CD8 T cell differentiation is orchestrated by dynamic metabolic changes that direct activation, proliferation, cytotoxic function, and epigenetic changes. We report that the BTB-ZF family transcriptional repressor Zbtb20 negatively regulates CD8 T cell metabolism and memory differentiation in mice. Effector and memory CD8 T cells with conditional Zbtb20 deficiency displayed enhanced mitochondrial and glycolytic metabolism, and memory CD8 T cells had enhanced spare respiratory capacity. Furthermore, Zbtb20-deficient CD8 T cells displayed increased flexibility in the use of mitochondrial fuel sources. Phenotypic and transcriptional skewing toward the memory fate was observed during the CD8 T cell response to Listeria monocytogenes. Memory cells mounted larger secondary responses and conferred better protection following tumor challenge. These data suggest that inactivation of Zbtb20 may offer an approach to enhance metabolic activity and flexibility and improve memory CD8 T cell differentiation, useful attributes for T cells used in adoptive immunotherapy.
Upon infection, small numbers of Ag-specific naive CD8 T cells differentiate into multiple types of effector and memory cells, which mediate pathogen clearance and provide long-term protective immunity. In an acute infection, which can be effectively cleared by the host, the CD8 T cell response is characterized by three distinct phases: clonal expansion, contraction, and memory (1). As Ag-specific CD8 T cells become activated, they undergo extensive clonal expansion to fight the infection. A large number of effector CD8 T (Teff) cells can be found in infected tissues by the time of pathogen clearance. Those Teff cells differ in effector cytokine production, migration patterns, proapoptotic and antiapoptotic protein expression, cytokine receptor expression, and proliferation capacity, and thus have varying potential to form memory CD8 T cells (2). The majority of Teff cells die during the ensuing contraction phase to bring the host immune system back into homeostasis (1), leaving only a small fraction to survive and differentiate into memory CD8 T cells. Certain surface markers have been shown to correlate with distinct CD8 T cell fates. Short-lived effector cells are characterized by low expression of IL-7R α-chain (CD127) and high expression of killer cell lectin-like receptor G1 (KLRG-1), whereas memory precursor effector cells are characterized by high expression of CD127 and low expression of KLRG-1 (2–6). The majority of short-lived effectors enter apoptosis during the contraction phase, whereas memory precursors are more likely to survive and become memory CD8 T cells (2–6).
Naive, Teff, and memory CD8 T cells have distinct metabolic profiles (7). Naive CD8 T cells are metabolically quiescent and mostly employ mitochondrial respiration to meet their energy demands by breaking down glucose, glutamine, and fatty acids (8). These cells only maintain basal nutrient uptake to support their slow homeostatic turnover. Upon activation, CD8 T cells promptly upregulate glycolysis as well as nutrient uptake. Although Teff cells preferentially use glycolysis to generate ATP, glycolysis and mitochondrial respiration are both upregulated to high levels. Intermediate metabolites from both pathways can be used for biosynthesis of lipid, nucleic acids, and proteins (7), which are crucial for growth, proliferation, and effector cytokine production. Memory CD8 T cells preferentially use mitochondrial respiration for ATP production. They also possess higher mitochondrial mass and larger spare respiratory capacity (SRC), which indicates the ability to generate ATP more quickly than a naive CD8 T cell (8).
There are many gaps in our knowledge regarding the regulation of immunometabolism; however, the roles for some central factors are clear. Signaling through the mammalian target of rapamycin (mTOR) pathway, culminating in activation of the transcription factors HIF-1α and Myc, is necessary to enhance glycolysis and glutaminolysis and for effector T cell differentiation (9, 10). In contrast, AMP-activated protein kinase (AMPK), which promotes the metabolic switch to catabolism when the AMP/ATP ratio is low, is required for memory CD8 T cell generation (11, 12). AMPK can also induce mitochondrial fusion and elongation (13–15), which is accompanied by tightly organized cristae, allowing more efficient use of the electron transport chain in memory cells. This is in contrast to mitochondria in Teff cells, which are more fragmented and exhibit looser conformation of the cristae (16).
