Edouard Guégain
email: edouard.guegain@univ-lille.fr

Amir Taherkordi
email: amirhost@ifi.uio.no

Clément Quinton
email: clement.quinton@univ-lille.fr

Configuration Optimization with Limited Functional Impact

Keywords: Software, Variability, Optimization, Performance

Dealing with a large configuration space is a complex task for developers, especially when configurations must comply with both functional constraints and non-functional goals. In this paper, we introduce an approach to optimize any set of performance indicators for an existing configuration, while meeting functional requirements. The efficiency of this approach is assessed by exhaustively optimizing a configurable system, and by analyzing how the algorithm navigates through the configuration space. This approach proves especially efficient at optimizing configurations through a minimal number of changes, thus limiting the impact on their functional behavior.

Introduction

Modern software-intensive systems are highly configurable. Software engineers thus have to develop, test and maintain a significant number of options, or features, that are then combined together to produce a specific software configuration. As the number of features grows, the number of configurations (i.e., the configuration space) consequently grows exponentially and modern systems face the ever-increasing complexity of their configurations [START_REF] Xu | Hey, you have given me too many knobs!: Understanding and dealing with over-designed configuration in system software[END_REF]. Dealing with large configuration spaces is challenging, especially when configurations must comply with both functional constraints and non-functional performance goals. To avoid facing this complexity, developers may stick to default configurations or sub-optimal ones [START_REF] Pereira | Learning software configuration spaces: A systematic literature review[END_REF].

Based on this observation and inspired by our previous work on measuring the energy consumption of configurable systems [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF], we propose in this paper an approach that optimizes a configuration regarding multiple performance objectives. Contrarily to prior work that samples or predicts performance models seeking for the best configuration of the whole configuration space [START_REF] Guo | Variability-aware performance prediction: A statistical learning approach[END_REF][START_REF] Kaltenecker | The interplay of sampling and machine learning for software performance prediction[END_REF][START_REF] Metzger | Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration[END_REF][START_REF] Nair | Finding faster configurations using flash[END_REF][START_REF] Švogor | An extensible framework for software configuration optimization on heterogeneous computing systems: Time and energy case study[END_REF], our approach optimizes existing configurations by maximizing performance gains while minimizing changes to such configurations. The objective is to provide the developer with the best-performing configuration by altering as little as possible the initial one, in order to remain as close as possible to the developer's functional requirements. Our contribution is threefold. First, we propose ICO, a novel optimization approach for configurable systems that addresses the aforementioned objective. Second, we release an up-and-running Java-based implementation of the approach. Third, we provide an in-depth analysis of the behavior of our approach and assess its efficiency on a real-world system.

In the remainder of this paper, Section 2 explains fundamentals and a running example. Section 3 explains our optimization approach. Section 4 and Section 5 present the design and results of our experiments, respectively. Section 6 provides a critical discussion. Section 7 discusses related work and Section 8 concludes the paper.

Motivation and Running Example

Feature models are commonly used to define the configuration space of highlyvariable software systems. A feature model is a tree or a directed acyclic graph of features [START_REF] Metzger | Software product line engineering and variability management: Achievements and challenges[END_REF], organized hierarchically in parent / sub-feature(s) relationships. Features can be mandatory, optional, or alternative and the selection of a feature may require or exclude the selection of other features. While most of these relationships can be encoded in a feature tree, require and exclude relationships are usually defined using cross-tree constraints. Therefore, the feature model describes the configuration space of a software system encoded both as a feature tree and a set of cross-tree constraints. It thus defines, in an implicit yet compact way, the set of possible configurations for that software. GPL-FH is a testbed, used in particular to evaluate different implementations and algorithms that can be executed on a graph. The graph under test is generated at runtime through the TestProg feature. GPL-FH exhibits 156 configurations for 37 features and 14 constraints. These features represent different characteristics of the generated graph, such as Weighted or Unweighted, and cross-tree constraints define what algorithm can be run depending on the implementation of the graph, e.g., MSTKruskal can only be run with a WithEdges implementation.

When running a configuration, a few questions arise regarding its performance, such as: Are there better ( e.g., faster regarding GPL-FH) configurations? If yes, is there one that is close enough so it still complies with the user's requirements? What would be the gain of running this configuration? How to make sure changing feature(s) will not result in a worst configuration? These questions arise for several reasons. In particular, the large number of configurations makes picking the best configuration on the first try almost impossible, unless having the proper background knowledge of the configuration space. Developers usually do not have this background knowledge and only consider less than 20% of the available configurations [START_REF] Xu | Hey, you have given me too many knobs!: Understanding and dealing with over-designed configuration in system software[END_REF]. Another reason is the use of the default configuration or a legacy one, e.g., to make sure functional requirements are met. Running such a configuration does not guarantee running the optimal one; On the contrary, it may result in running worst or incorrect configurations [START_REF] Pereira | Learning software configuration spaces: A systematic literature review[END_REF][START_REF] Nair | Finding faster configurations using flash[END_REF].

In both cases, it is necessary to explore the configuration space to seek configurations providing better performance. Yet, the size of the configuration space increases exponentially with the number of functionalities, making this exploration impractical manually. There is thus a need for an approach that optimizes the performance of an existing configuration while minimizing the impact on functional requirements for such a configuration.

ICO: Iterative Configuration Optimization Approach

To address these challenges, we propose the Iterative Configuration Optimization (ICO) approach. The core idea is as follows: From an initial configuration, ICO explores the remaining configuration space in search of configurations that (i) are neighbors of the initial configuration, (ii) comply with the user's functional requirements (i.e., features that have to be selected or excluded) and (iii) optimize given performance indicators. It then provides optimization suggestions to the developer. ICO is inspired by the energy consumption optimization approach presented in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF], but the approach in this paper differs from the one in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF] in several aspects. In particular, the approach proposed in this paper addresses the limitations listed in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF]. That is, we propose an approach that is feature model agnostic and supports multi-objective optimization, in contrast to an optimization method that was tightly coupled to the feature model under test and dedicated to only one performance indicator, the energy consumption. In addition, ICO supports cross-tree constraints in its optimization process, while the approach presented in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF] only focused on switching selected features based on the feature tree structure.

Optimizing Configurations

To perform the optimization process, ICO relies on the performance of each feature regarding all the considered metrics. That is, as shown by Equation 1, the overall performance P of a feature f with respect to n metrics is the sum, for each metric, of p if the normalized performance of the feature regarding this metric, multiplied by w i the weight associated to this metric and by d i the objective optimization for this metric, i.e., 1 or -1, respectively to maximize or minimize.

P f = n i=1 d i w i p if (1)
As interactions between features impact performance [START_REF] Siegmund | Predicting performance via automated feature-interaction detection[END_REF], ICO is able to optimize configurations w.r.t tuples of features of any size, in which case f defines a tuple of features instead of a single one. The performance of a configuration is then computed as the average performance of features -or tuples of features in interaction-wise optimization -contained in this configuration.

The ICO approach is realized by Algorithm 1, which takes the set of features, the list of constraints and the initial configuration as input to compute a set of improvement suggestions. The algorithm starts by creating a set of candidate configurations for the configuration to optimize (lines [START_REF] Hierons | Sip: Optimal product selection from feature models using many-objective evolutionary optimization[END_REF][START_REF] Horcas | Context-aware energy-efficient applications for cyber-physical systems[END_REF][START_REF] Kaltenecker | The interplay of sampling and machine learning for software performance prediction[END_REF][START_REF] Metzger | Software product line engineering and variability management: Achievements and challenges[END_REF][START_REF] Metzger | Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration[END_REF][START_REF] Nair | Finding faster configurations using flash[END_REF][START_REF] Olaechea | Modelling and multiobjective optimization of quality attributes in variability-rich software[END_REF][START_REF] Pereira | Learning software configuration spaces: A systematic literature review[END_REF][START_REF] Pereira | A featurebased personalized recommender system for product-line configuration[END_REF]

if isValid(c, constraints) ∧ perf(c) > perf(conf init ) then 17 suggestions ← suggestions ∪ diff(c, conf init ) 18 end
19 end 20 return suggestions configurations are the set of configurations that are one change away from the initial configuration, i.e., neighbor configurations, since they differ by the selection/deselection of one feature. For instance, a GPL-FH configuration for a Weighted graph is a neighbor of the same configuration where Unweighted graph is selected since both features are mutually exclusive. In a general way, each unselected feature leads to a candidate configuration where this feature is selected (lines 5-7), each selected feature leads to a candidate configuration where this feature is unselected (lines 8-10), and each exclusive relationship of both a selected and unselected features leads to a candidate configuration where the selected feature is deselected and the unselected one is selected (lines 11-13). Candidate configurations are then ordered by performance gain (line 14), and finally filtered regarding their validity and performance (line 16), to ensure that the returned suggestions (i) cannot turn a valid configuration into an invalid one and (ii) can only improve the performance of the configuration, according to the performance model 3 .

