Histone deacetylase 3 represses cholesterol efflux during CD4+ T-cell activation

  1. Drew Wilfahrt
  2. Rachael L Philips
  3. Jyoti Lama
  4. Monika Kizerwetter
  5. Michael Jeremy Shapiro
  6. Shaylene A McCue
  7. Madeleine M Kennedy
  8. Matthew J Rajcula
  9. Hu Zeng
  10. Virginia Smith Shapiro  Is a corresponding author
  1. Department of Immunology, Mayo Clinic, United States
  2. Division of Rheumatology, Department of Medicine, Mayo Clinic, United States

Abstract

After antigenic activation, quiescent naive CD4+ T cells alter their metabolism to proliferate. This metabolic shift increases production of nucleotides, amino acids, fatty acids, and sterols. Here, we show that histone deacetylase 3 (HDAC3) is critical for activation of murine peripheral CD4+ T cells. HDAC3-deficient CD4+ T cells failed to proliferate and blast after in vitro TCR/CD28 stimulation. Upon T-cell activation, genes involved in cholesterol biosynthesis are upregulated while genes that promote cholesterol efflux are repressed. HDAC3-deficient CD4+ T cells had reduced levels of cellular cholesterol both before and after activation. HDAC3-deficient cells upregulate cholesterol synthesis appropriately after activation, but fail to repress cholesterol efflux; notably, they overexpress cholesterol efflux transporters ABCA1 and ABCG1. Repression of these genes is the primary function for HDAC3 in peripheral CD4+ T cells, as addition of exogenous cholesterol restored proliferative capacity. Collectively, these findings demonstrate HDAC3 is essential during CD4+ T-cell activation to repress cholesterol efflux.

Editor's evaluation

This paper will be of interest to scientists in the field of T cell biology and immunometabolism. The data analysis is rigorous and the experiments performed are appropriate. The findings of the manuscript will expand upon previous findings of a role for histone deacetylase 3 in thymocyte development and CD8+ T cell function to that of CD4+ T cells.

https://doi.org/10.7554/eLife.70978.sa0

Introduction

After activation, CD4+ T cells must pass through a number of metabolic checkpoints in order to proliferate, differentiate, and generate robust immune responses. This metabolic transition shortly after TCR engagement has been defined as quiescence exit, and is characterized by several key cellular events including cell growth, interleukin-2 (IL-2) signaling, increased anabolic metabolism, and reprogramming of mitochondrial metabolism (Chapman et al., 2019; Chapman and Chi, 2018; Ron-Harel et al., 2016; Tan et al., 2017; Wang et al., 2011; Yang et al., 2013). Each of these checkpoints prepares CD4+ T cells for proliferation and effector function by generating the cellular building blocks required for activated T cells such as fatty acids and sterols, nucleotides, amino acids, and other metabolites (Bensinger et al., 2008; Geiger et al., 2016; Johnson et al., 2018; Kidani et al., 2013; Ma et al., 2017; Ricciardi et al., 2018; Wang et al., 2011). Given the importance of these molecules during activation, this metabolic reprogramming is under the control of keenly regulated molecular circuits to ensure resources are used efficiently. To date, our understanding of the transcriptional control of CD4+ T cells exiting quiescence is incomplete.

Mechanistic target of rapamycin (mTOR), particularly mTOR complex 1 (mTORC1), directs many aspects of metabolic reprogramming after T-cell activation (Tan et al., 2017; Yang et al., 2013). One role of mTORC1 is to drive lipid synthesis through the expression of sterol regulatory element-binding proteins (SREBPs) (Kidani et al., 2013). SREBPs are transcription factors that orchestrate lipid synthesis after T-cell activation (DeBose-Boyd and Ye, 2018). In addition to increasing lipid and sterol synthesis, recently activated T cells also halt cholesterol efflux (Bensinger et al., 2008). Activated T cells rapidly decrease expression of cholesterol efflux transporters in order to retain recently generated cholesterol (Michaels et al., 2021). Both of these steps, increased cholesterol synthesis and decreased cholesterol efflux, are required for successful proliferation and blast formation. Disruptions in cholesterol synthesis in CD8+ T cells inhibited blasting and proliferation after TCR engagement (Kidani et al., 2013), while enforced expression of cholesterol efflux transporter ABCG1 inhibited proliferation (Bensinger et al., 2008). Cholesterol metabolism may not be identically regulated in CD4+ and CD8+ T cells, as deletion of Acetyl-CoA Acetyltransferase 1 (ACAT1) enhanced proliferation and effector function in CD8+ T cells but not CD4+ T cells (Yang et al., 2016). Together, these studies point to an important ‘cholesterol checkpoint’ in which T cells require an optimal amount of cholesterol to exit quiescence.

Although previous work highlights the importance of transcription factors in the regulation of cholesterol homeostasis, less is known about the role of chromatin modifiers in the regulation of cholesterol availability in T cells. Histone deacetylase 3 (HDAC3) is a Class I HDAC that deacetylates lysine residues on histones H3 and H4 in order to repress gene expression. Previously, our group has shown that HDAC3 serves as a targeted regulator of key gene expression during T-cell development. During positive selection in the thymus, HDAC3 is required for downregulation of RORγt (Philips et al., 2016). Further, HDAC3 suppression of the purinergic-receptor P2RX7 is critical for survival of double positive thymocytes in the ATP-rich thymic cortex (Philips et al., 2019b). Recently, the role of HDAC3 as an inhibitor of the cytotoxicity program of CD8+ T cells was examined using E8I-Cre, which initiates deletion in CD8 SP thymocytes (Ellmeier et al., 1997; Tay et al., 2020). Collectively, this work supports the idea that HDAC3 is a highly specific transcriptional regulator in lymphocytes.

Little is known of the role that HDAC3 plays in peripheral CD4+ T cells. Here, we report that HDAC3-deficient CD4+ T cells have a loss of differentiated helper T-cell populations in vivo. This loss of differentiated T-cell numbers is due to an inability of HDAC3-deficient CD4+ T cells to blast and proliferate after activation. HDAC3-deficient CD4+ T cells upregulate cholesterol synthesis genes normally after activation, but fail to downregulate cholesterol efflux. This results in reduced cellular cholesterol levels in HDAC3-deficient T cells before and after T-cell activation. HDAC3-deficient cells upregulate mRNA expression of genes encoding the cholesterol efflux transporters ABCA1 and ABCG1. Increased mRNA expression is maintained after TCR ligation. Further, deletion of HDAC3 results in hyperacetylation of promoter sites for both Abca1 and Abcg1, consistent with direct gene regulation by HDAC3 deacetylase activity. Importantly, the addition of exogenous cholesterol restores proliferative capacity of HDAC3-deficient CD4+ T cells, indicating that a decreased cholesterol level is the primary block preventing proliferation and blasting. Thus, HDAC3 is required to maintain cholesterol availability after T-cell activation through the repression of cholesterol efflux.

Results

CD8+ T cells have intrathymic deletion of HDAC3 in dLck-Cre HDAC3 cKO, but CD4+ T cells initiate deletion in recent thymic emigrants

Previous studies have outlined several important roles for HDAC3 during T-cell development in the thymus (Hsu et al., 2015; Philips et al., 2016; 2019; Philips et al., 2019a; Stengel et al., 2015). To interrogate the role of HDAC3 in peripheral T cells, distal-Lck-Cre (dLck-Cre) HDAC3 cKO mice were generated. In this system, Cre recombinase expression is driven by the distal promoter of lymphocyte-specific protein tyrosine kinase (Lck). Previous studies showed this system drives Cre expression after positive selection in the thymus (Zhang et al., 2005). Adult dLck-Cre HDAC3 cKO mice had normal numbers of naive and memory CD4+ T-cell populations in the spleen, but had a significant decrease in CD8+ T-cell populations (Figure 1a). Previous work in which HDAC3 was deleted in the thymus revealed HDAC3 is required for T-cell maturation, leading to a block at the recent thymic emigrant (RTE) stage (Hsu et al., 2015).

Figure 1 with 1 supplement see all
CD8+ T cells have intrathymic deletion of histone deacetylase 3 (HDAC3) in dLck-HDAC3 cKO, but CD4+ T cells initiate deletion at the recent thymic emigrant (RTE) stage.

(a) Profile of primary splenic T-cell populations from wild-type (WT) and dLck-Cre HDAC3 cKO mice including total T cells (TCRβ+), total CD4+ (TCRβ+ CD4+), and total CD8+ (TCRβ+ CD8+), as well as memory (CD44hi CD62Llo) and naive (CD44lo CD62Lhi) from each of the CD4+ and CD8+ populations. Bar graph depicts mean ± standard deviation (SD). Total cell number from three independent experiments (n = 5 mice/group). Statistical significance was determined for the indicated comparisons with Mann–Whitney tests between each WT and cKO population. (b) Expression of HDAC3 in thymocyte populations from WT, dLck-Cre HDAC3 cKO and CD4-Cre HDAC3 cKO mice. Thymic populations are gated as in Figure 1—figure supplement 1, and quantification of normalized HDAC3 MFI (median fluorescent intensity) ± SD from three independent experiments is shown on the right (n = 2–4 mice/group). Statistical significance was determined for the indicated comparisons with a one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test. (c) Profile of HDAC3 deletion in dLck-Cre HDAC3 cKO mice. Splenocytes were gated on key populations including total CD4+ (CD4+), naive CD4+ (CD4+ CD44lo CD62Lhi), and memory CD4+ (CD4+CD44hiCD62Llo). Naive cells were further gated in mature naive T cells (MNTs; CD45RBhiCD55hi) or recent thymic emigrants (RTEs; CD45RBloCD55lo). HDAC3-positive and -negative population frequencies are shown below. Bar chart on right quantifies the mean frequency ± SD of HDAC3+ or HDAC3 events within each population (n = 8 mice/group from three independent experiments).

Since there were differences in peripheral CD8+ T-cell numbers in the dLck-Cre HDAC3 cKO mice, the kinetics of HDAC3 deletion were investigated to explore the possibility of intrathymic HDAC3 deletion. To do this, developing thymocyte populations were examined. Total numbers of double negative (DN), double positive (DP), and CD4 and CD8 single positive (CD4 SP/CD8 SP) in the dLck-Cre HDAC3 cKO thymus were roughly equivalent to wild-type (WT) mice (Figure 1—figure supplement 1). Surprisingly, the CD8 SP population had a loss of HDAC3 protein level compared to WT CD8 SP (Figure 1b). Thus, dLck-Cre HDAC3 cKO initiated deletion as early as the CD8 SP thymocyte stage. HDAC3 expression was unaffected in CD4 SP thymocytes. Given the critical roles for HDAC3 in developing thymocytes, we concluded that dLck-Cre HDAC3 cKO mice are not a suitable model for examination of mature peripheral CD8+ T cells in the absence of HDAC3. Thus, this work focuses on the role of HDAC3 in CD4+ T cells.

Having established that HDAC3 expression is intact in the developing CD4+ SP thymocytes, the HDAC3 protein expression in peripheral CD4+ T-cell populations at homeostasis was assessed. In previously published studies with dLck-Cre systems, deletion is inefficient in the mature CD4+ T-cell populations (Zhang et al., 2005; Zhang et al., 2010). To measure the efficiency of HDAC3 deletion in dLck-Cre HDAC3 cKO mice, naive (CD62LhiCD44lo) and memory phenotype (CD62loCD44hi) CD4+ T cells were examined. RTEs and mature naive T cells (MNTs) were distinguished using CD55 and CD45RB, both markers that are upregulated during peripheral T-cell maturation. HDAC3 deletion began soon after T cells egress from the thymus since ~25% of RTEs were HDAC3 deficient in the dLck-Cre HDAC3 cKO (Figure 1c). Further, MNTs were enriched for HDAC3 cells, with >75% of them being HDAC3 deficient (Figure 1c). Surprisingly, only 25% of the memory CD4+ T cells were HDAC3 deficient (Figure 1c) suggesting that HDAC3-deficient memory CD4+ T cells had a competitive disadvantage to the HDAC3-sufficient memory CD4+ cells in dLck-Cre HDAC3 cKO mice.

