Paxbp1 restrains cytotoxic and innate cell programs in CD4+ T cells to promote Th2 differentiation

Paxbp1 restrains cytotoxic and innate cell programs in CD4+ T cells to promote Th2 differentiation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Paxbp1 restrains cytotoxic and innate cell programs in CD4+ T cells to promote Th2 differentiation Danielle Chisolm, Yuki Morimoto, Hiroyuki Nagashima, Stephen Brooks, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7199764/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract CD4 + T helper cell differentiation is important for efficient host defense and has largely been defined by the activities of a limited number of lineage-specifying transcription factors. However, these factors alone cannot explain the intricate series of events that concurrently induce a specialization program while repressing competing gene programs. In this study, we define a new role for Paxbp1 as an important regulatory factor in T helper cell specification events. We show that Paxbp1 expression is selectively induced in Th2 cells and is required for repression of innate and cytotoxic gene programming potential during Th2 differentiation. Paxbp1-deficiency enhances chromatin accessibility at genomic regions associated with innate lymphocyte programs in both in vitro and in vivo models of Th2 differentiation, indicating that Paxbp1 restrains activities that promote alternative cytotoxic programming potential. Mechanistically, we found Paxbp1 interacts with Bcl11b and Runx1, transcription factors established as negative and positive regulators of innate cytotoxic programming potential, respectively. Our data show that Paxbp1 ensures proper regulation of Bcl11b and Runx1-associated gene programs, and it provides new molecular insights into the complexity of the transcriptional regulatory network involved in CD4 + T cell specialization events. Biological sciences/Immunology/Gene regulation in immune cells/Epigenetics in immune cells Biological sciences/Immunology/Lymphocytes/T cells/CD4-positive T cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The capacity of CD4 + T cells to differentiate into specialized helper T cell states is crucial for the immune system to eliminate a diversity of pathogenic insults. CD4 + T cell differentiation is tightly regulated by antigens, cytokines, and metabolites, in part through integrating this external information into internal transcriptional programs 1 . T helper 2 (Th2) cells are characterized by the expression of the lineage-specifying transcription factor GATA3 and the production of type 2 cytokines, IL-4, IL-5, and IL-13 2,3 . Much work has been done to understand how the Th2 transcriptional program is turned on by GATA3, with an emphasis on the events that regulate the type 2 cytokine loci 4,5 . In contrast, less research has focused on defining how alternative programs are repressed in Th2 cells to maintain the integrity of the overall differentiation state. Of note, adaptive lymphocytes and their innate counterparts are similar in their transcriptional programs 6-9 , so it is important to understand how transcription factors work cooperatively to regulate these programs in a distinguishing manner. Extensive research has been performed to understand how networks of transcription factors work together to promote developmental states in T cells differentiating in the thymus 10,11 . In contrast, while many transcription factors have been studied individually for their role in CD4 + T helper cell specification, less work has characterized the cooperativity between the different transcription factors needed to maintain these specialized states. Related to this topic, single cell technologies highlight that lineage-specifying factors are not exclusively expressed in one lineage or context 12 , raising the question of how the combination of factors present in CD4 + T cells contribute to their specialization. Paxbp1 is a regulatory factor that was first identified in mouse muscle stem cells to interact with Pax3 or Pax7 where it plays a role in cell proliferation and survival in the context of regeneration during injury 13,14 . Subsequent studies have similarly shown Paxbp1 is important for proliferation and survival at different stages of T cell and epidermal cell development in mice, with its role influencing cellular differentiation states 15-17 . In humans, PAXBP1 is found on chromosome 21, with trisomy of chromosome 21 associated with Down syndrome and aberrant immune responses including myeloid leukemia. Interestingly, a microarray performed on a partial chromosome triplication mouse model of Down syndrome found that Paxbp1 expression was decreased relative to an increase in expression of interferon genes 18 . Taken together, the information currently available suggests PAXBP1 is a common essential gene with cell type specific roles in differentiation and dysregulated PAXBP1 expression is associated with aberrant developmental and immunopathological consequences 14-19 . However, it is currently unknown whether Paxbp1 plays a role in CD4 + helper T cell specialization events. In this study, we used single cell analyses to identify novel factors that contribute to the regulation of the Th2 gene program and found that the expression of Paxbp1 is selectively induced in Th2 cells as compared to alternative CD4 + T cell subtypes. Utilizing a conditional deletion model, we found Paxbp1 is involved in Th2 lineage-specialization by mechanistically repressing an alternative innate-like cytotoxic program in both in vitro and in vivo models of Th2 differentiation. Notably, Paxbp1 interacts with Bcl11b and Runx1, transcription factors involved in T cell lineage programming, with Bcl11b deletion in several T cells models inducing an innate-like cytotoxic program similar to what we observed in Paxbp1-deficient cells. Collectively, our study defines new aspects of the complex transcriptional network required to sustain a specific specialized state in CD4 + T cells. Results Paxbp1 expression is selectively induced in Th2 cells. To define novel factors important for Th2 cell differentiation, we reanalyzed single cell RNA-seq data from CD4 + T cells isolated from the lungs of mice infected with Nippostrongylus brasiliensis 20 , a pathogen that elicits a type 2 immune response. The UMAP from this analysis separated the data into 6 clusters that we defined by the expression profiles of CD4 + T helper cell lineage-specifying genes (Fig. 1 a, Extended Data Fig. 1 a). Notably, in the cluster with high Gata3 expression (“Th2” cluster, Fig. 1 b, Extended Data Fig. 1 b), the nuclear factor Paxbp1 was also highly expressed (Fig. 1 c, Extended Data Fig. 1 c). These data indicate that Paxbp1 is predominantly expressed in Th2 cells as compared to alternative CD4 + T cell subtypes. Paxbp1 is involved in cellular differentiation potential for a variety of cell types. Therefore, we next wanted to determine whether Paxbp1 plays a role in coordinating the Th2 specification program. To address this question, we used a Paxbp1-floxed mouse model crossed to CD4-cre (referred to as Paxbp1-cKO) to conditionally delete Paxbp1 in T cells. As a first step, we used multi-dimension flow cytometry to immunophenotype cells isolated from Paxbp1-cKO mice compared to WT mice. Consistent with previous results 16,17 , we observed a decrease in the numbers of CD4 + T cells recovered from Paxbp1-cKO mice relative to WT mice in both the spleen (Extended Data Fig. 1 d) and peripheral lymph nodes (pLNs) (data not shown). Additionally, we examined the impact of Paxbp1 deletion on thymic development and observed reduced numbers of single positive CD4 + T cells in the thymus of Paxbp1-cKO compared to WT mice (Extended Data Fig. 1 e). Taken together with previous results, CD4 + T cell numbers decrease in the absence of Paxbp1, but cells do develop and migrate to the periphery. Paxbp1 is important for establishing a Th2 program. We next sought to determine whether Paxbp1 plays a functional role in the Th2 differentiation program. To address this, we challenged WT and Paxbp1-cKO mice in an ovalbumin (OVA) asthma model to elicit an in vivo Th2 response (Extended Data Fig. 2 a). Despite a decrease in the number of T cells prior to OVA immunization, there was no significant difference in the numbers of CD4 + T cells recovered from the lungs of WT and Paxbp1-cKO mice (Fig. 1 d). We did not observe a significant decrease in the number of alternative lymphocytes found in the lungs of WT and Paxbp1-cKO mice (Extended Data Fig. 2 c). Eosinophil recruitment is a hallmark of a type 2 response. We detected decreased numbers of eosinophils in the lungs of Paxbp1-cKO mice compared to WT mice in response to OVA (Fig. 1 e), conversely neutrophil numbers in the lungs of Paxbp1-cKO mice trended upward but did not reach statistical significance (Fig. 1 e). Together these data suggest an impaired type 2 environment in Paxbp1-cKO mice. Consistent with this, CD4 + T cells isolated from the lungs of Paxbp1-cKO compared to WT mice had increased production of IFN-g and TNF-a while having decreased production of type 2 cytokines IL-4 and IL-13 after stimulation ex vivo (Fig. 1 f). Collectively, the data indicate a shift away from a Th2 response in Paxbp1-cKO mice. Paxbp1-deficiency leads to aberrant expression of an innate and cytotoxic program. To understand the impact of Paxbp1 on Th2 gene programming we wanted to see if we could recapitulate the in vivo findings. To address this, we took a reductionist approach by polarizing equal numbers of naïve CD4 + T cells isolated from either WT or Paxbp1-cKO mice in type 2 conditions in vitro. This approach allowed us to rule out issues with reduced cell numbers or other contributing signals in vivo. As expected, there was reduced Paxbp1 in Paxbp1-cKO Th2 cells (Extended Data Fig. 3 a). Similar to the OVA immunization experiments, Th2 polarized cells deficient in Paxbp1 had increased frequencies of cells producing IFN-γ and expressing T-bet relative to WT Th2 cells (Extended Data Fig. 3 b,c). Of note, GATA3 expression was only modestly decreased in Paxbp1-cKO Th2 cells (Extended Data Fig. 3 b). Paxbp1 deficiency is associated with defects in cell survival and proliferation 14–17 . Therefore, we stained cells with Annexin V or cell trace violet to compare apoptosis and proliferation between Paxbp1-cKO and WT Th2 cells. Interestingly, while engaged with TCR, Paxbp1-cKO CD4 + T cells had a decrease in proliferation, but this defect was recovered when removed from TCR signals and maintained in type 2 polarizing cytokine conditions (Extended Data Fig. 3 e). A similar trend was seen with Annexin V staining (Extended Data Fig. 3 d). These data indicated that Paxbp1-deficiency, in part, initiated some aspects of a Th1 program in Paxbp1-cKO Th2 cells (e.g. increased production of IFN-γ and T-bet) (Extended Data Fig. 3 b,c). Therefore, we next sought to determine the overall impact of Paxbp1-deficiency on the transcriptional program. To accomplish this, we first performed bulk RNA-seq analyses on naïve CD4 + T cells isolated from WT or Paxbp1-cKO mice that were polarized in type 1 and type 2 conditions (Extended Data Fig. 3 f). Th1 conditions were used as a comparison because presumably Paxbp1 deletion should not have as robust of an impact on Th1 cells. Consistent with our flow cytometry results, transcripts for Tbx21 (encodes T-bet) and Ifng (encodes IFN-γ) were increased in Paxbp1-cKO compared to WT Th2 cells (Table 1). Additionally, there was an increase in the expression of genes from the cytotoxic program, including granzymes (e.g. Gzma , Gzmb , Gzmk ), transcription factors (e.g. Eomes ) and receptors (e.g. Klrg1 ) (Fig. 2 a, Table 1). We next performed a Metascape analysis 21 to characterize the functional pathways impacted by the genes differentially regulated by Paxbp1. Consistent with findings from other cellular settings, the genes downregulated by Paxbp1-deficiency were enriched for pathways such as cell proliferation and IL-7 signaling (Fig. 2 b). In contrast, the genes upregulated in Paxbp1-cKO Th2 cells were enriched for pathways associated with Natural Killer (NK) cells and cytotoxic functions (Fig. 2 b). For example, we observed increased expression of NK lineage-associated genes (e.g. Klrg1 , Nkg7 and Zbtb32 22 ) in Paxbp1-cKO compared to WT Th2 cells (Fig. 2 a, Table 1). Of note, we found similarities in the genes and pathways that were induced in Paxbp1-cKO Th1 cells, including genes associated with innate or cytotoxic lineages, although the fold changes were less robust in type 1 conditions presumably because Paxbp1 expression is much lower (Fig. 2 b, Extended Data Fig. 3 i, Table 1). Together, these data indicate that Paxbp1 plays a role in repressing innate and cytotoxic defining gene programs, especially in Th2 cells. We next performed bulk ATAC-seq on WT and Paxbp1-cKO CD4 + T cells polarized in type 1 or type 2 conditions to define how Paxbp1-deficiency influences genome accessibility (Extended Data Fig. 4 a). In a principal component analysis (PCA), differences in accessibility were attributed to genotype along PC1 rather than by polarizing conditions (Extended Data Fig. 4 a). Consistent with the RNA-seq analyses, there was decreased accessibility surrounding genes associated with a type 2 program in Paxbp1-cKO compared to WT Th2 cells (e.g. Il4 ; Fig. 2 c), whereas there was increased accessibility at genes associated with a type 1 or cytotoxic program (e.g. Ifng ; Fig. 2 c). Notably, clustering of differential ATAC peaks from the samples showed 5 distinct clusters: 1) Paxbp1-cKO-specific, 2) Paxbp1-cKO and WT Th1, 3) WT Th2-specific, 4) WT Th1-specific, and 5) WT-specific (Fig. 2 d). In the WT- and Paxbp1-cKO- specific clusters, we observed accessibility changes in genes associated with adaptive and innate lymphocyte lineages respectively such as Zbtb7b (WT-specific (cluster 5)) as well as Klr1bc and Fcer1g (Paxbp1-cKO- specific (cluster 1)). In support of this finding, we observed increased accessibility at innate (e.g. Nkg7 ) and cytotoxic (e.g. Prf1 and Klrg1 ) gene loci in the Paxbp1-cKO Th2 cells and decreased accessibility for genes associated with T cell identity (e.g. S1pr1 ) (Fig. 2 c and Extended Data Fig. 4 b). These data suggest that Paxbp1-deficiency affects genome accessibility particularly around loci associated with lineage identities. We next wanted to determine whether Paxbp1 broadly influences the accessibility of gene loci for innate and cytotoxic programs. To address this, we defined Paxbp1-cKO-specific ATAC peaks by comparing peaks from WT and Paxbp1-cKO Th2 cells, and Th2-specific peaks by comparing peaks between WT Th2 and Th1 cells (Extended Data Fig. 4 c). We then used the genomic coordinates from the Paxbp1-cKO-specific and Th2-specific peaks and intersected these with the Tn5 transposase insertions from previously published datasets for NK, ILC1, ILC2 and ILC3 cells 23 . We detected a statistically significant overlap between chromatin states in Paxbp1-cKO Th2 cells and innate lymphocytes, in particular with NK, ILC1 and ILC3 cells (Fig. 2 e). Both the WT Th2 and Paxbp1-cKO Th2 peaks overlapped with ILC2 peaks, but the overlap was more robust in Paxbp1-cKO Th2 cells (Fig. 2 e). Interestingly, we observed a Paxbp1-cKO specific peak in the Il5 locus (Extended Data Fig. 4 b). Relatively, Paxbp1-deficient Th1 cells did not have the same increased chromatin accessibility in innate programs (Extended Data Fig. 4 d). This might be because the peaks were already accessible to a degree in WT Th1 cells and Paxbp1 plays less of a role in this setting (Fig. 2 d (Cluster 2)). While being predisposed for accessibility is a reasonable explanation for some of the overlap with Th1 cells, we made several observations where the accessibility in Paxbp1-cKO Th2 cells was higher than any of the type 1 conditions (Fig. 2 d, Extended Data Fig. 4 b). Taken together, Paxbp1-deficiency in Th2 polarized cells enhanced accessibility of loci associated with innate and cytotoxic gene programs, consistent with the findings that Paxbp1 inhibits the expression of these programs in Th2 cells. Paxbp1 interacts with lineage-specifying transcription factors Bcl11b and Runx1. We next wanted to determine the molecular mechanisms by which Paxbp1 regulates Th2 programming potential. Not much is known about the molecular activities of Paxbp1. Paxbp1 was originally identified in a yeast two hybrid screen characterizing binding partners for Pax3 or Pax7 13 . These data indicated it served as an adaptor protein linking Pax3/7 to Wdr5-containing MLL complexes to regulate cellular differentiation programs in muscle stem cells 13,14 . Additionally, a recent study using cryo-electron microscopy showed Paxbp1 involvement with spliceosome disassembly 24 . With so little information available, especially in immune cells 16,17 , we decided to take an unbiased approach to identify Paxbp1 interacting partners in T cells. We also wanted to focus on conserved mechanisms between species because of potential connections to immune-mediated pathologies in humans 19 . It is interesting to note that the top 50 genes that were more highly expressed in human NK cells compared to human T cells (from the CZ Cell x Gene database 25 ) substantially overlapped with the genes upregulated in Paxbp1-cKO Th2 cells (Extended Fig. 5a, Table 1). This suggests that the regulatory mechanisms Paxbp1 utilizes to inhibit the innate-like program in T cells might be conserved between species. Therefore, we performed co-immunoprecipitation (co-IP) coupled with mass-spectrometry (spec) experiments in human Jurkat T cells, which have high endogenous Paxbp1 expression levels. Similar to previous reports, we found Paxbp1 interacted with proteins associated with splicing, including TFIP11 and DHX15 24 , in the co-IP mass spec experiments in T cells (Table 2 and Extended Data Fig. 5b). A STRING 26 analysis on Paxbp1-interacting proteins confirmed it clustered with splicing factors (Fig. 3 a). However, of particular interest regarding the role for Paxbp1 in restraining innate and cytotoxic gene programs in Th2 cells, we also found that the lineage-specifying transcription factors Bcl11b and Runx1 interacted with Paxbp1 in T cells. Notably, a second STRING cluster of Paxbp1-interacting proteins placed Bcl11b and Runx1 with other transcriptional regulators, including Mef2d, a transcription factor shown to play a role in Treg and Th2 lymphocytes 27,28 , as well as the chromatin remodeler Chd1, the highest hit in the mass spec dataset (Table 2). Bcl11b has previously been shown to repress an NK/innate-like or cytotoxic program in several T cell subsets, including Tregs, CD8 + T cells, and relevant for this study, Th2 cells 29–37 . In Th2 cells, loss of Bcl11b expression caused a failure to develop an appropriate Th2 response in asthma and helminth immune challenges 36,37 . This bears remarkable resemblance to the phenotype of Paxbp1-cKO Th2 cells. Additionally, in naïve T cells, overexpression of Runx1 inhibits the expression of the Th2 program and induces an innate lymphoid program in T cells 38,39 . Collectively, this raises the possibility of whether Bcl11b and Runx factors play an antagonizing role to regulate the innate program in mature T cells, and if these activities are modulated by Paxbp1 in Th2 cells. To start to test this hypothesis, we examined whether there were any detectable alterations in the activities of Bcl11b and/or Runx1 in our Paxbp1-cKO genomics datasets. We first performed a gene set enrichment analysis (GSEA) on the genes that were upregulated in Paxbp1-cKO Th2 cells compared to WT Th2 cells 40,41 . The top pathway identified was from a study by Li et al. that found Bcl11b-deficient T cells induced a NK cell-like gene program 29 (Fig. 3 b). This suggests Bcl11b activity was diminished in Paxbp1-cKO Th2 cells and this might contribute to the induction of an NK or innate-like program. We also performed a GSEA 40,41 analysis on genes upregulated in Paxbp1-cKO Th1 cells, which similarly identified enrichment of this pathway (Extended Data Fig. 5c). This suggests Paxbp1 might be more broadly involved in repressing the NK or innate cell program across different T cell subsets, with the levels of Paxbp1 determining the magnitude of its role. To further explore a possible interplay between Bcl11b and Paxbp1, we examined published Bcl11b ChIP-seq datasets and compared these to the ATAC-seq peaks induced in Paxbp1-cKO Th2 cells. We observed increased expression and chromatin accessibility of well-known Bcl11b target genes, such as Fcer1g and Klrb1c in Paxbp1-cKO T cells relative to WT cells (Fig. 2 d, Table 1), suggesting that Paxbp1 modulates Bcl11b activity. We next aligned peaks from a publicly available Bcl11b Th2 ChIP-seq dataset 36 with our ATAC-seq data and performed a motif analysis on reproducible peaks found in either the WT or Paxbp1-cKO Th2 samples. Overlapping coordinates were then analyzed for motifs using HOMER (Fig. 3 c). We observed differential motif enrichment in the overlap between WT compared to Paxbp1-cKO peaks with Bcl11b peaks. In WT-specific peaks, the most enriched motifs were from the bZIP transcription factor family. In contrast, Paxbp1-cKO-specific peaks were enriched with motifs from the Runt and Ets transcription families (Fig. 3 c, Extended Data Fig. 5d,e), implicating antagonistic roles for Runx proteins and Bcl11b. Of note, GATA motifs were not enriched in the Paxbp1-cKO specific peaks when overlapped with Bcl11b (Fig. 3 c), consistent