For each candidate configuration, the algorithm then computes the difference between this candidate configuration and the initial one (line 17). This difference takes the form of a feature to add or a feature to remove -or both, and its estimated performance gain. As a result, the algorithm provides a set of improvement suggestions, ordered by potential performance gains. For instance, a possible suggestion for a GPL-FH configuration is to replace the Undirected feature by the Directed feature which offers better performances, while other features remain unchanged.

The approach can thus be entirely automated by applying, while new suggestions are provided, the one providing the highest performance gain. ICO also offers an interactive mode, where developers select the suggestion to apply according to their functional requirements and domain knowledge.

Implementation

ICO has been implemented as a series of tools, namely the ICO tool suite. This tool suite has been built with the objectives of (i) providing developers with feedback about the performances of a given configuration and (ii) providing suggestions to optimize its performance by adding or removing a feature. ICO is composed of three software components: (1) ICOlib, a Java implementation of the proposed approach; (2) ICOcli, a command-line interface; and (3) ICOplugin, an Eclipse plugin. The ICO tool suite takes as input a configuration, a feature model and performance files, and then returns optimized configurations based on the suggestions provided by Algorithm 1. Figure 2 The tool suite is centered on ICOlib, a Java library that exposes the API managing all operations that can be performed with ICO: loading a project, displaying current performances, managing constraints (i.e., the lists of features required or excluded by the developer), listing or applying improvement suggestions and saving the new configurations. In particular, ICOlib delegates to the FeatureIDE [START_REF] Thüm | Featureide: An extensible framework for feature-oriented software development[END_REF] library the responsibility to load, update, validate and save the configurations. Taken as a standalone component, ICOlib can be integrated as a Java dependency into any tool requiring an implementation of Algorithm 1.

ICOplugin is an Eclipse Plugin developed to interact with ICOlib and implemented as an Eclipse view. It thus provides a GUI that assists developers when seeking optimized configurations, in particular by proposing visual feedback on suggested optimizations. ICOcli is a command-line interface to interact with ICOlib, enabling an in-depth exploration of the variability of the software and its performances. It can be used directly by the developer or integrated into automated processes such as CI/CD. The source code of ICO is publicly available 4 , and [START_REF] Guégain | The ICO Tool Suite: Optimizing Highly Configurable Systems[END_REF] covers the specifics of its implementation.

Experimental Methodology

Our goal is to assess the validity and effectiveness of our approach. In particular, we aim to answer the following research questions: RQ 1: Can any configuration be optimized? Considering a configuration space, we investigate whether or not any configuration from that space can be optimized using our approach.

RQ 2: How effective is the ICO optimization approach? When the ICO approach provides a better configuration, we measure the performance discrepancy between that configuration and the initial one.

RQ 3: How many iterations does it take to optimize a configuration? We evaluate the number of iterations of ICO required to converge from an initial configuration to its respective optimal one. We evaluate our approach on the real-world configurable system GPL-FH presented in Section 2. This system was selected for several reasons. First, both its source code and feature model are publicly available, and they seamlessly integrate as GPL-FH can be run from the command line. Second, its feature model (presented in Figure 1) exhibits 156 configurations, thus providing a largeenough configuration space for the optimization process to be significant.

The experiments consist in optimizing all 156 configurations regarding a pair of performance indicators, namely the execution time (time) and the number of lines of code (LoC). This exhaustive optimization highlights how the approach navigates through the configuration space. To not interfere with the time measurements, the logging functionality that comes as a default option of the GPL-FH system was disabled, as it might misrepresent the actual execution time. The GPL-FH default number of vertices was changed from 10 to 3500 to yield a larger graph and be able to properly measure the time, thus getting meaningful readings. The building time of the graph itself is excluded from the time measurement, since constant across configurations. In order to consolidate the measure of the time of each configuration, the experiment was repeated 20 times. Beyond that point, the average execution time converges.

The performance of each feature w.r.t LoC and time is computed according to the method proposed in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF], i.e., the performance of a feature w.r.t a metric is the average performance in this metric of configurations containing this feature. The global performance of each feature (i.e., the performance taking all metrics into consideration) is then calculated using Formula 1. Both metrics were given the same weight, while the optimization goal was set to a minimization of both performance indicators. The optimization algorithm has then been applied on each of the 156 configurations of GPL-FH: for each initial configuration, it seeks for a better neighbor configuration that minimizes LoC and time. All measurements were performed on a machine with an Intel Core i5 CPU at 2.9GHz and 8GB of RAM.

Results

The configuration space of GPL-FH has been exhaustively measured, providing insight into the performance of each of the 156 configurations w.r.t LoC and time. Figure 3 presents such performances. The best and worst time are respectively 0.09 and 23.4 seconds, while LoC ranges from 282 to 632. The optimization of a configuration should thus provide higher variations in time than in LoC, as the ratio between the worst and best readings for time (260) is orders of magnitude higher than the one for LoC (2.2).

Investigating RQ1: Can any configuration be optimized? Applying the best suggestion (if any) provided by Algorithm 1 to a given configuration results in either one of the following situations: (S 1 ) the configuration improved regarding both performance indicators; (S 2 ) the configuration improved regarding one performance indicator and worsened regarding the other; (S 3 ) the configuration did not improve nor worsen, i.e., ICO returned no suggestion; (S 4 ) the configuration worsened on both indicators 5 . Table 1 summarizes the performance gains resulting from applying Algorithm 1 on the GPL-FH configuration space regarding the four situations discussed above. Out of the 156 configurations, 138 were modified while 18 remained unchanged. Among the 138 modified configurations, 110 were improved regarding both performance indicators, and 16 regarding only one. As a matter of fact, all these 16 single-indicator optimizations relate to an improvement of LoC at the expense of time. The remaining 12 configurations worsened on both performance indicators.

RQ 1: These results show the efficiency of ICO: only 8% of the configuration space could not be improved by our approach. 12% remained unchanged as there was no way to further optimize them, and 80% were successfully optimized. Investigating RQ2: How effective is the ICO optimization approach? Figure 4 shows the performance gains when running ICO on the GPL-FH configuration space. As anticipated above, variations in time were more significant than the Loc-related ones, i.e., ranging from +96,6% to -133,6% regarding time and from +26,5% to -17,7% regarding Loc. The 12 configurations discussed in Table 1 worsen both performance indicators (situation S 4 ) thus represent a negative gain and as depicted below the horizontal axis and the left side of the vertical axis. The figure highlights that the performance loss on such features is very limited when compared to the performance gains in other situations. RQ 2: ICO provides efficient optimizations, especially for poorly performing configurations, but can sometimes worsen configurations' performance. Nevertheless, although worsened, these configurations remain in the top-tier performance ranking.

Investigating RQ3: How many iterations does it take to optimize a configuration? Since an initial configuration cannot be turned into an invalid one by Algorithm 1 (see line 16), running the algorithm on each configuration of the configuration space thus results in a set of optimized configurations which are a subset of the initial configurations. These optimized configurations cannot be further optimized, as they have no neighbor configuration with better performances. Based on this inclusion, it is then possible to build a directed graph representing all successive iterations of the algorithm.

Figure 5 depicts such a graph for the GPL-FH case study, where each node represents a configuration. For the sake of readability, nodes are placed on a relative logarithmic scale representing their related configuration's time and LoC, respectively on the vertical axis and on the horizontal axis. Each edge represents the application of the first suggestion returned by Algorithm 1: the initial configuration is the source node for that edge, while the optimized configuration resulting from applying this first suggestion is the target node. Thus, an edge represents the removal of a feature, the addition of a feature, or the substitution of a feature by another one. This graph is composed of 18 disconnected sub-graphs. Each sub-graph converges towards one of the 18 configurations that could not be optimized and remained unchanged (see Table 1, situation S 3 ). These 18 configurations are thus local optima, and one of them is the global optimum. Table 2: Applying ICO on the GPL-FH configuration space.

Table 2 shows the number of iterations of Algorithm 1 required by all configurations to converge towards their related optimized configuration. As explained before, 18 configurations remain unchanged and therefore do not need any iteration of the ICO algorithm to reach their convergence point. Regarding the 138 other configurations, a single iteration drives 61 of them (44.2%) toward their Fig. 5: ICO transition graph between configurations of GPL-FH. Configurations on a relative logarithmic scale for readability, time on the vertical axis, LoC on the horizontal axis, lower left is better. convergence point. That is, after one iteration, 79 configurations (more than half the configuration space) have already converged. After a second iteration, 81% of configurations have reached their convergence point. Up to five iterations are required to optimize the whole set of configurations, but the last two iterations only apply to 3.8% of the configurations.

RQ 3:

The number of iterations required by ICO to optimize a configuration is very limited, as (i) half of configurations are optimized after a single iteration and (ii) the number of configurations yet to be optimized decreases dramatically after each iteration. In this experiment, only 1 configuration required the maximum number of five iterations to be optimized.

