land reliably and thus have a CRAL score near zero.

In both cases, the agents with l = 17 and g = 3 or g = 5 show the best performance with respect to their flatten layer size, justifying the selection in Table I. Besides these two parameter combinations, it is noteworthy that the agents with l = 9 and g = 7 also perform well in both scenarios, despite their small flatten layer size of only 33 neurons.

V. CONCLUSION

We have presented a method for generalizing autonomous UAV path planning over two distinctly different mission types, coverage path planning and data harvesting. Through the flexibility afforded by combining specific mission goals and navigation constraints in the reward function, we trained DDQNs with identical structures in both scenarios to make IV; the black stars correspond to agents without global-local map processing. efficient movement decisions. We have introduced a novel global-local map processing scheme that allows to feed large maps directly into convolutional layers of the DRL agent and analyzed the effects of map processing parameters on learning performance. In future work, we will investigate still existing hindrances for applying our method to even larger maps, namely avoiding small-scale decision alternation through the use of macro-actions or options [START_REF] Sutton | Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning[END_REF]. Combining the presented high-level path planning approach with a lowlevel flight dynamics controller will also make it possible to conduct experiments with realistic open-source UAV simulators in the future. Additionally, we will investigate the effect of irregularly shaped, non-convex obstacles on the path planning performance.

Fig. 1 :

 1 Fig. 1: DQN architecture with map centering and global and local mapping, showing differences of layer size in blue for the 'Manhattan32' and orange for the 'Urban50' scenario.

Fig. 2 :

 2 Fig. 2: Example trajectories from the Monte Carlo simulations for CPP (a)+(b) and DH (c)+(d) on 32 × 32 Manhattan map (a)+(c) and 50 × 50 Urban map (b)+(d).

contain 5 .

 5 0-20.0 data units. The movement range was set to 50-150 steps for the 'Manhattan32' scenario and 100-200 for the 'Urban50' scenario. Four scenarios are evaluated in detail.

Fig. 3 :

 3 Fig. 3: Parameter grid search for CPP and DH with parameters from Table IV; the black stars correspond to agents without global-local map processing.

TABLE I :

 I Hyperparameters for 32 × 32 and 50 × 50 maps.

TABLE II :

 II Legend for scenario plots, DH and CPP are only applicable in data harvesting and coverage path planning scenarios, respectively.

TABLE III :

 III Performance metrics averaged over 1000 random scenario Monte Carlo iterations.

TABLE IV :

 IV Flatten layer size for 'Manhattan32' with different global map scaling and local map sizes; Without globallocal map processing the size is 48,401 neurons.

	Global			Local map scaling l
	map	9		17		25		33
	scaling g CPP DH CPP DH CPP DH CPP DH
	2	2.7	2.2	2.3	2.0	1.8	1.6	1.3	1.1
	3	3.5	3.0	3.0	2.5	2.2	1.9	1.6	1.4
	5	4.2	3.6	3.4	3.0	2.5	2.2	1.9	1.6
	7	4.7	3.8	3.6	3.0	2.5	2.2	2.5	2.1

TABLE V :

 V Training time speedup for the CPP and DH problem relative to without global-local map processing.

Harald Bayerlein and David Gesbert are with the Communication Systems Department, EURECOM, Sophia Antipolis, France

https://www.github.com/theilem/uavSim.git

ACKNOWLEDGMENTS

Marco Caccamo was supported by an Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research. Harald Bayerlein and David Gesbert were partially supported by the French government, through the 3IA Côte d'Azur project number ANR-19-P3IA-0002, as well as by the TSN CARNOT Institute under project Robots4IoT.


Rafael Gomes Von Borowski

Sophie Chat

Rafael Schneider

Sylvie Nonin-Lecomte

Serge Bouaziz

Emmanuel Giudice

Aline Rigon Zimmer

Simone Cristina

Baggio Gnoatto

Alexandre José Macedo
email: alexandre.jose.macedo@gmail.com

Reynald Gillet
email: reynald.gillet@univ-rennes1.fr.

Rafael Gomes

Borowski

Gillet

Capsicumicine, a New Bioinspired Peptide from Red Peppers Prevents Staphylococcal Biofilm In Vitro and In Vivo via a Matrix Anti-Assembly Mechanism of Action

Keywords: antibiofilm, biofilm, matrix, peptides, anti-assembly, resistance, tolerance, Staphylococcus, antimicrobial peptides

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

include physiological and biochemical changes, beneficial quorum sensing, higher (up to 100 times) mutation rates, and persister cell development [START_REF] Davies | Understanding biofilm resistance to antibacterial agents[END_REF][START_REF] Brauner | Distinguishing between resistance, tolerance and persistence to antibiotic treatment[END_REF][START_REF] Defraine | Fighting bacterial persistence: current and emerging anti-persister strategies and therapeutics[END_REF]. Staphylococci are the most frequent sources of nosocomial infections, particularly Staphylococcus epidermidis and Staphylococcus aureus. While S. aureus expresses many virulence factors such as toxins and proteases, for S. epidermidis, the formation of biofilm is the most important mechanism in infection development [START_REF] Otto | Physical stress and bacterial colonization[END_REF]. Staphylococcus epidermidis is the most frequent coagulase-negative staphylococcal (CoNS) infection-causing disease (12), surviving on various surfaces for months [START_REF] Otto | Staphylococcal biofilms[END_REF]. It is present in 30% of health care-involved bloodstream infections and is significantly associated with medical device infections, including 15 to 40% of prosthetic valve endocarditis [START_REF] Nishizaki | Japanese features of native valve endocarditis caused by coagulase-negative staphylococci: case reports and a literature review[END_REF] and 30 to 43% of prosthetic orthopedic device infections [START_REF] Abad | Prosthetic joint infections: an update[END_REF]. More than 150 million intravascular catheters are used per year in the United States, and there are about 250,000 catheter-related infections [START_REF] Rupp | Clinical characteristics of infections in humans due to Staphylococcus epidermidis[END_REF][START_REF]Vital signs: central lineassociated blood stream infections-United States, 2001, 2008, and 2009[END_REF]. These bacteria are developing antibiotic multiresistances, such as elevated glycopeptide MICs [START_REF] Lee | Global spread of three multidrug-resistant lineages of Staphylococcus epidermidis[END_REF], and 73 to 88% of isolates display resistance to oxacillin, fluoroquinolones, macrolides, clindamycin, and trimethoprim-sulfamethoxazole [START_REF] Streit | Assessment of pathogen occurrences and resistance profiles among infected patients in the intensive care unit: report from the SENTRY Antimicrobial Surveillance Program (North America, 2001)[END_REF][START_REF] Flamm | Linezolid surveillance results for the United States (LEADER surveillance program 2014)[END_REF][START_REF] Hope | Non-susceptibility trends among staphylococci from bacteraemias in the UK and Ireland, 2001-06[END_REF].

