Alexandre Blanchet
email: blanchet.alexandre@hotmail.com

Nathalie Julien
email: nathalie.julien@univ-ubs.fr

Mohammed Adel Hamzaoui
email: mohammed.hamzaoui@univ-ubs.fr

Typology as a Deployment Tool for Digital Twins: Application to Maintenance in Industry

Keywords: Digital Twin, Architecture, Maintenance, Smart Manufacturing

The digital twin is recognized as a key technology for the digital transition in the industry. Nevertheless, even if the digital twin is relatively a new concept, this term has been overused in the last years, and this without any unanimous definitions, models, or standards. Moreover, the main issue to be tackled when developing digital twins, is defining an architecture capable of efficiently connecting data and models. However, there is few generic architectures proposed in the literature, without any consensus. The aim here is to define a digital twin generic architecture suitable for all applications, and technology independent. This architecture is based on a typology composed of 10 criteria characterizing various aspects of the digital twin and its interactions with its physical, virtual and human environment. This architecture is part of a 5-step deployment methodology based on a 5C decomposition. At the end of the paper, we address as validation some specific test cases in the literature relating to maintenance applications .

Introduction

The digital twin is a relatively new technology, that is increasingly spreading in several fields, may it be the industry, healthcare, transportation, energy, building, etc. However, it is used as a fad because of the lack of a shared vision. We have noticed that sometimes it is even difficult to differentiate between a digital shadow and a true digital twin, as many tools developed on the basis of digital technologies are labelled as digital twins, without having clear classification criteria, defined models and architecture, and ultimately any deployment methodology. What is meant here by architecture is a formalization that defines the components of the TD and their nature, as well as their integration and organization, and the relationships between them.

As far as our exploration of the literature has brought us, few works deal with the standardization of digital twin concepts, the formalization of its maturity process, or even the development of generic descriptive models or architectures. This lack of common vision around the development of this technology represents a real glass ceiling regarding its maturity and sustainability in the long term.

It is indeed at the level of this impasse that we position our work, and through this paper we propose building blocks for a descriptive typology, the first step in the development of our generic deployment methodology.

In the sequel, the paper is organized as follows: Firstly, we present an analysis of works dealing with the digital twin (DT) and its overuse. Secondly, we highlight the lack of standards at this level, hence the relevance of our contribution. In the following section, we present our generic methodology for DT deployment, before addressing a section dedicated to the typology of the digital twin. Thereafter, we test the robustness of our typology by applying it to 2 Alexandre Blanchet, Nathalie Julien, Mohammed Adel Hamzaoui different works from the literature that focus on concrete applications of digital twins for maintenance. Finally, we end this paper by a conclusion and perspectives section.

Overexploitation of the Digital Twin

The digital twin is a dynamic virtual representation of an object, a process, or a service, which follows its operation throughout its lifecycle and reports on the different events, changes or actions that may occur over time. If the technology has seen the light of day in the aerospace industry in the 1960s, the actual term Digital Twin has been first defined in 2002 by Mickael Grieves. Over the years, digital twin has made its appearance in various fields such as industry, transportation, building, energy, etc. This has led to an increasing interest from companies and several scientific communities, and a rapid growth of this technology, but also to a certain anarchy in its development. Moreover, since the digital twin is a springboard for digital transformation, many companies overuse it in their race to business digitalization, and in most cases with an almost non-existent or very poorly defined architecture.

We can see in Fig. 1 that digital twin was at the top of the Gartner Hype Cycle for IT in 2019 (Gartner 2019), capitalizing on the trend surrounding this theme. However, this has fostered the anarchy of initiatives centered on this technology. As a result, we are now at the bottom of the curve in a "disillusionment" zone, where the implementation of robust models and architecture, as well as standardization and convention processes, would accelerate the transition to a period of technological maturity. During the hype phase, many DTs were developed in several fields, which required some investment in the use of proprietary software and close collaboration with business experts.

This indicates that it was mainly large companies that embarked on this innovation at the beginning and that the DT was not enough well formalized. It should also be noted, that in many cases the DTs were designed to meet a specific demand, where the lack of adaptability and modularity made it difficult to upgrade the digital twin when the physical asset evolves or a part of it may be changed. This, because of the lack of standard models or formalized methods.

Lack of standards

In [START_REF] Aheleroff | Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model[END_REF], an approach for digital twin definition was proposed by putting forward an inclusion relation between different concepts. And according to which the digital twin is encompassed by the hybrid twin, which is itself encompassed by the cognitive twin. In this paper, the authors have also proposed a digital twin architecture using the RAMI 4.0 model described in 4 levels of integration: first as a "model"; then as a "digital shadow"; next as a "digital twin"; and finally, as a "digital twin predictive" (DTP) in its last level. But in most of the papers, the DT architecture is not addressed or poorly defined. However, there is no formalism that clearly defines a standard for building a digital twin that can meet different needs. We can also mention the recently introduced 23247 ISO standard that "defines a framework to support the creation of digital twins of observable manufacturing elements" (Standard ISO/DIS 23247 2021).

As mentioned earlier, most of the existing digital twins have been developed for a specific application and for a limited use which requires a very important technological expertise. This makes them a very complicated tool to implement for SMEs or companies with low digital maturity. However, all companies, no matter how limited their development is, may benefit from this new technology to initiate a digital transition that allows them to remain competitive Therefore, to make this technology accessible to all, including low digital maturity companies, Typology as a Deployment Tool for Digital Twins: Application to Maintenance in Industry 3 we propose the development of a generic deployment methodology based on a 5C decomposition, and where the first level stipulates the development of a generic architecture based in the first place on a digital twin typology.

Deployment method

As seen before, digital twin is a dynamic representation of an object, a process or a service. It is used for applications in industry, such as: factory construction, training, scheduling and organization, production control or maintenance. To set up this dynamic representation, we need data and models. Sensors and IoT are used to capture data (physical system parameters, events, environmental sensing, etc.) and combine it with different models (such as geometric, behavioral, physical). Therefore, the digital twin is then characterized by a continuous digital thread between real and virtual spaces, at an adapted synchronization rate that can be close to real time or not (ISO 23247). Hence, thanks to a simple Digital Twin, one can perform basic applications, and then with its evolution throughout the maturity levels ( Ultimately, the DT has the ability to manage relevant, reliable and secure data with appropriate accuracy, as well as to increment, enrich and also validate models both online and offline, and this, according to its maturity level. Moreover, the DT may have partial or total control of the physical object via a formalized decision loop, hence the need to clearly formalize the allocation of decision making between the DT and the human agent. Since advanced DT are now provided with cognitive capabilities based on artificial intelligence algorithms for decision support.

Basically, this shows us that the digital twin must be apprehended according to several levels of abstraction, starting with the criteria and needs (strategic/tactical/operational), the interactions with the human agent, as well as the technical characteristics.

To define a deployment method, we started from the 5C Cyber Physical System (CPS) implementation architecture (Lee, J., Bagheri, B., & Kao, H. A. 2015). It proposes a 5-level CPS pyramid and offers a step-by-step guide for developing and deploying a CPS for manufacturing applications. At the top of the pyramid, we find the Configuration level, followed by the Cognitive one. These two layers are technology independent (see Fig. 3), therefore, a generic development approach can be considered at an early stage. We propose for the first level, the setting up of a typology for a "Generic architecture", while for the cognitive level it will be a "Decision architecture" using a CWA (Cognitive Work Analysis) method (See Fig. 3). The third part concerns the cyber, conversion, and connection aspects, which will then allow us to set up a more specific architecture for the digital twin to be developed and implies iterative refinement including technological choices

For this paper, we will focus on the first level of the pyramid (Configuration), and more precisely in establishing a generic architecture of the digital twin. For this purpose, we present a typology of the digital twin as a first building block. The latter is necessary to identify the characteristics of the DT to be developed according to the real needs of the application. This typology offers the designer a basis for the development of an initial sketch to contain the decision architecture and the specific technical architecture in the dedicated development steps. 4 Alexandre Blanchet, Nathalie Julien, Mohammed Adel Hamzaoui

Typology

As outlined before, to put in place our generic architecture, we first need to define a typology for digital twin (Julien, N., & Martin, E. 2022). This latter is composed of 10 criteria, that define as comprehensively as possible the DT and its interactions.

DT Entity

The ISO/DIS 23247 standard defines the entity as "thing (physical or non-physical) having a distinct existence" together with the observable manufacturing element (OME) as an "item that as an observable physical presence or operation in manufacturing". OMEs include equipment, material, personnel, process, facility, environment, product and supporting document. However, as our typology is not restricted to manufacturing applications, we choose to qualify this object as DT entity.

Type

There are 3 different types of DT, as defined by Grieves in (Grieves, M., & Vickers, J. 2017) : DT Prototype; DT Instance; DT aggregate. The protype describes the prototypical artifact who is mainly used in the design phase. The instance describes the individual object related to the production phase. The aggregate is composed of all DT instances for the same entity used in exploitation or maintenance phase.