HDAC3-deficient CD4+ T cells are capable of differentiation, but produce fewer cells than WT CD4+ T cells

Given the incongruence of HDAC3 deletion between the naive and memory CD4+ T-cell populations in the dLck-Cre HDAC3 cKO, we examined whether HDAC3 could play a role in the formation and expansion of memory CD4+ T cells. To test this, differentiated helper T-cell populations were measured at homeostasis in vivo. There were very few HDAC3-deficient T helper (Th) cells including Th1, Th2, Th17, Treg, and Tfh cells in the spleen in dLck-Cre HDAC3 cKO mice when compared to WT (Figure 2). The frequency of HDAC3-deficient Th17 cells in the mesenteric lymph nodes (mLNs) was also reduced in dLck-Cre HDAC3 cKO mice (Figure 2—figure supplement 1). The frequency of the other Th populations in mLN and Peyer’s patches was not statistically different (Figure 2—figure supplement 1, Figure 2—figure supplement 2). In fact, total numbers of the HDAC3-deficient differentiated splenic populations more closely resembled CD4-Cre HDAC3 cKO mice, which are highly lymphopenic (Hsu et al., 2015). Of note, HDAC3-deficient cells could differentiate in a noncompetitive environment. CD4-Cre HDAC3 cKO mice generated Th2, Th17 and Treg and Tfh cells at about the same frequency as WT cells in the spleen and the mesenteric lymph node although total numbers were highly reduced (Figure 2, Figure 2—figure supplement 1). Since those mice are highly lymphopenic, they exist in a relatively noncompetitive environment when compared to the competitive dLck-Cre HDAC3 cKO mice where HDAC3-sufficient cells are present. With this information, we hypothesized that HDAC3 plays a role in maintaining CD4+ T cell fitness to successfully differentiate in vivo.

Figure 2 with 2 supplements see all
Histone deacetylase 3 (HDAC3)-deficient CD4+ T cells from dLck-Cre HDAC3 cKO mice have reduced differentiated Th-cell populations.

Identification of helper T-cell populations in vivo. Splenocytes were harvested from wild-type (WT) and HDAC3 cKO mice, and labeled for flow cytometry. Cells were first gated on HDAC3+ or HDAC3 events, then gating for Th1 (T-bet+), Th2 (GATA3+), Th17 (RORγt+), Treg (Foxp3+ CD25+), and Tfh (CXCR5+ PD-1+ Bcl-6hi) is shown (left). Bar plots on right represent pooled data for the total cell number ± standard deviation (SD) from three independent experiments (n = 4–5 mice/group in total). Non-Tfh CXCR5 PD-1 cells (dark gray histograms) were used as a negative control for Bcl-6 expression to set the gate on the Bcl-6 histograms. Statistical significance was determined for the indicated comparisons using ordinary one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

To define the importance of HDAC3 in T-cell differentiation, in vitro differentiation assays were performed to differentiate naive CD4+ T cells from WT or dLck-Cre HDAC3 cKO mice to Th1, Th2, Th17, and Treg lineages, and lineage-defining transcription factor and cytokine expression was measured after 4 days. Notably, HDAC3 T cells from dLck-Cre HDAC3 cKO mice differentiated normally and expressed the transcription factors Foxp3, RORγt, GATA3, and T-bet under appropriate polarizing conditions (Figure 3). Likewise, HDAC3 T cells from dLck-Cre HDAC3 cKO mice had a normal frequency of cells expressing IFN-γ, IL-4, and IL-17A under appropriate conditions and coexpression of each lineage-defining transcription factor and cytokine was observed for each Th lineage (Figure 3—figure supplement 1). Thus, HDAC3 was not required for Th differentiation in vitro. To further test whether CD4+ T cells from dLck-Cre HDAC3 cKO mice that differentiated in vivo were functionally impaired, magnetically enriched CD4+ T cells from WT and dLck-Cre HDAC3 cKO spleens were stimulated with PMA/ionomycin for 6 hr, and subsequently examined for expression of IFN-γ and T-bet. Consistent with the in vitro differentiation assays, HDAC3-deficient memory CD4+ T cells from dLck-Cre HDAC3 cKO mice had a similar frequency of IFN-γ+ events to the WT, although the frequency of IFN-γ+ T-bethi cells was reduced among total CD4+ T cells (Figure 3—figure supplement 2). Thus, HDAC3-deficient CD4+ T cells that undergo differentiation in vivo are not functionally impaired.

Figure 3 with 2 supplements see all
Histone deacetylase 3 (HDAC3)-deficient CD4+ T cells are capable of differentiation, but produce fewer cells than wild-type (WT).

(a) In vitro differentiation assays were performed to examine differentiation into the Th1, Th2, Th17, and Treg lineages characterized by transcription factor expression. Splenocytes were harvested and magnetically enriched for naive (CD44) CD4+ T cells by negative selection. Cells in all assays were stimulated with 2 µg/ml plate-bound αCD3 and 0.5 µg/ml αCD28 for 4 days. For Th1 differentiation, 1 µg/ml αIL-4 antibody and 10 ng/ml of IL-12 were added to the media. For Th2 differentiation, 1 µg/ml of each αIFNγ and αIL-12 antibody, as well as 10 ng/ml of IL-4 was added to the media. For Th17 differentiation, media was supplemented with 10 µg/ml of αIFNγ and αIL-4 antibody as well as 10 ng/ml of rIL-23, 5 ng/ml TGF-β1, and 20 ng/ml IL-6. For Treg differentiation, media was supplemented with 10 µg/ml αIFNγ and αIL-4 antibody as well as 2 ng/ml TGF-β1, and 2 ng/ml interleukin-2 (IL-2). Unstimulated control samples did not receive αCD3/αCD28 stimulation, but did receive 10 ng/ml IL-7 to maintain cell survival during culture. Bar plots on the right show % of cells from the total culture that are positive for the transcription factor ± standard deviation (SD), total cell number ± SD, and geometric mean of expression ± SD (n = 2–4 mice/group from two to three independent experiments for each). Two nonlittermate, but age and sex matched, WT B6 controls were used in these experiments. Negative controls (black histograms) represent unstimulated, but stained samples from the same mouse as the stimulated sample shown. Statistical significance was determined for the indicated comparisons using an unpaired t-test.

Interestingly, the total number of cells harvested from dLck-Cre HDAC3 cKO mice in the day 4 in vitro differentiation assay cultures was >4-fold lower than WT for each of the Th1, Th2, and Th17 differentiation assays (Figure 3, right). The output of Foxp3+ Treg cells in the dLck-Cre HDAC3 cKO culture was not statistically different than WT, and intriguingly, HDAC3 T cells from the dLck-Cre HDAC3 cKO mice had increased Foxp3 MFI and a higher percentage of cells that were Foxp3+ among all cells in the culture (Figure 3). Thus, differentiation as measured by transcription factor expression is not impaired in the HDAC3-deficient T cells, but the cellular output is diminished after T-cell activation.

HDAC3-deficient CD4+ T cells have reduced proliferation, diminished mTORC1 signaling after in vitro stimulation

Since HDAC3-deficient cells exhibited a reduced cell number in the differentiation assays, proliferation was examined. To test this, CD4+ T cells were magnetically enriched, labeled with CFSE (carboxyfluorescein succinimidyl ester), stimulated with αCD3/αCD28 for 3 days, and examined by flow cytometry. There was a severe defect in the ability of HDAC3-deficient CD4+ T cells to proliferate upon CD3/CD28 stimulation (Figure 4a). However, HDAC3-deficient CD4+ T cells did not have a global impairment in their ability to respond to CD3/CD28 stimulation, as induction of CD69 expression was only slightly reduced. Greater than 80% of HDAC3-deficient T cells upregulated CD69 expression, and the MFI was similar between WT and HDAC3-deficient CD4+ T cells, indicating that HDAC3-deficient CD4+ T cells can respond to TCR signals (Figure 4b). However, IL-2 expression was reduced by 50 % in HDAC3-deficient T cells compared to WT after αCD3/αCD28 stimulation (Figure 4c). In addition, the percentage of HDAC3 cells that induced CD25 expression 3 days after αCD3/αCD28 stimulation was greatly reduced (Figure 4d). Since IL-2R signaling plays a critical role in T-cell proliferation after TCR activation (Boyman and Sprent, 2012; Ross and Cantrell, 2018), reduced expression of both IL-2 cytokine and IL-2 receptor likely contribute to defective proliferation in the HDAC3 population. IL-2 also sends key survival signals to activated T cells (Boyman and Sprent, 2012; Ross and Cantrell, 2018). Cell death was also measured in the HDAC3 dLck-Cre HDAC3 cKO cells by labeling activated cells with fixable viability dye 24 hr after activation. HDAC3-deficient T cells had a significant decrease in the percentage of viable cells compared to WT (Figure 4e). Since, P2RX7 expression was increased in HDAC3-deficient thymocytes (Philips et al., 2019b), P2RX7 expression was examined in HDAC3-deficient CD4+ T cells after CD3/CD28 stimulation in vitro. HDAC3-deficient CD4+ T cells did not have a statistically significant change of P2RX7 expression in either naive or memory CD4+ T cells (Figure 4f). Altogether, deletion of HDAC3 in peripheral CD4+ T cells results in impaired proliferation and survival after activation, suggesting that HDAC3 protein is necessary for successful expansion of CD4+ T-cell populations after TCR engagement.

Figure 4 with 1 supplement see all
Histone deacetylase 3 (HDAC3)-deficient CD4+ T cells have reduced proliferation and diminished mechanistic target of rapamycin (mTOR) signaling after in vitro stimulation.

(a) Splenocytes were isolated from wild-type (WT) or dLck-Cre HDAC3 cKO mice, and magnetically enriched for CD4+ T cells by magnetic negative selection. Enriched CD4+ T cells were labeled with CFSE and stimulated with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28 for 3 days and examined by flow cytometry for proliferation. Cells from dLck-Cre HDAC3 cKO were gated on HDAC3 cells to eliminate contaminating HDAC3+ events. Bar graphs on right show proliferation index ± standard deviation (SD) and % divided ± SD (n = 6 mice/group from three independent experiments). One nonlittermate, but age- and sex-matched WT B6 control was used in this experiment. (b) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for CD69 expression after 16 hr. Bar graph on right shows MFI ± SD (n = 6–7 mice/group from three independent experiments). (c) Enriched CD4+ T cells were analyzed for interleukin-2 (IL-2) production 48 hr after stimulation with 2 µg of plate-bound αCD3 and 0.5 µg/ml of αCD28. Bar graph on right shows fold induction ± SD (n = 4 mice/group from two independent experiments). (d) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for CD25 receptor expression after 3 days. Bar graph on right shows percent CD25 positive ± SD (n = 8 mice/group from four independent experiments). (e) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for viability after 24 hr. Bar graph on right shows percent viability dye positive ± SD (n = 7 mice/group from four independent experiments). (f) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for expression of P2RX7 after 20 hr. Bar graph on right shows MFI ± SD (n = 4 mice/group from two independent experiments). (g) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for Early Growth Response Protein 2 (Egr2) expression after 16 hr. Bar graphs on right shows percent Egr2+ ± SD and Egr2 MFI of Egr2+ population ± SD (n = 4–5 mice/group from three independent experiments). (h) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for phosphorylation of ribosomal protein S6 after 4 hr. Bar graph on right shows percent p-S6 positive ± SD (n = 5 mice/group from three independent experiments). (i) Enriched CD4+ T cells were stimulated overnight with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28, then a Mito Stress Assay was conducted to measure oxygen consumption rate (OCR). Data are pooled, and show mean OCR ± standard error of the mean (SEM; n = 3 mice/group from two independent experiments). Statistical significance for indicated comparisons in all panels except for (f) was determined by an unpaired t-test. For (f), statistical significance was determined for the indicated comparisons using one-way analysis of variance (ANOVA) and with Tukey’s multiple comparisons test.

