
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

39253451
10.1101/2024.08.26.609422
preprint
1
Article
IRF5 mediates adaptive immunity via altered glutamine metabolism, mTORC1 signaling and post-transcriptional regulation following T cell receptor activation
Brune Zarina 12
Lu Ailing 1
Moss Matthew 1
Brune Leianna 1
Huang Amanda 1
Matta Bharati 1
Barnes Betsy J 123*
1 Center of Autoimmune, Musculoskeletal and Hematopoietic Diseases, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
2 Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
3 Departments of Molecular Medicine and Pediatrics, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
Author contributions:

Conceptualization: ZB, BJB

Methodology: ZB, AL, BM, MM, BJB

Investigation: ZB, AL, AH, LB

Visualization: ZB, BJB

Funding acquisition: ZB, BJB

Project administration: BJB

Supervision: BJB

Writing - original draft: ZB

Writing – review and editing: ZB, BJB

* Corresponding author: bbarnes1@northwell.edu
27 8 2024
2024.08.26.609422https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
nihpp-2024.08.26.609422.pdf
Although dynamic alterations in transcriptional, translational, and metabolic programs have been described in T cells, the factors and pathways guiding these molecular shifts are poorly understood, with recent studies revealing a disassociation between transcriptional responses and protein expression following T cell receptor (TCR) stimulation. Previous studies identified interferon regulatory factor 5 (IRF5) in the transcriptional regulation of cytokines, chemotactic molecules and T effector transcription factors following TCR signaling. In this study, we identified T cell intrinsic IRF5 regulation of mTORC1 activity as a key modulator of CD40L protein expression. We further demonstrated a global shift in T cell metabolism, with alterations in glutamine metabolism accompanied by shifts in T cell populations at the single cell level due to loss of Irf5. T cell conditional Irf5 knockout mice in a murine model of experimental autoimmune encephalomyelitis (EAE) demonstrated protection from clinical disease with conserved defects in mTORC1 activity and glutamine regulation. Together, these findings expand our mechanistic understanding of IRF5 as an intrinsic regulator of T effector function(s) and support the therapeutic targeting of IRF5 in multiple sclerosis.

Sentence Summary:

Findings provide new insight into the mechanisms by which T cell intrinsic IRF5 regulates the adaptive immune response via modulation of mTORC1 signaling, glutamine metabolism, and protein translation.

Lupus Foundation of America Gina M. Finzi Fellowship (ZB)National Institutes of health grant 1R01AR076242 (BJB)National Institutes of health grant R03TR004623 (BJB)Department of Defense (DoD) CDMRP Lupus Research Program grant W81XWH-18-1-0674 (BJB)The Lupus Research Alliance (BJ.B)
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pmcINTRODUCTION

The transcription factor interferon (IFN) regulatory factor 5 (IRF5) has been characterized as a regulator of type I IFNs and a key mediator of proinflammatory cytokine expression in response to Toll-like receptor (TLR) signaling. Dysregulation of IRF5 has been linked to infection, autoimmune disease, cancer, metabolic diseases, and neuropathic pain (1–3). Initial mechanistic studies on IRF5 were performed in myeloid and B cells; however, more recent studies support a role for IRF5 in CD4 T cells (3, 4). Loss of IRF5 expression in CD4 T cells drives Th2 skewing in the context of Th1 and Th17 polarizing conditions, alters inflammatory cytokine production, regulates apoptosis, and inhibits T cell proliferation and chemotaxis (4). Many of these IRF5-mediated functions have been reported downstream of IRF5 transcriptional regulation, yet the role of IRF5 in T cell metabolism and translational reprogramming, global shifts of which are required to assume effector function(s), has yet to be examined. Indeed, a lack of correlation between transcriptional responses and protein expression following T cell activation has been documented, with regulation of the T cell proteome occurring far more rapidly than can be explained by de novo transcription (5). Conversely, rapid metabolic changes in response to T cell stimulation correlate with the kinetics of T activation. One of the key mediators of both normal and pathologic metabolic and translational responses is the mammalian target of rapamycin (mTOR) signaling complex (6).

Signaling from mTOR kinase complexes, mTORC1 and mTORC2, regulates T cell effector programs, migration, proliferation, and survival. Similar to previous observations in Irf5−/− mice showing skewing towards IL4 secreting Th2 cells, reductions in Th1 and Th17 cells, and increased Treg generation (4), reduced mTORC1 signaling is correlated with increased regulatory T cells (Tregs) and a reduction in Th1 and Th17 generation (7). In addition, mTORC1 is a key post-transcriptional and translational regulator. Prior studies revealed that naïve CD4 T cells have reserves of ribosomal machinery which, upon TCR stimulation, are activated by mTORC1 to support increased protein synthesis (8, 9). Not only has repression of protein synthesis and mTORC1 signaling been linked to T cell quiescence, but effector immune signaling pathways are also regulated by these translational molecules (10, 11). Further studies elucidating how manipulation of ribosomal machinery regulates T cell function remain to be completed.

The emerging importance of metabolic pathways, mTOR signaling and translational regulation in T cells has inspired a new generation of drug development and target discovery. In vitro studies using 2-deoxyglucose and metformin to reprogram reactive T cells from patients with systemic lupus erythematosus (SLE) were met with success (12). In the murine model of multiple sclerosis (MS), experimental autoimmune encephalomyelitis (EAE), mTORC1 inhibition with rapamycin protected mice from classical disease development and reversed symptom onset (7, 13, 14). In addition, manipulation of the Th17-Treg axis through alterations in glutamine metabolism is protective in EAE (15). Through a combination of targeted inhibition studies, scRNAseq, flow cytometry, and unbiased metabolomics, we identify IRF5 in the regulation of CD4 T cell metabolism, mTOR signaling and protein translation, and demonstrate a disease modifying role for T cell intrinsic IRF5 in EAE.

RESULTS

IRF5 regulates T cell support of B cell adaptive responses

Previous studies by our lab and others revealed defects in plasmablast (PB) generation and IgG2a/c production in mouse and human Irf5-deficient B cells by in vitro culture and in vivo immunization (16, 17). Although these defects were attributed to B cell intrinsic IRF5 function, more recent studies suggest a role for T cell intrinsic IRF5 in the regulation of B cell adaptive immune responses (4) We thus utilized the T cell-dependent antigen NP-conjugated chicken gamma globulin (CGG) emulsified in Complete Freund’s Adjuvant (CFA) to assess PB differentiation in 8–10 weeks-old Irf5+/+ (WT) and Irf5−/− (KO) littermate mice. 7 days following immunization, WT and KO spleens were harvested for immunohistochemistry (IHC) and flow cytometric analysis. We detected a significant reduction in B220+ staining in KO spleens with no difference in spleen size (Fig. 1A, B, Supp. 1A, B). Flow cytometric analysis of KO splenocytes showed reductions in T follicular helper (Tfh) cells (BCL6+CXCR5+) and significantly increased regulatory T cells (Tregs) (CD25+FoxP3+) (Fig. 1C–E). Further examination in non-immunized mice revealed significant reductions in splenic Tfh cells in KO compared to WT mice (Supp. 1C). Prior studies showed KO T cells to have decreased activation and proliferation following anti-CD3/CD28 stimulation. We confirmed these findings in purified CD4 T cells (Supp. 1D–G). Additionally, although KO mice had no significant reductions in NP-specific PBs (CD19+IgDlowCD138+), there were significant reductions in IgG2a production (Fig. 1F–H). Next, we examined by in vitro co-culture assay if KO CD4 T cells were sufficient to replicate the observed B cell defects in the NP-CGG CFA immunization model. B cells (CD45+B220+) and CD4 T cells (CD45+CD4+) were sorted from WT and KO splenocytes and co-cultured for 4 days in the presence of anti-IgM, CpG-B and anti-CD3/CD28 Dynabeads. Of interest, significant reductions in CD19+IgDlowCD138+ PB generation were observed in KO B cell:KO T cell, WT B cell:KO T cell and KO B cell:WT T cell cocultures as compared to WT B cell:WT T cell cultures with only slight differences in IgG2a production (Fig. 1I, J, Supp. 1H, I). Together, these data support a role for CD4 T cell intrinsic Irf5 in B cell adaptive immunity.

scRNAseq reveals alterations in metabolism, ribosome biogenesis and identifies novel IRF5 transcriptional targets

Current dogma in the field suggests that loss of Irf5 inhibits Th1 and Th17 effector subsets, enhances Treg differentiation and Th2 responses, and alters Tfh function through transcriptional regulation (3, 4, 18, 19). To gain further insight into the cellular pathways by which IRF5 regulates T cell differentiation/function, we performed scRNAseq on 5,000–7,000 TCR-stimulated WT and KO CD4 T cells. Using Seurat, we identified seven distinct T cell clusters (Fig. 2A). The subsets were classified as follows: activated naïve (Nr4a1, Nfkbid, Cd69, Egr3), naïve (Sell, Lef1, Klf2, Ifit3), memory-like (TMEM) (Spry1, Fabp5, Ccr7) (20, 21), IFN Enriched (Gbp2, Gbp5, Trat1, Nme1, Nme2) (22–25), Tfh (Tigit, Il21, Cxcr5, Pdcd1), Th Complex (Rora, Ccr5, Ccr2, Serpin6b6) (26), and Treg (FoxP3, Ikzf2, Il2ra, Il2rb) (Fig. 2A, B). Further subclustering revealed two distinct populations comprising both Treg (termed Treg 0 and Treg 1) and Tfh clusters (identified as Tfh 0 and Tfh 1) (Supp. Table 1).

Pseudotime trajectory analysis was performed using Slingshot (27) to examine the relationships between each subset. Four trajectory lineages were identified in WT and KO CD4 T cells (Supp. 2A). The Tfh and Treg, Th complex subset and IFN enriched, TMEM and activated naïve T cell clusters were predicted to exist within independent lineages. Paired with cluster enrichment analysis (Fig. 2C), the pseudotime trajectory findings support that loss of Irf5 skews T cells toward the specific Tfh/Treg lineage and away from activated naïve and TMEM trajectories. Indeed, further examination of subset enrichment in KO compared to WT CD4 T cells showed KO mice to have reduced activated naïve cells, enrichment in both Treg 0 and Treg 1 and a decrease in TMEM populations, supporting previously described flow cytometric CD4 T cell subset profiling by our lab and others (4). There was also a distinct increase in the Tfh 0 and a slight decrease in Tfh 1 KO T cell populations. The Th complex subset, named thus due to increased expression of genes encoding migratory receptors, inflammatory cytokines, inhibitory molecules, and transcription factors, was also highly enriched in KO CD4 T cells (Fig. 2C, Supp. 2B). Of interest, a significant proportion of genes dysregulated in the KO Th complex subset comprised of ribosomal transcripts and alternative splicing machinery. In addition, KO Th complex cells had downregulation of proinflammatory and metabolic genes, including Il18r1, Tomm5, Nkg7, Mif, Klrk1 and Irf8 (Supp. 2B) (28–32). Unlike the Th complex subset, despite the clear shifts in the Tfh and Treg subcluster distribution, few transcripts within each of these clusters demonstrated significant differences with loss of Irf5 (Fig. 2C, Supp. 2C, D). DEG analysis identified only three genes with clear dysregulation across all KO T cell clusters: Uba52, Speckled protein (Sp) 110 (Sp110) and Speckled protein 140 (Sp140) (Fig. 2D–F). Analysis of the third SP family member, Sp100, revealed no change in expression (Fig. 2G) (33). The specific and dramatic reductions in Sp110 and Sp140, but not Sp100, were independently confirmed by qPCR in purified WT and KO CD4 T cells (Fig. 2H–J). Of note, previous studies in myeloid cells demonstrated that IRF4 can bind to and regulate similar target genes as IRF5 (34). To assess specificity of IRF5 in Sp regulation, we examined Sp100, Sp110 and Sp140 expression in Irf4−/− T cells. No significant differences in either Sp100, Sp110 or Sp140 transcript expression were detected (Supp. 2E–G).