Zbtb20, also known as HOF or DPZF, belongs to an evolutionarily conserved transcription factor family named broad complex, tramtrack, bric-à-brac, and zinc finger (BTB-ZF) family. There are more than 49 BTB-ZF genes in mammalian genomes (17), characterized by one or more C-terminal C2H2 zinc finger DNA-binding domain in combination with an N-terminal BTB domain that mediates protein–protein interactions (18). Zinc finger domains bind to specific DNA target sequences in the regulatory regions of target genes; then, the BTB domain recruits corepressor proteins that can lead to remodeling of chromatin and repression of target gene expression (18). Other member of this gene family include Bcl-6, BAZF, and PLZF, which play important regulatory roles both in the immune system and in other cell types, regulating cellular differentiation, development, oncogenesis, and the maintenance of stem cell pools (18). Importantly, many BTB-ZF proteins, like Bcl-6 and BAZF, are also key factors in the development and function of lymphocytes and myeloid cells. Zbtb20 is widely expressed in neuronal and hematopoietic tissues (19), promotes Ab-secreting B cell longevity and differentiation, and is indispensable for maintaining the long-lived plasma cell response (20). In addition, Zbtb20 induces cell survival factors, including Bcl-2, Bcl-w, and Bcl-x, and blocks cell cycle progression in a plasma cell line (20). Global Zbtb20 deficiency causes premature death of mice because of growth retardation and metabolic dysfunction (21). Transcriptional profiling of liver tissue from Zbtb20 knockout (KO) pups revealed dysregulation of a number of genes related to metabolism and mitochondrial function, including AKT, PGC1α, PDK4, CPT, PI3K, and fatty acid synthase (21).
In this study, we show Zbtb20 deletion after CD8 T cell activation enhanced both glycolytic and mitochondrial metabolism and allowed more flexibility in mitochondrial fuel sources. These CD8 T cells were phenotypically and transcriptionally biased toward memory cells and mounted enhanced secondary responses. Adoptive transfer experiments showed Zbtb20-deficient memory cells conferred superior antitumor protection when compared with wild-type (WT) cells. This implies that Zbtb20 is an important negative regulator of CD8 T cell metabolism and memory differentiation, and deletion of this gene may offer a novel immunotherapeutic approach to enhance adoptive T cell therapy.
Materials and Methods
Mice, virus, and bacteria
Zbtb20-GFP mice (Mutant Mouse Resource and Research Center no. 030006-UCD) were obtained from the Knockout Mouse Project. Zbtb20–fl/fl mice were generated by Dr. W. J. Zhang (Second Military Medical University, China) (22). OT-I mice were originally purchased from The Jackson Laboratory (003831). CD45.1 mice were purchased from The Jackson Laboratory (002014). Granzyme B (GZB)–cre mice were kindly provided by Dr. R. Ahmed (Emory University). CD45.1 OT-I mice, Zbtb20-GFP CD45.1 OT-I mice, and GZB-cre Zbtb20-flox CD45.1 OT-I mice were crossed and bred in-house at Dartmouth College. Murine gammaherpesvirus-68 encoding OVA (MHV-68–OVA) virus was kindly provided by Dr. P. Stevenson (University of Queensland, Australia). LM-actA–OVA was kindly provided by Dr. J. Harty (University of Iowa).
Primers 5′-GCAAGTTGCAGGCACAGCTAGTT-3′ and 5′-TAGCGGCTGAAGCACTGCA-3′ were used to genotype Zbtb20-GFP mice. Primers 5′-GZACCGCTGGCAACACCTATCTG-3′ and 5′-CTCTCCCCTCCTCCCTCTGG-3′ were used to genotype Zbtb20-floxed mice. Primers 5′-GCATTACCGGTCGATGCAACGAGTGATGAG-3′ and 5′-GAGTGAACGAACCTGGTCGAAATCAGTGCG-3′ were used to genotype GZB-cre mice. Primers 5′-CCTGCCTGAACTTTGAAGCTGTT-3′ and 5′-GCAACTGATGTCACAATCAGATGACC-3′ were used for ZBTB20 quantitative fluorescent PCR.
