Mohit Raghuwanshi

Arantxa Vilalta-Clemente

Celia Castro

Sébastien Duguay

Emmanuel Cadel

Philip Jackson

Dimitrios Hariskos

Wolfram Witte

Philippe Pareige

MD, PhD Jean-Emmanuel Bibault
email: jean-emmanuel.bibault@aphp.fr

Benjamin Chaix

MSc Pierre Nectoux

PharmD Benoît Brouard

Benoit Brouard

Influence of RbF post deposition treatment on heterojunction and grain boundaries in high efficient (21.1%) Cu(In,Ga)Se2 solar cells

Keywords: Conversational agent, digital assistant, chatbot, oncology, cancer

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

Introduction

In 1950, Alan Turing envisioned a future where a computer would be able to express itself with a level of sophistication that would render it indistinguishable from humans 1 . The famous "Turing Test" was born: a trial-by-fire where a computer and a human are asked various questions while a third party attempts to distinguish between the two. Today, chatbots can imitate human conversation by using a field of artificial intelligence (AI) known as Natural Language Processing. They are widely available as text or voice-based assistants on smartphones or computers. They provide information and create a dynamic interaction between the agent and the user, without human back-end intervention. In healthcare, the first example of a computer program used as a conversational agent was Joseph Weizenbaum's ELIZA, a program that mimicked a Rogerian psychotherapist that was able to rephrase the patient's sentences as questions and provide prerecorded answers [START_REF] Weizenbaum | ELIZA-a computer program for the study of natural language communication between man and machine[END_REF] . In 1991, Dr. Sbaitso was created as an AI speech synthesis program created for MS DOS Personal Computers. In this software, Dr Sbaitso was a psychologist, with very limited possibilites 3 . Four years later, A.L.I.C.E (Artificial Linguistic Internet Computer Entity) included 40.000 knowledge categories that was later awarded the Loebner Prize thrice [START_REF]AISB -The Society for the Study of Artificial Intelligence and Simulation of Behaviour -Loebner Prize[END_REF] . In 2001, SmarterChild was made available as a bot distributed across SMS networks and is now considered as a precursor to Apple's Siri, which was released on iPhones in 2010.

Chatbots are now widely used in several forms as voice-based agents, such as Siri (Apple, Palo Alto, California, USA), Google Now (Google, Mountain View, California, USA), Alexa (Amazon, Seattle, Washington, USA) or Cortana (Microsoft, Seattle, Washington, USA). But they are not used in healthcare yet. Text-based chatbots are available as Messenger (Facebook, Menlo Park, California, USA) agents or as stand-alone mobile or web applications. Patients can now use chatbots to check for symptoms and to monitor their health [START_REF] Miner | Smartphone-Based Conversational Agents and Responses to Questions About Mental Health, Interpersonal Violence, and Physical Health[END_REF] . But their relevance and validity has rarely been assessed [START_REF] Brouard | Mobile applications in oncology: is it possible for patients and healthcare professionals to easily identify relevant tools[END_REF] . In this review we examine the current landscape of the use of conversational agents, or chatbots, in oncology. We address available literature that demonstrates the power of chatbots and discuss the few examples of their applications in oncology. We also describe the ongoing trials evaluating chatbots in oncology and finally discuss the challenges that remain to be addressed for a successful implementation.

Methods

Systematic search

This review focuses on articles published in peer-reviewed journals and conference proceedings. The review is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Search Strategy

We conducted a comprehensive search of PubMed, Embase and Google Scholar for relevant peer-reviewed publications from 1980, to October 2018. A comprehensive list of MeSH terms and keywords was used to query Medline; category 1: "conversational agents", "chatbots", and category 2: "healthcare", "oncology", "cancer". Each term of category 1 was researched separately and as an association with the second category. The search strategy also included screening of reference lists of relevant publications ("snowball" search technique).

ClinicalTrial.gov was also searched for ongoing trials using chatbots in healthcare.

Eligibility Criteria

Publications were selected if they met the following criteria: clinical studies reporting on the use of conversational agents or chatbots in healthcare, randomized studies comparing chatbots to standard interventions, ongoing studies registered on ClinicalTrials.gov. Studies reporting on the use of AI systems for image segmentation and classification, diagnosis and outcome prediction were excluded.

Results

Published studies

The search returned 25 studies that were individually screened. Six studies were included in the final analysis. The PRISMA flowchart is shown in figure 1.

Cancer screening

The iDecide chatbot is a conversational agent that delivers information about prostate cancer to patients (epidemiology, risk factors, treatment options and their side-effects). A study was designed to evaluate the effects of iDecide on prostate cancer knowledge, informed decisionmaking self-efficacy, technology use self-efficacy, and intention to engage in informed decision-making among African American men [START_REF] Owens | Evaluation of a Computer-Based Decision Aid for Promoting Informed Prostate Cancer Screening Decisions Among African American Men: iDecide[END_REF] . This metrics were measured on the complete cohort before and after use of the chatbot. Participants were African American men, aged 40 years and over, without a prior prostate cancer diagnosis (n = 354). Participants experienced significant improvements in their prostate cancer knowledge (p≤0.001), informed decision-making self-efficacy (p≤0.001), and technology use self-efficacy (p≤0.001), post intervention. Sixty-seven percent of participants reported an intention to engage in informed decision-making.

Mental health

As much as 40% of cancer patients will face severe anxiety or depression [START_REF] Kadan-Lottick | Psychiatric Disorders and Mental Health Service Use in Patients with Advanced Cancer[END_REF][START_REF] Mitchell | Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: a meta-analysis of 94 interview-based studies[END_REF] . In that setting, chatbots could be used to engage patients and provide them support at any time. Woebot is a Web-based cognitive-behavioral therapeutic (CBT) that was evaluated by Fitzpatrick et al. [START_REF] Fitzpatrick | Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial[END_REF] The objective their study was to determine the feasibility, acceptability, and preliminary efficacy of a fully automated conversational agent to deliver a self-help program for people with symptoms of anxiety and depression. In an unblinded trial, 70 individuals were recruited online from a social media site and were randomized to receive either 2 weeks (up to 20 sessions) of self-help content from CBT in a conversational format with a Woebot (n=34) or were directed to the National Institute of Mental Health ebook, "Depression in College Students," as an information-only control group (n=36). The participants completed Webbased versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Participants in the Woebot group engaged with the conversational agent an average of 12.14 (SD 2.23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Patients in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6.47; P=.01), in an intent-totreat univariate analysis, while those in the information control group did not. Another chatbot, Koko, was designed to express empathy, which could be positively leveraged in cancer-patients [START_REF] Morris | Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions[END_REF] . In this study, Morris et al report on the design of a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. The authors also assessed how users might appraise this system. The authors used responses from an existing pool of online peer support data that were repurposed by the chatbot and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. Ratings from 37,169 users were collected to evaluate the system. A controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions was performed: the majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. But, users still made the difference between the chatbot's and the human answers and significantly preferred the efforts of their peers (p<0.001). This effect was maintained in a controlled study (p=0.02). Only one randomized controlled trial has been published [START_REF] Ly | A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods[END_REF] . In this study, Ly et al assessed the effectiveness and adherence to a smartphone app delivering strategies used in positive psychology to improve happiness and reduce negative symptoms, via an automated chatbot (Shim) for a non-clinical population. A total of 28 participants were randomized to either receive the chatbot intervention (n = 14) or to a wait-list control group (n = 14). Findings revealed that participants who adhered to the intervention (n = 13) showed a significant interaction effects with the chatbot compared to the wait-list control group.

Expression of caring and understanding is valued in supportive human communications. A study recently published by Liu et al. assessed whether humans could perceive empathy from a chatbot: in the first group of people (N = 158), participants simply read a dialogue between a chatbot and a human user. In a second group, participants (N = 88) interacted with a real chatbot. Three types of empathic expression were tested: sympathy, cognitive, and affective empathy, on the individuals' perceptions of the service and the chatbot. Data revealed that expression of sympathy and empathy is favored over unemotional provision of advice, in support of the Computers are Social Actors (CASA) paradigm [START_REF] Liu | Should Machines Express Sympathy and Empathy? Experiments with a Health Advice Chatbot[END_REF] .

Lifestyle change

FitTrack was developed to assess the ability of an Embodied Conversational Agents (ECA) called "Laura" to establish a long-term therapeutic alliance with users, and to determine if these relationships could be used to induce health behavior change [START_REF] Bickmore | Relational Agents: Effecting Change through Human-Computer Relationships[END_REF] . In the FitTrack system, the ECA used nonverbal behavior to convey information in addition to the speech channel.

Laura played the role of an exercise advisor who motivated sedentary persons to obtain the minimum level of physical activity recommended by current public health guidelines 15 over a two-month period of time. The system ran on standard home desktop computers so that participants could interact with the system on a daily basis. The system used the BEAT textto-embodied-speech translator [START_REF] Cassell | BEAT: the Behavior Expression Animation Toolkit[END_REF] to generate nonverbal behavior for the agent. FitTrack was successfully used in two randomized clinical trials, one involving MIT students and the second an urban, older adult population [START_REF] Bickmore | Establishing and maintaining long-term human-computer relationships[END_REF][START_REF] Bickmore | Health dialog systems for patients and consumers[END_REF] .

Ongoing trials

A search on ClinicalTrials.gov returns four trials evaluating chatbots in healthcare. In the United Kingdom, a non-randomized trial is being performed by the National Health Service to compare the Babylon chatbot to the non-emergency 111 telephone number 19 . Patients can interact with an automatic agent in order to describe their symptoms. Advices and information are given in return by the chatbot. Randomized studies demonstrating the superiority (or at least non-inferiority) of chatbots, compared to an intervention performed by a physician, do not exist. If chatbots are to be safely used by a large number of patients, they must be evaluated like a medical device, or even a drug.

The Buddy Study (NCT02742740) evaluates an ECA Oncology Trial Advisor for Cancer

Trials that acts as an advisor to patients on chemotherapy regimens, promoting protocol adherence and retention, providing anticipatory guidance and answering questions. The chatbot also serves as a conduit to capture information about complaints or adverse events. Usability metrics will include session time, satisfaction, and error rates. Subjects will be identified from among patients on chemotherapy regimens at the Boston Medical Center [START_REF]ECA Oncology Trial Advisor) for Cancer Trials -Full Text View -ClinicalTrials[END_REF] . All subjects will be enrolled for 2 months and randomized to the chatbot group or control group. Baseline data will be collected after enrollment and before randomization and will include: sociodemographic data, mental and social health, medical diagnosis (cancer type and stage), the Short Assessment of Health Literacy and the Short Portable Mental Status Questionnaire (SPMSQ) for cognitive impairment. The exit interview will be conducted 2 months after baseline. Data collected at the exit interview will include: mental and social health and the satisfaction with ECA Review of the subject's medical chart will be performed bi-weekly while the subject is enrolled. Chart review will be performed by a staff member who is blinded to the subject's group (intervention vs control). Primary outcome will be treatment protocol adherence, defined by the number of treatment visits attended/number of treatment visits scheduled. The secondary outcome will measure: subject satisfaction, number of adverse events as reported through the ECA and directly to clinic by patient, time to detect and resolve adverse events as reported through the ECA and directly to clinic by patient, adverse event false alarm rate, as reported through ECA and directly to clinic by patient.