SP110 and SP140 are genes of interest in both inflammatory and autoimmune diseases. Mutations in these factors are associated with Crohn’s disease, chronic lymphocytic leukemia, and MS, while hyperactivation of SP110 and SP140 is associated with SLE and MS (33). Given the striking reduction in Sp110 and Sp140 expression in Irf5−/− T cells and the implications of elevated IRF5 expression and hyperactivation as a driver of SLE (35, 36), we examined Sp110 and Sp140 expression in a published RNAseq dataset (GSE149050) from healthy donors and SLE patients with differing IFN levels (37). Interestingly, we found increased SP110 expression in PBMCs from IFN high expressing SLE patients compared to both IFN low SLE patients and healthy controls (HC). Similar trends were observed for SP140 (Supp. 2H, I). Previous studies from our lab and others demonstrated that SLE is mediated in part by the aberrant production of autoantibodies regulated by B cell intrinsic IRF5 (16, 38). Notably, one of the pathologies associated with speckled protein inactivating mutations is inhibition of B cell antibody production (39). Analysis of Sp110 expression in KO B cells revealed a similar reduction in transcript expression as KO T cells (Supp. 2J).

To further assess functional differences between WT and KO T cells, we performed Gene Set Enrichment Analysis (GSEA) using the C5 Gene Ontology (GO) sub-collection. We found significant downregulation in gene sets for Cytoplasmic Translation, Ribosomal Synthesis, Ribosome Metabolism, Ribosome Assembly and Function, RNA Processing, Mitochondrial Depolarization and Mitochondrial structure in KO CD4 T cells (Fig. 2K). GSEA using C2 KEGG also revealed significant downregulation of Ribosomes, Oxidative Phosphorylation and Systemic Lupus Erythematosus, supporting conserved regulatory roles for IRF5 in oxidative phosphorylation, mitochondrial function, and ribosome regulation (Supp. 2K, Supp. Table 2). Given these striking reductions in cytoplasmic translation and ribosomal synthesis, we re-examined a previously unpublished study from our lab identifying IRF5-protein interactions in Ramos B cells by immunoprecipitation (anti-IgG control or anti-IRF5 antibodies) and mass spectrometry analysis (Supp. Table 3). Among the most significantly enriched for IRF5 interacting partners, as compared to Ig control, were the 60S and 40S ribosomal proteins (RPLs), and translation initiating factors EIF4A2 and EIF6. Despite these studies being performed in B cells, IRF5 interaction with proteins involved in ribosomal assembly and biogenesis (RPLs, EIF6) and translation (EIF4A2) imply a conserved role for cytoplasmic IRF5 in the regulation of the translational apparatus, findings supported by prior independent studies (40).

Irf5−/− mice are protected from Experimental Autoimmune Encephalomyelitis

Although inhibition or loss of IRF5 is protective in autoimmune and inflammatory diseases (4, 35, 41–43), apart from Leishmania donovani and inflammatory bowel disease (IBD) models, the contribution(s) of IRF5 to T cell-mediated disease pathogenesis has remained largely unexplored. Using scRNAseq, we found downregulation of proinflammatory factors previously implicated in EAE disease pathogenesis including Il18r1, Irf8, and Mif, and increased expression of Rora in the KO Th complex subset (28, 32, 44, 45). In addition, we detected a significant reduction in Sp110 and Sp140 expression, gene loci associated with risk of MS (33). Thus, we next expanded our studies to examine if loss of Irf5 impacts the clinical progression of experimental autoimmune encephalomyelitis (EAE) using MOG35–55/PTX injections (46). Using a common clinical rating scale (46), we found that KO mice had both decreased and delayed incidence and onset of disease as well as significantly attenuated disease progression compared to WT littermate mice, indicating a protective role for loss of Irf5 in T cell-mediated EAE (Fig. 3A, B).

One of the T cell-mediated adaptive immune signaling pathways dysregulated in MS involves the expression and binding of T cell CD40L to its receptor, CD40, on B cells (47, 48). Given the observed reductions in PB generation by coculture of WT B cells with KO T cells and the stark protection of Irf5 KO mice from T cell-mediated EAE disease onset and progression, we next examined if loss of Irf5 dysregulated the CD40L/CD40 pathway. Following stimulation of WT and KO total splenocytes with either anti-CD3/CD28 Dynabeads or anti-IgM (B cell receptor stimulation), CD40L and CD40 expression was quantified on Live CD3+CD4+ T cells and Live CD45+CD19+B220+ B cells, respectively. Anti-CD3/CD28-stimulated KO T cells demonstrated a significant decrease in both CD40L surface expression, as quantified by mean fluorescence intensity (MFI), and CD40L expressing CD3+CD4+ T cell populations 24 hours following stimulation (Fig. 3C–E). There were slight, albeit significant reductions in the percentage of CD40+ B cells and CD40 MFI on unstimulated and anti-IgM stimulated KO B cells (Supp. 3A, B). Examination of basal CD40L expression revealed a slight but significant increase in the percentage of CD40L expressing KO T cells, but no significant difference in MFI (Supp. 3C, D). Cd40l transcript expression was not significantly detected via scRNAseq, thus we evaluated Cd40l transcript expression from sorted CD3+CD4+ T cells (purity > 95%) from WT and KO mice following 6-hour in vitro TCR stimulation. There was no significant difference in expression between genotypes (Fig. 3F). This was unsurprising as previous studies have indicated that Cd40l expression is extensively regulated post-transcriptionally (49, 50). Notably, flow cytometric examination revealed, like that seen in NP-CGG CFA immunized KO mice, a significant decrease in Tfh cells in KO spleens following EAE induction (Supp. 3E, F). Together, aberrant CD40L protein expression on KO CD4 T cells and reductions in Tfh cells supports a role for IRF5 in T cell support of the adaptive immune response and in post-transcriptional or translational regulation of CD40L following TCR signaling.

IRF5 regulates protein translation in T cells via mTORC1

Given our findings, we next sought to examine the function of one of the most highly conserved mediators of translation, metabolism, and post-transcriptional regulation, whose aberrant function also has been implicated in EAE pathogenesis, the mammalian target of rapamycin 1 (mTORC1) signaling complex. mTORC1 is a key regulator of T cell proliferation, differentiation, and effector function (51–53). Despite current and prior studies demonstrating KO T cell defects in activation and proliferation, the regulatory mechanisms underlying these dysfunctions have yet to be fully elucidated (4, 17, 42, 43). Considering our findings that suggest a role for IRF5 in translational regulation, we next determined if mTORC1 activity was altered in KO T cells by examining total ribosomal S6 protein (RPS6) and phosphorylated RPS6 (phosphoRPS6) levels, both of which are canonical downstream effectors of mTORC1 signaling. Flow cytometric analysis revealed a significant defect in the phosphorylation of RPS6 in TCR stimulated KO T cells, indicating decreased mTORC1 activity (Figs. 4A, B). No significant difference in total RPS6 expression was detected (Supp. 4A). Examination of phospho(Thr389)-p70 S6 kinase (P70S6K), the kinase responsible for RPS6 phosphorylation, showed decreased phosphorylation, albeit insignificant (Supp. 4B). Additional studies revealed similar total mTOR protein expression in KO and WT CD4 T cells basally and following TCR stimulation, further supporting that IRF5 regulates mTORC1 activity rather than mTOR expression (Supp. 4C).

One of the main regulators of mTORC1 is the serine/threonine protein kinase, Akt (54). Akt activation promotes many of the pathways regulated by mTOR, including proliferation, survival, and metabolism (55). Analysis of Akt activity in KO CD4 T cells following 24-hour TCR stimulation revealed a significant reduction in Akt (Ser473) phosphorylation (Fig. 4C). This supports previous findings of Akt dysregulation in Irf5-deficient myeloid cells (56). Akt in T cells can be activated via signaling from IL2 (57). Following TCR stimulation, KO CD4 T cells had decreased expression of both IL2 and the IL2 receptor, CD25 (Fig. 4D, E). Prior studies have shown that, unlike in humans, IL2 does not regulate murine CD40L expression (58). Nonetheless, to confirm that alterations in IL2 expression and signaling through the IL2R were not responsible for reduced CD40L expression in KO CD4 T cells, total splenocytes were harvested and stimulated with anti-CD3/anti-CD28 in the presence or absence of recombinant IL2. Results showed no significant difference in CD40L expression (Supp. 4D). Of interest, treatment with the Akt inhibitor, MK2206, inhibited CD40L expression, indicating that Akt activity is required for CD40L expression (Supp. 4E). These results provide initial evidence that IRF5 regulates translation through mTORC1 signaling, whose activity is inhibited by reduced Akt activity.

Rapamycin is a small molecule inhibitor of mTORC1 that binds to cytosolic FKBP12 and inhibits mTOR S2448 phosphorylation and subsequent activation (59). Given the reduction in phosphoRPS6 in KO CD4 T cells, we next investigated if mTORC1 regulates the expression of CD40L. We stimulated WT CD4 T cells in the presence or absence of rapamycin for 24 hours, then analyzed T cell activation (CD4+CD69+) and CD40L expression. Although there was no change in the activation of CD4 T cells with rapamycin treatment (Fig. 4F), there was a significant decrease in CD40L expression that replicated levels seen in KO T cells (Fig. 4G–I, Fig. 3C–E). There was no difference in viability following rapamycin treatment (Supp. 4F). Next, to examine if T cell intrinsic mTORC1 inhibition was sufficient to inhibit the B cell adaptive response, we sorted and pretreated WT CD4 T cells with rapamycin (+Rap) or PBS (-Rap) then cocultured T cells with B cells as previously described. Quite strikingly, we observed a significant decrease in CD45+CD19+CD138+IgDlow PB generation and IgG2a production despite the relative increase in CD45+CD19+ B cells when B cells were cocultured with rapamycin-treated CD4 T cells. Taken together, these findings further support that mTORC1 signaling in CD4 T cells is required for T cell support of B cell adaptive immune responses (Fig. 4J–M, Supp 4G).

Notably, the mTORC1 signaling axis is a key regulator of protein synthesis. With the previous findings demonstrating defects in T cell activation, proliferation, and CD40L expression in the context of mTORC1 dysfunction, we next examined rates of protein translation in unstimulated and TCR stimulated KO T cells by measuring incorporation of an alkyne analog using chemoselective fluorochrome ligation with the OPP assay. We found a significant decrease in the rate of protein translation in TCR stimulated KO T cells, indicating a role for IRF5 in the positive regulation of protein synthesis (Fig. 4N, O). Recent studies exploring mechanisms by which mTORC1 regulates translation have revealed ATF4 as a metabolic effector of mTORC1. When expressed, ATF4 promotes protein synthesis and stimulates the uptake of various amino acids. Immunoblot analysis of ATF4 expression in KO T cells following TCR stimulation revealed significantly reduced expression in KO T cells (Fig. 4P, Q). We also examined the expression of eukaryotic elongation factor 2 (eEF2), a global mediator of protein translation. Prior studies have demonstrated that NF-kB activating stimuli can activate eEF2 by repressing transcription of the inhibitory calcium/calmodulin dependent eEF2 kinase (eEF2K) (60). Other studies have shown that eEF2K inhibitory phosphorylation of eEF2 is regulated through the rapamycin-sensitive mTOR pathway, shown here to be dysregulated in KO T cells (61, 62). We thus examined eEF2 levels following TCR stimulation of purified CD4 T cells and detected a significant reduction in expression within KO T cells (Fig. 4R, S). Altogether, these findings support a role for IRF5 in the regulation of protein translation at the transcriptional and post-transcriptional level.