IL-2/IL-15 in vitro CD8 T cell differentiation
Total splenocytes were harvested from OT-I mice and GZB-cre Zbtb20-fl/fl OT-I mice, then seeded at 2 × 106 cells/ml with 10 μg/ml SIINFEKL peptide for 48 h without exogenous IL-2. Activated cells were further cultured with 100 U/ml recombinant human IL-2 (rhIL-2) only at 0.5 × 106 cells/ml or with 50 μg/ml recombinant mouse IL-15 (rmIL-15) at 1 × 106 cells/ml for 7 d. Cultures were split and provided fresh media every 2–3 d.
Assays were performed according to the manufacturer’s protocols. One hundred and fifty thousand cells were seeded per well for IL-2/IL-15 in vitro differentiated CD8 T cells. Two hundred thousand cells were seeded per well for ex vivo CD8 T cells. One micromolar oligomycin, 1.5 μM 4-(trifluoromethoxy)phenyl)carbonohydrazonoyl dicyanide (FCCP), and 0.5 μM rotenone/antimycin A were used for mitochondrial stress assays (catalog no. 103015-100, Seahorse XF Cell Mito Stress Test Kit; Seahorse Agilent); 0.5 μM rotenone/antimycin A and 50 mM 2-deoxyglucose were used for glycolytic rate assays (catalog no. 103344-100, Seahorse XF Glycolytic Rate Assay; Seahorse Agilent).
Ex vivo Seahorse Bioanalyzer assays
Naive CD8 T cells were harvested from CD45.1 OT-I mice (WT) or GZB-cre Zbtb20-fl/fl CD45.1 OT-I mice (KO) using EasySep Mouse Naive CD8 T Cell Isolation Kits (catalog no. 19858A; STEMCELL Technologies). Fifty thousand naive OT-I cells were retro-orbitally injected into B6 recipients, which were then retro-orbitally infected with 1 × 106 CFU LM-actA–OVA 1 d later. On day 7 and day 28 postinfection, splenocytes were harvested from recipients, stained with anti-CD45.1–allophycocyanin Ab, then purified with MojoSort Mouse anti-allophycocyanin Nanobeads (catalog no. 480072; BioLegend). Two hundred thousand enriched cells (purity >95%) were seeded into each well for Seahorse Mitochondrial Stress Tests and Glycolytic Rate Tests. One micromolar oligomycin, 1.5 μM FCCP, and 0.5 μM rotenone/antimycin A were used for mitochondria stress assays. A total of 0.5 μM rotenone/antimycin A and 50 mM 2-deoxyglucose were used for glycolytic rate assays.
Mitochondrial fuel flexibility assays
Total splenocytes were harvested from OT-I mice and GZB-cre ZBTB20-fl/fl OT-I (KO) mice, then activated with SIINFEKL peptide for 48 h without exogenous IL-2. Activated cells were further cultured with 50 μg/ml rmIL-15 for 7 d. Cultured cells were then analyzed using Seahorse XFe96 Analyzer. Cells were treated with no inhibitors or combinations of different inhibitors that prevented the use of different mitochondrial fuel source (etomoxir for long-chain fatty acid, UK5099 for pyruvate, and bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (BPTES) for l-glutamine; use of short and medium chain fatty acid were not manipulated), followed by a conventional Seahorse Agilent Mito Stress test. The maximal respiratory capacity of each condition was normalized to the group without inhibitor treatment. Four micromolar etomoxir, 2 μM UK5099, 3 μM BPTES, 1 μM oligomycin, 1.5 μM FCCP, and 0.5 μM rotenone/antimycin A were used for mitochondrial fuel flexibility assay (catalog no.103015-100, Seahorse XF Cell Mito Stress Test Kit; Seahorse Agilent).