Since IL-2 signaling, survival and proliferation are key outcomes of successful quiescence exit, HDAC3-deficient CD4+ T cells from dLck-Cre HDAC3 cKO mice were examined for participation in the required metabolic switch during T-cell activation. Since mTORC1 activity orchestrates many of these changes, including lipid synthesis (Mossmann et al., 2018), events downstream of mTORC1 activity were investigated. One important mTORC1-S6K1-dependent signaling event is expression of Early Growth Response Protein 2 (Egr2) (Kurebayashi et al., 2012). Sixteen hours after αCD3/αCD28 stimulation, CD4+ HDAC3 T cells from dLck-Cre HDAC3 cKO mice exhibited a comparable percentage of Egr2+ cells with WT CD4+ T cells, but the MFI of HDAC3-deficient cells was significantly reduced (Figure 4g). Additionally, dLck-Cre HDAC3 cKO CD4+ T cells had reduced phosphorylation of ribosomal protein S6 (Figure 4h), indicating that mTOR activity was diminished in dLck-Cre HDAC3 cKO cells. To more accurately assess the energetic activity of recently activated dLck-Cre HDAC3 cKO T cells, magnetically enriched CD4+ T cells from WT and KO spleens were stimulated with αCD3/αCD28 antibodies overnight, and then a Mito Stress Assay was conducted. The assay revealed the dLck-Cre HDAC3 cKO CD4+ T cells had a decrease in both basal and maximal respiratory capacity compared to WT as measured by oxygen consumption rate (OCR) (Figure 4i). Moreover, basal extracellular acidification rate was also reduced in the dLck-Cre HDAC3 cKO cells (Figure 4—figure supplement 1). Collectively, these data suggest that dLck-Cre HDAC3 cKO cells have a disruption in the metabolic reprogramming required for successful T-cell activation.

HDAC3-deficient CD4+ T cells have defective blasting, reduced cholesterol levels, and increased cholesterol efflux transporter expression

mTORC1 activity plays a pivotal role in lipid synthesis after T-cell activation (Chapman et al., 2019). As such, we investigated whether dLck-Cre HDAC3 cKO T cells had a phenotype consistent with defective lipid and cholesterol availability. HDAC3-deficient CD4+ T cells from dLck-Cre HDAC3 cKO mice were examined to see whether they were capable of cell growth and blasting after activation, which is dependent upon the production and retention of lipids (Armstrong et al., 2010; Bensinger et al., 2008; Kidani et al., 2013). Three days after αCD3/αCD28 stimulation, HDAC3-deficient CD4+ T cells had a reduced frequency of blasting cells, and their median size measured by forward scatter area (FSC-A) was significantly reduced compared to WT cells (Figure 5a). Granularity as measured by side scatter area (SSC-A) was also reduced in the HDAC3-deficient CD4+ T cells (Figure 5a). Filipin III, a naturally fluorescent antibiotic that binds to cholesterol and has previously been used as a probe for cellular cholesterol levels (Muller et al., 1984), was utilized to determine whether cellular cholesterol levels were altered in HDAC3-deficient T cells. Notably, volume-adjusted Filipin III signal was significantly reduced in HDAC3-deficient CD4+ T cells both before and after a 20-hr stimulation with αCD3/αCD28 (Figure 5b), indicating that cellular cholesterol concentrations were decreased in the absence of HDAC3. Since cholesterol is implicated in the formation of lipid rafts, which are key components of T-cell receptor signaling (Bietz et al., 2017; Fessler, 2016; Fessler and Parks, 2011), we utilized a fluorescently conjugated Cholera Toxin Subunit B (CTB) molecule to probe for lipid rafts (Lencer and Tsai, 2003). After 20 hr of αCD3/αCD28 stimulation, HDAC3-deficient T cells had a small but significant reduction in CTB fluorescence, consistent with a reduction of lipid raft concentration (Figure 5c).

Histone deacetylase 3 (HDAC3)-deficient CD4+ T cells have defective blasting, reduced cholesterol levels, and increased cholesterol efflux transporter expression.

(a) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 and analyzed for blasting by size (forward scatter area, FSC-A) and granularity (side scatter area, SSC-A) after 3 days. Bar graphs on right represent mean ± standard deviation (SD; n = 6 mice/group from three independent experiments). One nonlittermate, but age- and sex-matched wild-type (WT) B6 control was used in this experiment. Statistical significance was determined for the indicated comparisons using an unpaired t-test. (b) Splenocytes were isolated from WT or dLck-Cre HDAC3 cKO mice, and labeled for cholesterol using Filipin III 20 hr after stimulation with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28. Flow cytometry was conducted. Bar plot (right) shows volume-adjusted MFI ± SD quantified across three independent experiments (n = 6 mice/group). MFI was adjusted to approximate cell volume by taking MFI divided by FSC-W3. Statistical significance was determined for the indicated comparisons using one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test. (c) Enriched CD4+ T cells were labeled with AF488-conjugated Cholera Toxin Subunit B 20 hr after stimulation with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28. Bar graph on right represents mean ± SD (n = 4 mice/group from two independent experiments). MFI was adjusted to approximate cell volume by taking MFI divided by FSC-W3. Statistical significance was determined for the indicated comparison using an unpaired t-test. (d, e) Enriched CD4+ T cells from Rag1-GFP WT and Rag1-GFP dLck-Cre HDAC3 cKO mice were sorted by fluorescence-activated cell sorting (FACS) for mature naive T cells (GFP). After sorting, cells were stimulated with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28, and RT-qPCR was conducted to examine gene expression of genes involved in cholesterol homeostasis. Bar graphs represent mean ± SD (n = 3–4 mice/group from three independent experiments). Statistical significance was determined for the indicated comparisons using an unpaired t-test.

Since cholesterol levels were disrupted in HDAC3-deficient CD4+ T cells, key genes involved in cholesterol biosynthesis were examined. To enrich for HDAC3 MNTs without intracellular staining of HDAC3, dLck-Cre HDAC3 cKO mice were interbred to Rag1-GFP reporter mice. These mice have a knock in of GFP into the Rag1 locus (Kuwata et al., 1999). The half-life of GFP has been estimated to be ~56 hr in in vivo (McCaughtry et al., 2007). This stability allows GFP+ T cells to be detected 2–3 weeks after T cell egress from the thymus even though Rag1 transcription ceases before T cells leave the thymus. Thus, Rag1-GFP naive CD4+ T cells are MNTs, and HDAC3 T cells comprise 75% of the MNT pool in dLck-Cre HDAC3 cKO (Figure 1c). Splenocytes from Rag1-GFP WT and Rag1-GFP dLck-Cre HDAC3 cKO mice were sorted using fluorescently activated cell sorting (FACS) for Rag1-GFP mature naive CD4+ T cells and cultured with or without αCD3/αCD28 antibodies for 12 hr to measure changes in gene expression. Srebf1 and Srebf2, genes that produce sterol response element-binding proteins and serve as key regulators of lipid synthesis (Bertolio et al., 2019) showed similar expression between WT and HDAC3 cKO T cells before or after stimulation (Figure 5d). In addition, key genes involved in cholesterol synthesis and homeostasis such as Hmgcr, Dhcr7, Sult2b1, Abcc1, Idi1, and Sqle were all expressed normally in dLck-HDAC3 cKO cells (Figure 5d). Additionally, expression of Nr1h2, which encodes the protein Liver X Receptor Beta (LXRβ) and serves as a cholesterol sensitive transcription factor, also showed normal expression in dLck-Cre HDAC3 cKO T cells (Figure 5d). This finding is unexpected, as previous studies showed that disruptions in mTORC1 activity altered the expression of cholesterol synthesis genes (Zeng et al., 2013). However, HDAC3-deficient CD4+ T cells had a higher Filipin III signal after TCR stimulation (Figure 5b), indicating they were still capable of increasing cholesterol synthesis despite reduced mTORC1 activation. Collectively, these data indicate TCR/CD28 signaling in HDAC3-deficient CD4+ T cells is sufficient to drive the expression of cholesterol biosynthesis genes.

Since cholesterol levels were reduced despite intact cholesterol synthesis, changes in sterol export could be responsible for the reduced Filipin III signal in HDAC3-deficient CD4+ T cells. To test this, gene expression of two ATP-binding cassette transporters, ABCA1 and ABCG1, was measured. ABCA1 and ABCG1 are pivotal players in cholesterol efflux (Tarling and Edwards, 2012). ABCG1 in particular has been identified as a key cholesterol transporter downregulated after T-cell activation and important for LXR’s antiproliferative effects (Bensinger et al., 2008). At baseline, mature naive CD4+ T cells from dLck-Cre HDAC3 cKO mice had a 3.4-fold increase in expression of Abcg1 transcripts and a 3.5-fold increase in Abca1 transcripts compared to WT (Figure 5e). Twelve hours after activation, Abcg1 transcript expression in dLck-Cre HDAC3 cKO mice was 8.1-fold higher than WT activated T cells, while Abca1 transcript expression in the activated dLck-Cre HDAC3 cKO was 7.1-fold higher than WT activated T cells (Figure 5e). Abcg1 and Abca1 expression was decreased with TCR stimulation in HDAC3-deficient cells after activation, but not to the level that occurred in WT CD4+ T cells.

Defective proliferation and blasting after activation of HDAC3-deficient CD4+ T cells is rescued by addition of exogenous cholesterol in vitro

The decrease in cholesterol concentration in HDAC3-deficient CD4+ T cells could drive inhibition of T-cell proliferation and blasting. Studies have shown that the addition of cholesterol conjugated to methyl-β-cyclodextrin (MBCD-Chol) can rescue CD8+ T cells with defects in lipid homeostasis (Kidani et al., 2013). To test whether the addition of exogenous cholesterol improves HDAC3-deficient CD4+ T-cell proliferation, CD4+ T cells from WT and dLck-Cre HDAC3 cKO mice were labeled with CFSE and cultured with αCD3/αCD28 simulation in the presence or absence of 5 μg/ml MBCD-Chol for 3 days. Remarkably, HDAC3-deficient CD4+ T cells that received exogenous cholesterol proliferated equivalently to WT T cells (Figure 6a). Additionally, HDAC3 dLck-Cre HDAC3 cKO CD4+ T cells treated with cholesterol had a strong recovery in the percentage of blasting cells compared to WT (Figure 6b). Importantly, cell size measured by FSC-A recovered to levels equivalent to WT (Figure 6b). However, cell granularity as measured by SSC-A did not return to WT levels in the cholesterol-treated HDAC3 cKO cells (Figure 6b), indicating HDAC3 may play a role in other pathways downstream of TCR signaling. WT CD4+ T cells that received exogenous cholesterol also increased their blasting and proliferation (Figure 6a, b), reinforcing the idea that cholesterol regulates the rate of CD4+ T-cell proliferation. These data confirmed that the reduction in cholesterol availability drove the defect in proliferation and blasting in the HDAC3-deficient CD4+ T cells. Interestingly, concentration of lipid rafts remained reduced in cholesterol-treated HDAC3 T cells (Figure 6c). Filipin III signal, as expected, was increased in the cholesterol-treated HDAC3 T cells (Figure 6d). Since lipid rafts remained disrupted in dLck-Cre HDAC3 cKO CD4+ T cells, we next asked whether the addition of cholesterol altered signaling events in HDAC3-deficient T cells. CD25 expression 3 days after in vitro stimulation was similar to WT levels in HDAC3-deficient T cells given exogenous cholesterol (Figure 6e). Conversely, p-S6 and Egr2 remained disrupted 16 hr after αCD3/αCD28 stimulation in the presence of exogenous cholesterol (Figure 6f, g). These data reveal that although mTORC1 signaling remained reduced in HDAC3-deficient CD4+ T cells, the residual signal was sufficient to drive normal proliferation given exogenous cholesterol. The ability of exogenous cholesterol to restore proliferation of HDAC3-deficient CD4+ T cells demonstrated that the primary function of HDAC3 is to repress cholesterol efflux during T-cell activation.

Defective proliferation and blasting in histone deacetylase 3 (HDAC3)-deficient CD4+ T cells is rescued by addition of exogenous cholesterol in vitro.