In this circumstance, the extracellular matrix is a complex physicochemical barrier representing one of the biggest challenges in microbial treatments [START_REF] Flemming | The biofilm matrix[END_REF]. Therefore, the development of antivirulence strategies, such as antibiofilm agents, is crucial for the current antibiotic crisis, and peptides are an increasing arsenal for controlling pathogenic biofilms [START_REF] Strempel | Potential application of antimicrobial peptides in the treatment of bacterial biofilm infections[END_REF][START_REF] De La Fuente-Nunez | D-enantiomeric peptides that eradicate wild-type and multidrug-resistant biofilms and protect against lethal pseudomonas aeruginosa infections[END_REF][START_REF] El-Halfawy | Discovery of an antivirulence compound that reverses b-lactam resistance in MRSA[END_REF]. Here, we describe the discovery of the antibiofilm peptide capsicumicine, inspired by natural peptides from the seeds of the red pepper Capsicum baccatum. Our data suggest that capsicumicine is a nonantibiotic peptide displaying a matrix anti-assembly (MAA) mechanism of action by interfering with polysaccharides. Notably, we report an in vivo anti-infective proof of concept toward the use of capsicumicine for complementary treatment of infectious diseases.

RESULTS

Capsicumicine prevents biofilm formation without antibiotic activity. We synthesized the following three peptides inspired by a natural antibiofilm fraction previously identified from Capsicum baccatum var. pendulum pepper seeds [START_REF] Borowski | Red pepper peptide coatings control Staphylococcus epidermidis adhesion and biofilm formation[END_REF]: P1 (RVQSEEGEDQISQRE), P2 (RAEAFQTAQALPGLCRI), and P3 (RSCQQQIQQAQQLSSCQQYLKQ). To find the most active one, we exposed these compounds to strong biofilm-forming S. epidermidis RP62A (ATCC 35984). After 24 h, crystal violet was used to quantify the remaining biofilm (Fig. 1A). P3, named "capsicumicine," was the most active with particularly strong antibiofilm activity. Biofilm decreases were observed at all tested concentrations but especially at 10 mM. There, biofilm was reduced by over 91%, independently of cell growth inhibition (Fig. 1B). To examine the effects of capsicumicine on growth, we checked S. epidermidis CFU counts after peptide exposure. As expected, the CFU were unchanged by capsicumicine, so the peptide's biofilm inhibition is not due to bactericidal activity (Fig. 1B). To verify the interactions between capsicumicine and established biofilm, we exposed a preexisting S. epidermidis biofilm to a single concentration (100 mM) of the peptide. At that concentration, capsicumicine accounts for 15% of the disruption of preexisting biofilm (see Fig. S1 in the supplemental material). To study the peptide's possible mechanisms of action, we selected several genes involved in different stages of biofilm development (atlE, aap, agrC, icaA, leuA, saeR, saeS, and sarA); primers are listed in Table S1 in the supplemental material. Fold changes were analyzed by quantitative real-time PCR (qRT-PCR). Since exposed bacteria remain planktonic, we compared their relative gene expressions to planktonic control cells. For all tested genes, capsicumicine-exposed cells show the same fold changes as the control (Fig. 1C).

Capsicumicine is not cytotoxic in mammalian cells. To ensure that capsicumicine is safe before propose in vivo trials, we verified its cytotoxicity in seven different representative human cell lines. We used automated image-based cellular content analysis and found that capsicumicine-treated cells perform exactly the same as untreated controls, displaying no cytotoxicity (Fig. 1D).

Independently of cell interactions, capsicumicine impairs biofilm attachment, aggregation, and accumulation. To explore its activity during the first stages of biofilm development, we analyzed biofilm cultures on polystyrene coupons with and without capsicumicine after 1, 4, and 24 h. Scanning electron microscopy (SEM) analysis shows that bacterial attachment decreases after 1 h of capsicumicine exposure, with biofilm accumulation and cell aggregation profiles strongly reduced after 4 and 24 h (Fig. 2A). This demonstrates that capsicumicine prevents S. epidermidis coupon adhesion, and notably this activity still occurs after 24 h of incubation. In order to localize the peptide, we used capsicumicine conjugated to fluorescein isothiocyanate (capsicumicine-FITC) and confocal fluorescence microscopy (CFM). Analysis of the CFM images showed that capsicumicine-FITC stays associated with extracellular components, not entering into bacterial cells or the walls or membranes (Fig. 2B).

Capsicumicine disturbs S. epidermidis matrix assembly. Since capsicumicine's antibiofilm activity was not associated with direct bacterial interactions or gene expression modulation, we used various microscopic approaches to investigate the interactions between the peptide and the extracellular matrix. In the biofilm control, macroscopic observation shows a homogenous whitish adhered layer covering the walls and bottoms of the well (Fig. 2D, middle), but when capsicumicine is present, we see whitish flocculent nonadhered heterogeneous agglutinates (Fig. 2D, right). Transmitted light microscopy (TLM) images match with these macroscopic observations (Fig. 2E). Crucially, SEM and transmission electron microscopy (TEM) techniques yield ultrastructural descriptions that support these results, with the control biofilm matrix showing denser assembled globular-like structures, while the capsicumicine-exposed matrix is less dense and has thin fibrillary branch-like structures (Fig. 2F andG). However, cellular morphologies are not different from the controls. Therefore, different imaging techniques prove that matrix assembly changes when capsicumicine is present.

Capsicumicine interacts with matrix polysaccharides. To explore the peptide's affinity with these major matrix components, we exposed S. epidermidis cultures to capsicumicine (-FITC; green) and saccharide staining, and then analyzed them all by CFM. We used concanavalin A and calcofluor to selectively target matrix saccharides (blue). Amounts of capsicumicine-FITC appear exclusively on the matrix (Fig. 2B left; see also Fig. S2B in the supplemental material), and its colocalization indicates the interaction between both marked elements (Fig. 2C, right; see also Fig. S2A). Visualization was done by individual (Fig. 2C, left and center) and colocalization fluorescence (Fig. 2C, right). The peptide control was pseudonajide-FITC, an antibacterial peptide; it showed green fluorescence in the cells but not in the matrix (Fig. 2B right; see also Fig. S2C). To explore whether capsicumicine features are compatible with carbohydrate-binding modules (CBMs), we performed a BLAST and amino acid alignments between capsicumicine and chitin-, chitosan-, and polysaccharide intercellular adhesin (PIA)-binding proteins (see Table S2 in the supplemental material). UniProt tools [START_REF]UniProt: the universal protein knowledgebase[END_REF] and CAZy information crossing [START_REF] Lombard | The carbohydrate-active enzymes database (CAZy) in 2013[END_REF] showed that capsicumicine does in fact present CBM homology with all tested proteins (see Fig. S3 in the supplemental material).