Naive CD8 T cells were harvested from CD45.1 OT-I mice (WT) or GZB-cre Zbtb20-fl/fl CD45.1 OT-I mice (KO) and purified using EasySep Mouse Naive CD8 T Cell Isolation Kits (catalog no. 19858A; STEMCELL Technologies). Fifty thousand naive OT-I cells were retro-orbitally injected into congenic B6 recipient mice, which were then retro-orbitally infected with 1 × 106 CFU LM-actA–OVA 1 d later.
MC38-OVA tumor protection
Naive CD8 T cells were harvested from CD45.1 OT-I mice (WT) or GZB-cre Zbtb20-fl/fl CD45.1 OT-I mice (KO) using EasySep Mouse Naive CD8 T Cell Isolation Kits (catalog no. 19858A; STEMCELL Technologies). Fifty thousand naive OT-I cells were retro-orbitally injected into B6 recipients, which were then retro-orbitally infected with 1 × 106 CFU LM-actA–OVA 1 d later. On day 80 postinfection, splenocytes were harvested from recipients, stained with anti–CD45.1-allophycocyanin Ab, then purified with MojoSort Mouse anti-allophycocyanin Nanobeads (catalog no. 480072; BioLegend). A total of 1 × 106 enriched memory OT-I cells were adoptively transferred into MC38-OVA tumor-bearing mice, which were s.c. inoculated with 1 × 106 MC38-OVA tumor cells 4 d earlier. Tumor areas were measured three times a week.
Cells were mounted using poly-d-lysine, fixed with 2% glutaraldehyde, then quenched with 1 mg/ml NaBH4. Cells were then rendered permeable using 0.25% Triton X-100 solution, blocked, and stained with polyclonal anti-rabbit TOM20 Ab (ab78547 LOT:GR3199811-2; Abcam) to label mitochondrial outer membranes, DAPI for nuclear staining. Texas Red anti-rabbit IgG (TI-1000; VECTOR) was used as a secondary Ab for TOM20 staining. Quantification was performed with Bitplane Imaris software (Oxford Instruments). Outlines were traced manually for each mitochondrion in all images, and Imaris software used to calculate the total mitochondrial volume and surface area for each cell. All microscopy was performed in the Dartmouth Institute for Biomolecular Targeting.
ATP detection assay
Naive CD8 T cells were purified from spleens of CD45.1 OT-I mice (WT) or GZB-cre Zbtb20-fl/fl CD45.1 OT-I mice (KO) using EasySep Mouse Naive CD8 T Cell Isolation Kits (catalog no. 19858A; STEMCELL Technologies). Fifty thousand naive OT-I cells were retro-orbitally injected into congenic recipient mice, which were then retro-orbitally infected with 1 × 106 CFU LM-actA–OVA 1 d later. On day 7 and day 28 postinfection, splenocytes were harvested from recipients, stained with anti–CD45.1-allophycocyanin, then purified with MojoSort Mouse anti-allophycocyanin Nanobeads (catalog no. 480072; BioLegend). Purified cells (purity >95%) were then analyzed using a luminescence-based ATP detection assay (catalog no. 700410; Cayman Chemical).
Cell preparation for single-cell RNA sequencing
For isolation of CD8 T cells 10 d postinfection, single-cell suspensions were generated from four mice per recipient group by macerating spleens through nylon filters. CD8 T cells were enriched from these suspensions using a STEMCELL EasySep Mouse CD8 T Cell Isolation Kit (no. 19853). These samples were stained to block Fc receptors then stained with Abs and live/dead stain (LIVE/DEAD Fixable Violet Dead Cell Stain Kit, no. L34955; Thermo Fisher Scientific) for 30 min on ice shielded from light. The Abs used for cell surface staining from BioLegend were as follows: PE anti-mouse CD8β Ab (YTS156.7.7), allophycocyanin anti-mouse CD45.1 Ab (A20), and allophycocyanin anti-rat CD90/mouse CD90.1 (Thy-1.1) Ab (OX-7). Samples were subsequently washed twice, and ∼1 × 106 congenically marked OT-I cells were purified using fluorescence activated cell sorting for each group of recipients. The samples purified in this way from each group of recipients were then suspended in 100 μl buffer and labeled with 1 μg per sample of the following TotalSeq A Abs from BioLegend: TotalSeq-A0198 anti-mouse CD127 (A7R34), TotalSeq-A0250 anti-mouse/human KLRG-1 (2F1/KLRG-1), TotalSeq-A0073 anti-mouse/human CD44 (IM7), and TotalSeq-A0112 anti-mouse CD62L (MEL-14). Samples were labeled for 30 min on ice and subsequently washed with 1 ml PBS twice.