(a) Enriched CD4+ T cells were labeled with CFSE and stimulated with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin for 3 days and examined by flow cytometry for proliferation. Bar graphs represent mean proliferation index ± standard deviation (SD; n = 5 mice/group from three independent experiments). (b) Splenocytes were isolated from wild-type (WT) or dLck-Cre HDAC3 cKO mice, and magnetically enriched for CD4+ T cells by magnetic negative selection. Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 in the presence or absence of 5 µg cholesterol-methyl-β- cyclodextrin, and analyzed for blasting by size (forward scatter area, FSC-A) and granularity (side scatter area, SSC-A) after 3 days. Bar graphs represent mean ± SD (n = 6–8 mice/group from three independent experiments). (c) Enriched CD4+ T cells were labeled with AF488-conjugated Cholera Toxin Subunit B 20 hr after stimulation with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin. Bar graph on right represents mean ± SD (n = 4 mice/group from two independent experiments). MFI was adjusted to approximate cell volume by taking MFI divided by FSC-W3. (d) Enriched CD4+ T cells were labeled with Filipin III 20 hr after stimulation with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin. MFI was adjusted to approximate cell volume by taking MFI divided by FSC-W3. Bar graph on right represents adjusted MFI ± SD (n = 6 mice/group from three independent experiments). (e) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin and analyzed for CD25 receptor expression after 3 days. Bar graphs represent mean percent CD25+ ± SD (n = 4 mice/group from two independent experiments). (f) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin and analyzed for Early Growth Response Protein 2 (Egr2) expression after 16 hr. Bar graphs represent percent Egr2+ ± SD and normalized Egr2 MFI ± SD among Egr2+ population (n = 8 mice/group from four independent experiments). (g) Enriched CD4+ T cells were stimulated with 5 µg plate-bound αCD3 and 1 µg/ml soluble αCD28 in the presence or absence of 5 µg cholesterol-methyl-β-cyclodextrin and analyzed phosphorylation of S6 after 4 hr. Bar graphs represent percent p-S6+ ± SD (n = 4 mice/group from two independent experiments). Statistical significance was determined for all indicated comparisons in this figure using a one-way analysis of variance (ANOVA) with Tukey’s multiple comparisons test.

HDAC3 loss results in increased expression and hyperacetylation of Abca1 and Abcg1 genes

HDAC3 represses genes through removal of acetyl groups from histone tails and other nonhistone proteins. Considering HDAC3-deficient CD4+ T cells have increased expression of ABCA1 and ABCG1, the Abca1 and Abcg1 gene loci were examined for changes in histone acetylation in the absence of HDAC3. Our group previously conducted both RNA-Seq and ChIP-Seq on FACS-sorted selecting (Vβ5intCD69+) thymocytes in OT-II WT and OT-II CD2-iCre HDAC3 cKO mice (Philips et al., 2019a). These data were examined to identify potential changes in transcript levels as well as histone acetylation at the gene loci for Abca1 and Abcg1. HDAC3 cKO thymocytes had a significant upregulation of mRNA expression of Abca1 and Abcg1, while Hmgcr expression was unaffected by HDAC3 loss (Figure 7a). This mirrored the expression data in the dLck-Cre HDAC3 cKO in peripheral CD4+ T cells (Figure 5e). Next, H3K9 and H3K27 acetylation at each of the gene loci for Abca1, Abcg1, and Hmgcr was examined. HDAC3 cKO thymocytes had a substantial increase of both H3K27 and H3K9 histone acetylation at the promoter regions of Abca1 and Abcg1 (Figure 7b). Previously, HDAC3 ChIP-Seq was conducted on human CD4+ T cells (Wang et al., 2009). These data were reexamined for HDAC3 enrichment at the Abca1 and Abcg1 loci. HDAC3 was enriched at five sites within the Abcg1 locus, and HDAC3 was enriched at one site ~50 kilobases upstream of the Abca1 locus (Figure 7—figure supplement 1). To further investigate whether the enzymatic activity of HDAC3 was required to suppress expression of these cholesterol efflux transporters in thymocytes, the 16610D9 thymocyte cell line was treated for 24 or 48 hr with RGFP966, a competitive tight-binding inhibitor of HDAC3, and mRNA expression of Abca1, Abcg1, and Hmgcr was measured after treatment. HDAC3 inhibition produced a profound upregulation of both Abca1 and Abcg1 expression after both 24 and 48 hr of HDAC3 inhibition (Figure 7c). Similarly, treatment of WT CD4+ T cells for 24 hr with RGFP966 significantly upregulated both Abca1 and Abcg1 expression in primary CD4+ T cells (Figure 7d). These experiments demonstrated the deacetlyase activity of HDAC3 was required to repress expression of Abca1 and Abcg1 expression during primary CD4+ T-cell activation.

Figure 7 with 1 supplement see all
Histone deacetylase 3 (HDAC3) loss results in increased expression and hyperacetylation of ABCA1 and ABCG1.

(a) Gene expression (RNA-Seq) of Abca1, Abcg1, and Hmgcr in selecting (TCRβint CD69+) thymocytes from OT-II and OT-II CD2-iCre HDAC3 cKO mice. Bar graphs show mean RPKM (reads per kilobase million) ± standard deviation (SD). The exactTest (edgeR software) was used to compare mRNA levels (RPKM) from individual genes in RNA-Seq datasets. (b) Snapshot of H3K27Ac (left) and H3K9Ac (right) ChIP-seq tracks for the Abca1, Abcg1, and Hmgcr loci in selecting thymocytes from OT-II (wild-type, WT) and OT-II CD2-iCre HDAC3 cKO (CD2-iCre HDAC3-cKO) mice. (c) 16610D9 thymocytes were treated with competitive HDAC3 inhibitor RGFP966 for 24 or 48 hr, and expression of Abca1, Abcg1, and Hmgcr was measured by RT-qPCR at timepoints. Bar graphs show mean expression ± SD (n = 3 from three independent experiments). Statistical significance was determined for all indicated comparisons using an unpaired t-test. (d) WT splenocytes were harvested and magnetically enriched for CD4+ T cells. Cells were cultured with 5 µg of plate-bound αCD3 and 1 µg/ml of αCD28 or left unstimulated. Cells were also treated with competitive inhibitor of HDAC3 RGFP966. After 24 hr, expression of Abca1 and Abcg1 was examined by RT-qPCR. Bar graphs show mean expression ± SD (n = 4–5 mice/group from three independent experiments).

Discussion

In this study, we utilized a dLck-Cre HDAC3 cKO mouse system to examine the role of HDAC3 in peripheral CD4+ T cells. HDAC3-deficient CD4+ T cells have a defect in blasting and proliferation after in vitro αCD3/αCD28 stimulation. This defect is dependent upon reduced intracellular cholesterol levels as addition of exogenous cholesterol in culture restores proliferation and blasting in HDAC3-deficient CD4+ T cells. Importantly, the addition of cholesterol does not restore diminished mTORC1 signaling in HDAC3 T cells, which implies that reduced mTORC1 in HDAC3 cKO T cells is sufficient to drive proliferation. Moreover, we have outlined a role for HDAC3 in the regulation of cholesterol efflux. In the absence of HDAC3, both resting and activated CD4+ T cells have increased expression of the cholesterol efflux transporters ABCA1 and ABCG1. Inhibition of HDAC3 in WT CD4+ T cells revealed that repression of Abca1 and Abcg1 was dependent upon HDAC3 enzymatic activity. Previous work has shown that components of the SMRT/NCOR complex play a critical role in regulating gene expression of cholesterol efflux transporter ABCG1 in macrophages (Jakobsson et al., 2009). However, the functional importance of the regulation of ABCG1 was not elucidated. Chromatin immunoprecipitation assays showed that HDAC3 localized to the promoter region of both ABCA1 and ABCG1 in human macrophages (Jakobsson et al., 2009). Further, HDAC3 ChIP-Seq in human CD4+ T cells revealed HDAC3 binding at the Abcg1 locus, and upstream of the Abca1 locus. In this study, we show that HDAC3-deficient thymocytes had hyperacetylation of the promoter region of the Abca1 and Abcg1 genes. In addition, we show that HDAC3 enzymatic inhibition by RGP966 led to increased ABCA1 and ABCG1 expression, demonstrating a critical role for HDAC3-mediated histone deacetylation in active suppression of ABCA1 and ABCG1. Thus, HDAC3 is a direct regulator of the Abca1 and Abcg1 genes in CD4+ T cells. Collectively, these data demonstrate that HDAC3 regulates cholesterol availability in CD4+ T cells and that HDAC3 is required during T-cell activation to transcriptionally repress cholesterol efflux through ABCA1 and ABCG1. Importantly, the regulation of cholesterol availability was the limiting factor blocking proliferation of HDAC3-deficient CD4+ T cells, as proliferation was restored by the addition of exogenous cholesterol.

T cells are uniquely sensitive to changes in cholesterol concentration shortly after activation, as recently activated T cells rapidly upregulate cholesterol synthesis and simultaneously abolish cholesterol efflux in preparation for the membrane production required for proliferation. Thus, disruptions in cholesterol synthesis produce profound defects in T-cell proliferation (Kidani et al., 2013). Likewise, it has been shown that enforced expression of the cholesterol efflux transporter ABCG1 through treatment with LXR agonists results in defective T-cell proliferation (Bensinger et al., 2008). Conversely, loss of LXRβ activity through genetic deletion alters T-cell fitness in the context of activation (Michaels et al., 2021), while genetic deletion of Abcg1 in CD4+ T cells results in increased proliferation (Armstrong et al., 2010). Collectively, this demonstrates that transcriptional control of genes involved in cholesterol availability is critical for successful T-cell responses, and here we demonstrate a role for HDAC3 in controlling cholesterol availability in activated CD4+ T cells. This work demonstrates a highly specific role for HDAC3 in suppressing cholesterol efflux during T-cell activation. Importantly, critical components of the cholesterol synthesis pathway are unaltered by HDAC3 loss, demonstrating that HDAC3 serves as a specific regulator, rather than a generic regulator, of gene expression.

Materials and methods

Mice

Hdac3 fl/fl mice were generously provided by Dr. Scott Hiebert (Knutson et al., 2008). dLck-Cre mice were purchased from The Jackson Laboratory. Rag1-GFP knock-in mice were generously provided by Dr. Nobuo Sakaguchi (Kuwata et al., 1999). Mice were kept in barrier facilities and experiments were conducted with approval from the Institutional Animal Care and Use Committee at Mayo Clinic. Mice were analyzed between the age of 5–12 weeks, and both males and females were used. dLck-Cre HDAC3 cKO mice were examined with age-matched controls or littermates. WT mice may represent mice that have the floxed allele (Hdac3 fl/fl) alone, or mice that had no genetic alteration. The three instances of nonlittermate controls are noted in the figure legends. Sample size in the figure legends represents individual mice. Mouse genotypes were verified by flow cytometry analysis for HDAC3 protein expression or by PCR after use.

Flow cytometry

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Flow analysis was performed on the Attune NxT flow cytometer (Thermo Fisher) or the ZE5 Cell Analyzer (Bio-Rad), and all experiments were analyzed using FlowJo software (v10.5.3). To conduct intracellular flow cytometry, lymphocytes from spleen, thymus, or lymph nodes were labeled with surface markers, and then fixed with Foxp3/Transcription Factor Staining Buffer (eBioscience and Tonbo Biosciences). For analysis of phosphorylated protein analysis, the BD Phosflow kit was used. All flow analyses included size exclusion (FSC/SSC), doublet exclusion (FSC height/FSC area), and dead cell exclusion (Ghost Dyes; Tonbo Biosciences). Antibodies were purchased from eBioscience, BioLegend, Tonbo Biosciences, BD Biosciences, and Cell Signaling. Cells were labeled with Filipin III (Sigma #F4767) for 60 min in PBS at room temperature after fixation with Foxp3/Transcription Factor Staining Buffer. CTB (Thermo #34775) labeling was conducted for 30 min after Foxp3/Transcription Factor Staining Buffer.

Magnetic enrichment of CD4+ T cells

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For stimulation and FACS sorting, cells were magnetically enriched using the EasySep Mouse Streptavadin RapidSpheres Isolation Kit (Stem Cell Technologies #19860) to remove non-CD4+ T-cell populations from total splenocytes. Biotin-conjugated antibodies against CD8 (53–6.7), TCRγδ (UC7-13D5), NK1.1 (PK136), B220 (RA3-6B2), CD11b (M1/70), CD19 (6D5), CD11c (N418), Gr-1 (RB6-8C5), and Ter-119 (TER-119) were used. CD44 (IM7) was additionally used for negative selection to isolate naive CD4+ T cells.

FACS sorting

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Cell sorting for qPCR was performed using the BD FACSMelody Cell Sorter. Magnetically enriched CD4+ T cells from spleens of Rag1-GFP WT and RagGFP dLck-Cre HDAC3 cKO mice were labeled with anti-CD4 (RM4-5), anti-CD62L (MEL-14), and-CD44 (IM7), and Ghost Viability 510. Sorted MNTs were gated as live, CD4+ CD62L+ CD44 RagGFP cells.