Capsicumicine shifts staphylococcal synthetic matrix. To confirm these interactions in the absence of bacteria metabolic or regulatory influences, we adapted a model of artificial staphylococcal biofilm assembly to test it. For this purpose, we used chitosan in order to mimic natural poly-N-acetylglucosamine (PNAG) biofilm. Briefly, we monitored the real-time molecular self-assembly (RTMSA) reaction (based on the assembly reaction of the synthetic staphylococcal matrix; see Materials and Methods) by measuring the optical density at 600 nm (OD 600 ) as a function of time with or without capsicumicine. OD increases when capsicumicine is present, which shows that the molecular self-assembly reaction is quicker overall (Fig. 3A). The profiles of assembled matrices are visually different, with larger agglutinates in the presence of capsicumicine, although both controls are similar (Fig. 3A). Remarkably, these profiles are comparable to those previously observed in the presence of bacteria (Fig. 2D). To evidence the interactions between capsicumicine and target saccharides, we performed nuclear magnetic resonance (NMR) titration experiments using chitosan as a mimic of the matrix PNAG. The evolutions of the capsicumicine NMR spectra upon chitosan addition were monitored. Reference one-dimensional (1D) proton NMR spectra were recorded at all tested conditions. Chitosan was gradually added from a concentrated solution, and spectra changes were monitored. This solution remained clear during the 7 days of recording. The addition of soluble chitosan drives noticeable changes of the resonance frequency of some of the amide protons mainly in the N-terminal part of the peptide (S2, C3, Q4, and R1 [not shown]) (Fig. 3B). Conversely, to Y19, L20, and K21 NH and the Y19 aromatic proton resonances, between 6.8 and 7.2 are not sensitive to the presence of chitosan (Fig. 3E). The concentration variation of chitosan was estimated to be less than 0.5 eq at the end of the titration. This small quantity of chitosan drives spectral modifications in the N-terminal part of capsicumicine and strongly supports the interaction between both partners. The important broadening observed after 1 day shows that the peptide structure keeps evolving over time and that this is not due to cysteine oxidation (Fig. 4D andE). These experiments were repeated, adding gelled chitosan instead of soluble chitosan, mimicking an assembled matrix. It does not drive any significant spectral changes except for a general line broadening, which probably arises from an increased viscosity of the solution (Fig. 3C). The sudden addition of gelled chitosan pellets in the NMR solution does not modify the spectrum either (Fig. 3D).

Structural requirements toward molecular interactions with target saccharides. To better understand the mechanisms involved in capsicumicine bioactivity, we step toward the knowledge of its conformation in solution and molecular interactions with matrix representative saccharides. To monitor the conformation of free capsicumicine in response to time and temperature, we first used circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopies. Spectral deconvolution using the CDSSTR algorithm discloses a helical structure (about 43 to 44%) with a nonnegligible proportion of b strands (31 to 33%) and unfolded conformations (about 20%) (see Table S3 in the supplemental material). The structure stability was assessed over 5 days, recording the CD spectra at 5°C (Fig. 4A). The main structure observed over the first 24 h is helical. After 5 days, deconvolutions show a slight decrease in the helical content. Furthermore, on the fifth day, CD spectra were also recorded at 15 and 30°C (Fig. 4B). The helical content decreases to 18 to 23%, while the b strand and turn mean proportions rose around 37% and 23%, respectively. The proportion of unfolded structures also increased. After 2 days at room temperature, a new CD spectrum was recorded at 30°C. The helix proportion became less than 15% in favor of b strand and unfolded conformations (respectively, 38 to 41% and 31 to 35%). During these days, the peptide solution remained clear, showing no macroscopic signs of aggregation. The 1D and two-dimensional (2D) NMR spectra of a 0.3 mM freshly prepared solution of capsicumicine were recorded at 10°C, pH 5.0. All amide resonances were unambiguously assigned using total correlation spectroscopy (TOCSY) and nuclear Overhauser effect spectroscopy (NOESY) spectra, except for Q9 and Q11 residues (see Fig. S4 in the supplemental material). The spectral dispersion and the spreading of the amide proton resonances disclose that the peptide is folded with one conformation in these conditions. In accordance with CD spectra, a second set of NH resonances appears as a function of time (Fig. 4C andD), revealing conformational changes in the slow exchange regime on the NMR time scale. After 5 days, the most important chemical shift perturbations (CSP) observed on the TOCSY spectrum are clustered around the two cysteines apart from the two terminal residues (boxed cross-peaks) (Fig. 4E). Interestingly, I7 located one helix turn apart from C3 is among the most sensitive residues to the conformational perturbation. This is due to a disulfide bridge (DB), since no reducing agent was added. Consequently, large proton chemical shift perturbations would be expected for every 2NH amino acid, and this was not detected (Fig. 4D andE). The prevalence of the second conformation reaches more than 50% after 7 days (Fig. 4C, top). The solution remained clear over all of the spectra recording time.

Capsicumicine attenuates the dynamics of S. aureus (Xen36) infection on a central venous catheter. We performed a translational proof of concept evaluating long-term bacterial biofilm and related infection in mice implanted with capsicumicine precoated central venous catheter (CVC). These CVCs were previously coated using an immobilization polymer ("hydrogel") encompassing capsicumicine. This coating confers amorphous and biocompatible surfaces to CVC (Fig. 5A). They were first validated in vitro, decreasing $51% of S. aureus colonization (Fig. 5B). Then, bacterial development was evaluated in vivo by bioluminescence imaging and bacterial load of harvested CVC (Fig. 5C to F). Two days (D2) after S. aureus systemic infection, 40% of the control group (CVC-hydrogel) presented a high bioluminescence signal (red zones) related to region of interest (ROI) against none in the treated group (CVC-hydrogel 1 capsicumicine) (Fig. 5C). Four days (D4) after infection, 75% of the control presented high ROI red zones against 20% in the treated group (Fig. 5C). In addition, at D4, 1 animal was found dead in the control group. Therefore, bioluminescence quantifications show that the treated group decreased 56% (D2) and 54% (D4) of the total flux compared to that of the control (Fig. 5D). This trend was also observed on the CFU load at D4 (Fig. 5E). After image acquisitions (D4), 2 animals/group were euthanized due to ethical criteria. At the end of the experiment, 7 days after infection (D7), the treated group showed a decrease of 86% of bacterial load compared to that of the control (Fig. 5E). Macroscopic observation revealed that one animal from the control presented several organs with necrosis (liver, spleen, intestine, kidneys, and bladder). Finally, the treated group had an increased survival rate of 50% at D7 compared to that of the control (Fig. 5E).

DISCUSSION

Bioinspired peptides are increasingly being explored as alternative biofilm controls and have become important allies in the fight against bacterial tolerance and resistance [START_REF] Feuillie | Molecular interactions and inhibition of the staphylococcal biofilm-forming protein SdrC[END_REF][START_REF] Schmitt | Analysis of modular bioengineered antimicrobial lanthipeptides at nanoliter scale[END_REF]. As shown here, capsicumicine, a peptide derived from Capsicum baccatum red pepper seeds, possesses strong antibiofilm activity in vitro and in vivo. We demonstrated that capsicumicine strongly prevents biofilm formation for the most frequent bacteria related to nosocomial infections, S. epidermidis and S. aureus. However, all staphylococci do not produce a biofilm rich in polysaccharides, and considering the proposed mechanism of action of capsicumicine, this might be a limitation to our approach.