Level

A DT level is defined with 4 different levels:

 Component, elementary level, a part of equipment or system  Equipment, composed of components, a part of system or system of systems  System, composed of equipment, can be a part of system of system  System of system, composed of systems

Maturity

To distinguish between the digital shadow, the digital mirror, and the digital twin, we have classified the digital representation by its maturity as shown in Fig 4 . The digital mirror is a set of mathematical models representing the object and its behavior like 3D simulation with no direct communication between physical and digital entity.

The digital shadow represents all the numerical activity of the DT entity and provides its dynamic footprint. In the final step, both physical and virtual entity are merged in Cyber-Physical System (CPS). There is first a communication between physical object to the DT but also with a partial or total control feedback from the DT to the object, this is a control DT. It can evolve if adding predictive ability in Cognitive DT. Then, with more decision and control it becomes Collaboratives DT.

Topology

It qualifies the connection between the observable manufacturing asset and the digital twin. From (Schroeder, G. N., Steinmetz, C., Rodrigues, R. N., Rettberg, A., & Pereira, C. E. 2021), we have defined 4 topologies which are:  Disconnected, there is no direct connection between the DT and its entity, the information comes from external components or users.  Connected, the DT is directly connected to its entity  Embedded, the DT is included in its entity, the communication may be facilitated but the computational capabilities will be limited.  Combined, the DT is composed of two or more of the topologies described above.

Synchronization

In many publications the digital twin is assumed to be real-time synchronized in real time, but We decided to enlarge this vision by including synchronous and asynchronous communication. Synchronous synchronization can be done in real time, near real time or periodically. Asynchronous synchronization can be eventbased, conditional or on-demand.

Decision loop

We have extended the works in (Traoré, M. K. 2021) by proposing 3 different types. The first type is the open loop, the human receives the information and decides. The second type is the closed loop, the human just receives information, and the DT is autonomous regarding the decision making. The third type is the mixed loop, the human receives the information but can also decide just like the DT; in this case, the assignment between human and system decision must be clearly formalized.

Users

According to (Standard ISO/DIS 23247 2021), appropriate interfaces must be provided to different DT users who may be the human, the device, the application or another DT.

Usages

The digital twin can be used to analyze, to simulate, to optimize, to predict, to compare, to collaborate and to conceptualize; details can be found in (Julien, N., & Martin, 2021). Different usages can be combined to respond to the application.

Applications

Applications consist in the final service delivered by the DT to the users. In (Standard ISO/DIS 23247 2021) the digital twin can be used for real time control, off-line analytics, predictive maintenance, health check, engineering design.

Comparison to literature

In their quest to conceptualize and formalize the digital twin, many researchers in the community have tried to propose models that faithfully represent the digital twin. One of the models that seems to be the most robust is the one proposed by (Tao, F., Zhang, M., & Nee, A. Y. C. 2019), namely the five-dimension DT model (Fig. 5). In this representation, five main elements are considered: (1) Physical entity; (2) Virtual entity;

(3) Data; (4) Services; and ( 5) Interactions. This model has demonstrated a very good robustness, both by the adherence of the scientific community and by its confrontation with several real case studies. al., 2019) This reinforced our choice to take this model as a reference to add other elements which seems to us essential as for the formal definition of the digital twin, and then to see how far this model and the proposed typology are consistent with each other (Fig. 6). The additional dimensions we have considered are related to the environment in which the digital twin evolves, in other words, to the interactions it can have with its surroundings (physical environment, virtual environment, and human environment).

According to the 5D model, one of the main dimensions is the "Virtual Element", because it is the one that encompasses the various models that mimic the behavior of the physical element (Geometry model; Physics model; Behavior model; Rule model). These two entities (Physical Element and Virtual Element) will interact with each other via data exchanges (Connections), these data (the data element) can be multitemporal, multidimensional, multi-sources and heterogeneous. Thanks to this interaction, the DT can then offer different services (the services elements) to its users.

Firstly, it is important to know that the digital twin can be a collaborative tool, which means that this latter interact with its environment. Therefore, we highlight the presence of three distinct environments of its ecosystem which allows him to be part of a Social Cyber-Physical System. Secondly, we believe that the "Physical Element" of the 5D model should not be part of the DT model, since this latter is considered to be the "Physical Twin" or the "Physical counterpart" of the Digital Twin. Finally, we complement this model with all the elements of our typology, which fit perfectly into this representation in Fig. 6, illustrating the coherence and complementarity of these two 6 Alexandre Blanchet, Nathalie Julien, Mohammed Adel Hamzaoui formalizations. The objective of this, is to ensure that the proposed typology allows the designer to identify any technical specificities (or capabilities) that the DT must possess in order to satisfy the expected needs, while not omitting any of its fundamental elements (the main 5 dimensions of the 5D model).

Maintenance applications

To validate our typology, we decided to confront it with different works dealing with the digital twin for maintenance.

To do so, we conducted a survey of papers dealing with this subject, and then selected those that best described the digital twins they had developed.

Moreover, for those who presented DT architectures, they were in most cases too dependent on the technology and dedicated to the problem being addressed to our knowledge no generic architecture for the digital twin has been presented.

For this study we have limited the scope to choose only 4 articles that cover several aspects of maintenance and are different enough to validate our architecture (Table 1).

The first paper (Centomo, S., Dall'Ora, N., & Fummi, F. 2020) deals with maintenance.

A general approach is based on the EDA (Electronic Design Automation) approach, which allows to set up a maintenance strategy by analyzing the data recovered via different sensors. The study described in this article is the analysis of vibrations according to the size of the machine.

This health state detection is an important information that will be used to reflect the health state detected in the model allowing to obtain the digital twin of the equipment.

Via this study we have applied our DT typology, as far as the entity concerned is of course the machines. We have here an instance twin, the level of this twin is rather centered on the equipment. At the level of maturity, we have a cognitive system. For the topology level, we have a system that is disconnected because the vibration data is recovered and processed much later for analysis. As for the synchronization, it is not clearly defined but it is assumed that it is the same as the synchronization rate of the sensors.

We are here in an open loop because we only recover the health status of an equipment, and it is up to the human to take a decision. Concerning the users of this twin, we suppose that it could concern the managers, or the operators on the production line. In terms of use, we are sure of the prediction, to make a health diagnosis and to enable predictive maintenance.

The second paper (Barthelmey, A., Lee, E., Hana, R., & Deuse, J. 2019) addresses predictive maintenance in a so-called flexible system. This study presents the effects of flexibility on the predictive maintenance strategy. The application proposed here is based on a reference plant which is put in the form of a flexible hybrid assembly system.

Here we have the digital twin of each component which is stored as an AML file. In this article we have as entity a hybrid assembly system, this digital twin is a prototype.

Concerning the level, we are here in a system, the digital twin takes all the components present in the assembly system.

The maturity of this twin is rather collaborative because the twin includes HMI (Human machine interface) that includes the important elements present in the database to help the maintenance technician to create labels that will describe the problems concerned.

To have a flexible system, this DT must be connected. Regarding the synchronization we have found no information, nevertheless we assume that it is linked to the same frequency as the sensors present on the assembly system.

Here again we are in an open loop, the twin only sends information back to the human who decides at his turn. The users of this platform will be the maintenance technicians and the manager.

This twin is used for prediction or optimization, the application is health diagnosis of the different components of the system for predictive maintenance.

The third paper (Xu, Y., Sun, Y., Liu, X., & Zheng, Y. 2019) deals with failure diagnostics using deep transfer learning. This paper presents a method of failure diagnosis assisted by DT using deep transfer learning, these diagnostics are performed during the development and maintenance phase.

This study is performed on a physical workshop which is then digitized via a model for the digital twin. So, for the entity part we have a workshop, the digital twin is an instance. Compared to the level, we are more towards the system. This twin is a cognitive twin that must be connected in real time with the physical workshop. Concerning the synchronization, we take the same as the frequency of the PLC.

We are always on an open decision loop because the digital twin does not act alone and only reports to the human. Maintenance engineers will interact with this DT. For the uses, we are still with prediction as well as diagnosis with the aim of setting up a diagnosis of the health state for the workshop.

Finally, the last paper (Mi, S., Feng, Y., Zheng, H., Wang, Y., Gao, Y., & Tan, J. 2021) deals with predictive maintenance with the integration of decision making driven by the digital twin. The digital twin is integrated here to improve the accuracy of the diagnosis, the prediction of defects as well as to support the development of a more reliable maintenance plan. The OME part here is quite generic, this part concerns the site environment (workshop, production line, etc.). We are here in a system rather aggregated; the twin is part of a system which groups several systems. The level is then rather a system. This twin is cognitive and must be continuously connected with the production environment of the factory to function.