Stimulations

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To examine the expression of TCR signaling events, cells were stimulated with 5 μg/ml anti-CD3 (2C11, Bio X Cell #BE0001-1) and 1 µg/ml soluble anti-CD28 (37.51, Bio X Cell # BE0015-1). Tissue culture plates were coated with 5 µg/ml anti-CD3 in PBS for 3 hr at 37 °C. Splenocytes were magnetically enriched for CD4+ T cells. Enriched cells were then cultured in complete culture media (RPMI 1640 with 10 % FCS, L-glutamine, penicillin and streptomycin, and β-mercaptoethanol) with stimulation. Unstimulated samples received 10 ng/ml IL-7 (PeproTech # 217-17) in complete media. Timing of each stimulation is noted in each figure legend. After stimulation, cells were immediately stained for flow cytometry on ice for 30 min, and fixed/permeabilized for intracellular staining as needed with the Tonbo Foxp3/Transcription Factor Staining Buffer Kit. For IL-2 detection, Protein Transport Inhibitor Cocktail (eBioscience # 00-4980-93) was added 6 hr prior to harvest. For antibodies targeting p-S6, stimulated cells were immediately fixed with BD Lyse/Fix Buffer (BD Phosflow kit), permeabilized with BD Perm Buffer III, and stained with anti-p-S6 antibodies (Cell Signaling #5364S) for 30 min at room temperature. PE-conjugated αRabbit secondary antibody (Southern Biotech #4050-09) was used and stained for 15 min on ice.

T-cell differentiation assays

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For T-cell differentiation assays, cells were stimulated with 2 μg/ml plate-bound anti-CD3 (2C11) and 0.5 μg/ml soluble anti-CD28 (37.51) antibody. For Th1 differentiation, complete culture media (RPMI 1640 with 10 % FCS, L-glutamine, penicillin and streptomycin, and β-mercaptoethanol) was supplemented with 1 μg/ml anti-IL-4 antibody (11B11, Bio X Cell #BE0045), 10 ng/ml IL-12 (PeproTech #210-12). For Th2 differentiation, media was supplemented with 1 μg/ml of each anti-IFNγ (XMG1.2, Bio X Cell #BE0055) and anti-IL-12 antibody (R2-9A5, Bio X Cell BE0233) as well 10 ng/ml of IL-4 (PeproTech #214–14). For Th17 differentiation, media was supplemented with 10 μg/ml of anti-IFNγ (XMG1.2, Bio X Cell #BE0055) and anti-IL-4 antibody (11B11, Bio X Cell #BE0045) as well as as 10 ng/ml of rIL-23 (PeproTech #200-23), 5 ng/ml TGF-β1 (PeproTech #100-21), and 20 ng/ml IL-6 (PeproTech 216-16). For Treg differentiation, media was supplemented with 10 μg/ml anti-IFNγ (XMG1.2, Bio X Cell #BE0055) and anti-IL-4 antibody (11B11, Bio X Cell #BE0045) as well as 2 ng/ml TGF-β1 PeproTech #100-21, and 2 ng/ml IL-2 (PeproTech #212-12). Cells were stimulated for 4 days, and stained for flow cytometry on ice. Unstimulated samples received 10 ng/ml IL-7 (PeproTech # 217-17) in complete media.

CFSE labeling and proliferation assays

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For proliferation assays, enriched CD4+ T cells were labeled with 1.25 μM CFSE (Sigma # 21888) for 2.5 min in PBS. Cells were washed with complete RPMI three times after labeling, and stimulated with 5 μg/ml plate-bound anti-CD3 (2C11) and 1 μg/ml soluble anti-CD28 (37.51) for 3–4 days. Cells were harvested and stained for flow cytometry on ice.

Metabolic assays

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The bioenergetic activity of CD4+ T cells was measured using the Seahorse XFe96 Analyzer. Magnetically enriched T cells were seeded at 2 × 105 cells/well. Cells were seeded on a Cell-Tak (Corning #354240)-coated XFe96 plate with fresh XF media (Seahorse XF RPMI medium with 2 mM glutamine, 10 mM glucose, 1 mM sodium pyruvate, and 5 mM HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid), pH 7.4). For the Mito Stress Assay, OCR was measured with additions of oligomycin (1.5 μM), FCCP (2-[2-[4-(trifluoromethoxy)phenyl]hydrazinylidene]-propanedinitrile, 1.5 μM), rotenone (1 μM), and antimycin A (1 μM) during the assay.

Real-time quantitative PCR analysis

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For qPCR analyses, mRNA was isolated from FACS sorted CD4+ T-cell populations or from 16610D9 thymocytes using Qiagen RNeasy Mini Kit. cDNA was generated using Superscript IV Reverse Transcriptase (Invitrogen). cDNA was amplified and detected using TaqMan probes for Srebf1 (Thermo #Mm00550338_m1), Srebf2 (Thermo #Mm01306292_m1), Idi1 (Thermo #Mm01337454_m1), Sqle (Thermo #Mm00436772_m1), Nr1h2 (Thermo #Mm00437265_g1), Sult2b1 (Thermo #Mm00450550_m1), Ldlr (Thermo #Mm00440169_m1), Abcc1 (Thermo #Mm01344332_m1), Dhcr7 (Thermo #Mm00514571_m1), Hmgcr (Thermo #Mm01282499_m1), Abca1 (Thermo #Mm00442646_m1), Abcg1 (Thermo #Mm00437390_m1) as well as an 18S rRNA (Applied Biosystems #4352930) to serve as an internal control. A StepOnePlus Real-Time PCR system was used, and differences in abundance were calculated using the 2-ΔΔCT method (Livak and Schmittgen, 2001).

Cholesterol addition

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Cholesterol-methyl-β-cyclodextrin (Sigma Prod# C4951) was added to complete RPMI at a concentration of 5 μg/ml (cholesterol weight). Stimulations and proliferation assays were performed as described above with or without cholesterol present in the media.

ChIP-Seq and RNA-Seq

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Chip-Seq and RNA-Seq data were previously published (Philips et al., 2019a; Wang et al., 2009). Data were retrieved from GEO Series GSE109531 and GSM393952. ChIP-Seq data were visualized with Integrated Genomics Viewer (mm10 for GSE109531, hg18 for GSM393952).

Cell line

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The 16610D9 murine double positive thymocyte cell line is used in Figure 7c only. This cell line was generated and received from Dr. Stephen Hedrick at UCSD. This cell line is not on the list of commonly misidentified cell lines and authentication for this cell line is not available from ATCS. To verify the identity of this cell line, flow cytometry using murine-specific antibodies for CD4, CD8, CD5, TCRβ, and CD24 was performed. The 16610D9 cells in this manuscript were CD4+ CD8+ double positive cells with intermediate expression of TCRβ, high expression of CD24, and low expression of CD5. This is consistent with the original description of the cell line. Mycoplasma testing was performed using the Universal Mycoplasma Detection Kit (ATCC Product # 30-1012 K), and the 16610D9 cell line was negative for mycoplasma contamination.