Single-cell RNA sequencing
Single-cell RNA sequencing (RNAseq) library preparation was carried out by the Center for Quantitative Biology Single Cell Genomics Core and the Genomics and Molecular Biology Shared Resource at Dartmouth. Droplet-based, 3′-end single-cell RNAseq was performed using the 10x Genomics Chromium platform, and libraries were prepared using the Single Cell v3 3′ Reagent Kit according to the manufacturer’s protocol (10x Genomics, Pleasanton, CA). Recovery of Ab-DNA tags (ADTs) from single cells (i.e., cellular indexing of transcriptomes and epitopes by sequencing [CITEseq]) was performed by adding 1 μl of ADT additive primer (10 μM, 5′-CCTTGGCACCCGAGAATT*C*C-3′) to the cDNA amplification reaction and following the 10x protocol for separation of the ADT and mRNA-derived cDNA fractions. ADT libraries were further amplified using 1 μl sample index PCR primer (10 μM, 5′-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGC*T*C-3′) and 1 μl Illumina RPI_X index primer, in which X represents a unique index sequence per sample. ADT and mRNA libraries were normalized to 4 μM and pooled at a 1:9 ratio before loading onto a NextSeq 500 instrument. Libraries were sequenced using 75 cycle kits, with 28 bp on read1 and 56 bp for read2.
Data analysis for single-cell RNAseq
The Cell Ranger Single-Cell Software Suite (10x Genomics) was used to perform barcode processing and transcript counting after alignment to the mm10 reference genome with default parameters. A total of 7267 cells in the conditional KO and 10119 cells in the WT were analyzed for 10784 genes. Analysis of the gene-level transcript counts output by Cell Ranger was performed in R (Version 3.5.2) (79) on the merged KO and WT datasets (24) using the Seurat R package (Version 3.1.4) (24, 25). All ribosomal genes and genes with counts in fewer than 25 cells were excluded. Cells with mitochondrial DNA content >10% (80) or nonzero counts for fewer than 500 genes or more than 3000 genes were also removed. The filtered gene expression data were normalized using the SCTransform (81) method and subsequent computations were performed on the matrix of corrected counts. Unsupervised clustering was performed using Seurat implementation of shared nearest neighbor modularity optimization with the resolution parameter set to 0.2 (28). For data visualization, single-cell gene expression data were projected onto a reduced dimensional space as computed by the Uniform Manifold Approximation and Projection (UMAP) (27) method for the first 30 principal components of the expression data. The variance-adjusted Mahalanobis (VAM) (23) method was used to compute cell-specific scores for pathways from Molecular Signature Database collections (Version 7.0) (82) that were filtered to remove pathways with fewer than five members or more than 200 members. We identified differentially expressed genes and pathways between KO and WT cells using Wilcoxon rank-sum tests applied to either the normalized counts for each gene or the VAM scores for each pathway with p values adjusted using the Bonferroni method.
EasySep Mouse Naive CD8 T Cell Isolation Kits (catalog no. 19858A; STEMCELL Technologies), MojoSort Mouse anti-allophycocyanin Nanobeads (catalog no. 480072; BioLegend), ATP detection assay kit-luminescence (catalog no. 700410; Cayman Chemical), DAPI (catalog no. D1306; Thermo Fisher Scientific), Seahorse XF Cell Mito Stress Test Kit (catalog no. 103015-100; Seahorse Agilent), 2-DG (catalog no. 14325; Cayman Chemical), SIINFEKL peptide (peptide Lot:V1355-37/40; New England Biolabs), rhIL-2 (catalog no. Ro23-6019; TECIN), rmIL-15 (catalog no. 210-15; PeproTech), poly-d-lysine (catalog no. P6407; MilliporeSigma), glutaraldehyde (catalog no. 16000; Electron Microscopy Science), NaBH4 (stock no.:35788; Alfa Aesar), and Triton X-100 (catalog no. N9300260; PerkinElmer).