Capsicumicine prevents the establishment and maintenance of biofilm architecture through a mechanism of action that we named "matrix anti-assembly" (MAA). MAA differs from matrix disassembly [START_REF] Roy | Strategies for combating bacterial biofilms: a focus on anti-biofilm agents and their mechanisms of action[END_REF], as instead of destructuring preestablished matrices, it acts on the initial phase of assembly preventing its correct structuration. In fact, established biofilms are harder to treat than initial biofilms because they have more complex structures [START_REF] Jabbouri | Characteristics of the biofilm matrix and its role as a possible target for the detection and eradication of Staphylococcus epidermidis associated with medical implant infections[END_REF], and increased structural complexity means that more energy is required for disassembly [START_REF] Fleming | Approaches to dispersing medical biofilms[END_REF]. Bacterial surface proteins can passively interact with surfaces such as medical devices, generating an initial and reversible adhesion after electrostatic and hydrophobic interactions, Van der Waals forces, and others [START_REF] Armbruster | New insight into the early stages of biofilm formation[END_REF]. Bacteria will then require extracellular matrix production in order to remain attached after these weak interactions [START_REF] Otto | Staphylococcal infections: mechanisms of biofilm maturation and detachment as critical determinants of pathogenicity[END_REF]. During this process, physicochemical interactions drive molecular and colloidal matrix self-assembly, establishing a chain of dense architecture that results in stable adhesion (Fig. 6A) [START_REF] Stewart | Artificial biofilms establish the role of matrix interactions in staphylococcal biofilm assembly and disassembly[END_REF]. In contrast, capsicumicine might interact with matrix saccharides and modify the self-assembly chain, resulting in a less dense and nonfunctional matrix and impaired biofilm formation (Fig. 6B). The capsicumicine amino acid sequence contains several residue characteristics for recognizing putative chitin-binding domains, including polar and hydrophobic residue (45% of Q) and cysteines [START_REF] Hemmi | Structural basis for new pattern of conserved amino acid residues related to chitin-binding in the antifungal peptide from the coconut rhinoceros beetle Oryctes rhinoceros[END_REF][START_REF] Suetake | Chitin-binding proteins in invertebrates and plants comprise a common chitin-binding structural motif[END_REF][START_REF] Wright | Evolution of a family of Nacetylglucosamine binding proteins containing the disulfide-rich domain of wheat germ agglutinin[END_REF][START_REF] Yin | Identification and molecular characterization of a chitin-binding protein from the beet webworm, Loxostege sticticalis L[END_REF]. The chitin-binding domain is a well-conserved amino acid stretch that binds specifically to N-acetylglucosamine, a homologous structure of PIA [START_REF] Lombard | The carbohydrate-active enzymes database (CAZy) in 2013[END_REF][START_REF] El-Gebali | The Pfam protein families database in 2019[END_REF][START_REF] Suginta | The chitin-binding domain of a GH-18 chitinase from Vibrio harveyi is crucial for chitin-chitinase interactions[END_REF]. Additionally, noncatalytic carbohydrate-binding modules (CBMs) are contiguous amino acid sequences with a discreet fold displaying carbohydrate-binding activity. In this context, capsicumicine displays homologies with all tested CBM proteins, notably with the IcaA protein, which is a PIA synthetase from the same S. epidermidis strain studied here (see Fig. S3 and Table S2 in the supplemental material). According to the CAZY web site, there are currently 86 CBM families defined (http://www.cazy .org/Carbohydrate-Binding-Modules.html). CBMs exhibit different folds, with secondary structures ranging from mainly b-sheet-based organization to a mixture of a-helices and b-sheets, and usually display a high content of loops and unfolded parts. Likewise, the capsicumicine secondary fold is a mixture of a-helix, b-sheet, and unfolded regions, with conformational time and temperature interconversion in favor of b-sheet and unfolded segments. The b-sheet substructure observed in the CD concerns the Nter part of the peptide according to the chemical shifts of the corresponding amino acids (between 8.6 and 9.0). The TOCSY spectra analyses disclose that the conformational changes observed through time originate around the two cysteines, possibly involved in intermolecular disulfide bonds. This disulfide bond is possibly intermolecular since the formation of an intramolecular disulfide bond is only possible by bending the structure to get both cysteines close to each other. The size of different conformers and potential polymers are limited as shown by the NMR peaks of free peptides, which are not broadened at day 7 (Fig. 4C). As a result, these conformational changes may correspond to structural requirements for interactions with the staphylococcal matrix.

The proposed mechanism of action of capsicumicine matrix anti-assembly (MAA) is based on a set of intermolecular and cooperative forces that might be triggered by the peptide to disrupt matrix assembly.

Intermolecular forces. The staphylococcal matrix is composed mainly of polysaccharides but also contains proteins (AtlE, Aap, Empb), teichoic acids, and extracellular DNA [START_REF] Paharik | The staphylococcal biofilm: adhesins, regulation, and host response[END_REF]. These saccharides are PIA/PNAG, homoglycans of beta-1,6-linked 2-amino-2deoxy-D-glucopyranosyl residues. The matrix contains positively charged amino groups (PNAG) as well as negative charges from O-succinylation, which confers electrical charge lability on the matrix. Supported by NMR observations, capsicumicine interacts with free chitosan, a PNAG mimetic, and then switches to a higher-molecular-weight organization (Fig. 3B). The line broadening observed with the peptide-chitosan mixture after 1 day evidences the aggregation of chitosan particles with capsicumicine over time as schematized on Fig. 6. Likewise, CSP analysis reveals that chitosan is able to bind preferentially the capsicumicine N terminus (Fig. 4E) before intermolecular disulfide bridge formation and the relative conformational transition. This is supported by the fact that chitosan is substoichiometric at the end of the NMR titration and because the capsicumicine spectrum of the mixture is different from the spectra of the free peptide (Fig. 4C). These interactions may proceed by both hydrogen bonding between the hydrophilic Q lateral chains and hydrophobic/stacking interactions involving I and L residues. According to NMR, the N-terminal part of the peptide is probably the favored interacting region with the carbohydrate moieties. Thus, these data support the MAA model, in which the peptide supra organization would compete with matrix self-assembly preventing intra h-bonds over a large range of residues. However, the NMR results obtained with assembled chitosan, mimicking a preformed biofilm, show that capsicumicine does not interact with the preassembled matrix (Fig. 3C andD), such as demonstrated in the eradication test. Additionally, the amino acids in capsicumicine are mostly neutral, counting 13 Q and 3 S, although due to the Q amide group and S hydroxyl group, they can generate electronegative (dipole-dipole) zones. This high electronegativity suggests that capsicumicine may interact with the positive free charges of the PIA/PNAG. Thus, these polar nonionic forces perform moderate interactions with the polysaccharides. Since strong interactions, such as ionic forces, may trigger unwanted effects including matrix repulsion or sequestration [START_REF] Batoni | Antimicrobial peptides and their interaction with biofilms of medically relevant bacteria[END_REF][START_REF] Sharma | dPABBs: a novel in silico approach for predicting and designing anti-biofilm peptides[END_REF], the moderate interactions of capsicumicine seem to be ideal.