with a shift away from the Th2 program in the absence of Paxbp1. Finally, we compared our ATAC-seq dataset to Bcl11b ChIP-seq datasets 33 performed in Th2 cells and tissue resident-like CD8 + T cells to assess how alterations in accessibility aligns with Bcl11b recruitment in type 2 and type 1 conditions, respectively. Differential ATAC-seq peaks often aligned with Bcl11b ChIP-seq peaks in accordance with their respective gene expression including at the Il4 and Nkg7 loci (Fig. 3 d,e). As expected, this mechanism did not explain the regulation for all differentially expressed genes. At a subset of loci, there was no difference in Bcl11b association between Th2 and CD8 + T cells such as the case for Prf1 despite increased accessibility and gene expression in Paxbp1-cKO compared to the WT samples (Fig. 3 f). Collectively, these data suggest Paxbp1 modulates Bcl11b activity to restrain aspects of the innate and cytotoxic programs in T cells. Paxbp1-cKO mice infected with N. brasiliensis fail to induce a Th2 program and instead divert CD4 + T cells toward innate- and cytotoxic-like programs. Collectively, our data suggest that Paxbp1 impacts the activity of multiple transcription factors relevant to immune cell differentiation. To better understand how Paxbp1 influences gene regulatory networks, we performed a combined single cell RNA-seq and ATAC-seq multiomics experiment from the nuclei of CD4 + T cells isolated from the lungs of WT and Paxbp1-cKO mice infected with N. brasiliensis . There was no difference in the worm burden detected in WT and Paxbp1-cKO mice. The multiomics experiment measures gene expression and chromatin accessibility from the same nuclei, linking this information to identify gene programs for individual cells. Projecting the data onto a UMAP resulted in the combined WT and Paxbp1-cKO data to have 9 clusters (Fig. 4 a). When separated by genotype, the WT UMAP identified 5 clusters whereas Paxbp1-cKO cells had 8 clusters (Fig. 4 b,c). Comparing the gene expression patterns between the clusters, we found 3 of the WT clusters were largely preserved in Paxbp1-cKO cells: 1) naïve (as defined by the expression of Sell ), 2) Tregs (as defined by the expression of Foxp3 ), and 3) a cluster of unknown identity with high expression of the RNA helicase gene Dhx40 (Fig. 4 d,e and Extended Data Fig. 6b). Similar to the data in Fig. 2 , the absence of Paxbp1 in CD4 + T cells resulted in the inability to mount a Th2 differentiation program (WT1), with Gata3 expression absent in the Paxbp1-cKO clusters (Fig. 4 e). Noteworthy, the top gene for the “Th2” (WT1) cluster in WT cells was Paxbp1 (Fig. 4 d), and as a control, Paxbp1 expression was not induced in the Paxbp1-cKO samples (Fig. 4 e). Consistent with the data from the bulk RNA-seq and ATAC-seq analyses from in vitro polarized Th2 cells, the novel clusters in Paxbp1-cKO mice expressed genes from innate and cytotoxic gene programs (KO3, KO5, KO6, and KO7). Interestingly, one cluster (KO7) appeared to have similarities with NKT cells (e.g. Zbtb16 (encodes PLZF)) and another cluster (KO5) with aged CD8 + T cells 42 (e.g. Gzmk and Eomes ) (Fig. 4 g, Extended Data Fig. 6e). To better define the transcriptional regulatory networks between WT and Paxbp1-cKO cells, we analyzed the data with the SCENIC + package 43 . SCENIC + is a python package that constructs gene regulatory networks by combining both gene expression and accessibility data to extrapolate relationships between transcription factors, enhancers, and target genes 43 . Applying this analysis, we found the Th2 cluster (i.e. WT1) was enriched for GATA3 TF activity (Fig. 4 f, Extended data 6d), confirming that SCENIC + can define relevant “regulons” in our datasets. In addition, the Paxbp1-cKO cluster 5 (KO5) was enriched for EOMES TF activity, with both Eomes and its target gene Gzmk found in the cluster (Fig. 4 g, Extended Data Fig. 6e). Taken together, the data indicate that SCENIC + is sensitive enough to pick up both known and novel regulons in WT and Paxbp1-cKO CD4 + T cells. To better understand how Paxbp1 deficiency impacts transcriptional gene programs in T cells we first compared the “naïve” clusters from WT (WT0) and Paxbp1-cKO (KO2) samples. A comparison of the gene expression profiles in the WT (WT0) and Paxbp1-cKO (KO2) “naïve” clusters indicated they were grossly similar (Extended Fig. 6b,c). Therefore, comparing these clusters provided an ideal opportunity to define the chromatin events that occur as Paxbp1 expression starts to be induced during a primary Th2 response, but prior to the onset of aberrant gene programs in the Paxbp1-deficient cells. In the WT0 compared to KO2 cluster, two activating regulons, Bach2 and Lef1, were preserved, whereas no overlapping regulons were detected with repressive activity (Fig. 4 f,g). Bach2 and Lef1 are transcription factors known to regulate the programming of naïve cells, making these regulons of interest for exploring how the initiation of Paxbp1 might influence the naïve program. Comparisons of the Bach2 and Lef1 regulons revealed many similarities in the “naïve” clusters between WT and Paxbp1-cKO cells. This included genes in the Bach2 regulon (e.g. Il7r and Ccr7 ), Lef1 regulon (e.g. Bach2 and Zfp281 ), and in both the Bach2 and Lef1 regulons ( Foxp1 and Ifngr2 ) (Fig. 4 h,i). However, there were some interesting differences in the Bach2 and Lef1 regulons in WT and Paxbp1-cKO clusters. For example, Fli1 was found to be positively regulated by both Bach2 and Lef1 in WT cells but was not identified as a target in Paxbp1-cKO cells (Fig. 4 h,i). In addition, Lef1 was predicted to regulate some genes associated with the CD4 lineage 28,44 (e.g. Satb1 , Mef2d ) in WT cells, whereas in Paxbp1-cKO cells, the prediction was for the cytotoxic lineage 45,46 ( Ugcg and Tox ) (Fig. 4 i). These data suggest that while gene regulatory networks associated with a naïve program were initiated in Paxbp1-cKO cells, the potential for alternative programs was enhanced by the availability of additional transcription factors generally repressed in WT Th2 cells such as Tox . Of note, both Bach2 and Lef1 have been shown to play context dependent roles in T cells to influence differentiation gene programs 47–57 , and our analysis provides some insight into how these transcription factors might be involved in regulating the programming potential of T cells. We also examined the regulons for the clusters that were gained in the Paxbp1-deficient cells. Consistent with our findings from in vitro polarized Th2 cells, CD4 + T cells deficient in Paxbp1 differentiating in response to N. brasiliensis gained a number of novel regulons indicative of a shift towards a cytotoxic program (e.g. Runx3 and Eomes) or a type 1 program (e.g. Ets1 and Tbx21 (T-bet)) (Fig. 4 g). Additionally, we identified multiple transcription factors predicted to positively regulate genes like Ifng (Runx1 and Tbx21(T-bet)), Il2rb (Runx2 and Rora), and Gzma (Runx3) in the Paxbp1-cKO clusters (Fig. 4 g), and in the KO2 “naïve” cluster, Ifnar1 (Ets2 and Elf1) was identified to be positively regulated by multiple transcription factors. Notably, in Paxbp1-cKO cluster 3, SCENIC + inferred increased activity of Runx proteins, and importantly, the Runx family was also identified to interact with Paxbp1 in our co-IP mass-spec analysis (Fig. 3 a, Table 2). In addition, there was an enrichment of transcription factor activity for multiple factors associated with a type 1 and type 3 program in KO0 and KO7, along with increased activity of NF-kB factors in KO7. The SCENIC + analysis highlights how the loss of Paxbp1 dysregulates several gene regulatory networks during Th2 differentiation. Finally, in accordance with our mass spec data that identified a physical interaction between Paxbp1 with Runx1 and Chd1 (Fig. 3 a, Table 2), regulons for Runx1 and Chd2 (family member to Chd1) were gained in Paxbp1-cKO “naïve” cells (KO2) (Fig. 4 g). In the literature, increased activity of Runx1 has been shown to inhibit the Th2 program in naïve T cells and enhance NK cell genes in T cells 38,39 . Therefore, increased repressive activity of Runx1 provides a potential mechanism that contributes to the inhibition of the Th2 program and enhancement of genes associated with NK cells in Paxbp1-cKO cells. Therefore, these data suggest a role for Paxbp1 in regulating the activities of Runx and Chd proteins to promote Th2 differentiation while restraining cytotoxic potential. Discussion In this study, we found that Paxbp1 is important for the appropriate expression and differentiation of the Th2 program in CD4 + T cells. Using single cell RNA-seq data, we uncovered how the expression of a seemingly commonly utilized protein was also selectively increased in Th2 cells. Using a conditional mouse model to delete Paxbp1 in T cells, we characterized a role for Paxbp1 in specifying the type 2 program. Molecular experiments performed in Paxbp1-cKO Th2 cells compared to WT Th2 cells determined that Paxbp1 was important for restraining cytotoxic and innate-like programs in CD4 + T cells. At the chromatin level, loss of Paxbp1 resulted in an increased accessibility of genomic regions associated with innate cell lineages, in particular NK cells. Mechanistically, our data also indicate Paxbp1 impacted the activity of Bcl11b and Runx1, with single nuclei multiomics data suggesting Runx1 had increased activity in Paxbp1-cKO T cells responding to a N. brasiliensis infection. Collectively, our data characterizes a new role for Paxbp1 in maintaining the fidelity of the Th2 differentiation program by inhibiting alternative cytotoxic programming potential. Until recently, Paxbp1 was thought to be a ubiquitously expressed factor that plays a role in the survival of various cell types including T cells, keratinocytes, and muscle stem cells 13–17 . Mechanistically, Paxbp1 has been connected to the spliceosome 24 , possibly explaining why it is needed in a wide range of cell types. Clinically, dysregulation of Paxbp1 is associated with worse health outcomes in viral infections and cancer 19 , which suggests it has a more specific function in immune cells. An interesting finding from our study is the implication that Paxbp1 regulates the activities of Bcl11b and Runx1. Bcl11b has been shown to play multiple roles in different lymphocytes including NK cells, CD8 + T cells, and ILC2 cells but less is known about what additional proteins are required for its context dependent activity in immune cells 58,59 . Of note, Bcl11b has been shown to repress an innate cell program in T cells 29–31 and recent research has explored the molecular details that contribute to this activity 60,61 . Our work extends the molecular network to suggest that Paxbp1 contributes to the ability of Bcl11b to repress an innate program in T cells. Of note, in Shin et. al. an analysis comparing chromatin and gene expression relationships of different transcription factors highlighted an antagonistic role for Bcl11b and Runx1 activities when they were associated with the same genomic regions 39 . In this study, we show that Paxbp1 interacts with Bcl11b and Runx1 in T cells, and we observed modulation of their activities at the transcriptional and epigenetic level. Interestingly, Bcl11b and Runx1 were previously shown to work cooperatively to regulate the T effector landscape in early T cell development 62 , which highlights the importance of defining the molecular details for how these transcription factors work together to regulate lineage defining programs in mature T cells as well. The connection between Paxbp1 and these transcription factors provide new insight into their contextual contributions to T helper specification and T cell gene programs. The PAXBP1 locus is located on human chromosome 21, and RUNX1 is located about 2.5 MB away from it in the 3’ direction. In addition to being associated with Down Syndrome, chromosome 21 is associated with aberrant immune states and RUNX1 dysregulation is often associated with leukemia 63,64 . Of note, chromosome 21 has a relatively high concentration of genes associated with immune cell functions. The observation that genes encoding proteins with cooperative functions in immunity are located in proximity on chromosome 21 suggests its will be important to understand how these genes are modulated and how their molecular interactions impact lymphocyte regulation. The role for Paxbp1 in regulating Runx1 activity highlights the importance of obtaining a deeper understanding of this topic. Individual cells in an organism have the same DNA content, and as they differentiate, gene programs are defined through the fine tuning of the activities for a limited number of transcription factors. Transcriptionally, innate and adaptive lymphocytes are quite similar, and factors such as GATA3, TCF1, and Bcl11b have roles in both adaptive and innate lymphocytes 6–9 . This means we need a more thorough understanding of the molecular mechanisms for how each factor works in different lineages to define the conserved and divergent mechanisms that control innate and adaptive lymphocyte differentiation programming potential. In our study, we found Paxbp1-deficiency in CD4 + T cells leads to elements of the chromatin resembling innate lymphocyte lineages. Enhancer availability can alter the activity of transcription factors and might represent one aspect contributing to how Paxbp1 influences cell type specificity. For example, a recent study identified cell type specific usage of a super enhancer for Il2ra , which allowed for selective expression in NK and peripheral T cells compared to Tregs and early thymic T cells 65 . This highlights how unique mechanisms can regulate similar genes in distinct cellular states. In summary, our study illuminates molecular circuits in CD4 + T cells that specify to a Th2 differentiation state while preventing activation of innate and cytotoxic programming potential. Materials and methods Mice Paxbp1 fl/fl mice were purchased from RIKEN BioResource Research Center (Ibaraki, Japan). Cd4 -Cre mice were purchased from The Jackson Laboratory (Bar Harbor, Maine, USA). All animal experiments were performed in the AAALAC-accredited animal housing facilities at NIH. All animal studies were performed according to the NIH guidelines for the use and care of live animals and were approved by the Institutional Animal Care and Use Committee of NIAMS. Mice of 6–12 weeks old were used in all experiments. The number of mice used in each experiment are indicated in the corresponding figure legends. The littermates of the same sex were randomly assigned to experimental groups of WT (flox/flox-CD4Cre − ) or Paxbp1-cKO (flox/flox-CD4Cre + ). scRNAseq analysis We reanalyzed CD4 + T cells isolated from the lungs responding to N. brasilineas: GSE131996 20 using Seurat. Seurat was used for QC and downstream analysis. The following samples: Day 0 (GSM4192248) Day 2 (GSM4192249), Day 5 (GSM4192250), Day 9 (GSM4192251), and Day 14 (GSM4192252) were merged for the analysis. Samples were integrated, clustered, and visualized using Seurat and ggplot. Flow cytometry The following antibodies were used in combination for flow cytometry: anti-CD8a-BV711(53 − 6.7, BioLegend), anti-CD25-BV605(PC61, BioLegend), anti-Foxp3-AF700(FJK-16s, eBioscience), anti-H2-K-Pacific Blue(AF6-88.5, BioLegend), anti-CD44-BV570(IM7, BioLegend), anti-BCL6-PE(BCL-DWN, eBioscience), anti-CD62L-BUV737(MEL-14, BD Pharmingen), anti-CD69-FITC(H1.2F3, BD Pharmingen), anti-CXCR5-BV711(L138D7, BioLegend), anti-PD-1-PE-ef610 (J43, eBioscience), anti-CD62L-BUV395(MEL-14, BD Pharmingen), anti-CD4-BUV496(GK1.5, BD Pharmingen), anti-CD11c-BUV661(N418, BD Pharmingen), anti-γδTCR-BUV805(GL3, BD Pharmingen), anti-CD8a-BV421(53 − 6.7, BioLegend), anti-Foxp3-ef450(FJK-16s, eBioscience), anti-TCRβ-BV480(H57-597, BD Pharmingen), anti-CD19-BV510(1D3, BD Pharmingen), anti-Ly6G-BV605(1A8, BioLegend), anti-CD11b-BV785(M1/70, BioLegend), anti-Ly6C-PerCP-Cy5.5(HK1.4, eBioscience), anti-CD24-PE-ef610(M1/69, eBioscience), anti-IL-7Rα-PE-Cy5(A7R34, BioLegend), anti-NK1.1-APC(PK136, eBioscience), anti-Siglec-F-ef660(1RNM44N, eBioscience), anti-CD90.2-AF700(53 − 2.1, BioLegend), anti-CD64-APC-ef780(X54-5/7.1, eBioscience), anti-TNFα-BV510(MP6-XT22, BD Pharmingen), anti-IFNγ-FITC(XMG1.2, BD Pharmingen), anti-IL-5-PE(TRFK5, BioLegend), anti-IL-17a-PE-ef610(eBio17B7, eBioscience), anti-IL-4-APC(11B11, BioLegend), anti-IL-13-APC-ef780(eBio13A, eBioscience), anti-GATA3-AF488(16E10A23, BioLegend), and analyzed by FACS Fortessa (BD Biosciences) or Cytek Aurora (Cytek Biosciences). Zombie-NIR (BioLegend) and Fixable Viability Dye ef780 (eBioscience) was used for gating live cells. For cytokine staining, the cells were stimulated with PMA and Ionomycin (eBioscience Cell Stimulation cocktail, ThermoFisher Scientific) and Protein Transport Inhibitor (ThermoFisher Scientific) for 4 hours before staining. Anti-PAXBP1 antibody was custom-made by GenScript and anti-rabbit IgG-AF647(Poly4064, BioLegend) was used as the secondary antibody for intracellular TF staining. Intracellular staining was performed using eBioscience™ Foxp3 / Transcription Factor Staining Buffer Set (ThermoFisher Scientific) according to manufactures’ protocol. Immunization and challenge with OVA Mice were immunized intraperitoneally with 250µg of OVA (chicken egg albumin Sigma-Aldrich, cat. no. 9006-59-1) in 4mg of aluminum hydroxide gel (Alum) for OVA-immunized group or only PBS for control group on days 0 and 7. OVA was administered to mice intranasally: 100µg on days 14, 16, 18, 20 and 22. At day 23, cells were recovered from the lung and various assays were performed. Cell isolation from mouse lungs The lungs were perfused before dissection, then cells were isolated using Lung Dissociation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany). After RBC lysis, cells were analyzed by flow cytometry. In vitro T helper cell differentiation Naïve CD4 + T cells (CD4 + CD62L high CD44 low CD8a − γδTCR − CD19 − ) were purified from spleens using a cell sorter (BD Aria), yielding a purity of > 98%, or by EasySep Mouse Naïve CD4 + T Cell Isolation Kit (STEMCELL Technologies Inc., Vancouver, Canada). Purified naive CD4 + T cells were stimulated with immobilized anti-TCRβ mAb (H57-597; 10µg/ml) in the presence of IL-2 (25U/ml), IL-12 (10 U/ml; R&D), anti-IL-4 mAb (11B11; 5µg/ml), and anti-CD28 mAb (37.51; 1µg/ml) for Th1 cell differentiation; IL-2 (25U/ml), IL-4 (100U/ml; R&D), anti-IFN-γ mAb (R46A2; 5µg/ml), and anti-CD28 mAb (37.51; 1µg/ml) for Th2 cell differentiation. Following treatment with anti-TCR mAb and anti-CD28 mAb for 48 hours, cells were further cultured with indicated cytokines and neutralizing antibodies, then harvested on day 5. Western blotting Whole cell extract was prepared with NP-40 lysis buffer and subjected for western blotting. Anti-PAXBP1 antibody (PA5-52002, ThermoFisher) and anti-HDAC1 antibody (D5C6U, Cell Signaling Technology) were used as the primary antibodies and anti-rabbit IgG, HRP-linked antibody (7074, Cell Signaling Technology) was used as the secondary antibody. Cell viability assay Experiment was performed with FITC Annexin V Apoptosis Detection Kit I (BD Biosciences). Cells were washed, resuspended in 100 mL 1X Binding Buffer, then treated with 5 mL of FITC Annexin V and 5mL propidium iodide and incubated for 15 min at room temperature in the dark while supplemented with 400 mL of 1X Binding Buffer before being analyzed by flow cytometry. Cell proliferation assay Experiments were performed with the CellTrace Violet Cell Proliferation Kit (ThermoFisher). Cells were washed, resuspended with PBS, and incubated in 5µM CellTrace Violet for 20 minutes at 37°C in the dark. To quench any unbound dye, 5-fold volume of complete culture medium was added to the cells and incubated for 5 minutes. After centrifugation, cells were resuspended in fresh pre-warmed complete culture medium and cultured for 20 minutes at room temperature. The proliferation index was calculated using FlowJo. RNA-seq and analysis Total cellular RNA was extracted with Buffer RLT (QIAGEN), and RNA samples were then processed by Lexogen (Greenland, New Hampshire, USA) for mRNA-sequencing. RNAseq data were processed using the RNA-seek workflow v1.2.1 ( https://doi.org/10.5281/zenodo.5223025 ). In brief, reads were trimmed using cutadapt v1.18 and aligned to the Mmul_10 reference genome and Gencode release 108 using STAR v2.7.6a in 2-pass basic mode. Expression levels were quantified with RSEM v1.3.3. Differential analysis was completed with idep v0.96 and DESeq2 with the following pre-filtering parameters: minimum CPM of 0.5 in five libraries. Differentially expressed genes (DEGs) were defined as RM = at least two-fold change and adjusted P < 0.01, PTM = two-fold change and adjusted P < 0.1. For viral load analyses, idep v1.13 code was adapted to handle continuous variables. RNAseq and ATACseq pipelines were performed utilizing the computational resources of the NIH HPC Biowulf cluster. ( http://hpc.nih.gov ). Metascape analysis Metascape ( https://metascape.org/gp/index.html#/main/step1 ) was used for gene ontology analysis of differential gene sets. ATAC-seq ATAC-seq was performed with 50,000 from WT or Paxbp1-cKO in vitro polarized Th2 or Th1 cells. Dead cells were removed using the Dead Cell Removal Kit (Miltenyi). The ATAC-seq protocol was performed as previously published 66 . Briefly, cells were lysed in ice-cold lysis buffer (10mM TrisCl pH 7.4, 10mM NaCl, 3mM MgCl 2 , 0.1% Igepal). Transposase reactions were performed using