A third study, the RAISE project (NCT01458002 ) [START_REF]Online Tailored Interventions & Relational Agents for Exercise and Sun Protection -Full Text View -ClinicalTrials[END_REF] , is designed to promote exercise and sun protection. The primary aims are to develop and assess the effectiveness of a tailored internet intervention on a national sample, to develop and assess the effectiveness of the internet intervention enhanced by a relational agent, and to determine if the intervention with the relational agent can outperform the regular tailored internet intervention. The study will include three groups (Control, Internet, Internet plus Relational Agent). A representative national sample of 1639 individuals at risk for both behaviors will be recruited.

A fourth study, INCASE, a randomized, controlled, blind study will be launched in France, comparing the information given by the Vik chatbot vs a multidisciplinary group of physicians (from medical, radiation and surgical oncology) to breast cancer patients (NCT03556813). One hundred and forty patients will be randomized in this non-inferiority trial. The EORTC QLQ-INFO25 questionnaire, that was validated to assess cancer patients' information [START_REF] Arraras | An international validation study of the EORTC QLQ-INFO25 questionnaire: an instrument to assess the information given to cancer patients[END_REF] , will be used to compare the quality of the information provided to the two groups of patients by the physician or the chatbot.

Applications in radiation oncology

In the field of Radiation Oncology (RO), chatbots could be leveraged in each step of the process: from cancer screening, diagnosis to patient information on treatment modalities and potential toxicities. They could also be used to seamlessly collect patient-reported outcomes (PRO) during and after treatments in a convenient manner. Algorithms could then be used on curated data to analyze in real-time PRO in order to red-flag the most at-risk patients or trigger an additional consultation with the physician and adapt supportive care treatments.

Considering the documented discrepancies in available RO professionals in Europe, this kind of tool could have a significant interest [START_REF] Dunscombe | Guidelines for equipment and staffing of radiotherapy facilities in the European countries: final results of the ESTRO-HERO survey[END_REF][START_REF] Lievens | Radiotherapy staffing in the European countries: final results from the ESTRO-HERO survey[END_REF] .

Conclusion

The scarcity of clinical trials evaluating conversational agents in healthcare in general and oncology in particular is in sharp contrast with their potential benefits for the patients and the healthcare system. Health chatbots will need to be used by many and have access to rich data sets in order to increase their knowledge of medical terms, symptoms, and treatments. These systems will not replace the physicians and should be considered as a resource to enhance the efficacy of healthcare interventions [START_REF] Goldstein | Human-Machine Collaboration in Cancer and Beyond: The Centaur Care Model[END_REF] . If chatbots are shown to be effective and safe, they could be prescribed like any drug to improve patient information, monitoring, or treatment adherence. The best conversational agents may save patients with minor health concerns from a visit to the doctor. This could allow clinicians to spend more time to treat patients who need a consultation the most. Consultations for whose symptoms that don't necessitate an actual consultation could be avoided, potentially saving a significant amount of money and resources. But if the quality of these computer programs is not rigorously assessed, they could be unable to actually detect the difference between minor and major symptoms, without anyone knowing. The lack of objective evidence for the relevance and efficacy of this kind of applications is concerning since they are poised to be used by more patients. There are several differences between a conversational agent and a drug. For example, a standard drug formulary is locked down, evaluated, and then distributed. Conversational agents can effectively learn and adapt while deployed such that a conversational agent today deployed to millions of users will likely be vastly different in response to its operation within weeks.

Significant hurdles currently exist in the widespread application of chatbots at this time. One major limitation stems from noncompliance of many commercially available systems with the Health Insurance Portability and Accountability Act.

Table 1 .

 1 Studies evaluating conversational agents

	Tables
	Tool	Intervention	Inclusion criteria	Number of patients	Endpoints
			-African-American
			-Men
		Population	-40 years-old or		-Prostate cancer knowledge,
	iDecide	information to all	older	354	-Informed decision-making self-efficacy,
		cohort	-With or without		-Intention to engage in informed decision-making
			prostate cancer
			diagnosis
		2 weeks of self-help
		content from CBT
		in a conversational
		format with a
	Woebot (Web-based cognitive-behavioral therapeutic)	Woebot (n=34) in College or National Institute of Mental Health ebook, "Depression	Online recruitment on social media	70	Feasibility, acceptability, and preliminary efficacy of the tool
		Students," as an
		information-only
		control group
		(n=36)
		Answers from a
	Koko	database of 37169 real users vs answers from the conversational	Online recruitment on Koko platform	1284	User perceptions of a response depending on the alleged source of a response (human or machine)
		agent


Véronique Adoue
email: veronique.adoue@inserm.fr

Bénédicte Binet

Agathe Malbec

Joanna Fourquet

Paola Romagnoli

Joost Pm Van Meerwijk

Sebastian Amigorena

Olivier P Joffre
email: olivier.joffre@inserm.fr

Joost P M Van Meerwijk

Setdb1 +/+

The Histone Methyltransferase SETDB1 Controls T Helper Cell Lineage Integrity by Repressing Endogenous Retroviruses

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

INTRODUCTION

T lymphocytes protect vertebrates against a wide variety of endogenous and exogenous dangers. Their efficacy comes at least in part from their ability to adapt their phenotype and function to the threat detected by the cells of the innate immune system. Depending on the nature and strength of the signals delivered by these cells and the surrounding tissues, T lymphocytes mobilize different networks of transcription factors to induce distinct developmental programs that coordinate the acquisition of lineage-specific and dangeradapted phenotypes and functions [START_REF] O'shea | Mechanisms Underlying Lineage Commitment and Plasticity of Helper CD4+ T Cells[END_REF][START_REF] Wilson | Epigenetic control of T-helper-cell differentiation[END_REF]. This plasticity is best illustrated by naïve CD4 + T cells, which are able to differentiate into multiple distinct effector populations.

The transcription factors mobilized in response to environmental signals orchestrate a massive remodeling of the epigenetic landscape of T cells [START_REF] Kanno | Transcriptional and Epigenetic Control of T Helper Cell Specification: Molecular Mechanisms Underlying Commitment and Plasticity[END_REF][START_REF] Wilson | Epigenetic control of T-helper-cell differentiation[END_REF]. These dynamic changes in chromatin composition and compaction are necessary to set up and stabilize gene expression programs and to allow their faithful transmission to the progeny. Indeed, interfering with the post-translational modifications of histones or with DNA methylation critically affects the differentiation and stability of effector and memory T cells [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF][START_REF] Wilson | Epigenetic control of T-helper-cell differentiation[END_REF][START_REF] Xiao | The Costimulatory Receptor OX40 Inhibits Interleukin-17 Expression through Activation of Repressive Chromatin Remodeling Pathways[END_REF][START_REF] Young | Differentiation of the T helper phenotypes by analysis of the methylation state of the IFN-gamma gene[END_REF]. In CD4 + T lymphocytes, epigenetic remodeling is largely coordinated by STAT proteins and by the master regulators specific to each lineage, such as T-bet and GATA-3 for, respectively, Th1 and Th2 cells [START_REF] Kanno | Transcriptional and Epigenetic Control of T Helper Cell Specification: Molecular Mechanisms Underlying Commitment and Plasticity[END_REF][START_REF] O'shea | Genomic views of STAT function in CD4+ T helper cell differentiation[END_REF]. These transcriptional regulators finetune the balance between T helper cell determination and plasticity by directing the deposition of permissive epigenetic marks at lineage-specific cis-regulatory elements, and by targeting repressive epigenetic pathways to the loci associated with alternative fates [START_REF] Kanno | Transcriptional and Epigenetic Control of T Helper Cell Specification: Molecular Mechanisms Underlying Commitment and Plasticity[END_REF][START_REF] O'shea | Genomic views of STAT function in CD4+ T helper cell differentiation[END_REF][START_REF] Vahedi | STATs shape the active enhancer landscape of T cell populations[END_REF][START_REF] Wilson | Epigenetic control of T-helper-cell differentiation[END_REF].

Trimethylation of Histone H3 on Lysine 9 (H3K9me3) has varied roles in the control of genome functions [START_REF] Mozzetta | Sound of silence: the properties and functions of repressive Lys methyltransferases[END_REF]. This epigenetic mark was first implicated in the scaffolding and function of constitutive heterochromatin [START_REF] Lachner | Methylation of histone H3 lysine 9 creates a binding site for HP1 proteins[END_REF][START_REF] Peters | Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability[END_REF]. H3K9me3 deposition at promoters of genes encoding developmental regulators is necessary to repress these loci and maintain embryonic stem cell pluripotency [START_REF] Bilodeau | SetDB1 contributes to repression of genes encoding developmental regulators and maintenance of ES cell state[END_REF]. In adult cells, H3K9me3-dependent repression of gene expression in euchromatin and facultative heterochromatin is also important to define and maintain cell identity [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF]2016;[START_REF] Liu | Chromatin Landscape Defined by Repressive Histone Methylation during Oligodendrocyte Differentiation[END_REF]. However, the repertoires of loci and genomic elements that are targeted, as well as the molecular mechanisms at work remain poorly characterized.

H3K9me3 also accumulates on the body of active genes where it may affect transcription elongation and alternative splicing [START_REF] Saint-André | Histone H3 lysine 9 trimethylation and HP1γ favor inclusion of alternative exons[END_REF][START_REF] Vakoc | Histone H3 Lysine 9 Methylation and HP1γ Are Associated with Transcription Elongation through Mammalian Chromatin[END_REF]. H3K9me3 is thus a versatile chromatin mark, with multiple and at times potentially opposing functions.

Several lysine methyltransferases trimethylate H3K9. These include SUV39H1, SUV39H2 and SETDB1, which all belong to the SUV39H family [START_REF] Mozzetta | Sound of silence: the properties and functions of repressive Lys methyltransferases[END_REF]. SUV39H1 and SUV39H2 were first identified as key components of constitutive heterochromatin [START_REF] Peters | Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability[END_REF]2002), whereas SETDB1 was initially found to be involved in the dynamic repression of gene transcription at euchromatin and facultative heterochromatin [START_REF] Schultz | SETDB1: a novel KAP-1-associated histone H3, lysine 9-specific methyltransferase that contributes to HP1-mediated silencing of euchromatic genes by KRAB zinc-finger proteins[END_REF]. SUV39H1 can also repress euchromatic gene expression through H3K9me3 deposition at promoters [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF][START_REF] Liu | Chromatin Landscape Defined by Repressive Histone Methylation during Oligodendrocyte Differentiation[END_REF], whereas the maintenance of H3K9me3 at pericentromeric heterochromatin during DNA replication might depend on a stepwise process involving H3K9 mono-and tri-methylation by SETDB1 and SUV39H1, respectively [START_REF] Loyola | The HP1α-CAF1-SetDB1-containing complex provides H3K9me1 for Suv39-mediated K9me3 in pericentric heterochromatin[END_REF]. In embryonic stem cells, these two enzymes also collaborate to repress ERVs [START_REF] Buenrostro | Suv39hdependent H3K9me3 marks intact retrotransposons and silences LINE elements in mouse embryonic stem cells[END_REF]. Since various cell types use these repeat elements as cis-regulatory modules to shape and control gene networks [START_REF] Chuong | Regulatory activities of transposable elements: from conflicts to benefits[END_REF], SETDB1 and SUV39H1 may therefore also control cell integrity through deposition of H3K9me3 at ERVs.