Cooperative polymerization forces. In living systems, biomolecules perform their functions in the presence of various macromolecules of different shapes and sizes, and these interactions can include cooperative polymerization forces such as depletion forces (DFs) and subsequent molecular crowding (MC) [START_REF] Banani | Biomolecular condensates: organizers of cellular biochemistry[END_REF][START_REF] Rest | Strategies to create hierarchical self-assembled structures via cooperative non-covalent interactions[END_REF]. These forces are noncovalent and nonspecific physicochemical interactions, leading to bridging, aggregation, and rheological variations [START_REF] Kudlay | Influence of the shape of crowding particles on the structural transitions in a polymer[END_REF][START_REF] Even | Recent advances in studying single bacteria and biofilm mechanics[END_REF], as is observed in the presence of capsicumicine (Fig. 2D to G and Fig. 3A), and also may contribute to MAA. In a suspension containing different molecules, DFs are the pressures exerted by small particles, which in turn cause attractive forces between the macromolecules. DFs are only expressed in crowded environments like biofilms, driving the assembly and final shape of these structures [START_REF] Marenduzzo | The depletion attraction: an underappreciated force driving cellular organization[END_REF]. Taking together NMR and CD experimental results as well as the TANGO and SALSA analysis (see Fig. S5B in the supplemental material), we can picture that capsicumicine can self-organize to form extended structures both by disulfide bridges and b-sheet-mediated fibril formation. We propose that capsicumicine-associated DFs may coagulate in polymer solutions forming fibers and parallel bundles (50), explaining the observed branch-like profile (Fig. 2D to G) (51). This higher organization would facilitate the interaction with the saccharides of the matrix preventing their pattern assembly and resulting in flocculation (52). In the same way, MC in macromolecule solutions is characterized by a decrease in accessible volume due to high macromolecule concentrations, as well as attractive and repulsive forces between them [START_REF] Banani | Biomolecular condensates: organizers of cellular biochemistry[END_REF][START_REF] Aumiller | Phase separation as a possible means of nuclear compartmentalization[END_REF]. Some molecular crowders modulate refolding kinetics and decrease competing aggregation and segregation [START_REF] Biswas | Mixed macromolecular crowding: a protein and solvent perspective[END_REF]. In this way, capsicumicine might enhance the aggregation kinetics (Fig. 3A), acting as an agent of MC, decreasing entropic forces, and leading to segregation [START_REF] Polson | Segregation of polymers under cylindrical confinement: effects of polymer topology and crowding[END_REF]. The in vitro interactions between capsicumicine and chitosan demonstrate that capsicumicine' structures are chemically able to interact with staphylococcal matrix saccharides. However, natural staphylococcal matrices are rather rich in PNAG and of course far more complex, making interactions with other matrix constituents also possible. These combined intermolecular and cooperative forces might disturb matrix self-assembly at both molecular and colloidal levels. Consequently, matrix functionality would shift and antibiofilm activity occur. Additionally, planktonic microorganisms are more available for the innate immune system to recognize and clear [START_REF] De La Fuente-Núñez | Bacterial biofilm development as a multicellular adaptation: antibiotic resistance and new therapeutic strategies[END_REF]. Furthermore, nonantibiotic activity appears less susceptible to the development of bacteria resistance because these microorganisms are under less evolutionary pressure then when exposed to conventional antibiotics [START_REF] Krachler | Targeting the bacteria-host interface: strategies in anti-adhesion therapy[END_REF][START_REF] Travier | Escherichia coli resistance to nonbiocidal antibiofilm polysaccharides is rare and mediated by multiple mutations leading to surface physicochemical modifications[END_REF].

Finally, although there are no antibiofilm drugs available yet, here, we relate the discovery of a new carbohydrate-binding peptide as a promising candidate for complementary drug/treatment of infectious diseases. In particular, we propose its antibiofilm mechanism of action, matrix anti-assembly (MAA), and validated a proof of concept for an in vivo application.

MATERIALS AND METHODS

Peptides. All peptides were synthesized by Biomatik and ProteoGenix at purity grades over 95% in salts suitable for cell culture. For the assays, the peptides were all solubilized in ultrapure sterile water.

Bacterial strains and growth conditions. Staphylococcus epidermidis ATCC 35984 was grown overnight on blood agar (Thermo Scientific Oxoid; PB5039A) at 37°C. Oxoid LB agar was used for the CFU assay. The other assays were done using a bacterial suspension of 3 Â 10 8 CFU/ml in tryptone soya broth (TSB) (Oxoid) or 0.9% NaCl.

Biofilm formation. At least three technical and biological replicates were done for each assay of 1, 10, or 100 mM peptide concentrations.

(i) Biofilm formation inhibition. A protocol adapted from Trentin et al. ( 59) was used with crystal violet (CV) in 96-well BD Falcon polyvinyl chloride (PVC) microtiter plates. The cell-bound stains were solubilized with absolute ethanol (Sigma-Aldrich), and absorbance was measured at 570 nm using a BioTek PowerWave XS plate reader. The biofilm formation control represents 100% of biofilm formation.

(ii) Biofilm eradication. Biofilm was preformed as described above for 24 h at 37°C without treatment. Afterwards, the wells were washed to remove planktonic cells, peptide solutions and controls were added, and all were incubated for 24 h. Biofilm eradication was verified by CV as previously described.

Bacterial growth assays. (i) Microtiter plates. Bacterial growth was evaluated by comparing OD 600 values at the start and end of incubation in 96-well PVC microtiter plates. After incubation at 37°C for 24 h, CFU per milliliter was calculated to determine the peptide solution's bactericidal effects. The untreated bacteria were considered as the growth control. At least three technical and biological replicates were performed for all assays.

(ii) Quantitative reverse transcription-PCR. After culturing for 24 h, RNAs were isolated from planktonic controls, biofilm controls, and from total cells exposed to 10 mM capsicumicine. An Invitrogen TRIzol Max bacterial RNA isolation kit and an Ambion TURBO DNase treatment were used as per manufacturer instructions. Total RNA concentrations and purities were assessed using a Biochrom SimpliNano spectrophotometer, and PCRs were performed to ensure the complete absence of DNA. Each qRT-PCR was then subjected to previously established quantities of cDNA (10 ng) and primers (0.2 mM). Reactional volumes and conditions were used as per the manufacturer's instructions (SYBR Select master mix; Applied Biosystems). Primers (see Table S1 in the supplemental material) were designed using the Primer3 program and then produced by Eurofins Genomics. Applied Biosystems StepOnePlus equipment and software were used. Relative transcript levels were determined by the 2 2DDct method ( 60).

