Chromatin bound by survivin regulates the glycolytic switch in interferon-γ producing CD4+ T cells


 Upon activation, CD4+ T cells adapt metabolically to fulfill their effector function in autoimmunity. Here we show that nuclear survivin is essential for transcriptional regulation of glucose utilization. We found that the glycolytic switch in interferon (IFN) g–producing CD4+ cells is dependent on a complex of survivin with interferon regulatory factor 1 (IRF1), and Smad3 and was reversed by survivin inhibition. Transcriptome analysis of CD4+ cells and sequencing of survivin-bound chromatin identified a hub of metabolism regulating genes whose transcription depended on survivin. Direct binding of survivin to IRF1 and SMAD3 promoted IRF1-mediated transcription, repressed SMAD3 activity, and lowered PFKFB3 production. Inhibiting survivin upregulated PFKFB3, restored glycolysis, and reduced glucose uptake, improving control over IFNg-dependent T-cell functions. Thus, IRF1-survivin-SMAD3 interactions are important for metabolic adaptation of CD4+ cells and provide an attractive strategy to counteract IFNg-dependent inflammation.


Introduction
Activated CD4 + effector T cells are key players in autoimmune in ammation. These cells migrate, proliferate, and produce signal molecules at sites of in ammation to mobilize immunity. Upon activation, CD4 + cells metabolize glucose via the pentose phosphate pathway rather than the tricarboxylic acid (TCA) cycle 1 -a change re ected in the remarkable shift of energy production and biosynthetic pathways that fuel effector responses 2,3 . The increase in glucose consumption sustains the increased energy demands and facilitates production of IFNg 4,5 -the principal coordinator of adaptive immune responses in chronic in ammation. Shared IFNg-dependent processes are central for the pathogenesis of autoimmune diseases 4,6 , but the exact mechanism connecting glucose metabolism with IFNg production remains unclear. Strategies to interfere with these processes by targeting concordant changes in the expression of IFN-sensitive genes in blood leukocytes and target tissues may have broad therapeutic potential for autoimmune disorders. Inhibition of anabolic adaptation, which fuels IFNg production, is an appealing approach to reduce the effects of IFNg in autoimmunity.
Survivin, an oncoprotein encoded by BIRC5 and widely expressed in solid and hematological malignancies, is essential for renewal of nonmalignant cells 7,8 and may help regulate gene expression 7,[9][10][11] . It is also essential for T-cell development. Conditional deletion of survivin in thymocytes reduces mature CD4 + and CD8 + T cell populations 12 and leads to dysfunctional T-cell receptors and inability to mount a proper immune response to an antigen challenge 13 . Survivin expression declines in mature T cells, but re-appears during critical phases of phenotype transition, such as the effector phenotype acquisition by CD4 + or CD8 + memory T cells 14 . Survivin is associated with increased severity and are-up of autoimmune conditions, including arthritis 15 , psoriasis, and multiple sclerosis 16 . Targeting survivin in experimental and clinical autoimmunity e ciently reduces in ammation, proliferation, and tissue damage [17][18][19][20][21] . However, the role of survivin in cell metabolism has not been investigated.
In this study, we explored the role of nuclear survivin in maintaining the effector phenotype in IFNgproducing Th1 cells acting through the transcriptional control of glucose utilization. To study this, we performed a genome-wide deep sequencing of the survivin precipitated chromatin regions; identi ed survivin interactors on chromatin, and the biological processes regulated by survivin in cooperation with the identi ed interactors. Combining chromatin and transcriptome analyses with functional studies we searched for the genes sensitive to survivin inhibition and proposed a novel survivin-dependent mechanism coordinating metabolic adaptations during activation of CD4 + T cells in autoimmunity.

Results
Survivin is an essential marker of the IFNg-producing cells phenotype. Flow-cytometry analysis of CD4 + cells from the blood of 22 patients with rheumatoid arthritis (RA) showed that the effector cells (T EFF , CD62L neg CD45RA +/-CD27 neg ) had higher levels of survivin than memory cells; 5.4-16.4% (mean 9.2%) of T EFF cells contained survivin and had highest amount of survivin per cell (Fig. 1a). The phenotype of survivin-producing T EFF cells was determined by RNA-seq analysis of CD4 + cells.