Statistical analysis

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The unpaired Student’s t-test was used to compare between two groups for total cell counts, in vitro differentiation assays, proliferation, blasting, signal transduction flow cytometry, phosflow, filipin, and qPCR analysis. For comparisons with three or more groups, a one-way analysis of variance (ANOVA was used for total cell counts and HDAC3 expression in thymocytes, cell counts in splenocytes and mesenteric lymph nodes, proliferation, blasting and signaling events after cholesterol addition). The exactTest (edgeR software) was used to compare mRNA levels (RPKM) from individual genes in RNA-Seq datasets. T-tests and ANOVA analysis were calculated using GraphPad Prism. To calculate proliferation index in proliferation assays, the ‘Generation 0’ peak was set by drawing a gate around the unstimulated peak from an unstimulated sample in FlowJo. This gate was then applied to stimulated samples derived from the same mouse, and Flowjo calculated the proliferation index for each. For Filipin and CTB analysis, MFI was normalized to cell size by taking MFI divided by spheroid time of flight (FSC-W3) to estimate cell volume as has been described previously (Stein et al., 2017; Tzur et al., 2011) Details for each statistical test used are included in each figure legend.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (M. musculus)Hdac3floxScott HiebertMGI: 379477PMID:18406327
Strain, strain background (M. musculus)dLck-CreJackson Laboratory # 012837MGI: 4819511PMID:11748274
Strain, strain background (M. musculus)Cd4-CreJackson Laboratory # 017336MGI: 2386448RRID:IMSR_JAX:017336PMID:11728338
Strain, strain background (M. musculus)Rag1-GFPNobuo SakaguchiMGI: 2388344PMID:10586023
Strain, strain background (M. musculus)Cd2-iCreJackson Labs # 008520MGI: 2449947RRID:IMSR_JAX:008520PMID:12548562
Cell line (M. musculus)16610D9Stephen HedrickRRID:CVCL_0111PMID:10587351
AntibodyAnti-CD4 PerCP (rat monoclonal)BiolegendCat #100538RRID:AB_893325Flow cytometry (1:500)
AntibodyAnti-CD4, BV510 (rat monoclonal)BiolegendCat #100559 RRID:AB_2562608Flow cytometry (1:1000)
AntibodyAnti-CD55 af647 (Armenian hamster monoclonal)BiolegendCat #131806 RRID:AB_1279261Flow cytometry (1:200)
AntibodyAnti-CD45RB Pacific Blue (rat monoclonal)BiolegendCat #103316 RRID:AB_2174405Flow cytometry (1:200)
AntibodyAnti-CD8a BV510 (rat monoclonal)BiolegendCat #100752 RRID:AB_2563057Flow cytometry (1:200)
AntibodyAnti-HDAC3 (rabbit monoclonal)Cell SignalingCat #85057S RRID:AB_2800047Flow cytometry (1:1500)
AntibodyAnti-HDAC3 (mouse monoclonal)Cell SignalingCat #3949S RRID:AB_2118371Flow cytometry (1:1000)
AntibodyAnti-TCRβ FITC (Armenian hamster monoclonal)BiolegendCat #109206 RRID:AB_313429Flow cytometry (1:500)
AntibodyAnti-CD62L APC-Cy7 (rat monoclonal)Tonbo BiosciencesCat #25-0621U100 RRID:AB_2893432Flow cytometry (1:200)
AntibodyAnti-CD62L BV510 (rat monoclonal)BiolegendCat #104441 RRID:AB_2561537Flow cytometry (1:200)
AntibodyAnti-CD44 BV510 (rat monoclonal)BiolegendCat #103043 RRID:AB_2561391Flow cytometry (1:200)
AntibodyAnti-CD44 BV785 (rat monoclonal)BiolegendCat #103041 RRID:AB_11218802Flow cytometry (1:200)
AntibodyAnti-CD44 v450 (rat monclonal)Tonbo BiosciencesCat #75-0441U025 RRID:AB_2621946Flow cytometry (1:200)
AntibodyAnti-Tbet PE-Cy7 (mouse monoclonal)BiolegendCat #644824 RRID:AB_2561761Flow cytometry (1:100)
AntibodyAnti-GATA3 eFluor 660 (rat monoclonal)eBioscienceCat #50-9966-42 RRID:AB_10596663Flow cytometry (1:100)
AntibodyAnti-RORγt BV421 (mouse monoclona)BD HorizonCat #562894 RRID:AB_2687545Flow cyotmetry (1:100)
AntibodyAnti-RORγt PE (rat monoclonal)eBioscienceCat #12-6981-82 RRID:AB_10807092Flow cytometry (1:100)
AntibodyAnti-Foxp3 Biotin (rat monoclonal)eBioscienceCat #13-5773-82 RRID:AB_763540Flow cytometry (1:100)
AntibodyAnti-Foxp3 PE (rat monclonal)Tonbo BiosciencesCat #50-5773U100 RRID:AB_11218868Flow cytometry (1:100)
AntibodyAAnti-CD25 PE-Cy7 (rat monoclonal)BiolegendCat #102016 RRID:AB_312865Flow cytometry (1:500)
AntibodyAnti-PD-1 FITC (Armenian hamster monoclonal)eBioscienceCat #11-9985-85 RRID:AB_465473Flow cytometry (1:100)
AntibodyAnti-IL-4 BV421 (rat monoclonal)BiolegendCat #504120 RRID:AB_2562102Flow cytometry (1:100)
AntibodyAnti-IFNγ FITC (rat monoclonal)InvitrogenCat #RM9001 RRID:AB_10375014Flow cytometry (1:100)
AntibodyAnti-IL-17A Af488 (rat monoclonal)BiolegendCat #506910 RRID:AB_536012Flow cytometry (1:100)
AntibodyAnti-CXCR5 BV421 (rat monoclonal)BiolegendCat #145512 RRID:AB_2562128Flow cytometry (1:100)
AntibodyAnti-Bcl6 PE (mouse monoclonal)BiolegendCat #648304 RRID:AB_2561375Flow cytometry (1:200)
AntibodyAnti-TNF-α PE (rat monoclonal)BiolegendCat #506306 RRID:AB_315427Flow cytometry (1:100)
AntibodyAnti-IL-2 PE (rat monoclonal)BiolegendCat #503808 RRID:AB_315302Flow cytometry (1:100)
AntibodyAnti-Egr2 PE (rat monoclonal)eBioscienceCat #12-6691-82 RRID:AB_10717804Flow cytometry (1:100)
AntibodyAnti-p-S6 (rabbit monoclonal)Cell SignalingCat #5364S RRID:AB_10694233Flow cytometry (1:100)
AntibodyAnti-CD69 (Armenian hamster monoclonal)BiolegendCat #104512 RRID:AB_493564Flow cytomery (1:200)
AntibodyAnti-P2RX7 (rabbit polyclonal)Enzo Life SciencesCat #ALX-215-035R100RRID:AB_2052434Flow cytometry (1:100)
AntibodyAnti-CD3 (Armenian hamster monoclonal)Bio X CellCat #BE0001-1 RRID:AB_1107634Stimulation of cultured cells (coated plate 3 hr at 37°C)
AntibodyAnti-CD28 (syrian hamster monoclonal)Bio X CellCat # BE0015-1 RRID:AB_1107624Stimulation of cultured cells (soluble)
AntibodyAnti IL-4 (rat monoclonal)Bio X CellCat #BE0045 RRID:AB_1107707Blocking for in vitro differentiation assays
AntibodyAnti-IFNγ (rat monoclonal)Bio X CellCat #BE0055 RRID:AB_1107694Blocking for in vitro differentiation assays
AntibodyAnti-IL-12 (rat monoclonal)Bio X CellCat #BE0233 AB_2687715Blocking for in vitro differentiation assays
Peptide, recombinant proteinTGF-β1PeproTechCat #100-21In vitro differentiation assays
Peptide, recombinant proteinIL-12PeproTechCat #210-12In vitro differentiation assays
Peptide, recombinant proteinIL-4PeproTechCat #214-14In vitro differentiation assays
Peptide, recombinant proteinIL-23PeproTechCat #200-23In vitro differentiation assays
Peptide, recombinant proteinIL-6PeproTechCat #216-16In vitro differentiation assays
Peptide, recombinant proteinIL-2PeproTechCat #212-12In vitro differentiation assays
Chemical compound, drugMethyl-β-cyclodextrin-cholesterolMillipore SigmaCat #C4951
Chemical compound, drugFilipin IIIMillipore SigmaCat #F4767Flow cytometry (1:100)
Chemical compound, drugCholera Toxin Subunit B – Af488Thermo FisherCat #C34775Flow cytometry (1:200)
Commercial assay or kitBD Fix/Perm Buffer Kit (Phosflow)BD BiosciencesCat #558049; 558050
Commercial assay or kitFoxp3/ Transcription Factor Staining Buffer KitTonbo BiosciencesCat #TNB-0607-KIT
Chemical compound, drugCFSESigmaCat #21888
Commercial assay or kitEasySep Mouse Streptavadin Rapidspheres Isolation KitStemCellCat #19860
AntibodyAnti-CD8 Biotin (rat monoclonal)BiolegendCat #100704 RRID:AB_312743Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-TCRγδ Biotin (Armenian hamster monoclonal)eBioscienceCat #13-5811-85RRID:AB_466685Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-NK1.1 Biotin (mouse monoclonal)BioegendCat #13-5941-85 RRID:AB_466805Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-B220 Biotin (rat monoclonal)eBioscienceCat #13-0452-85 RRID:AB_466450Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-CD11b Biotin (rat monoclonal)BioegendCat #101207 RRID:AB_312787Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-CD19 Biotin (rat monoclonal)BioegendCat #115503 RRID:AB_313638Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-Cd11c Biotin (Armenian hamster monoclonal)BioegendCat #117303 RRID:AB_313772Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-Gr-1 Biotin (rat monoclonal)BioegendCat #117303 RRID:AB_313368Magnetic Enrichment – Negative Selection (1:100)
AntibodyAnti-Ter-119 Biotin (rat monoclonal)BioegendCat #116203 RRID:AB_313704Magnetic Enrichment – Negative Selection (1:100)
OtherFixable Viability Dye Ghost 510Tonbo BiosciencesCat #13-0870Flow cytometry (1:1000)
OtherFixable Viability Ghost 780Tonbo BiosciencesCat #13-0865Flow cytometry (1:1000)
OtherFixable Viability Dye Ghost 510Tonbo BiosciencesCat #13-0870Flow cytometry (1:1000)
Commercial assay or kitSeahorse XF Cell Mito Stress Test KitAgilentCat #103015-100
Sequence-based reagentSrebf1Thermo Fisher Scientific#Mm00550338_m1
Sequence-based reagentSrebf2Thermo Fisher Scientific#Mm01306292_m1
Sequence-based reagentIdi1Thermo Fisher Scientific#Mm01337454_m1
Sequence-based reagentSqleThermo Fisher Scientific#Mm00436772_m1
Sequence-based reagentNr1h2Thermo Fisher Scientific#Mm00437265_g1
Sequence-based reagentSult2b1Thermo Fisher Scientific#Mm00450550_m1
Sequence-based reagentldlrThermo Fisher Scientific#Mm00440169_m1
Sequence-based reagentAbcc1Thermo Fisher Scientific#Mm01344332_m1
Sequence-based reagentDhcr7Thermo Fisher Scientific#Mm00514571_m1
Sequence-based reagentHmgcrThermo Fisher Scientific#Mm01282499_m1
Sequence-based reagentAbcg1Thermo Fisher Scientific#Mm00437390_m1
Sequence-based reagentAbca1Thermo Fisher Scientific#Mm00442646_m1
Sequence-based reagenthmgcrThermo Fisher ScientificMm01282499_m1
Sequence-based reagent18SApplied BiosystemsCat #4352930
Software, algorithmPrism 8GraphPadRRID:SCR_002798
Software, algorithmFlowJo v10TreestarRRID:SCR_008520
Software, algorithmIllustratorAdobeRRID:SCR_010279
Software, algorithmIGVBroad InstituteRRID:SCR_011793

Data availability

All RNAseq and ChIP-seq data are publicly available in GEO (GSE109531 and GSE15735).

The following previously published data sets were used
    1. Philips RL
    2. Lee JH
    3. Gaonkar K
    4. Chanana P
    5. Chung JY
    6. Schwab A
    7. Ordog T
    8. Sinibaldo R
    9. Arocha R
    10. Shapiro VS
    (2019) NCBI Gene Expression Omnibus
    ID GSE109531. HDAC3 restrains CD8-lineage genes to maintain a bi-potential state in CD4+CD8+ thymocytes for CD4-lineage commitment.
    1. Wang Z
    2. Zang C
    3. Cui K
    4. Schones DE
    5. Barski A
    6. Peng W
    7. Zhao K
    (2009) NCBI Gene Expression Omnibus
    ID GSE15735. Genome-wide mapping of HATs and HDACs in human CD4+ T cells.

References

    1. Kuwata N
    2. Igarashi H
    3. Ohmura T
    4. Aizawa S
    5. Sakaguchi N
    (1999)
    Cutting edge: absence of expression of RAG1 in peritoneal B-1 cells detected by knocking into RAG1 locus with green fluorescent protein gene
    Journal of Immunology 163:6355–6359.

Decision letter

  1. Juan Carlos Zúñiga-Pflücker
    Reviewing Editor; University of Toronto, Sunnybrook Research Institute, Canada
  2. Satyajit Rath
    Senior Editor; Indian Institute of Science Education and Research (IISER), India

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "Histone deacetylase 3 represses cholesterol efflux during CD4 + T cell activation" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Satyajit Rath as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Both reviewers provided detailed recommendations which are also provided below for clarity and context. Among these, it would be good to focus on the following:

1) The staining for Tfh cells in Figure 2 is not convincing. The shift of the Bcl6-positive population is rather low and therefore it is difficult to gate of Tfh cells. In addition, the number of Tfh cells for non-immunized mice seems to be rather high. This might be related to the gating strategy. It might be better to use PD1 vs CXCR5 for the definition of Tfh cells (and then gate on Bcl6). Did the authors analyze the frequency of Tfh cells in peyer's patches (in WT mice, Tfh cells are present in PP at steady state)?

2) Have the authors analyzed cytokine expression in ex vivo isolated CD4+ T cells (stimulated with PMA/ionomycin). It would be very informative to know whether T-bet+ or RORgt+ HDAC3- CD4+ T cells display changes in IFNγ or IL-17A expression, respectively.

3) The authors need to more convincingly demonstrate that the blasting/proliferation phenotype is related to the cholesterol content. There is some reservations about the filipin staining that can be easily addressed by looking at their existing data. It would be beneficial to see the cholesterol loading experiments redone where they aren't overloading the cells with cholesterol and have the proper control.

Reviewer #1:

The study "Histone deacetylase 3 represses cholesterol efflux during CD4+ T cell activation" represents a detailed study on the role of HDAC3 on CD4+ T cell activation. The reader is presented with detailed analysis on how loss of HDAC3 alters cholesterol metabolism in CD4+ T cells. The provided data are well presented and conclusive.

Overall, this is a nice study. Some comments are listed below.

(1) In Figure 1c, the data would be easier to read if the order of the statistical summary showing the percentage of the various cell populations correlates with the sequence of the representative FACS plots.

(2) In Figure 2 the authors should display data from the same organ (spleen) for all subsets, while mLN data should be presented exclusively in the corresponding supplementary Figure.

(3) The staining for Tfh cells in Figure 2 is not convincing. The shift of the Bcl6-positive population is rather low and therefore it is difficult to gate of Tfh cells. In addition, the number of Tfh cells for non-immunized mice seems to be rather high. This might be related to the gating strategy. It might be better to use PD1 vs CXCR5 for the definition of Tfh cells (and then gate on Bcl6). Did the authors analyze the frequency of Tfh cells in peyer's patches (in WT mice, Tfh cells are present in PP at steady state)?

(4) Have the authors analyzed cytokine expression in ex vivo isolated CD4+ T cells (stimulated with PMA/ionomycin). It would be very informative to know whether T-bet+ or RORgt+ HDAC3- CD4+ T cells display changes in IFNγ or IL-17A expression, respectively.

(5) It is not clear, at least to the reviewer, why the authors show the % of CD4+ T cells as well as total cell numbers from CD4-Cre HDAC3cKO mice in the diagrams in the right panels of Figure 2. If they include the data, one would also have to show the % and number of the corresponding WT cells from the study.

(6) Which cells were used as negative control in Figure 3 as well as in Figure 3—figure supplement 1. This is not described in the manuscript. Ideally, the negative controls are unboosted but stained cells. If the negative controls are unstained cells, the negative control peaks might in fact not correlate with the negative cytokine peak of the actual sample. The gating for IFNγ, IL-4 and IL-17 (is it IL-17A?) cytokine expression doesn't look okay, e.g. there are two peaks for IFNγ expression, but the gates include cells that appear IFNγ-negative. Similarly, the authors show 90% IL-17+ cells. This is very high, however, there seems to be a small fraction of cells that express very high levels. Are these cells the "real" IL-17A positive cells?

Showing contour plots (or dot plots) CD4 vs cytokine might help to discriminate between cytokine negative and positive peaks.

Have the cells been restimulated/boosted with PMA/ionomycin?

(7) Approx. 50% of naïve CD4+ T cells have deleted HDAC3 (as shown in Figure 1c). After differentiation into the various lineages shown in Figure 3, what was the % of cells that had deleted HDAC3. Was there a competitive advantage of cells that didn't delete HDAC3?

(8) The authors should briefly explain why they used Rag1-GFP mice for the data presented in Figure 5d and 5e and why the sorted GFP- cells for this experiment. Not all readers might be familiar with these mice.

(9) Related to Figure 6a: would the addition of cholesterol enhance the proliferation of WT CD4+ T cells?

(10) Why was a thymocyte cell line used for the data presented in Figure 7c? Wouldn't it be better to show results from primary cells?