Concerning the synchronization, it will have to respect the frequency of the sensors or the PLC (Programmable Logic Controller) which could be present in a production line. Regarding the decision loop, we are in a mixed loop, the twin takes some minor decisions, especially regarding the maintenance plan, but the human also decides in this system. The users will rather be maintenance engineers. The uses of this twin are mainly prediction, and it is used in a case of organization and optimization of the maintenance plan.

Conclusion

Throughout this work, we are interested in the first step of our strategy for the development of a generic deployment methodology for the digital twin.

We first presented a typology for the digital twin, through which we aspire to set up an exhaustive generic architecture of all nontechnology dependent features. Afterwards, we contrasted the latter with the 5D model of the DT to see if it is generic enough and comprehensive. In the second part, we conducted a literature review to identify papers dealing with the digital twin for maintenance. Subsequently, we also exposed our typology to these use cases to validate its robustness. It has demonstrated a very good adequacy and a certain coherence with respect to the studied cases, while waiting to validate it with other works dealing with other concerns.

Future work on the Cognition part with the deployment of CWA (Cognitive Work Analysis) and the study of the data level architecture (respectively part 2 and 3 of our 5C CPS architecture) will allow us to refine and develop our generic methodology to provide a formalized DT deployment methodology.

Fig. 1 .

 1 Fig. 1. Gartner's hype cycle (Gartner 2019)

  Fig. it can be used for more advanced usages to conceptualize, analyze, simulate, compare, optimize, collaborate, and provide decision support.

Fig. 2 .

 2 Fig. 2. Generic definition of DT (Traoré, M. K. 2021)

Fig. 3 .

 3 Fig. 3. 5C CPS Architecture (Julien & Martin 2022)

Fig. 4 .

 4 Fig. 4. Numerical maturity (Julien & Martin 2021)

Fig. 5 .

 5 Fig. 5. The 5-Dimensions Digital Twin model (Tao et.al., 2019)

Fig. 6 .

 6 Fig. 6. Extended 5D model

Table 1 .

 1 Typology validation

						"Prediction maintenance
	Typology criteria	Papers	"The Design of a Digital-Twin for Predictive Maintenance" (Centomo et al., 2020)	"Dynamic digital twin for predictive maintenance in flexible production systems" ( Barthelmey et al., 2019)	"A digital-twin-assisted fault diagnosis using deep transfer learning" (Xu et al., 2019)	integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework" (Mi et al.,
						2021)
	Entity		Machines	Hybrid assembly system	Physical shop floor	Site environment/maintenance resource
	Type		Instance	Prototype	Instance	Aggregate
	Level		Equipment	System	System	System
	Maturity		Cognitive	Collaborative	Cognitive	Cognitive
	Topology		Connected	Connected	Connected	Connected
	Synchronization	Periodic	Near real time	Near real time	Near real time
	Decision loop	Open	Open	Open	Mixed loop
	User		Manager/operator Maintenance technician	Maintenance engineer	Manager/Maintenance engineer
	Usage		Analysis/Cognition Cognition/Collaboration Cognition/Analysis	Analysis/Cognition

Conference on

Emerging Technologies and Factory Automation (ETFA) (Vol. 1, pp. 1781(Vol. 1, pp. -1788)). IEEE. Gartner (2019)


Antoine Bernard

Christophe Hibos

Corentin Richard

Etienne Viltard

Sandy Chevrier

Sophie Lemoine

Joséphine Melin

Etienne Humblin

Romain Mary

Théo Accogli

Fanny Chalmin

Mélanie Bruchard

Paul Peixoto

Eric Hervouet

Lionel Apetoh

François Ghiringhelli

Frédérique Végran
email: frederique.vegran@inserm.fr

Romain Boidot
email: rboidot@cgfl.fr

The tumor microenvironment impairs Th1 IFNγ secretion through alternative splicing modifications of Irf1 pre-mRNA

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

Introduction

The tumor microenvironment (TME) is conditioned by complex interactions between malignant and non-malignant cells. Tumor cells mainly secrete immunosuppressive mediators, which contribute to immune surveillance evasion [START_REF] Anderson | Obstacles Posed by the Tumor Microenvironment to T cell Activity: A Case for Synergistic Therapies[END_REF][START_REF] Hanahan | Accessories to the crime: functions of cells recruited to the tumor microenvironment[END_REF]. The TME consists of a wide variety of cells, immune and not. These cells dynamically communicate amongst themselves, as well as with tumor cells, leading to tumor progression or regression. Transforming growth factor-beta (TGFβ) is secreted by malignant and non-malignant cells. This cytokine has an antagonist role in the TME and can either inhibit tumor cell proliferation or disturb activity of antitumor immune cells [START_REF] Colak | Targeting TGF-beta Signaling in Cancer[END_REF][START_REF] Ikushima | TGFbeta signalling: a complex web in cancer progression[END_REF]). An increasing number of studies show that alternative splicing is involved in immune system differentiation and homeostasis [START_REF] Baralle | Alternative splicing as a regulator of development and tissue identity[END_REF][START_REF] Yabas | The Role of Alternative Splicing in the Control of Immune Homeostasis and Cellular Differentiation[END_REF].

This molecular mechanism provides cell flexibility and diversity because it increases the number of protein isoforms from a unique gene. For example, it has been shown that 1319 alternative-splicing events occur upon T-cell receptor (TCR) engagement [START_REF] Butte | CD28 costimulation regulates genome-wide effects on alternative splicing[END_REF]. Alternative splicing allows the immune system to adapt to different environments, such as those within lymph nodes and tumors. Splicing is controlled by a huge protein complex called the spliceosome, which is regulated by splicing factors that control its binding to pre-mRNA [START_REF] Shi | Mechanistic insights into precursor messenger RNA splicing by the spliceosome[END_REF].

Here, we showed that Irf1, encoding crucial transcription factor in Th1 cells IRF1, undergoes alternative splicing in tumors, giving rise to a shortened IRF1 isoform. TGFβ was involved in SFPQ (splicing factor, proline-and glutamine-rich) splicing factor activation and was responsible for the appearance of the short IRF1 isoform. This isoform acted on the full-length IRF1 transcriptional activity, decreasing Il12rb1 expression and subsequently Ifng expression. Targeting IRF1Δ7 could therefore potentiate Th1-cell antitumor responses.

Methods

Patients and clinical samples

Four tumors samples were kindly provided by the Cancer Center George François Leclerc (Dijon, France), and thirteen healthy donors buffy coats were obtained from Etablissement Français du Sang (Besançon, France). Tumor samples were dissociated using the gentleMACS™ Dissociator (Miltenyi Biotec) in association with Tumor Dissociation Kit (human; 130-095-929, Miltenyi Biotec) or purified with the RosetteSep TM Human CD4 + T-cell Enrichment Cocktail (STEMCELL TM Technologies). The study on patient samples was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the Centre Georges-François Leclerc (Dijon, France), the Comité Consultatif de Protection des Personnes en Recherche Biomédicale de Bourgogne. Written informed consent was obtained from all patients before enrollment.

Cell lines

Cell lines were cultured at 37°C under 5% CO2 in Dulbecco's Modified Eagle's Medium (DMEM) (MC-38, B16-F10, LLC1, EL4, 293T, platinum-E) or Roswell Park Memorial Institute medium (RPMI) (for CT-26, 4T1) supplemented with 10% (vol/vol) fetal calf serum (FCS; Lonza), 1% penicillinstreptomycin/amphotericin B (PSA; Gibco), and 4 mM of 4-(2-hydroxyethyl)-1piperazineethanesulfonic acid (HEPES; Gibco). The MC-38 (Kerafast; ENH204-FP) cell line was derived from C57BL/6 murine colon adenocarcinoma cells. B16-F10 (ATCC; CRL-6475) cell line was derived from a C57BL/6 murine melanoma. LLC1 (ATCC; CRL-1642) cell line was derived from a C57BL/6 murine lung carcinoma. EL4 (ATCC; TIB-39) cell line was derived from a C57BL/6 murine lymphoma.

293T (ATCC; CRL-3216) cell line was derived from a human embryonic kidney. Platinum-E (Plat-E) (Cell Biolabs; RV-101) cell line was generated from 293T cell line. Plat-E cells contain gag, pol, and env genes, allowing retroviral packaging with a single plasmid transfection. The CT-26 (ATCC; CRL-2639) cell line was derived from a BALB/c murine colorectal carcinoma. The 4T1 (ATCC; CRL-2539) cell line was derived from a BALB/c mammary tumor. All cells were routinely tested for Mycoplasma contamination using the Mycoalert Mycoplasma Detection Kit (Lonza) and were found to be negative.

Mice and tumor model

All the mice were maintained in specific pathogen-free conditions at Dijon Zootechnical Centre, and all experiments followed the guidelines of the Federation of European Animal Science Associations.