Untargeted metabolomics reveals global metabolic shifts in Irf5−/− CD4 T cells

mTORC1 activity is extensively regulated by and responsive to changes in cellular energy levels and metabolites, changes that are mediated in part through ATF4 signaling (63). In T cells, metabolites modulate survival, proliferation, and effector fate decision and function (64). Results from scRNAseq analysis of KO T cells revealed a significant reduction in the enrichment of genes involved in oxidative phosphorylation (Supp. 2K). Prior studies in Irf5−/− macrophages have also reported reduced oxidative phosphorylation capacity (56, 65, 66). Although a role for IRF5 in T cell metabolic regulation has yet to be investigated, the observed alterations in KO T cell function and mTOR signaling provided compelling initial evidence to support this. As such, we performed unbiased LC-MS metabolomics analysis on unstimulated and TCR stimulated WT and KO purified naïve CD4 T cells (Supp. Table 4). T cell purity and activation was confirmed by flow cytometric analysis (Supp. 5A). Basally and following TCR stimulation, the metabolic landscapes of WT and KO T cells had dramatic differences (Supp 5B). KO T cells significantly downregulating 21 metabolites relative to WT T cells (p < 0.05, FC > 1.5) (Fig. 5A, B). Following 24 hours of anti-CD3/CD28 stimulation, WT and KO T cells continued to demonstrate distinct metabolic profiles (Fig. 5A–D). Anti-CD3/CD28 stimulated KO T cells had 20 significantly upregulated and 4 significantly downregulated metabolites compared to unstimulated KO T cells (Fig. 5C), while anti-CD3/CD28 stimulated WT T cells had 28 significantly upregulated and 14 significantly downregulated metabolites compared to unstimulated WT T cells (Fig. 5D). Of those metabolites, TCR stimulated WT T cells revealed 10 unique ones that were upregulated while KO T cells had only 2 uniquely upregulated. Conversely, WT T cells showed no uniquely downregulated metabolites while KO T cells had 23 downregulated (p < 0.05, FC > 2.0) (Fig. 5E, F, Supp. Table 5). Subsequent metabolite enrichment pathway analysis demonstrated regulatory perturbations in some of the most highly enriched metabolite sets following TCR stimulation (Supp. 5C). Of particular interest was the significant enrichment in Malate-Aspartate Shuttle (MAS) metabolites: malate, aspartate, glutamate, and α-ketoglutaramate (α-KGM) in KO T cells (Supp. 5C, Fig. 5G–K). We next quantified transcript levels of the cytoplasmic Glutamic-oxaloacetic transaminase 1 (Got1) and mitochondrial Glutamic-oxaloacetic transaminase 2 (Got2) MAS transaminases. Following anti-CD3/CD28 stimulation, both Got1 and Got2 transcript levels were increased in KO compared to WT T cells (Supp. 5D, E). Finally, to confirm that the increased levels of MAS metabolites and transaminases were reflective of increased utilization of the MAS in KO T cells, we treated WT and KO T cells with the MAS inhibitor aminooxyacetate acid (AOAA). Following 24 hours of AOAA treatment, anti-CD3/CD28 stimulated KO T cells had significantly increased rates of apoptosis compared to WT T cells (Supp. 5F). Together, these findings indicate global shifts in T cell metabolism with loss of Irf5, and increased reliance of KO T cells on the malate-aspartate shuttle following T cell receptor stimulation.

IRF5 and glutamine metabolites regulate glutamine transporter protein expression

As previously described, the in vitro metabolomics analysis revealed increased levels of glutamate in KO T cells (Fig. 5J). However, previous studies demonstrated that inhibition rather than enrichment in glutamine metabolism is a molecular mechanism to inhibit Th1 and Th17 effector functions, drive Treg generation, and inhibit T cell activation and proliferation, phenotypes that have been described by us and others in KO T cells (Supp. 1) (67–70). Thus, given the results of our in vitro metabolic studies that demonstrated increased glutamine metabolites in KO T cells (Fig. 5), we next interrogated other factors involved in glutamine transport metabolism in CD4 T cells. Two key murine glutamine transporters expressed in CD4 T cells are ASCT2 and SNAT2. Following TCR stimulation, ASCT2 and SNAT2 expression were downregulated in KO T cells (Fig. 6A–C). However, there was no significant difference in Slc1a5 (ASCT2) or Slc38a2 (SNAT2) transcript expression (Fig. 6D, E). Further evaluation of key glutamine metabolic enzymes, Glutamate dehydrogenase 1 (Glud1) and Glutaminase 2 (Gls2) revealed no significant differences in transcript expression (Supp. 6A, B). Interestingly, although there was no change in GLUD1 expression (Supp. 6C, D), there was a dramatic, albeit insignificant reduction in GLS2 protein expression in KO T cells following anti-CD3/CD28 stimulation (Supp. 6E, F). Together, these results indicate that the increased levels of glutamine metabolites in the KO T cells are more likely due to contributions from upregulation of the MAS rather than an upregulation in glutamine metabolism.

Given the observed increase in the intermediary metabolite of α-ketoglutarate (α-KG), α-KGM, in KO T cells (Fig. 5J), we next examined if ASCT2 expression was influenced by the upregulation of this epigenetic regulator of immune responses (68, 71–74). Following treatment of WT CD4 T cells with the cell permeable α-KG analog, dimethyl-ketoglutarate (DMK), we observed a significant reduction in ASCT2 expression (Fig. 6F). As previously discussed, glutamine metabolism regulates T helper cell function. Thus, we examined if alterations in metabolites contribute to CD40L dysregulation observed in KO T cells. Following WT T cell DMK treatment, we found a significant downregulation of CD40L expression (Fig. 6G). As previously reported, treatment with DMK did not inhibit S6 phosphorylation, indicating that DMK modulation of CD40L expression signals through an mTORC1-independent pathway (Supp. 6G) (75–77). In support of the previously established regulatory role of α-KG in demethylation via the ten-eleven translocation (Tet) α-KG dependent dioxygenases, increasing levels of DMK resulted in a significant increase in transcript expression for both Cd40l and Slc1a5 despite the significant reductions in CD40L and ASCT2 protein expression (Fig. 6H, I) (77). These findings indicate a more complex role for α-KG regulation at a post-epigenetic level.

To further elucidate how the reductions in ASCT2 expression mechanistically contribute to defects in KO CD4 T cell function, we sought to mimic the reduction in ASCT2 expression by using the small molecule ASCT2 inhibitor, V-9302. Treatment of WT CD4 T cells with V-9302 dramatically inhibited CD40L expression (Fig. 6J, K). Further, inhibition of glutaminase, the enzyme responsible for converting glutamine into glutamate, with CB-839 (Telaglenastat) also inhibited CD40L expression in WT CD4 T cells (Fig. 6J, L). Together, our findings indicate that increased intracellular glutamine metabolites act as negative regulators of glutamine transporter and enzyme expression, which in turn are required for the positive regulation of CD40L.

T cell conditional Irf5−/− mice are protected from EAE

mTORC1 signaling, ASCT2 expression, and Speckled proteins have been linked as either risk factors for or protective against multiple sclerosis (7, 13, 70). Thus, to determine the specificity of our findings, we generated Irf5fl/fl-Lck-Cre+ T cell-specific KO mice (cKO) and examined if loss of Irf5 in T cells was sufficient to confer protection from EAE as previously observed in our whole-body KO studies (Fig. 3).

cKO mice had significantly attenuated disease progression with a marked improvement in disease scores compared to the Irf5fl/fl-Cre− littermate controls (Fig. 7A). Histologic analysis of spinal cord sections from cKO mice showed an intact myelin sheath and reduced inflammatory foci (Supp. 7A.), whereas typical demyelination and inflammation were observed in Irf5fl/fl-Cre− littermate mice. To examine the role of IRF5 in disease onset as well as disease progression, we harvested spleens at symptom onset (D13) and peak symptoms (D20). cKO spleen sizes showed trends towards increased size compared to Irf5fl/fl-Cre− mice at D20 (Fig. 7B, Supp. 7B). Despite this, flow cytometric analysis of total splenocytes revealed a significant decrease in the overall percentage of CD4 T cells in the spleens of cKO mice at both timepoints (Fig. 7C).

We next investigated if effector T cells were impacted by loss of Irf5. We observed a significant reduction in splenic IFNγ production by CD4 cKO T cells (Fig. 7D, E). There were no differences in IL17 or IL4 expression in cKO CD4 T cells (Supp. 7C–F). Despite the protective phenotype in cKO mice, there was no significant alteration in Tfh (CD4+BCL6+CXCR5+), Treg (CD4+FoxP3+) or CD40L expression (Supp. 7G–J). As previously discussed, ASCT2 expression and mTORC1 activity have been implicated in EAE. Examination of the expression and activation of these molecules, respectively, showed significant reductions in ASCT2 expression (Fig. 7F, G) and RPS6 phosphorylation (Fig. 7H, I) in cKO CD4 T cells following EAE induction, supporting a vital role for the expression of these molecules in EAE disease pathogenesis, as well as their regulation by T cell intrinsic IRF5.

Our previous studies in NP-CGG CFA immunized mice revealed defects in KO splenic follicles (Fig. 1A). Thus, we next examined the spleen at both D13 and D20 in cKO and Irf5fl/fl-Cre− mice by IHC. D13 IHC analyses revealed decreased CD4 and B220 cells in cKO spleens, while D20 IHC analyses revealed no significant differences in CD4, but a dramatic increase in B220 coverage (Figs. 7J, K, Supp. 7K, L). To better elucidate how these early histologic findings correlated with disease progression, spleens from Irf5fl/fl-Cre− and cKO mice were stratified by disease score (advanced score > 10, moderate score 6 to 8, minimal score 1 – 2) and correlations performed between B or T cell coverage and disease score. Our findings revealed that increasing EAE disease scores inversely correlated with B220 coverage (Fig. 7L) and positively correlated with CD4 coverage (Fig. 7M), with the coverage of CD4 thus inversely correlated with that of B220 (R2 = 0.78, p-val< 0.005) (Fig. 7N).

Notably, Sp110 and Sp140 dysregulation has been implicated in the pathogenesis of MS (33). Thus, to further assess the link between loss of Irf5 expression and Sp expression and validate our scRNAseq findings, we performed qPCR on sorted CD4 T cells and B cells from cKO mice following anti-CD3/CD28 stimulation, as previously described. Our findings revealed a select decrease in Sp110 and Sp140 in cKO CD4 T cells (Supp. 7M–O, Fig. 2). Taken together, these data demonstrate a clear relationship between the presence and function of CD4 T cells and B cells in modulating EAE disease pathogenesis, identifies regulatory pathways by which inhibition of T cell intrinsic IRF5 may offer protection from EAE disease onset and severity, and reveals novel IRF5 translational and transcriptional targets (Fig. 7O).

DISCUSSION

Recent studies have demonstrated the vital importance of metabolic and translational regulatory pathways in T cell function (78–81). IRF5 was previously implicated as a mediator of T helper cell-intrinsic cytokine/chemokine expression and migration. However, prior to this study, a thorough functional examination of T cell intrinsic IRF5 beyond its canonical role as a transcriptional regulator of proinflammatory cytokines had yet to be conducted (4, 43, 82).