Availability and e cient metabolism of glucose are required for IFNg production and effector function of Th1 cells. Expression of the main glucose metabolism regulators HIF-1a and the catalytic subunit of AMPK-associated kinase (PRKAA1) differed between BIRC5 hi and BIRC5 lo CD4 + cells, but their expression of MYC and MTOR was similar (Fig. 1e). Since HIF1A expression is controlled by hypoxia, the selective enrichment in HIF1A prompted us to evaluate other genes of the hypoxia signature 23 . We found that BIRC5 hi cells overexpress the canonical HIF-1a target genes, including lactate dehydrogenase (LDHA), enolase (ENO1), phosphoglycerate kinase 1 (PGK1), and aldolase A (ALDOA), corrrelated with glucose metabolism (Supplementary Fig. 1c). Speci cally, BIRC5 hi cells were de cient in the key regulator of glucose processing, PFKFB3 (Fig. 1e). As a result, glucose was shunted to the pentose phosphate pathway, as shown by increased expression of glucose-6-phosphate dehydrogenase (G6PD) and 6phosphogluconolactonate (PGLS) and by increased expression of ATP citrate lyase (ACLY), indicating increased fatty acid metabolism. The correlation matrix of the core set of Th1 genes and glycolysis markers revealed clear distinctions in biological processes between BIRC5 hi and BIRC5 lo cells (Fig. 1f).
The tight interactions in BIRC5 hi cells suggested that survivin expression is functionally connected to these processes.
Survivin-bound chromatin is predicted to regulate carbohydrate metabolism Since survivin has been reported to bind to genomic DNA elements that regulates gene transcription 9-11 , we investigate survivin-bound chromatin on the whole-genome level. Chromatin immunoprecipitation sequence (ChIP-seq) analysis of 12 CD4 + cell cultures pooled in 4 replicates (Fig. 2a) revealed 13,704 nonredundant survivin-ChIP peaks (enrichment against input, adjusted p < 10 -5 ). The peaks were unevenly distributed across the genome and were speci cally enriched in a region within 10-100 kb of the regulatory chromatin area occupied by promoters, enhancers, chromatin insulator regions, and CTCF binding sites (Fig. 2b, c).
To characterize the TF landscape of the survivin-ChIP peaks, we used the global ChIP-seq dataset for 1034 human transcriptional regulators in the ReMap database 24 to annotate the set of nonredundant survivin-ChIP peaks. We identi ed 146 TF candidates that were signi cantly enriched across the survivin-ChIP peaks with 0 kb (minimal threshold for the overlapping peaks 10%) and 100-kb anking regions (Fig.  2d) and compared them to regions within 1 Mb of the peaks. The TF candidates identi ed by this approach were tightly associated (Fig. 2d). The q signi cance of association with survivin was higher for TFs in the regions of 0 to 100 kb and lower for TFs within 1Mb.
To identify TFs in open chromatin of CD4 + cells, we used the ATAC-seq dataset (GSE138767 24 ) to annotate nonredundant survivin-ChIP peaks. Survivin was tightly associated with a subset of TFs comparable to those identi ed by whole-genome analysis (Fig. 2d, inset). The q signi cance of the association did not change between the chromatin regions accessible at 2 and 4 h. The top TFs identi ed by both analyses were those regulating glucose and insulin metabolism, including CREBBP, KDM5B, DDX5, FOXK2, CTBP1, and IKZF1.
To identify biological processes regulated by chromatin-bound survivin, we analyzed the functions of the 146 TFs that co-localized with survivin peaks (Fig. 2e) and 2749 protein-coding genes expressed in CD4 + cells (RNA-seq, normalized raw counts >0.5) and used Gene Ontology terms to annotate them to the survivn peaks ( Supplementary Fig. 2a). This approach identi ed functional groups that regulate chromatin, protein modi cation, and metabolism (Fig. 2e). Other functional groups regulated the response to hypoxia and organic substances, including glucose (Fig. 2e and Supplementary Table 1). In agreement with the functional annotation of TFs, gene set enrichment analysis of the protein-coding genes expressed in CD4 + cells and annotated to the survivin peaks revealed signi cant enrichment in processes regulating cellular biogenesis, carbohydrate metabolism, and hydrolase activity ( Supplementary Fig. 2b).