All animal experiments were approved by the Ethics Committee of Université de Bourgogne (Dijon, France; approved protocol #2212), in accordance with the Federation of Laboratory Animal Science Associations (FELASA). Only females between 6 and 10 weeks of age were used for the experiments.

RNA-sequencing

CD4 + T cells were isolated from spleens and tumors of into C57BL/6 (MC-38, B16-F10) or BALB/c (CT-26) mice as described above. For RNA-Seq library preparation, total RNA from CD4 + T cells was extracted using Trizol. Ribosomal (r)RNA was removed using the Ribo-zero rRNA Removal Kit (Illumina, San Diego, CA, USA). The RINs (RNA integrity numbers) were determined using the TapeStation system (Agilent) and were always above 8. A total of 100 ng of rRNA-depleted RNA was used for the library preparation using the TruSeq Stranded Total RNA Library Prep kit (Illumina)

following the manufacturer's instructions. RNA sequencing was performed on NextSeq device (Illumina). The RNA-seq libraries were sequenced with paired-end 75 bp reads. FASTQ files were mapped by using BWA (mm10 version of Mus Musculus genome) for Illumina. The analysis was performed by using TopHat for Illumina. Generated files were processed with Cufflinks software to obtain annotated expressed genes in each studied subset. Differential expression between the samples was analyzed with Cuffdiff. Unsupervised hierarchical clustering of samples was performed by using Gene Cluster 3.0 software and viewed with Treeview viewer. Genes were normalized in TPM. All sequencing data are available on the GEO website with the SuperSeries reference:

GSE134855.

Nanopore RNA-seq

For RNA-Seq library preparation, total RNA from CD4 + T cells cultured with IL12 and with or without TGFβ (3 or 10 ng/mL) was extracted using Trizol (Invitrogen, Carlsbad, CA, USA). The library was prepared using the cDNA-PCR Sequencing Kit (Oxford Nanopore Technologies). Sequencing was performed on GridION device (Oxford Nanopore Technologies). The analysis was performed in collaboration with the Genomic Platform, Institut de Biologie de l'ENS, Paris, France.

Sanger sequencing

Naïve CD4 + T cells were isolated from C57BL/6 mouse spleen and differentiated in Th1 cells for 2 hours. Total RNA was extracted using Trizol and was reverse-transcribed into cDNA as described below. cDNA was then amplified by high-fidelity PCR with the Platinum™ Taq DNA Polymerase High Fidelity (Thermo Scientific, 11304011) using primers targeting the exon 6 and exon 7 or exon 6/exon8 junction and intron of Irf1 (Supplementary Table S1). Sanger sequencing was performed using the Applied Biosystems™ BigDye™ Terminator v3.

Retroviral transduction

For retrovirus infection, full-length Irf1 and the alternatively splice isoform (Irf1Δ7) were cloned into the pMXs-IRES-GFP retroviral vector (Cell Biolabs). RNA from Th1 cells differentiated from naïve CD4 + T cells as described above, was extracted using Trizol and was reverse-transcribed into cDNA as FACSAria™ Cell Sorter (BD Bioscience) according to GFP expression. GFP + cells used for intracellular staining were fixed in 2% PFA. Otherwise, GFP + cells were differentiated as described above, and after 1 or 3 days, the cells were collected for PCR and ELISAs.

Reverse transcription PCR and quantitative PCR analysis

Total RNA from differentiated CD4 + T cells was extracted using Trizol. In total, 300 ng of RNA was reverse-transcribed into cDNA using M-MLV reverse transcriptase (28025-013, Invitrogen), Random Primers, and RNAseOUT inhibitor (10777-019, Invitrogen). cDNAs were quantified by real-time PCR using a Power SYBR Green Real-time PCR kit (4367659, Life technologies) on a StepOne detection system (Thermo Scientific). Relative mRNA levels were determined using the ΔCt method. Values were expressed relative to β-actin unless otherwise specified. The sequences of the oligonucleotides used are described in Supplementary Table S1.

ELISAs

After polarization for 72 hours, cell culture supernatants of different tumor cell lines (1/100 dilution)

were assayed by ELISA for mouse IFNγ (BD Biosciences; 555138). IFNγ concentration was also assessed in tumor shreds (pure). An ELISA for mouse TGFβ (R&D SYSTEMS; DY1679-05) was performed according to the manufacturer's protocol.

Western blotting and immunoprecipitation assays

Differentiated CD4+ T cells were lysed in boiling buffer (1% SDS, 1 mM sodium orthovanadate, and 10 mM Tris (pH 7.4)) containing protease inhibitor cocktail (Roche; 11697498001) for 20 minutes at 4°C.

Chromatin immunoprecipitation assay

Chromatin isolation and shearing were performed with the truChIP TM Chromatin Shearing Kit (Covaris) using a Focused-Ultrasonicator M220 device (Covaris). A chromatin immunoprecipitation (ChIP) assay was performed with the ChIP-IT kit (Active Motif Europe, Rixensart, Belgium) according to the manufacturer's instructions. A total of 7 μg of DNA were immunoprecipitated with 3 μg of anti-histidine or anti-YFP (Thermo Scientific), or 3 μg of negative control immunoglobulin at 4°C overnight. After chromatin elution, cross-links were reversed, and samples were analyzed by quantitative PCR as described above. The sequences of the oligonucleotides used are described in Supplementary Table S1.

RNA immunoprecipitation

RNA immunoprecipitation (RIP) was performed with Magna RIP TM RNA-Binding Protein Immunoprecipitation Kit according to the manufacturer's instructions. RNA was immunoprecipitated with 5 μg of anti-SFPQ (Abcam). RNA was reverse-transcribed into cDNA and analyzed by quantitative PCR as previously described.

siRNA transfection

siRNA knockdown experiments were performed in mouse and human with validated controls, or Irf1-, Irf1Δ7-, Sfpq (mouse)-specific siRNAs (Life Technologies). In brief, naive CD4 + T cells were transfected with Transit-TKO Transfection Reagent (MIR2154, Mirus) according to the manufacturer's instructions for 24 hours and then differentiated as previously described. The sequences of the siRNAs used are described in Supplementary Table S1 Promoter-activity reporter assay

The Il12rb1-luc luciferase construct was obtained by inserting 154 base pairs of Il12rb1 mouse promoters in the multicloning site of the pGl3 basic vector (Promega, Charbonnières, France).

Fragments were amplified by high-fidelity PCR with C57BL/6 mouse DNA as the template and specific primers (Supplementary Table S1). 1x10 5 Human 293T cells were transiently transfected for 48 hours with the pSV-β-galactosidase (Promega) reporter plasmid and the pcDNA3.1/CT-GFP-TOPO-IRF1 (Thermo Scientific), pcDNA3.1/CT-GFP-TOPO-IRF1Δ7 using Lipofectamine 2000 (Invitrogen).

Luciferase was measured using the Dual Glo Luciferase Assay System (Promega; E2920) according to

FLIM-FRET

The IRF1-YFP and IRF1Δ7-CFP constructs were obtained by inserting 819 bp of Irf1Δ7 in the multiple cloning site of the mVenus C1 vector (Addgene) and by inserting 990 bp of Irf1 into mCerulean C1 (Addgene) respectively. Fragments were amplified by high-fidelity PCR with C57BL/6 mouse cDNA as template and specific primers Supplementary Table S1. Fluorescence lifetime imaging (FLIM) was performed, and images were collected using a time-correlated single-photon counting (TCSPC) module (PicoQuant) on a Nikon A1-MP scanning microscope. Imaging was carried out with a ×60 Apo IR objective (NA: 1.27, Water Immersion, Nikon). Two-photon excitation at 820 nm was provided by an IR laser (Chameleon, Coherent) that delivered femtosecond pulses at a repetition rate of 80 MHz.

Fluorescence emission of CFP was collected through a FF01-494/20 band-pass emission filter (Semrock) using a single-photon avalanche diode detector. TCSPC lifetime recording was performed over 200 temporal channels (final resolution 0.64 ps). We performed a global lifetime analysis on regions of interest of the FLIM images using SymPhoTime software (PicoQuant). The fluorescence lifetimes were calculated by fitting the tail of the fluorescence decay with a biexponential model.