In T cells, while SETDB1 is implicated in OX40-dependent repression of the Il17a locus in Th17 cells [START_REF] Xiao | The Costimulatory Receptor OX40 Inhibits Interleukin-17 Expression through Activation of Repressive Chromatin Remodeling Pathways[END_REF], SUV39H1 controls Th2 cell stability by depositing H3K9me3 at the Ifng promoter [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF]. However, the deregulation of the Ifng locus observed in Suv39h1 -/-cells cannot by itself explain a loss of Th2 cell integrity. Other critical Th1 cell lineage-specific loci might therefore be controlled by H3K9me3-dependent repressive mechanisms. In addition, while a clear H3K9me3 signal is detected at the gene encoding Tbet in Th2 cells, SUV39H1 has no impact in the deposition of the repressive mark at this locus [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF]. Together with the fact that H3K9me3 disappearance at euchromatin and facultative heterochromatin is limited in SUV39H1-deficient cells [START_REF] Peters | Histone H3 lysine 9 methylation is an epigenetic imprint of facultative heterochromatin[END_REF], these observations suggest that other H3K9me3-dependent epigenetic pathways critically control Th2 cell stability.

Here we examined the effects of SETDB1-dependent H3K9me3 deposition on CD4 + T cell differentiation. We found that SETDB1 restricts Th1 cell priming and ensures Th2 cell integrity. Unlike their wild-type counterparts, Setdb1 -/-Th2 cells readily expressed Th1associated genes when exposed to the Th1-instructing cytokine interleukin (IL)-12. SETDB1 repressed Th1-related loci by depositing H3K9me3 at a subset of ERVs that flank and repress Th1 enhancers or behave themselves as cis-regulatory elements of Th1 genes. Our findings reveal a repertoire of ERVs that have been co-opted to behave as Th1-specific cisregulatory modules, and outline a model wherein H3K9me3 deposition by SETDB1 locks the Th1 gene network and ensures Th cell lineage integrity by repressing these repeat elements.

RESULTS

Th1 priming is enhanced in the absence of SETDB1

To analyze the role of SETDB1 in CD4 + T cell differentiation and plasticity, we generated mice homozygous for a LoxP-flanked Setdb1 allele and expressing (Setdb1 -/-), or not (Setdb1 +/+ ), the CRE recombinase under the control of the Cd4 promoter. This strategy resulted in the almost complete absence of SETDB1 from CD4 single-positive thymocytes (Figure S1A). As SETDB1-deficiency was not compensated by other methyltransferases targeting H3K9 (Figure S1B), we also observed a marked loss of H3K9me3 from naïve Setdb1 -/-CD4 + T cells (Figure S1C). The use of the Cd4-Cre transgene, which induces SETDB1 deletion relatively late in ontogeny, allowed for normal intrathymic T cell development. Indeed, the total number of cells in the thymus, the relative proportions of the four main populations of thymocytes, and the proportion of mature CD4 + T cells were similar in Setdb1 -/-mice and control littermates (Figures S1D-S1H).

In peripheral lymphoid tissues, we detected no consequences of T cell-specific SETDB1deficiency on other populations of immune cells (Figures S2A-S2C). SETDB1 was previously implicated in survival in various cell types. For example, conditional deletion of the enzyme in mice expressing the CRE recombinase under the control of the Mb1 promoter abolishes the B cell lineage [START_REF] Collins | The histone methyltransferase SETDB1 represses endogenous and exogenous retroviruses in B lymphocytes[END_REF]. The impact of Setdb1 deletion on T cell survival was less pronounced: despite substantially increased activity of caspase-3/7, we only observed a partial loss of the T cell pool (Figures S2D-S2G).

To obtain a global view of the changes in gene expression induced by Setdb1 deletion, we performed RNA sequencing (RNA-seq) on naïve Setdb1 -/-and Setdb1 +/+ CD4 + T cells. Most of the differentially expressed genes were more expressed in Setdb1 -/-than in Setdb1 +/+ cells (Figure S3A), consistent with a globally repressive effect of H3K9me3 on gene transcription.

Among the overexpressed genes, those involved in cell division were particularly enriched (Figures S3B andS3C). The proportion of CD4 + T cells expressing the nuclear antigen Ki67 being higher in Setdb1 -/-mice than in control littermates (Figure S3D), this increased expression of cell division-related genes most likely resulted from the observed lymphopenia rather than from a direct impact of Setdb1 deletion on the regulation of these genes. Moreover, we found no particular enrichment for H3K9me3 domains at cell cycle-related genes (Figure S3E).

To assess if Setdb1 deletion could affect T cell function, we next analyzed the differential transcription of a gene set related to Th cell differentiation. We found no major differences in the expression of these genes between Setdb1 -/-and Setdb1 +/+ cells (Figure 1A and Table S1), despite the presence of H3K9me3 domains close to loci involved in lymphocytemediated immunity (Figure S3E). The vast majority of the genes were equally expressed in Setdb1 -/-and Setdb1 +/+ cells, most of the differentially expressed loci were transcribed at very low levels, and no lineage-specific transcriptomic signature appeared when focusing on deregulated genes. This lack of effect of SETDB1 deficiency on naïve CD4 + T cell programming was further confirmed when we analyzed the production of lineage-specific mediators following acute ex vivo stimulation (Figure 1B). Together, these observations show that Setdb1 -/-cells are not a priori biased towards a specific Th lineage.

To test whether SETDB1 regulated Th cell lineage commitment in response to environmental signals, we analyzed Setdb1 -/-and Setdb1 +/+ CD4 + T cell fate in an IL-12-mediated Th1 differentiation assay. As expected from our experiments measuring caspase 3/7 activity ex vivo (Figures S2F andS2G), SETDB1 deficiency impaired T cell survival at early time points (Figure S3F). However, a significant proportion of cells remained viable and showed normal activation upon TCR triggering (Figures S3G andS3H). As T cell differentiation depends on cell cycle progression, we next analyzed the proliferative response of activated CD4 + T cells.

There were no differences between control and mutant cells (Figure 1C), which displayed similar proliferation indexes and percentages of divided cells (Figure 1D). Upon exposure to IL-12, Setdb1 -/-cells also expressed T-bet at a similar level as their Setdb1 +/+ counterparts (Figures 1E and1F). However, SETDB1-deficiency led to greater acquisition of lineagespecific functions. Indeed, both percentages of cells producing Th1-related cytokines and amounts of cytokine synthesized per cell were higher in Setdb1 -/-than in Setdb1 +/+ cells (Figures 1G-1J). This exacerbated production of cytokines was not the result of a global transcriptional derepression, since we did not evidence any aberrant secretion of soluble mediators related to alternative lineages (Figure 1J). It was also not the result of a greater sensitivity of the Setdb1 -/-cells to IL-12 (Figures S3I-S3K). Together, these results highlight a key role for SETDB1 in regulating the magnitude of Th1 responses.

Impaired acquisition of the Th2 phenotype by SETDB1-deficient cells

Most of the genes encoding lineage-specific cytokines in naïve CD4 + T cells have both permissive and repressive epigenetic marks on their promoters and enhancers. They are thus poised for transcription to guarantee the plasticity, while also preserving the identity of the cells. The enhanced Th1 response observed in Setdb1 -/-cells might, therefore, result from a loss of H3K9me3 at these cis-regulatory regions, and this may potentially affect other lineages. To test this hypothesis, we cultured Setdb1 +/+ and Setdb1 -/-naïve CD4 + T cells in Th2 polarizing conditions. The proliferative response and viability of Setdb1 +/+ and Setdb1 -/- cells were comparable at day six (Figures 2A-2C). Moreover, Setdb1 -/-cells seemed to commit to the Th2 lineage similar to their control counterparts, with almost all cells expressing GATA-3 (Figures 2D and2E) and no aberrant expression of T-bet (Figure 2F).

Production of IL-13 and IL-4 was also similar between Setdb1 +/+ and Setdb1 -/-cells (Figures 2G-2I). Thus, in contrast to what we observed in Th1-polarizing conditions, there was no enhanced production of Th2 lineage-specific mediators by Setdb1 -/-cells grown in the presence of IL-4. In fact, global transcriptional profiling revealed that the level of expression of the Th2 signature genes was significantly lower in Setdb1 -/-Th2 cells than in their wild-type counterparts, although both wild-type and mutant naïve CD4 + T cells efficiently switched on the Th2 program upon exposure to IL-4 (Figures 2J and2L). This impaired induction of the Th2 gene network correlated with lower expression of GATA-3 (Figures 2D and2E) and with decreased chromatin accessibility at Th2 gene enhancers (Figure 2K). Moreover, unlike their wild-type counterparts, Setdb1 -/-cells grown in Th2-polarizing conditions also produced small amounts of IFN-γ (Figures 2G-2I). This IFN-γ 'leak' may result from defective repression of Th1-related loci in Th2 cells, which could potentially antagonize the Th2 gene expression program and lead to functional and phenotypic instability.

SETDB1 is required for stable Th2 cell commitment

To assess if SETDB1 controlled Th2 cell plasticity, we cultured Setdb1 -/-and Setdb1 +/+ cells in Th2-polarizing conditions and then switched to culture in Th1-polarizing medium. In agreement with the Th1/Th2 paradigm, the control Setdb1 +/+ Th2 cells remained stable (Figures 3A-3C). By contrast, a large fraction of the Setdb1 -/-cell population secreted IFN-γ, this phenomenon being even more pronounced after four days of culture (Figures 3A and3B). IFN-γ secretion was accompanied by decreased expression of GATA-3 and increased expression of T-bet (Figures 3D and3E). In fact, SETDB1-deficiency allowed the virtually complete reprogramming of Th2 cells upon exposure to Th1-instructing signals, with extinction of Th2 gene expression and induction of a large part of the Th1 gene set (Figures 3F and3G and Table S2). This plasticity was not the result of a bias in Setdb1 -/-cells programming due to lymphopenia (Figures S4A-S4C).

SETDB1 plays a key role in silencing ERVs [START_REF] Buenrostro | Suv39hdependent H3K9me3 marks intact retrotransposons and silences LINE elements in mouse embryonic stem cells[END_REF][START_REF] Matsui | Proviral silencing in embryonic stem cells requires the histone methyltransferase ESET[END_REF]. Ectopic expression of these retrotransposons can lead to activation of the nucleic acid-sensing machinery and, eventually, to production of type I IFNs [START_REF] Chiappinelli | Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses[END_REF]. Together with IL-12 and IFN-γ, type I IFNs can reprogram Th2 cells into stable cells producing IFN-γ and expressing both GATA-3 and T-bet [START_REF] Hegazy | Interferons Direct Th2 Cell Reprogramming to Generate a Stable GATA-3+T-bet+ Cell Subset with Combined Th2 and Th1 Cell Functions[END_REF]. Activation of ERVs in Setdb1 -/-Th2 cells might thus account for the increased plasticity that we observed: ERV-induced secretion of IFN-α and IFN-β might reprogram the Th2 cells in combination with exogenous IL-12 and the observed aberrant production of IFN-γ. However, we found no abnormal levels of type I IFN mRNA in Setdb1 -/-cells (Figures S4D andS4E), and neutralization of IFN-γ did not prevent Setdb1 -/-Th2 cells from switching to a Th1 phenotype (Figures S4F andS4G). To assess directly if the ectopic expression of Th1-instructive mediators by Setdb1 -/-Th2 cells might account for their phenotypic instability, we co-cultured Setdb1 -/-and Setdb1 +/+ Th2 cells in Th1 polarizing conditions. In this setting, Setdb1 -/-cells still showed substantial plasticity, while their control counterparts did not (Figures S4H-S4J).