Thus, survivin is frequently located near cis-regulatory elements (REs) and is functionally linked to regulation of protein and carbohydrate metabolism.
Survivin restricts PFKFB3 expression and changes the metabolic requirements of CD4 + cells. To investigate the role of survivin in the predicted biological processes, we used YM155 to inhibit survivin function 10,25 in freshly isolated CD4 + T cells using YM155. Cells were polarized with IFNg for the nal 2 h.
Comparison of differentially expressed genes (DEGs) identi ed by RNA-seq analysis of YM155-treated (0 and 10 nM) CD4 + cells (nominal p < 0.05, DESeq2) with those annotated to survivin peaks showed that 11.8% (24 h) and 4.5% (72 h) of the protein-coding genes expressed in CD4 + cells were sensitive to survivin inhibition (Fig. 3a). Using the curated TRRUST database of gene regulatory relationships, we identi ed the central metabolism regulators HIF-1a, c-MYC, and SP1 as the upstream transcriptional supervisors of the DEGs after 24 h and 72 h of survivin inhibition. Other effects were attributed to the activity of SMAD4, JUN, NF-kB, RELA, ETS1 TFs at 24 h and to interferon regulatory factor 1 (IRF1) and the MHC class II transactivator at 72 h (Fig. 3b).
To study in detail the enzymes involved in cellular glucose utilization (Fig. 3c), we analyzed YM155treated and IFNg-polarized CD4 + cells by RNA-seq. We found that mRNA levels of PFKFB3 and LDHA increased rapidly, promoting conversion of pyruvate into lactate, and that PGLS and ACLY mRNA levels decreased, indicating downregulation of the pentose phosphate pathway and fatty acid metabolism (Fig.   3d). These effects of survivin inhibition blocked the alterations in carbohydrate metabolism seen in the BIRC5 hi CD4 + cells from RA patients ( Fig. 1f) but did not alter the mRNA levels of HIF1A or its metabolic targets HK2, ALDOA, ENO1, and GAPDH.
Survivin colocalizes with IRF1 and SMAD3 on chromatin. To characterize chromatin bound by survivin, we used the JASPAR database of human TF binding sites to analyze motifs in genomic regions covered by the survivin peaks. This analysis revealed enrichment in IRF-binding motifs in all 4 independent ChIPseq replicates. Predominant among the IRF motifs were IRF1 and IRF8, both containing the conserved GAAA repeat (Fig. 5a). The survivin peaks were also enriched in the composite motifs AP1:IRF (AICE motif, GAAAnnnTGAc/gTCA) and SPI1:IRF (EICE motif, GGAAnnGAAA). Multiple binding sites for each motif were frequently present in a single survivin peak. The ISRE motif (GRAASTGAAAST), which bound two IRFs, was also enriched compared to the whole genome, yet infrequent within the survivin peaks ( Fig.   5a).
To connect survivin peaks with transcription, we annotated the whole set of survivin-ChIP peaks to open chromatin in aCD3/aCD28 activated CD4 + cells, using ATAC-seq data (GSE138767 24 ). We found that  Supplementary Fig. 4a). No enrichment in JUND and JUN motifs was found. These nding con rmed the functional speci city of survivin binding.
Next, we looked for evidence of physical interaction between survivin and the predicted TF partners, we immunoprecipitated survivin from lysates of THP1 cells. After a nity isolation, protein denaturation, and separation by electrophoresis, western blotting with speci c antibodies showed that IRF1 and SMAD3 coprecipitated with survivin ( Fig. 5c). Neither IRF8 nor c-MYC, JUN, or JUND were identi ed in the precipitated material from two independent experiments.