Our mechanistic studies using immunophenotyping, targeted inhibitor assays, metabolomics, co-immunoprecipitation and scRNAseq analyses revealed dysregulation of ribosomes, protein translation and metabolism in Irf5−/− T cells. Specifically, we found an overall reduction in Irf5−/− T cell translational capacity and mTORC1 activity. Uba52, ATF4, and eEF2 were identified as candidate regulatory targets of IRF5 that may contribute to the observed reduction in protein translation. UBA52, a ubiquitin-ribosomal fusion protein, is an understudied master regulator of translation that functions through roles in both the assembly of the ribosome translational complex and as a major supplier of ubiquitin. Inhibition of global protein synthesis and decreased proliferation in KO T cells with decreased Uba52 expression closely replicates findings from prior studies, indicating a likely role for IRF5-dependent UBA52 expression in the regulation of CD4 T cells (83). Analysis of IRF5 interacting partners in Ramos B cells revealed significant enrichment for EIF6 and EIF4A2. Both of these factors are of particular interest as EIF6 plays a key role in ribosome biogenesis (84) while EIF4A2 is a RNA helicase involved in translational repression via the miRNA degradation pathway (85, 86). Thus, it stands to reason that loss of Irf5 may alter the function and/or expression of EIF6 and EIF4A2 and hence reduce protein translation. Lastly, as previously discussed, ATF4 is a key regulator of protein translation and metabolism, whose expression we reveal as dysregulated with loss of Irf5. Further validation of these candidate regulatory targets and their impacts on T cell function will be a focus of future study.

Beyond this, rapamycin inhibition assays provided evidence that mTORC1 signaling is crucial for CD4 T cell support of B cell adaptive immunity through regulation of CD40L expression. Prior studies demonstrated correlations between reduced CD40L expression on T cells and impaired Th1 polarization, recapitulating our findings in KO CD4 T cells (87, 88). However, the T cell intrinsic regulatory mechanisms governing CD40L expression have yet to be fully elucidated. Here, we provide compelling evidence that mTORC1 activity and glutamine metabolism are molecular regulators of CD40L expression. We further demonstrate that elevated levels of the metabolite α-KG were sufficient to reduce both glutamine transporter expression and CD40L through post-transcriptional mechanisms, thereby curtailing the adaptive immune response. Interestingly, prior studies showed increased α-KG levels produced from glutamine metabolism drives M2-like macrophage polarization, while other studies have demonstrated the promotion of alternative (M2-like) anti-inflammatory macrophages from M1 occurs with loss of Irf5 (89–91). Our data suggest that IRF5-mediated alterations in α-KG may contribute to its role in macrophage polarization. Beyond glutamine metabolism, another metabolite of interest that was uniquely downregulated in KO but not WT T cells following stimulation was glycerolphosphorylethanolamine (GPE), levels of which directly correlate with phosphatidyl ethanolamine (PE) (Supp. Table 4, 5) (92). Recent studies have identified PE as a key metabolite in Tfh differentiation and support of humoral immunity. It is tempting to speculate that dysregulation of these metabolic pathways in KO mice may contribute to our findings of reduced Tfh cells (93). Lastly, our findings demonstrate increased reliance of KO CD4 T cells on the malate aspartate shuttle. The MAS is an alternative method by which cells can generate NADH and provide electrons to the electron transport chain. Our current findings, along with prior work demonstrating defects in oxidative phosphorylation with loss of Irf5 (56, 65), suggest that mechanisms exist by which KO T cells compensate for decreased oxidative phosphorylation. Taken together, we propose that loss of Irf5 in CD4 T cells drives aberrant effector function through a combination of transcriptional, translational, and metabolic reprograming (Fig. 7O).

Metabolic intervention to combat disease has been met with success in both preclinical and clinical studies. Increasing our understanding of how immune cells respond to metabolic dysregulation is crucial for continued advancement. Through unbiased metabolomics analysis, we reveal global alterations in T cell metabolism with loss of Irf5. Prior studies have shown that reducing glutamine in media drives Tregs while inhibition of glutamate conversion to α-KG with the MAS inhibitor, AOAA, drives Th17 inflammatory cells through 2-hydroxyglutarate dependent methylation of the FoxP3 promoter. Here, our studies reveal a shift towards a Treg phenotype with loss of Irf5 and an increase in intracellular α-KG and glutamate, supporting a role for IRF5 in the metabolic regulatory axis governing Th17 and Treg fate decision. Of further interest, α-KGM is an intermediary molecule in the conversion of glutamate to α-KG through the understudied glutaminase II pathway (94). Unlike the conversion of glutamate to α-KG by glutamate dehydrogenase, conversion of α-KGM to α-KG results in the production of NH4+. High ammonia levels inhibit T cell activation, proliferation and contribute to T cell exhaustion (95). Further studies elucidating the role of T cell intrinsic ammonia production in T effector functions, and by extension how increasing levels of α-KGM might contribute to the dysregulation of Irf5−/− T cells, remains to be performed.

IRF5 has canonically been described as a transcription factor. Our scRNAseq analyses highlighted the differentiating potential and transcriptional regulation of T cell subsets, particularly the Th complex, by IRF5. DEG analysis in WT CD4 T cells revealed Th complex cells to express chemokines and chemokine receptors in addition to proinflammatory and inhibitory cytokines, transcription factors and proteins. These factors describe a migratory CD4 T cell population poised to rapidly respond to stimuli with the potential to gain proinflammatory or inhibitory effector functions. Notably, we found the Th complex subset to be significantly enriched in Irf5−/− mice. Interestingly, analysis of the transcriptional profiles of Irf5−/− Th complex subset showed them poised for an anti-inflammatory response with reduced translational capacity compared to their WT counterparts. It is tempting to speculate that these transcriptional differences contribute to the protection observed in EAE cKO mice. Of note, a recent paper revealed that CXCR4-dependent MOG autoreactive T cell migration into the bone marrow was required for CCL5-dependent aberrant inflammatory myelopoiesis that escalates the CNS demyelination characteristic of EAE (96). These same pathways were downregulated in Irf5−/− Th complex cells.

Our scRNAseq analysis additionally revealed two autoimmune risk genes under IRF5 transcriptional regulation: Sp110 and Sp140. SP110 and SP140 are nuclear body proteins hypothesized to regulate gene transcription, ribosome biogenesis and apoptosis (97, 98). Of interest, SP110 missense mutations and deletions are associated with Hepatic Venoocclusive Disease with Immunodeficiency (VODI), which clinically presents with severe hypogammaglobulinemia, a T-cell immunodeficiency, absent lymph node germinal centers (GCs) and absent tissue plasma cells; findings remarkably similar to those reported in Irf5−/− mice (99). SP140 has been identified as a modulator of chromatin accessibility in macrophages (100). Previous ATAC-seq studies performed in Irf5−/− T cells revealed significant alterations in chromatin accessibility. This finding was not attributed to IRF5’s role as a transcription factor (4). Furthermore, analysis of the SP110 and SP140 promoter region with Genome.UCSC revealed predicted binding sites for IRF5. We propose that Sp110 and Sp140 are IRF5-specific regulatory targets. Examination of this novel IRF5 regulatory axis in other immune cells will be the focus of future studies.

Prior work described alterations in SP110 expression, glutamine transporter expression and aberrant mTORC1 activity as significant risk factors for MS. Here, we show each of these risk factors to be regulated by IRF5 (101, 102). cKO protection from EAE further demonstrated the preclinical relevance of IRF5 expression in CD4 T cells. The alterations in secondary lymphoid architecture and reduced B cell egress from the spleens provide additional compelling evidence that the expression of IRF5 in CD4 T cells is crucial for the B cell adaptive response. In the context of MS, mounting evidence demonstrates the crucial role for pathogenic B cell maturation and antibody secretion in driving demyelination (103). Treatment with rituximab to deplete B cells is gaining traction as an effective therapy for treatment resistant disease (104, 105). However, this fails to counter existing plasma cells which lack CD20 expression (106). Thus, inhibiting the generation of pathogenic autoantibody secreting B cells has remained a challenge due to a lack of understanding of modulators of disease pathogenesis (106–109). Based on the results of our studies, we propose that inhibiting B cell maturation and autoantibody production by targeting IRF5 activity in CD4 T cells through recently developed IRF5 specific inhibitors is a potential therapeutic strategy for the treatment of MS (35, 41).

In summary, our findings highlight novel regulatory roles for IRF5 in mTORC signaling, as well as in the metabolic, transcriptional, and translational regulation of CD4 T cells. We elucidate how subtle shifts in metabolism have a profound impact on the adaptive immune response with clinical implications. Altogether, these data significantly increase our knowledge of IRF5 regulatory pathways in CD4 T cells at a transcriptional, translational and metabolic level, expands our understanding of metabolic regulation of signaling pathway molecules, broadens our knowledge of the KO CD4 single cell landscape, and highlights IRF5 expression in CD4 T cells as a potential therapeutic target in the treatment of multiple sclerosis.

In the context of metabolic dysregulation, previous studies established that disruption of metabolites drives transcriptional alterations and epigenetic remodeling, which in turn reprograms immune cells. Thus, a limitation of this study is an inability to definitively determine the primary perturbation driving dysregulation in these highly interlinked transcription-translation-metabolic feedback loops. We attempted to address this using small molecule inhibitors in unperturbed WT systems. However, our findings only provide partial insight into the complex interplay between metabolism and T cell function. In addition, in the cKO EAE model, we did not observe reductions in CD40L. Review of literature shows that CD40L has historically been difficult to study directly due to the highly transient and early expression of this signaling molecule. Further studies investigating the kinetics of CD40L expression in EAE models are of interest.

MATERIALS AND METHODS

Study design

This study was designed to understand the molecular mechanisms by which IRF5 modulates T cell effector function in response to TCR activation in vitro and in the in vivo EAE disease model. Endpoint analyses for in vitro and in vivo studies included flow cytometry, quantitative polymerase chain reaction (qPCR), scRNAseq, and unbiased metabolomics. All in vitro studies used randomly assigned mice without investigator blinding. We compared the clinical response to EAE induction following MOG35–55/PTX injections of 8–12 weeks-old C57BL/6 Irf5+/+ (WT) and Irf5−/− (KO) littermate mice, and Irf5fl/fl-Lck-Cre− and Irf5fl/fl-Lck-Cre+ littermate mice. Mechanisms of immunopathology were investigated during early onset and peak disease as determined by a common clinical scoring method. Clinical scoring was performed with investigator blinding. All data points and n values reflect biological replicates. The specific numbers and genotypes of the mice and the statistics performed are included in the figure legends such as the two-way unpaired t test, 1-way analysis of variance (ANOVA) with Tukey’s post-hoc test for multiple comparisons, and 2-way ANOVA with Holm-Sidak correction for multiple comparisons as indicated. Experiments were repeated in multiple settings using complementary methods of molecular interrogation and laboratory techniques to validate findings. All data points from the studies were included unless methodologic errors occurred resulting in decreased cell viability, poor activation, sample contamination or low cell numbers.

Mice

C57BL/6J Irf5−/− mice were originally obtained from Taniguchi via the Rifkin Lab and backcrossed 11 generations (110). C57BL/6J Irf5+/+WT littermate mice were used as controls. Irf5fl/fl-Lck-Cre+ mice were a kind gift from Simona Stager’s lab (1INRS-Institut Armand-Frappier). C57BL/6J Irf4−/− mice were a kind gift from Dr. Alessandra Pernis (Hospital for Special Surgery). Genotype was confirmed using PCR. Mice used in experiments were between 6 weeks to 4 months of age and of both genders. Experiments were performed in agreement with our Institutional Care and Use Committee and according to National Institutes of Health guidelines. All mice were bred in house at the Feinstein Institutes for Medical Research. Irf5fl/fl cage-matched littermates (Irf5fl/fl-Lck-Cre−) were used as wild-type controls in experiments utilizing Irf5fl/fl-Lck-Cre+ (cKO) mice. For all experiments, mice were age- and sex-matched.