Violet fluorescent reactive dye (reference no. L34955; Life Technologies), CD45.1-BV421 (catalog no. 110732; BioLegend), Blimp1-BV421 (catalog no. 564270; BD Bioscience), CD8-BV510 (catalog no. 100752; BioLegend), CD45.1-BV510 (catalog no. 110741; BioLegend), CD45.1-allophycocyanin (catalog no. 110714; BioLegend), CD62L-BV510 (catalog no. 104441; BioLegend), CD127-BV510 (catalog no. 135033; BioLegend), CD8-BV650 (catalog no. 100742; BioLegend), MitoTracker Green FM (reference no. M7514; Invitrogen), CD62L-FITC (catalog no. 11-0621-85; eBioscience), Thy1.1-A488 (catalog no. 202506; BioLegend), Thy1.1–allophycocyanin (catalog no. 202526; BioLegend), TCF1-A488 (reference no. 02/2018; cell signaling), TNFa-FITC (catalog no. 506304; BioLegend), MitoSOX Red Mitochondrial Superoxide Indicator (reference no. M36008; Invitrogen), CD45.2-PE (catalog no. 109808; BioLegend), CD62L-PE (catalog no. 104408; BioLegend), CD127-PE (catalog no. 135010; BioLegend), EOMES-PE (reference no. 12-4875-82; Invitrogen), IL-2–PE (catalog no. 503808; BioLegend), Thy1.1-PE (catalog no. 202524; BioLegend), TNFa-PE (catalog no. 506306; BioLegend), CD8-PerCP-Cy5.5 (catalog no. 100734; BioLegend), CD44-PerCP-Cy5.5 (reference no. 45-0441-82; Invitrogen ), Bcl6-PerCP-Cy5.5 (catalog no. 562198; BD Pharmingen), IFNy-PerCP-Cy5.5 (catalog no. 505822; BioLegend), Thy1.1-PE-Cy7 (catalog no. 202518; BioLegend), KLRG-1–PE-Cy7 (catalog no. 138416; BioLegend), CD27-PE-Cy7 (catalog no. 124216; BioLegend), T-bet–PE-Cy7 (reference no. 25-5825-82; Invitrogen), GZB-PE-Cy7 (reference no. 25-8898-82; eBioscience), CD25–allophycocyanin (catalog no. 102008; BioLegend), CD44–allophycocyanin (catalog no. 103012; BioLegend), CXCR3–allophycocyanin (catalog no. 126512; BioLegend), IFNy–allophycocyanin (catalog no. 505810; BioLegend), Thy1.1–allophycocyanin (reference no. 17-0900-82; Invitrogen), p79–allophycocyanin tetramer (National Institutes of Health tetramer facility), Bcl2-A647 (catalog no. 633510; BioLegend), Bcl6-A647 (catalog no. 561525; BD Pharmingen), CD8–allophycocyanin–ef780 (reference no. 47-0081-82; eBioscience), near-infrared fluorescent reactive dye (reference no. L10119; Invitrogen), polyclonal anti-rabbit TOM20 (ab78547, LOT:GR3199811-2; Abcam), Texas Red anti-rabbit IgG (TI-1000; VECTOR), TotalSeq-A0198 CD127 (catalog no. 135045; BioLegend), TotalSeq-A0073 CD44 (catalog no. 103045; BioLegend), and TotalSeq-A0112 CD62L (catalog no. 104451; BioLegend).