Reviewer #2:

Past work of this lab and others have described a role for the histone deacetylase 3 (HDAC3) in the regulation of thymocyte development and CD8+ T cell function. This study used a loss-of-function approach to study the role of HDAC3 in CD4+ T cells. This was done by studying the T cell compartment in HDAC3 floxed mice that were crossed to mice expressing the distal Lck-Cre promoter (HDAC3 T KO mice). They observed that HDAC3 T KO mice exhibited CD8, but not CD4 T cell lymphopenia and a normal naïve to memory CD4+ T cell ratio convincingly establishing that Th cell development was intact in these mice. They then established using in vitro assays that CD4+ T cells had defects in blasting and proliferation that correlated with reduced expression IL-2 and CD25, and reduced expression and activity of mTORC1 targets. They then focused their analysis on cholesterol metabolism-related genes in the CD4+ T cells and discovered that two cholesterol efflux proteins were upregulated in HDAC3-deficient CD4+ T cells at the mRNA level. Past work in the literature had established that cholesterol efflux is downregulated during T cell activation to support T cell growth. They also showed that loading T cells with cholesterol restored the defect in T cell blasting and proliferation and CD25 expression in HDAC3 KO CD4+ T cells.

Strengths:

The manuscript was well-written and concisely presented. The experiments that were performed generated data of high quality. Experimental approaches were appropriate for surveying defects in the T cell compartment and effector T cell function. Experiments were replicated and, in most instances, means represented means of experiments, which is to be commended. Statistics were appropriate for each analysis that was performed.

With the exception of some of the data in Figure 6, the conclusions match the data presented.

Weaknesses:

The conclusion that "HDAC3-deficiency results in cholesterol deficiency and this is the cause of the proliferation defects" could be strengthened by the following considerations. First, since they gated on total CD4+ T cells, which were overall smaller in size in the HDAC3 KO mice, it would be important to show that the lowered filipin and cholera toxin staining is not a result of reduced autofluorescence.

In addition, the loading of the cells with cholesterol appeared to be almost too efficient and resulted in higher filipin staining than the WT T cells (Figure 6). To prove that the effects of the HDAC3-deficiency on T cell blasting and proliferation are indeed related to the cholesterol deficiency, the experiment should attempt to restore, not increase cholesterol levels. A cholesterol-loaded WT should also be included as a control. In addition, since cholesterol efflux is not being measured in the study, conclusions could have been strengthened by staining for cholesterol transporters using commercial antibodies.

In addition, the manuscript would have been improved if certain details were provided about the nature of the WT mice used in each experiment. The methods described that the littermate floxed mice were used as controls in some experiments and "off-the-shelf" C57BL6/J mice were used in others and the term WT was used interchangeably to describe these different mice. The legends also did not detail what the sample size represented (individual mice or cultures) and what sex of mice were used for each experiment.

Comments for the authors:

1. It is surprising that the filipin staining is lowered in T cells cultured in full serum media. I have conducted such studies in T cells cultured with HMG CoA reductase inhibitors and as unable to see decreases in filipin staining in T cells unless the T cells were cultured with HMG-CoA reductase inhibitors with serum-free media. This suggested to me that the media could be an important source of cholesterol for these cells. This aspect was not considered.

2. It would be good to gate the blasting and non-blasting cells with similar sized gates in the WT and KO T cells in the FSC/SSC plot before looking at filipin staining to rule out size effects on autofluorescence.

3. The finding that the deletion appears to be more efficient in the naïve mature T cells (25% still expressing), versus the memory T cells (75%), suggests that the HDAC3 defect may have given the remaining HDAC3+ T cells a selective advantage to enter the memory T cell pool as a result of homeostatic proliferation. This possibility was not considered.

4. Legends are sometimes confusing. For example, for Figure 4 (are these HDAC- gated cells from dLck-Cre mice?). If so, what is the WT control. Do you have the internal WT control (HDAC3+ cells in the dLck-Cre floxed mice)? Why was this shown for some experiments and not others.

5. I didn't find the rationale completely convincing to justify the focus on cholesterol metabolism, when IL-2 and IL-2R expression were decreased by more than 50%! mTORC and metabolic changes occur in part downstream of IL-2R (and CD28). IL-2 and IL-2R defects could have explained the phenotype.

https://doi.org/10.7554/eLife.70978.sa1

Author response

Essential revisions:

Both reviewers provided detailed recommendations which are also provided below for clarity and context. Among these, it would be good to focus on the following:

1) The staining for Tfh cells in Figure 2 is not convincing. The shift of the Bcl6-positive population is rather low and therefore it is difficult to gate of Tfh cells. In addition, the number of Tfh cells for non-immunized mice seems to be rather high. This might be related to the gating strategy. It might be better to use PD1 vs CXCR5 for the definition of Tfh cells (and then gate on Bcl6). Did the authors analyze the frequency of Tfh cells in peyer's patches (in WT mice, Tfh cells are present in PP at steady state)?

We have altered the gating scheme to match what was proposed by the reviewer. Figure 2 and its associated supplements 1 and 2 now utilize the two-step strategy of gating on PD-1+ CXCR5+ cells and then gating on the Bcl-6hi cells. We also added data for Tfh cells in the Peyer’s patch in Figure 2—figure supplement 2. Using this gating strategy, we see a significant reduction in total Tfh numbers in the spleen in the dLck-Cre HDAC3 cKO compared to WT. There was not a significant difference in Tfh frequency between dLck-Cre HDAC3 cKO and WT in the mLN or Peyer’s patches.

2) Have the authors analyzed cytokine expression in ex vivo isolated CD4+ T cells (stimulated with PMA/ionomycin). It would be very informative to know whether T-bet+ or RORgt+ HDAC3- CD4+ T cells display changes in IFNγ or IL-17A expression, respectively.

We have added this data into Figure 3 – supplement 2, and lines 171-178. Short-term stimulation worked well for IFN-γ in splenocytes, and was comparable between WT and HDAC3-deficient CD4+ Tbet+ T cells. This is consistent with our in vitro results (Figure 3—figure supplement 1) We conducted short-term (6 hour) stimulation of cells from mesenteric lymph node, where RORγt+ CD4+ T cells are more abundant, but we were unable to detect IL-17A production in either the WT or HDAC3 cKO cells after PMA/ionomycin stimulation.

3) The authors need to more convincingly demonstrate that the blasting/proliferation phenotype is related to the cholesterol content. There is some reservations about the filipin staining that can be easily addressed by looking at their existing data.

The HDAC3-deficient CD4 T cells have reduced cholesterol content (as measured by Filipin III signal) at all sizes compared to WT cells. This can be seen in Author response image 1 which shows FSC-A vs. Filipin III signal.

Author response image 1

We have now normalized the Filipin III and Cholera Toxin Subunit B MFI to cell size in our data using a previously established method by taking MFI and dividing by an estimated volume of the cell using FSC-W3 (Stein et al., 2017; Tzur, Moore, Jorgensen, Shapiro, and Kirschner, 2011). The data in figure 5 still demonstrates a significant reduction of Filipin III signal and Cholera Toxin Subunit B signal in the HDAC3- dLck-Cre HDAC3 cKO CD4+ T cells, confirming that HDAC3- dLck-Cre HDAC3 cKO CD4+ T cells have a reduction in cholesterol content independent from changes in cellular volume during blasting and activation.

It would be beneficial to see the cholesterol loading experiments redone where they aren't overloading the cells with cholesterol and have the proper control.

The concentration of methyl-β-cyclodextrin cholesterol (MBCD-Chol) we used in the paper (5ug/mL) was the lowest biologically relevant concentration for restoration of proliferation. Titration experiments using lower concentrations had little to no effect on the function of dLck-Cre HDAC3-cKO or WT cells. We speculate that this may be related to the mechanism of MBCD-Chol incorporation into cells. Little is known as to whether MBCD-Chol incorporates into membranes or lipid rafts with the same efficiency or frequency into the various cellular compartments as unconjugated cholesterol. It is possible that although the cells are taking up a great deal of cholesterol (as shown by the increase in Filipin III staining), only a fraction of that is providing a physiological benefit to the cells. We have added in the WT+ Cholesterol control to all experiments in figure 6 to demonstrate the effects of exogenous cholesterol on wild type cells as a control.

Reviewer #1:

The study "Histone deacetylase 3 represses cholesterol efflux during CD4+ T cell activation" represents a detailed study on the role of HDAC3 on CD4+ T cell activation. The reader is presented with detailed analysis on how loss of HDAC3 alters cholesterol metabolism in CD4+ T cells. The provided data are well presented and conclusive.

Overall, this is a nice study. Some comments are listed below.

(1) In Figure 1c, the data would be easier to read if the order of the statistical summary showing the percentage of the various cell populations correlates with the sequence of the representative FACS plots.

This has been corrected, the order of the FACS plots and bar graphs now match.

(2) In Figure 2 the authors should display data from the same organ (spleen) for all subsets, while mLN data should be presented exclusively in the corresponding supplementary Figure.

This has been changed with all spleen samples in Figure 2, all mLN in Figure 2 —figure supplement 1 and the updated Peyer’s patch addition in Figure 2 —figure supplement 2.

(3) The staining for Tfh cells in Figure 2 is not convincing. The shift of the Bcl6-positive population is rather low and therefore it is difficult to gate of Tfh cells. In addition, the number of Tfh cells for non-immunized mice seems to be rather high. This might be related to the gating strategy. It might be better to use PD1 vs CXCR5 for the definition of Tfh cells (and then gate on Bcl6).

Figure 2 and its supplements have been updated, please see the full response is in the “essential revisions 1”.

(4) Have the authors analyzed cytokine expression in ex vivo isolated CD4+ T cells (stimulated with PMA/ionomycin).

Figure for IFN-γ after short term PMA/Ionomycin stim has been added (Figure 3—figure supplement 2). Please see the full response is in “essential revisions 2”.

(5) It is not clear, at least to the reviewer, why the authors show the % of CD4+ T cells as well as total cell numbers from CD4-Cre HDAC3cKO mice in the diagrams in the right panels of Figure 2. If they include the data, one would also have to show the % and number of the corresponding WT cells from the study.

To simplify this figure, we removed the % of CD4+ T cell bar graphs from figure 2. For Figure 2 – supplement 1 we now only report the frequency of CD4+ T cells from the mLN as the number of lymph nodes harvested from each mouse was variable, the frequency of CD4+ T cells is a more appropriate comparator than total cell number. For clarity, we added in the flow analysis for the CD4-Cre HDAC3 cKO mice. The CD4-Cre HDAC3 cKO cells have a block in thymocyte development that results in very few mature T cells (and thus differentiated CD4+ T cells) in vivo, and thus serve as a good control for the loss of the differentiated CD4+ T cell numbers. Further, this loss of mature T cells in CD4-Cre HDAC3 cKO mice produces a relatively non-competitive environment, which allows for the development of relatively normal frequencies of differentiated Treg, Th2, and Th17 CD4 populations in the spleen (Figure 2), despite dramatically reduced total numbers. This reinforces the idea that HDAC3-deficient T cells are capable of differentiating, but are outcompeted by the WT cells in the competitive environment of the dLck-Cre HDAC3 cKO in vivo due to poor proliferative capacity.

(6) Which cells were used as negative control in Figure 3 as well as in Figure 3—figure supplement 1. This is not described in the manuscript. Ideally, the negative controls are unboosted but stained cells. If the negative controls are unstained cells, the negative control peaks might in fact not correlate with the negative cytokine peak of the actual sample.

We apologize for this oversight. This information has been added into the Figure legends of Figure 3 and Figure 3—figure supplement 1. The negative control (black histograms) is indeed unstimulated but stained cells as recommended by the reviewer.

The gating for IFNγ, IL-4 and IL-17 (is it IL-17A?) cytokine expression doesn't look okay, e.g. there are two peaks for IFNγ expression, but the gates include cells that appear IFNγ-negative. Similarly, the authors show 90% IL-17+ cells. This is very high, however, there seems to be a small fraction of cells that express very high levels. Are these cells the "real" IL-17A positive cells?

The stain is using an IL-17A antibody (Biolegend Cat #506910). We set a positive gate for all cytokine gates based on the unstimulated controls (black histograms figure 3 – supplement 1). As over 90% of the cells in our Th17 differentiation assays were RORγt+ (Figure 3), the percentage of IL-17A producing cells in this figure is consistent with this. Likewise, over 75% of the cells in the Th1 differentiation assays were T-bet+ (Figure 3), so the percentage of IFN-γ+ T cells is consistent with this.