Naïve CD4 T cell isolation

Freshly isolated splenocytes from WT and KO mice were homogenized in 4 mL PBS. Red blood cell (RBC) lysis was performed in 8 mL of RBC lysis buffer (Biolegend) for 5 minutes on ice and quenched with excess PBS + 2% FBS (vol/vol). Naïve CD4 T cells were isolated using Miltenyi T cell Isolation Kit following manufacturer’s protocol (Cat#: 130-104-453). Magnetic separation was performed to achieve a >95% enriched population of naïve CD4 T cells. For T cell activation, cells were cultured in RPMI 1640 supplemented with 10% FBS and 1X Pen/Strep in the presence of either 1 ng/mL Recombinant murine IL-7 (PeproTech, Cat#217–17) or stimulated with anti-CD3/CD28 Dynabeads at a 4:1 bead:cell ratio (Fisher Scientific, cat#: 11–452-D).

Total B cell and CD4 T cell isolation

Total splenocytes were isolated from mice and RBCs lysed as previously described. Cells were stained with B220R PerCP and CD4 BV510 in FACS buffer for 30 minutes at room temperature, briefly washed in FACS buffer, and sorted using BDFacsAria.

In vitro CD4 T cell proliferation

CD4 T cells were sorted then stained with CellTrace™ Violet Proliferation Dye as per manufacturer protocols (Invitrogen™ Cat#:C34557). Labeled CD4 T cells were activated with anti-CD3/CD28 Dynabeads and cultured for four days before being analyzed by flow cytometry.

Immunizations

Intraperitoneal injections were performed using 200 μl/mouse. Sterile PBS solution was used to reconstitute 4-Hydroxy-3-nitrophenylacetyl hapten conjugated Chicken Gamma Globulin (NP-CGG, Ratio 29:20) to a final concentration of 2 mg/mL (Biosearch Technologies, Cat#: N-5055C-5). Complete Freund’s Adjuvant (CFA) was purchased from Invivogen (Cat#: vac-cfa-60). Intraperitoneal injections of NP-CGG-CFA were conducted using 100 μg/100 μL NP-CGG emulsified in 100 μL CFA.

Flow cytometry

Samples were harvested, washed in PBS + 2% FBS (vol/vol), blocked with anti-CD16/CD32, and stained with a Live/Dead Fixable Dead Cell stain prior to surface and intracellular stains. For a detailed list of antibodies used in staining panels, see Supplemental Methods. Intracellular staining was performed per manufacturer’s protocol using Transcription Factor Staining Buffer Set (Cat#: 00-5523-00, eBioscience™). Phospho-flow cytometry was performed using the two-step fixation/methanol protocol (Protocol C: Two-step Protocol for Fixation/Methanol, ThermoFisher).

In vitro CD4 T cell activation and cocultures

Sorted CD4+ T cells or total splenocytes were cultured in 24-well flat-bottom plates at a density of 2 × 106 cells/500 μL media (Fischer Scientific, cat# 3473). For T cell activation, cells were cultured in RPMI 1640 supplemented with 10% FBS (v/v) and 1X Penn/Strep and stimulated with anti-CD3/CD28 Dynabeads as per manufacturer’s protocol (Fisher Scientific, cat#: 11–452-D). For cocultures, 2 × 106 sorted CD4+ T cells and 2 × 106 sorted B cells were co-cultured for 4 – 5 days in the presence of Dynabeads, 10 ng/mL CpG-B (ODN 2006) (Fischer Scientific, Cat#: HC4039) and 10 μg/mL anti-IgM (Southern Biotech, Cat#: 1021–01). For rapamycin pretreatment, CD4+ T cells were sorted and plated in RPMI 1640 supplemented with 10% FBS and 1X Penn/Strep and then incubated at 37°C with either 100 nM of rapamycin or equal volume of PBS (control) for 2 hours (Selleck Chemicals, Cat#: S1039). Media was removed and cells were washed three times with excess volume of PBS prior to being plated with sorted B cells. Cocultures were stimulated following rapamycin treatment as previously described.

In vitro protein synthesis quantification

OPP staining was performed using Click-iT plus OPP Alexa-555 protein synthesis kit per manufacturer’s protocol (ThermoFischer, Cat#: C10456). Briefly, 2 × 106 cells were incubated at 25°C for 30 minutes with 1:400 dilution of the Click-iT OPP reagent. Cells were washed three times with PBS and then fixed in PBS with 4% formaldehyde (v/v). Following fixation, cells were permeabilized in PBS supplemented with 2.0% saponin and 3.0 % FBS (v/v) for 15 minutes. For the Click-iT reaction, cells were incubated in the dark at room temperature for 30 minutes in the Click-iT reaction cocktail. Samples were then washed twice with PBS supplemented with 2% FBS and immediately analyzed by flow cytometry.

qPCR

RNA was isolated from sorted CD4+ T cells following specified stimulation and treatments using the Qiagen RNeasy Mini Kit (Cat#: 74106). cDNA synthesis was performed with the GoScript reverse transcription system (Cat #: A5001). qPCR was performed in triplicate for each sample using the PowerUp SYBR green real-time PCR master mix with 5–10 ng input cDNA (cat#: A25776). Threshold values (CT) were averaged across sample replicates, followed by normalization via the ΔΔCT method to β-actin. For primer sets, see Supp. Table 6.

Apoptosis analysis

Total splenocytes were cultured for 24 hours as previously described. All stains were performed protected from light. Cells were washed twice with prewarmed FACS buffer and surface stained as previously described. Cells were then washed once with prewarmed FACS buffer and Annexin V binding buffer. Samples were resuspended in 100 μL binding buffer supplemented with 5 μL of Annexin V-FITC (Cat#: 10040–02, SouthernBiotech) and stained for 20 minutes at room temperature. Cells were washed once with Annexin V binding buffer (Cat#: 10045–01, SouthernBiotech) then resuspended in 200 μL of binding buffer and stained with 5 μL of 7-AAD (Cat#: 420403, Biolegend) for 15 min at room temperature. Samples were placed on ice and analyzed using Fortessa.

Immunoblot analysis

Briefly, whole cell lysates were prepared using NP-40 lysis buffer (50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1% NP-40 and 5 mM EDTA) (Thermo Scientific, J60766-AP) supplemented with Halt Protease Inhibitor Cocktail (Thermo Scientific, 87786) and PhosStop Phosphatase Inhibitor Cocktail (Roche, 4906845001). Sample protein concentrations were quantified using the DC protein assay (Bio-Rad, 5000112). 20 μg of protein per sample were separated by SDS-PAGE using the Bolt Bis-Tris system (Invitrogen). Proteins were transferred to 0.45 μm nitrocellulose membranes (MDI, SCNX8402XXXX101) using a wet tank transfer system. The membranes were blocked for 1 hour at room temperature with 5% bovine serum albumin (BSA) in TBST and incubated overnight at 4°C with the primary antibody diluted in the blocking buffer. The membranes were washed three times for 5 min each with TBST and incubated with the secondary antibody diluted in the blocking buffer for 1 hour at room temperature. The membranes were washed five times for 5 min each with TBST and incubated with 1 mL of chemiluminescent detection reagent (Cytiva, RPN2232) for 3 min before image acquisition using a ChemiDoc MP Imaging System (Bio-Rad Laboratories). Horseradish peroxidase (HRP)-conjugated antibodies against β-actin (Cell Signaling, 12620, 1:5000) were used as loading controls for protein normalization. Densitometric analysis was performed using the Image Lab software (Bio-Rad Laboratories).

Metabolomics analysis

For each sample, approximately 3 × 106 naïve splenic CD4+ T cells were purified and stimulated as previously described. LC-MS/MS analysis was performed by The Metabolomics Innovation Centre (Alberta, Canada). For detailed protocol, see Supplemental Methods.

Metabolomics Pathways Interpretation

Biological interpretation was performed by MetaboAnalyst 5.0 using the metabolite set library Homo sapiens based on normal human metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Human Metabolome Database (HMDB).

scRNA-Seq sample preparation

FACS-sorted WT and KO CD4+ T cells were purified and stimulated as previously described, then directly processed for scRNA-seq with 10X Genomics 3′ kit (10X Genomics, Pleasanton, CA) following the manufacturer’s instructions. 10,000 CD4 T cells were used to construct single-cell libraries with the Chromium Single Cell 3′ Reagent Kits (v2 Chemistry) according to manufacturer’s instructions. Libraries were sequenced using the Illumina platform.

scRNA-Seq data analysis

scRNA-seq data were aligned to mm10 using CellRanger v.3.1.0 and downstream processing was performed using Seurat v3.1.1 (111). Briefly, cells with fewer than 200 features, higher than 1% mitochondrial gene content, or transcripts expressed by fewer than 3 cells were removed prior to log normalization. Principal component analysis (PCA) was performed, and the subsequent Uniform Manifold Approximation and Projection (UMAP) analysis was conducted using the first 30 principal components. Cluster-specific genes were determined using the FindMarkers algorithm in the Seurat suite. Clustering was performed by calculating a shared nearest neighbor graph with a resolution of 0.5. The FindAllMarkers function determined differentially expressed genes (DEG) based on the non-parametric Wilcoxon rank sum test for each subset. Subsetting was performed using previously published markers of T cell subsets. Trajectory analysis was performed using Slingshot (112).

Histological analysis

For histological analysis, spleens, right inguinal lymph nodes, and spinal cords were harvested as indicated and fixed in 10% Neutral Buffer Formalin (NBF) overnight before being transferred to 70% EtOH. For spinal cord harvest, mice were CO2 euthanized, then immediately perfused with 10 mL PBS followed by 10 mL of 10% NBF. All samples were sent to Histowiz for paraffin embedding and histological analysis (Brooklyn, NY). A minimum of three independent samples were sent for each study. Immune cell infiltration and lymphoid architecture were examined using Hematoxylin and Eosin (H&E), anti-CD4 (Cat# ab183685, Abcam) and anti-B220 (Cat#: NB100–77420, Novus Biologicals). Demyelination was analyzed using Luxol Fast Blue. IHC quantification was performed using ImageJ.

EAE immunization

8–12 weeks-old male and female mice, minimum of 3 per gender and genotype, were subcutaneously injected at three sites with 200 μg of MOG peptide 35–55 emulsified in Complete Freund’s Adjuvant (CFA) containing 400 μg of Mycobacterium (Cat#: EK-0111, Hooke Laboratories). On day 0 (D0) and 1 day after (D1) immunization, mice were intraperitoneally injected with 200 ng of pertussis toxin (Cat#: 180, List Biological Laboratories). All mice were examined and graded daily for neurological signs in a blinded manner as previously described. For the tail: 0, no disease; 1, half paralyzed; 2, full paralysis. For each hind and forelimb assessed separately: 0, no disease; 1, weak or altered gait; 2, paresis; 3, limb paralysis; and 5, moribund state (46). Average clinical scores were calculated daily for each group of mice and plotted. Immunological studies were performed on the onset (13 days after immunization) or peak (20 days after immunization) of disease. 4 female mice were chosen for each genotype for further molecular analyses according to typical and representative clinical symptoms.

Statistical analysis

GraphPad Prism v.9.2 was used for statistical analysis. Statistical analysis was performed using. P value calculated by two-way unpaired t tests, one-way ANOVA with Tukey’s post-hoc test for multiple comparisons, or 2-way ANOVA with Holm-Sidak correction for multiple comparisons as indicated. Differences were considered statistically significant when p < 0.05.

Supplementary Material

1 Supplementary Tables

Supp. Table 1. scRNA-Seq enrichment analysis comparing Treg0/Treg1 and Tfh0/Tfh1 clusters

Supp. Table 2. scRNA-Seq gene set enrichment analysis using KEGG_Hallmark_C5GO

Supp. Table 3. LC/MS/MS analysis of IRF5 interacting partners from immunoprecipitation in Ramos B cells

Supp. Table 4. Tier 1 and Tier 2 metabolites from Mass Spectrometry Analysis

Supp. Table 5. Fold-change of metabolites from TCR stimulated vs. unstimulated WT and KO T cells

Supp. Table 6. Primers for qPCR analysis

Data file S1: scRNAseq files will be made publicly available at GSE267271.