Thus, survivin is recruited to open chromatin containing sequence-speci c motifs through its binding to IRF1 and SMAD3. This nding provides molecular evidence that the IRF/survivin/SMAD3 complex helps coordinate the survivin-dependent transcriptional control we observed in the functional experiments (Figs. 3 and 4).
IRF1 and SMAD3 partner with survivin to regulate gene transcription. Since survivin-ChIP peaks accumulated in regulatory chromatin occupied by enhancers (Fig. 2c), we analyzed their presence in the cis-REs of the top protein-coding DEGs ( Supplementary Fig. 3). Using the likelihood score for the enhancer-gene pairing 32 , we identi ed 117 REs that were paired to DEGs and associated with survivin peaks within 0-10 kb and 852 REs with no survivin peaks (Fig. 6a, b). These two groups of REs were similar in GeneHancer (GH) score, length/size of REs, and distance to the transcription start site (TSS) ( Supplementary Fig. 5a).
Among the TF ChIP-seq peaks that co-localized with survivin peaks (10% overlap, 0-kb anks) in ChIP-seq datasets (Fig. 2d), 58 TFs were expressed in CD4 + cells and were more abundant in survivin-containing REs than in the whole genome and or in the remaining REs (all p<10 -3 ) (Fig. 6c). IRF1 and SMAD3 were among the most frequent and abundant survivin partners in REs paired to DEGs, as shown by density distribution analysis (Fig. 6d). Principal component analysis of the distribution of the enriched TFs across the REs, followed by unsupervised clustering of the components (Fig. 6e) revealed that REs clustered by total density of TFs (TF-poor and TF-rich) ( Supplementary Fig. 5b) rather than by gene association and further by association of TFs with IRF1 or SMAD3 (Fig. 6e). Thus, survivin is present in TF complexes with distinct functions and diverse protein compositions.
Using the BioGrid database to analyze protein-protein interactions, we identi ed histone acetyltransferase EP300 and glycogen synthetase kinase 3B as the only common interactors of IRF1 and SMAD3 (Fig. 6f). EP300, a polyvalent protein that recruits TFs to distant enhancers, was enriched in survivin-containing REs and physically interacted with several other enriched TFs (Fig. 6e, f), providing a broad platform for building multiprotein complexes. Some of the IRF1 and SMAD3 interactors were differentially expressed in BIRC5 hi CD4 + cells of RA patients (Fig. 6g, Supplementary Fig. 4b, c).
Survivin has a speci c pattern of transcriptional regulation. To explore the mode of survivin-speci c transcriptional regulation, we analyzed chromatin regions containing genes highly sensitive to survivin inhibition. Several common features emerged, including (1) long-range interactions between survivincontaining REs and the promoters of target genes, (2) the location of survivin-containing REs among REs clustered into regulatory modules, and (3) the location of survivin-containing REs on repressed/poised chromatin. These features are clearly seen in three genes critical for survivin-dependent metabolism in CD4 + cells: PFKFB3, BIRC2, and SMURF2 (Fig. 7a-c).
PFKFB3 was the main target of the survivin-dependent metabolic effects in CD4 + cells. We identi ed 4 survivin-ChIP peaks associated with 5 high-scored REs paired to PFKFB3 (Fig. 7a). These REs covered a region extending from ~20 kb upstream to 100 kb downstream of PFKFB3. Both the upstream and the downstream REs contained ChIP peaks for IRF1 and SMAD3 grouped together with the survivin peaks (Fig. 6e). Publicly available data (e.g.,, HiC, eQTL) indicated internal connection between these REs and the PFKFB3 promoter. Functional segmentation in CD4 + cells annotate the upstream REs to the active promoter and the downstream REs to the repressed region. Thus, activation of those areas after survivin inhibition would enhance PFKFB3 expression. Near the upstream REs were single nucleotide polymorphisms (SNPs) identi ed by genome-wide association studies (GWAS) provide additional weight to those loci and implicated in T-cell-mediated disorders, including latent autoimmune diabetes, and thyroiditis at the upstream REs and type 1 diabetes, RA, thyroiditis, and celiac disease at the downstream REs (Fig. 7a). Thus, the binding of survivin to those REs may be important for PFKFB3 expression and glucose utilization in T cells.