(iii) Cytotoxicity assays. Cytotoxicity assays were performed on the ImPACcell robotic platform (BIOSIT, Université de Rennes 1). Multiparameter high-content screening (HCS) and high-content analysis (HCA) of chemical markers related to cell viability were done on 7 different mammalian lines as follows: HuH7 (hepatocellular carcinoma), Caco-2 (colorectal adenocarcinoma), MDA-MB231 (breast adenocarcinoma), HCT116 (colorectal carcinoma), PC3 (prostatic adenocarcinoma), NCL-H727 (lung carcinoma), and MCF7 (breast cancer). The number of viable cells is presented as residual cell percentage compared to the dimethyl sulfoxide (DMSO) control average.

Microscopic analysis. S. epidermidis ATCC 35984 biofilm was cultured as described above.

(i) Scanning electron microscopy. Sterile 10-by 4-mm polystyrene coupons were inserted into bacterial cultures in the presence or absence of capsicumicine for 1, 4, and 24 h. The coupons were then washed with sterile 0.9% NaCl and fixed with 2.5% glutaraldehyde, 2% paraformaldehyde, and 0.1 M cacodylate buffer (pH 7.2). Afterward, they were washed with 0.1 M cacodylate buffer and 0.2 M sucrose and then dehydrated with increasing concentrations of ethanol. A Leica EM CPD300 was used for critical point drying of the dehydrated samples. These were then sputtered with palladium in a Leica EM ACE200 and analyzed with a JEOL JSM-7100F microscope with energy-dispersive X-ray spectrometry (EDS) and electron backscatter diffraction (EBSD) at 10 kV.

(ii) Transmission electron microscopy. All well content was carefully detached at 1, 4, and 24 h, centrifuged at 10,000 Â g for 15 min at 4°C, and then washed with sterile 0.9% NaCl. Fixation was performed at 4°C with sodium 0.1 M cacodylate, 2% paraformaldehyde, 2.5% glutaraldehyde, and 75 mM lysine. Samples were washed with 0.1 M sodium cacodylate and 0.2 M sucrose and contrasted with 1% osmium tetroxide and 1.5% potassium ferrocyanide. Dehydration was done with a gradual solution of ethanol and infiltration of increasing concentrations of LR white resin (Delta Microscopies, France). LR white resin inclusion and polymerization were then performed over 24 h at 60°C in the absence of O 2 . Thin 80-nm sections were collected onto carbon grids and visualized at 200 kV with an FEI Tecnai Sphera microscope equipped with a Gatan 4k x 4k charge-coupled device (CCD) UltraScan camera.

(iii) Confocal fluorescence microscopy. Capsicumicine-fluorescein isothiocyanate (capsicumicine-ITC) (10 mM) was used to detect the capsicumicine peptide, whose antibiofilm activity was previously verified. After incubation for 1, 4, or 24 h, the well contents were carefully detached, centrifuged at 11,000 Â g for 2 min at 4°C, and then washed with sterile 0.9% NaCl. The suspension was visualized directly or after adding 0.1 mg/ml concanavalin A conjugates (Alexa Fluor 633; Invitrogen) or 2 mg/ml Calcofluor white dye (Fluorescent Brightener 28; Sigma-Aldrich). The pseudonajide FITC-labeled peptide is a known antimicrobial peptide that permeates the bacterial cell and was used as a permeability reference [START_REF] Schneider | Pseudonajide peptide derived from snake venom alters cell envelope integrity interfering on biofilm formation in Staphylococcus epidermidis[END_REF]. Images were acquired via resonant scanner with a Leica SP8 DMI 6000 CS confocal microscope with hybrid detector, and ImageJ software was used for image analysis.

Real-time molecular self-assembly assay. The assembly reaction of the synthetic staphylococcal matrix, starting at pH 7.2, was recorded by measuring the OD 600 as a function of time every 30 s for 30 min [START_REF] Stewart | Artificial biofilms establish the role of matrix interactions in staphylococcal biofilm assembly and disassembly[END_REF]. Molecular self-assembly reactions were calculated to a final volume of 4 ml, with 0.3% chitosan (medium molecular weight, 75 to 85% deacetylation), 0.15% bovine serum albumin, and 0.015% lambda DNA (all from Sigma) in TSB. The concentration (mM) of tested peptides was calculated for a final volume of 4 ml. Before getting the assembly reaction pH starting points, a calibration record was done using the same reactional tube containing all reagents (auto zero). Acetic acid and NaOH were used to adjust pH, and the reaction temperature was about 30°C. As a negative control, a similarly sized peptide was used, PA-1 (62). NMR. NMR spectra were recorded in 3-mm tubes on a Bruker Avance III 600-MHz spectrometer equipped with a TXI (1H,13C,15N) probe and a Z-gradient unit. Spectra were processed with TopSpin 4.0.8 (Bruker Biospin) and CcpNmr analysis [START_REF] Skinner | CcpNmr AnalysisAssign: a flexible platform for integrated NMR analysis[END_REF]. TOCSY and NOESY experiments were respectively recorded at 10°C and pH 5.0 with a 70 ms and 300 ms mixing time. Capsicumicine concentration in water was set at 0.3 mM and the pH adjusted either to 5.0 or to 3.5 with a few microliters of deuterated HCl 0.1 M and/or NaOH 0.1 M solutions. Chitosan powder (purchased from Sigma) was dissolved in pure water and the pH adjusted to either 5.0 or 3.5. Reference 1D proton NMR spectra were recorded at 10°C and either at pH 3.5 and 5.0. Chitosan was stepwise added from a concentrated solution at pH 5.0 or 3.5, and spectra changes were monitored. We first titrated capsicumicine solution with chitosan solution at pH 5.0. The concentration variation of chitosan was estimated using the peaks intensities, resulting in 3.6 and 3.8 ppm (not shown) in the beginning to less than 0.5 eq at the end of the titration. These experiments were repeated with a starting capsicumicine solution at pH 3.5 and the gradual addition of gelled chitosan at pH 3.5 and the sudden addition of gelled chitosan pellets in the NMR solution at pH 3.5.

Circular dichroism. The peptide was dissolved in 18 M Xwater at a concentration of 50 mM at pH 5.0. The UV CD spectra were recorded at 5, 15, and 30°C in a 0.1-cm path length quartz cell on a Jobin Yvon CD6 spectrometer equipped with a temperature controller unit over a 180-to 260-nm range with a 2-nm bandwidth, a step size of 1 nm, and an integration time of 2 s per point. The samples were conserved at 5°C between each recording. Spectra were averaged over 5 records. Water CD contributions were subtracted from CD spectra before processing. Spectra were processed using KaleidaGraph (Synergy Software). Molar circular dichroism (D« ) per residue and molar ellipticity per residue (u ) (MER) were computed from the difference of the delta absorbance recorded by the spectrometer. Raw delta epsilon per residue spectra were analyzed using the CDSSTR program (64) and different reference protein data sets provided by the DICHROWEB facility [START_REF] Whitmore | DICHROWEB, an online server for protein secondary structure analyses from circular dichroism spectroscopic data[END_REF]. MER data curves were smoothed for presentation, using the interpolate and weighted data (5%) routines provide by KaleidaGraph.