Discussion

A thorough analysis of the GPL-FH optimization graph depicted in Figure 5 provides additional findings regarding the ICO approach. The graph is composed of four clusters of nodes: (C 1 ) the green nodes at the bottom left; (C 2 ) the black nodes in the center; (C 3 ) the "horizontal" cluster of blue nodes in the upper part above cluster (C 2 ); and (C 4 ) the red nodes in the upper right corner. In particular, we observe that all the configurations located in C 4 move to C 2 once optimized. Such configurations thus share the same optimization. In particular, they are optimized by removing the MSTPrim feature, which is characterized by both the worst LoC and time performance.

Feature Model Design. One can also notice that no optimization edge enters or leaves clusters C 1 and C 3 . These two clusters are characterized by the presence of features that are mutually dependent such as Directed, WithEdges and DirectedWithEdges (a sub-feature of the collapsed HiddenGtp feature), which are tightly-coupled by the Directed ∧ WithEdge ↔ DirectedWithEdge cross-tree constraint. In addition, C 3 contains configurations whose couple of features StronglyConnected and Transpose are selected, complying with the StronglyConnected ↔ Transpose cross-tree constraint, thus preventing the removal or addition of these features. The best-performing configuration from C 3 is actually similar to a configuration from C 1 , with the addition of Transpose and StronglyConnected features that, as explained before, must be removed together. It is thus impossible to enter or leave these clusters without changing two or three features at once, or without turning the configuration into an invalid transitional state, which is not supported by the ICO approach. Configurations from these clusters can only be optimized by changing other features, and configurations from C 2 and C 4 cannot be optimized towards C 1 or C 3 . Feature relationships and cross-tree constraints also explain why the optimization process converges towards 18 different configurations: these configurations only contain mutually-dependent features and cannot be further improved while remaining valid. The shape of the feature model and related cross-tree constraints can thus hinder the capacity of ICO to optimize the entire configuration space.

Performance Model. To perform its optimization process, ICO relies on a performance model. This model provides an estimated performance for each feature, measured based on the method proposed in [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF]. As the performance model is estimated, it may contain measurement inaccuracies which in turn may impact the efficiency of the approach. For instance, we observed that, while optimizing GPL-FH, the performance of twelve configurations worsened after an iteration. When analyzing the initial and "optimized" configurations, we found out that the twelve performance regressions were caused by the addition of either the feature Number or Cycle. Both of these features happen to be present in all the configurations from C 1 , the cluster of best-performing configurations. However, such good performances are actually not related to Number or Cycle alone, but to the presence of other features in combination. The performance model seems thus biased toward Number and Cycle, which causes inaccuracies during the execution of ICO.

Validity Threats. To assess our approach, we ran our experiments on a specific configurable system (GPL-FH) and optimized it based on specific metrics, i.e., minimizing the execution time and the number of lines of code of configurations from this system. Results such as the performance gains or the number of iterations are thus only related to this single system, and cannot be generalized. Nonetheless, our contribution can be easily applied to any configurable system as long as a feature model is provided. The optimization gains resulting from applying our approach to other configurable systems will depend on the initial performance of each configuration for such systems. We leave the evaluation of our approach across a larger set of domains to a future study.

We ran the ICO optimization algorithm on the whole configuration space of GPL-FH.While relying on an exhaustive performance model was convenient, we acknowledge that this may not be practical for any case study, in particular regarding performance models of software systems exhibiting larger configuration spaces. Yet, it is still possible to use our approach by sampling or predicting performance models for such larger spaces, using approaches such as [2,10,1].

Related Work

The management of highly configurable systems has been widely studied in the last decade. Research has mainly addressed one of the following three areas: (i) performance prediction, intending to estimate the performance of a configuration without actually measuring it, (ii) performance optimization, to generate optimal configurations of a system, and (iii) recommender systems, to assist developers during the feature selection process.

Performance prediction. Performance prediction approaches have been proposed by many researchers [START_REF] Kaltenecker | The interplay of sampling and machine learning for software performance prediction[END_REF][START_REF] Guo | Variability-aware performance prediction: A statistical learning approach[END_REF][START_REF] Siegmund | Performance-influence models for highly configurable systems[END_REF]. The aim of such approaches is to build an estimated performance model of the system's features. These approaches rely on machine learning techniques to infer performance data from a sample of configurations. One of their main objectives is to detect feature interactions -as they can have significant impact on performances, and provide more accurate prediction than approaches that do not consider such interactions. Relying on such performance models, Siegmund et al. [START_REF] Siegmund | Predicting performance via automated feature-interaction detection[END_REF][START_REF] Siegmund | Scalable prediction of non-functional properties in software product lines: Footprint and memory consumption[END_REF] predict the performance of configurations as the sum of the impact of each feature on performances. Such approaches are complementary to the current performance model of ICO, which originates from [START_REF] Guégain | On reducing the energy consumption of software product lines[END_REF], and can extend its current implementation.

Performance optimization. Many approaches have been proposed to address performance optimization for configurable systems. Such approaches strive to locate optimal or near-optimal configurations w.r.t some performance indicators. Several studies provide deterministic approaches [START_REF] Švogor | An extensible framework for software configuration optimization on heterogeneous computing systems: Time and energy case study[END_REF][START_REF] Olaechea | Modelling and multiobjective optimization of quality attributes in variability-rich software[END_REF][START_REF] Nair | Finding faster configurations using flash[END_REF] to tackle this challenge. Other authors like Hierons et al. rely on genetic algorithms to minimize the number of measurements needed to optimize configurations [START_REF] Hierons | Sip: Optimal product selection from feature models using many-objective evolutionary optimization[END_REF], or leverage the performance predictions methods discussed above [START_REF] Siegmund | Spl conqueror: Toward optimization of non-functional properties in software product lines[END_REF]. Such approaches do not take an initial configuration into consideration. For instance, Nair et al. [START_REF] Nair | Finding faster configurations using flash[END_REF] start their optimization process from a random configuration. In contrast to such approaches, the approach proposed in this paper aims at optimizing a set of performance indicators while remaining as close as possible to the initial user-defined configuration. This optimization goal is shared with the approach of Soltani et al., which takes user's preferences into consideration to optimize a configuration, but yet does not support the optimization of pre-existing configurations [START_REF] Soltani | Automated planning for feature model configuration based on functional and non-functional requirements[END_REF].

Recommender systems. In recent years, we observed an increased interest in studies applying tools and approaches to assist users during the configuration of their systems. Such systems provide recommendations to the users based on their functional needs. For instance, Pereira et al. propose a visual recommender system [START_REF] Pereira | A featurebased personalized recommender system for product-line configuration[END_REF] based on proximity and similarity between features. Similarly, Zhang et al. [START_REF] Zhang | Which configuration option should i change?[END_REF] use dynamic profiling and analyze the stack trace of the system to locate features that can be changed without altering the functional behavior of the system. Other approaches, such as [START_REF] Metzger | Realizing self-adaptive systems via online reinforcement learning and feature-model-guided exploration[END_REF] or [START_REF] Horcas | Context-aware energy-efficient applications for cyber-physical systems[END_REF], aim at updating the configuration of the system while it is running, in order to adapt it to the evolution of its environment. To the best of our knowledge, no recommender system provides suggestions based on both functional and performance considerations.

Conclusion

This paper introduced ICO, an iterative approach to optimize configurations regarding defined performance indicators. Considering an initial configuration to optimize, ICO estimates the performance of neighbor configurations, i.e., configurations that distinguish from the initial one by a single feature (de)selection change. ICO provides performance improvement suggestions to drive the optimization process, which can be run either in fully automated mode or based on the developer's inputs. We evaluated our approach on a real-world example by running ICO on its entire configuration space, and our experiments showed that ICO significantly improved 80% of the configurations.

As future work, we plan to improve the accuracy of performance suggestions provided by ICO by relying on more accurate performance models and performance prediction techniques. We also plan to extend the capabilities of ICO supporting multiple feature changes at once, e.g., in highly heterogeneous systems such as Edge and Fog computing systems. In such systems, the wide range of possibilities (e.g., component-level resource allocation and computation models) results in a huge configuration space that is not manually manageable.