BIRC2 was signi cantly upregulated by survivin inhibition. Fig. 7b shows an extended region of ~550 kb containing 26 REs paired to and surrounding BIRC2. Two of these REs, located ~100 kb and ~400 kb downstream of the TSS, were associated with 3 survivin-ChIP peaks. Despite their distant location, both REs were strongly linked to BIRC2 (GH scores of 1.56 and 10.95, respectively). Both REs contained multiple IRF1 and SMAD3 ChIP-seq peaks and were located within the repressed/poised chromatin according to the functional chromatin segmentation in CD4 + cells. The BIRC2 locus contains few immunologically relevant SNPs.
Survivin activated transcription of SMURF2. Regulatory chromatin around SMURF2 forms a dense cluster of 25 adjacent REs that span the region of ~100 kb upstream of the TSS and cover the gene start (Fig.  7c). Four survivin peaks are annotated to 12 of those REs, 11 of which were in poised/inactive chromatin. An additional survivin peak was in SMURF2, outside of any RE. REs paired to SMURF2 differed in length, TF density, and presence of survivin partners IRF1 and SMAD3 (Fig. 6e). Simultaneous activation of the poised REs triggered by survivin inhibition was predicted by te RoadMap data that reveal a higher-order regulatory unit at this site. Therefore, cooperative activation of the clustered REs is a plausible mechanism for the pronounced upregulation of SMURF2 expression.

Discussion
This study demonstrates a survivin-dependent mechanism of metabolic adaptation existing in the IFNγproducing CD4 + cells. We show that nuclear survivin has a genome-wide and motif speci c binding to chromatin with unappreciated function in gene transcription control. The exact position of survivin binding is de ned here by its physical interaction with the TFs IRF1 and SMAD3. We describe that the IRF1/survivin/SMAD3 complex keeps control of PFKFB3, the major point of metabolic adaptation for autoreactive T cells, and other genes responsible for glycolysis and sugar transport through binding to clusters of REs at a distance from the target genes. Thus, survivin binding to chromatin acts as epigenetic check-point coordinating metabolic switch required for effector function of the IFNγ-producing CD4 + cells.
We found that survivin/BIRC5 hi CD4 + cells have elevated expression of HIF1A and the canonical HIF-1a targets, including numerous glycolytic enzymes and hypoxia-sensing proteins, consistent with reports indicating HIF1α-dependent expression pattern of survivin [33][34][35] . In RA, hypoxia is an important mechanism of immune cell invasion and excessive proliferation in joints 36,37 . We also found that survivin is an essential mediator of the metabolic effects of HIF1α and links them to IFNg-driven in ammation. By analyzing DEGs, we found effects of survivin downstream of HIF1a. Inhibition of survivin repressed the hypoxia-sensing proteins SESN2, PYHIN2, and NLRX1, which inhibited GLUT1 expression and glucose uptake. Subsequently, this strengthened c-Myc driven glutamate metabolism seen in upregulation of the major glutamine transporter SLC1A4 and neutral amino acid transporter SLC7A5 38 .
Repression of the key glycolytic enzyme PFKFB3 was central for the survivin-dependent metabolic effects in CD4 + cells and led to activation of LDHA and aerobic glycolysis and a cessation of the pentose phosphate pathway. PFKFB3 is highly responsive to a growth factors, in ammation, and ischemia, all of which activate estrogen receptor-, hypoxia-, or progesterone response elements on its promoter 39 . Thus, maintenance of PFKFB3 repression requires energy. Integrative analysis of ChIP-seq and protein binding data identi ed IRF1/survivin/Smad3 complexes as potent repressors of the REs paired to PFKFB3. Inhibition of survivin activated PFKFB3 expression and restored conventional aerobic glycolysis through the TCA cycle. This survivin-dependent change in the mode of glucose utilisation is consistent with the logical connection between hypoxia, survivin, and IRF1-dependent effector function of CD4 + cells. IRF1 is the lineage-speci c TF that mediates IFNg signaling and forms the phenotype of Th1 cells. Survivin is mobilized to chromatin sequences containing IRF-binding motifs and directly binds IRF1, enabling it to help regulate the transcriptional of IRF1 target genes. Inhibition of survivin signi cantly impaired both IFNg production and the sensitivity of CD4 + cells to IFNg stimulation, which is required to maintain their effector phenotype and chronic in ammation.