Acknowledgements:

We thank the Feinstein Flow Cytometry Core Facility for flow cytometry technical assistance, the CCP animal facility for assistance with breeding, The Metabolomics Innovation Centre Canada for metabolomics, and the Center for Advanced Proteomics Research, New Jersey Medical School Cancer Research Center (Rutgers). We thank Dr. Lionel Blanc for OPP reagents and techniques and Amy Pitler and Hong Li at Rutgers University for assistance in the LC/MS/MS analysis of IRF5 interacting partners by immunoprecipitation.

Funding:

Lupus Foundation of America Gina M. Finzi Fellowship (ZB)

National Institutes of health grant 1R01AR076242 (BJB)

National Institutes of health grant R03TR004623 (BJB)

Department of Defense (DoD) CDMRP Lupus Research Program grant W81XWH-18-1-0674 (BJB)

The Lupus Research Alliance (BJ.B)

Data and materials availability:

Raw and processed bulk scRNA-seq files are available from Gene Expression Omnibus (GEO) under accession number GSE267271. All other data are available in the main text or the supplementary materials.

Fig. 1. T cell intrinsic IRF5 regulates T cell dependent B cell maturation

Mice were immunized intraperitoneally with 50 ug NP-CGG/CFA. Spleens harvested 7 days following immunization. (A) Representative histology and (B) quantification of B220+ staining from WT and Irf5−/− spleens; scale bar represents 500μm. (C) Representative flow gating strategies for Tfh (CD3+CD4+BCL6+CXCR5+) and Tregs (CD3+CD4+CD25+FoxP3+) and their respective quantification (D, E). (F) Representative flow cytometric gating strategy for PBs (CD19+IgDlowCD138+). (G) Quantification of NP specific PBs (NP+CD45+CD19+CD138+IgDlow) and (H) IgG2a production from (G). P values calculated by two-way unpaired t test. (I) Representative flow gating strategy and (J) summary graphs of B220+CD138+IgDlow plasmablast generation following 4-day in vitro coculture. P value calculated by one-way analysis of variance (ANOVA) with Tukey’s post-hoc test for multiple comparisons. Bar graphs show means +/− SEMs. **p < 0.01, ***p < 0.001, ****p <0.0001, ns = not significant. Data pooled from two to three independent experiments with each point representing an independent biological replicate.

Fig. 2. Loss of IRF5 alters CD4 single cell transcriptional landscape

(A) Uniform Manifold Approximation and Projection (UMAP) of 5000 – 7000 CD4+ single cells. Each dot corresponds to a single cell, colored according to cell type. Representative graph of two WT and two Irf5−/− biological replicates, with one male and one female representing each genotype. (B) Heatmap of genes used to identify CD4 T cell clusters. Data are colored according to expression levels. Legend is labeled in log scale. (C) Log2 fold change enrichment values of Irf5−/− relative to WT T cell clusters. (D-G) Violin plots showing the transcript expression of (D) Uba52, (E) Sp110, (F) Sp140 and (G) Sp100 in WT and Irf5−/− CD4 T cells in indicated T cell cluster. (H-J) Sorted CD3+CD4+ T cells from WT and Irf5−/− splenocytes were activated for 6-hours in vitro with anti-CD3/CD28 (TCR). (H) Sp110, (I) Sp140 and (J) Sp100 transcript expression normalized to β-Actin. Three biological replicates, representative of two independent experiments. P value calculated by two-way unpaired t tests. (K) Preranked gene set enrichment analyses using C5 gene ontology (GO) gene sets. Top gene ontology (GO) terms sorted by Normalized Enrichment Score (NES). Padj < 0.05. *P < 0.05, ****P < 0.0001. Each point represents an independent biological replicate.

Fig. 3. Irf5 KO mice are protected from Experimental Autoimmune Encephalomyelitis

(A) Composite means +/− SEM of clinical scores. Minimum of 12 biological replicates per experiment. Data pooled from two independent experiments. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (B) Disease incidence. Minimum of four biological replicates per genotype. (C, D) WT and Irf5−/− total splenocytes were sorted for CD3+CD4+ T cells, then activated for 24- or 48-hours in vitro with anti-CD3/CD28 Dynabeads (TCR). Live CD3+CD4+ T cells were gated for flow cytometric marker analysis. (E) Representative CD40L flow cytometry gating in WT and KO CD4 T cells. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (F) Cd40l transcript expression normalized to β-Actin following 6-hours in vitro anti-CD3/CD28 (TCR) stimulation. Data pooled from two to three independent experiments, with each point representing an independent biological replicate. P value calculated by two-way unpaired t test. *P < 0.05, **P < 0.01. Bar graphs show means +/− SEMs.

Fig. 4. IRF5 regulates protein translation, mTORC1 and Akt signaling

(A) Phosphorylated RPS6 quantification in CD3+CD4+ T cells sorted from WT and Irf5−/− splenocytes following 24-hours in vitro stimulation with anti-CD3/CD28 (TCR) or without (IL7). Data pooled from three independent experiments. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (B) Representative histogram of phosphorylated RPS6 quantification from (A). (C-E) Summary quantification of (C) phospho-AKT, (D) IL2 and (E) CD25 expression in WT and Irf5−/− CD3+CD4+ T cells gated from total splenocytes following 24-hours in vitro stimulation with anti-CD3/CD28 (TCR) or without (Unstim). Three to five biological replicates, representative of two independent experiments. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (F-H) Summary quantification of (F) CD69, (G) CD40L mean fluorescence intensity (MFI) and (H) CD40L% expression in Live CD3+CD4+ T cells gated from total splenocytes following 24-hours in vitro stimulation in the presence (TCR + Rap) or absence (TCR) of rapamycin. Data pooled from three independent experiments. P value calculated by Ordinary 1-way ANOVA with Tukey correction for multiple comparisons. (I) Representative gating for CD40L expression in CD3+CD4+ gated on Live Singlets in the absence (TCR) or presence (+Rap) of rapamycin. (J-M) Summary quantification of (J) CD19+ B cells, (K) plasma blasts (PBs) (CD45 +CD19+CD138+IgDlow) and (L, M) IgG2a production. Five biological replicates. P value calculated by two-way unpaired t tests. (N) CD3+CD4+ T cells gated from total splenocytes were activated for 24-hours in vitro with anti-CD3/CD28 (TCR) and puromycin incorporation measured via O-propargyl-puromycin assay. Representative histogram of puromycin incorporation is shown. (O) Quantification of puromycin incorporation from three to four biological replicates. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (P-R) Quantification and representative immunoblot of (P, Q) ATF4 and (P, R) eEF2 in sorted CD4 T cells following anti-CD3/CD28 stimulation. N = 4 biological replicates. P value calculated by two-way unpaired t tests. Bar graphs show means +/− SEMs. *p < 0.05, **p < 0.01, ***p < 0.001. Each point represents an independent biological replicate.

Fig. 5. IRF5 regulates CD4 T cell metabolism

(A) Purified naive CD4 T cells from 3 WT and 3 Irf5−/− mouse spleens were analyzed using untargeted LC-MS approach following either 24-hours in vitro stimulation with anti-CD3/CD28 (TCR) or IL7 (Unstim). Heatmap of top 40 differentially expressed metabolites in WT and KO CD4 T cells generated using Metaboanalyst 5.0. (B-D) Volcano plots of significantly altered metabolites in (B) IL7 unstimulated (Unstim) KO compared to WT, (C) TCR vs Unstim KO, and (D) TCR vs Unstim WT. Significance determined by P value < 0.05 and log2FC >1.5. (E, F) Venn diagrams showing shared and unique (E) upregulated and (F) downregulated metabolites following TCR stimulation in WT and KO T cells. Significance determined by P value < 0.05 and log2FC >2.0. (G) Schematic of the malate-aspartate shuttle. Metabolites significantly enriched in KO T cells are written in red. (H-K) Summary graphs of the following normalized metabolite levels detected in LC-MS analysis of WT and KO T cells (H) malate, (I) aspartate, (J) glutamate, (K) alpha-ketoglutaramate. P value calculated by two-way unpaired T test. *p < 0.05, **p < 0.01.

Fig. 6. IRF5 regulation of glutamine metabolism modulates effector protein expression

(A) WT and Irf5−/− total splenocytes were activated for 24- (D1) and 48-hours (D2) in vitro with anti-CD3/CD28 (TCR). Summary graphs of ASCT2 expression pooled from two independent experiments. P value calculated by 2-way ANOVA with Holm-Sidak correction for multiple comparisons. (B) ASCT2 representative gating strategy in Live CD45+CD4+ T cells gated from WT and Irf5−/ total splenocytes. (C) Summary graph of SNAT2 expression pooled from two independent experiments. Sorted CD3+CD4+ T cells WT and Irf5−/− splenocytes were activated for 6 hours in vitro with anti-CD3/CD28 (TCR). (D) Slc1a5 and (E) Slc38a2 transcript expression normalized to β-Actin. (F, G) WT and Irf5−/− total splenocytes were activated for 24-hours in vitro with anti-CD3/CD28 in the presence (DMK) or absence (TCR) of dimethyl ketoglutarate (DMK). Summary graphs of (F) ASCT2 and (G) CD40L expressing CD3+CD4+ T cells pooled from three independent experiments. (H, I) WT sorted CD3+CD4+ T cells were activated for 6-hours in vitro with anti-CD3/CD28 (TCR) in the presence (DMK) or absence (TCR) of dimethyl-ketoglutarate. Summary graphs of (H) CD40l expression and (I) Slc1a5 transcript levels normalized to B-Actin. Four biological replicates. (J) Representative flow gating strategy for CD40L expression in Live CD3+CD4+ T cells under indicated treatment conditions. WT total splenocytes were activated for 24-hours in vitro with anti-CD3/CD28 (TCR) in the presence of ASCT2 inhibitor (V-9302) or absence (TCR). Summary graph of (K) CD40L expression in CD3+CD4+ T cells. Data pooled from three independent experiments. (L) WT total splenocytes were activated for 24-hours in vitro with anti-CD3/CD28 (TCR) in the presence (CB839) or absence (TCR) of CB839. Summary graphs of CD40L expression in activated CD3+CD4+ T cells. Data pooled from three independent experiments. P value calculated by one-way analysis of variance (ANOVA) with Tukey’s post-hoc test for multiple comparisons unless otherwise noted. Bar graphs show means +/− SEMs. *p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant. Each point represents an independent biological replicate.

Fig. 7. T cell conditional Irf5−/− mice are protected from EAE

(A) Composite means +/− SEM of clinical scores. Minimum of 10 biological replicates per experiment. Pooled data from three independent experiments comparing clinical progression of Irf5fl/fl-Lck-Cre+ (cKO), Irf5fl/fl-Lck-Cre− (fl/fl), Irf5−/− (KO), and WT mice following EAE induction. (B) Spleen weights from WT control, cKO and fl/fl (Flox) mice at peak disease. Five-six biological replicates pooled from two independent experiments. P value calculated by one-way analysis of variance (ANOVA) with Tukey’s post-hoc test for multiple comparisons. (C-I) Summary graphs of flow cytometric analyses from day 13 post immunization (clear circles) and day 20 post immunization (colored circles). (C) CD3+CD4+ T cells gated from live cells, (D, E) IFNγ production from live CD3+CD4+ T cells, (F, G) ASCT2 expression, and (H, I) S6 phosphorylation (phosphoRPS6). P value calculated by multiple T tests with Holm-Sidak correction for multiple comparisons. (J, K) Quantification of splenic CD4+ and B220+ immunohistochemistry staining at (J) day 13 post-immunization and (K) day 20 post-immunization. Data represents four biological replicates. (L, M) Predicted correlation between (L) B220 B cells and disease severity and (M) CD4 T cells and disease severity. Partial residual plot generated using visreg analysis package (115). Shaded area represents 95% confidence interval. (N) Linear regression analysis of B220 and CD4 IHC coverage in spleens harvested from mice at Day 13 and Day 20 timepoints post EAE induction. Outer lines represent 95% confidence interval. Bar graphs show means +/− SEMs. *p < 0.05, **p < 0.01, ***p < 0.001, ns = not significant. Each point represents an independent biological replicate. (O) Summary schematic of the transcriptional, translational and metabolic regulatory roles for IRF5.