Fig. 1 :

 1 Fig. 1: Excerpt of the feature model of GPL-FH-Java.

Figure 1

 1 Figure1presents an excerpt of the GPL-FH feature model (some features, like HiddenWgt, are collapsed and only two constraints are shown). GPL-FH is a testbed, used in particular to evaluate different implementations and algorithms that can be executed on a graph. The graph under test is generated at runtime through the TestProg feature. GPL-FH exhibits 156 configurations for 37 features and 14 constraints. These features represent different characteristics

Fig. 2 :

 2 Fig. 2: The architecture of the ICO tool suite.

Fig. 3 :

 3 Fig. 3: Performance of each GPL-FH configuration w.r.t LoC and time (lower left corner is better).

Fig. 4 :

 4 Fig. 4: Performance gains for each GPL-FH configuration w.r.t LoC and time (top right corner is better).

  (f eatures\conf init )\constraints exclude 4 removable ← conf init \constraints include 5 for rem ∈ removable do

	. Candidate
	Algorithm 1: ICO optimization algorithm
	Input: features, constraints, conf init ;
	Output: suggestions
	1 candidates ← ∅
	2 suggestions ← ∅
	3 addable ←

6 candidates ← candidates ∪ newConfig(conf init \rem) 7 end 8 for add ∈ addable do 9 candidates ← candidates ∪ newConfig(add ∪ conf init ) 10 end 11 for add ∈ addable, rem ∈ removable do 12 candidates ← candidates ∪ newConfig(addable ∪ conf init \removable) 13 end 14 candidates ← sortByPerfGain(candidates) 15 for c ∈ candidates do 16

Table 1 :

 1 The effect of ICO on the GPL-FH configuration space.

The computation of the performance model is out of the scope of this paper. Yet, we discuss this particular point in Section 6.

https://gitlab.inria.fr/ico

Due to inaccuracies in the performance model. See Section

for further analysis.

Acknowledgements

The research leading to these results received funding from French Research Agency through the ANR-19-CE25-0003-01 KOALA project and from the Norwegian Research Council through the DILUTE project (Grant No. 262854/F20).


Nessrine Bellamri

Marie Lelong

Audrey Joannes

Erwan Le Tallec

Stéphane Jouneau

Laurent Vernhet

Alain Lescoat

Valérie Lecureur
email: valerie.lecureur@univ-rennes1.fr

Effects of Ruxolitinib on fibrosis in preclinical models of systemic sclerosis

Keywords: systemic sclerosis, ruxolitinib, lung fibroblast, fibrosis, macrophage, interferon

Systemic sclerosis (SSc) is an autoimmune fibrotic disorder notably characterized by the production of antinuclear autoantibodies, which have been linked to an excess of apoptotic cells, normally eliminated by a macrophagic efferocytosis. As interferon (IFN) signature and phosphorylation of JAK-STAT proteins are hallmarks of SSc tissues, we tested the hypothesis that a JAK inhibitor, ruxolitinib, targeting the IFN signaling, could improve efferocytosis of IFN-exposed human macrophages in vitro as well as skin and lung fibrosis

In vivo, BLM-and HOCl-induced skin thickness and fibrosis is associated with an increase of caspase-3 positive dermal cells and a significant increase of IFN-stimulated genes expression.

In BLM-SSc model, ruxolitinib prevented dermal thickness, fibrosis and significantly decreased the number of cleaved caspase-3 cells in the dermis. Ruxolitinib also improved lung architecture and fibrosis although IFN signature was not entirely decreased by ruxolitinib. In vitro, ruxolitinib improves efferocytosis capacity of human monocyte-differentiated macrophages exposed to IFN-γ or IFN-β. In human fibroblasts derived from lung (HLF) biopsies isolated from patients with idiopathic pulmonary fibrosis, the reduced mRNA expression of typical TGF-β-activated markers by ruxolitinib was associated with a decrease of the phosphorylation of SMAD2 /3 and STAT3.

Our finding supports the anti-fibrotic properties of ruxolitinib in a systemic SSc mouse model and in vitro in human lung fibroblasts.

Introduction

Systemic sclerosis (SSc) is a systemic autoimmune disease characterized by endothelial dysfunction, immune over-activation and fibrosis. Interstitial lung disease (ILD) represents the main cause of SSc-related mortality [START_REF] Perelas | Systemic sclerosis-associated interstitial lung disease[END_REF]. The pathophysiology of SSc remains poorly understood, but it is recognized that genetic and environmental risk factors initiate vascular damages which in turn induce dysregulated immune response [START_REF] Asano | Systemic sclerosis[END_REF]. This latter is considered as a major contributor of skin fibrosis, also participating in visceral damage including heart and lung involvement [START_REF] Brown | The immunopathogenesis of fibrosis in systemic sclerosis[END_REF].

There is increasing evidence for an involvement of macrophages in the pathogenesis of SSc [START_REF] Lescoat | Contribution of monocytes and macrophages to the pathogenesis of systemic sclerosis: recent insights and therapeutic implications[END_REF], notably attested by tissue-omics analyses which identified an activated macrophage signature [START_REF] Mahoney | Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms[END_REF][START_REF] Taroni | A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis[END_REF]. Macrophages are a heterogeneous population comprising resident macrophages and blood monocytes-derived macrophages that are recruited in inflamed tissues. Inhibition of monocyte recruitment by immunomodulatory drugs such as mycophenolate mofetil was associated with a lower count of skin macrophages and an improvement of skin fibrosis [START_REF] Hinchcliff | Mycophenolate Mofetil Treatment of Systemic Sclerosis Reduces Myeloid Cell Numbers and Attenuates the Inflammatory Gene Signature in Skin[END_REF].

Moreover, a reduction of macrophage activation by nintedanib improved the fibrotic manifestations into the Fra-2 SSc mouse model [START_REF] Huang | Nintedanib inhibits macrophage activation and ameliorates vascular and fibrotic manifestations in the Fra2 mouse model of systemic sclerosis[END_REF].

Autoantibodies targeting nuclear antigens (antinuclear antibodies, ANA) are detectable in 90-95% of SSc patients [START_REF] Stochmal | Antinuclear Antibodies in Systemic Sclerosis: an Update[END_REF] and represent key biomarkers for the diagnosis and classification of SSc patients [START_REF] Tiniakou | Insights into origins and specificities of autoantibodies in systemic sclerosis[END_REF]. The presence of autoantibodies might be explained by the accumulation and persistence of apoptotic bodies that would not efficiently cleared by phagocytic cells such as macrophages, providing some antigenic materials composed of nucleic acids and nucleoproteins [START_REF] Lescoat | Contribution of monocytes and macrophages to the pathogenesis of systemic sclerosis: recent insights and therapeutic implications[END_REF][START_REF] Kawano | Efferocytosis and autoimmune disease[END_REF]. Such a defect of efferocytosis was observed in the monocyte-derived macrophages of SSc patients [START_REF] Ballerie | Efferocytosis capacities of blood monocyte-derived macrophages in systemic sclerosis[END_REF] and an accumulation of apoptotic cells was found in the skin biopsies of SSc patient [START_REF] Maehara | Cytotoxic CD4+ T lymphocytes may induce endothelial cell apoptosis in systemic sclerosis[END_REF]. However, the association between impaired efferocytosis and apoptotic cell accumulation has not been explored in SSc to date.

ACCEPTED MANUSCRIPT / CLEAN COPY "Self" DNA and the clearance of immune complexes, composed by autoantibodies bound to uncleared cellular debris, might favor the production of interferon (IFN), thereby contributing to the IFN signature detected in blood, PBMCs and skin biopsies from SSc patients [START_REF] Tan | Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients[END_REF][START_REF] Brkic | The interferon type I signature is present in systemic sclerosis before overt fibrosis and might contribute to its pathogenesis through high BAFF gene expression and high collagen synthesis[END_REF][START_REF] Assassi | Dissecting the Heterogeneity of Skin Gene Expression Patterns in Systemic Sclerosis[END_REF].

This IFN signature was associated with the presence of anti-topoisomerase or anti-U1-RNP antibodies [START_REF] Skaug | Type I interferon dysregulation in Systemic Sclerosis[END_REF][START_REF] Assassi | Systemic sclerosis and lupus: Points in an interferon-mediated continuum[END_REF], the worsening of musculoskeletal symptoms and skin involvement [START_REF] Liu | Correlation of interferon-inducible chemokine plasma levels with disease severity in systemic sclerosis[END_REF].

Moreover, a large microarray-type study performed on lung tissues of SSc-ILD patients demonstrated that the increased expression of IFN-stimulated genes (ISGs) was associated with ILD-progression [START_REF] Christmann | Association of Interferon-and Transforming Growth Factor β-Regulated Genes and Macrophage Activation With Systemic Sclerosis-Related Progressive Lung Fibrosis[END_REF]. Although the importance of IFN signature in SSc pathogenesis is well recognized, its link with the defect of efferocytosis remains to be confirmed.