Our ndings showed that survivin represses TGFb/SMAD-dependent processes in CD4 + cells. Indeed, genes encoding proteins downstream of TGFb/SMAD were among the top DEGs upregulated after survivin inhibition, and SMAD3 was one of the most densely present TFs in the REs of those DEGs. Finally, our ndings showed a close interaction between survivin and SMAD3, as shown by immunoprecipitation studies. JUN did not interact with survivin in western blot and was not enriched in the survivin peaks in open chromatin; but neither of those ndings exclude the possibility of an interaction between AP-1 TFs and SMAD3 40 or their consolidating effect on the IRF1/survivin/SMAD3 complex. Conversely, SMAD3/4 and AP1 proteins are frequently found on distant cis-regulatory regions, where they facilitate promoter-enhancer interactions through chromatin looping and triggering transcription 41 . Cell activation with TGFb elicits a widespread SMAD-dependent increase in chromatin accessibility 62 . Hypoxia affects the level of SMAD3 phosphorylation and SMAD4 binding 42,43 . Hypothetically, formation of the survivin/SMAD3 complex might anchor SMAD3 to inactive/poised chromatin, protecting it from degradation and creating a predisposition for rapid changes in transcriptional activity, as observed in our study.
The identi ed interaction within the IRF1/survivin/SMAD3 complex could participate in transcriptional regulation by other mechanisms. The EP300/CREB1 complex is involved in RNA polymerase IIdependent recruitment of TFs to distant REs in inactive/poised chromatin, where REs paired to DEGs are predominantly located. Both EP300 and CREB1 ChIP-seq binding sites were signi cantly enriched in REs and were the only common interactors for IRF1 and SMAD3. An interaction between EP300 and SMAD3 could facilitate the upregulation of the DEGs we detects after survivin inhibition. In this scenario, survivin functions as a guardian of the functional chromatin state by preventing this interaction. Remarkably, the activity of EP300/CREB1 is mediated by glucose 44 and integrates the immune processes initiated by IFNγ 45 and TGFb-signaling, potentially by patronising the epigenetic activity of the IRF1/survivin/SMAD3 complex.
In agreement with our ndings, repression of PFKFB3, which switched glucose processing to the pentose phosphate pathway, has been suggested as the major point of metabolic adaptation for autoreactive T cells in RA 6 . In type 1 diabetes, multiple sclerosis, and systemic lupus erythematosus, the cells experience no energetic starvation and rely on pyruvate kinase-dependent hyperproduction of lactate 46-48 . Experimentally, inhibition of PFKFB3 could restore the metabolic alterations and disable the effector function of T cells in multiple sclerosis, graft-versus-host disease, and type 1 diabetes 46,49 . It could also induce insulin resistance and accelerate in ammation in these conditions 50,51 . GWAS have shown that the genomic region around PFKFB3 harbors several critical polymorphisms associated with autoimmune diabetes, RA, and celiac disease, suggesting that this region is strongly linked to metabolic and autoimmune conditions through variation in T-cell transcription [52][53][54] . Recently, new SNPs near PFKFB3 have been linked to differences in TNFa and IL6 production 55 .
Our studies identify a novel epigenetic mechanism that connects regulation of the PFKFB3 locus to the metabolic phenotype of effector CD4 + cells. Although we studied primary CD4 + cells from RA patients and healthy women, our ndings deepen our understanding of the molecular mechanisms of autoimmunity. The tight interaction within the IRF1/survivin/SMAD3 complex maintains expression of IFN-sensitive genes that are clinically relevant to several autoimmune diseases, including RA 26 , systemic lupus erythematosus 27 , and Sjögren's syndrome 28 . The fundamental role of survivin in bridging the transcriptional programs governed by IRF1 and SMAD3 sheds light on the regulation of the balance between IFNγ-and TGFb-dependent processes. Pharmacological interventions that selectively target these molecular interactions of survivin could be an attractive approach to improve control of IFNγdependent autoimmunity.