Zbtb20 deficiency negatively regulates glycolytic and mitochondrial metabolism in CD8 T cells
Previous research has shown Zbtb20 regulates glucose metabolism in liver cells (21), so we tested whether CD8 T cell metabolism was affected. To address how Zbtb20 deletion affected metabolism in effector and memory CD8 T cells, we differentiated OT-I cells in vitro into either terminal Teff cell or central memory CD8 T (Tcm) cell. As there was the potential for Zbtb20 to affect naive CD8 T cell development or function, we elected to use a GZB-cre Zbtb20-fl/fl CD45.1 OT-I transgenic mouse model in which Zbtb20 is deleted in CD8 T cells only after T cell activation. Splenocytes from either GZB-cre Zbtb20-fl/fl CD45.1 OT-I mice (KO) or CD45.1 OT-I mice (WT) were stimulated with peptide in the presence of either IL-2 or IL-15. Consistent with previous reports, culture with IL-2 induced Teff-like cells, which are characterized by high expression of CD25 and low expression of CD62L, and culture with IL-15 induced Tcm-like cells, which express low levels of CD25 and high levels of CD62L (24) (Supplemental Fig. 1A, 1B). Cells were then subjected to metabolic analysis to test mitochondrial respiration and glycolytic metabolism using the Seahorse Bioanalyzer.
We observed WT Tcm cells had higher SRC compared with Teff cells, consistent with previous studies (8) (Fig. 1A, 1E). Zbtb20 KO Teff cells had significantly lower basal mitochondrial respiration, indicated by basal oxygen consumption rate, compared with WT Teff, but maximal respiration was not different between WT and KO Teff (Fig. 1A, 1C). This resulted in a higher SRC in KO cells. We also interrogated the glycolytic capacity (glycolytic proton efflux rate) of KO and WT Teff cells, as Teff cell are known to heavily depend on glycolysis for production of ATP and effector functions (25). We found that KO Teff displayed higher basal glycolysis compared with WT cells, but maximal glycolytic capacity (compensatory glycolysis) was not different between the groups. This resulted in little spare glycolytic capacity (SGC) in KO Teff, in contrast to WT Teff, which possessed significantly higher SGC (Fig. 1B, 1D). Taken together, our data suggested that in vitro–derived Zbtb20 KO Teff had the same maximal capacity for performing glycolysis as well as mitochondrial respiration as WT Teff. However, under basal conditions KO Teff displayed higher glycolytic activity.
In T cells cultured with IL-15, we found that KO Tcm displayed higher basal mitochondrial respiration, higher maximal respiration as well as higher SRC when compared with WT Tcm (Fig. 1E, 1G). Zbtb20 KO Tcm displayed similar basal glycolysis and compensatory glycolysis but significantly lower SGC compared with WT CD8 T cells (Fig. 1F, 1H).
Collectively, these data show that Zbtb20 deletion increased spare capacity for both glycolysis and mitochondrial respiration in both Teff and Tcm. Interestingly, we also found Zbtb20 deletion played opposite roles regulating basal and maximal mitochondrial respiration in in vitro–generated Teff and Tcm, as it decreased those two parameters in Teff but increased them in Tcm compared with WT. Therefore, Zbtb20 appears to regulate both glycolysis and mitochondrial respiration in CD8 T cells cultured in vitro.
Zbtb20-deficient memory CD8 T cells have increased mitochondrial mass
We wished to establish whether enhanced mitochondrial metabolism observed in Zbtb20-deficient Teff or Tcm cells was accompanied by increased mitochondrial content. In vitro generated Teff or Tcm CD8 T cells were fixed then stained to visualize the mitochondrial outer membrane. Examination by confocal microscopy and image analysis was used to quantify mitochondrial surface area and volume. This revealed that Zbtb20 KO Teff had less mitochondrial surface area and volume than WT cells, whereas Zbtb20 KO Tcm had larger mitochondrial surface area and volume than WT cells (Fig. 2A–E).