IFNs exert their immune functions by binding with IFN receptors IFNAR1 and IFNAR2 which dimerize, interacting with the elements of the Janus Kinase (JAK) protein family tyrosine kinase 2 (Tyk2)/ JAK1 and JAK1, respectively. These latter phosphorylate STAT1, STAT2 and IRF9 to form the ISGF3 complex which in turn initiates the transcription of ISGs [START_REF] Jiang | Type I Interferons in the Pathogenesis and Treatment of Autoimmune Diseases[END_REF]. Several drugs have been designed to target IFN signaling pathway [START_REF] Ciechomska | Targeting interferons as a strategy for systemic sclerosis treatment[END_REF]. These treatments include monoclonal antibodies targeting IFNARs [START_REF] Guo | Suppression of T Cell Activation and Collagen Accumulation by an Anti-IFNAR1 mAb, Anifrolumab, in Adult Patients with Systemic Sclerosis[END_REF] and small molecule inhibitors like the family of JAK inhibitors (JAKi) [START_REF] Jiang | Type I Interferons in the Pathogenesis and Treatment of Autoimmune Diseases[END_REF][START_REF] Gadina | Janus kinases to jakinibs: from basic insights to clinical practice[END_REF]. JAKi such as tofacitinib or ruxolitinib are FDA-approved drugs for autoimmune or inflammatory disorders including rheumatoid arthritis or for graft-versushost disease, that share many features with SSc. JAKi represent promising treatments as JAK/STAT pathway is activated in both skin and lung of patients with SSc [START_REF] Escamilla Gómez | Ruxolitinib in refractory acute and chronic graftversus-host disease: a multicenter survey study[END_REF]. Ruxolitinib, considered as a pan-JAKi, has anti-inflammatory effects by decreasing the expression of IFNβ in human macrophages [START_REF] Febvre-James | Repression of interferon β-regulated cytokines by the JAK1/2 inhibitor ruxolitinib in inflammatory human macrophages[END_REF] and also exerted anti-fibrotic property in the hypochloric acid (HOCl) induced SSc-ILD mouse model [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF]. However, its effects on the IFN signature and on the efferocytosis capacities of macrophages have never been studied.

In this study, we explored the protective effects of the JAKi ruxolitinib in the bleomycin (BLM)-induced SSc-ILD in mice, with a special focus on IFN signature and associated effects.

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We secondly analyzed the anti-fibrotic effects of ruxolitinib on human lung fibroblasts from patients with fibrotic ILD.

Materials and methods

Chemicals and reagents

Human recombinant cytokines IFN-γ, IFN-β and TGF-β1 were purchased from Peprotech (Neuilly sur Seine, France). Human recombinant M-CSF was obtained from Miltenyi Biotec SAS (Paris, France). Ruxolitinib (Ruxo, Hy-50856) and Bleomycin sulfate were provided by MedChemExpress, whereas camptothecin was purchased from Sigma-Aldrich (St-Quentin Fallavier, France). Antibodies (Ab) directed against: collagen I was from Southern Biotechnology Associates, Birmingham, AL, fibronectin (2413) was obtained from abcam, GAPDH (mab90009-P) was purchased from Covalab Bron France, α-SMA (clone 1A4) were from Sigma-Aldrich, phospho-smad2 (3104), smad2 (D43B4), phospho-smad3 (C25A9) and smad3 (C67H9), phospho-STAT3 (Tyr705), STAT3 were from Cell Signaling Technology (Ozyme, Montigny-le-Bretonneux, France).

Cell culture and treatments

Primary cultures of human macrophage:

Human macrophages were differentiated from peripheral blood monocyte. Briefly, buffy coat were obtained from healthy donors who gave a written consent for the use of blood samples for experimental research (Etablissement Français du Sang, Rennes, France). After a first separation by a Ficoll gradient centrifugation, the peripheral blood mononuclear cells were generated. The monocytes were isolated due to their adherence capacities and were differentiated into macrophages (M0) during 6 days in RPMI 1640 medium GlutaMAX (Gibco, Life technologies), containing 10 % heat-inactivated fetal bovine serum (FBS, Lonza, Levallois-Perret, France), 2 mM L-glutamine, 20 IU/mL penicillin, 20 μg/mL streptomycin ACCEPTED MANUSCRIPT / CLEAN COPY (ThermoFisher Scientific, Courtaboeuf, France) and 50 ng/ml of M-CSF. M0 were treated with 1 µM ruxolitinib and polarized in fresh RPMI 5 % FBS 10 ng/ml M-CSF supplemented with 20 ng/ml IFN-γ or IFN-β.

Primary cultures of human Lung Fibroblasts (HLFs):

Idiopathic pulmonary fibrosis (IPF)-HLFs were isolated from fibrotic lung tissue samples as previously described [START_REF] Joannes | Arsenic trioxide inhibits the functions of lung fibroblasts derived from patients with idiopathic pulmonary fibrosis[END_REF]. The IPF samples were from patients undergoing open lung biopsy (mean age 64 year; range 57-75 years). IPF was diagnosed according to the ATS/ERS/JRS/ALAT criteria, including histopathological features of usual interstitial pneumonia [START_REF] Raghu | Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline[END_REF]. This study was approved by the local ethics committee (Ethics Committee CHU Rennes, no 16123). Written informed consent was obtained from all subjects. All experiments were performed in accordance with the World Medical Association declaration of Helsinki [START_REF]World Medical Association Declaration of Helsinki: Recommendations Guiding Physicians in Biomedical Research Involving Human Subjects[END_REF]. Cell morphology of fibroblasts was checked by phase contrast microscopy. IPF-HLFs (n=8) were cultured in Dulbecco's Modified Eagle Medium (DMEM) (Gibco™, Life Technologies, Courtaboeuf, France) supplemented with 10% fetal bovine serum (FCS) (Eurobio scientific, Evry, France), L-glutamine, antibiotic-antimycotic solution (Gibco™, Life Technologies) and used at passages 5 and 6. The IPF-HLFs were seeded in 6-well plates for 24 h, and starved in FCS-free DMEM for 16 h. TGF-β1-induced myofibroblast differentiation was measured by treating HLFs with 1µM ruxolitinib for 2 h and then with 1 ng/ml TGF-β1 for 24 h. The concentrations of ruxolitinib (1 µM) were in a comparable range to the maximal plasma concentrations (0,6 -1,3 µM) that can be reached in patients treated with ruxolitinib (31).

Mouse model of BLM-induced systemic sclerosis

Male C57BL/6J mice weighing about 20 gr, used at 8 weeks of age, were acquired from Janvier Labs (Le Genest Saint Isle, France). Experimental scleroderma-associated ILD was induced by daily intradermal injections (ID) of 100 µl of BLM solution (at 0.4 mg/ml) into the shaved back of mice (5 days a week) for 4 weeks. Mice were randomly divided into 3 groups ACCEPTED MANUSCRIPT / CLEAN COPY

Mouse model of HOCl-induced systemic sclerosis

Female C57BL/6J mice weighing between 18 and 20 gr, used at 8 weeks of age and purchased from Janvier Labs (Le Genest Saint Isle, France) were randomly divided into 2 groups: daily intradermal injections of 100 µl of PBS (n = 9) or HOCl (n = 8, one death due to biopsy sample procedure at week 3) as previously described [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF]. Animal studies were reviewed and approved by the Committee on the Ethics of Animal Experiments under the French Ministry of Higher Education and Research (permission#: 17011-2018100812449655).

Induction of apoptosis in Jurkat cells and quantification of efferocytosis in human macrophages

The human Jurkat cells CD4 T lymphocyte cell line was cultured in RPMI 1640 Glutamax medium supplemented with 10% of heat-inactivated FCS and antibiotics. Apoptosis was induced in Jurkat cells (Japo) through an exposure to 10 μM camptothecin for 4 h. The apoptosis was confirmed with FITC-Annexin V/iodide propidium staining using flow cytometry.

After labeling with 250 ng/mL pHrodo (IncuCyte® pHrodo® Red Cell Labeling Kit,Sartorius, Ann Arbor, USA), a pH-dependent fluorescent probe, the pHrodo + apoptotic Jurkat cells were washed then add to macrophages (ratio 10/1) at 37°C. Quantification of engulfment of apoptotic ACCEPTED MANUSCRIPT / CLEAN COPY cells by macrophages was evaluated by measuring the intensity of the pHrodo red fluorescence emitted (560nm/585nm) in an acid medium of lysosomes, by fluorescence microscopy in real time (IncuCyte® live-cells Analysis system, Sartorius), with photographs carried out every 15 min for 3 h. The average fluorescence intensity (expressed as RCUxµm2/image) at 90 min was used to compare the level of efferocytosis between the different treatment conditions.

Sample collection for in vivo experiments

At the end of experimental procedure, mice were euthanized with an overdose of ketamine and xylazine (respectively at 120 and 20 mg/kg) and intracardiac exsanguination. Lungs were collected from each mouse and skin biopsies were performed using standardized biopsy punch near BLM injection sites. All samples were fixed in 10 % acetic acid formol for histopathological analyses or stored at -80 °C until used.

Hydroxyproline content in the skin

Collagen content in the skin was assessed using a colorimetric Hydroxyproline Kit Assay according to the manufacturer's protocol (BioVision, USA). Briefly, a skin punch of each mouse was homogenized in 100 μl of water and 100µl of NaOH (10 M) and hydrolyzed at 120°C for 1 h. The hydrolysate was secondly neutralized using equimolar adjunction of HCl (10 M). Then, a colorimetric product, visualized at 560 nm and proportional to the hydroxyproline content was generated using supplied regents, based on an oxidization reaction, allowing the quantification of hydroxyproline in skin punch for each mouse [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF].