Tn5 transposase (Illumina) and were incubated for 30 min at 37°C. DNA was purified with the MinElut Kit (QIAGEN). Libraries were amplified using Nextara primers with NEBNext High-Fidelity 2X PCR Master Mix (New England Biolegends), and reactions were purified with the PCR Purification Kit (QIAGEN). Samples were sequenced in the NIAMS Genomics Technology Core. ATAC-seq analysis All samples had over 80 million reads. Samples were trimmed for adapters using Cutadapt v 1.18 before alignment. The trimmed reads were aligned to the Mmul_10 reference using Bowtie2 v2.3.4.1 with flag -k 10. The peaks were called using Genrich v0.6 with the following flags: -j -y -r -v -d 150m 5 -e chrM,chrY. PCA and differential peaks were determined by DiffBind v 2.10.0. PCR-duplicate reads were identified by picard v2.18.26 for filtering prior to DiffBind analysis for both PCA and differential peak analyses. Differential peaks were identified using the EdgeR algorithm and were considered significant with an FDR less than 0.05. Differential peaks were annotated using Uropa v4.0.2 f. Since multiple peaks often were assigned to a single gene, genes were associated with the most significant peak. Peak data were visualized using IGV 2.14.1. and differential peaks were rechecked manually to resolve potential conflicts with automatic assignments. To generate Fig. 2 d ATAC-seq samples were processed using the ENCODE ATAC-sequence pipeline v2.2.0 ( https://doi.org/10.5281/zenodo.3564813 ) with standard parameters on the NIH HPC Biowulf cluster ( https://hpc.nih.gov ). Briefly, the ATAC-seq pipeline initially trims the raw sequences for adapters and poor-quality sequencing reads. The high-quality sequencing reads were then aligned to the mm10 reference genome. Alignments are then purged based on confidence and blacklisted regions 67 . Duplicate reads which mostly arise from the PCR amplification process are also purged to identify peaks with higher confidence. Peaks are then shifted by + 4bp and − 5bp for the positive and negative strands respectively as recommended by the standard ATAC-seq analysis protocol 68 . The MACS2 program 69 was used to identify peaks and differential peaks analysis was performed using the Diffbind R package ( http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf ). To compare WT and Paxbp1-cKO samples to innate cell populations ATAC-seq samples were processed as followed. Adaptor sequences were trimmed using SeqPurge (v2019_11) and trimmed reads were mapped to mm10 mouse genome assembly using Bowtie2 (v2.2.9) with settings -- very-sensitive -X 2000 . PCR duplicates were removed using Picard (v2.21.8) MarkDuplicates REMOVE_DUPLICATES = true VALIDATION_STRINGENCY = LENIENT . Reads with MAPQ scores below 30 were purged using samtools (v1.9) view with settings -b -q 30 -f 2 -F 1804 . Peak calling and sample normalization were carried out as described 70 . Briefly, peaks were called for each sample using MACS2 (v2.2.7.1) with settings --shift − 75 --extsize 150 --nomodel --call-summits --nolambda --keep-dup all -p 0.01 --min-length 100 . For each sample, a 501 bp fixed-width peak set was generated by extending the MACS2 summits by 250 bp in both directions. Peaks were ranked by significance (MACS2 peak score) and overlapping peaks with lower peak scores were removed iteratively to create non- overlapping sample peak sets. To facilitate comparison of peaks across samples, the MACS2 peak scores (-log10(p-value)) for each sample were converted to a score per million (SPM) by dividing each peak score by the sum of all the peak scores in a sample divided by 1 million. Sample peak sets were merged, less significant overlapping peaks removed, and remaining peaks were filtered for those that were observed in at least two samples with an SPM value 5. To generate peak-by-sample count matrices, ATAC fragment counts within each peak were normalized by the number of inserts intersecting nucleosome-depleted promoter regions (-300 bp to + 100 bp relative to transcriptional start-sites). Mass Spectrometry To prepare samples for mass spectrometry the Pierce MS-compatible magnetic IP kits were used. Briefly 10x10 6 cells were lysed in the presence of protease and phosphatase inhibitor. The lysates were incubated with 5ug of either aIgG or aPaxbp1 antibody overnight at 4C. Lysates were then incubated with protein A/G beads at room temperature for 1 hour. Washed beads were stored at -80C for sequencing. LC/MS was performed by Poochon (Frederick, MD). Samples were processed for trypsin/LysC digestion, concentrated, desalted and reconstituted in 0.1% formic acid. Peptides were analyzed against human protein sequences database Proteome Discoverer 2.4 software (Thermo, SanJose, CA) based on the SEQUEST algorithm. STRING analysis Proteins identified in mass spec were input into the STRING database ( https://string-db.org/ ) using default parameters to determine their relationship to each other. Gene set enrichment analysis (GSEA) Pre-ranked lists of genes determined by DESeq2 were used in the GSEA function in the clusterProfiler program with default parameters to perform gene set enrichment analysis. Motif enrichment analysis MACS3 software was used to identify peaks in ATAC-seq data. Diffbind R Package was used to identify differentially accessible open chromatin regions (DORCs) between WT and Paxbp1-cKO conditions. We downloaded identified Bcl11b ChIP-seq 36 (GEO accession: GSE108633) and used bedtools intersect to find DORCs that overlap with Bcl11b peaks. Motif analysis was conducted using Homer findMotifs.pl on the overlapping DORCs. ChIPseq analysis Publicly available Bcl11b ChIPseq datasets performed in Th2 36 (GSE108633) or CD8 + T cells 33 (GSE186907) were downloaded as raw sequence read files. Files were trimmed and mapped to the mm10 reference genome browser with Trim Galore and Bowtie2 respectively. PCR duplicates were removed using Picard and filtered peaks were called with MACS3. Bigwig files were generated using the tool bedGraphToBigWig. Processed files were visualized using UCSC genome browser view 71 . Nippostrongylus brasiliensis infection Age-matched female WT and Paxbp1-cKO mice were given subcutaneous injection of 400 infective third-stage N. brasiliensis larvae as described previously 20 . Lung cells were harvested on day 10 after infection for single-cell multiomics. Single-cell multiomics CD3ε + TCRβ + CD4 + T cells (> 200,000 cells each, cell viability > 98%) were freshly sorted out from lungs of WT and Paxbp1-cKO mice infected with N. brasiliensis , followed by isolated nuclei using 10x Genomics Nuclei Isolation for Single Cell Multiome protocol (CG000365). The single-cell libraries were prepared using Chromium Next GEM Single Cell Multiome ATAC + Gene Expression kit according to manufacturer’s protocol (10X Genomics, CG000338). The generated libraries were sequenced using NovaSeq6000. Multiomics analysis The 10x Genomics Cell Ranger ARC (v2.0.0) pipeline was used to process the multiome data. Raw sequencing data were first converted to fastq format using “cellranger-arc mkfastq”. The raw files of RNA-seq and ATAC-seq libraries from the same sample were aligned to the UCSC mouse genome (mm10) and quantified using “cellranger-arc count”. SCENIC + analysis The scRNA-seq portion of the multiome data was analyzed using Scanpy version 1.10.2 and scATAC-seq data using pycisTopic version 2.0a0 following the workflow provided at https://scenicplus.readthedocs.io/en/latest/tutorials.html . A custom cisTarget database was generated off of the conserved peaks from pycisTopic results. Finally, the scRNA-seq and scATAC-seq results were integrated using SCENIC + version 1.0a1 SCENIC + results were filtered and visualized using Cytoscape version 3.10.3. UCSC genome browser ATAC-seq and ChIP-seq data are displayed using the University of California Santa Cruz (UCSC) genome browser ( https://genome.ucsc.edu/ ). Statistical analysis Data were analyzed with GraphPad Prism software (version 9). Comparisons of two groups were calculated with unpaired t test. Differences with p -values \(\:\le\:\) 0.05 were considered significant and marked with asterisk(s) in the figures. Declarations Data availability statement Sequencing data is publicly available on the NCBI GEO website under the accession number XXX. References Chisolm, D. A. & Weinmann, A. S. Connections Between Metabolism and Epigenetics in Programming Cellular Differentiation. Annu Rev Immunol 36 , 221–246 (2018). https://doi.org:10.1146/annurev-immunol-042617-053127 Nakayama, T. et al. Th2 Cells in Health and Disease. Annu Rev Immunol 35 , 53–84 (2017). https://doi.org:10.1146/annurev-immunol-051116-052350 Walker, J. A. & McKenzie, A. N. J. T(H)2 cell development and function. Nat Rev Immunol 18 , 121–133 (2018). https://doi.org:10.1038/nri.2017.118 Ansel, K. M., Djuretic, I., Tanasa, B. & Rao, A. Regulation of Th2 differentiation and Il4 locus accessibility. Annu Rev Immunol 24 , 607–656 (2006). https://doi.org:10.1146/annurev.immunol.23.021704.115821 Nagashima, H. et al. Remodeling of Il4-Il13-Il5 locus underlies selective gene expression. Nat Immunol 25 , 2220–2233 (2024). https://doi.org:10.1038/s41590-024-02007-4 De Obaldia, M. E. & Bhandoola, A. Transcriptional regulation of innate and adaptive lymphocyte lineages. Annu Rev Immunol 33 , 607–642 (2015). https://doi.org:10.1146/annurev-immunol-032414-112032 Fang, D., Healy, A. & Zhu, J. Differential regulation of lineage-determining transcription factor expression in innate lymphoid cell and adaptive T helper cell subsets. Front Immunol 13 , 1081153 (2022). https://doi.org:10.3389/fimmu.2022.1081153 McCullen, M. & Oltz, E. The multifaceted roles of TCF1 in innate and adaptive lymphocytes. Adv Immunol 164 , 39–71 (2024). https://doi.org:10.1016/bs.ai.2024.10.001 Spinner, C. A. & Lazarevic, V. Transcriptional regulation of adaptive and innate lymphoid lineage specification. Immunol Rev 300 , 65–81 (2021). https://doi.org:10.1111/imr.12935 Rothenberg, E. V. Logic and lineage impacts on functional transcription factor deployment for T-cell fate commitment. Biophys J 120 , 4162–4181 (2021). https://doi.org:10.1016/j.bpj.2021.04.002 Rothenberg, E. V. Single-cell insights into the hematopoietic generation of T-lymphocyte precursors in mouse and human. Exp Hematol 95 , 1–12 (2021). https://doi.org:10.1016/j.exphem.2020.12.005 Efremova, M., Vento-Tormo, R., Park, J. E., Teichmann, S. A. & James, K. R. Immunology in the Era of Single-Cell Technologies. Annu Rev Immunol 38 , 727–757 (2020). https://doi.org:10.1146/annurev-immunol-090419-020340 Diao, Y. et al. Pax3/7BP is a Pax7- and Pax3-binding protein that regulates the proliferation of muscle precursor cells by an epigenetic mechanism. Cell Stem Cell 11 , 231–241 (2012). https://doi.org:10.1016/j.stem.2012.05.022 Zhou, S. et al. Paxbp1 controls a key checkpoint for cell growth and survival during early activation of quiescent muscle satellite cells. Proc Natl Acad Sci U S A 118 (2021). https://doi.org:10.1073/pnas.2021093118 Huang, C. et al. Paxbp1 Is Indispensable for the Maintenance of Epidermal Homeostasis. J Invest Dermatol (2024). https://doi.org:10.1016/j.jid.2024.08.012 Li, W. et al. Paxbp1 is indispensable for the survival of CD4 and CD8 double-positive thymocytes. Front Immunol 14 , 1183367 (2023). https://doi.org:10.3389/fimmu.2023.1183367 Li, W. et al. Paxbp1 is indispensable for the maintenance of peripheral CD4 T cell homeostasis. Immunology 172 , 641–652 (2024). https://doi.org:10.1111/imm.13802 Ling, K. H. et al. Functional transcriptome analysis of the postnatal brain of the Ts1Cje mouse model for Down syndrome reveals global disruption of interferon-related molecular networks. BMC Genomics 15 , 624 (2014). https://doi.org:10.1186/1471-2164-15-624 Pahl, M. C. et al. Implicating effector genes at COVID-19 GWAS loci using promoter-focused Capture-C in disease-relevant immune cell types. Genome Biol 23 , 125 (2022). https://doi.org:10.1186/s13059-022-02691-1 Nagashima, H. et al. Neuropeptide CGRP Limits Group 2 Innate Lymphoid Cell Responses and Constrains Type 2 Inflammation. Immunity 51 , 682–695 e686 (2019). https://doi.org:10.1016/j.immuni.2019.06.009 Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10 , 1523 (2019). https://doi.org:10.1038/s41467-019-09234-6 Beaulieu, A. M., Zawislak, C. L., Nakayama, T. & Sun, J. C. The transcription factor Zbtb32 controls the proliferative burst of virus-specific natural killer cells responding to infection. Nat Immunol 15 , 546–553 (2014). https://doi.org:10.1038/ni.2876 Shih, H. Y. et al. Developmental Acquisition of Regulomes Underlies Innate Lymphoid Cell Functionality. Cell 165 , 1120–1133 (2016). https://doi.org:10.1016/j.cell.2016.04.029 Vorlander, M. K. et al. Mechanism for the initiation of spliceosome disassembly. Nature 632 , 443–450 (2024). https://doi.org:10.1038/s41586-024-07741-1 Program, C. Z. I. C. S. et al. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Nucleic Acids Res 53 , D886-D900 (2025). https://doi.org:10.1093/nar/gkae1142 Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51 , D638-D646 (2023). https://doi.org:10.1093/nar/gkac1000 Di Giorgio, E. et al. MEF2D sustains activation of effector Foxp3 + Tregs during transplant survival and anticancer immunity. J Clin Invest 130 , 6242–6260 (2020). https://doi.org:10.1172/JCI135486 Szeto, A. C. H. et al. Mef2d potentiates type-2 immune responses and allergic lung inflammation. Science 384 , eadl0370 (2024). https://doi.org:10.1126/science.adl0370 Li, P. et al. Reprogramming of T cells to natural killer-like cells upon Bcl11b deletion. Science 329 , 85–89 (2010). https://doi.org:10.1126/science.1188063 Ikawa, T. et al. An essential developmental checkpoint for production of the T cell lineage. Science 329 , 93–96 (2010). https://doi.org:10.1126/science.1188995 Li, L., Leid, M. & Rothenberg, E. V. An early T cell lineage commitment checkpoint dependent on the transcription factor Bcl11b. Science 329 , 89–93 (2010). https://doi.org:10.1126/science.1188989 Drashansky, T. T. et al. Bcl11b prevents fatal autoimmunity by promoting T(reg) cell program and constraining innate lineages in T(reg) cells. Sci Adv 5 , eaaw0480 (2019). https://doi.org:10.1126/sciadv.aaw0480 Helm, E. Y. et al. Bcl11b sustains multipotency and restricts effector programs of intestinal-resident memory CD8(+) T cells. Sci Immunol 8 , eabn0484 (2023). https://doi.org:10.1126/sciimmunol.abn0484 Forkel, H. et al. BCL11B depletion induces the development of highly cytotoxic innate T cells out of IL-15 stimulated peripheral blood alphabeta CD8 + T cells. Oncoimmunology 11 , 2148850 (2022). https://doi.org:10.1080/2162402X.2022.2148850 Sottile, R. et al. Human cytomegalovirus expands a CD8(+) T cell population with loss of BCL11B expression and gain of NK cell identity. Sci Immunol 6 , eabe6968 (2021). https://doi.org:10.1126/sciimmunol.abe6968 Fang, D. et al. Bcl11b, a novel GATA3-interacting protein, suppresses Th1 while limiting Th2 cell differentiation. J Exp Med 215 , 1449–1462 (2018). https://doi.org:10.1084/jem.20171127 Lorentsen, K. J. et al. Bcl11b is essential for licensing Th2 differentiation during helminth infection and allergic asthma. Nat Commun 9 , 1679 (2018). https://doi.org:10.1038/s41467-018-04111-0 Naoe, Y. et al. Repression of interleukin-4 in T helper type 1 cells by Runx/Cbf beta binding to the Il4 silencer. J Exp Med 204 , 1749–1755 (2007). https://doi.org:10.1084/jem.20062456 Shin, B., Zhou, W., Wang, J., Gao, F. & Rothenberg, E. V. Runx factors launch T cell and innate lymphoid programs via direct and gene network-based mechanisms. Nat Immunol 24 , 1458–1472 (2023). https://doi.org:10.1038/s41590-023-01585-z Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102 , 15545–15550 (2005). https://doi.org:10.1073/pnas.0506580102 Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34 , 267–273 (2003). https://doi.org:10.1038/ng1180 Mogilenko, D. A. et al. Comprehensive Profiling of an Aging Immune System Reveals Clonal GZMK(+) CD8(+) T Cells as Conserved Hallmark of Inflammaging. Immunity 54 , 99–115 e112 (2021). https://doi.org:10.1016/j.immuni.2020.11.005 Bravo Gonzalez-Blas, C. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods 20 , 1355–1367 (2023). https://doi.org:10.1038/s41592-023-01938-4 Ribeiro de Almeida, C. et al. Critical role for the transcription regulator CCCTC-binding factor in the control of Th2 cytokine expression. J Immunol 182 , 999–1010 (2009). https://doi.org:10.4049/jimmunol.182.2.999 Morrison, T. A. et al. Selective requirement of glycosphingolipid synthesis for natural killer and cytotoxic T cells. Cell (2025). https://doi.org:10.1016/j.cell.2025.04.007 Ngiow, S. F. et al. LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity. Cell 187 , 4336–4354 e4319 (2024). https://doi.org:10.1016/j.cell.2024.07.018 Roychoudhuri, R. et al. BACH2 represses effector programs to stabilize T(reg)-mediated immune homeostasis. Nature 498 , 506–510 (2013). https://doi.org:10.1038/nature12199 Afzali, B. et al. BACH2 immunodeficiency illustrates an association between super-enhancers and haploinsufficiency. Nat Immunol 18 , 813–823 (2017). https://doi.org:10.1038/ni.3753 Kim, E. H. et al. Bach2 regulates homeostasis of Foxp3 + regulatory T cells and protects against fatal lung disease in mice. J Immunol 192 , 985–995 (2014). https://doi.org:10.4049/jimmunol.1302378 Kuwahara, M. et al. Bach2-Batf interactions control Th2-type immune response by regulating the IL-4 amplification loop. Nat Commun 7 , 12596 (2016). https://doi.org:10.1038/ncomms12596 Roychoudhuri, R. et al. BACH2 regulates CD8(+) T cell differentiation by controlling access of AP-1 factors to enhancers. Nat Immunol 17 , 851–860 (2016). https://doi.org:10.1038/ni.3441 Thakore, P. I. et al. BACH2 regulates diversification of regulatory and proinflammatory chromatin states in T(H)17 cells. Nat Immunol 25 , 1395–1410 (2024). https://doi.org:10.1038/s41590-024-01901-1 Yao, C. et al. BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8(+) T cells. Nat Immunol 22 , 370–380 (2021). https://doi.org:10.1038/s41590-021-00868-7 Choi, Y. S. et al. LEF-1 and TCF-1 orchestrate T(FH) differentiation by regulating differentiation circuits upstream of the transcriptional repressor Bcl6. Nat Immunol 16 , 980–990 (2015). https://doi.org:10.1038/ni.3226 Steinke, F. C. et al. TCF-1 and LEF-1 act upstream of Th-POK to promote the CD4(+) T cell fate and interact with Runx3 to silence Cd4 in CD8(+) T cells. Nat Immunol 15 , 646–656 (2014). https://doi.org:10.1038/ni.2897 Carr, T. et al. The transcription factor lymphoid enhancer factor 1 controls invariant natural killer T cell expansion and Th2-type effector differentiation. J Exp Med 212 , 793–807 (2015). https://doi.org:10.1084/jem.20141849 Yang, B. H. et al. TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases. Cell Rep 27 , 3629–3645 e3626 (2019). https://doi.org:10.1016/j.celrep.2019.05.061 Avram, D. & Califano, D. The multifaceted roles of Bcl11b in thymic and peripheral T cells: impact on immune diseases. J Immunol 193 , 2059–2065 (2014). https://doi.org:10.4049/jimmunol.1400930 Sidwell, T. & Rothenberg, E. V. Epigenetic Dynamics in the Function of T-Lineage Regulatory Factor Bcl11b. Front Immunol 12 , 669498 (2021). https://doi.org:10.3389/fimmu.2021.669498 Okuyama, K. et al. A mutant BCL11B-N440K protein interferes with BCL11A function during T lymphocyte and neuronal development. Nat Immunol 25 , 2284–2296 (2024). https://doi.org:10.1038/s41590-024-01997-5 Pease, N. A., Denecke, K. M., Chen, L., Gerges, P. H. & Kueh, H. Y. A timed epigenetic switch balances T and ILC lineage proportions in the thymus. Development 151 (2024). https://doi.org:10.1242/dev.203016 Gamble, N. et al. PU.1 and BCL11B sequentially cooperate with RUNX1 to anchor mSWI/SNF to poise the T cell effector landscape. Nat Immunol 25 , 860–872 (2024). https://doi.org:10.1038/s41590-024-01807-y Malle, L. et al. Autoimmunity in Down's syndrome via cytokines, CD4 T cells and CD11c(+) B cells. Nature 615 , 305–314 (2023). https://doi.org:10.1038/s41586-023-05736-y Gialesaki, S. et al. RUNX1 isoform disequilibrium promotes the development of trisomy 21-associated myeloid leukemia. Blood 141 , 1105–1118 (2023). https://doi.org:10.1182/blood.2022017619 Spolski, R. et al. Distinct use of super-enhancer elements controls cell type-specific CD25 transcription and function. Sci Immunol 8 , eadi8217 (2023). https://doi.org:10.1126/sciimmunol.adi8217 Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol 109 , 21 29 21–21 29 29 (2015). https://doi.org:10.1002/0471142727.mb2129s109 Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Sci Rep 9 , 9354 (2019). https://doi.org:10.1038/s41598-019-45839-z Yan, F., Powell, D. R., Curtis, D. J. & Wong, N. C. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biol 21 , 22 (2020). https://doi.org:10.1186/s13059-020-1929-3 Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol 9 , R137 (2008). https://doi.org:10.1186/gb-2008-9-9-r137 Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362 (2018). https://doi.org:10.1126/science.aav1898 Perez, G. et al. The UCSC Genome Browser database: 2025 update. Nucleic Acids Res 53 , D1243-D1249 (2025). https://doi.org:10.1093/nar/gkae974 Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files ChisolmTable1072325.txt Table 1: DESEQ of genes upregulated with a log2 fold change of 2 or higher in Paxbp1-cKO polarized in type 2 conditions. ChisolmTable2072325.txt Table 2: Top 50 proteins identified from mass spectrometry in Jurkat cells with anti-Paxbp1, related to Figure 3. ExtendedFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7199764","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":514136033,"identity":"fe3ef0ab-a637-43c2-8904-7b87422849f3","order_by":0,"name":"Danielle Chisolm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3RIU8DMRTA8XeGmbLZd2Z8hUdqJu6bYK5mmG1BznEJSWcWZu9jkJCg21Rgbpm9BAQKNXG4g5zgVYyEhLLhyNJ/Kp75pa8pQCz2HzP+XI14En4ewQASfQAh3BGEtDiAwI6AJ2Tg5FfRf1xb0xAOobe2tp3jhXy60S/wkakiQNJqltuSUIKY5U5UOH14tgtKbsdBQmZCThCqAgW5RDOplcZk6WRoMdpsyXWE157Ydyb35T5S8y38/ByYmFMmd8gEWjcMkbTekl0SnmvhN+S3lH4xVYyDpL+ZyKadZ2eDXiXfeJiuystXbLpMhMhX3/9C7fnNn+r+TmKxWOxo+wQKDltfXTeDwAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9932-1450","institution":"National Institutes of Health","correspondingAuthor":true,"prefix":"","firstName":"Danielle","middleName":"","lastName":"Chisolm","suffix":""},{"id":514136034,"identity":"929d85d2-1a0b-4380-87d3-1918b3369262","order_by":1,"name":"Yuki Morimoto","email":"","orcid":"https://orcid.org/0000-0002-2352-0469","institution":"Chiba University","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Morimoto","suffix":""},{"id":514136035,"identity":"57c4ade3-1f93-4a0d-86f5-33d4a9e38415","order_by":2,"name":"Hiroyuki Nagashima","email":"","orcid":"https://orcid.org/0000-0001-8405-6746","institution":"NIH / NIAMS","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Nagashima","suffix":""},{"id":514136036,"identity":"767fda55-6ad8-4aec-9e9e-44beeafd0e3b","order_by":3,"name":"Stephen Brooks","email":"","orcid":"https://orcid.org/0000-0001-5592-5332","institution":"NIAMS","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Brooks","suffix":""},{"id":514136037,"identity":"06d9c1fe-c484-4d54-813a-1cc2ea9826c0","order_by":4,"name":"Ijeoma Meremikwu","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Ijeoma","middleName":"","lastName":"Meremikwu","suffix":""},{"id":514136038,"identity":"660c74e0-c924-4f9d-ae68-5298f231606f","order_by":5,"name":"Micala Hammond","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Micala","middleName":"","lastName":"Hammond","suffix":""},{"id":514136039,"identity":"1d17d4d2-8cae-41d3-bc5a-4f5827a25fe7","order_by":6,"name":"Jian Sun","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Sun","suffix":""},{"id":514136040,"identity":"fd460da9-d8f3-4031-bdab-171195fd9b4d","order_by":7,"name":"Ebru Ermis","email":"","orcid":"","institution":"MIT","correspondingAuthor":false,"prefix":"","firstName":"Ebru","middleName":"","lastName":"Ermis","suffix":""},{"id":514136041,"identity":"4476e962-1a68-431b-9271-9aa2aa6339f3","order_by":8,"name":"Rachael Philips","email":"","orcid":"","institution":"National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Rachael","middleName":"","lastName":"Philips","suffix":""},{"id":514136042,"identity":"29fd69d6-5417-45ae-a6ad-ada4eb4795ec","order_by":9,"name":"Vijender Chaitankar","email":"","orcid":"","institution":"University of Nebraska","correspondingAuthor":false,"prefix":"","firstName":"Vijender","middleName":"","lastName":"Chaitankar","suffix":""},{"id":514136043,"identity":"ac95c5f0-1cec-405a-afa2-775ee72eb96a","order_by":10,"name":"Andrew Koh","email":"","orcid":"https://orcid.org/0000-0002-4198-0143","institution":"University of Chicago","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Koh","suffix":""},{"id":514136044,"identity":"b5e53ca1-6411-48dd-807e-cb6f83c24f1a","order_by":11,"name":"Yuka Kanno","email":"","orcid":"https://orcid.org/0000-0001-5668-9319","institution":"NIH / NIAMS","correspondingAuthor":false,"prefix":"","firstName":"Yuka","middleName":"","lastName":"Kanno","suffix":""},{"id":514136045,"identity":"5ecc5b16-7794-44a2-8b93-a3027c13fd89","order_by":12,"name":"John O'Shea","email":"","orcid":"","institution":"NIH/NIAMS","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"O'Shea","suffix":""}],"badges":[],"createdAt":"2025-07-23 21:27:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7199764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7199764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92231115,"identity":"e05a3a55-bfd4-4548-94fc-730711f693c7","added_by":"auto","created_at":"2025-09-26 06:25:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20984115,"visible":true,"origin":"","legend":"","description":"","filename":"Chisolmmanuscript072325.docx","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/0f0125217fdd9a08296b02e6.docx"},{"id":92229872,"identity":"580d8133-7b07-4a4a-88ba-eb6db7191a7a","added_by":"auto","created_at":"2025-09-26 06:17:38","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12657,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS2571643T.json","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/7b00b10f932f1e74be13fee7.json"},{"id":92229876,"identity":"0cb6f1e0-f0d0-4915-9dc2-bbe8a82d69e2","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141145,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS2571643T0enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/37c1c21ebcb930f971ed466f.xml"},{"id":92229874,"identity":"864e9722-b6c5-4ac4-bac9-4fc0108061d0","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"eps","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149415,"visible":true,"origin":"","legend":"","description":"","filename":"drawingimage1.eps","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/629f51f632d722a17564198e.eps"},{"id":92229878,"identity":"71d9338a-b5c0-4caa-bd18-71cc232ea325","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":787714,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/3358ac323002d25fbea6e002.jpeg"},{"id":92229898,"identity":"1f62f998-33d8-436a-8545-2c80d11bda9c","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194561,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/2e1b9e7fd065fe7fa446707e.jpeg"},{"id":92229910,"identity":"acfb49f7-f103-49a7-973f-5fc77ea3dc1b","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1615742,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/d87bab9bf200664e59b81736.jpeg"},{"id":92229909,"identity":"9514916e-d535-4fee-bfae-cdfa0af25883","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/b4ed8bd746bf37641733abea.jpeg"},{"id":92229886,"identity":"5cc195cc-9e5d-4adc-9ac3-2acfabac6fb3","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":802015,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/7db2977e49e86dfa8f142a42.jpeg"},{"id":92231112,"identity":"85bbe9cb-3433-4a14-8f16-bd25b2e36420","added_by":"auto","created_at":"2025-09-26 06:25:39","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":852887,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/c43b444c6db17f944659f3e8.jpeg"},{"id":92231110,"identity":"4aefca32-041f-4a11-9481-9abe157e4451","added_by":"auto","created_at":"2025-09-26 06:25:39","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1199994,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/714b621c918fdbf977461b7f.jpeg"},{"id":92231272,"identity":"a4d16345-7324-468e-b1b8-2d8656d7f332","added_by":"auto","created_at":"2025-09-26 06:33:40","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":809455,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/92a4f08e8d5b092c4f3a1729.jpeg"},{"id":92229900,"identity":"50d732f4-bc62-4132-bb70-a329146cbb47","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1849407,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/dcbead2acebc7b8614f1dce1.jpeg"},{"id":92231108,"identity":"bf4310df-063d-477f-88ad-0b9b0dfcf144","added_by":"auto","created_at":"2025-09-26 06:25:39","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":754413,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/f96f2b533034ae093e38aeb1.jpeg"},{"id":92231121,"identity":"f8bf8b97-6303-4fd7-b1e4-4e9dfbeb0d6d","added_by":"auto","created_at":"2025-09-26 06:25:40","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1962626,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/e82b27b553628e735995ee4f.jpeg"},{"id":92231113,"identity":"ba5f2775-466d-4bdb-a868-3613f4f55822","added_by":"auto","created_at":"2025-09-26 06:25:39","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2003419,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/02825438a3b72d2aea34fff8.jpeg"},{"id":92229902,"identity":"24b93beb-eb01-41b5-a3b3-a00ef10b1f52","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140594,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/2b76a11890d1a6dbb4c9a47b.jpeg"},{"id":92229887,"identity":"3f53bc96-95a7-4a07-854a-b3b475675707","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":223448,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/c6705a8dd6f4dee6eefe99fb.png"},{"id":92229883,"identity":"dc9f391b-21e7-449b-8552-105a143a9ba2","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38907,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/ce170bc122c7fe9af81d7a2e.png"},{"id":92229891,"identity":"bd9b6c0f-4713-41e4-b2fc-0a5c8d2324b0","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":293717,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/9698b465983567f16767101d.png"},{"id":92229904,"identity":"ff9a8706-3e2d-41ae-a62f-67dc34bdefb5","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/9360d8bc140c277ab10fc315.png"},{"id":92229893,"identity":"81a18ea5-b626-410b-ab81-3490da251ba7","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180726,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/e5d99110ff68b11735050df9.png"},{"id":92229896,"identity":"990e29af-925c-4c5a-9586-f8dd6ba989bd","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":246660,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/3bb3b361b1162614504883d7.png"},{"id":92231118,"identity":"0f4d8842-2f63-4392-8971-873506d73683","added_by":"auto","created_at":"2025-09-26 06:25:40","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":335615,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/f37076c1bb5d0260adb91f68.png"},{"id":92229888,"identity":"559dc743-13b4-4230-b193-267730fc2a6a","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":230169,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/38b3dfe16f1ce77afea3877e.png"},{"id":92229907,"identity":"d2db268b-177a-49b3-8112-1967f322da4f","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":333958,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/b6fa570ebf2c187eb6a497c1.png"},{"id":92229895,"identity":"fc6e784c-c516-4c32-9bb1-bd41a98a7740","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180989,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/6b4ca98e96526149b9f66001.png"},{"id":92231117,"identity":"ac28d04d-4569-4ac0-8cb2-633ecac52774","added_by":"auto","created_at":"2025-09-26 06:25:40","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":383759,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/1c3abc65f794cec70dc39862.png"},{"id":92229901,"identity":"310eaa35-2208-40eb-b114-a29688ef0170","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":346045,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/e31a34925d5a42606a693129.png"},{"id":92229894,"identity":"65a4b0ad-4e76-4c6a-99a3-6e67643b28e2","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38577,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/8f7ed826fe8165030adfde1f.png"},{"id":92229897,"identity":"6bbce27d-0c77-4c2f-b744-1377c28dcc8c","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"xml","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":137222,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS2571643T0structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/6817a0725ae674dca875874f.xml"},{"id":92229899,"identity":"2483c834-a62a-4b5d-82ce-23eb09e8d965","added_by":"auto","created_at":"2025-09-26 06:17:40","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":153617,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/4491b237cb2beb2b473808f3.html"},{"id":92231107,"identity":"b419871b-d758-4719-b965-2adc78f5c85b","added_by":"auto","created_at":"2025-09-26 06:25:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211259,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of \u003cem\u003ePaxbp1\u003c/em\u003e is selectively induced in Th2 cells and is important for the induction of a Type 2-allergic immune response to OVA. a, UMAP plot depicting clusters from scRNA-seq data (GSE131996) of CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of mice infected with \u003cem\u003eN.\u003c/em\u003e \u003cem\u003ebrasiliensis\u003c/em\u003e. b,c, Feature plots illustrating the expression profiles of (b) \u003cem\u003eGata3\u003c/em\u003e and (c) \u003cem\u003ePaxbp1\u003c/em\u003e from Fig. 1a. d, Pooled results of absolute cell numbers of CD4\u003csup\u003e+\u003c/sup\u003e T cells, Tregs cells, or CD8\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of WT (blue) and Paxbp1-cKO (yellow) mice responding to OVA. PBS: n=3 OVA: n=5 for either genotype. T cells gated on Live CD45\u003csup\u003e+\u003c/sup\u003e SiglecF\u003csup\u003e-\u003c/sup\u003e Ly6G\u003csup\u003e-\u003c/sup\u003e CD19\u003csup\u003e-\u003c/sup\u003e Thy1.2\u003csup\u003e+\u003c/sup\u003e. e, Pooled results of absolute cell numbers for eosinophils or neutrophils isolated from the lungs of WT and Paxbp1-cKO mice responding to OVA. Cells were gated on Live CD45\u003csup\u003e+\u003c/sup\u003e CD11b\u003csup\u003e+\u003c/sup\u003e CD11c\u003csup\u003e-\u003c/sup\u003e. f, Cells recovered from lungs were restimulated with PMA and ionomycin for 4 hours and then stained for cytokines. CD4+ T cells were gated on Live CD45\u003csup\u003e+\u003c/sup\u003e TCRb\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e CD8a\u003csup\u003e-\u003c/sup\u003e Foxp3\u003csup\u003e-\u003c/sup\u003e. The genotype (WT or Paxbp1-cKO) and treatment (PBS or OVA) of mice are indicated for the representative FACS plots showing the frequency (top) and absolute cell numbers (bottom) of the indicated cytokines. Data are shown as the mean SEM with p values indicated (* \u003cu\u003e\u0026lt;\u003c/u\u003e 0.05, **\u003cu\u003e\u0026lt;\u003c/u\u003e 0.001, *** \u003cu\u003e\u0026lt;\u003c/u\u003e 0.0001, n.s. = not significant by an unpaired student t test). PBS: n=3 OVA: n=5 for either genotype.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/819b2c52b37a5f872cb57e37.png"},{"id":92229879,"identity":"05631d6d-4d92-4cc0-9d3f-c0ee3116b7b3","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":189140,"visible":true,"origin":"","legend":"\u003cp\u003ePaxbp1-deficiency leads to the increased gene expression and chromatin accessibility at loci associated with innate or cytotoxic programs in Th2 cells. a, Volcano plot depicting differential gene expression analysis of bulk RNAseq performed in WT or Paxbp1-cKO Th2 cells polarized \u003cem\u003ein vitro\u003c/em\u003e. Data for three biological replicates are shown. b, Bar plots depicting the pathways enriched for genes upregulated WT (top) or Paxbp1-cKO (bottom) Th2 cells. Genes with a log2 fold change of 2 or higher were used to perform the pathway analysis. c, Genome browser view of select gene loci displaying ATAC-seq performed in WT or Paxbp1-cKO Th2 \u003cem\u003ein vitro\u003c/em\u003e polarized cells. One replicate of three is displayed. Differential ATAC peaks are highlighted by blue boxes. d, Heatmap displaying ATAC-seq peaks from Th2 and Th1 \u003cem\u003ein vitro\u003c/em\u003e polarized cells from WT and Paxbp1-cKO mice. Data for three biological replicates are shown. Diffbind was used to calculate differential peaks between the conditions. e, Histograms depicting the relationship of genomic regions (Tn5 insertions) in WT and Paxbp1-cKO cells in comparison to ATAC-seq peaks specific to innate cell populations (GSE77695) as indicated above each graph. a.u., arbitrary unit.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/11681e4792d3872f6b05a8e2.png"},{"id":92231122,"identity":"a7231ca7-55f7-459b-adfe-6a8c57333b5b","added_by":"auto","created_at":"2025-09-26 06:25:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230714,"visible":true,"origin":"","legend":"\u003cp\u003ePaxbp1 interacts with Runx1 and Bcl11b in T cells. a, STRING analysis of proteins identified by mass spectrometry to interact with Paxbp1. Edges represented by the colored lines indicate protein-protein interactions. Mass spectrometry data represents three biological replicates. b, Pre-ranked GSEA analysis of genes induced in Paxbp1-cKO Th2 cells compared to WT Th2 cells. Pre-ranked genes were determined by DESeq2. c, Scatter plot depicting the transcription factor motif enrichment of differentially accessible open chromatin regions (DORCS) that intersect with Bcl11b Th2 ChIP-seq peaks (GSE108633) from WT and Paxbp1-cKO Th2 cells. Visualized are motifs with a pvalue of 10\u003csup\u003e-10\u003c/sup\u003e or less. d,e,f, Representative genome browser view of DORCS aligned with publicly available Bcl11b ChIP-seq peaks in either Th2 (GSE108633) or CD8\u003csup\u003e+\u003c/sup\u003e T cells (GSE186907). Peaks of interest are highlighted by blue boxes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/b4d6382f8886061dc643cb0b.png"},{"id":92232392,"identity":"1e88800a-0429-4d9b-a425-a110bcd6a761","added_by":"auto","created_at":"2025-09-26 06:41:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":346425,"visible":true,"origin":"","legend":"\u003cp\u003ePaxbp1 deficiency alters the enhancer regulatory network of CD4\u003csup\u003e+\u003c/sup\u003e T cells responding to \u003cem\u003eN.\u003c/em\u003e \u003cem\u003ebrasiliensis\u003c/em\u003e. a,b,c UMAP plot of a single nuclei multiomics (Gene Expression (GEX) and ATAC) from CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of WT and Paxbp1-cKO mice 10 days after \u003cem\u003eN. brasiliensis\u003c/em\u003e infection. UMAP plots depict (a) combined WT and Paxbp1-cKO samples and separately (b) WT and (c) Paxbp1-cKO clustering analysis. n=2. d, e, Dotplot displaying gene expression of select genes from single nuclei multiomics experiment from (d) WT-specific clusters or (e) Paxbp1-cKO-specific clusters. f,g, SCENIC+ eGRN enrichment dot plot predicted for (f) WT and (g) Paxbp1-cKO clusters. The color of the square indicates the enrichment for the TF target genes in each cluster. The size of the circle represents the enrichment of the TF motif. Each transcription factor is predicted to have either activating or repressive activities for genes identified in each regulon. h,i, Network visualization of activating eGRN for (h) Bach2 and (i) Lef1 in WT (WT0) and Paxbp1-cKO (KO2) “naïve” clusters. Genes identified in eGRN were visualized using cytoscape. Immunological related genes are highlighted in red.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/9035651517ed677145ceff0d.png"},{"id":92232471,"identity":"912fdfa9-9585-4c84-913f-46c90d1d980e","added_by":"auto","created_at":"2025-09-26 06:49:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2177877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/230981db-dcd1-4c85-97d0-c77ec87be3d0.pdf"},{"id":92231270,"identity":"5eaf5c8e-b60f-4782-baf9-4e835e5e7678","added_by":"auto","created_at":"2025-09-26 06:33:39","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20753,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1: DESEQ of genes upregulated with a log2 fold change of 2 or higher in Paxbp1-cKO polarized in type 2 conditions.\u003c/p\u003e","description":"","filename":"ChisolmTable1072325.txt","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/5db2407d802da72006273206.txt"},{"id":92229877,"identity":"085f5908-c0ba-46a3-94de-2ae6d88aa7b8","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":136273,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2: Top 50 proteins identified from mass spectrometry in Jurkat cells with anti-Paxbp1, related to Figure 3.\u003c/p\u003e","description":"","filename":"ChisolmTable2072325.txt","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/578717cff263d18bf343fa9d.txt"},{"id":92229884,"identity":"365a65c2-ef88-46de-ba78-cc86228084f3","added_by":"auto","created_at":"2025-09-26 06:17:39","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9949878,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7199764/v1/220e290f5c98690a0906b54e.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Paxbp1 restrains cytotoxic and innate cell programs in CD4+ T cells to promote Th2 differentiation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe capacity of CD4\u003csup\u003e+\u003c/sup\u003e T cells to differentiate into specialized helper T cell states is crucial for the immune system to eliminate a diversity of pathogenic insults. CD4\u003csup\u003e+\u003c/sup\u003e T cell differentiation is tightly regulated by antigens, cytokines, and metabolites, in part through integrating this external information into internal transcriptional programs\u003csup\u003e1\u003c/sup\u003e. T helper 2 (Th2) cells are characterized by the expression of the lineage-specifying transcription factor GATA3 and the production of type 2 cytokines, IL-4, IL-5, and IL-13\u003csup\u003e2,3\u003c/sup\u003e. Much work has been done to understand how the Th2 transcriptional program is turned on by GATA3, with an emphasis on the events that regulate the type 2 cytokine loci\u003csup\u003e4,5\u003c/sup\u003e. In contrast, less research has focused on defining how alternative programs are repressed in Th2 cells to maintain the integrity of the overall differentiation state. Of note, adaptive lymphocytes and their innate counterparts are similar in their transcriptional programs\u003csup\u003e6-9\u003c/sup\u003e, so it is important to understand how transcription factors work cooperatively to regulate these programs in a distinguishing manner. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExtensive research has been performed to understand how networks of transcription factors work together to promote developmental states in T cells differentiating in the thymus\u003csup\u003e10,11\u003c/sup\u003e. In contrast, while many transcription factors have been studied individually for their role in CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT helper cell specification, less work has characterized the cooperativity between the different transcription factors needed to maintain these specialized states. Related to this topic, single cell technologies highlight that lineage-specifying factors are not exclusively expressed in one lineage or context\u003csup\u003e12\u003c/sup\u003e, raising the question of how the combination of factors present in CD4\u003csup\u003e+\u003c/sup\u003e T cells contribute to their specialization.\u003c/p\u003e\n\u003cp\u003ePaxbp1 is a regulatory factor that was first identified in mouse muscle stem cells to interact with Pax3 or Pax7 where it plays a role in cell proliferation and survival in the context of regeneration during injury\u003csup\u003e13,14\u003c/sup\u003e. Subsequent studies have similarly shown Paxbp1 is important for proliferation and survival at different stages of T cell and epidermal cell development in mice, with its role influencing cellular differentiation states\u003csup\u003e15-17\u003c/sup\u003e. In humans, \u003cem\u003ePAXBP1\u003c/em\u003e is found on chromosome 21, with trisomy of chromosome 21 associated with Down syndrome and aberrant immune responses including myeloid leukemia. Interestingly, a microarray performed on a partial chromosome triplication mouse model of Down syndrome found that \u003cem\u003ePaxbp1\u003c/em\u003e expression was decreased relative to an increase in expression of interferon genes\u003csup\u003e18\u003c/sup\u003e. Taken together, the information currently available suggests \u003cem\u003ePAXBP1\u003c/em\u003e is a common essential gene with cell type specific roles in differentiation and dysregulated PAXBP1 expression is associated with aberrant developmental and immunopathological consequences\u003csup\u003e14-19\u003c/sup\u003e. However, it is currently unknown whether Paxbp1 plays a role in CD4\u003csup\u003e+\u003c/sup\u003e helper T cell specialization events.\u003c/p\u003e\n\u003cp\u003eIn this study, we used single cell analyses to identify novel factors that contribute to the regulation of the Th2 gene program and found that the expression of \u003cem\u003ePaxbp1\u003c/em\u003e is selectively induced in Th2 cells as compared to alternative CD4\u003csup\u003e+\u003c/sup\u003e T cell subtypes. Utilizing a conditional deletion model, we found Paxbp1 is involved in Th2 lineage-specialization by mechanistically repressing an alternative innate-like cytotoxic program in both in vitro and in vivo models of Th2 differentiation. Notably, Paxbp1 interacts with Bcl11b and Runx1, transcription factors involved in T cell lineage programming, with Bcl11b deletion in several T cells models inducing an innate-like cytotoxic program similar to what we observed in Paxbp1-deficient cells. Collectively, our study defines new aspects of the complex transcriptional network required to sustain a specific specialized state in CD4\u003csup\u003e+\u003c/sup\u003e T cells.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePaxbp1 expression is selectively induced in Th2 cells.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo define novel factors important for Th2 cell differentiation, we reanalyzed single cell RNA-seq data from CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of mice infected with \u003cem\u003eNippostrongylus brasiliensis\u003c/em\u003e\u003csup\u003e\u003cem\u003e20\u003c/em\u003e\u003c/sup\u003e, a pathogen that elicits a type 2 immune response. The UMAP from this analysis separated the data into 6 clusters that we defined by the expression profiles of CD4\u003csup\u003e+\u003c/sup\u003e T helper cell lineage-specifying genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Notably, in the cluster with high \u003cem\u003eGata3\u003c/em\u003e expression (\u0026ldquo;Th2\u0026rdquo; cluster, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), the nuclear factor \u003cem\u003ePaxbp1\u003c/em\u003e was also highly expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). These data indicate that \u003cem\u003ePaxbp1\u003c/em\u003e is predominantly expressed in Th2 cells as compared to alternative CD4\u003csup\u003e+\u003c/sup\u003e T cell subtypes.\u003c/p\u003e\u003cp\u003ePaxbp1 is involved in cellular differentiation potential for a variety of cell types. Therefore, we next wanted to determine whether Paxbp1 plays a role in coordinating the Th2 specification program. To address this question, we used a Paxbp1-floxed mouse model crossed to CD4-cre (referred to as Paxbp1-cKO) to conditionally delete Paxbp1 in T cells. As a first step, we used multi-dimension flow cytometry to immunophenotype cells isolated from Paxbp1-cKO mice compared to WT mice. Consistent with previous results\u003csup\u003e16,17\u003c/sup\u003e, we observed a decrease in the numbers of CD4\u003csup\u003e+\u003c/sup\u003e T cells recovered from Paxbp1-cKO mice relative to WT mice in both the spleen (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) and peripheral lymph nodes (pLNs) (data not shown). Additionally, we examined the impact of Paxbp1 deletion on thymic development and observed reduced numbers of single positive CD4\u003csup\u003e+\u003c/sup\u003e T cells in the thymus of Paxbp1-cKO compared to WT mice (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Taken together with previous results, CD4\u003csup\u003e+\u003c/sup\u003e T cell numbers decrease in the absence of Paxbp1, but cells do develop and migrate to the periphery.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePaxbp1 is important for establishing a Th2 program.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next sought to determine whether Paxbp1 plays a functional role in the Th2 differentiation program. To address this, we challenged WT and Paxbp1-cKO mice in an ovalbumin (OVA) asthma model to elicit an in vivo Th2 response (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Despite a decrease in the number of T cells prior to OVA immunization, there was no significant difference in the numbers of CD4\u003csup\u003e+\u003c/sup\u003e T cells recovered from the lungs of WT and Paxbp1-cKO mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We did not observe a significant decrease in the number of alternative lymphocytes found in the lungs of WT and Paxbp1-cKO mice (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Eosinophil recruitment is a hallmark of a type 2 response. We detected decreased numbers of eosinophils in the lungs of Paxbp1-cKO mice compared to WT mice in response to OVA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), conversely neutrophil numbers in the lungs of Paxbp1-cKO mice trended upward but did not reach statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Together these data suggest an impaired type 2 environment in Paxbp1-cKO mice. Consistent with this, CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of Paxbp1-cKO compared to WT mice had increased production of IFN-g and TNF-a while having decreased production of type 2 cytokines IL-4 and IL-13 after stimulation ex vivo (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Collectively, the data indicate a shift away from a Th2 response in Paxbp1-cKO mice.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePaxbp1-deficiency leads to aberrant expression of an innate and cytotoxic program.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo understand the impact of Paxbp1 on Th2 gene programming we wanted to see if we could recapitulate the in vivo findings. To address this, we took a reductionist approach by polarizing equal numbers of na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from either WT or Paxbp1-cKO mice in type 2 conditions in vitro. This approach allowed us to rule out issues with reduced cell numbers or other contributing signals in vivo. As expected, there was reduced Paxbp1 in Paxbp1-cKO Th2 cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Similar to the OVA immunization experiments, Th2 polarized cells deficient in Paxbp1 had increased frequencies of cells producing IFN-γ and expressing T-bet relative to WT Th2 cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,c). Of note, GATA3 expression was only modestly decreased in Paxbp1-cKO Th2 cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Paxbp1 deficiency is associated with defects in cell survival and proliferation\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. Therefore, we stained cells with Annexin V or cell trace violet to compare apoptosis and proliferation between Paxbp1-cKO and WT Th2 cells. Interestingly, while engaged with TCR, Paxbp1-cKO CD4\u003csup\u003e+\u003c/sup\u003e T cells had a decrease in proliferation, but this defect was recovered when removed from TCR signals and maintained in type 2 polarizing cytokine conditions (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). A similar trend was seen with Annexin V staining (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eThese data indicated that Paxbp1-deficiency, in part, initiated some aspects of a Th1 program in Paxbp1-cKO Th2 cells (e.g. increased production of IFN-γ and T-bet) (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,c). Therefore, we next sought to determine the overall impact of Paxbp1-deficiency on the transcriptional program. To accomplish this, we first performed bulk RNA-seq analyses on na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from WT or Paxbp1-cKO mice that were polarized in type 1 and type 2 conditions (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Th1 conditions were used as a comparison because presumably Paxbp1 deletion should not have as robust of an impact on Th1 cells. Consistent with our flow cytometry results, transcripts for \u003cem\u003eTbx21\u003c/em\u003e (encodes T-bet) and \u003cem\u003eIfng\u003c/em\u003e (encodes IFN-γ) were increased in Paxbp1-cKO compared to WT Th2 cells (Table\u0026nbsp;1). Additionally, there was an increase in the expression of genes from the cytotoxic program, including granzymes (e.g. \u003cem\u003eGzma\u003c/em\u003e, \u003cem\u003eGzmb\u003c/em\u003e, \u003cem\u003eGzmk\u003c/em\u003e), transcription factors (e.g. \u003cem\u003eEomes\u003c/em\u003e) and receptors (e.g. \u003cem\u003eKlrg1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;1). We next performed a Metascape analysis\u003csup\u003e21\u003c/sup\u003e to characterize the functional pathways impacted by the genes differentially regulated by Paxbp1. Consistent with findings from other cellular settings, the genes downregulated by Paxbp1-deficiency were enriched for pathways such as cell proliferation and IL-7 signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In contrast, the genes upregulated in Paxbp1-cKO Th2 cells were enriched for pathways associated with Natural Killer (NK) cells and cytotoxic functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). For example, we observed increased expression of NK lineage-associated genes (e.g. \u003cem\u003eKlrg1\u003c/em\u003e, \u003cem\u003eNkg7\u003c/em\u003e and \u003cem\u003eZbtb32\u003c/em\u003e\u003csup\u003e\u003cem\u003e22\u003c/em\u003e\u003c/sup\u003e) in Paxbp1-cKO compared to WT Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;1). Of note, we found similarities in the genes and pathways that were induced in Paxbp1-cKO Th1 cells, including genes associated with innate or cytotoxic lineages, although the fold changes were less robust in type 1 conditions presumably because \u003cem\u003ePaxbp1\u003c/em\u003e expression is much lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei, Table\u0026nbsp;1). Together, these data indicate that Paxbp1 plays a role in repressing innate and cytotoxic defining gene programs, especially in Th2 cells.\u003c/p\u003e\u003cp\u003eWe next performed bulk ATAC-seq on WT and Paxbp1-cKO CD4\u003csup\u003e+\u003c/sup\u003e T cells polarized in type 1 or type 2 conditions to define how Paxbp1-deficiency influences genome accessibility (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In a principal component analysis (PCA), differences in accessibility were attributed to genotype along PC1 rather than by polarizing conditions (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Consistent with the RNA-seq analyses, there was decreased accessibility surrounding genes associated with a type 2 program in Paxbp1-cKO compared to WT Th2 cells (e.g. \u003cem\u003eIl4\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), whereas there was increased accessibility at genes associated with a type 1 or cytotoxic program (e.g. \u003cem\u003eIfng\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Notably, clustering of differential ATAC peaks from the samples showed 5 distinct clusters: 1) Paxbp1-cKO-specific, 2) Paxbp1-cKO and WT Th1, 3) WT Th2-specific, 4) WT Th1-specific, and 5) WT-specific (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In the WT- and Paxbp1-cKO- specific clusters, we observed accessibility changes in genes associated with adaptive and innate lymphocyte lineages respectively such as \u003cem\u003eZbtb7b\u003c/em\u003e (WT-specific (cluster 5)) as well as \u003cem\u003eKlr1bc\u003c/em\u003e and \u003cem\u003eFcer1g\u003c/em\u003e (Paxbp1-cKO- specific (cluster 1)). In support of this finding, we observed increased accessibility at innate (e.g. \u003cem\u003eNkg7\u003c/em\u003e) and cytotoxic (e.g. \u003cem\u003ePrf1\u003c/em\u003e and \u003cem\u003eKlrg1\u003c/em\u003e) gene loci in the Paxbp1-cKO Th2 cells and decreased accessibility for genes associated with T cell identity (e.g. \u003cem\u003eS1pr1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). These data suggest that Paxbp1-deficiency affects genome accessibility particularly around loci associated with lineage identities.\u003c/p\u003e\u003cp\u003eWe next wanted to determine whether Paxbp1 broadly influences the accessibility of gene loci for innate and cytotoxic programs. To address this, we defined Paxbp1-cKO-specific ATAC peaks by comparing peaks from WT and Paxbp1-cKO Th2 cells, and Th2-specific peaks by comparing peaks between WT Th2 and Th1 cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). We then used the genomic coordinates from the Paxbp1-cKO-specific and Th2-specific peaks and intersected these with the Tn5 transposase insertions from previously published datasets for NK, ILC1, ILC2 and ILC3 cells\u003csup\u003e23\u003c/sup\u003e. We detected a statistically significant overlap between chromatin states in Paxbp1-cKO Th2 cells and innate lymphocytes, in particular with NK, ILC1 and ILC3 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Both the WT Th2 and Paxbp1-cKO Th2 peaks overlapped with ILC2 peaks, but the overlap was more robust in Paxbp1-cKO Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Interestingly, we observed a Paxbp1-cKO specific peak in the \u003cem\u003eIl5\u003c/em\u003e locus (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Relatively, Paxbp1-deficient Th1 cells did not have the same increased chromatin accessibility in innate programs (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). This might be because the peaks were already accessible to a degree in WT Th1 cells and Paxbp1 plays less of a role in this setting (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed (Cluster 2)). While being predisposed for accessibility is a reasonable explanation for some of the overlap with Th1 cells, we made several observations where the accessibility in Paxbp1-cKO Th2 cells was higher than any of the type 1 conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Taken together, Paxbp1-deficiency in Th2 polarized cells enhanced accessibility of loci associated with innate and cytotoxic gene programs, consistent with the findings that Paxbp1 inhibits the expression of these programs in Th2 cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePaxbp1 interacts with lineage-specifying transcription factors Bcl11b and Runx1.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next wanted to determine the molecular mechanisms by which Paxbp1 regulates Th2 programming potential. Not much is known about the molecular activities of Paxbp1. Paxbp1 was originally identified in a yeast two hybrid screen characterizing binding partners for Pax3 or Pax7\u003csup\u003e13\u003c/sup\u003e. These data indicated it served as an adaptor protein linking Pax3/7 to Wdr5-containing MLL complexes to regulate cellular differentiation programs in muscle stem cells\u003csup\u003e13,14\u003c/sup\u003e. Additionally, a recent study using cryo-electron microscopy showed Paxbp1 involvement with spliceosome disassembly\u003csup\u003e24\u003c/sup\u003e. With so little information available, especially in immune cells\u003csup\u003e16,17\u003c/sup\u003e, we decided to take an unbiased approach to identify Paxbp1 interacting partners in T cells. We also wanted to focus on conserved mechanisms between species because of potential connections to immune-mediated pathologies in humans\u003csup\u003e19\u003c/sup\u003e. It is interesting to note that the top 50 genes that were more highly expressed in human NK cells compared to human T cells (from the CZ Cell x Gene database\u003csup\u003e25\u003c/sup\u003e) substantially overlapped with the genes upregulated in Paxbp1-cKO Th2 cells (Extended Fig.\u0026nbsp;5a, Table\u0026nbsp;1). This suggests that the regulatory mechanisms Paxbp1 utilizes to inhibit the innate-like program in T cells might be conserved between species. Therefore, we performed co-immunoprecipitation (co-IP) coupled with mass-spectrometry (spec) experiments in human Jurkat T cells, which have high endogenous Paxbp1 expression levels.\u003c/p\u003e\u003cp\u003eSimilar to previous reports, we found Paxbp1 interacted with proteins associated with splicing, including TFIP11 and DHX15\u003csup\u003e24\u003c/sup\u003e, in the co-IP mass spec experiments in T cells (Table\u0026nbsp;2 and Extended Data Fig.\u0026nbsp;5b). A STRING\u003csup\u003e26\u003c/sup\u003e analysis on Paxbp1-interacting proteins confirmed it clustered with splicing factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). However, of particular interest regarding the role for Paxbp1 in restraining innate and cytotoxic gene programs in Th2 cells, we also found that the lineage-specifying transcription factors Bcl11b and Runx1 interacted with Paxbp1 in T cells. Notably, a second STRING cluster of Paxbp1-interacting proteins placed Bcl11b and Runx1 with other transcriptional regulators, including Mef2d, a transcription factor shown to play a role in Treg and Th2 lymphocytes\u003csup\u003e27,28\u003c/sup\u003e, as well as the chromatin remodeler Chd1, the highest hit in the mass spec dataset (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eBcl11b has previously been shown to repress an NK/innate-like or cytotoxic program in several T cell subsets, including Tregs, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and relevant for this study, Th2 cells\u003csup\u003e29\u0026ndash;37\u003c/sup\u003e. In Th2 cells, loss of Bcl11b expression caused a failure to develop an appropriate Th2 response in asthma and helminth immune challenges\u003csup\u003e36,37\u003c/sup\u003e. This bears remarkable resemblance to the phenotype of Paxbp1-cKO Th2 cells. Additionally, in na\u0026iuml;ve T cells, overexpression of Runx1 inhibits the expression of the Th2 program and induces an innate lymphoid program in T cells\u003csup\u003e38,39\u003c/sup\u003e. Collectively, this raises the possibility of whether Bcl11b and Runx factors play an antagonizing role to regulate the innate program in mature T cells, and if these activities are modulated by Paxbp1 in Th2 cells.\u003c/p\u003e\u003cp\u003eTo start to test this hypothesis, we examined whether there were any detectable alterations in the activities of Bcl11b and/or Runx1 in our Paxbp1-cKO genomics datasets. We first performed a gene set enrichment analysis (GSEA) on the genes that were upregulated in Paxbp1-cKO Th2 cells compared to WT Th2 cells\u003csup\u003e40,41\u003c/sup\u003e. The top pathway identified was from a study by Li et al. that found Bcl11b-deficient T cells induced a NK cell-like gene program\u003csup\u003e29\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This suggests Bcl11b activity was diminished in Paxbp1-cKO Th2 cells and this might contribute to the induction of an NK or innate-like program. We also performed a GSEA\u003csup\u003e40,41\u003c/sup\u003e analysis on genes upregulated in Paxbp1-cKO Th1 cells, which similarly identified enrichment of this pathway (Extended Data Fig.\u0026nbsp;5c). This suggests Paxbp1 might be more broadly involved in repressing the NK or innate cell program across different T cell subsets, with the levels of Paxbp1 determining the magnitude of its role.