Supplementary Materials and Methods

Detailed metabolomics analysis, immunoprecipitation and LC/MS/MS, flow cytometry and immunoblot analysis.

Supplementary Figures

Supp. F1 – Supp. F7

Competing interests: The authors declare that they have no competing interests.
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References and Notes

1. Banga J. , Inhibition of IRF5 cellular activity with cell-penetrating peptides that target homodimerization. Science Advances 6 , eaay1057 (2020).32440537
2. Bi X. , Loss of interferon regulatory factor 5 (IRF5) expression in human ductal carcinoma correlates with disease stage and contributes to metastasis. Breast Cancer Res 13 , R111 (2011).22053985
3. Fabie A. , IRF-5 Promotes Cell Death in CD4 T Cells during Chronic Infection. Cell Rep 24 , 1163–1175 (2018).30067973
4. Yan J. , Pandey S. P. , Barnes B. J. , Turner J. R. , Abraham C. , T Cell-Intrinsic IRF5 Regulates T Cell Signaling, Migration, and Differentiation and Promotes Intestinal Inflammation. Cell Rep 31 , 107820 (2020).32610123
5. Ricciardi S. , The Translational Machinery of Human CD4(+) T Cells Is Poised for Activation and Controls the Switch from Quiescence to Metabolic Remodeling. Cell Metab 28 , 895–906.e895 (2018).30197303
6. Klein Geltink R. I. , Kyle R. L. , Pearce E. L. , Unraveling the Complex Interplay Between T Cell Metabolism and Function. Annual Review of Immunology 36 , 461–488 (2018).
7. Delgoffe G. M. , The kinase mTOR regulates the differentiation of helper T cells through the selective activation of signaling by mTORC1 and mTORC2. Nature Immunology 12 , 295–303 (2011).21358638
8. Myers D. R. , Norlin E. , Vercoulen Y. , Roose J. P. , Active Tonic mTORC1 Signals Shape Baseline Translation in Naive T Cells. Cell Rep 27 , 1858–1874.e1856 (2019).31067469
9. Wolf T. , Dynamics in protein translation sustaining T cell preparedness. Nat Immunol 21 , 927–937 (2020).32632289
10. Poddar D. , An Extraribosomal Function of Ribosomal Protein L13a in Macrophages Resolves Inflammation. The Journal of Immunology 190 , 3600–3612 (2013).23460747
11. Wan F. , Ribosomal Protein S3: A KH Domain Subunit in NF-κB Complexes that Mediates Selective Gene Regulation. Cell 131 , 927–939 (2007).18045535
12. Yin Y. , Normalization of CD4+ T cell metabolism reverses lupus. Sci Transl Med 7 , 274ra218 (2015).
13. Hou H. , Rapamycin Ameliorates Experimental Autoimmune Encephalomyelitis by Suppressing the mTOR-STAT3 Pathway. Neurochem Res 42 , 2831–2840 (2017).28600752
14. Li X.-L. , Rapamycin Alleviates the Symptoms of Multiple Sclerosis in Experimental Autoimmune Encephalomyelitis (EAE) Through Mediating the TAM-TLRs-SOCS Pathway. Front Neurol 11 , 590884–590884 (2020).33329339
15. Xu T. , Metabolic control of TH17 and induced Treg cell balance by an epigenetic mechanism. Nature 548 , 228–233 (2017).28783731
16. De S. , B Cell-Intrinsic Role for IRF5 in TLR9/BCR-Induced Human B Cell Activation, Proliferation, and Plasmablast Differentiation. Front Immunol 8 , 1938 (2017).29367853
17. Feng D. , Irf5-deficient mice are protected from pristane-induced lupus via increased Th2 cytokines and altered IgG class switching. Eur J Immunol 42 , 1477–1487 (2012).22678902
18. Paun A. , Bankoti R. , Joshi T. , Pitha P. M. , Stäger S. , Critical Role of IRF-5 in the Development of T helper 1 responses to Leishmania donovani infection. PLOS Pathogens 7 , e1001246 (2011).21253574
19. Brune Z. , Rice M. R. , Barnes B. J. , Potential T Cell-Intrinsic Regulatory Roles for IRF5 via Cytokine Modulation in T Helper Subset Differentiation and Function. Frontiers in Immunology 11 , (2020).
20. Kaji T. , CD4 memory T cells develop and acquire functional competence by sequential cognate interactions and stepwise gene regulation. Int Immunol 28 , 267–282 (2016).26714588
21. Poon M. M. L. , Heterogeneity of human anti-viral immunity shaped by virus, tissue, age, and sex. Cell Rep 37 , 110071 (2021).34852222
22. Krapp C. , Guanylate Binding Protein (GBP) 5 Is an Interferon-Inducible Inhibitor of HIV-1 Infectivity. Cell Host & Microbe 19 , 504–514 (2016).26996307
23. Care M. A. , Westhead D. R. , Tooze R. M. , Gene expression meta-analysis reveals immune response convergence on the IFNγ-STAT1-IRF1 axis and adaptive immune resistance mechanisms in lymphoma. Genome Med 7 , 96 (2015).26362649
24. Taylor J. , Transcriptomic profiles of aging in naïve and memory CD4(+) cells from mice. Immun Ageing 14 , 15 (2017).28642803
25. Szabo P. A. , Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease. Nature Communications 10 , 4706 (2019).
26. Fantuzzi L. , Tagliamonte M. , Gauzzi M. C. , Lopalco L. , Dual CCR5/CCR2 targeting: opportunities for the cure of complex disorders. Cell Mol Life Sci 76 , 4869–4886 (2019).31377844
27. Cannoodt R. , Saelens W. , Saeys Y. , Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol 46 , 2496–2506 (2016).27682842
28. Cox G. M. , Macrophage Migration Inhibitory Factor Potentiates Autoimmune-Mediated Neuroinflammation. The Journal of Immunology 191 , 1043–1054 (2013).23797673
29. Harrison O. J. , Epithelial-derived IL-18 regulates Th17 cell differentiation and Foxp3+ Treg cell function in the intestine. Mucosal Immunology 8 , 1226–1236 (2015).25736457
30. Ng S. S. , The NK cell granule protein NKG7 regulates cytotoxic granule exocytosis and inflammation. Nat Immunol 21 , 1205–1218 (2020).32839608
31. Wensveen F. M. , Jelenčić V. , Polić B. , NKG2D: A Master Regulator of Immune Cell Responsiveness. Frontiers in Immunology 9 , (2018).
32. Yoshida Y. , The transcription factor IRF8 activates integrin-mediated TGF-β signaling and promotes neuroinflammation. Immunity 40 , 187–198 (2014).24485804
33. Fraschilla I. , Jeffrey K. L. , The Speckled Protein (SP) Family: Immunity’s Chromatin Readers. Trends in Immunology 41 , 572–585 (2020).32386862
34. Jefferies C. A. , Regulating IRFs in IFN Driven Disease. Frontiers in Immunology 10 , (2019).
35. Song S. , Inhibition of IRF5 hyperactivation protects from lupus onset and severity. J Clin Invest 130 , 6700–6717 (2020).32897883
36. Li D. , IRF5 genetic risk variants drive myeloid-specific IRF5 hyperactivation and presymptomatic SLE. JCI Insight 5 , (2020).
37. Panwar B. , Multi-cell type gene coexpression network analysis reveals coordinated interferon response and cross-cell type correlations in systemic lupus erythematosus. Genome Res 31 , 659–676 (2021).33674349
38. Pellerin A. , Monoallelic IRF5 deficiency in B cells prevents murine lupus. JCI Insight 6 , (2021).
39. Cliffe S. T. , Clinical, molecular, and cellular immunologic findings in patients with SP110-associated veno-occlusive disease with immunodeficiency syndrome. Journal of Allergy and Clinical Immunology 130 , 735–742.e736 (2012).22621957
40. Warner J. D. , Interferon Regulatory Factor 5 (IRF5) Interacts with the Translation Initiation Complex and Promotes mRNA Translation During the Integrated Stress Response to Amino Acid Deprivation. bioRxiv, 163998 (2017).
41. Ban T. , Genetic and chemical inhibition of IRF5 suppresses pre-existing mouse lupus-like disease. Nature Communications 12 , 4379 (2021).
42. Yasuda K. , Interferon Regulatory Factor-5 Deficiency Ameliorates Disease Severity in the MRL/lpr Mouse Model of Lupus in the Absence of a Mutation in DOCK2. PLOS ONE 9 , e103478 (2014).25076492
43. Feng D. , Protection of Irf5-deficient mice from pristane-induced lupus involves altered cytokine production and class switching. 2013.
44. Chung Y. , Critical Regulation of Early Th17 Cell Differentiation by Interleukin-1 Signaling. Immunity 30 , 576–587 (2009).19362022
45. Shi F.-D. , Takeda K. , Akira S. , Sarvetnick N. , Ljunggren H.-G. , IL-18 Directs Autoreactive T Cells and Promotes Autodestruction in the Central Nervous System Via Induction of IFN-γ by NK Cells. The Journal of Immunology 165 , 3099–3104 (2000).10975822
46. Weaver A. , An elevated matrix metalloproteinase (MMP) in an animal model of multiple sclerosis is protective by affecting Th1/Th2 polarization. Faseb j 19 , 1668–1670 (2005).16081501
47. Elgueta R. , Molecular mechanism and function of CD40/CD40L engagement in the immune system. Immunological reviews 229 , 152–172 (2009).19426221
48. Aarts S. , The CD40-CD40L Dyad in Experimental Autoimmune Encephalomyelitis and Multiple Sclerosis. Front Immunol 8 , 1791 (2017).29312317
49. Vavassori S. , Covey L. R. , Post-transcriptional regulation in lymphocytes: the case of CD154. RNA Biol 6 , 259–265 (2009).19395873
50. Narayanan B. , A Posttranscriptional Pathway of CD40 Ligand mRNA Stability Is Required for the Development of an Optimal Humoral Immune Response. The Journal of Immunology 206 , 2552 (2021).34031147
51. Pollizzi K. N. , Powell J. D. , Regulation of T cells by mTOR: the known knowns and the known unknowns. Trends in Immunology 36 , 13–20 (2015).25522665
52. Salmond R. J. , Brownlie R. J. , Meyuhas O. , Zamoyska R. , Mechanistic Target of Rapamycin Complex 1/S6 Kinase 1 Signals Influence T Cell Activation Independently of Ribosomal Protein S6 Phosphorylation. J Immunol 195 , 4615–4622 (2015).26453749
53. Yeh H. S. , Yong J. , mTOR-coordinated Post-Transcriptional Gene Regulations: from Fundamental to Pathogenic Insights. J Lipid Atheroscler 9 , 8–22 (2020).32821719
54. Dan H. C. , Akt-dependent activation of mTORC1 complex involves phosphorylation of mTOR (mammalian target of rapamycin) by IκB kinase α (IKKα). The Journal of biological chemistry 289 , 25227–25240 (2014).24990947
55. Wofford J. A. , Wieman H. L. , Jacobs S. R. , Zhao Y. , Rathmell J. C. , IL-7 promotes Glut1 trafficking and glucose uptake via STAT5-mediated activation of Akt to support T-cell survival. Blood 111 , 2101–2111 (2008).18042802
56. Hedl M. , Yan J. , Abraham C. , IRF5 and IRF5 Disease-Risk Variants Increase Glycolysis and Human M1 Macrophage Polarization by Regulating Proximal Signaling and Akt2 Activation. Cell Rep 16 , 2442–2455 (2016).27545875