TANGO and SALSA predictions. Tango (66) and Salsa (67) predictions were run using the AMYpdb online web server (http://amypdb.genouest.org/e107_plugins/amypdb_project/project.php) [START_REF] Pawlicki | AMYPdb: a database dedicated to amyloid precursor proteins[END_REF]. For Tango temperature was set to 298 K. Predictions were both run with pH set either to 5.0, 7.0, or 9.0. SALSA predictions were run with a window size dynamic 512 of 4 to 20 residues, a cutoff of 1.2, and a minimal hot spot length of 5.

CVC coatings. We adapted an approach to immobilize peptides based on poly(ethylene glycol)diacrylate (PEGDA) hydrogel [START_REF] Cleophas | Convenient preparation of bactericidal hydrogels by covalent attachment of stabilized antimicrobial peptides using thiol-ene click chemistry[END_REF]. Briefly, polyurethane (PU) tubes (Instech) of 2 Fr or 25 G equivalent (0.4 to 0.7 mm of diameter) were used as coating framework. Hydrogel base was prepared using pentaerythritol tetrakis(3-mercaptopropionate) (PTMP) (4.1 mmol), PEGDA (10 mmol), PEG-600 (20 mM), 2,2-dimethoxy-2-phenylacetophenone (DMPA) (0.1 wt%), THF soluble PU (10 wt%), and methanol (qs). Peptides were solubilized in DMSO and added to a hydrogel base under vortex agitation. PU tubes were first internally coated by suction using a needle syringe and then externally by immersion. The reaction and polymerization conditions such as room temperature and oxygen tolerance were easily implemented. After polymerization, successive washes with methanol under agitation were used to eliminate undesirable monomers. All reagents were purchased from Sigma-Aldrich.

CVC infection assay. This study was conducted by Voxcan s.a.r.l. The ethical agreement is part of project APAFiS number 10756-2017072522272676 v4, approved by the Voxcan ethical committee (CEAA-129) and the French authorities (Ministry of National Education, the Higher Education and Research). This study used S. aureus rather than S. epidermidis for the in vivo experiment because of the poor bioluminescent activity of the known S. epidermidis strain. Indeed, the previously described bioluminescent S. epidermidis strain is no longer available, as its light-emission levels are not stable (see Fig. S1 in reference 70). Since the use of bioluminescence for quantifying bacterial colonization and biofilm formation significantly reduces the number of animals needed for testing, thus decreasing the need for euthanasia at each point/time in the analysis, we decided to base the study on bioluminescent S. aureus. We therefore first validated the in vitro activity of capsicumicine with the bioluminescent S. aureus (see Fig. 5b). This study used SKH1 mice, females, immunocompetent, and specific pathogen free (SPF) provided by Charles River Laboratories. Animals were acclimated at least 2 days, housed collectively in disposable standard cages in ventilated racks A3 biological safety, at 121 6 3°C, 30 to 70% humidity, and 12-h dark and light cycles, with filtered water and autoclaved standard food provided ad libitum. Catheters were blind implanted (n = 5/group) in mice jugular vein followed by an intravenous (i.v.) inoculation of S. aureus Xen36 5 days later, which was in turn to colonize the device from blood circulation. The bacterial development was evaluated and compared between the different catheters by in vivo bioluminescence imaging (IVIS Spectrum; acquisition and analysis with Living Image version 4.5.5) performed 2 and 4 days after mice infection. In addition, 7 days after inoculation or at the time of mice euthanasia for ethical reason, catheters were harvested and bacterial load was evaluated by CFU counting (SCAN500). All along the study, mice clinical state was evaluated using a scoring grid and body weight measurements 3 times a week. At each time point, a bioluminescence acquisition was also performed on the background (BKG) mouse to measure the flux level corresponding to the autobioluminescence. Images were radiance-thresholded with respect to the background radiance level and smoothed with a bidimensional Gaussian filter (3 by 3).

FIG 1

 1 FIG 1 Antibiofilm activity of bioinspired peptides and cytotoxicity. (A) Antibiofilm activity of peptides P1, P2, and P3 (capsicumicine) at 1, 10, and 100 mM. Quantification of Staphylococcus epidermidis (ATCC 35984) biofilm and growth were done at an optical density of 570 (crystal violet) and 600 nm (without crystal violet), respectively, bacteria without peptide exposure (control) and the antibiotic control, 16 mg/ml rifampin. Student's t test, *, P # 0.05; **, P # 0.01. (B) Growth of bacteria at 10 mM using CFU. (C) Gene expression (mean log fold changes 6 standard errors of the mean) of the encoding genes involved in S. epidermidis biofilm formation compared to the planktonic (gray) and biofilm controls (black) with the ssrA gene used as a reference. The group exposed to capsicumicine is shown in white. CWA, cell wall-anchored proteins. Two-way analysis of variance (ANOVA) and Tukey's multiple-comparison test, **, P # 0.0015. (D) Capsicumicine cytotoxicity evaluation in representative human cell lines shown via automated image-based cellular content analysis. HuH7, hepatocellular carcinoma; Caco-2, colorectal adenocarcinoma; MDA-MB231, breast adenocarcinoma; HCT116, colorectal carcinoma; PC3, prostatic adenocarcinoma; NCL-H727, lung carcinoma; MCF7, breast cancer. Cell counts are presented as residual cell percentages (%) compared to the average of the DMSO control, with water control also shown (gray bars). The black bars on the left show cytotoxic controls (roscovitine, doxycycline, and taxol), while the white bars are cells exposed to 10 mM capsicumicine.

FIG 2

 2 FIG 2 Biological characterization of capsicumicine by microscopic approaches. (A) SEM images of polystyrene coupons after 1, 4, or 24 h of culture with Staphylococcus epidermidis (ATCC 35984). (Top) Peptide-less biofilm control. (Bottom) Cultures exposed to 10 mM capsicumicine. Magnification Â500, with insets at Â5,000; scale bars, 10 mm. (B) CFM images. (Left) S. epidermidis after exposure to 10 mM capsicumicine-FITC with the peptide in green (fluorescence) and the bacterial cells in black (no fluorescence). (Right) Control, S. epidermidis after exposition to pseudonajide-FITC, an antibacterial peptide, this time showing the extracellular components in gray (no fluorescence). White arrows indicate extracellular content. Scale bars, 5 mm. (C) CFM images. S. epidermidis after exposure to 10 mM capsicumicine-FITC (left) and after exposure to concanavalin A conjugates (selectively reveals saccharides) (center) and their colocalization (right). Scale bars, 5 mm. (D to G) The organizational state of S. epidermidis biofilm after 24 h in the presence (right) or absence (control, left) of capsicumicine. (D) Macroscopic examination by pictures from the bottom of 24-well plates. The "sterile control" shows no bacteria or biofilm formation, the "biofilm control" shows homogenous adhered layers of bacteria, and "capsicumicine" shows nonadhered bacteria but agglutinates. Scale bar, 5 mm. (E) TLM images show the biofilm control with overlapping attached bacterial clusters, while the capsicumicine-exposed culture shows agglutinated nonadhered cells. Scale bar, 20 mm. (F) SEM images show the control with dense globular-like matrix features, while the capsicumicine-exposed culture shows fibrillary branch-like oligomer structures. Scale bar, 200 nm. (G) TEM images of the biofilm control show denser assembled structures, while the capsicumicine-exposed displays thin fibrillary oligomer structures. Scale bar, 200 nm. Arrows indicate the matrices.