\u003c/p\u003e\u003cp\u003eTo further explore a possible interplay between Bcl11b and Paxbp1, we examined published Bcl11b ChIP-seq datasets and compared these to the ATAC-seq peaks induced in Paxbp1-cKO Th2 cells. We observed increased expression and chromatin accessibility of well-known Bcl11b target genes, such as \u003cem\u003eFcer1g\u003c/em\u003e and \u003cem\u003eKlrb1c\u003c/em\u003e in Paxbp1-cKO T cells relative to WT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Table\u0026nbsp;1), suggesting that Paxbp1 modulates Bcl11b activity. We next aligned peaks from a publicly available Bcl11b Th2 ChIP-seq dataset\u003csup\u003e36\u003c/sup\u003e with our ATAC-seq data and performed a motif analysis on reproducible peaks found in either the WT or Paxbp1-cKO Th2 samples. Overlapping coordinates were then analyzed for motifs using HOMER (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). We observed differential motif enrichment in the overlap between WT compared to Paxbp1-cKO peaks with Bcl11b peaks. In WT-specific peaks, the most enriched motifs were from the bZIP transcription factor family. In contrast, Paxbp1-cKO-specific peaks were enriched with motifs from the Runt and Ets transcription families (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, Extended Data Fig.\u0026nbsp;5d,e), implicating antagonistic roles for Runx proteins and Bcl11b. Of note, GATA motifs were not enriched in the Paxbp1-cKO specific peaks when overlapped with Bcl11b (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), consistent with a shift away from the Th2 program in the absence of Paxbp1. Finally, we compared our ATAC-seq dataset to Bcl11b ChIP-seq datasets\u003csup\u003e33\u003c/sup\u003e performed in Th2 cells and tissue resident-like CD8\u003csup\u003e+\u003c/sup\u003e T cells to assess how alterations in accessibility aligns with Bcl11b recruitment in type 2 and type 1 conditions, respectively. Differential ATAC-seq peaks often aligned with Bcl11b ChIP-seq peaks in accordance with their respective gene expression including at the \u003cem\u003eIl4\u003c/em\u003e and \u003cem\u003eNkg7\u003c/em\u003e loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed,e). As expected, this mechanism did not explain the regulation for all differentially expressed genes. At a subset of loci, there was no difference in Bcl11b association between Th2 and CD8\u003csup\u003e+\u003c/sup\u003e T cells such as the case for \u003cem\u003ePrf1\u003c/em\u003e despite increased accessibility and gene expression in Paxbp1-cKO compared to the WT samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Collectively, these data suggest Paxbp1 modulates Bcl11b activity to restrain aspects of the innate and cytotoxic programs in T cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePaxbp1-cKO mice infected with\u003c/b\u003e \u003cb\u003eN. brasiliensis\u003c/b\u003e \u003cb\u003efail to induce a Th2 program and instead divert CD4\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eT cells toward innate- and cytotoxic-like programs.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCollectively, our data suggest that Paxbp1 impacts the activity of multiple transcription factors relevant to immune cell differentiation. To better understand how Paxbp1 influences gene regulatory networks, we performed a combined single cell RNA-seq and ATAC-seq multiomics experiment from the nuclei of CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs of WT and Paxbp1-cKO mice infected with \u003cem\u003eN. brasiliensis\u003c/em\u003e. There was no difference in the worm burden detected in WT and Paxbp1-cKO mice. The multiomics experiment measures gene expression and chromatin accessibility from the same nuclei, linking this information to identify gene programs for individual cells. Projecting the data onto a UMAP resulted in the combined WT and Paxbp1-cKO data to have 9 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). When separated by genotype, the WT UMAP identified 5 clusters whereas Paxbp1-cKO cells had 8 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb,c). Comparing the gene expression patterns between the clusters, we found 3 of the WT clusters were largely preserved in Paxbp1-cKO cells: 1) na\u0026iuml;ve (as defined by the expression of \u003cem\u003eSell\u003c/em\u003e), 2) Tregs (as defined by the expression of \u003cem\u003eFoxp3\u003c/em\u003e), and 3) a cluster of unknown identity with high expression of the RNA helicase gene \u003cem\u003eDhx40\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed,e and Extended Data Fig.\u0026nbsp;6b). Similar to the data in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the absence of Paxbp1 in CD4\u003csup\u003e+\u003c/sup\u003e T cells resulted in the inability to mount a Th2 differentiation program (WT1), with \u003cem\u003eGata3\u003c/em\u003e expression absent in the Paxbp1-cKO clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Noteworthy, the top gene for the \u0026ldquo;Th2\u0026rdquo; (WT1) cluster in WT cells was \u003cem\u003ePaxbp1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), and as a control, \u003cem\u003ePaxbp1\u003c/em\u003e expression was not induced in the Paxbp1-cKO samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Consistent with the data from the bulk RNA-seq and ATAC-seq analyses from in vitro polarized Th2 cells, the novel clusters in Paxbp1-cKO mice expressed genes from innate and cytotoxic gene programs (KO3, KO5, KO6, and KO7). Interestingly, one cluster (KO7) appeared to have similarities with NKT cells (e.g. \u003cem\u003eZbtb16\u003c/em\u003e (encodes PLZF)) and another cluster (KO5) with aged CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e42\u003c/sup\u003e (e.g. \u003cem\u003eGzmk\u003c/em\u003e and \u003cem\u003eEomes\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, Extended Data Fig.\u0026nbsp;6e).\u003c/p\u003e\u003cp\u003eTo better define the transcriptional regulatory networks between WT and Paxbp1-cKO cells, we analyzed the data with the SCENIC\u0026thinsp;+\u0026thinsp;package\u003csup\u003e43\u003c/sup\u003e. SCENIC\u0026thinsp;+\u0026thinsp;is a python package that constructs gene regulatory networks by combining both gene expression and accessibility data to extrapolate relationships between transcription factors, enhancers, and target genes\u003csup\u003e43\u003c/sup\u003e. Applying this analysis, we found the Th2 cluster (i.e. WT1) was enriched for GATA3 TF activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, Extended data 6d), confirming that SCENIC\u0026thinsp;+\u0026thinsp;can define relevant \u0026ldquo;regulons\u0026rdquo; in our datasets. In addition, the Paxbp1-cKO cluster 5 (KO5) was enriched for EOMES TF activity, with both \u003cem\u003eEomes\u003c/em\u003e and its target gene \u003cem\u003eGzmk\u003c/em\u003e found in the cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, Extended Data Fig.\u0026nbsp;6e). Taken together, the data indicate that SCENIC\u0026thinsp;+\u0026thinsp;is sensitive enough to pick up both known and novel regulons in WT and Paxbp1-cKO CD4\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e\u003cp\u003eTo better understand how Paxbp1 deficiency impacts transcriptional gene programs in T cells we first compared the \u0026ldquo;na\u0026iuml;ve\u0026rdquo; clusters from WT (WT0) and Paxbp1-cKO (KO2) samples. A comparison of the gene expression profiles in the WT (WT0) and Paxbp1-cKO (KO2) \u0026ldquo;na\u0026iuml;ve\u0026rdquo; clusters indicated they were grossly similar (Extended Fig.\u0026nbsp;6b,c). Therefore, comparing these clusters provided an ideal opportunity to define the chromatin events that occur as \u003cem\u003ePaxbp1\u003c/em\u003e expression starts to be induced during a primary Th2 response, but prior to the onset of aberrant gene programs in the Paxbp1-deficient cells. In the WT0 compared to KO2 cluster, two activating regulons, Bach2 and Lef1, were preserved, whereas no overlapping regulons were detected with repressive activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef,g). Bach2 and Lef1 are transcription factors known to regulate the programming of na\u0026iuml;ve cells, making these regulons of interest for exploring how the initiation of Paxbp1 might influence the na\u0026iuml;ve program. Comparisons of the Bach2 and Lef1 regulons revealed many similarities in the \u0026ldquo;na\u0026iuml;ve\u0026rdquo; clusters between WT and Paxbp1-cKO cells. This included genes in the Bach2 regulon (e.g. \u003cem\u003eIl7r\u003c/em\u003e and \u003cem\u003eCcr7\u003c/em\u003e), Lef1 regulon (e.g. \u003cem\u003eBach2\u003c/em\u003e and \u003cem\u003eZfp281\u003c/em\u003e), and in both the Bach2 and Lef1 regulons (\u003cem\u003eFoxp1\u003c/em\u003e and \u003cem\u003eIfngr2\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh,i). However, there were some interesting differences in the Bach2 and Lef1 regulons in WT and Paxbp1-cKO clusters. For example, Fli1 was found to be positively regulated by both Bach2 and Lef1 in WT cells but was not identified as a target in Paxbp1-cKO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh,i). In addition, Lef1 was predicted to regulate some genes associated with the CD4 lineage\u003csup\u003e28,44\u003c/sup\u003e (e.g. \u003cem\u003eSatb1\u003c/em\u003e, \u003cem\u003eMef2d\u003c/em\u003e) in WT cells, whereas in Paxbp1-cKO cells, the prediction was for the cytotoxic lineage\u003csup\u003e45,46\u003c/sup\u003e (\u003cem\u003eUgcg\u003c/em\u003e and \u003cem\u003eTox\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei). These data suggest that while gene regulatory networks associated with a na\u0026iuml;ve program were initiated in Paxbp1-cKO cells, the potential for alternative programs was enhanced by the availability of additional transcription factors generally repressed in WT Th2 cells such as \u003cem\u003eTox\u003c/em\u003e. Of note, both Bach2 and Lef1 have been shown to play context dependent roles in T cells to influence differentiation gene programs\u003csup\u003e47\u0026ndash;57\u003c/sup\u003e, and our analysis provides some insight into how these transcription factors might be involved in regulating the programming potential of T cells.\u003c/p\u003e\u003cp\u003eWe also examined the regulons for the clusters that were gained in the Paxbp1-deficient cells. Consistent with our findings from in vitro polarized Th2 cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells deficient in Paxbp1 differentiating in response to \u003cem\u003eN. brasiliensis\u003c/em\u003e gained a number of novel regulons indicative of a shift towards a cytotoxic program (e.g. Runx3 and Eomes) or a type 1 program (e.g. Ets1 and Tbx21 (T-bet)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Additionally, we identified multiple transcription factors predicted to positively regulate genes like \u003cem\u003eIfng\u003c/em\u003e (Runx1 and Tbx21(T-bet)), \u003cem\u003eIl2rb\u003c/em\u003e (Runx2 and Rora), and \u003cem\u003eGzma\u003c/em\u003e (Runx3) in the Paxbp1-cKO clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), and in the KO2 \u0026ldquo;na\u0026iuml;ve\u0026rdquo; cluster, \u003cem\u003eIfnar1\u003c/em\u003e (Ets2 and Elf1) was identified to be positively regulated by multiple transcription factors. Notably, in Paxbp1-cKO cluster 3, SCENIC\u0026thinsp;+\u0026thinsp;inferred increased activity of Runx proteins, and importantly, the Runx family was also identified to interact with Paxbp1 in our co-IP mass-spec analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table\u0026nbsp;2). In addition, there was an enrichment of transcription factor activity for multiple factors associated with a type 1 and type 3 program in KO0 and KO7, along with increased activity of NF-kB factors in KO7. The SCENIC\u0026thinsp;+\u0026thinsp;analysis highlights how the loss of Paxbp1 dysregulates several gene regulatory networks during Th2 differentiation.\u003c/p\u003e\u003cp\u003eFinally, in accordance with our mass spec data that identified a physical interaction between Paxbp1 with Runx1 and Chd1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table\u0026nbsp;2), regulons for Runx1 and Chd2 (family member to Chd1) were gained in Paxbp1-cKO \u0026ldquo;na\u0026iuml;ve\u0026rdquo; cells (KO2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). In the literature, increased activity of Runx1 has been shown to inhibit the Th2 program in na\u0026iuml;ve T cells and enhance NK cell genes in T cells\u003csup\u003e38,39\u003c/sup\u003e. Therefore, increased repressive activity of Runx1 provides a potential mechanism that contributes to the inhibition of the Th2 program and enhancement of genes associated with NK cells in Paxbp1-cKO cells. Therefore, these data suggest a role for Paxbp1 in regulating the activities of Runx and Chd proteins to promote Th2 differentiation while restraining cytotoxic potential.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that Paxbp1 is important for the appropriate expression and differentiation of the Th2 program in CD4\u003csup\u003e+\u003c/sup\u003e T cells. Using single cell RNA-seq data, we uncovered how the expression of a seemingly commonly utilized protein was also selectively increased in Th2 cells. Using a conditional mouse model to delete Paxbp1 in T cells, we characterized a role for Paxbp1 in specifying the type 2 program. Molecular experiments performed in Paxbp1-cKO Th2 cells compared to WT Th2 cells determined that Paxbp1 was important for restraining cytotoxic and innate-like programs in CD4\u003csup\u003e+\u003c/sup\u003e T cells. At the chromatin level, loss of Paxbp1 resulted in an increased accessibility of genomic regions associated with innate cell lineages, in particular NK cells. Mechanistically, our data also indicate Paxbp1 impacted the activity of Bcl11b and Runx1, with single nuclei multiomics data suggesting Runx1 had increased activity in Paxbp1-cKO T cells responding to a \u003cem\u003eN. brasiliensis\u003c/em\u003e infection. Collectively, our data characterizes a new role for Paxbp1 in maintaining the fidelity of the Th2 differentiation program by inhibiting alternative cytotoxic programming potential.\u003c/p\u003e\u003cp\u003eUntil recently, Paxbp1 was thought to be a ubiquitously expressed factor that plays a role in the survival of various cell types including T cells, keratinocytes, and muscle stem cells\u003csup\u003e13\u0026ndash;17\u003c/sup\u003e. Mechanistically, Paxbp1 has been connected to the spliceosome\u003csup\u003e24\u003c/sup\u003e, possibly explaining why it is needed in a wide range of cell types. Clinically, dysregulation of Paxbp1 is associated with worse health outcomes in viral infections and cancer\u003csup\u003e19\u003c/sup\u003e, which suggests it has a more specific function in immune cells. An interesting finding from our study is the implication that Paxbp1 regulates the activities of Bcl11b and Runx1. Bcl11b has been shown to play multiple roles in different lymphocytes including NK cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, and ILC2 cells but less is known about what additional proteins are required for its context dependent activity in immune cells\u003csup\u003e58,59\u003c/sup\u003e. Of note, Bcl11b has been shown to repress an innate cell program in T cells\u003csup\u003e29\u0026ndash;31\u003c/sup\u003e and recent research has explored the molecular details that contribute to this activity\u003csup\u003e60,61\u003c/sup\u003e. Our work extends the molecular network to suggest that Paxbp1 contributes to the ability of Bcl11b to repress an innate program in T cells. Of note, in Shin et. al. an analysis comparing chromatin and gene expression relationships of different transcription factors highlighted an antagonistic role for Bcl11b and Runx1 activities when they were associated with the same genomic regions\u003csup\u003e39\u003c/sup\u003e. In this study, we show that Paxbp1 interacts with Bcl11b and Runx1 in T cells, and we observed modulation of their activities at the transcriptional and epigenetic level. Interestingly, Bcl11b and Runx1 were previously shown to work cooperatively to regulate the T effector landscape in early T cell development\u003csup\u003e62\u003c/sup\u003e, which highlights the importance of defining the molecular details for how these transcription factors work together to regulate lineage defining programs in mature T cells as well. The connection between Paxbp1 and these transcription factors provide new insight into their contextual contributions to T helper specification and T cell gene programs.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003ePAXBP1\u003c/em\u003e locus is located on human chromosome 21, and \u003cem\u003eRUNX1\u003c/em\u003e is located about 2.5 MB away from it in the 3\u0026rsquo; direction. In addition to being associated with Down Syndrome, chromosome 21 is associated with aberrant immune states and RUNX1 dysregulation is often associated with leukemia\u003csup\u003e63,64\u003c/sup\u003e. Of note, chromosome 21 has a relatively high concentration of genes associated with immune cell functions. The observation that genes encoding proteins with cooperative functions in immunity are located in proximity on chromosome 21 suggests its will be important to understand how these genes are modulated and how their molecular interactions impact lymphocyte regulation. The role for Paxbp1 in regulating Runx1 activity highlights the importance of obtaining a deeper understanding of this topic.\u003c/p\u003e\u003cp\u003eIndividual cells in an organism have the same DNA content, and as they differentiate, gene programs are defined through the fine tuning of the activities for a limited number of transcription factors. Transcriptionally, innate and adaptive lymphocytes are quite similar, and factors such as GATA3, TCF1, and Bcl11b have roles in both adaptive and innate lymphocytes\u003csup\u003e6\u0026ndash;9\u003c/sup\u003e. This means we need a more thorough understanding of the molecular mechanisms for how each factor works in different lineages to define the conserved and divergent mechanisms that control innate and adaptive lymphocyte differentiation programming potential. In our study, we found Paxbp1-deficiency in CD4\u003csup\u003e+\u003c/sup\u003e T cells leads to elements of the chromatin resembling innate lymphocyte lineages. Enhancer availability can alter the activity of transcription factors and might represent one aspect contributing to how Paxbp1 influences cell type specificity. For example, a recent study identified cell type specific usage of a super enhancer for \u003cem\u003eIl2ra\u003c/em\u003e, which allowed for selective expression in NK and peripheral T cells compared to Tregs and early thymic T cells\u003csup\u003e65\u003c/sup\u003e. This highlights how unique mechanisms can regulate similar genes in distinct cellular states. In summary, our study illuminates molecular circuits in CD4\u003csup\u003e+\u003c/sup\u003e T cells that specify to a Th2 differentiation state while preventing activation of innate and cytotoxic programming potential.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eMice\u003c/h2\u003e\u003cp\u003e\u003cem\u003ePaxbp1\u003c/em\u003e\u003csup\u003e\u003cem\u003efl/fl\u003c/em\u003e\u003c/sup\u003e mice were purchased from RIKEN BioResource Research Center (Ibaraki, Japan). \u003cem\u003eCd4\u003c/em\u003e-Cre mice were purchased from The Jackson Laboratory (Bar Harbor, Maine, USA). All animal experiments were performed in the AAALAC-accredited animal housing facilities at NIH. All animal studies were performed according to the NIH guidelines for the use and care of live animals and were approved by the Institutional Animal Care and Use Committee of NIAMS. Mice of 6\u0026ndash;12 weeks old were used in all experiments. The number of mice used in each experiment are indicated in the corresponding figure legends. The littermates of the same sex were randomly assigned to experimental groups of WT (flox/flox-CD4Cre\u003csup\u003e\u0026minus;\u003c/sup\u003e) or Paxbp1-cKO (flox/flox-CD4Cre\u003csup\u003e+\u003c/sup\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003escRNAseq analysis\u003c/h3\u003e\n\u003cp\u003eWe reanalyzed CD4\u003csup\u003e+\u003c/sup\u003e T cells isolated from the lungs responding to N. brasilineas: GSE131996\u003csup\u003e20\u003c/sup\u003e using Seurat. Seurat was used for QC and downstream analysis. The following samples: Day 0 (GSM4192248) Day 2 (GSM4192249), Day 5 (GSM4192250), Day 9 (GSM4192251), and Day 14 (GSM4192252) were merged for the analysis. Samples were integrated, clustered, and visualized using Seurat and ggplot.\u003c/p\u003e\n\u003ch3\u003eFlow cytometry\u003c/h3\u003e\n\u003cp\u003eThe following antibodies were used in combination for flow cytometry: anti-CD8a-BV711(53\u0026thinsp;\u0026minus;\u0026thinsp;6.7, BioLegend), anti-CD25-BV605(PC61, BioLegend), anti-Foxp3-AF700(FJK-16s, eBioscience), anti-H2-K-Pacific Blue(AF6-88.5, BioLegend), anti-CD44-BV570(IM7, BioLegend), anti-BCL6-PE(BCL-DWN, eBioscience), anti-CD62L-BUV737(MEL-14, BD Pharmingen), anti-CD69-FITC(H1.2F3, BD Pharmingen), anti-CXCR5-BV711(L138D7, BioLegend), anti-PD-1-PE-ef610 (J43, eBioscience), anti-CD62L-BUV395(MEL-14, BD Pharmingen), anti-CD4-BUV496(GK1.5, BD Pharmingen), anti-CD11c-BUV661(N418, BD Pharmingen), anti-γδTCR-BUV805(GL3, BD Pharmingen), anti-CD8a-BV421(53\u0026thinsp;\u0026minus;\u0026thinsp;6.7, BioLegend), anti-Foxp3-ef450(FJK-16s, eBioscience), anti-TCRβ-BV480(H57-597, BD Pharmingen), anti-CD19-BV510(1D3, BD Pharmingen), anti-Ly6G-BV605(1A8, BioLegend), anti-CD11b-BV785(M1/70, BioLegend), anti-Ly6C-PerCP-Cy5.5(HK1.4, eBioscience), anti-CD24-PE-ef610(M1/69, eBioscience), anti-IL-7Rα-PE-Cy5(A7R34, BioLegend), anti-NK1.1-APC(PK136, eBioscience), anti-Siglec-F-ef660(1RNM44N, eBioscience), anti-CD90.2-AF700(53\u0026thinsp;\u0026minus;\u0026thinsp;2.1, BioLegend), anti-CD64-APC-ef780(X54-5/7.1, eBioscience), anti-TNFα-BV510(MP6-XT22, BD Pharmingen), anti-IFNγ-FITC(XMG1.2, BD Pharmingen), anti-IL-5-PE(TRFK5, BioLegend), anti-IL-17a-PE-ef610(eBio17B7, eBioscience), anti-IL-4-APC(11B11, BioLegend), anti-IL-13-APC-ef780(eBio13A, eBioscience), anti-GATA3-AF488(16E10A23, BioLegend), and analyzed by FACS Fortessa (BD Biosciences) or Cytek Aurora (Cytek Biosciences). Zombie-NIR (BioLegend) and Fixable Viability Dye ef780 (eBioscience) was used for gating live cells. For cytokine staining, the cells were stimulated with PMA and Ionomycin (eBioscience Cell Stimulation cocktail, ThermoFisher Scientific) and Protein Transport Inhibitor (ThermoFisher Scientific) for 4 hours before staining. Anti-PAXBP1 antibody was custom-made by GenScript and anti-rabbit IgG-AF647(Poly4064, BioLegend) was used as the secondary antibody for intracellular TF staining. Intracellular staining was performed using eBioscience\u0026trade; Foxp3 / Transcription Factor Staining Buffer Set (ThermoFisher Scientific) according to manufactures\u0026rsquo; protocol.