Therefore, SETDB1 critically controls Th2 cell commitment through a cell-intrinsic mechanism.

To test the role of SETDB1 in CD4 + T cell programming in vivo, we next immunized Setdb1 +/+ and Setdb1 -/-mice with the 1W1K variant of the I-E alpha chain immunodominant peptide formulated in RIBI adjuvant. Using 1W1K/I-A b tetramers, we first showed that antigen-specific Setdb1 -/-CD4 + T cells expanded and accumulated in the lymph nodes draining the site of immunization (Figures 3H and3I). The frequency of tetramer-positive cells was lower than in control mice but this quantitative defect did not prevent Setdb1 -/- cells from differentiating into Th1 and Th2 effector cells. However, relative to the size of the antigen-specific T cell compartment, the frequency of CD4 + T cells producing IFN-γ was strongly increased in Setdb1 -/-as compared to Setdb1 +/+ mice (Figure 3J). This exacerbated Th1 response shifted the Th1/Th2 balance towards Th1 dominant immunity (Figure 3K). Therefore, as observed in vitro, SETDB1 controls the Th1 gene expression program in vivo.

SETDB1-dependent H3K9 trimethylation at a subset of ERVs

To determine how SETDB1 controlled Th2 cell commitment and stability, we performed highthroughput chromatin immunoprecipitation (ChIP) of H3K9me3 in Setdb1 +/+ and Setdb1 -/-Th2 cells. We first analyzed H3K9me3 signal at gene promoters. As expected from the literature, our genome-wide analysis revealed an inverse correlation between the deposition of the mark and gene expression (Figure S5A). However, SETDB1-deficiency did not significantly impact on H3K9me3 deposition at Th1-related genes (Figure S5B). We then analyzed the genome-wide distribution of H3K9me3 domains in Setdb1 +/+ Th2 cells. We observed no more peaks at gene bodies or promoters than would be expected by a random distribution (Figures 4A and4B). By contrast, we found statistically significant enrichment of H3K9me3 domains at enhancers (defined as non-promoter H3K4me1 + genomic regions) and at ERVs (Figures 4A and4B and Table S3). Since 74% of the peaks at enhancers also overlapped with ERVs (Figure 4C), these observations suggest that the ERVs rather than the enhancers themselves are the main targets for H3K9 trimethylation. To test this hypothesis, we analyzed the distribution of H3K9me3 across the length of individual H3K9me3 + ERV and enhancer sequences. The H3K9me3 signal clearly peaked at and aligned with the center of the ERVs (Figures 4D and4E). By contrast, the signal appeared randomly distributed across the enhancer sequences, with only a modest accumulation on their flanking regions. In agreement with the repressive role of the histone mark and with the status of the cisregulatory elements, H3K9me3 signal was higher on the flanks of poised than of active enhancers, and the mark only overlapped inactive cis-regulatory elements (Figure 4F).

H3K9me3 accumulation over the enhancer sequence might thus result from a spreading from neighboring ERVs (Figure 4G), or might be due to physical overlap between the enhancers and the retroelements (Figure 4H). Whatever the model, we observed that H3K9me3 domains were closer to the center of the ERVs than to the center of the enhancers (Figure 4I). Therefore, these results support the hypothesis that H3K9me3 is deposited at a subset of ERVs that overlap or flank enhancers, which might lead to their repression.

Finally, to determine which lysine methyltransferase is necessary for H3K9me3 deposition at ERVs, we determined if the retroelements marked by H3K9me3 in wild-type cells were still covered by the repressive mark in Setdb1 -/-or Suv39h1 -/-Th2 cells. While SUV39H1deficiency had no major impact on H3K9me3 deposition at these genomic locations, most of the peaks disappeared from the ERVs in the SETDB1-deficient cells (Figure 4J), even if some residual signal persisted at certain locations (Figures S6A-S6C). Together, these data indicate that SETDB1 targets H3K9me3 at a subset of ERVs in Th2 cells, and that some of these retrotransposons overlap or flank enhancers.

Increased expression of ERVs and neighboring genes in Setdb1 -/-cells

Recent studies have provided evidence for the hypothesis that transposable elements are co-opted for the regulation of host gene networks [START_REF] Chuong | Regulatory activities of transposable elements: from conflicts to benefits[END_REF]2016). The impact of SETDB1 deletion on CD4 + T cell fate might, therefore, result from a loss of H3K9me3 at ERVs that behave as cis-regulatory modules of transcription, and/or regulate the activity of enhancers. To test this hypothesis, we analyzed the consequences of SETDB1 deletion on ERV accessibility and activity, on the status of their nearest enhancers, and on the expression of associated genes. We first compared ERV expression levels in Setdb1 -/-and Setdb1 +/+ Th2 cells. In Setdb1 -/-Th2 cells, the expression of 22% of the ERVs that lost H3K9me3 was deregulated, 77% of which being overexpressed (Figures 5A and5B). As the expression levels were very low, we used other parameters to confirm that H3K9me3 disappearance from ERVs led to local chromatin remodeling. Loss of H3K9me3 at ERVs in Setdb1 -/-Th2 cells also correlated with the accumulation and spreading of permissive histone marks and with increased chromatin accessibility (Figures 5C andS6A-S6C). Since we observed a decompaction of the chromatin on both sides of the retrotransposons overexpressed in mutant cells (Figure 5C), we hypothesized that the cis-regulatory elements that flank ERVs might be de-repressed in the absence of SETDB1. To test this hypothesis, we analyzed the activation status of enhancers located in the vicinity of ERVs marked by H3K9me3 in Setdb1 +/+ cells and whose expression was increased in Setdb1 -/-cells. The enhancers associated with ERVs that were activated following H3K9me3 disappearance were themselves more expressed in Setdb1 -/-than in Setdb1 +/+ Th2 cells (Figure 5D) As expected from their increased transcription, they also accumulated permissive histone marks in mutant cells (Figure 5E). We then tested if this cascade of events resulted in deregulation of gene expression. Even if the Th2 gene expression program was impaired in Setdb1 -/-Th2 cells (Figures 2J and2L), our analysis showed that the genes associated with enhancers flanking or overlapping ERVs whose expression was increased in Setdb1 -/-cells were also significantly more expressed (Figure 5F). Finally, we observed a positive correlation between the degree of gene expression change per gene between Setdb1 -/-and Setdb1 +/+ cells and the number of activated retroelements associated with a gene (Figure 5G).

Therefore, we propose that SETDB1-dependent H3K9me3 deposition at ERVs inactivates neighboring enhancers and thus participates to the silencing of their target genes.

H3K9me3 + ERVs mark the Th1 enhancer landscape in Th2 cells

In our functional assays, SETDB1 deletion led to enhanced Th1 priming and to Th2 cell instability. As discussed above, loss of regulation of the Th1 gene network might underlie these observations. Based on our epigenetic and transcriptomic studies, we postulated that SETDB1 controls Th1 gene expression by either repressing ERVs operating as cisregulatory elements of these genes, and/or regulating the activity of their enhancers. To test this hypothesis, we first assigned biological significance to the ERVs marked by H3K9me3 in a SETDB1-dependent manner by analyzing the annotations of their nearby genes. We observed a strong association of the retrotransposons with genes involved in immune processes, including leukocyte activation and cytokine production (Figure 6A). This distribution was cell type-specific as there was very little overlap between the ERVs marked by H3K9me3 in Th2 cells and those marked in adipocytes (Figure S7A). Moreover, the ERVs marked by H3K9me3 in white adipose cells were associated with genes that have no direct link with immunity (Figures S7B andS7C). As IFN-γ plays a critical role in Th1 cell programming, we next investigated whether these ERVs more specifically targeted IFN-γstimulated genes (ISGs). In contrast to H3K9me3 -ERVs or to the repertoire of H3K9me3 + ERVs found in adipocytes, the ERVs marked by H3K9me3 in Th2 cells were located in the vicinity of ISGs (Figure 6B). Motif enrichment analysis of H3K9me3 + ERV sequences strengthened this observation; it revealed a strong enrichment for the binding sites of STAT1, the main transcription factor responsible for the diverse cellular effects induced by IFN-γ (Figure 6C), as well as for other critical Th1-related transcription factors. The "Upstream Regulator Analysis" of our RNA-seq data also identified these five transcription factors as very likely to be responsible for the differences in gene expression observed in Setdb1 -/-vs Setdb1 +/+ Th2 cells upon culture in Th1-polarizing conditions (Figures 6D andS7D). To strengthen these in silico-based predictions, we next tested whether ERV sequences marked by H3K9me3 in Th2 cells were associated with the genomic localizations of STAT1 and STAT4 in Th1 cells. In contrast to H3K9me3 -ERVs or to the repertoire of H3K9me3 + ERVs found in adipocytes, we observed that the ERVs marked by the repressive histone mark in Th2 cells were strongly enriched near Th1-specific STAT1 and STAT4 binding sites (Figures S7E andS7F). In fact, almost 2,000 H3K9me3 + retroelements were associated with Th1specific STAT1/4 genomic locations in Th2 cells (Figures 6E-6H). In addition, whereas a substantial number of these ERVs overlapped STAT ChIP-seq peaks, and were thus very likely to behave as Th1 gene enhancers, most of them only flanked the transcription factors binding sites (Figure S7G). To confirm that SETDB1 deposits H3K9me3 at a subset of ERVs associated with Th1 enhancers in Th2 cells, we analyzed the location of the retroelements relative to Th1 enhancers. We found 4,411 putative Th1 enhancers associated with H3K9me3 + ERVs in Th2 cells (Figure 6I). In contrast, we did not detect any enrichment of the Th1-specific cis-regulatory elements at H3K9me3 + ERVs in adipocytes (Figure 6J).

Together these data suggest that the differences in stability observed between Setdb1 -/-and Setdb1 +/+ Th2 cells are very likely explained by SETDB1 causing H3K9me3 deposition at, and thus suppression of, ERVs that overlap or flank a large network of Th1-specific enhancers. Since 70% of the Th1 cis-regulatory elements that were covered by the repressive mark overlapped an ERV onto which H3K9me3 signal was centered (Figures 6K and6L), this epigenetic silencing pathway is probably central for the H3K9me3-dependent suppression of the Th1 gene network in Th2 cells.

SETDB1-dependent H3K9 trimethylation at ERVs represses Th1-specific enhancers

H3K9me3 + ERVs marked a large repertoire of Th1 enhancers in Th2 cells. To test whether the deposition of H3K9me3 at these genomic elements was associated with their repression, we first analyzed the activation status of the Th1 enhancers associated with H3K9me3 + ERVs in wild-type Th2 cells. As expected, most of these cis-regulatory elements were repressed (Figure 7A), and we detected an accumulation of the histone mark over "poised" or "ghost" enhancers but not on active cis-regulatory elements (Figure 7B). To test whether the histone mark had a causal role in the repression of Th1 enhancers, we next compared their status in Setdb1 +/+ and Setdb1 -/-Th2 cells. H3K9me3 disappearance led to a strong increase in chromatin accessibility at Th1 enhancers associated with ERVs whose expression was increased in a mutant context (Figure 7C). This derepression correlated with an accumulation of permissive histone marks on the enhancer sequences (Figures 7D-7G).