Histological analyses

2.8.1 Masson's trichrome, picrosirius-red staining: Skin and lung samples embedded in paraffin were sliced into serial 4 μm sections. Sections were then stained using Masson's trichrome or picrosirius-red staining. Slides were then scanned with the NanoZoomer 2.0 RS (Hamamatsu, ACCEPTED MANUSCRIPT / CLEAN COPY Tokyo, Japan). Images were captured for analysis using NDPview2 software (Hamamatsu).

Skin thickness was evaluated by measuring the distance between the epidermal and the dermalsubcutaneous fat junction at a 10-fold magnification. Six random measurements per section were performed by a same blinded investigator and averaged for each section. The severity of fibrosis in Masson's trichrome stained lungs was quantified by the Ashcroft scoring system [START_REF] Ashcroft | Simple method of estimating severity of pulmonary fibrosis on a numerical scale[END_REF]. Lung analyses were performed by a same investigator who was also blinded to the animal's group assignment 2.8.2 Caspase 3 apoptotic cell staining: Skin samples embedded in paraffin were dewaxing with EZ prep solution (DISCOVERY Wash) at 72°C. After washing with a reaction buffer, a liquid coverslip was automatically applied during all the IHC process to avoid drying of the slides.

After cell conditioning with ultra CC1 solution and the inhibition of endogenous HRP with application of discovery inhibitor, a manual application of the anti-caspase3 cleaved (Asp175) antibody diluted with antibody diluent at 1/500 was performed. The slice was incubated during 60 min at 37°C followed by an automatic application of secondary antibody Omni Map anti-Rb HRP. The staining was visualized by automatic addition of the substrate (H2O2+purple) or (H2O2+DAB) and the HRP of the secondary antibody. A counter-staining with hematoxyline II during 16 min and Bluing Reagent during 4 min. After the IHC staining, the slides were washed with hot tap water and dishwasher gel to take away the oil of liquid coverslip. The slides were then dehydrated and mounted with Pertex mounting media.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from human cells and mouse tissue using the Nucleospin RNA Extraction Kit (Macherey-Nagel, Hoerdt, France) according to the manufacturer's instructions.

RNA concentrations were measured by spectrofluorimetry using a NanoDrop 1000 (Thermo Fisher scientific) and was submitted to reverse transcription in complementary DNA (cDNA) using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Thermo Fisher ACCEPTED MANUSCRIPT / CLEAN COPY Scientific). Quantitative PCR (qPCR) experiments were performed using the fluorescent probe SYBR Green methodology. Human and mouse predesigned KiCqStart® SYBR® Green primers for gene expression analysis were purchased from Sigma-Aldrich. PCR amplification was performed with the CFX384 Real-Time PCR detector (Bio-Rad Laboratories, Marnes-la-Coquette, France). The quantification of amplified genes was evaluated using the comparative cycle threshold (Cq) method (CFX Manager Software). Mean of Cq values were used to normalize expression of the steady-state target mRNA levels to those of the 18S ribosomal protein, using the 2(-ΔΔCq) method. It is noted that some samples of mouse tissues were not obtained in a sufficient quantity to evaluate the expression of all the genes of interest.

Western Blotting

Total IPF-HLF extract was lysed in ice-cold RIPA buffer containing 150 mM NaCl, 50 mM Tris-HCl, 0,1 % SDS, 1 % Triton X-100, 0,5 % Na deoxycholate, 50 mM sodium fluoride, 5 mM EDTA, 0,5 mM dithiotréitol, 1X protease inhibitor cocktail (Roche Diagnostic, Meylan, France) and phosphatase inhibitor cocktail 2, phosphatase inhibitor cocktail 3 (Sigma-Aldrich).

The lysate was centrifuged at 17900g for 30 min. The supernatant protein was denatured by heated for 5 min at 95°C in a loading buffer (glycerol 25 %(V/V), 0,3 % bromophenol (V/V), 10 % SDS (V/V), 0,5 M Tris pH 6,8 and 5 % β mercaptoetanol) and protein concentration was determined using the Bio-Rad Protein Assay Kit. 35µg of protein was separated by 10% SDS-PAGE and transferred overnight (30V at 4°C) into nitrocellulose membrane (Pall Corporation).

Immunoblot were blocked with T-TBS (0,5 M Tris pH 7,4, 1,5 M NaCl, Tween-20 0,1 %(V/V)) containing 4% of bovine serum albumin, were then hybridized overnight at 4°C with appropriate primary antibodies (1/1000) and, after washing, were incubated with horseradish peroxidase-conjugated secondary antibodies (1/2000) before analysis by chemiluminescence.

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The densitometry of blots was measured using the Image Lab™ Software for total protein normalization (Bio-Rad) and normalized to GAPDH.

Statistical analyses

Data were presented as means ± standard error on the mean (SEM). Comparisons between more than 2 groups were performed by repeated measure analysis of variance for paired or oneway analysis of variance followed by Dunnett's multiple comparison post-hoc tests for independent groups. Depending on conditions and Gaussian distribution, Student's t test, paired-t-test or Mann and Whitney test were used to compare 2 groups. A p-value < 0.05 was considered significant. Data analyzes were performed with GraphPad Prism 5.0 software (GraphPad Software, La Jolla, CA, USA).

Results

BLM-and HOCl-induced skin thickness is associated with an increase of apoptotic cells and an interferon signature

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Ruxolitinib improves efferocytosis capacity of human macrophages exposed to IFN-β and IFN-γ.

To assess the effect of ruxolitinib on efferocytosis, human monocyte-derived-macrophage (MDM) were treated with 1 µM ruxolitinib and polarized into pro-inflammatory macrophages by adding IFN-β or IFN-γ, they were then exposed to pHRodo-labeled apoptotic Jurkat cells.

IFN-β-or IFN

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Ruxolitinib improves skin and lung parameters induced in BLM-SSc mouse model

We next analyzed ruxolitinib effects on skin and lung tissues of BLM-SSc mouse model. The

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The antifibrotic properties of ruxolitinib may be related to its direct impact on human lung fibroblasts

The impact of ruxolitinib on human fibroblasts was evaluated in idiopathic pulmonary fibrosis-human lung fibroblasts (IPF-HLF), as IPF is a prototypical fibrotic lung disease sharing many similarities with SSc-ILD, in terms of pathogenesis and treatment strategy. It is noted that the effect of ruxolitinib has been not tested on TGF-β1-induced control fibroblasts in this study as the basal expression levels of fibrotic markers in control fibroblasts were previously demonstrated to be similar to IPF-HLF [START_REF] Joannes | Arsenic trioxide inhibits the functions of lung fibroblasts derived from patients with idiopathic pulmonary fibrosis[END_REF]. Incubation of IPF-HLF with 1 ng/ml of TGF-β1 for 24 h significantly up-regulated mRNA expressions of pro-fibrotic genes (Col1A1, Col3A1, Col4A2, Serpin, Fn1 and ACTA2) and ruxolitinib exposure prevented the over-expression of all these typical TGF-β-activated fibroblast markers except for Fn1 mRNA expression (Fig 5A ).

Moreover, ruxolitinib significantly reduced α-SMA protein expression and tended to reduce collagen-1 but not fibronectin protein levels in TGF-β-activated IPF-HLFs (Fig 5B and5C), suggesting that improvement of ILD by ruxolitinib could be related to its effects on lung fibroblasts. Ruxolitinib significantly decreased TGF-β1-induced phosphorylation of SMAD2 and SMAD3 proteins (Fig 6A and6B) but also decreased TGF-β1-induced phosphorylation of STAT3 (Fig 6C ), supporting that ruxolitinib could directly limit the profibrotic properties of human lung fibroblast in vitro by targeting both canonical and non-canonical TGF-β signaling pathways.

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Discussion

In this study, we demonstrated anti-fibrotic effects of the pan-JAKi ruxolitinib in the skin and the lung of BLM induced-SSc-ILD model and in TGF-β-induced lung fibroblasts isolated from patients with IPF. IFN signature and the presence of apoptotic cells in skin tissues were associated with dermal thickness in BLM-and HOCl-induced SSc-ILD mouse model.