Materials And Methods
Patients. Blood samples of 46 RA patients and 7 healthy female controls were collected at the Rheumatology Clinic, Sahlgrenska Hospital, Gothenburg. Clinical characteristics of the patients are shown in Supplementary Table 2. All RA patients ful lled the EULAR/ACR classi cation criteria 56 and gave written informed consent before the blood sampling. The study was approved by the Swedish Ethical Evaluation Board (659-2011) and done in accordance with the Declaration of Helsinki. The trial is registered at ClinicalTrials.gov (ID NCT03449589).
Isolation and stimulation of CD4 + cells. Human peripheral blood mononuclear cells (PBMC) were isolated from venous peripheral blood by density gradient separation on Lymphoprep (Axis-Shield PoC As, Dundee, Scotland). CD4 + cells were isolated by positive selection (Invitrogen, 11331D), and cultured (1.25x10 6 cells/ml) in complete RPMI-medium supplemented with concanavalin A (ConA, 0.625 μg/ml, Sigma-Aldrich), and lipopolysaccharide (LPS) (5 μg/ml, Sigma-Aldrich), for 24 or 72 h. In the inhibition experiments, CD4 + cells were cultured in wells coated with anti-CD3 antibody (0.5 mg/ml; OKT3, Sigma-Aldrich), in RPMI-medium supplemented with the survivin inhibitor YM155 25  RNA-seq analysis. Transcripts were mapped with the UCSC Genome Browser using the annotation set for the hg38 human genome assembly and analyzed with the core Bioconductor packages in R-studio Conventional qPCR. RNA was isolated with the Total RNA Puri cation Kit (17200, Norgen Biotek). RNA concentration and quality were evaluated with a NanoDrop spectrophotometer (Thermo Fisher Scienti c) and Experion electrophoresis system (Bio-Rad Laboratories). cDNA was synthesized from RNA (400 ng) with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Realtime ampli cation was done with RT2 SYBR Green qPCR Mastermix (Qiagen) and a ViiA 7 Real-Time PCR System (Thermo Fisher Scienti c) as described 10 . Primers used are shown in Supplementary Table 3. Expression was calculated by the ddCt method.
For western blotting, 30 mg of total protein from whole-cell lysates and the IP material was separated on NuPage 4-12% Bis-Tris gels (Novex). Proteins were transferred to polyvinylidene di uoride membranes Cytokine measurement. Cytokine levels in supernatants of CD4 + cells were measured with a sandwich enzyme-linked immune assay. Brie y, high-performance 384-well plates (Corning Plasticware, Corning, NY, USA) were coated with capture antibody, blocked, and developed according to the manufacturers' instructions. IFNg (PelikineM1933, detection limit 3 pg/ml, Sanquin, Amsterdam, The Netherlands), IL10 (DY217B, detection limit 15 pg/ml), IL9 (DY209, detection limit 1 pg/ml), IL13 (DY213, detection limit 50 pg/ml), and IL4 (DY204, detection limit 0.25 pg/ml (all from R&D Systems). Developed plates were read in a SpectraMax340 Microplate reader (Molecular Devices, San Jose, CA, USA), and absolute protein levels were calculated after serial dilutions of the recombinant protein provided by the manufacturer.
Glucose uptake assay. Glucose uptake was monitored with 2NBDG (Abcam). CD4 + cells were cultured for 24 h in anti-CD3 coated plates (0.5 mg/ml) supplemented with IFNg (50 ng/ml) and YM155 (0 and 10 nM). Cells were starved in glucose-free RPMI-medium for 2 h and then supplemented with 2-NBDG (100 µM). 2NBDG uptake was registered after 30 min using ow cytometry (Verse, BD) and quanti ed as the ratio of mean uorescence intensity to baseline.