Among the large set of Th1 enhancers associated with at least one ERV marked by H3K9me3 in Th2 cells, we identified the conserved non-coding sequence (CNS) located 17-20kb downstream of the Ifng gene [START_REF] Shnyreva | Evolutionarily conserved sequence elements that positively regulate IFN-γ expression in T cells[END_REF]. While this region was poised in naïve T cells, with a strong H3K4me1 signal flanked by a large domain of H3K9me3, it lost competence upon Th2 cell commitment, with an accumulation of H3K9me3 and a complete loss of H3K4me1 (Figure 7F). In contrast, this enhancer exhibited a diminished H3K9me3 signal in Setdb1 -/-Th2 cells, which may make this region, as well as the hundreds of other Th1 enhancers associated with an ERV marked by H3K9me3 in wild-type cells, accessible to Th1-specific transcription factors. Consistent with this hypothesis, the H3K4me1 and ATACseq signals at Ifng CNS 17-20 were substantially higher in Setdb1 -/-than in Setdb1 +/+ Th2 cells (Figures 7G and7H). We identified many other critical Th1-related genes whose expression might be regulated by an ERV acting as a cis-regulatory module. They included those encoding T-bet, the "master regulator" of the lineage, and other critical transcriptional regulators such as STAT4, IRF1 and RUNX3 (Figure 7I). These genes had at least one enhancer associated with an ERV marked by H3K9me3 in Th2 cells and were, in addition, more expressed in Setdb1 -/-than in Setdb1 +/+ Th2 cells upon culture in Th1-inducing conditions. In conclusion, our data reveal that Th2 cell stability is controlled at the level of chromatin by the SETDB1-dependent deposition of H3K9me3 at a restricted set of ERVs flanking or behaving as Th1-gene enhancers.

DISCUSSION

Up to 10% of the mouse genome is comprised of ERVs, which had long been considered to be junk DNA sequences. Recently, however, regulatory functions over gene expression have been assigned to transposable elements. In mouse CD4 + T cells, we documented that a set of ERVs enriched in binding sites for pro-Th1 transcription factors overlapped or flanked the enhancers of genes from the Th1 cell transcriptomic signature. We further showed that the accessibility of these repeat elements was regulated at the epigenetic level by SETDB1. Indeed, in Setdb1 -/-cells, the lack of deposition of H3K9me3 at this subset of ERVs correlated with their activation and with the increased expression of their closest genes. At the cellular level, this deregulation of gene expression translated into increased Th2 cell plasticity and enhanced Th1 cell priming. Together, these data suggest that SETDB1 controls Th2 cell integrity by repressing a restricted and cell-type specific repertoire of ERVs.

While the use of Setdb1 -/-cells allowed us to establish cause and effect links between SETDB1 depletion, H3K9me3 disappearance and ERV-derepression, we have not strictly demonstrated that the ERVs marked by H3K9me3 in Th2 cells acted as Th1 gene enhancers. The most direct way to prove that SETDB1 controlled the Th1 gene expression program through the regulation of ERVs that behave as Th1 gene enhancers would have been to selectively inactivate the transposable elements marked by H3K9me3 in Th2 cells using CRISPR/Cas9. Unfortunately, due to the large number of ERVs and to the absence of a consensus sequence to target, we have not been able to perform this experiment.

ERVs potentially control Th1 genes expression through two non-mutually exclusive mechanisms: they may either behave as cis-regulatory elements, or they may regulate chromatin accessibility at nearby enhancers. Our evidence that the binding motifs for critical Th1-associated transcription factors were enriched in H3K9me3 + ERV sequences suggests that the ERVs directly act as cis-regulatory elements. The existence of such a subset of regulatory ERVs, which might have shaped the Th1 transcriptional network over time, is supported by a recent study showing that ERVs containing binding sites for IFN-induced transcription factors are necessary for AIM2 inflammasome activation [START_REF] Chuong | Regulatory evolution of innate immunity through co-option of endogenous retroviruses[END_REF].

However, when we fractionated the repertoire of H3K9me3 + ERVs associated with Th1 enhancers in Th2 cells, we observed that most of them only flanked STAT-1/4 binding sites.

This result suggests that ERVs regulate the Th1 gene network mainly by modulating the activity of the Th1 enhancers located in their vicinity. Interestingly, the H3K9me3 + ERVs that flanked Th1 enhancers accumulated at a distance of 3 to 5 kb from the STAT peaks. This distribution of the retrotransposons overlaps with the distribution of the H3K9me3 signal observed on the flanking regions of enhancers whose activity is regulated by this histone mark in DC and fibroblasts [START_REF] Zhu | Cell-type-specific control of enhancer activity by H3K9 trimethylation[END_REF]. Although the authors of that study do not implicate SETDB1 in H3K9me3 deposition and do not identify ERVs as the targeted genomic elements, they correlate the accumulation of H3K9me3 at this location with the repression of adjacent enhancer activity. This observation reinforces, and extends to other cell types, our model supporting that retrotransposons are the genetic elements that are targeted by the H3K9me3-dependent silencing machinery to regulate enhancer activity in a cell type-specific manner. The underlying molecular mechanism probably relies on local heterochromatin spreading from ERVs to nearby regulatory elements as suggested by our ChIP-seq data and by studies from the literature [START_REF] Rebollo | Retrotransposon-Induced Heterochromatin Spreading in the Mouse Revealed by Insertional Polymorphisms[END_REF].

SETDB1 is ubiquitously expressed in mouse tissues and more than 900,000 ERVs are dispersed through the mouse genome. Nevertheless, the repertoire of ERVs under the control of SETDB1 was limited and highly cell type-specific. Only 17,349 ERVs were associated with H3K9me3 domains in Th2 cells. The vast majority of these ERVs were not associated with H3K9me3 domains in white adipose cells. Moreover, the biological functions of genes associated with the ERVs marked by H3K9me3 in Th2 and white adipose cells were fully different since they were associated with genes involved in immune processes only in differentiated lymphocytes. The enrichment of H3K9me3 at a specific set of ERVs might be explained by the fact that SETDB1 is recruited to the chromatin by Krüppel-associated box zinc-finger proteins (KZFPs) using the scaffold protein TRIM28 as a molecular intermediate. The mouse genome encodes hundreds of KZFPs, whose expression depends on the cell type and its physiological state [START_REF] Imbeault | KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks[END_REF].

These transcriptional regulators have different DNA binding sites and ERVs are one of their main genomic targets. Indeed, KZFPs have co-evolved with transposable elements and use evolutionarily conserved regions located mainly within their regulatory sequences to control gene expression [START_REF] Chuong | Regulatory activities of transposable elements: from conflicts to benefits[END_REF][START_REF] Imbeault | KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks[END_REF]. The cell-type specific and SETDB1-dependent deposition of H3K9me3 at ERVs that we observed in Th2 cells was therefore probably orchestrated by a specific set of KZFPs that await identification.

Of the three lysine methyltransferases from the SUV39H family, only SETDB1 was necessary for silencing ERVs in differentiated lymphocytes. This finding is consistent with those obtained in neural progenitor cells and immortalized mouse embryonic fibroblasts, in which SUV39H1-deficiency does not severely affect ERV silencing [START_REF] Buenrostro | Suv39hdependent H3K9me3 marks intact retrotransposons and silences LINE elements in mouse embryonic stem cells[END_REF]. Although they apparently argue against a direct collaboration of SUV39H1 and SETDB1 in H3K9 trimethylation at ERVs, our data do not fully exclude cooperation between these two enzymes in regulating Th2 cell commitment. In fact, the H3K9me3-dependent epigenetic regulation of CD4 + T cell differentiation involves both SUV39H1 and SETDB1. In differentiated Th2 cells, SUV39H1 controls H3K9me3 deposition at the Ifng promoter [START_REF] Allan | An epigenetic silencing pathway controlling T helper 2 cell lineage commitment[END_REF] S1. S2. domains overlapping ERVs in Setdb1 -/-and Suv39h1 -/-Th2 cells (relative to Setdb1 +/+ Th2 cells). All data are from two independent biological replicates for each genotype. See also Figure S5 and Table S3. (A) Proportions and numbers of ERVs that were differentially ("Changed") or similarly ("Stable") expressed between Setdb1 +/+ and Setdb1 -/-Th2 cells are shown on the left. The percentages of differentially expressed ERVs that were up-regulated ("Up") or downregulated ("Down") are shown on the right. (B) Expression levels of all ERVs that were differentially expressed in Setdb1 +/+ and Setdb1 -/-Th2 cells. (C) Average ATAC-Seq signal profiles at ERVs that overlapped (H3K9me3 + ), or not (H3K9me3 -), with H3K9me3 domains in Th2 cells and that were more expressed ("H3K9me3 + up", fold change >10, expression in Setdb1 -/-≥1), or not (H3K9me3 + ), in Setdb1 -/-than in Setdb1 +/+ cells. The data represent the signal measured in Setdb1 -/-Th2 cells relative to that measured in their wild-type counterparts. (D) Expression levels of the 238 enhancers located in the vicinity of ERVs that overlapped with H3K9me3 domains in Setdb1 +/+ cells and were more expressed in Setdb1 -/-cells. ***p<0.001 Pearson's chi-squared test. (E) Average H3K4me1 or H3K27ac signal profiles at Th2 enhancers associated with H3K9me3 + ERVs that were more expressed in Setdb1 -/-Th2 cells. (F) GSEA of Setdb1 -/-Th2 cells, with genes ranked on the basis of their expression in Setdb1 -/-vs Setdb1 +/+ Th2 cells. The gene set comprises all the genes associated with enhancers located in the vicinity of ERVs that overlapped with H3K9me3 domains and were more expressed in Setdb1 -/-cells (left panel). A random selection of 500 genes associated with enhancers that remained silent in Setdb1 -/-cells was used as control (right panel). (G) Gene expression change between Setdb1 -/-and Setdb1 +/+ Th2 cells for genes that were expressed in at least one population and that were located in the vicinity of H3K9me3 + ERVs that remained silent in Setdb1 -/-cells or were more expressed in Setdb1 -/-than in Setdb1 +/+ cells. *p<0.05, ***p<0.001 (Wilcoxon test). All transcriptomic data are from three independent biological replicates. ChIP-and ATAC-seq data are from two independent biological replicates. See also Figure S6. (A) Proportions of Th1 enhancers associated with H3K9me3 + ERVs in Th2 cells that were either active (H3K4me1 + H3K27ac + ) or repressed (H3K4me1 + H3K27ac -or H3K4me1 - H3K27ac -). The percentages of repressed enhancers that were poised (H3K4me1 + H3K27ac -) or "ghost" (H3K4me1 -H3K27ac -) are shown on the right. (B) Average H3K9me3 signal profiles in Setdb1 +/+ Th2 cells at Th1 enhancers that were associated with H3K9me3 + ERVs and that were either active, poised or repressed in Setdb1 +/+ Th2 cells. (C) Average ATACseq signal profiles in Setdb1 -/-Th2 cells at Th1 enhancers that were repressed and associated with H3K9me3 + ERVs in Setdb1 +/+ Th2 cells and that were more expressed, or not ("Silent"), in the absence of SETDB1. (D, E) Average H3K4me1 (D) and H3K27ac (E) signal profiles in Setdb1 -/-and Setdb1 +/+ Th2 cells at Th1 enhancers that were repressed and associated with H3K9me3 + ERVs in Setdb1 +/+ Th2 cells and that were more expressed, or showing the main Th1-related genes that had at least one enhancer in the vicinity (± 5kb) of a SETDB1-dependent H3K9me3 + H3K4me1 -ERV in Th2 cells. buffer, Sigma). CD4 + T cells were enriched by negative selection by using antibodies specific for CD16/32 (2.4G2), I-A/I-E (M5/114.15.2), CD8α (H59) and B220 (RA3-6B2), and Dynabeads sheep anti-rat IgG (Thermo Fisher Scientific). Naïve CD4 + T cells, defined as CD4 + CD25 -CD62L high CD44 low , were labeled with fluorochrome-conjugated monoclonal antibodies specific for CD4 (GK1.5, BD Biosciences), CD25 (PC61, BD Biosciences), CD62L (MEL14, Thermo Fisher Scientific) and CD44 (IM7, BD Biosciences), and purified from the enriched fraction of CD4 + T cells by fluorescence-activated cell sorting (FACS Aria, BD Biosciences).