Showing contour plots (or dot plots) CD4 vs cytokine might help to discriminate between cytokine negative and positive peaks.

We did not find any differences in the delineation of cytokine positive and negative peaks using either of these methods, and thus kept the original histogram plots shown.

Have the cells been restimulated/boosted with PMA/ionomycin?

These cells were not restimulated/boosted with PMA/ionomycin prior to harvest. They were, however, kept in the presence αCD3/CD28 stimulation throughout the duration of the in vitro differentiation cultures.

(7) Approx. 50% of naïve CD4+ T cells have deleted HDAC3 (as shown in Figure 1c). After differentiation into the various lineages shown in Figure 3, what was the % of cells that had deleted HDAC3. Was there a competitive advantage of cells that didn't delete HDAC3?

We didn’t find a significant enrichment of HDAC3+ cells from the dLck-Cre HDAC3 cKO mice in the differentiation assays. This result is likely confounded by the deletion timing of HDAC3 in the dLck-Cre HDAC3 cKO system. Most (~65-70%) of the recent thymic emigrants (RTEs) are HDAC3 sufficient, while very few (~20%) of the mature naïve T cells are HDAC3 sufficient (Figure 1c). Thus, most of the magnetically-enriched naïve HDAC3-sufficient cells are RTEs, which have been previously shown to have significant functional impairment compared to mature naïve T cells (Cunningham, Helm, and Fink, 2018).Thus, the naïve HDAC3-sufficient cells from the dLck-Cre HDAC3 cKO mice likely do not have a competitive advantage in the stimulation cultures since most of them are also functionally impaired RTEs.

(8) The authors should briefly explain why they used Rag1-GFP mice for the data presented in Figure 5d and 5e and why the sorted GFP- cells for this experiment. Not all readers might be familiar with these mice.

We apologize for this oversight. This has been added into the body of the manuscript within the Results section of figure 5 (lines 260-268).

(9) Related to Figure 6a: would the addition of cholesterol enhance the proliferation of WT CD4+ T cells?

We have added the “WT + Cholesterol” control to all panels in figure 6. WT cells readily take up the exogenous cholesterol (as measured by Filipin III), and this improves their proliferation as it likely bypasses their need for upregulation of production of cholesterol after activation.

(10) Why was a thymocyte cell line used for the data presented in Figure 7c? Wouldn't it be better to show results from primary cells?

We have added qPCR data from primary CD4 T cells treated with the competitive HDAC3 inhibitor, RGFP966, with and without the presence of CD3/CD28 stimulation. This data is in figure 7d.

Reviewer #2:

[…] First, since they gated on total CD4+ T cells, which were overall smaller in size in the HDAC3 KO mice, it would be important to show that the lowered filipin and cholera toxin staining is not a result of reduced autofluorescence.

Our Filipin III and Cholera Toxin Subunit B analysis was at a 20hr stimulation timepoint, which should be before the cells have significantly begun to blast. Still, to account for any modest changes in cell size, we normalized the Filipin III and Cholera Toxin Subunit BMFI to cell volume. Detailed information on this volume adjustment can be seen in “essential revisions 3”.

In addition, the loading of the cells with cholesterol appeared to be almost too efficient and resulted in higher filipin staining than the WT T cells (Figure 6). To prove that the effects of the HDAC3-deficiency on T cell blasting and proliferation are indeed related to the cholesterol deficiency, the experiment should attempt to restore, not increase cholesterol levels.

Please see response in “Essential Revisions 4”.

A cholesterol-loaded WT should also be included as a control.

All panels in figure 6 now include a cholesterol loaded wild type as a control.

In addition, since cholesterol efflux is not being measured in the study, conclusions could have been strengthened by staining for cholesterol transporters using commercial antibodies.

We attempted to detect ABCA1 and ABCG1 proteins using commercially available antibodies by western blot on sorted CD4 T cells, but the antibodies we tried (Novus Biologicals #NB100-2068 for ABCA1, and Novus Biologicals# NB400-132 for ABCG1) failed to produce bands at the expected molecular weight by western blot in any of our WT or HDAC3 cKO samples. Given that much work has been done to show that both ABCA1 and ABCG1 protein levels are tightly controlled by LXR-dependent transcriptional events (Bensinger et al., 2008; Chen et al., 2011; Tan et al., 2017), we believe that the qPCR analysis in this manuscript demonstrates the role of HDAC3 in suppressing Abca1 and Abcg1 gene expression.

In addition, the manuscript would have been improved if certain details were provided about the nature of the WT mice used in each experiment. The methods described that the littermate floxed mice were used as controls in some experiments and "off-the-shelf" C57BL6/J mice were used in others and the term WT was used interchangeably to describe these different mice. The legends also did not detail what the sample size represented (individual mice or cultures) and what sex of mice were used for each experiment.

The wild type mice in this manuscript were all littermates with the exception of 3 individual mice. 2 straight “off the shelf” B6 wild type mice were used in the in vitro differentiation assay experiments. One non-littermate wild type B6 mouse from our colony was used for the blasting and proliferation experiments. For all three of these mice, the mice were age and sex-matched to the dLck-Cre HDAC3 cKO mice in the experiment to prevent any other variation. We have updated the Materials and methods and the figure legends to point out where non-littermate controls were used.

Overall, 106 male and 116 female mice were used in the experiments described in this manuscript. We didn’t put the number of male and female mice for each panel of each figure to prevent overcrowding of the figure legends. The sample size represents individual mice, this has been updated in the Materials and methods.

Comments for the authors:

1. It is surprising that the filipin staining is lowered in T cells cultured in full serum media. I have conducted such studies in T cells cultured with HMG CoA reductase inhibitors and as unable to see decreases in filipin staining in T cells unless the T cells were cultured with HMG-CoA reductase inhibitors with serum-free media. This suggested to me that the media could be an important source of cholesterol for these cells. This aspect was not considered.

It is possible that the serum-supplemented RPMI we are using to culture the CD4+ T cells could be providing an important source of cholesterol to our cultured cells. Even with our current formulation, the Filipin signal is reduced even when normalized for cell volume, indicating that although the media may provide an important source of cholesterol, the HDAC3-deficient cells fail to maintain cholesterol levels to the level of their wild-type counterparts in culture. We hypothesize this is due to the overexpression of the cholesterol export proteins ABCA1 and ABCG1. Certainly, culture with serum-free media could exacerbate the difference in Filipin III signal when there is no longer any exogenous cholesterol to take up.

2. It would be good to gate the blasting and non-blasting cells with similar sized gates in the WT and KO T cells in the FSC/SSC plot before looking at filipin staining to rule out size effects on autofluorescence.

To rule out the effects of size on autofluorescence, we normalized the signals to the median FSC-W3 to approximate the volume of the cells. For the gating, lymphocytes (blasting and non-blasting) were both captured in the lymphocyte gate, which was applied universally to the WT and KO samples in each flow experiment to maintain consistency in the gating strategy. Collectively, these two measures allow us to differences in cholesterol content for Filipin III signal, and not due to changes in cell size or autofluorescence. For more information on the Filipin III analysis, please see the response to “essential revisions 3”

3. The finding that the deletion appears to be more efficient in the naïve mature T cells (25% still expressing), versus the memory T cells (75%), suggests that the HDAC3 defect may have given the remaining HDAC3+ T cells a selective advantage to enter the memory T cell pool as a result of homeostatic proliferation. This possibility was not considered.

We agree and have now added a sentence addressing that the HDAC3-sufficient cells appear to have a competitive advantage to populate the memory pool in this system (lines 139-141).

4. Legends are sometimes confusing. For example, for Figure 4 (are these HDAC- gated cells from dLck-Cre mice?). If so, what is the WT control. Do you have the internal WT control (HDAC3+ cells in the dLck-Cre floxed mice)? Why was this shown for some experiments and not others.

We have added a sentence in the figure 4 legend to clarify that these cells are HDAC3- cells to eliminate the contaminating HDAC3+ cells. We omitted the HDAC3-sufficient cells from the graphs because most of the naïve HDAC3-sufficient CD4+ are recent thymic emigrants (RTEs), which have been shown to have significant functional impairment. We see results consistent with the previously described RTE phenotype, and thus they are not a proper WT control in these experiments. The WT controls are from completely separate WT mice.

References

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Chen, X., Zhao, Y., Guo, Z., Zhou, L., Okoro, E. U., and Yang, H. (2011). Transcriptional Regulation of ATP-binding Cassette Transporter A1 Expression by a Novel Signaling Pathway. The Journal of Biological Chemistry, 286(11), 8917. https://doi.org/10.1074/JBC.M110.214429

Cunningham, C. A., Helm, E. Y., and Fink, P. J. (2018). Reinterpreting recent thymic emigrant function: defective or adaptive? Current Opinion in Immunology, 51, 1. https://doi.org/10.1016/J.COI.2017.12.006

Stein, M., Dütting, S., Mougiakakos, D., Bösl, M., Fritsch, K., Reimer, D., … Mielenz, D. (2017). A defined metabolic state in pre B cells governs B-cell development and is counterbalanced by Swiprosin-2/EFhd1. Cell Death and Differentiation, 24(7), 1239. https://doi.org/10.1038/CDD.2017.52

Tan, H., Yang, K., Li, Y., Shaw, T. I., Wang, Y., Blanco, D. B., … Chi, H. (2017). Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation. Immunity, 46(3), 488–503. https://doi.org/10.1016/j.immuni.2017.02.010

Tzur, A., Moore, J. K., Jorgensen, P., Shapiro, H. M., and Kirschner, M. W. (2011). Optimizing Optical Flow Cytometry for Cell Volume-Based Sorting and Analysis. PLOS ONE, 6(1), e16053. https://doi.org/10.1371/JOURNAL.PONE.0016053

https://doi.org/10.7554/eLife.70978.sa2

Article and author information

Author details

  1. Drew Wilfahrt

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Validation, Writing – original draft, Validation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Rachael L Philips

    Department of Immunology, Mayo Clinic, Rochester, United States
    Present address
    Molecular Immunology and Inflammation Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Validation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Jyoti Lama

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Monika Kizerwetter

    Department of Immunology, Mayo Clinic, Rochester, United States
    Present address
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6084-7471
  5. Michael Jeremy Shapiro

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Shaylene A McCue

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Madeleine M Kennedy

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Matthew J Rajcula

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Hu Zeng

    1. Department of Immunology, Mayo Clinic, Rochester, United States
    2. Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Virginia Smith Shapiro

    Department of Immunology, Mayo Clinic, Rochester, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Validation, Writing – original draft, Writing – review and editing
    For correspondence
    shapiro.virginia1@mayo.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9978-341X

Funding

National Institute of Allergy and Infectious Diseases (NIH R01 AI150100-01A1)

  • Virginia Smith Shapiro

National Institute of Allergy and Infectious Diseases (F31AI147438-01A1)

  • Drew Wilfahrt

A. Gary and Anita Klesch Predoctoral Fellowship (Mayo Clinic Graduate School of Biomedical Sciences)

  • Drew Wilfahrt

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr. Scott Hiebert for Hdac3flox mice. We also thank Dr. Nobuo Sakaguchi for Rag1-GFP knock-in mice. We also thank members of the VSS, HZ, and Kay Medina (Mayo Clinic) laboratory for thoughtful discussions.

Ethics

This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#A00001984-16 and #A00004560-19) of the Mayo Clinic.

Senior Editor

  1. Satyajit Rath, Indian Institute of Science Education and Research (IISER), India

Reviewing Editor

  1. Juan Carlos Zúñiga-Pflücker, University of Toronto, Sunnybrook Research Institute, Canada

Publication history

  1. Received: June 4, 2021
  2. Accepted: November 15, 2021
  3. Version of Record published: December 2, 2021 (version 1)

Copyright

© 2021, Wilfahrt et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Drew Wilfahrt
  2. Rachael L Philips
  3. Jyoti Lama
  4. Monika Kizerwetter
  5. Michael Jeremy Shapiro
  6. Shaylene A McCue
  7. Madeleine M Kennedy
  8. Matthew J Rajcula
  9. Hu Zeng
  10. Virginia Smith Shapiro
(2021)
Histone deacetylase 3 represses cholesterol efflux during CD4+ T-cell activation
eLife 10:e70978.
https://doi.org/10.7554/eLife.70978
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