FIG 3

 3 FIG 3 Molecular interactions between capsicumicine and target-saccharides. (A) RTMSA curves of artificial staphylococcal biofilm assembly. Optical densities (OD 600 ) were recorded as a function of the time in the presence of capsicumicine (blue dots) or PA1 as peptide control (orange dots) and the synthetic matrix without peptides (black dots); each reaction tub is shown under the graph. (B-D) NMR titration of capsicumicine by chitosan at 600 MHz, 10°C. (B) From the bottom to the top: increasing concentrations of soluble chitosan [,0.5 eq. and 8% of volume variation] were gradually added to capsicumicine water solution. Specific line broadenings and frequency shifts are shown in the NMR spectrum for some amide protons upon titration; white bullets for terminal R1 and Q22 and black bullets for S2, C3 and Q4. (C) Assembled chitosan gradually added to soluble capsicumicine [0.5 eq.]; an overall line broadening was observed. (D) Assembled chitosan suddenly added to soluble capsicumicine [0.5 eq.]; the orange spectrum was recorded after few minutes of stirring. (E) Superposition of capsicumicine 1D NMR and TOCSY spectra immediately after sample preparation (blue) and chitosan [10 eq.] addition (red) and; capsicumicine structure predicted by Phyre2 server; according to NMR results the aggregation nucleating regions are shown as orange triangles and favored interacting region with sugar moieties is shown in gray.

FIG 4

 4 FIG 4 Capsicumicine conformational changes. CD spectra. (A) Evolution at 5°C over 5 days. (B) Evolution versus time and temperature. (C) NMR spectra recorded at pH 5.0, 0.3 mM, and 10°C immediately after extemporaneous preparation (bottom); a second conformation appears over the time corresponding to the second set of amide resonances (stars). (D) Superposition of capsicumicine TOCSY spectra immediately after sample preparation (red) and at day 5 (brown). The most important chemical shift perturbations (CSP) between the original and the new conformations are boxed and clustered around the two cysteines. (E) Proton chemical shift perturbations computed from the TOSCY spectra are displayed in the box.

FIG 5

 5 FIG 5 Assessment of capsicumicine precoated central venous catheter (CVC) in mice (SKH1) infected by Staphylococcus aureus Xen36. (A and B) In vitro CVC development. (A) From the top to the bottom, a picture of up to half capsicumicine precoated CVC (whitish) (scale bar, 1 cm), SEM image of precoated CVC (scale bar, 0.5 mm and its magnification scale bar, 1 mm), and a compilation of images of fluorescence microscopy of precoated CVC (capsicumicine-FITC in green). (B) Bacterial load on CVC. (C to F) In vivo CVC assessment. (C) IVIS images (radiance thresholded and smoothed with a bidimensional Gaussian filter) of ventral position for each mouse at each time point. The control group (CVC-hydrogel) is shown in the top and the treated (CVC-hydrogel 1 capsicumicine) in the bottom. White circles delimitate the CVC localization, and the luminescence scale is presented in radiance. (D) Bioluminescence quantification (ROI mean signals 6 standard deviation [SD]) is shown as photons total flux (p/s) for each group at each time point. (E) Bacterial load (CFU/ml) on harvested CVC for each mouse at each time point (day 4 and 7 after infection). (F) The Kaplan-Meier survival curve (% of survival) at each group during the 7 days after infection. All bioluminescence data and analyses were performed using IVIS Spectrum of Perkin Elmer. All experiments were conducted in accordance with ethical committee and the French authorities (n = 5/group). Bioluminescence images are presented with a defined pseudocolor scale to visualize the intensity of signals emitted.

FIG 6

 6 FIG 6 The matrix anti-assembly (MAA) antibiofilm mechanism of action identified in capsicumicine. (A) Untreated biofilm development on abiotic surfaces: planktonic cells interact passively with the surface and start the extracellular matrix production. Initial adhesion is established, after which matrix self-assembly occurs, leading to irreversible biofilm structuring and adhesion. (B) Biofilm development in the presence of capsicumicine shows the MAA mechanism of action: planktonic cells are still able to interact passively with the surface. At that point, capsicumicine acts, attracting extracellular saccharides and shifting the matrix self-assembly. (C) Capsicumicine (red) tends to organize itself in beta leaves favoring the interactions with matrix target-saccharides (yellow). These interactions occur between the noncatalytic carbohydrate-binding modules of the peptide and the saccharides. This peptide supra organization can compete with matrix assembly, preventing/destroying intramatrix H-bonds over a large range of residues. Consequently, the MAA decreases adhesion and aggregation, forcing bacteria to remain planktonic. The arrows indicate reversible (white background) and irreversible stages (black background) during biofilm formation.

  Gomes Von Borowski et al.

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ACKNOWLEDGMENTS

We thank Daniel Thomas for his support and Juliana Berland for insightful comments on the manuscript.

We thank the Microscopy Rennes Imaging Center (MRic) and CMEBA facilities. This study was funded by the CAPES-COFECUB program, whose institutional partners are the Brazilian Ministry of Education's CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) agency and the French Ministère de l'Europe et des Affaires Etrangères (MEAE) and Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation (MESRI). Support was also received from the Transfer Acceleration Company SATT Ouest-Valorisation. R.G.V.B., S.C.B.G., A.J.M., S.N-L., and R.G. designed the research; R.G.V.B., S.C., R.S., S.N-L., and S.B. performed research; R.G.V.B., A.R.Z., E.G., S.C.B.G., A.J.M., S.N-L., S.B., and R.G. analyzed data; and R.G.V.B., A.J.M., S.N-L., and R.G. wrote the paper. R.G.V.B., S.C.B.G., A.R.Z., A.J.M., and R.G. are co-authors of a patent register of capsicumicine. Application number WO 2020/169709 A1 at European Patent Office. Specific aspects of this article are covered in the patent application (its amino acid sequence and bioactivity).

Data availability. All data needed to evaluate the conclusions in this article are present in the paper and/or the Supplemental Material. Additional data related to this paper may be requested from the authors.

SUPPLEMENTAL MATERIAL

Supplemental material is available online only. SUPPLEMENTAL FILE 1, PDF file, 2.5 MB.