Th cell cultures

Naïve CD4 + T cells were cultured for three days in 96-well flat bottom plates coated with 10 µg/mL anti-CD3ε antibody (145-2C11, InVivoMab TM , BioXcell) in RPMI 1640 Glutamax TM supplemented with 1 mM sodium pyruvate, non-essential amino acids, 10 mM HEPES, 100 units/mL penicillin, 100 µg/mL streptomycin, 50 mM 2β-mercaptoethanol, 10% fetal calf serum (all from Thermo Fisher Scientific) and 1 µg/mL anti-CD28 antibody (37.51, InVivoMab TM , BioXcell). Unless stated otherwise, Th1 medium also contained 10 ng/mL recombinant mouse IL-12 (R&D Systems) and 10 µg/mL anti-IL-4 neutralizing antibody (11B11, InVivoMab TM , BioXcell). Th2 medium contained 50 ng/mL recombinant mouse IL-4 (R&D systems) and 10 µg/mL anti-IFN-γ neutralizing antibody (XMG1.2, InVivoMab TM , BioXcell). At day 3, the cells were re-plated in the same conditioning medium but without the anti-CD3ε and anti-CD28 antibodies and with 30 IU/mL recombinant IL-2 (Proleukin). To test for Th2 cell lineage commitment, cells were harvested at day 6, extensively washed in complete medium, and re-plated in Th1-polarizing conditions as indicated above. To assess the role of the IFN-γ pathway in Th2 cell plasticity, Th1 medium was supplemented with 10 µg/mL anti-IFN-γ. In co-culture experiments, Setdb1 +/+ and Setdb1 -/-Th2 cells were differentiated separately, mixed at a 1:3 ratio, and then plated in Th1 culture conditions.

T cell proliferation and differentiation analysis by flow cytometry

To analyze intracellular transcription factor expression upon Th cell differentiation, cells were collected at the requires time points, stained with the fixable viability dye eFluor 506 (Thermo Fisher Scientific), and labeled with fluorochrome-conjugated antibodies specific for T-bet (ebio4B10, Thermo Fisher Scientific) and Thermo Fisher Scientific) by means of the Transcription Factor Staining Buffer Set (Thermo Fisher Scientific). For intracellular cytokine staining, cells were first stimulated at 37°C with 20 ng/mL phorbol 12myristate 13-acetate (Millipore) and 1 µg/mL ionomycin (Millipore) for 5 hours in the presence of GolgiStop™ (BD Biosciences). Cells were then labelled with the fixable viability dye eFluor 506 and stained with fluorochrome-coupled antibodies specific for IL-13 (ebio13A, Thermo Fisher Scientific), IFN-γ (XMG1.2, Thermo Fisher Scientific), GM-CSF (MP1-22E9, BD Biosciences), TNF (MP6-XT22, Thermo Fisher Scientific) or IL-4 (11B11, BD Biosciences) by using the Cytofix/Cytoperm™ Fixation/Permeabilization Kit (BD Biosciences). When indicated, naïve CD4 + T cells were labeled prior to culture with 0.5 µM CellTrace Violet (Thermo Fisher Scientific). Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) or MACSQuant analyzer 10 (Myltenyi) and the data were analyzed by using FlowJo software (Tree Star).

Mouse phenotyping

To determine the frequency and phenotype of immune cells in primary and secondary lymphoid organs, thymus, spleen and lymph nodes were collected from Setdb1 -/-and Setdb1 +/+ mice and single-cell suspensions were obtained by mechanical disruption. Cells were then incubated on ice in FACS buffer (PBS, 3 mM EDTA, 3% fetal calf serum) containing 10 µg/mL anti-CD16/32 antibody for 20 minutes. Fluorochrome-conjugated antibodies were added at saturating concentrations, and cell suspensions were incubated on ice and protected from light for a further 20 minutes. For intracellular staining, cells were fixed and permeabilized by using the Transcription Factor Staining Buffer Set (Thermo Fisher Scientific) according to the manufacturer's instructions. The following antibodies were used for phenotyping: anti-TCR-β (H57-597), anti-CD4 (GK1.5), anti-NKp46 (29A1.4), anti-CD11b (M1/70), anti-CD19 (1D3), anti-CD25 (PC61), anti-CD69 (H1.2F3), anti-Siglec-F (E50-2440), anti-H-2K b (AF6-88.5) and anti-Ki67 (B56) all from BD Biosciences; anti-CD8β (ebioH35-17.2), anti-CD8α (53-6.7), anti-PDCA1 (ebio927), anti-I-A/I-E (M5/114), anti-CD11c (N418), anti-Gr1 (RB6-8C5), anti-B220 (RA3-6B2), anti-CD44 (IM7) and anti-CD62L (MEL-14), all from Thermo Fisher Scientific. Dendritic cells (DC) were gated based on I-A b and CD11c expression (CD19 -TCR-β -CD11c + I-Ab + ) and CD8α, CD11b and PDCA-1 were used as markers to identify the conventional type 1 (cDC1, CD8α + CD11b -), conventional type 2 (cDC2, CD8α -CD11b + ) and plasmacytoid (pDC, PDCA-1 + ) sub-populations.

Monocytes/macrophages (Macro) and B cells (B cell) were defined as lin -CD11c - CD11b + SSC-A low Gr-1 low/-and TCR-β -CD19 + B220 + , respectively. Neutrophils (Neutro), natural killer cells (NK) and eosinophils (Eosino) were identified as lin -CD11c -CD11b + SSC-A high Gr-1 + , TCR-β -NKP46 + and lin -CD11c -CD11b + SSC-A high Gr-1 -, respectively. Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) and the data were analyzed by using FlowJo software (Tree Star).

Ex vivo measurement of apoptosis

Single-cell suspensions of spleen, mesenteric lymph nodes and thymus were obtained as described above. Spleen and lymph node cells were labeled with antibodies specific for TCR-β (H57-597, Thermo Fisher Scientific) and CD4 (GK1.5, BD Biosciences), whereas thymocytes were stained with antibodies specific for CD4 and CD8β (ebioH35-17.2, ThermoFisher Scientific). Apoptotic cells were then labeled by using the Cell Event TM Caspase-3/7 Green Detection Reagent (Thermo Fisher Scientific) according to the manufacturer's instructions. Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) and the data were analyzed by using FlowJo software (Tree Star).

Measurement of cytokines in cell culture supernatants

Following 6 days of culture in Th1 or Th2 polarizing conditions, T cells were collected and extensively washed in complete medium. The differentiated cells (7.5 x 10 4 per well) were cultured overnight in 96-well flat bottom plates coated with anti-CD3ε antibody in complete culture medium containing anti-CD28 antibody. The concentrations of cytokines in the cell culture supernatants were then measured by flow cytometry using the FlowCytomix Kit (a bead-based multiple cytokines detection system) according to the manufacturer's instructions (FlowCytomix, eBiosciences). Flow cytometry was performed by using a MACSquant Q10 flow cytometer (Miltenyi).

Phospho-STAT4 (Tyr693) staining

Naïve CD4 + T cells were cultured for three days in 96-well flat bottom plates in complete medium supplemented with 30 UI/mL of recombinant IL-2 (Peprotech) at a ratio of 1 to 1 with Dynabeads Mouse T-Activator CD3/CD28 (Thermo Fisher Scientific). After 3 washes in complete medium, 10 5 cells were stimulated for 30 minutes with recombinant mouse IL-12 (10 ng/mL). Cells were then fixed with BD Phosflow™ Lyse/Fix Buffer, permeabilized with BD Phosflow™ Perm Buffer III in the presence of 10mM Sodium Fluoride and 1mM Sodium Orthovanadate (Sigma), and stained with fluorochrome-conjugated monoclonal antibodies specific for phosphorylated STAT4 (pY693, clone 38/pSTAT4, BD). Flow cytometry was performed by using a LSRII Fortessa cytometer and the data were analyzed by using FlowJo software.

Mixed hematopoietic bone marrow chimeras

Bone marrow from femurs and tibias was collected from CD45.2 + CD90.1 + C57BL/6 mice and from Setdb1 +/+ or Setdb1 -/-CD45.2 + CD90.2 + littermates. Single-cell suspensions were washed in complete medium. CD90.1 + and CD90.2 + cells were then eliminated using HO-22 and AT83 hybridoma supernatants, respectively, and rabbit complement (TCS Biosciences). Following treatment with DNase I (Sigma), single-cell suspensions were washed three times in PBS and filtered through a 70 µm cell strainer. CD45.2 + CD90.1 + cells were mixed at a ratio of 3 to 7 with either Setdb1 +/+ or Setdb1 -/-CD45.2 + CD90.2 + cells. 10 7 cells were then injected intravenously into γ-irradiated CD45.1 + C57BL/6 hosts (11 Gy; 137 Cs source) that were kept on antibiotic-containing water (0.28% pediatric suspension of Bactrim; Roche) for the next 4 weeks. Spleen and lymph nodes were isolated from these mice at least 6 weeks after injection of the bone marrow cells.

Immunization

Mice were immunized subcutaneously on each side of the base of the tail with 20 µg of 1W1K peptide (EAWGALANKAVDKA, Genecust) in 100 µL of RIBI adjuvant (Sigma). 8 days after immunization, the lymph nodes draining the site of immunization were collected and homogenized. To determine the frequency of Ag-specific CD4 + T cells, 4.10 6 cells were incubated with the 1W1K-I-A b tetramer (NIH tetramers core facility) for 2h at room temperature. Cells were then labelled with fluorochrome-conjugated monoclonal antibodies specific for CD4 (RM4.5), CD44 (IM7), CD8α (53-6.7) and B220 (RA3-6B2) and with the fixable viability dye eFluor 506 (Thermo Fisher Scientific) according to the manufacturer's instructions. To determine the frequency of antigen-specific CD4 + T cells producing IFN-γ, 4.10 6 lymph node cells were stimulated overnight with 7µM of the 1W1K peptide in complete medium and in the presence of Brefeldin A (5µg/mL, Sigma) and GolgiStop™ (BD Biosciences) during the last 5h. Cells were then harvested and stained with fluorochromeconjugated monoclonal antibodies specific for CD4 and CD44 and with the fixable viability dye eFluor 506. Following fixation and permeabilization (Cytofix/Cytoperm™ Fixation/Permeabilization Kit, Thermo Fisher Scientific), cells were finally stained with fluorochrome-coupled antibodies specific for IFN-γ. To determine the Th1/Th2 balance of the antigen-specific T cell response, 2.10 6 cells were stimulated for 72h with 7µM of 1W1K peptide in HL-1 medium (Lonza) supplemented with 2 mM of glutamine, 100 units/mL penicillin and 100 µg/mL streptomycin. IL-13 and IFN-γ levels were then measured by ELISA in the cell culture supernatants.