Flow cytometry

To phenotype and sort distinct mouse CD4

Results

Tumor microenvironment impacts CD4 + T-cell alternative splicing

First, we tested the pattern of alternative splicing in bulk of CD4 + T cells from spleens or tumors by RNA-sequencing (Supplementary Figure S1). We observed a global increase in splicing events in tumors compared to spleen (Figure 1A). GSEA analysis of genes with a higher expression in tumors showed a significant enrichment in splicing factor gene expression (Figure 1B). When comparing transcriptomic profiles of bulk CD4 + T cells from spleens and tumors, we observed a clear impact on Th1-(Figure 1C), Th2-, Th17-, and Treg-related gene expression (Supplementary Figure S1B). Th1 cell presence in tumors is reported as a good prognosis indicator [START_REF] Pages | Immune infiltration in human tumors: a prognostic factor that should not be ignored[END_REF]; therefore, we wondered whether TME-induced alternative splicing modifications had an impact on Th1 master controller genes Tbx21 and Irf1. To determine if Tbx21 and Irf1 pre-mRNA where alternatively spliced in Th1 cells, we generated Th1 CD4 + T cells in vitro and analyzed both Tbx21 and Irf1 by PCR and Sanger sequencing.

We detected an Irf1-spliced isoform in which exon 7 had been excluded (Irf1Δ7), whereas no splice variant was found for Tbx21 mRNA (Supplementary Figure S2A). In addition to exon 7 skipping, we identified the inclusion of intron 8 together with an in-phase stop codon by Sanger sequencing (Supplementary Figure S2B) and through Nanopore sequencing (Supplementary Figure S2C), avoiding nonsense-mediated decay degradation and enabling mRNA stabilization (Supplementary Figure S2D).

Quantification by digital PCR revealed that the strongest expression level of Irf1 was detected 2 hours after Th1 differentiation start (Figure 1D), and 4 hours for Irf1Δ7 mRNA (Figure 1E). After 4 hours of differentiation, Irf1Δ7 mRNA represented almost 20% of the Irf1 gene expression (Figure 1F). In different subsets of in vitro-differentiated helper T cells, we observed that Irf1 mRNA was 56%, 30%, and 78% less expressed in Th2, Th17, and Treg respectively, than in Th1 cells, and that Irf1Δ7 mRNA was 83%, 46%, and 87% less expressed in Th2, Th17, and Treg respectively, than in Th1 cells (Supplementary Figure S3). We confirmed the expression of Irf1 (Figure 1G) and Irf1Δ7 mRNA (Figure 1H) in in vivo Th1 cells isolated from spleens (Supplementary Figure S4). To determine whether the short form of IRF1 was detected at the protein level, we performed Western blotting using an antibody targeting the protein N-terminal domain. We detected two proteins of 37 and 35 KDa, corresponding to the predicted sequence molecular weight (Supplementary Figure S5), indicating that both proteins were expressed (Figure 1I). Collectively, these findings indicated that IRF1, a master controller of Th1 cell differentiation, could be affected by alternative splicing at the mRNA level, giving rise to an another stable isoform that is translated in Th1 cells.

TGFβ affects Irf1Δ7 isoform appearance

TGFβ is one of the most common cytokines found in TME [START_REF] Trapani | The dual adverse effects of TGF-beta secretion on tumor progression[END_REF] and is described as modifying alternative splicing in cancer cells [START_REF] Tripathi | Direct Regulation of Alternative Splicing by SMAD3 through PCBP1 Is Essential to the Tumor-Promoting Role of TGF-beta[END_REF]. By comparing the transcriptomic profiles of CD4 + T cells isolated from spleens and tumors, we underlined TGFβ-related gene enrichment in tumor CD4 + T cells (Figure 2A-B), whereas no enrichment was found with IL6 (Supplementary Figure S6A), another cytokine in TME [START_REF] Johnson | Targeting the IL-6/JAK/STAT3 signalling axis in cancer[END_REF]. By dosing TGFβ in the tumor shred of different cell lines (Figure 2C) and directly in the cell culture supernatant (Supplementary Figure S6B), we validated the secretion of this cytokine by different mouse tumor cell lines, CT-26 and MC-38. The amount of TGFβ was higher in tumor shreds than in supernatants (except for EL4 cells), suggesting that TGFβ could be secreted by tumor cells and tumor-infiltrating cells. On this basis, we hypothesized that TGFβ from TME could have an impact on Th1 CD4 + T-cell alternative splicing and consequently on Irf1Δ7 isoform expression. To test this hypothesis, naïve CD4 + T cells were cultured for 2 hours with IL12 and increasing doses of TGFβ and then differentiated for 24 hours with IL12 only (Supplementary Figure S7). Using Nanopore sequencing, we confirmed that TGFβ affected Irf1 mRNA exon 7 inclusion at 2 hours (Figure 2D), and we also noted an increase in intron 8 inclusion (Figure 2E). After 2 hours of TGFβ exposure, we did not observe, by PCR, differences in Irf1 mRNA expression (Figure 2F) but did see an increase in Irf1Δ7 mRNA expression, indicating an increase in exon 7 exclusion (Figure 2G), confirming the Nanopore data. Thus, in these conditions, the fold-change ratio was in favor of Irf1Δ7 mRNA (Figure 2H). After 24 hours of Th1 differentiation without TGFβ, Tbx21 (Figure 2I) and Foxp3

(Figure 2J) mRNA expression remained unchanged, while Il12rb1 (Figure 2K) and Ifng mRNA (Figure 2L) expression were decreased in a dose-dependent manner. To confirm the involvement of TGFβ in Irf1 mRNA alternative splicing, we monitored tumor growth in MC-38 (TGFβ-producing)-bearing C57BL/6 mice treated with TGFβ-blocking antibody or control (Supplementary Figure S8A). We observed inhibition of tumor growth upon TGFβ targeting compared to controls (Supplementary Figure S8B). After sorting Th1 cells from spleens and tumors from anti-TGFβ-treated and untreated mice, we analyzed the Irf1Δ7/Irf1 ratio by RT-qPCR. We observed an increase in Irf1Δ7 mRNA expression in Th1 cells from tumors compared to those from spleens in controls (Figure 2M). In parallel, we observed a significant decrease in Irf1Δ7 mRNA in tumors when mice were treated with TGFβ-blocking antibody (Figure 2M). We also found a significant increase in IFNγ in tumors from mice treated with TGFβ-blocking antibody (Figure 2N). This observation was validated by an increase in CD4 + IFNγ + cells in tumors from mice treated with TGFβ-blocking antibody (Figure 2O, Supplementary Figure S9A and-S9B). We observed no significant differences in CD4 + Foxp3 + cells in blocking antibodytreated and -untreated tumors (Figure 2P, Supplementary Figure S9B). We obtained similar results in BALB/c mice injected with CT-26 cells in terms of tumor growth, Irf1Δ7 mRNA expression, and IFNγ secretion (Supplementary Figure S10A-C). Together, these data showed that TGFβ was able to induce Irf1Δ7 isoform appearance in vitro and in vivo.

SFPQ splicing factor regulates Irf1 pre-mRNA alternative splicing

Because TGFβ impacted Irf1 pre-mRNA alternative splicing, we wondered what could be the mechanism at play. Using transcriptomic analysis of CD4 + T cells isolated from spleens and tumors, we observed 26 splicing factor genes overexpressed in CD4 + T cells infiltrating tumors (Figure 3A).

Through in silico analysis (Supplementary Figure S11-S14), we analyzed intron 6, exon 7, and intron 7 of Irf1 pre-mRNA sequence to identify splicing factor binding sites. By crossing RNAseq results and in silico results, we identified SFPQ as the splicing factor potentially involved in Irf1Δ7 mRNA expression (Figure 3B). Transcriptomic analysis of Th1 cells isolated from spleens and B16-F10 tumors validated SFPQ and hnRNPF (heterogeneous nuclear ribonucleoprotein F) in Irf1Δ7 mRNA expression (Supplementary Figure S215A and S15B). To determine whether any splicing factors were involved in TGFβ-induced exon 7 alternative splicing, naïve CD4 + T cells were cultured for 2 hours with TGFβ and IL12. We then analyzed the transcriptional expression of Hnrnpf, Sfpq, and Hnrnph1 (another splicing factor upregulated in tumors but not selected in silico). Only Sfpq mRNA expression was significantly increased, whereas neither Hnrnpf nor Hnrnph1 mRNA was statistically impacted by TGFβ pretreatment (Figure 3C). SFPQ activity is described to be regulated by the PI3K/AKT pathway, leading to its fixation on CD45 mRNA [START_REF] Heyd | Phosphorylation-dependent regulation of PSF by GSK3 controls CD45 alternative splicing[END_REF]. After naïve CD4 + T-cell treatment using IL12 and TGFβ, we observed, by immunofluorescence, that SFPQ expression in the nucleus was increased in response to TGFβ (Figure 3D). GSEA analysis showed a positive enrichment of PI3K/AKT pathway-related genes in tumors (Figure 3E). To confirm the involvement of PI3K/AKT pathway, we pretreated IL12-and TGFβincubated naïve CD4 + T cells with a PI3K/AKT pharmacological inhibitor. This completely abolished the increase of Irf1Δ7 mRNA expression in response to TGFβ (Figure 3F). To further study the effect of TGFβ in SFPQ activity, we analyzed SFPQ interaction on Irf1 mRNA upon mRNA immunoprecipitation. We observed that TGFβ increased the binding of SFPQ to Irf1Δ7 mRNA (Figure 3G). When we transfected Th1 cells with an siRNA-targeting Sfpq (Supplementary Figure S16A-C), we observed a decrease in Irf1Δ7 mRNA expression (Figure 3H), whereas Irf1 was not impacted (Supplementary Figure S16D). These data suggest the involvement of SFPQ in Irf1Δ7 mRNA appearance, through PI3K/AKT pathway activation in response to TGFβ.