ChIP-seq analysis. The fastq sequencing les were mapped to the human reference genome (hg38) with the STAR aligner 57 ; the alignIntronMax ag was set to 1 for end-to-end mapping. The quality of sequenced material was assessed with the FastQC tool and MultiQC (v.0.9dev0) (Babraham Institute, Cambridge, UK). Peaks were called with MACS2 algorythm for narrow peaks and default parameters. Peaks were ltered for the survivin antibody IP fraction (IP) and unprocessed DNA (Input) and annotated with HOMER software 58 in standard mode to the closest TSS with no distance restriction. A set of peaks with enrichment versus surrounding region and Input (adjusted p < 10 -5 ) was identi ed and quanti ed separately for each sample. Peaks that overlapped by at least 1 nucleotide in several samples were merged as survivin-ChIP peaks. Peaks in all samples were scored by the number of tags of difference between IP and Input (average of these differences between samples). HOMER ( ndMotifsGenome.pl) and the homer2 engine were used for de novo motif discovery and motif scanning. The most common de novo motifs were identi ed separately for each IP sample and examined for detected motifs in the JASPAR database of human TF binding sites 59  For genomic interval datasets, including survivin-ChIP peaks and REs, the Table Browser for the hg38 human genome assembly (http://genome.ucsc.edu/cgi-bin/hgTables) and Galaxy suite tools (https://usegalaxy.org/) were used for estimating distances between nearest intervals, merging, overlapping, calculating genomic coverage, and other standard procedures. The genome-wide distribution of survivin-ChIP peaks was initially screened with the cis-regulatory annotation system (CEAS v0.9.8; accessed November 1, 2020 with Cistrome Galaxy, http://cistrome.org/ap/root). For enrichment analysis, we used the list of all survivin-ChIP peaks and the fraction of them located within 100 kb of the known genes. To estimate pairwise distances and statistical signi cance of pairwise interval overlaps for survivin-ChIP peaks with genome elements de ned above, we used Bedtools suite (https://github.com/arq5x/bedtools2; accessed 01feb2021-15apr 2021). For each comparison, a pairwise, two-tailed Fisher's exact test was used. Comparison was based on initial survivin-ChIP peak positions as intervals and extended regions with 1-kb, 10-kb and 50-kb anks.
Peak colocalization analysis with transcription regulators. To identify transcription regulators near survivin-ChIP peaks, we used the ReMap database (http://remap.univ-amu.fr/; accessed November 15, 2020) for colocalization analysis of aggregated cell-and tissue-agnostic human ChIP-seq datasets of 1034 transcriptional regulator. ReMapEnrich R-script (https://github.com/remap-cisreg/ReMapEnrich; accessed November 15, 2020) was used for colocalization enrichment analysis. The hg38 human genome assembly was used for all comparisons. Two-tailed p values were estimated and normalized with the Benjamini-Yekutielli test, using the maximal allowed value of shu ed genomic regions for each dataset (n = 15), kept on the same chromosome (shu ing genomic regions parameter byChrom=TRUE).
The default fraction of minimal overlap for input and catalogue intervals was set to 10%. Bed interval les of survivin-ChIP peaks with 0-and 100-kb anks were prepared. The dataset with 0-kb anks was compared with the Universe sets of genomic regions, de ned as within 1 Mb of the same ChIP-seq peaks.
For analysis of the regulatory chromatin paired with DEGs, input bed les were selected according to their distance from the genome region containing REs paired to DEGs; bed les for individual TFs were downloaded from ReMap2020.
Analysis of candidate partner TFs. TFs with statistically signi cant enrichment of overlaps (q value <0.05, n >100) were selected. TFs that were enriched with respect to the genomic background were identi ed within each RE by using ReMap database, as described above. A subset of TFs enriched within the survivin-associated REs was identi ed by chi-square test (chisq.test, R-studio) and false-discovery rate correction (R-studio). To explore the involvement of these TFs in regulating DEGs, we prepared the