Western blotting

The different subpopulations of thymocytes were sorted on a FACS Aria (BD Biosciences) based on their expression of CD4 and CD8. Naïve CD4 + T cells were purified from the spleen and lymph nodes as described above. Cells were lysed in 1X NuPAGE LDS sample buffer and 1X NuPAGE sample reducing agent (Thermo Fisher Scientific). Whole cell lysates were then sonicated briefly and proteins were separated by SDS-PAGE on 4-12% (ab107225, Abcam), total H3 (ab1791, Abcam), H3K9me3 (D4W1U, Cell Signaling Technology) or beta ACTIN (ab8227, Abcam). The bands were detected by using Amersham ECL western blotting detection reagent (GE Healthcare Life Sciences) and the ChemiDoc XRS+ imaging system (Bio-Rad) after staining with secondary antibodies coupled to horseradish peroxidase. Images were analyzed by using Image Lab software (Bio-Rad).

RNA-seq sample preparation and analysis

Total RNA was extracted by using the RNeasy Micro Kit (Qiagen) and its quality was assessed on a 2100 Bioanalyzer (Agilent Technologies). Only high-quality RNA (i.e. RNA of integrity number > 7) was subsequently used to prepare the libraries according to the ScriptSeq RNA-seq protocol (Illumina). Quality controls of the libraries were performed by using standard methods, including quantification by Qubit (Thermo Fisher Scientific) and assessment of size distribution by using the 2100 Bioanalyzer. Samples were indexed and sequenced on an Illumina HiSeq 2500 or 3000 (paired-end reads of 100 or 150 bp, respectively). After trimming of adaptor sequences (Cutadapt 1.3) and removal of lowquality bases (-q value, < 15), high-quality reads were aligned to the mouse reference genome mm10 (Genome Reference Consortium) by using TopHat version 2.0.5 [START_REF] Trapnell | TopHat: discovering splice junctions with RNA-Seq[END_REF]. Count of the reads mapping to each gene was performed using Htseq-count.

Differential expression analysis was performed by using the DESeq package (Bioconductor software) [START_REF] Anders | Differential expression analysis for sequence count data[END_REF]). An adjusted P value of < 0.1 (P value adjusted for multiple testing with the Benjamini-Hochberg procedure) was used as cutoff to select the genes differentially expressed.

ChIP, semi-quantitative PCR and library preparation and sequencing

ChIP was performed as previously described [START_REF] Lee | Chromatin immunoprecipitation and microarray-based analysis of protein location[END_REF]. Briefly, following cell lysis, the chromatin was sonicated with a Bioruptor Pico (Diagenode) to obtain fragments of 100-300 bp. In each assay, we used 5-50 million cells and 2-10 µg of antibody specific for H3K9me3 (ab8898, Abcam), H3K27ac (Ab4729, Abcam) or H3K4me1 (ab8895, Abcam).

Immunoprecipitation was performed by using Dynabead ® Protein G (Thermo Fisher Scientific). Library preparation was carried out by using the TruSeq ChIP Sample Preparation Kit (Illumina). Library quality was assessed by using the 2100 Bioanalyzer and sequencing was performed on an Illumina HiSeq 3000 (paired-end reads of 150 bp). When indicated, semi-quantitative PCR was performed on a Light Cycler® 480 (Roche) using LightCycler 480 SYBR Green I Master (Roche). Primers specific for Ifng CNS 17-20 (forward: tccctagactctgccactct; and reverse: gctcaccatcaataggcgtg) and for the glyceraldehyde-3phosphate dehydrogenase (Gapdh) promoter (forward: gctccttgcccttccagatt and reverse: cccttcccaccctgttcatc) were used. The results were expressed as the percentage of input DNA normalized to the signal from the Gapdh promoter. More details on STAT1, STAT4 and H3K4me1 ChIP-seq data in Th1 cells can be found in [START_REF] Vahedi | STATs shape the active enhancer landscape of T cell populations[END_REF].

ChIP-seq data processing

Reads were filtered as described for RNA-seq and aligned to the mm10 reference genome by using BWA v.0.7.10 (Li and Durbin, 2009). H3K4me1 and H3K27ac peaks were detected by using the 'broad' option of MACS2 v.2.1.0 [START_REF] Zhang | Model-based Analysis of ChIP-Seq (MACS)[END_REF]. To detect H3K9me3 domains, we used the R Bioconductor package CSAW v.1.4.1 [START_REF] Lun | csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows[END_REF]. The minimum mapping quality score and the FDR threshold were set to 50 and 0.05, respectively. We tested 11 window sizes ranging from 200 to 600 bp. In the end, we selected 300 bp as it allowed for the most accurate detection of H3K9me3-enriched domains, as determined using the visualization tool IGV. Differential binding windows were clustered in regions with the 'mergeWindows' function and the Benjamini-Hochberg method was applied to control the False Discovery Rate (FDR) across all detected clusters ('combineTests' function). For vizualizing H3K4me1 and H3K27ac signals in Setdb1 +/+ and Setdb1 -/-Th2 cells, reads were extended by 200 bp because of low reads coverage.

ATAC-seq sample preparation and analysis

ATAC-seq was performed as previously described [START_REF] Buenrostro | Suv39hdependent H3K9me3 marks intact retrotransposons and silences LINE elements in mouse embryonic stem cells[END_REF] with some modifications. Briefly, 50 000 cells were lysed in ice-cold lysis buffer and the transposition reaction was performed using the Tn5 transposase at 37 °C for 30 min. DNA was purified using the Qiagen MinElute kit (Qiagen). The libraries were prepared using the Nextera DNA Library Prep Kit (Illumina) and purified using AMPure XP beads (Beckman) following a double-sided protocol to remove primer dimers and large fragments. Samples were performed in duplicates, multiplexed and sequenced on NextSeq-500 (75 bp paired-end reads) at the Transcriptomic & Genomic Platform Marseille Luminy (TGML, Marseille, France). Reads were aligned with BWA mem (version 0.7.12-r1039). Samples were normalized by scaling reads numbers according to the signal found at promoter of genes which were highly (expression level > 5000) and equally (0.9 < adjusted p-value) expressed in Setdb1 +/+ and Setdb1 -/-samples, as determined by RNA-seq.

ERV reconstruction

Annotations of ERV elements were downloaded from the UCSC Genome Browser (assembly GRCm38, release of RepeatMasker: 2012-02-07). We used the four major subfamilies (ERV1, ERVK, ERVL and ERVL-MaLR) of LTRs and excluded elements for which the curator was unsure of the classification. We merged ERV fragments from the same family (identical 'repName') into a single ERV when located within 20 bp, as previously described [START_REF] Göke | Dynamic Transcription of Distinct Classes of Endogenous Retroviral Elements Marks Specific Populations of Early Human Embryonic Cells[END_REF]. Count of the reads mapping to each ERV was performed using Htseq-count [START_REF] Anders | HTSeq-a Python framework to work with highthroughput sequencing data[END_REF] and normalization was performed with DESeq. ERVs with an expression score ≥1 were considered as expressed.

Bioinformatics analyses

R (https://www.R-project.org), SAMtools (Li et al., 2009) and the BEDtools suite v2.22.1 [START_REF] Quinlan | BEDTools: a flexible suite of utilities for comparing genomic features[END_REF] were used to analyze high-throughput sequencing files. To determine the genome-wide distribution of H3K9me3 domains, we defined the different genomic regions as follows: gene body coordinates were extracted from assembly GRCm38; promoters were defined as transcription start sites +1kb/-2kb; enhancers were identified as H3K4me1 peaks with no overlap with promoters; ERV coordinates were rebuilt from the RepeatMasker database as described above. As a control, we randomly distributed H3K9me3 domains through the genome using the shuffle sub-command of the BEDtools suite. R package 'Genomation' was used to visualize genomic intervals [START_REF] Akalin | Genomation: a toolkit to summarize, annotate and visualize genomic intervals[END_REF]. To represent to what extend the retroelements marked by H3K9me3 in wild-type cells were still covered by the repressive mark in Setdb1 -/-or Suv39h1 -/-Th2 cells, we expressed the number of peaks that remained in mutant contexts as percentage of wildtype. Biological functions analysis of H3K9me3 ChIP-seq peak coordinates was performed by using the Genomic Regions Enrichment of Annotations Tool (GREAT) [START_REF] Mclean | GREAT improves functional interpretation of cis-regulatory regions[END_REF] with default settings and using the 'single nearest gene' option (each gene is assigned a regulatory domain that extends in both directions (within 100kb) to the midpoint between the gene's TSS and the nearest gene's TSS but no more than the maximum extension in one direction). For Gene Ontology analysis, the "enrichment analysis" tool from the Gene Ontology Consortium was used (http://geneontology.org). For analysis of motif enrichment, we used AME software of the MEME Suite version 4.11.3 with defaults options (0.05 ≥ adjusted p-value) [START_REF] Mcleay | Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data[END_REF]; the HOCOMOCOv11 MOUSE was used as the input transcription factor motif database. We also used the gene set enrichment analysis (GSEA) software (http://software.broadinstitute.org/gsea/index.jsp) with default settings except for the 'Collapse dataset to gene symbols' and 'the permutation type' which were set as 'false' and 'gene set', respectively. Heat maps were generated by using matrix2png version 1.2.1 (http://www.chibi.ubc.ca/matrix2png). To measure H3K9me3, H3K4me1 or H3K27ac signal, we used bamCoverage from the deepTools suite (v2.3.4) to generate normalized bedgraph files (we normalized by sequencing depth and ignored chrX, chrY and chrM) and Bedtools map to calculate the average signal. Correlation analysis between H3K9me3 + or H3K9me3 -ERVs and IFN-γ-stimulated genes (ISG) was performed as described elsewhere [START_REF] Chuong | Regulatory evolution of innate immunity through co-option of endogenous retroviruses[END_REF]. The absolute distance between ERVs and the nearest ISG (n=4,276) was first determined for all 13,303 H3K9me3 + H3K4me1 - retroelements. The distances were then grouped by 10 kb bin sizes. As control, a similar analysis was performed using an equal number of randomly sampled H3K9me3 -ERVs.

Sampling was repeated 10 times and the mean number of elements was used. Statistical significance was determined for the first 10 kb bin by chi-squared test.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical parameters including the exact value and significance of n and precision measures (Mean +/-SEM or SD) as well as statistical significance are reported in the figures and figure legends. Unless stated otherwise, asterisks denote statistical significance as calculated by Student's t test in GraphPad PRISM 6 ( * , p < 0.05; * * , p < 0.01; * * * , p < 0.001).

When large sets of unpaired data were compared, Pearson's chi-squared test was calculated in R to determine whether the observed difference between the sets of data arose by chance.

DATA AVAILABILITY

Figure 1 .