57. Ray John P. , The Interleukin-2-mTORc1 Kinase Axis Defines the Signaling, Differentiation, and Metabolism of T Helper 1 and Follicular B Helper T Cells. Immunity 43 , 690–702 (2015).26410627
58. Snyder J. T. , Direct inhibition of CD40L expression can contribute to the clinical efficacy of daclizumab independently of its effects on cell division and Th1/Th2 cytokine production. Blood 109 , 5399–5406 (2007).17344465
59. Zheng Y. , Jiang Y. , mTOR Inhibitors at a Glance. Mol Cell Pharmacol 7 , 15–20 (2015).27134695
60. Bianco C. , Thompson L. , Mohr I. , Repression of eEF2K transcription by NF-κB tunes translation elongation to inflammation and dsDNA-sensing. Proceedings of the National Academy of Sciences 116 , 22583–22590 (2019).
61. Browne G. J. , Proud C. G. , A novel mTOR-regulated phosphorylation site in elongation factor 2 kinase modulates the activity of the kinase and its binding to calmodulin. Mol Cell Biol 24 , 2986–2997 (2004).15024086
62. Wang X. , Eukaryotic elongation factor 2 kinase activity is controlled by multiple inputs from oncogenic signaling. Mol Cell Biol 34 , 4088–4103 (2014).25182533
63. Howell J. J. , Manning B. D. , mTOR couples cellular nutrient sensing to organismal metabolic homeostasis. Trends Endocrinol Metab 22 , 94–102 (2011).21269838
64. Pearce E. L. , Metabolism in T cell activation and differentiation. Curr Opin Immunol 22 , 314–320 (2010).20189791
65. Albers G. J. , IRF5 regulates airway macrophage metabolic responses. Clin Exp Immunol 204 , 134–143 (2021).33423291
66. Byrne A. J. , A critical role for IRF5 in regulating allergic airway inflammation. Mucosal Immunology 10 , 716–726 (2017).27759022
67. Carr E. L. , Glutamine Uptake and Metabolism Are Coordinately Regulated by ERK/MAPK during T Lymphocyte Activation. The Journal of Immunology 185 , 1037–1044 (2010).20554958
68. Klysz D. , Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci Signal 8 , ra97 (2015).26420908
69. W. M. M. Johnson Marc O. , Madden Matthew Z. , Andrejeva Gabriela , Sugiura Ayaka , Contreras Diana C. , Maseda Damian , Liberti Maria V. , Paz Katelyn , Kishton Rigel J. , Johnson Matthew E. , de Cubas Aguirre A. , Wu Pingsheng , Li Gongbo , Zhang Yongliang , Newcomb Dawn C. , Wells Andrew D. , Restifo Nicholas P. , Rathmell W. Kimryn , Locasale Jason W. , Davila Marco L. , Blazar Bruce R. , Rathmell Jeffrey C. ,, Distinct Regulation of Th17 and Th1 Cell Differentiation by Glutaminase-Dependent Metabolism. Cell 175 , 1780–1795.e1719 (2018).30392958
70. Nakaya M. , Inflammatory T Cell Responses Rely on Amino Acid Transporter ASCT2 Facilitation of Glutamine Uptake and mTORC1 Kinase Activation. Immunity 40 , 692–705 (2014).24792914
71. Yang R. , Hydrogen Sulfide Promotes Tet1- and Tet2-Mediated Foxp3 Demethylation to Drive Regulatory T Cell Differentiation and Maintain Immune Homeostasis. Immunity 43 , 251–263 (2015).26275994
72. Liu S. , Yang J. , Wu Z. , The Regulatory Role of α-Ketoglutarate Metabolism in Macrophages. Mediators of inflammation 2021 , 5577577–5577577 (2021).33859536
73. Bravo Iniguez A. , Du M. , Zhu M.-J. , α-Ketoglutarate for Preventing and Managing Intestinal Epithelial Dysfunction. Advances in Nutrition 15 , 100200 (2024).38438107
74. Liu M. , α-Ketoglutarate Modulates Macrophage Polarization Through Regulation of PPARγ Transcription and mTORC1/p70S6K Pathway to Ameliorate ALI/ARDS. Shock 53 , (2020).
75. Durán R. V. , Glutaminolysis activates Rag-mTORC1 signaling. Mol Cell 47 , 349–358 (2012).22749528
76. Ichiyama K. , The Methylcytosine Dioxygenase Tet2 Promotes DNA Demethylation and Activation of Cytokine Gene Expression in T Cells. Immunity 42 , 613–626 (2015).25862091
77. Tran K. A. , Dillingham C. M. , Sridharan R. , The role of α-ketoglutarate-dependent proteins in pluripotency acquisition and maintenance. The Journal of biological chemistry 294 , 5408–5419 (2019).30181211
78. Yerinde C. , Siegmund B. , Glauben R. , Weidinger C. , Metabolic Control of Epigenetics and Its Role in CD8+ T Cell Differentiation and Function. Frontiers in Immunology 10 , (2019).
79. Phan A. T. , Goldrath A. W. , Glass C. K. , Metabolic and Epigenetic Coordination of T Cell and Macrophage Immunity. Immunity 46 , 714–729 (2017).28514673
80. Chang C.-H. , Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis. Cell 153 , 1239–1251 (2013).23746840
81. Wang R. , Green D. R. , Metabolic reprogramming and metabolic dependency in T cells. Immunological reviews 249 , 14–26 (2012).22889212
82. Barnes B. J. , Kellum M. J. , Field A. E. , Pitha P. M. , Multiple regulatory domains of IRF-5 control activation, cellular localization, and induction of chemokines that mediate recruitment of T lymphocytes. Mol Cell Biol 22 , 5721–5740 (2002).12138184
83. Kobayashi M. , The ubiquitin hybrid gene UBA52 regulates ubiquitination of ribosome and sustains embryonic development. Scientific Reports 6 , 36780 (2016).27829658
84. Gandin V. , Eukaryotic initiation factor 6 is rate-limiting in translation, growth and transformation. Nature 455 , 684–688 (2008).18784653
85. Meijer H. A. , Translational Repression and eIF4A2 Activity Are Critical for MicroRNA-Mediated Gene Regulation. Science 340 , 82–85 (2013).23559250
86. Wilczynska A. , eIF4A2 drives repression of translation at initiation by Ccr4-Not through purine-rich motifs in the 5′UTR. Genome Biology 20 , 262 (2019).31791371
87. Lacy M. , Cell-specific and divergent roles of the CD40L-CD40 axis in atherosclerotic vascular disease. Nature Communications 12 , 3754 (2021).
88. van Os B. W. , CD40L modulates CD4(+) T-cell activation through receptor for activated C kinase 1. Eur J Immunol 53 , e2350520 (2023).37683186
89. Liu P.-S. , α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nature Immunology 18 , 985–994 (2017).28714978
90. Weiss M. , IRF5 controls both acute and chronic inflammation. Proc Natl Acad Sci U S A 112 , 11001–11006 (2015).26283380
91. Krausgruber T. , IRF5 promotes inflammatory macrophage polarization and TH1-TH17 responses. Nature Immunology 12 , 231–238 (2011).21240265
92. Daniele S. , Mangano G. , Durando L. , Ragni L. , Martini C. , The Nootropic Drug Α-Glyceryl-Phosphoryl-Ethanolamine Exerts Neuroprotective Effects in Human Hippocampal Cells. Int J Mol Sci 21 , 941 (2020).32023864
93. Fu G. , Metabolic control of TFH cells and humoral immunity by phosphatidylethanolamine. Nature 595 , 724–729 (2021).34234346
94. Dorai T. , Pinto J. T. , Denton T. T. , Krasnikov B. F. , Cooper A. J. L. , The metabolic importance of the glutaminase II pathway in normal and cancerous cells. Analytical Biochemistry 644 , 114083 (2022).33352190
95. Bell H. N. , Microenvironmental Ammonia Enhances T cell Exhaustion in Colorectal Cancer. bioRxiv, 2022.2005.2025.493422 (2022).
96. Shi K. , Bone marrow hematopoiesis drives multiple sclerosis progression. Cell 185 , 2234–2247.e2217 (2022).35709748
97. Wu Y. , Sp110 enhances macrophage resistance to Mycobacterium tuberculosis via inducing endoplasmic reticulum stress and inhibiting anti-apoptotic factors. Oncotarget 8 , 64050–64065 (2017).28969051
98. Leu J.-S. , Chang S.-Y. , Mu C.-Y. , Chen M.-L. , Yan B.-S. , Functional domains of SP110 that modulate its transcriptional regulatory function and cellular translocation. Journal of Biomedical Science 25 , 34 (2018).29642903
99. Z. J. B. Roscioli Tony , Buckley Michael , Wong Melanie , GeneReviews®, Ed. (University of Washington, Seattle, Seattle (WA), 2007).
100. Mehta S. , Maintenance of macrophage transcriptional programs and intestinal homeostasis by epigenetic reader SP140. Science Immunology 2 , eaag3160 (2017).28783698
101. Hollinger K. R. , Glutamine antagonism attenuates physical and cognitive deficits in a model of MS. Neurology - Neuroimmunology Neuroinflammation 6 , e609 (2019).31467038
102. Beecham A. H. , Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nature Genetics 45 , 1353–1360 (2013).24076602
103. Cencioni M. T. , Mattoscio M. , Magliozzi R. , Bar-Or A. , Muraro P. A. , B cells in multiple sclerosis — from targeted depletion to immune reconstitution therapies. Nature Reviews Neurology 17 , 399–414 (2021).34075251
104. Brancati S. , Gozzo L. , Longo L. , Vitale D. C. , Drago F. , Rituximab in Multiple Sclerosis: Are We Ready for Regulatory Approval? Frontiers in Immunology 12 , (2021).
105. Nissimov N. , B cells reappear less mature and more activated after their anti-CD20–mediated depletion in multiple sclerosis. Proceedings of the National Academy of Sciences 117 , 25690–25699 (2020).
106. Pröbstel A. K. , Hauser S. L. , Multiple Sclerosis: B Cells Take Center Stage. J Neuroophthalmol 38 , 251–258 (2018).29561328
107. Bjornevik K. , Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis. Science 375 , 296–301 (2022).35025605
108. Robinson W. H. , Steinman L. , Epstein-Barr virus and multiple sclerosis. Science 375 , 264–265 (2022).35025606
109. Lanz T. V. , Clonally expanded B cells in multiple sclerosis bind EBV EBNA1 and GlialCAM. Nature 603 , 321–327 (2022).35073561
110. Takaoka A. , Integral role of IRF-5 in the gene induction programme activated by Toll-like receptors. Nature 434 , 243–249 (2005).15665823
111. Stuart T. , Comprehensive Integration of Single-Cell Data. Cell 177 , 1888–1902.e1821 (2019).31178118
112. Street K. , Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19 , 477 (2018).29914354
113. Zhao S. , Li H. , Han W. , Chan W. , Li L. , Metabolomic Coverage of Chemical-Group-Submetabolome Analysis: Group Classification and Four-Channel Chemical Isotope Labeling LC-MS. Analytical Chemistry 91 , 12108–12115 (2019).31441644
114. Li L. , MyCompoundID: using an evidence-based metabolome library for metabolite identification. Anal Chem 85 , 3401–3408 (2013).23373753
115. Breheny P. J. , Burchett W. , Visualization of Regression Models Using visreg. R J. 9 , 56 (2017).