\u003c/p\u003e\n\u003ch3\u003eImmunization and challenge with OVA\u003c/h3\u003e\n\u003cp\u003eMice were immunized intraperitoneally with 250\u0026micro;g of OVA (chicken egg albumin Sigma-Aldrich, cat. no. 9006-59-1) in 4mg of aluminum hydroxide gel (Alum) for OVA-immunized group or only PBS for control group on days 0 and 7. OVA was administered to mice intranasally: 100\u0026micro;g on days 14, 16, 18, 20 and 22. At day 23, cells were recovered from the lung and various assays were performed.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCell isolation from mouse lungs\u003c/h2\u003e\u003cp\u003eThe lungs were perfused before dissection, then cells were isolated using Lung Dissociation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany). After RBC lysis, cells were analyzed by flow cytometry.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIn vitro T helper cell differentiation\u003c/h3\u003e\n\u003cp\u003eNa\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells (CD4\u003csup\u003e+\u003c/sup\u003e CD62L\u003csup\u003ehigh\u003c/sup\u003e CD44\u003csup\u003elow\u003c/sup\u003e CD8a\u003csup\u003e\u0026minus;\u003c/sup\u003e γδTCR\u003csup\u003e\u0026minus;\u003c/sup\u003e CD19\u003csup\u003e\u0026minus;\u003c/sup\u003e) were purified from spleens using a cell sorter (BD Aria), yielding a purity of \u0026gt;\u0026thinsp;98%, or by EasySep Mouse Na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T Cell Isolation Kit (STEMCELL Technologies Inc., Vancouver, Canada). Purified naive CD4\u003csup\u003e+\u003c/sup\u003e T cells were stimulated with immobilized anti-TCRβ mAb (H57-597; 10\u0026micro;g/ml) in the presence of IL-2 (25U/ml), IL-12 (10 U/ml; R\u0026amp;D), anti-IL-4 mAb (11B11; 5\u0026micro;g/ml), and anti-CD28 mAb (37.51; 1\u0026micro;g/ml) for Th1 cell differentiation; IL-2 (25U/ml), IL-4 (100U/ml; R\u0026amp;D), anti-IFN-γ mAb (R46A2; 5\u0026micro;g/ml), and anti-CD28 mAb (37.51; 1\u0026micro;g/ml) for Th2 cell differentiation. Following treatment with anti-TCR mAb and anti-CD28 mAb for 48 hours, cells were further cultured with indicated cytokines and neutralizing antibodies, then harvested on day 5.\u003c/p\u003e\n\u003ch3\u003eWestern blotting\u003c/h3\u003e\n\u003cp\u003eWhole cell extract was prepared with NP-40 lysis buffer and subjected for western blotting. Anti-PAXBP1 antibody (PA5-52002, ThermoFisher) and anti-HDAC1 antibody (D5C6U, Cell Signaling Technology) were used as the primary antibodies and anti-rabbit IgG, HRP-linked antibody (7074, Cell Signaling Technology) was used as the secondary antibody.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCell viability assay\u003c/h2\u003e\u003cp\u003eExperiment was performed with FITC Annexin V Apoptosis Detection Kit I (BD Biosciences). Cells were washed, resuspended in 100 mL 1X Binding Buffer, then treated with 5 mL of FITC Annexin V and 5mL propidium iodide and incubated for 15 min at room temperature in the dark while supplemented with 400 mL of 1X Binding Buffer before being analyzed by flow cytometry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\u003cp\u003eExperiments were performed with the CellTrace Violet Cell Proliferation Kit (ThermoFisher). Cells were washed, resuspended with PBS, and incubated in 5\u0026micro;M CellTrace Violet for 20 minutes at 37\u0026deg;C in the dark. To quench any unbound dye, 5-fold volume of complete culture medium was added to the cells and incubated for 5 minutes. After centrifugation, cells were resuspended in fresh pre-warmed complete culture medium and cultured for 20 minutes at room temperature. The proliferation index was calculated using FlowJo.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRNA-seq and analysis\u003c/h2\u003e\u003cp\u003eTotal cellular RNA was extracted with Buffer RLT (QIAGEN), and RNA samples were then processed by Lexogen (Greenland, New Hampshire, USA) for mRNA-sequencing. RNAseq data were processed using the RNA-seek workflow v1.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5223025\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5223025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e In brief, reads were trimmed using cutadapt v1.18 and aligned to the Mmul_10 reference genome and Gencode release 108 using STAR v2.7.6a in 2-pass basic mode. Expression levels were quantified with RSEM v1.3.3. Differential analysis was completed with idep v0.96 and DESeq2 with the following pre-filtering parameters: minimum CPM of 0.5 in five libraries. Differentially expressed genes (DEGs) were defined as RM\u0026thinsp;=\u0026thinsp;at least two-fold change and adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, PTM\u0026thinsp;=\u0026thinsp;two-fold change and adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1. For viral load analyses, idep v1.13 code was adapted to handle continuous variables. RNAseq and ATACseq pipelines were performed utilizing the computational resources of the NIH HPC Biowulf cluster. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hpc.nih.gov\u003c/span\u003e\u003cspan address=\"http://hpc.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMetascape analysis\u003c/h2\u003e\u003cp\u003eMetascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html#/main/step1\u003c/span\u003e\u003cspan address=\"https://metascape.org/gp/index.html#/main/step1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for gene ontology analysis of differential gene sets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eATAC-seq\u003c/h2\u003e\u003cp\u003eATAC-seq was performed with 50,000 from WT or Paxbp1-cKO in vitro polarized Th2 or Th1 cells. Dead cells were removed using the Dead Cell Removal Kit (Miltenyi). The ATAC-seq protocol was performed as previously published\u003csup\u003e66\u003c/sup\u003e. Briefly, cells were lysed in ice-cold lysis buffer (10mM TrisCl pH 7.4, 10mM NaCl, 3mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.1% Igepal). Transposase reactions were performed using Tn5 transposase (Illumina) and were incubated for 30 min at 37\u0026deg;C. DNA was purified with the MinElut Kit (QIAGEN). Libraries were amplified using Nextara primers with NEBNext High-Fidelity 2X PCR Master Mix (New England Biolegends), and reactions were purified with the PCR Purification Kit (QIAGEN). Samples were sequenced in the NIAMS Genomics Technology Core.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eATAC-seq analysis\u003c/h2\u003e\u003cp\u003eAll samples had over 80\u0026nbsp;million reads. Samples were trimmed for adapters using Cutadapt v 1.18 before alignment. The trimmed reads were aligned to the Mmul_10 reference using Bowtie2 v2.3.4.1 with flag -k 10. The peaks were called using Genrich v0.6 with the following flags: -j -y -r -v -d 150m 5 -e chrM,chrY. PCA and differential peaks were determined by DiffBind v 2.10.0. PCR-duplicate reads were identified by picard v2.18.26 for filtering prior to DiffBind analysis for both PCA and differential peak analyses. Differential peaks were identified using the EdgeR algorithm and were considered significant with an FDR less than 0.05. Differential peaks were annotated using Uropa v4.0.2 f. Since multiple peaks often were assigned to a single gene, genes were associated with the most significant peak. Peak data were visualized using IGV 2.14.1. and differential peaks were rechecked manually to resolve potential conflicts with automatic assignments.\u003c/p\u003e\u003cp\u003eTo generate Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed ATAC-seq samples were processed using the ENCODE ATAC-sequence pipeline v2.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.3564813\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.3564813\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e)\u003c/span\u003e with standard parameters on the NIH HPC Biowulf cluster (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hpc.nih.gov\u003c/span\u003e\u003cspan address=\"https://hpc.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, the ATAC-seq pipeline initially trims the raw sequences for adapters and poor-quality sequencing reads. The high-quality sequencing reads were then aligned to the mm10 reference genome. Alignments are then purged based on confidence and blacklisted regions\u003csup\u003e67\u003c/sup\u003e. Duplicate reads which mostly arise from the PCR amplification process are also purged to identify peaks with higher confidence. Peaks are then shifted by +\u0026thinsp;4bp and \u0026minus;\u0026thinsp;5bp for the positive and negative strands respectively as recommended by the standard ATAC-seq analysis protocol\u003csup\u003e68\u003c/sup\u003e. The MACS2 program\u003csup\u003e69\u003c/sup\u003e was used to identify peaks and differential peaks analysis was performed using the Diffbind R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf\u003c/span\u003e\u003cspan address=\"http://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo compare WT and Paxbp1-cKO samples to innate cell populations ATAC-seq samples were processed as followed. Adaptor sequences were trimmed using \u003cem\u003eSeqPurge\u003c/em\u003e (v2019_11) and trimmed reads were mapped to mm10 mouse genome assembly using \u003cem\u003eBowtie2\u003c/em\u003e (v2.2.9) with settings \u003cem\u003e-- very-sensitive -X 2000\u003c/em\u003e. PCR duplicates were removed using \u003cem\u003ePicard\u003c/em\u003e (v2.21.8) \u003cem\u003eMarkDuplicates REMOVE_DUPLICATES\u0026thinsp;=\u0026thinsp;true VALIDATION_STRINGENCY\u0026thinsp;=\u0026thinsp;LENIENT\u003c/em\u003e. Reads with MAPQ scores below 30 were purged using \u003cem\u003esamtools\u003c/em\u003e (v1.9) \u003cem\u003eview\u003c/em\u003e with settings \u003cem\u003e-b -q 30 -f 2 -F 1804\u003c/em\u003e. Peak calling and sample normalization were carried out as described\u003csup\u003e70\u003c/sup\u003e. Briefly, peaks were called for each sample using \u003cem\u003eMACS2\u003c/em\u003e (v2.2.7.1) with settings \u003cem\u003e--shift \u0026minus;\u0026thinsp;75 --extsize 150 --nomodel --call-summits --nolambda --keep-dup all -p 0.01 --min-length 100\u003c/em\u003e. For each sample, a 501 bp fixed-width peak set was generated by extending the MACS2 summits by 250 bp in both directions. Peaks were ranked by significance (MACS2 peak score) and overlapping peaks with lower peak scores were removed iteratively to create non- overlapping sample peak sets. To facilitate comparison of peaks across samples, the MACS2 peak scores (-log10(p-value)) for each sample were converted to a score per million (SPM) by dividing each peak score by the sum of all the peak scores in a sample divided by 1\u0026nbsp;million. Sample peak sets were merged, less significant overlapping peaks removed, and remaining peaks were filtered for those that were observed in at least two samples with an SPM value 5. To generate peak-by-sample count matrices, ATAC fragment counts within each peak were normalized by the number of inserts intersecting nucleosome-depleted promoter regions (-300 bp to +\u0026thinsp;100 bp relative to transcriptional start-sites).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMass Spectrometry\u003c/h2\u003e\u003cp\u003eTo prepare samples for mass spectrometry the Pierce MS-compatible magnetic IP kits were used. Briefly 10x10\u003csup\u003e6\u003c/sup\u003e cells were lysed in the presence of protease and phosphatase inhibitor. The lysates were incubated with 5ug of either aIgG or aPaxbp1 antibody overnight at 4C. Lysates were then incubated with protein A/G beads at room temperature for 1 hour. Washed beads were stored at -80C for sequencing. LC/MS was performed by Poochon (Frederick, MD). Samples were processed for trypsin/LysC digestion, concentrated, desalted and reconstituted in 0.1% formic acid. Peptides were analyzed against human protein sequences database Proteome Discoverer 2.4 software (Thermo, SanJose, CA) based on the SEQUEST algorithm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSTRING analysis\u003c/h2\u003e\u003cp\u003eProteins identified in mass spec were input into the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using default parameters to determine their relationship to each other.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eGene set enrichment analysis (GSEA)\u003c/h2\u003e\u003cp\u003ePre-ranked lists of genes determined by DESeq2 were used in the GSEA function in the clusterProfiler program with default parameters to perform gene set enrichment analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eMotif enrichment analysis\u003c/h2\u003e\u003cp\u003eMACS3 software was used to identify peaks in ATAC-seq data. Diffbind R Package was used to identify differentially accessible open chromatin regions (DORCs) between WT and Paxbp1-cKO conditions. We downloaded identified Bcl11b ChIP-seq\u003csup\u003e36\u003c/sup\u003e (GEO accession: GSE108633) and used bedtools intersect to find DORCs that overlap with Bcl11b peaks. Motif analysis was conducted using Homer findMotifs.pl on the overlapping DORCs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eChIPseq analysis\u003c/h2\u003e\u003cp\u003ePublicly available Bcl11b ChIPseq datasets performed in Th2\u003csup\u003e36\u003c/sup\u003e (GSE108633) or CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e33\u003c/sup\u003e (GSE186907) were downloaded as raw sequence read files. Files were trimmed and mapped to the mm10 reference genome browser with Trim Galore and Bowtie2 respectively. PCR duplicates were removed using Picard and filtered peaks were called with MACS3. Bigwig files were generated using the tool bedGraphToBigWig. Processed files were visualized using UCSC genome browser view\u003csup\u003e71\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eNippostrongylus brasiliensis infection\u003c/h2\u003e\u003cp\u003eAge-matched female WT and Paxbp1-cKO mice were given subcutaneous injection of 400 infective third-stage \u003cem\u003eN. brasiliensis\u003c/em\u003e larvae as described previously\u003csup\u003e20\u003c/sup\u003e. Lung cells were harvested on day 10 after infection for single-cell multiomics.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eSingle-cell multiomics\u003c/h2\u003e\u003cp\u003eCD3ε\u0026thinsp;+\u0026thinsp;TCRβ\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;T cells (\u0026gt;\u0026thinsp;200,000 cells each, cell viability\u0026thinsp;\u0026gt;\u0026thinsp;98%) were freshly sorted out from lungs of WT and Paxbp1-cKO mice infected with \u003cem\u003eN. brasiliensis\u003c/em\u003e, followed by isolated nuclei using 10x Genomics Nuclei Isolation for Single Cell Multiome protocol (CG000365). The single-cell libraries were prepared using Chromium Next GEM Single Cell Multiome ATAC\u0026thinsp;+\u0026thinsp;Gene Expression kit according to manufacturer\u0026rsquo;s protocol (10X Genomics, CG000338). The generated libraries were sequenced using NovaSeq6000.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eMultiomics analysis\u003c/h2\u003e\u003cp\u003eThe 10x Genomics Cell Ranger ARC (v2.0.0) pipeline was used to process the multiome data. Raw sequencing data were first converted to fastq format using \u0026ldquo;cellranger-arc mkfastq\u0026rdquo;. The raw files of RNA-seq and ATAC-seq libraries from the same sample were aligned to the UCSC mouse genome (mm10) and quantified using \u0026ldquo;cellranger-arc count\u0026rdquo;.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eSCENIC\u0026thinsp;+\u0026thinsp;analysis\u003c/h2\u003e\u003cp\u003eThe scRNA-seq portion of the multiome data was analyzed using Scanpy version 1.10.2 and scATAC-seq data using pycisTopic version 2.0a0 following the workflow provided at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scenicplus.readthedocs.io/en/latest/tutorials.html\u003c/span\u003e\u003cspan address=\"https://scenicplus.readthedocs.io/en/latest/tutorials.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. A custom cisTarget database was generated off of the conserved peaks from pycisTopic results. Finally, the scRNA-seq and scATAC-seq results were integrated using SCENIC\u0026thinsp;+\u0026thinsp;version 1.0a1 SCENIC\u0026thinsp;+\u0026thinsp;results were filtered and visualized using Cytoscape version 3.10.3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eUCSC genome browser\u003c/h2\u003e\u003cp\u003eATAC-seq and ChIP-seq data are displayed using the University of California Santa Cruz (UCSC) genome browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genome.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://genome.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData were analyzed with GraphPad Prism software (version 9). Comparisons of two groups were calculated with unpaired t test. Differences with \u003cem\u003ep\u003c/em\u003e-values \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 0.05 were considered significant and marked with asterisk(s) in the figures.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eSequencing data is publicly available on the NCBI GEO website under the accession number XXX.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Chisolm, D. A. \u0026amp; Weinmann, A. S. Connections Between Metabolism and Epigenetics in Programming Cellular Differentiation. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 221\u0026ndash;246 (2018). https://doi.org:10.1146/annurev-immunol-042617-053127\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Nakayama, T. \u003cem\u003eet al.\u003c/em\u003e Th2 Cells in Health and Disease. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 53\u0026ndash;84 (2017). https://doi.org:10.1146/annurev-immunol-051116-052350\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Walker, J. A. \u0026amp; McKenzie, A. N. J. T(H)2 cell development and function. \u003cem\u003eNat Rev Immunol\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 121\u0026ndash;133 (2018). https://doi.org:10.1038/nri.2017.118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Ansel, K. M., Djuretic, I., Tanasa, B. \u0026amp; Rao, A. Regulation of Th2 differentiation and Il4 locus accessibility. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 607\u0026ndash;656 (2006). https://doi.org:10.1146/annurev.immunol.23.021704.115821\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Nagashima, H. \u003cem\u003eet al.\u003c/em\u003e Remodeling of Il4-Il13-Il5 locus underlies selective gene expression. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 2220\u0026ndash;2233 (2024). https://doi.org:10.1038/s41590-024-02007-4\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e De Obaldia, M. E. \u0026amp; Bhandoola, A. Transcriptional regulation of innate and adaptive lymphocyte lineages. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 607\u0026ndash;642 (2015). https://doi.org:10.1146/annurev-immunol-032414-112032\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Fang, D., Healy, A. \u0026amp; Zhu, J. Differential regulation of lineage-determining transcription factor expression in innate lymphoid cell and adaptive T helper cell subsets. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1081153 (2022). https://doi.org:10.3389/fimmu.2022.1081153\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e McCullen, M. \u0026amp; Oltz, E. The multifaceted roles of TCF1 in innate and adaptive lymphocytes. \u003cem\u003eAdv Immunol\u003c/em\u003e \u003cb\u003e164\u003c/b\u003e, 39\u0026ndash;71 (2024). https://doi.org:10.1016/bs.ai.2024.10.001\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Spinner, C. A. \u0026amp; Lazarevic, V. Transcriptional regulation of adaptive and innate lymphoid lineage specification. \u003cem\u003eImmunol Rev\u003c/em\u003e \u003cb\u003e300\u003c/b\u003e, 65\u0026ndash;81 (2021). https://doi.org:10.1111/imr.12935\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Rothenberg, E. V. Logic and lineage impacts on functional transcription factor deployment for T-cell fate commitment. \u003cem\u003eBiophys J\u003c/em\u003e \u003cb\u003e120\u003c/b\u003e, 4162\u0026ndash;4181 (2021). https://doi.org:10.1016/j.bpj.2021.04.002\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Rothenberg, E. V. Single-cell insights into the hematopoietic generation of T-lymphocyte precursors in mouse and human. \u003cem\u003eExp Hematol\u003c/em\u003e \u003cb\u003e95\u003c/b\u003e, 1\u0026ndash;12 (2021). https://doi.org:10.1016/j.exphem.2020.12.005\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Efremova, M., Vento-Tormo, R., Park, J. E., Teichmann, S. A. \u0026amp; James, K. R. Immunology in the Era of Single-Cell Technologies. \u003cem\u003eAnnu Rev Immunol\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 727\u0026ndash;757 (2020). https://doi.org:10.1146/annurev-immunol-090419-020340\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Diao, Y. \u003cem\u003eet al.