Our study demonstrated that the non-selective JAKi ruxolitinib improved skin histological changes and dermal thickness induced by BLM-intradermal injections. These skin effects of ruxolitinib have also been observed in previous publications based on other SSc-ILD models [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF][START_REF] Zhang | JAK1-dependent transphosphorylation of JAK2 limits the antifibrotic effects of selective JAK2 inhibitors on long-term treatment[END_REF], confirming the positive effects of pan-JAKi on SSc-related fibrosis. Skin fibrosis was associated with an increase of apoptotic cells in the dermis of BLM-induced SSc model [START_REF] Yamamoto | Increased expression of p53 and p21 (Waf1/Cip1) in the lesional skin of bleomycin-induced scleroderma[END_REF], in the genetic Fra-2 SSc mouse model [START_REF] Wollin | The effect of nintedanib versus mycophenolate mofetil in the Fra2 mouse model of systemic sclerosis-associated interstitial lung disease[END_REF] and also, as we demonstrated in this article, in the HOCl-induced SSc model; therefore showing that the presence of apoptotic cells in dermis is a common feature of skin fibrosis in SSc. In human skin, apoptotic cells were mostly identified as endothelial cells and not as immune cells [START_REF] Maehara | Cytotoxic CD4+ T lymphocytes may induce endothelial cell apoptosis in systemic sclerosis[END_REF]. Although we have not characterized the nature of these apoptotic cells in our study, their absence of co-localization with vessels does no support an endothelial origin. Additional immuno-histological experiments will be required to fully characterize their nature. In the BLM-induced SSc model, induction of apoptosis occurs via the Fas/Fas ligand pathway [START_REF] Yamamoto | Increased expression of p53 and p21 (Waf1/Cip1) in the lesional skin of bleomycin-induced scleroderma[END_REF], and interestingly, the reduction of skin fibrosis and collagen deposition is associated with the reduction of cellular apoptosis [START_REF] Yamamoto | Fas-and FasL-deficient mice are resistant to the induction of bleomycin-induced scleroderma[END_REF]. These results are consistent with ours, as the reduction of apoptotic cell number by ruxolitinib is associated with skin fibrosis improvement as assessed by dermal thickness and the level of hydroxyproline.

Altogether, these data support a contribution of cell death to skin remodeling and suggest that the positive effects of ruxolitinib on skin fibrosis may be related, at least in part, to its ability to limit the presence of apoptotic cells in the dermis.

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In agreement with our previous results in the HOCl induced-SSc model, the pan-JAKi ruxolitinib decreases pulmonary fibrosis assessed by Sirius red and the expression of fibrosis markers. This result is in agreement with those of Zhang et al, which demonstrates that ruxolitinib has decreased pulmonary fibrosis and the differentiation of fibroblasts into myofibroblasts in the intratracheal instillation model of BLM [START_REF] Zhang | JAK1-dependent transphosphorylation of JAK2 limits the antifibrotic effects of selective JAK2 inhibitors on long-term treatment[END_REF]. This anti-fibrotic effect of ruxolitinib can also be explained by the inhibition of the JAK-STAT signaling pathways driving macrophage polarization as this JAKi reduced the expression of some pro-inflammatory (or M1) and pro-fibrotic (or M2a) macrophage-associated markers in the HOCl mouse model [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF].

Analysis of blood and tissues of SSc patients as well as in HOCl-SSc mouse model showed a mixed phenotype of M1/M2 [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF][START_REF] Skaug | Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile[END_REF]. The latter is associated with SSc-ILD and thus the severity of the disease [START_REF] Trombetta | A circulating cell population showing both M1 and M2 monocyte/macrophage surface markers characterizes systemic sclerosis patients with lung involvement[END_REF]. SSc patients also exhibit an IFN signature in the very early phase [START_REF] Tan | Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients[END_REF][START_REF] Skaug | Type I interferon dysregulation in Systemic Sclerosis[END_REF] concomitantly with a potentially IFN mediated-M1 macrophage signature in skin tissue of human [START_REF] Skaug | Type I interferon dysregulation in Systemic Sclerosis[END_REF] or HOCl-SSc mice [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF]. These data suggest that M1 activation and concomitant IFN signature are hallmarks of early SSc.

In our study, we showed a decrease in efferocytosis capacity of human macrophages exposed to IFN-β-or IFN-γ. This result is in agreement with those obtained in M1 macrophages [START_REF] Michlewska | Macrophage phagocytosis of apoptotic neutrophils is critically regulated by the opposing actions of pro-inflammatory and antiinflammatory agents: key role for TNF-α[END_REF] and could explain the defect in efferocytosis observed in macrophages of SSc patients [START_REF] Ballerie | Efferocytosis capacities of blood monocyte-derived macrophages in systemic sclerosis[END_REF] as an IFN signature was also described in peripheral blood mononuclear cells [START_REF] York | A macrophage marker, siglec-1, is increased on circulating monocytes in patients with systemic sclerosis ACCEPTED MANUSCRIPT / CLEAN COPY and induced by type i interferons and toll-like receptor agonists[END_REF]. This hypothesis is in accordance with the defect of efferocytosis observed in other systemic autoimmune diseases characterized by a high IFN signature such as systemic lupus [START_REF] Herrmann | Impaired phagocytosis of apoptotic cell material by monocyte-derived macrophages from patients with systemic lupus erythematosus[END_REF].

The pulmonary anti-fibrotic effect of ruxolitinib cannot be fully explained by its action on the IFN signature. Indeed, only Ifi44 mRNA lung expression was decreased in the presence of ruxolitinib in comparison to BLM group. This was not the case in the HOCl induced SSc-ILD model where CXCL10 and IRF3 expression were decreased by ruxolitinib but at early phase [START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF]. Additional experiments in the early phase might provide some answers [START_REF] Khanna | Long-Term Safety and Efficacy of Tocilizumab in Early Systemic Sclerosis-Interstitial Lung Disease: Open-Label Extension of a Phase 3 Randomized Controlled Trial[END_REF].

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Beyond macrophages, fibroblasts are the other key cells involved in the pathogenesis of SSc.

Similarly to skin fibroblasts of SSc patients, IPF lung fibroblasts strongly express JAK2 and STAT3 in comparison with control fibroblasts [START_REF] Milara | The JAK2 pathway is activated in idiopathic pulmonary fibrosis[END_REF], and treatments targeting fibroblasts initially approved in IPF-ILD have also been recently approved in SSc-ILD [START_REF] Distler | Nintedanib for Systemic Sclerosis-Associated Interstitial Lung Disease[END_REF], suggesting common activation mechanisms in these two fibrotic diseases. By contrast with IPF, lung biopsy is rarely performed in SSc-ILD and SSc-lung fibroblasts are rarely available. In our study, we used IPF-HLFs and demonstrated a decrease of STAT3 phosphorylation by ruxolitinib supporting an efficacy of JAKi also via the non-canonical pathway [START_REF] Montero | Role of JAK/STAT in Interstitial Lung Diseases; Molecular and Cellular Mechanisms[END_REF]. The anti-fibrotic effects of ruxolitinib could also be attributed to a decrease of type I-IFN/IFNAR/IRF7/SMAD3 signaling as both IRF7 mRNA and protein are overexpressed in SSc skin fibroblasts [START_REF] Wu | Interferon regulatory factor 7 (IRF7) represents a link between inflammation and fibrosis in the pathogenesis of systemic sclerosis[END_REF]. Wu et al. showed a colocalization of IRF7 and SMAD3 in SSc fibroblasts and demonstrated that TGF-β-induced collagen and fibronectin expressions were significantly decreased in cells transfected with siRNA IRF7 [START_REF] Wu | Interferon regulatory factor 7 (IRF7) represents a link between inflammation and fibrosis in the pathogenesis of systemic sclerosis[END_REF]. The decrease of several TGF-β-induced fibrotic markers by ruxolitinib may be explained by its capacity to reduce the canonical signaling pathway involving SMAD2 and 3 along with an impact on the non-canonical pathway. In accordance with this hypothesis, the JAK2 inhibitor TG101209 that reduced fibrotic markers in TGF-β-exposed healthy skin human fibroblasts was not efficient in fibroblast nucleofected with Jak2 siRNA, suggesting that a noncanonical pathway involving phospho-Jak2 was activated by TGF-β in skin fibroblasts and that this JAK pathway may be an promising therapeutic target [START_REF] Montero | Role of JAK/STAT in Interstitial Lung Diseases; Molecular and Cellular Mechanisms[END_REF][START_REF] Dees | JAK-2 as a novel mediator of the profibrotic effects of transforming growth factor β in systemic sclerosis[END_REF]. Such data were also supported by the efficiency of ruxolitinib to reduce fibrotic markers expression in skin fibroblasts [START_REF] Zhang | JAK1-dependent transphosphorylation of JAK2 limits the antifibrotic effects of selective JAK2 inhibitors on long-term treatment[END_REF]. Altogether, these data suggest a link between inflammation illustrated by the IFN/JAK/STAT signaling pathway and fibrosis, characterized by TGF-β signaling pathway, in lung fibroblasts from patients with chronic ILD such as IPF or SSc. Examining the effects of ruxolitinib in TGF-β and IFN co-treated lung fibroblasts on the p-SMAD3 and IRF3 interaction may help to confirm these results in the future. Nonetheless, comparing signaling pathways of ACCEPTED MANUSCRIPT / CLEAN COPY skin tissues with lung tissues in a same disease or of lung samples from two fibrotic diseases can be hazardous as divergent patterns of IFN signaling have been described (50).

In this study, we demonstrated that ruxolitinib has prevented the increase of apoptotic cell number and confirmed the anti-fibrotic effects of ruxolitinib in vivo in a systemic SSc mouse model and in vitro in human lung fibroblasts. Overall, our results strengthened the relevance of using JAKi as a therapeutic approach for skin and lung manifestations of SSc, a systemic autoimmune disease without efficient disease modifying drug available for all patients to date.