IRF1Δ7 impairs Th1-cell activity

After seeing TGFβ's effects on Irf1Δ7 occurrence, we focused on determining if this isoform may have a direct effect in Th1 activity. Using siRNA, we saw that Irf1 depletion (Supplementary Figure S16E) associated with a decrease in Il12rb1 and Ifng mRNA expression. Irf1Δ7 (Supplementary Figure S16F)

and Sfpq (Supplementary Figure S16A-C) mRNA depletion associated with an increase in Il12rb1 and

Ifng mRNA expression (Figure 4A-D). Irf1Δ7 and Sfpq mRNA depletion induced higher IFNγ secretion (Figure 4E). In contrast, overexpression of IRF1Δ7 using a retroviral strategy (Figure 4F) inhibited Th1 cell differentiation, as demonstrated by the decreased production of both Il12rb1 and Ifng mRNA (Figure 4G-H). Th1 IFNγ + cells (Figure 4I, Supplementary Figure S17) and IFNγ secretion (Figure 4J)

were also decreased by IRF1Δ7 overexpression.

To understand, how IRF1Δ7 impacted Il12rb1 and Ifng expression in Th1 cells, we first performed a ChIP analysis to determine whether IRF1Δ7 could interact with DNA. We used 293T cells transfected with either Irf1 or Irf1Δ7 or both expression plasmids tagged with histidine or YFP. Cells were transfected with Il12rb1 plasmid containing 154 basepairs of the promoter region and 18 basepairs downstream of the transcription-initiation site known to be the DNA binding site of IRF1 [START_REF] Kano | The contribution of transcription factor IRF1 to the interferon-gamma-interleukin 12 signaling axis and TH1 versus TH-17 differentiation of CD4+ T cells[END_REF]. We confirmed an interaction of IRF1 with Il12rb1 promoter but no interaction of IRF1Δ7 was observed (Figure 4K). When both IRF1 isoforms were present, we observed a disruption of IRF1 interaction with Il12rb1 promoter (Figure 4K). To confirm the negative effect of IRF1Δ7 on IRF1 transcriptional activity, we cloned Il12rb1 promoter into a luciferase reporter plasmid. Il12rb1 reporter plasmid was transfected either alone or with an Irf1-expressing plasmid with an increasing yield of Irf1Δ7expressing plasmid. IRF1 alone significantly activated the Il12rb1 promoter, as evidenced by an increase luciferase activity. The activity of the Il12rb1 promoter, due to the IRF1 isoform, was IFNG expression (Figure 5M-N) was decreased in a dose-dependent manner. To attest the effect of the TME on alternative splicing profile of IRF1 mRNA, we sorted Th1 cells from healthy blood donors and from colon tumors, and observed a significant increase in the IRF1Δ7 isoform in Th1 cells isolated from tumors (Figure 5O). Together, these data emphasize that the short IRF1 mRNA isoform is also expressed in different human T-helper cells, particularly Th1 cells, where it down-modulates Th1 activation, as was observed in mouse models.

Discussion

Alternative splicing of pre-mRNA provides a higher level of genetic diversity and complexity by increasing the number of protein forms of the same gene [START_REF] Nilsen | Expansion of the eukaryotic proteome by alternative splicing[END_REF]. This molecular phenomenon provides a way respond and adapt to a given environment [START_REF] El Marabti | The Cancer Spliceome: Reprograming of Alternative Splicing in Cancer[END_REF]. It is now well-recognized that the immune system is an integral part of the disrupted microenvironment in tumors [START_REF] Binnewies | Understanding the tumor immune microenvironment (TIME) for effective therapy[END_REF]. Our report showed an impact of TME in CD4 + Th1 cells. By comparing the same cells isolated from spleen or tumors, we observed a global alternative splicing increase in CD4 + T cells in tumors. Among them, we focused on Th1 cells, in which we identified a new splicing isoform of IRF1, a master controller of Th1 differentiation. From our results we identified an isoform lacking exon 7 and included a part of intron 8 containing an in-frame stop codon.

TGFβ is the most common cytokine found in TME [START_REF] Papageorgis | Role of TGFbeta in regulation of the tumor microenvironment and drug delivery (review)[END_REF]. It can be secreted both by tumor cells and by non-tumor cells, like Tregs. TGFβ immunosuppressive mechanisms are not fully understood. Studies suggest that TGFβ signaling pathway in tumor cells has an important impact on alternative splicing of CD44, favoring epithelial-to-mesenchymal transition and metastasis [START_REF] Tripathi | Direct Regulation of Alternative Splicing by SMAD3 through PCBP1 Is Essential to the Tumor-Promoting Role of TGF-beta[END_REF]. Here, our data showed that targeting TGFβ with blocking antibodies during tumor growth induced a decrease in Irf1Δ7

expression and an increase in IFNγ secretion in tumors. In vitro, we observed that increasing TGFβ concentration in presence of IL12for 2 hours, corresponding to the peak level of Irf1 expression, increased Irf1Δ7 expression. This treatment did not affect the expression of Tbx21, the master regulator of Th1 cells, and did not induce the expression of Foxp3, the key transcription factor of Treg differentiation. However, we saw a decrease in Il12rb1 expression, a gene whose expression is under IRF1 control, and also a decrease in Ifng expression in a dose-dependent manner. Thus, Irf1 pre-mRNA alternative splicing could exacerbate TGFβ immunosuppressive effects on Th1 cells.

Splicing factors are key regulators of spliceosome activity because they modulate its binding to pre-mRNA [START_REF] Matera | A day in the life of the spliceosome[END_REF]. Their activity is finely regulated by extracellular signals, but underlying molecular mechanisms remain unclear [START_REF] Tarn | Cellular signals modulate alternative splicing[END_REF]. In human T cells, it has been reported that antigen stimulation induces skipping of 3 exons of CD45 to produce CD45RO ( 13), and the shorter isoform is less active than the full-form and tempers T-cell activation. The splicing factor involved in this phenomenon is SFPQ. When phosphorylated by GSK3, SFPQ stays inactive because of its sequestration by TRAP150.

Our results showed that SFPQ is the only splicing factor overexpressed in the TME that could be specific to Irf1 pre-mRNA splicing. Indeed, Sfpq had a significant expression increase when cells were treated with TGFβ. We also found that SFPQ interacted more with Irf1 mRNA when naïve CD4 + T cells were exposed to IL12 and TGFβ. When SFPQ was targeted by siRNA, Irf1Δ7 mRNA expression was decreased, and the inhibition of PI3K (controlling GSK3 activity) activity with a pharmacologic inhibitor, completely canceled the effect of TGFβ on Irf1Δ7 mRNA expression. Nevertheless, the full mechanism leading to SFPQ activation by this cytokine remains to be fully decrypted. However, it is known that SR (serine-and arginine-rich) proteins [START_REF] Zhou | Regulation of splicing by SR proteins and SR protein-specific kinases[END_REF] and hnRNPs [START_REF] Doltra | Magnetic resonance imaging of cardiovascular fibrosis and inflammation: from clinical practice to animal studies and back[END_REF] activity are modulated by the PI3K/AKT pathway. Thus, the disruptions of alternative splicing mediated by TGFβ most likely impact some splicing factors, and SFPQ is responsible for Irf1Δ7 production.

The transcription factor IRF1 has been identified to regulate the expression of a large number of genes in different cell types [START_REF] Kim | IRF-1 expression induces apoptosis and inhibits tumor growth in mouse mammary cancer cells in vitro and in vivo[END_REF][START_REF] Lohoff | Interferon regulatory factor-1 is required for a T helper 1 immune response in vivo[END_REF][START_REF] Ogasawara | Requirement for IRF-1 in the microenvironment supporting development of natural killer cells[END_REF][START_REF] Penninger | The interferon regulatory transcription factor IRF-1 controls positive and negative selection of CD8+ thymocytes[END_REF]. Concerning immune cells, Th1 lymphocyte development is supported by IRF1 through the transcriptional control of Il12rb1 gene expression [START_REF] Kano | The contribution of transcription factor IRF1 to the interferon-gamma-interleukin 12 signaling axis and TH1 versus TH-17 differentiation of CD4+ T cells[END_REF]. In Tregs, IRF1 negatively regulates Foxp3 expression [START_REF] Fragale | IFN regulatory factor-1 negatively regulates CD4+ CD25+ regulatory T cell differentiation by repressing Foxp3 expression[END_REF]. However, alternative splicing of IRF1 is still largely underexplored, especially in immune cells. Nevertheless, experiments using normal and malignant human cervical tissue samples reveals five IRF1 variants lacking some combinations of exons 7, 8, and 9 [START_REF] Lee | Alternative splicing variants of IRF-1 lacking exons 7, 8, and 9 in cervical cancer[END_REF]. The study also showed that these variants were able to inhibit the transcriptional activity of IRF1 full-form. In this context, we observed that IRF1Δ7 had a negative impact on Th1 cell activity.