\u003c/em\u003e Pax3/7BP is a Pax7- and Pax3-binding protein that regulates the proliferation of muscle precursor cells by an epigenetic mechanism. \u003cem\u003eCell Stem Cell\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 231\u0026ndash;241 (2012). https://doi.org:10.1016/j.stem.2012.05.022\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Zhou, S. \u003cem\u003eet al.\u003c/em\u003e Paxbp1 controls a key checkpoint for cell growth and survival during early activation of quiescent muscle satellite cells. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e (2021). https://doi.org:10.1073/pnas.2021093118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Huang, C. \u003cem\u003eet al.\u003c/em\u003e Paxbp1 Is Indispensable for the Maintenance of Epidermal Homeostasis. \u003cem\u003eJ Invest Dermatol\u003c/em\u003e (2024). https://doi.org:10.1016/j.jid.2024.08.012\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Li, W. \u003cem\u003eet al.\u003c/em\u003e Paxbp1 is indispensable for the survival of CD4 and CD8 double-positive thymocytes. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1183367 (2023). https://doi.org:10.3389/fimmu.2023.1183367\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Li, W. \u003cem\u003eet al.\u003c/em\u003e Paxbp1 is indispensable for the maintenance of peripheral CD4 T cell homeostasis. \u003cem\u003eImmunology\u003c/em\u003e \u003cb\u003e172\u003c/b\u003e, 641\u0026ndash;652 (2024). https://doi.org:10.1111/imm.13802\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Ling, K. H. \u003cem\u003eet al.\u003c/em\u003e Functional transcriptome analysis of the postnatal brain of the Ts1Cje mouse model for Down syndrome reveals global disruption of interferon-related molecular networks. \u003cem\u003eBMC Genomics\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 624 (2014). https://doi.org:10.1186/1471-2164-15-624\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Pahl, M. C. \u003cem\u003eet al.\u003c/em\u003e Implicating effector genes at COVID-19 GWAS loci using promoter-focused Capture-C in disease-relevant immune cell types. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 125 (2022). https://doi.org:10.1186/s13059-022-02691-1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Nagashima, H. \u003cem\u003eet al.\u003c/em\u003e Neuropeptide CGRP Limits Group 2 Innate Lymphoid Cell Responses and Constrains Type 2 Inflammation. \u003cem\u003eImmunity\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, 682\u0026ndash;695 e686 (2019). https://doi.org:10.1016/j.immuni.2019.06.009\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Zhou, Y. \u003cem\u003eet al.\u003c/em\u003e Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. \u003cem\u003eNat Commun\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1523 (2019). https://doi.org:10.1038/s41467-019-09234-6\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Beaulieu, A. M., Zawislak, C. L., Nakayama, T. \u0026amp; Sun, J. C. The transcription factor Zbtb32 controls the proliferative burst of virus-specific natural killer cells responding to infection. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 546\u0026ndash;553 (2014). https://doi.org:10.1038/ni.2876\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Shih, H. Y. \u003cem\u003eet al.\u003c/em\u003e Developmental Acquisition of Regulomes Underlies Innate Lymphoid Cell Functionality. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e165\u003c/b\u003e, 1120\u0026ndash;1133 (2016). https://doi.org:10.1016/j.cell.2016.04.029\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Vorlander, M. K. \u003cem\u003eet al.\u003c/em\u003e Mechanism for the initiation of spliceosome disassembly. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e632\u003c/b\u003e, 443\u0026ndash;450 (2024). https://doi.org:10.1038/s41586-024-07741-1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Program, C. Z. I. C. S. \u003cem\u003eet al.\u003c/em\u003e CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D886-D900 (2025). https://doi.org:10.1093/nar/gkae1142\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Szklarczyk, D. \u003cem\u003eet al.\u003c/em\u003e The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e, D638-D646 (2023). https://doi.org:10.1093/nar/gkac1000\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Di Giorgio, E. \u003cem\u003eet al.\u003c/em\u003e MEF2D sustains activation of effector Foxp3\u0026thinsp;+\u0026thinsp;Tregs during transplant survival and anticancer immunity. \u003cem\u003eJ Clin Invest\u003c/em\u003e \u003cb\u003e130\u003c/b\u003e, 6242\u0026ndash;6260 (2020). https://doi.org:10.1172/JCI135486\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Szeto, A. C. H. \u003cem\u003eet al.\u003c/em\u003e Mef2d potentiates type-2 immune responses and allergic lung inflammation. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e384\u003c/b\u003e, eadl0370 (2024). https://doi.org:10.1126/science.adl0370\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Li, P. \u003cem\u003eet al.\u003c/em\u003e Reprogramming of T cells to natural killer-like cells upon Bcl11b deletion. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e, 85\u0026ndash;89 (2010). https://doi.org:10.1126/science.1188063\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Ikawa, T. \u003cem\u003eet al.\u003c/em\u003e An essential developmental checkpoint for production of the T cell lineage. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e, 93\u0026ndash;96 (2010). https://doi.org:10.1126/science.1188995\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Li, L., Leid, M. \u0026amp; Rothenberg, E. V. An early T cell lineage commitment checkpoint dependent on the transcription factor Bcl11b. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e329\u003c/b\u003e, 89\u0026ndash;93 (2010). https://doi.org:10.1126/science.1188989\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Drashansky, T. T. \u003cem\u003eet al.\u003c/em\u003e Bcl11b prevents fatal autoimmunity by promoting T(reg) cell program and constraining innate lineages in T(reg) cells. \u003cem\u003eSci Adv\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, eaaw0480 (2019). https://doi.org:10.1126/sciadv.aaw0480\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Helm, E. Y. \u003cem\u003eet al.\u003c/em\u003e Bcl11b sustains multipotency and restricts effector programs of intestinal-resident memory CD8(+) T cells. \u003cem\u003eSci Immunol\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, eabn0484 (2023). https://doi.org:10.1126/sciimmunol.abn0484\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Forkel, H. \u003cem\u003eet al.\u003c/em\u003e BCL11B depletion induces the development of highly cytotoxic innate T cells out of IL-15 stimulated peripheral blood alphabeta CD8\u0026thinsp;+\u0026thinsp;T cells. \u003cem\u003eOncoimmunology\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2148850 (2022). https://doi.org:10.1080/2162402X.2022.2148850\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Sottile, R. \u003cem\u003eet al.\u003c/em\u003e Human cytomegalovirus expands a CD8(+) T cell population with loss of BCL11B expression and gain of NK cell identity. \u003cem\u003eSci Immunol\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, eabe6968 (2021). https://doi.org:10.1126/sciimmunol.abe6968\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Fang, D. \u003cem\u003eet al.\u003c/em\u003e Bcl11b, a novel GATA3-interacting protein, suppresses Th1 while limiting Th2 cell differentiation. \u003cem\u003eJ Exp Med\u003c/em\u003e \u003cb\u003e215\u003c/b\u003e, 1449\u0026ndash;1462 (2018). https://doi.org:10.1084/jem.20171127\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Lorentsen, K. J. \u003cem\u003eet al.\u003c/em\u003e Bcl11b is essential for licensing Th2 differentiation during helminth infection and allergic asthma. \u003cem\u003eNat Commun\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1679 (2018). https://doi.org:10.1038/s41467-018-04111-0\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Naoe, Y. \u003cem\u003eet al.\u003c/em\u003e Repression of interleukin-4 in T helper type 1 cells by Runx/Cbf beta binding to the Il4 silencer. \u003cem\u003eJ Exp Med\u003c/em\u003e \u003cb\u003e204\u003c/b\u003e, 1749\u0026ndash;1755 (2007). https://doi.org:10.1084/jem.20062456\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Shin, B., Zhou, W., Wang, J., Gao, F. \u0026amp; Rothenberg, E. V. Runx factors launch T cell and innate lymphoid programs via direct and gene network-based mechanisms. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 1458\u0026ndash;1472 (2023). https://doi.org:10.1038/s41590-023-01585-z\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Subramanian, A. \u003cem\u003eet al.\u003c/em\u003e Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e, 15545\u0026ndash;15550 (2005). https://doi.org:10.1073/pnas.0506580102\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Mootha, V. K. \u003cem\u003eet al.\u003c/em\u003e PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. \u003cem\u003eNat Genet\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 267\u0026ndash;273 (2003). https://doi.org:10.1038/ng1180\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Mogilenko, D. A. \u003cem\u003eet al.\u003c/em\u003e Comprehensive Profiling of an Aging Immune System Reveals Clonal GZMK(+) CD8(+) T Cells as Conserved Hallmark of Inflammaging. \u003cem\u003eImmunity\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 99\u0026ndash;115 e112 (2021). https://doi.org:10.1016/j.immuni.2020.11.005\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Bravo Gonzalez-Blas, C. \u003cem\u003eet al.\u003c/em\u003e SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. \u003cem\u003eNat Methods\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 1355\u0026ndash;1367 (2023). https://doi.org:10.1038/s41592-023-01938-4\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Ribeiro de Almeida, C. \u003cem\u003eet al.\u003c/em\u003e Critical role for the transcription regulator CCCTC-binding factor in the control of Th2 cytokine expression. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cb\u003e182\u003c/b\u003e, 999\u0026ndash;1010 (2009). https://doi.org:10.4049/jimmunol.182.2.999\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Morrison, T. A. \u003cem\u003eet al.\u003c/em\u003e Selective requirement of glycosphingolipid synthesis for natural killer and cytotoxic T cells. \u003cem\u003eCell\u003c/em\u003e (2025). https://doi.org:10.1016/j.cell.2025.04.007\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Ngiow, S. F. \u003cem\u003eet al.\u003c/em\u003e LAG-3 sustains TOX expression and regulates the CD94/NKG2-Qa-1b axis to govern exhausted CD8 T cell NK receptor expression and cytotoxicity. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e187\u003c/b\u003e, 4336\u0026ndash;4354 e4319 (2024). https://doi.org:10.1016/j.cell.2024.07.018\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Roychoudhuri, R. \u003cem\u003eet al.\u003c/em\u003e BACH2 represses effector programs to stabilize T(reg)-mediated immune homeostasis. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e498\u003c/b\u003e, 506\u0026ndash;510 (2013). https://doi.org:10.1038/nature12199\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Afzali, B. \u003cem\u003eet al.\u003c/em\u003e BACH2 immunodeficiency illustrates an association between super-enhancers and haploinsufficiency. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 813\u0026ndash;823 (2017). https://doi.org:10.1038/ni.3753\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Kim, E. H. \u003cem\u003eet al.\u003c/em\u003e Bach2 regulates homeostasis of Foxp3\u0026thinsp;+\u0026thinsp;regulatory T cells and protects against fatal lung disease in mice. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cb\u003e192\u003c/b\u003e, 985\u0026ndash;995 (2014). https://doi.org:10.4049/jimmunol.1302378\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Kuwahara, M. \u003cem\u003eet al.\u003c/em\u003e Bach2-Batf interactions control Th2-type immune response by regulating the IL-4 amplification loop. \u003cem\u003eNat Commun\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 12596 (2016). https://doi.org:10.1038/ncomms12596\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Roychoudhuri, R. \u003cem\u003eet al.\u003c/em\u003e BACH2 regulates CD8(+) T cell differentiation by controlling access of AP-1 factors to enhancers. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 851\u0026ndash;860 (2016). https://doi.org:10.1038/ni.3441\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Thakore, P. I. \u003cem\u003eet al.\u003c/em\u003e BACH2 regulates diversification of regulatory and proinflammatory chromatin states in T(H)17 cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 1395\u0026ndash;1410 (2024). https://doi.org:10.1038/s41590-024-01901-1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Yao, C. \u003cem\u003eet al.\u003c/em\u003e BACH2 enforces the transcriptional and epigenetic programs of stem-like CD8(+) T cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 370\u0026ndash;380 (2021). https://doi.org:10.1038/s41590-021-00868-7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Choi, Y. S. \u003cem\u003eet al.\u003c/em\u003e LEF-1 and TCF-1 orchestrate T(FH) differentiation by regulating differentiation circuits upstream of the transcriptional repressor Bcl6. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 980\u0026ndash;990 (2015). https://doi.org:10.1038/ni.3226\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Steinke, F. C. \u003cem\u003eet al.\u003c/em\u003e TCF-1 and LEF-1 act upstream of Th-POK to promote the CD4(+) T cell fate and interact with Runx3 to silence Cd4 in CD8(+) T cells. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 646\u0026ndash;656 (2014). https://doi.org:10.1038/ni.2897\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Carr, T. \u003cem\u003eet al.\u003c/em\u003e The transcription factor lymphoid enhancer factor 1 controls invariant natural killer T cell expansion and Th2-type effector differentiation. \u003cem\u003eJ Exp Med\u003c/em\u003e \u003cb\u003e212\u003c/b\u003e, 793\u0026ndash;807 (2015). https://doi.org:10.1084/jem.20141849\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Yang, B. H. \u003cem\u003eet al.\u003c/em\u003e TCF1 and LEF1 Control Treg Competitive Survival and Tfr Development to Prevent Autoimmune Diseases. \u003cem\u003eCell Rep\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 3629\u0026ndash;3645 e3626 (2019). https://doi.org:10.1016/j.celrep.2019.05.061\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Avram, D. \u0026amp; Califano, D. The multifaceted roles of Bcl11b in thymic and peripheral T cells: impact on immune diseases. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cb\u003e193\u003c/b\u003e, 2059\u0026ndash;2065 (2014). https://doi.org:10.4049/jimmunol.1400930\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Sidwell, T. \u0026amp; Rothenberg, E. V. Epigenetic Dynamics in the Function of T-Lineage Regulatory Factor Bcl11b. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 669498 (2021). https://doi.org:10.3389/fimmu.2021.669498\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Okuyama, K. \u003cem\u003eet al.\u003c/em\u003e A mutant BCL11B-N440K protein interferes with BCL11A function during T lymphocyte and neuronal development. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 2284\u0026ndash;2296 (2024). https://doi.org:10.1038/s41590-024-01997-5\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Pease, N. A., Denecke, K. M., Chen, L., Gerges, P. H. \u0026amp; Kueh, H. Y. A timed epigenetic switch balances T and ILC lineage proportions in the thymus. \u003cem\u003eDevelopment\u003c/em\u003e \u003cb\u003e151\u003c/b\u003e (2024). https://doi.org:10.1242/dev.203016\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Gamble, N. \u003cem\u003eet al.\u003c/em\u003e PU.1 and BCL11B sequentially cooperate with RUNX1 to anchor mSWI/SNF to poise the T cell effector landscape. \u003cem\u003eNat Immunol\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 860\u0026ndash;872 (2024). https://doi.org:10.1038/s41590-024-01807-y\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Malle, L. \u003cem\u003eet al.\u003c/em\u003e Autoimmunity in Down's syndrome via cytokines, CD4 T cells and CD11c(+) B cells. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e615\u003c/b\u003e, 305\u0026ndash;314 (2023). https://doi.org:10.1038/s41586-023-05736-y\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Gialesaki, S. \u003cem\u003eet al.\u003c/em\u003e RUNX1 isoform disequilibrium promotes the development of trisomy 21-associated myeloid leukemia. \u003cem\u003eBlood\u003c/em\u003e \u003cb\u003e141\u003c/b\u003e, 1105\u0026ndash;1118 (2023). https://doi.org:10.1182/blood.2022017619\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Spolski, R. \u003cem\u003eet al.\u003c/em\u003e Distinct use of super-enhancer elements controls cell type-specific CD25 transcription and function. \u003cem\u003eSci Immunol\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, eadi8217 (2023). https://doi.org:10.1126/sciimmunol.adi8217\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Buenrostro, J. D., Wu, B., Chang, H. Y. \u0026amp; Greenleaf, W. J. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. \u003cem\u003eCurr Protoc Mol Biol\u003c/em\u003e \u003cb\u003e109\u003c/b\u003e, 21 29 21\u0026ndash;21 29 29 (2015). https://doi.org:10.1002/0471142727.mb2129s109\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Amemiya, H. M., Kundaje, A. \u0026amp; Boyle, A. P. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. \u003cem\u003eSci Rep\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 9354 (2019). https://doi.org:10.1038/s41598-019-45839-z\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Yan, F., Powell, D. R., Curtis, D. J. \u0026amp; Wong, N. C. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 22 (2020). https://doi.org:10.1186/s13059-020-1929-3\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Zhang, Y. \u003cem\u003eet al.\u003c/em\u003e Model-based analysis of ChIP-Seq (MACS). \u003cem\u003eGenome Biol\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, R137 (2008). https://doi.org:10.1186/gb-2008-9-9-r137\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Corces, M. R. \u003cem\u003eet al.\u003c/em\u003e The chromatin accessibility landscape of primary human cancers. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e362\u003c/b\u003e (2018). https://doi.org:10.1126/science.aav1898\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Perez, G. \u003cem\u003eet al.\u003c/em\u003e The UCSC Genome Browser database: 2025 update. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, D1243-D1249 (2025). https://doi.org:10.1093/nar/gkae974\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7199764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7199764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T helper cell differentiation is important for efficient host defense and has largely been defined by the activities of a limited number of lineage-specifying transcription factors. However, these factors alone cannot explain the intricate series of events that concurrently induce a specialization program while repressing competing gene programs. In this study, we define a new role for Paxbp1 as an important regulatory factor in T helper cell specification events. We show that Paxbp1 expression is selectively induced in Th2 cells and is required for repression of innate and cytotoxic gene programming potential during Th2 differentiation. Paxbp1-deficiency enhances chromatin accessibility at genomic regions associated with innate lymphocyte programs in both in vitro and in vivo models of Th2 differentiation, indicating that Paxbp1 restrains activities that promote alternative cytotoxic programming potential. Mechanistically, we found Paxbp1 interacts with Bcl11b and Runx1, transcription factors established as negative and positive regulators of innate cytotoxic programming potential, respectively. Our data show that Paxbp1 ensures proper regulation of Bcl11b and Runx1-associated gene programs, and it provides new molecular insights into the complexity of the transcriptional regulatory network involved in CD4\u003csup\u003e+\u003c/sup\u003e T cell specialization events.\u003c/p\u003e","manuscriptTitle":"Paxbp1 restrains cytotoxic and innate cell programs in CD4+ T cells to promote Th2 differentiation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 06:17:34","doi":"10.21203/rs.3.rs-7199764/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"87d689d0-4c9c-4952-b56b-838e8de5b15f","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54622278,"name":"Biological sciences/Immunology/Gene regulation in immune cells/Epigenetics in immune cells"},{"id":54622279,"name":"Biological sciences/Immunology/Lymphocytes/T cells/CD4-positive T cells"}],"tags":[],"updatedAt":"2025-11-05T01:50:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 06:17:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7199764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7199764","identity":"rs-7199764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}