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 6 mice each: ID of NaCl and oral solvent (0.5% Weight/Volume carboxymethyl glucose in NaCl 0.9%), ID of BLM dissolved in NaCl and oral solvent, ID of BLM and ruxolitinib (20 mg/kg, oral gavage twice a day). Animals were housed in similar autoclaved cages and fed food and water with identical housing condition. They were maintained under a 12/12h light/dark cycle, with controlled room temperature and humidity. Animal studies were reviewed and approved by the Committee on the Ethics of Animal Experiments under the French Ministry of Higher Education and Research (#17011-2018100812449655). The study was conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals, EEC Council Directive 2010/63/EU.

  Histological changes and skin thickness was significantly increased in BLM and HOCl mouse models (Fig 1A). These changes were associated with a significant increase of cleaved caspase-3 positive cells, reflecting an increase of dermal apoptosis (Fig 1B). mRNA expression of ISGs such as the chemokine CXCL10, Ifi44 or the transcription factor IFR3 was significantly increased in both mouse models (Fig 1C). The accumulation of dermal apoptotic cells could be related to an IFN-dependent decreased of efferocytosis.

Fig. 1 .

 1 Fig.1. Skin thickness, apoptosis and IFN signature in two systemic mouse SSc models. A) Evaluation of skin thickness and hydroxyproline content in BLM and HOCl SSc-mouse models. C57BL/6 mice were intradermally injected with BLM, HOCl or saline every day for 4 weeks (BLM) or for 6 weeks (HOCl). Skin paraffin sections were stained according to the Masson's trichrome staining protocol. Data of skin thickness (μm,

  -γ-polarized MDM showed a decreased of the percentage of pHRodo + engulfed cells, with a strongest effect onto IFN-γ-treated MDM (almost 50% reduction) (Fig 2). Efferocytosis levels were restored by ruxolitinib in the both IFN-treated macrophages (Fig 2).A decreased phosphorylation of STAT1 in IFN-treated MDM after ruxolitinib treatment was previously validated[START_REF] Lescoat | Combined anti-fibrotic and anti-inflammatory properties of JAK-inhibitors on macrophages in vitro and in vivo: Perspectives for scleroderma-associated interstitial lung disease[END_REF].

Fig 2 .

 2 Fig 2. Ruxolitinib improves efferocytosis capacity of human macrophages exposed to IFN-β or IFN-γ Efferocytosis capacities of monocyte-derived macrophages (MDM) exposed or not to IFN-β or IFN-γ (20 µg/ml) in the presence or not of ruxolitinib (Ruxo) at 1 µM were evaluated by measuring the intensity of the pHrodo red fluorescence. Efferocytosis of untreated macrophages (M0) used as control was set as 100%. Experiments were conducted in MDM from n> 4 independent healthy donors and are expressed as means + SEM. p value was calculated with ANOVA Dunnet test, *p< 0.05.

  increased of skin thickness (Fig 3A) and of hydroxyproline content (Fig 3B) observed in BLM exposed mice were both significantly reduced by ruxolitinib. Ruxolitinib also significantly decreased the number of cleaved caspase-3 cells in the dermis (54.52% in BLM vs 25.83% in BLM+Ruxo, p<0.01) (Fig 3C) suggesting either a protective effect of ruxolitinib on BLMinduced cell apoptosis or an improvement of apoptotic cell clearance by ruxolitinib.

Fig 3 .

 3 Fig 3. Ruxolitinib improves skin parameters induced in BLM-SSc mouse model (A) Skin thickness, measured on paraffin sections stained to Masson's trichrome, is expressed in μm. Data represent the means + SEM of 6 measures per skin section for each 4 mice per group. p value was calculated with Student's t-test, * p<0.05. (B) Skin fibrosis was evaluated by the quantification of hydroxyproline content (µg/punch) which is expressed by the means + SEM (n = 4 mice per group). p value was calculated with Student's t-test, * p<0.05. (C) Dermal cell apoptosis was evaluated on skin sections stained to an anti-cleaved Caspase 3 antibody. Data expressed by the count of caspase 3 positive cells/mm 2 represented the means + SEM (n = 6 mice per group). p value was calculated with Student's t-test, * p<0.05.

Fig. 4 .

 4 Fig.4. Ruxolitinib improves lung parameters induced in BLM-SSc mouse model (A) Staining of lung (left upper lobe) showing total section (x1,25) and details of the sub-pleural, parenchymal and peri-bronchial regions (x20) by Sirius red. Sections of mouse lung are representative of each group (n = 6 mice per group). Ashcroft score (B) and the mRNA relative expressions of key pro-fibrotic markers (C) and of three ISGs (D) in the lungs are expressed as the means + SEM in comparison with the BLM group. p value was calculated with ANOVA followed by Newman-Keuls multiple comparison post-hoc test. * p<0.05; ** p< 0.01; *** p<0.001 (n = 3 to 6 mice per group).

Fig. 5 .

 5 Fig.5. Ruxolitinib tends to decrease TGF-β-induced cell differentiation in IPF-HLF (A) The mRNA relative expression in IPF-HLFs stimulated by TGF-β and treated or not with 1 µM of Ruxo was determined by arbitrarily setting at 1 mRNA concentrations measured in TGF-β treated cells. Results are expressed as with means ± SEM of 8 independent experiments. Significant differences were determined by ANOVA followed by Dunnett's multiple comparison t-test, * p<0.05; ** p< 0.01; *** p<0.001. (B) Representative Western blots of fibronectin (Fn1), collagen-1 (COL-1) and α-SMA in IPF-HLFs treated with 1 μM Ruxo for 24 h and then stimulated with 1 ng/ml TGF-β1 for 24 h. (C) Proteins were quantified by densitometry and normalized to GAPDH expression. The fold change in protein expression in stimulated cells was determined by arbitrarily setting at 1 the protein expression measured in TGF-β treated cells. Results are expressed as the means ± SEM of 8 IPF-HLF independent cell culture experiments. Significant differences were determined by ANOVA followed by the Dunnett's multiple comparison t-test, * p<0.05.

Fig. 6 .

 6 Fig.6. Ruxolitinib prevents the phosphorylation of SMAD2/3 induced by TGF-β in IPF-HLFs (A) Representative Western blots showing the phosphorylation of SMAD2 and SMAD3 in IPF-HLFs treated with 1 μM Ruxo for 24 h and then stimulated with 1 ng/ml TGF-β for 24 h. (B) Proteins were quantified by densitometry and then normalized to the GAPDH expression. The fold change in protein expression in stimulated cells was determined by arbitrarily setting at 1 the protein expression measured in TGF-β treated cells. Results are expressed as with means ± SEM of 8 IPF independent cell culture experiments. (C) Representative Western blots showing the phosphorylation of STAT3 and protein quantification and normalization to GAPDH expression by densitometry. The fold change in protein expression in stimulated cells was determined by arbitrarily setting at 1 the protein expression measured in TGF-β treated cells. Results are expressed as with means ± SEM of 4 IPF independent cell culture experiments. Significant differences were determined by ANOVA followed by the Dunnett's multiple comparison t-test, * p< 0.05; ** p< 0.01.

  50. Valenzi E, Tabib T, Papazoglou A, Sembrat J, Trejo Bittar HE, Rojas M, et al. Disparate Interferon Signaling and Shared Aberrant Basaloid Cells in Single-Cell Profiling of Idiopathic Pulmonary Fibrosis and Systemic Sclerosis-Associated Interstitial Lung Disease. Front Immunol. 2021;12:595811.ACCEPTED MANUSCRIPT / CLEAN COPY

Acknowledgements

ACCEPTED MANUSCRIPT / CLEAN COPY

The authors thank the animal house facilities (ARCHE), the platform of Flow cytometry and the Platform of Histo-Pathology Hight Precision (H2P2) (Biosit, Rennes, France). This work was supported by the Institut National de la Santé et de la Recherche Médicale (INSERM), the Université de Rennes (Univ Rennes) and the "Groupe francophone de recherche sur la sclérodermie".

Data availability

Data will be made available on request.

Author Contributions

Design of the study: NB, AL, VL. Acquisition of the data: NB, ML, AL, ELT. Interpretation of the data: NB, AL, VL. Manuscript preparation: NB, AL, VL. Provided essential materials: AJ, SJ, LV.

Declaration of competing interest:

Outside the submitted work, Stéphane Jouneau has received fees, funding or reimbursement for national and international conferences, boards, expert or opinion groups, research projects over the past 3 years from AIRB, Bellorophon Therapeutics, Biogen, Boehringer, Chiesi, Fibrogen, Galecto Biotech, Gilead, LVL, Novartis, Olam Pharm, Pfizer, Pliant Therapeutics, Roche, Sanofi-Genzyme, Savara.

The other authors have no conflict to interest to declare.

Appendix A: Supplementary data

Supplementary data are available