 1 FIGURES LEGENDS Figure 1. Th1 priming is enhanced in the absence of SETDB1. (A) Expression of T helper-related genes by Setdb1 +/+ and Setdb1 -/-naïve CD4 + T cells. The names of the genes that were differentially expressed in the two genotypes (adjusted p-value<0.1 and fold difference >2 or <0.5) are highlighted in red. (B) Cytokine production by Setdb1 +/+ and Setdb1 -/-naïve CD4 + T cells, as measured flow cytometry. (C) Proliferation profiles of Setdb1 +/+ and Setdb1 -/-CD4 + T cells cultured in Th1 medium. (D) Percentage of divided cells (left) and proliferation index (right) as calculated from the data in (C). (E) Expression of T-bet by Setdb1 +/+ and Setdb1 -/-CD4 + T cells after 6 days of culture in Th1 medium, as measured by flow cytometry. (F) Average expression of T-bet by Setdb1 +/+ and Setdb1 -/-CD4 + T cells after 6 days of culture in Th1 medium. (G) Production of GM-CSF and IFN-γ by Setdb1 +/+ and Setdb1 -/-CD4 + T cells after 6 days of culture in Th1-inducing conditions. (H, I) Percentage of CD4 + T cells-producing cytokine (left) and average cytokine production per cell (right) after 6 days of culture in Th1 medium. (J) Production of cytokines by Setdb1 +/+ and Setdb1 -/-CD4 + T cells following 6 days of culture in Th1-inducing conditions. Data are representative of two (C) or three (B, E and G) independent experiments. Data are represented as mean ± SD of two (D) or three (J) independent experiments or of three biological replicates from one representative experiment out of three performed (F, H, I). *p<0.05, **p<0.01, ***p<0.001 (unpaired Student's t-test). See also Figure S1-S3 and TableS1.

Figure 2 .

 2 Figure 2. Impaired acquisition of the Th2 phenotype by SETDB1-deficient cells. Setdb1 +/+ and Setdb1 -/-naïve CD4 + T cells were cultured for 3 (A, B) or 6 (C-H) days in Th2-polarizing conditions. (A) Proliferation profiles, as determined by CTV dilution. (B) Percentages of divided cells and proliferation index, as calculated from the data in (A). (C) Percentage of live (acridin orange-positive and propidium iodide-negative) cells. (D) Expression of GATA-3, as determined by flow cytometry. (E) Percentage of GATA-3expressing cells and average expression of GATA-3 per cell. (F) Average expression of Tbet, as measured by flow cytometry. (G) Production of IL-13 and IFN-γ by Setdb1 +/+ and Setdb1 -/-CD4 + T cells after culture in Th2-inducing conditions, as determined by flow cytometry. (H) Percentage of cytokine-producing CD4 + T cells. (I) Production of Th1-(IFN-γ)and Th2-related (IL-13, IL-4) cytokines by Setdb1 +/+ and Setdb1 -/-CD4 + T cells following culture in Th2-polarizing conditions. (J) Box and whisker (min to max) diagrams representing the mRNA levels of 79 Th2-related genes in Setdb1 +/+ and Setdb1 -/-naïve and Th2 cells. (K) Average ATAC-seq signal at Th2 enhancers in Setdb1 +/+ and Setdb1 -/-Th2 cells. (L) Th2-related genes expression in Setdb1 +/+ and Setdb1 -/-naïve and Th2 cells. Data

Figure 3 .

 3 Figure 3. SETDB1-deficiency leads to impaired Th2 cell commitment in vitro and to deregulation of the Th1/Th2 balance in vivo. (A-G) Unless stated otherwise, Setdb1 +/+ and Setdb1 -/-Th2 cells were analyzed after being cultured for two days in Th1 polarizing conditions. (A) Production of IL-13 and IFN-γ, as determined by flow cytometry after two days (top) and four days (bottom) of culture in Th1 medium. (B) Percentages of cells producing IL-13 and/or IFN-γ after two days of culture, as calculated from the data in (A). (C) Percentages of cells producing IL-4, as determined by flow cytometry. (D) Expression of GATA-3 and T-bet, as determined by flow cytometry. (E) Average expression of GATA-3 and T-bet, as calculated from the data in (D). (F) Th2-and Th1-related gene expression. Differentially expressed genes (adjusted p-value <0.1 and fold difference >2 or <0.5) are indicated by the gene names in red. (G) GSEA of Setdb1 -/- Th2 cells, with genes ranked on the basis of expression in Setdb1 -/-Th2 cells relative to that in their Setdb1 +/+ counterparts. (H-K) Setdb1 +/+ and Setdb1 -/-mice were immunized s.c. with 1W1K peptide in RIBI adjuvant. Eight days after, the draining lymph nodes were collected and the antigen-specific CD4 + T cell response was analyzed. The frequency of antigenspecific cells among CD4 + T cells was first determined by flow cytometry using the 1W1K/I-A b tetramer. Representative dot-plots (H) and frequency of 1W1K/I-A b+ CD44 + cells among CD4 + T cells for each mouse (I) are shown. (J) Percentages of IFN-γ producing cells among 1W1K/I-A b+ CD4 + T cells, as determined by flow cytometry. The percentages are expressed relative to the size of the antigen-specific CD4 + T cell compartment, as determined in (I). (K) IFN-γ and IL-13 production by CD4 + T cells was determined by ELISA following antigenspecific restimulation. The IFN-γ to IL-13 ratio is shown for each mouse. Data are representative of eight independent experiments (A, D) or are represented as mean ± SEM (B, E) of eight independent experiments or as mean ± SD of three independent biological replicates I. The data in (F) and (G) are from two (Setdb1 +/+ ) or three (Setdb1 -/-) independent biological replicates. *p<0.05, ***p<0.001 (unpaired Student's t-test). See also Figure S4 and TableS2.

Figure 4 .

 4 Figure 4. SETDB1-dependent H3K9 trimethylation at a subset of ERVs.

Figure 5 .

 5 Figure 5. Increased expression of ERVs and neighboring genes in Setdb 1-/-cells Analyses were focused on ERVs overlapping a SETDB1-dependent H3K9me3 domain in Th2 cells. ERVs overlapping gene bodies or promoters were excluded from the analysis.

Figure 6 .

 6 Figure 6. H3K9me3 + ERVs mark the Th1 enhancer landscape in Th2 cells. Only ERVs that did not overlap with Th2 enhancers were analyzed. (A) Biological functions assigned to SETDB1-dependent H3K9me3 + ERVs by GREAT. (B) Frequency histograms of absolute distances from each H3K9me3 -(Th2) or H3K9me3 + (Th2 or adipocytes) ERV to the nearest ISG. Statistical significance of the observed enrichment within the first 10 kb of the nearest ISG was assessed by chi-squared test. (C) Th1-related transcription factor binding sites showing enrichment within the 13,303 ERVs sequences. (D) The transcriptional regulators that might account for the differences in gene expression observed between Setdb1 +/+ and Setdb1 -/-Th2 cells following culture in Th1-polarizing conditions were identified by using the Upstream Regulator Analysis module of IPA. The activation z-score infers the activation state of the transcriptional regulators in Setdb1 -/-cells by comparing the differences in gene expression observed in Setdb1 -/-and Setdb1 +/+ cells to the predicted effect (activating or inhibiting) of each transcriptional regulator on these genes. Hashtags highlight transcriptional regulators whose binding sites are enriched at ERVs. Only transcriptional regulators with an overlap p-value of less than 0.01 and a z-score lower than -2 or greater than 2 are shown. (E, G) Absolute numbers of H3K9me3 + ERVs associated with Th1-specific STAT4 (E) or STAT1 (G) binding sites in Th2 cells (black bars) compared to counts obtained with a random distribution (shuffle, white bar). (F, H) Relative enrichment of H3K9me3 + or H3K9me3 -ERVs at Th1-specific STAT4 (F) or STAT1 (H) peaks in adipocytes and Th2 cells. (I) Numbers of putative Th1 enhancers associated with SETDB1-dependent H3K9me3 + H3K4me1 -ERVs in Th2 cells. (J) Relative enrichment of Th1 enhancers at H3K9me3 + ERVs in adipocytes and Th2 cells. (K) Relative proportions of Th1 enhancers intersecting with H3K9me3 domains that overlapped, or not, with ERVs in Th2 cells. (L) Box plots representing the distance between H3K9me3 domains and ERVs or Th1 enhancers. ***p<0.001 (chi-squared test (B and E-J) or Wilcoxon test (L)). See also Figure S7.

Figure

  Figure 7. SETDB1-dependent H3K9 trimethylation at ERVs represses Th1-specific enhancers.

  not ("Silent"), in the absence of SETDB1. (F,G) IGV snapshots of the Ifng locus. Colored boxes indicate peaks for H3K4me1 (green) and H3K9me3 (red). Black boxes correspond to ERV coordinates. (H) Enrichment of H3K4me1 at CNS 17-20 of the Ifng gene in Setdb1 +/+ and Setdb1 -/-Th2 cells as measured by ChIP-qPCR. Data are means ± SEM of three independent biological replicates. (I) Th1 gene network generated with the IPA software

  Figure1

Th2 cell stability in a changing environment, two non-redundant epigenetic silencing pathways therefore converge to lock the Th1 transcriptional program at different genomic locations.In conclusion, our data support that SETDB1 controls CD4 T cell identity by repressing ERVs that flank or overlap Th1-specific enhancers. This enzyme is thus a potential target for drugs that might be useful, for example, to promote Th1 cell differentiation in various infectious diseases, or to prevent harmful Th2 responses in allergic disorders.

ACKNOWLEDGMENTS

We greatly acknowledge T. Jenuwein (Max Planck Institute, Freiburg, Germany) for providing the Suv39h1-deficient mice. We also thank F.-E. L'Faqihi-Olive, V. Duplan-Eche and A.-L. Iscache for technical assistance at the flow cytometry facility of INSERM U1043, the personnel of the US006 ANEXPLO/CREFRE animal facility for expert animal care, D. Rozet for administrative assistance, the GeT and Bioinformatics platforms from the Genotoul (Toulouse, Région Occitanie, France), the Transcriptomic & Genomic Platform Marseille Luminy (TGML, Marseille, France), and the Genomic and Transcriptomic platform from the CPTP (Toulouse, Région Occitanie, France). We are also grateful to members of the "T cell-mediated Immune Tolerance" laboratory for advice and discussions. We would like to thank D. Dunia for his careful reading of the manuscript. This work was supported by Agence Nationale de la Recherche (ANR JCJC "EpiTreg" to O.J.), Région Occitanie (NVEQ 2014 to O.J.) and Fondation pour la Recherche Médicale (FRM, AJE201212 to O.J.). B.B. was funded by a fellowship from the Association pour la Recherche sur le Cancer (ARC). A.M. was supported by fellowships from the Région Midi-Pyrénées and FRM (FDT20170437348).

Data Resources

Raw and processed data files from ChIP-seq and RNA-seq experiments have been deposited in the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE101546.

STAR METHODS

KEY RESOURCES TABLE

CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for reagents may be directed to, and will be fulfilled by the corresponding authors Véronique Adoue (veronique.adoue@inserm.fr) and Olivier P Joffr.fr).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

Suv39h1-deficient mice were kindly provided by T. Jenuwein [START_REF] Peters | Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability[END_REF]. The Setdb1 mutant mouse strain (common strain name EPD0028_1_B07; international strain designation B6Dnk;B6N-Setdb1 tm1a(EUCOMM)Wtsi ) was established as part of the International Mouse Phenotyping Consortium (EMMA ID: EM:04052) at the German Research Center for Environmental Health (Helmholtz Zentrum, Muenchen). The targeting vector was composed of the promoterless L1L2_gt1 cassette inserted in the L3L4_pZero_kan plasmid backbone. Table S2. Related to Figure 3. mRNA levels of genes related to the Th1-or Th2-lineages in Setdb1 +/+ and Setdb1 -/-Th2 cells following culture in Th1-polarizing conditions.

Table S3.