Thus, IRF1Δ7 impaired IRF1 transcriptional activity by preventing its interaction with the Il12rb1 promoter, probably by sequestration. Th1 cells are known to have potent antitumor properties by their faculty to secrete a large amount of IFNγ, a source of activation of other antitumor cells like CD8 + T cells [START_REF] Goedegebuure | The role of CD4+ tumor-infiltrating lymphocytes in human solid tumors[END_REF]. We found in vitro that in the absence of the short isoform of IRF1, Th1 cells can secrete even more IFNγ, which might potentiate their antitumor activity.

RNA-sequencing has shown that around 95% of human genes are alternatively spliced [START_REF] Sultan | A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome[END_REF]. However, few studies have analyzed the impact of alternative splicing on human immune system homeostasis [START_REF] Martinez | Control of alternative splicing in immune responses: many regulators, many predictions, much still to learn[END_REF]. Our study indicated that, as in mice, an IRF1 alternative isoform without the exon 7 exists in humans. When we targeted the IRF1Δ7 form, we observed an increase in IFNG expression that tended towards exacerbated antitumor activity. We noted that, when compared to Th1 cells from healthy donors' blood, Th1 cells from tumors had a higher expression of IRF1Δ7. Hence, a short form of IRF1 was also expressed in human Th1 cells and possessed similar negative effects on Th1 cell activity, as was observed in mouse models.

In summary, we have shown that a short isoform of IRF1 lacking exon 7 is expressed in Th1 cells, and that its expression is increased in the TME via a TGFβ-dependent mechanism. We also found that IRF1Δ7 had a negative impact in IRF1 isoform transcriptional activity in Th1 cells. Targeting IRF1Δ7 could therefore potentiate Th1 cell antitumor responses. EV IRF1Δ7

Female C57BL/ 6

 6 and BALB/c were purchased from Charles River Laboratories (Saint-Germain sur l'Arbresle, France). To induce tumor formation, 8x105 MC-38, 8x105 CT-26 and 2x10 5 B16-F10 cells were injected subcutaneously into C57BL/6 mice. 8x10 5 CT26 cells were injected subcutaneously into BALB/c mice. Tumor size was measured three times a week with an electronic caliper. After one week, animals were randomized according to tumor size before any treatment to ensure group homogeneity. Mice received three times a week an intraperitoneal injection of IgG1 control antibody (BioXCell; BE0083) or TGFβ blocking antibody (BioXCell; BE0057)(100 µg/mouse). After two weeks, according to our institutional ethical board, animals were sacrificed after anesthetizing. Lymph nodes and spleens were harvested and dissociated using syringe plunger and a 70 µm filter (Miltenyi Biotec). Red Blood Cell Lysis Buffer (155 mM NH 4 Cl, 12 mM NaHCO 3 and 0.1 mM EDTA) were added for 2 minutes, and cells were centrifuged (400 × g, 5 minutes) and then suspended in flow cytometry buffer (eBioscience; 00-4222-26) to be labeled. Tumors were harvested and dissociated using the gentleMACS™ Dissociator (Miltenyi Biotec) in association with the Tumor Dissociation Kit (Miltenyi Biotec; 130-096-730). The tumor shreds were filtered using syringe plunger and a 70 µm filter (Miltenyi Biotec) and centrifuged (400 × g, 5 minutes). Supernatants were harvested for IFNγ ELISA and CD45 + cells were purified with CD45 (TIL) MicroBeads (Miltenyi Biotec; 130-110-618). CD45 + cells were labeled in in flow cytometry buffer and analyzed or sorted by flow cytometry.

  Cell lysates were subjected to sonication (10 secondes at 10% intensity), and protein concentration was assessed using the Bio-Rad DC Protein Assay Kit (5000112, Bio-Rad). Proteins were then denatured, loaded, and separated on SDS-PAGE and transferred on nitrocellulose membranes (Schleicher & Schuell). After blocking with 5% non-fat milk in PBS containing 0.1% Tween 20 (PBST), membranes were incubated overnight with primary antibody diluted (1 μg/mL) in PBST containing 5% BSA, washed, and incubated for 1 hour with secondary antibody (1/10000) diluted in PBST-5% non-fat milk. After additional washes, membranes were incubated for 1 minute with luminol reagent (Santa Cruz Biotechnology). The following antibodies were used for in immunoblotting. Primary antibodies: rabbit anti-IRF1 N-terminal region was purchased from Aviva systems biology, and mouse anti-HSC70 (B-6) was purchased from Santa Cruz Biotechnology. Secondary antibodies: peroxidase AffiniPure goat polyclonal anti-rabbit IgG (H+L)(Jackson ImmunoReseach), and peroxidase AffiniPure goat polyclonal anti-mouse IgG (H+L) (Jackson ImmunoReseach).

Figure 1 .

 1 Figure 1. The tumor microenvironment impacts CD4 + T-cell alternative splicing. (A) Left: Pattern of alternative splicing obtained by RNA-sequencing of CD4 + T cells from three spleens and two MC-38

Figure 2 .

 2 Figure 2. TGFβ affects Irf1Δ7 isoform appearance. (A) GSEA analysis of RNA-sequencing of CD4 + T cells from three spleens and two MC-38 tumors. Geneset: untreated CD4 + T cells vs. TGFβ-treated CD4 + T cells. (B) MA-plot showing RNA-sequencing of CD4 + T cells from spleens and MC-38 tumors

Figure 3 .

 3 Figure 3. The SFPQ splicing factor regulates Irf1 pre-mRNA alternative splicing. (A) Heatmap showing RNA-sequencing of splicing factors in CD4 + T cells from three spleens and two

Figure 4 .

 4 Figure 4. IRF1Δ7 impairs Th1-cell activity. (A and B) RT-PCR analysis of Il12rb1 mRNA in Th1 cells differentiated for 24 hours from naive CD4 + T cells transfected with control siRNA (Ctrl), Irf1-Irf1Δ7-,

Figure 5 .

 5 Figure 5. IRF1Δ7 is present in human Th1 cells and modulates their activity. (A) RT-PCR of Irf1Δ7 mRNA in naive CD4 + T cells isolated from three human blood donors or after 1 hour of differentiation into Th1, Th2, Th17 cells. (B) RT-PCR of Irf1Δ7 mRNA in naive CD4 + T cells and Th1, Th2, Th17 cells

Figure 1 :

 1 Figure 1: The tumor microenvironment impacts CD4 + T cell alternative splicing

Figure 2 :FFigure 3 :

 23 Figure 2: TGFβ affects Irf1Δ7 isoform appearance

Figure 5 :

 5 Figure 5: IRF1Δ7 is present in human Th1 cells and modulates their activity

  Downloaded from http://aacrjournals.org/cancerimmunolres/article-pdf/doi/10.1158/2326-6066.CIR-19-0679/2943272/2326-6066_cir-19-0679v1.pdf by guest on 28 October 2022

  1 Cycle Sequencing Kit (Thermo Fisher Scientific) and the ABI PRISM 3130xl sequencer (Thermo scientific).

	IFNγ (10 μg/mL), anti-IL4 (10 μg/mL), and TGFβ (4 ng/mL) to obtain Tregs. Cells were cultured for 2,
	24, or 72 hours at 37 °C under 5% CO 2 in RPMI 1640 with 10% (vol/vol) FCS supplemented with
	sodium pyruvate, 1% penicillin and streptomycin, and 4 mM of HEPES. Anti-CD3 (Armenian Hamster
	IgG, clone 145-2C11, BE0001-1), anti-CD28 (Armenian Hamster IgG, clone PV-1, BE0015-5), anti-IL4
	(Rat IgG1, clone 11B11, BE0045), and anti-IFNγ (Rat IgG1, clone XMG1.2, BE0055) were obtained
	from BioXcell (West Lebanon, NH, USA), and IL12, IL6, and IL4 were purchased from R&D SYSTEMS
	and TGFβ from Miltenyi Biotec. For in vitro treatments, naive CD4 + T cells were pre-treated for 1 hour
	with PI3K inhibitor (0.1 µM; Merck Chemical France) before beginning the Th1 differentiation with or
	without TGFβ (10 ng/mL).
	Human: Naive CD4 + T cells (CD4 + CD62L hi ) and differentiated CD4 + T-cell subsets (Th1, Th2, Th17) were
	obtained from human healthy donor buffy coats. Cells were purified by flow cytometry and re-stimulated with plate-bound antibodies against CD3 (1 µg/mL) and CD28 (5 µg/mL). 5x10 5 naive T cells were cultured with anti-CD3 (2 μg/mL) and anti-CD28 (2 μg/mL) in the presence of anti-IFNγ (10 μg/mL) and anti-IL4 (10 μg/mL) to obtain Th0; anti-IL4 (10 μg/mL) and IL12 (10 ng/mL) to obtain Th1; anti-IFNγ (10 μg/mL) and IL4 (20 ng/mL) to obtain Th2; anti-IFNγ (10 μg/mL), anti-IL4 (10 μg/mL), IL6 (20 ng/mL), and TGFβ (2 ng/mL) to obtain Th17; anti-IFNγ (10 μg/mL), anti-IL4 (10 μg/mL), and TGFβ (4 ng/mL) to obtain Tregs. Cells were cultured for 2, 24, or 72 hours at 37 °C under 5% CO 2 in RPMI In In vitro T-cell differentiation 1640 with 10% (vol/vol) FCS supplemented with sodium pyruvate, 1% penicillin and streptomycin,
	Mouse: Naive CD4 + T cells (CD4 + CD62L + ) were obtained from spleen and lymph nodes of C57BL/6 and 4 mM of HEPES. Anti-CD3 (clone OKT-3; BE0001-2), anti-CD28 (clone 9.3; BE0248), anti-IL4 (clone
	wild-type (WT) mice. CD4 + T cells were purified from spleen and lymph nodes with anti-CD4 MP4-25D2; BE0240), and anti-IFNγ (clone B133.5, #BE0055) were obtained from BioXcell (West
	microbeads (Miltenyi Biotec; 130-093-227), then were further sorted as naïve CD4 + CD62L + T cells Lebanon, NH, USA), and IL12, IL6, TGFβ, and IL4 were purchased from Miltenyi Biotec.
	using LS column (Miltenyi Biotec; 130-042-401). The purity of the isolated naive T-cell population
	routinely exceeded 95%. 5x10 5 naive T cells were cultured with anti-CD3 (2 μg/mL) and anti-CD28 (2
	μg/mL) in the presence of anti-IFNγ (10 μg/mL) and anti-IL4 (10 μg/mL) to obtain Th0; anti-IL4 (10
	μg/mL) and IL12 (10 ng/mL) to obtain Th1; anti-IFNγ (10 μg/mL) and IL4 (10 ng/mL) to obtain Th2;
	anti-IFNγ (10 μg/mL), anti-IL4 (10 μg/mL), IL6 (20 ng/mL), and TGFβ (2 ng/mL) to obtain Th17; anti-

silico analysis Intron 6, exon 7, and intron 8 mouse sequences were used to search for splicing factors binding motifs. In silico analysis were performed with ENCODE (https://www.encodeproject.org/), RBPmap (http://rbpmap.technion.ac.il/ software), SFmap (http://sfmap.technion.ac.il/), and AVISPA (https://avispa.biociphers.org/galaxy/) software. The Venn diagrams were generated using the online tool provided by VIB and Ghent University (http://bioinformatics.psb.ugent.be/webtools/Venn/).

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	Immunofluorescence (IF)
	1×10 6 of CD4+ T cells per condition were used. Cells were washed, fixed for 10 minutes at room
	temperature with 4% paraformaldehyde (PFA) and permeabilized for 10 minutes on ice with
	methanol (100% glacial) and saturated for 45 minutes with a buffer of 5% BSA and 0.2% Triton X-100
	(Sigma-Aldrich) in PBS. Samples were then incubated overnight at 4°C with primary antibodies

(identified below and detailed in Supplementary Table

S1

), diluted in 5% BSA buffer. Cells were washed two times with a solution of 0.05% Tween 20 in PBS (PBS-Tween) and incubated for 1 hour at room temperature with the secondary antibody (identified below and detailed in Supplementary Table

S1diluted

in the IF blocking buffer. Cells were then washed two times with PBS-Tween solution and two times with ultrapure water. Stained cells resuspended in ultrapure water were dropped off on microscopy slides (Superfrost Ultra Plus®, ThermoFisher Scientific) and incubated at room temperature in dark until water evaporation. Dried slides were mounted with a drop of Mounting Medium containing DAPI (Molecular Probes) and covered with a cover slip (Knittelglass). Slides were imaged with a charge-coupled device-equipped upright microscope (Zeiss) and 40x or 63x objective with a numerical aperture of 1.4 using Zeiss ApoTome system (for 63x magnification). Images were analyzed with ZEN lite (ZEISS). Threshold were defined according to negative control fluorescence (IgG). The following antibodies were used for IF. Primary antibodies: rabbit anti-SFPQ (Abcam); Secondary antibodies: goat polyclonal anti-rabbit IgG (H+L) Cross-Adsorbed Alexa Fluor 568 (ThermoFisher Scientific).

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	4h
	19.8 %
	80.2 %
	H

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	CD4 +	IFNγ +	CD4 +	IFNγ +
	%P:31,22	%P:17,80
	IFNγ
	CD4

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	CD4 + IFNγ +	CD4 + IFNγ +
	%P:38,07	%P:44,44
		J
		**

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Acknowledgments

This work was funded by the Fondation ARC pour la recherche sur le cancer and the Ligue Nationale contre le Cancer. Authors would like to thank to the Plateforme de Cytométrie (http://www.cytometrie-dijon.fr) (Anabelle Sequeira, Dijon, France) for providing regents and access to their cytometers, the Centre de Zootechnie (http://recherche.u-bourgogne.fr/potentiel-derecherche/plateformes-technologiques/centre-de-zootechnie.html) (Valérie Saint-Giorgio, Dijon, France) for providing animal bred facilities and devices to perform ex vivo and in vivo experiments and the DImaCell Imaging Facility for the use of FLIM microscopy and for technical assistance.

DImaCell equipements are funded by the Regional Council of Bourgogne -Franche Comté and the "Fonds Européen de DEveloppement Régional (FEDER)". Finally, authors thank Isabel Grégoire, CGFL for manuscript editing.

We thank "Ligue contre le Cancer de Côte d'Or" and "Ligue contre le Cancer de Saône-et-Loire" for the financial support of this work. We thank "Ligue contre le Cancer" and "Fondation ARC" for the financial support of Antoine Bernard.

decreased in an IRF1Δ7 dose-dependent manner (Figure 4L). We therefore hypothesized that IRF1Δ7 could interact with IRF1 and prevent its binding to DNA. To address this question, we performed a FRET assay in 293T cells transfected with both IRF1 and short-expression plasmids tagged with CFP and YFP respectively. Our data indicated that the donor fluorescence (CFP) decreased faster when both isoforms were expressed together (Figure 4M), indicating a high proximity (<5nm) between the different isoforms. These data suggested a potential interaction between IRF1 and IRF1Δ7. Taken together, these data demonstrated that IRF1Δ7 reduced Th1 activity by decreasing DNA binding of IRF1 to the Il12rb1 promoter.

IRF1Δ7 is present in human Th1 cells and modulates their activity

We then asked whether inhibiting the impact of IRF1 alternative splicing isoform was transposable to human cells. We validated the exon 7 exclusion in T-helper cells in vitro after 1 hour of differentiation (Supplementary Figure S18). This isoform was similar to the Irf1Δ7 identified in mice. Expression of the short IRF1 mRNA isoform, detected by qPCR, was similar among Th1, Th2, and Th17 cells (Figure 5A). In vivo expression of IRF1Δ7, detected by qPCR, was significantly higher in Th1 and lower in Th17 cells compared to naïve T cells (Figure 5B, Supplementary Figure S19). Because IRF1Δ7 impaired Th1 differentiation in mice, we wondered whether this IRF1 isoform could still have an effect on human Th1 differentiation. Targeting this isoform using siRNA (Supplementary Figure S20A-C), led to an increase in Il12rb1 and Ifng mRNA expression (Figure 5C-D), an increase in IFNγ + cells (Figure 5E, Supplementary Figure S20D), and an increased IFNγ secretion evaluated by ELISA (Figure 5F). To determine if TGFβ also had an effect on the alternative splicing profile of IRF1 mRNA in human Th1 cells, naïve CD4 + T cells or ex vivo Th1 cells obtained from healthy blood donors were cultured for 1 hour with IL12 and increasing doses of TGFβ and then differentiated for 24 hours after TGFβ withdrawal (Figure S21). We did not note any differences in IRF1 mRNA expression (Figure 5G andH), but did observe an increase in IRF1Δ7 mRNA (Figure 5I-J). Thus, in these conditions, the fold-change ratio was in favor of IRF1Δ7 mRNA (Figure 5K-L