Enhanced glycolysis and mitochondrial respiration in Zbtb20-deficient CD8 T cell responses ex vivo
Metabolic changes were observed in Zbtb20-deficient CD8 T cells activated in vitro, so next we tested whether similar changes were present ex vivo. We adoptively transferred either KO or WT OT-I cells into recipient mice subsequently infected with LM-actA–OVA. Splenocytes were harvested at day 7 (effector) or day 28 (memory) postinfection and OT-I cells assayed for mitochondrial respiratory and glycolytic rates. We found that both effector and memory Zbtb20 KO CD8 T cells had higher basal and maximal mitochondrial respiration compared with WT (Fig. 3A, 3C, 3E). Zbtb20 KO memory, but not Teff, cells also had higher SRC compared with WT (Fig. 3A, 3C, 3E). In addition, both effector and memory Zbtb20 KO CD8 T cells exhibited higher basal and maximal glycolysis as well as SGC (Fig. 3B, 3D, 3F). Therefore, our data indicated that Zbtb20 KO effector and memory CD8 T cells directly taken from infected animals exhibited upregulated mitochondrial metabolism and glycolysis.
To determine the extent to which these observations extended to other, unrelated infections, we performed similar experiments infecting with MHV-68–OVA. Few changes were observed in the absence of Zbtb20 during the effector response to MHV-68–OVA (day 14) (Supplemental Fig. 2). In contrast, memory (day 28) CD8 T cells exhibited an increase in basal oxidative phosphorylation and elevation in all glycolytic parameters, similar to the changes observed after LM infection. Therefore, although the identity of the infection influences the phenotype, it is clear Zbtb20 regulates both mitochondrial and glycolytic metabolism in two distinct infection models.
Effector and memory CD8 T cells lacking Zbtb20 possess increased ATP content and higher mitochondrial mass
Elevated mitochondrial and glycolytic metabolism observed ex vivo in the absence of Zbtb20 implies these cells produce ATP at a higher rate. Therefore, we measured the ATP content in WT and Zbtb20-deficient CD8 T cells. WT or KO OT-I cells purified from recipient mice at day 7 or day 28 postinfection were used in a luminescence-based ATP detection assay. We found ex vivo–purified effector and memory Zbtb20 KO CD8 T cells consistently had higher ATP content than WT cells (Fig. 4A).
As our in vitro data indicated higher mitochondrial volume in the absence of Zbtb20 under in vitro conditions that skewed to memory, we measured mitochondrial mass ex vivo. Accurate confocal analysis of these cells was not possible because of the smaller size of effector and memory CD8 T cells directly ex vivo when compared with in vitro–generated cells. We therefore measured mitochondrial content by staining with the mitochondrial dye MitoTracker Green. We found that Zbtb20 KO OT-I cells had no significant difference in mitochondrial content at day 7 (Fig. 4B) but higher mitochondrial content at day 28 postinfection (Fig. 4C).
Increased mitochondrial fuel flexibility in the absence of Zbtb20
As we observed increased mitochondrial respiration in the absence of Zbtb20 in memory cells, we tested whether there were differences in the reliance on different mitochondrial fuels. We generated Tcm in vitro by culture with IL-15, then treated with inhibitors of different mitochondrial fuel sources (etomoxir for long-chain fatty acid, UK5099 for pyruvate, and BPTES for l-glutamine), followed by conventional Seahorse mitochondrial stress tests. The maximal respiratory capacity in each condition was normalized to values without inhibitor treatment. We found that WT Tcm cells displayed reduced mitochondrial activity when they could not use pyruvate or l-glutamine (Fig. 5). In contrast, Zbtb20 KO Tcm cells only displayed reduced mitochondrial activity when they could not use both pyruvate and l-glutamine. Overall, Zbtb20 KO Tcm cells were more flexible with regard to mitochondrial fuel sources compared with WT Tcm, which suggests that Zbtb20 KO Tcm cells could have survival and/or functional advantages over WT Tcm cells in nutrient-limiting environments.
Zbtb20 is induced in activated CD8 T cells
To dissect the expression pattern of Zbtb20 in CD8 T cells, we took advantage of a Zbtb20 reporter mouse strain in which GFP is expressed from the Zbtb20 promoter. Naive Zbtb20-GFP OT-I cells were adoptively transferred into recipient mice, which were then i.v. infected with LM-actA–OVA. Initial experiments showed little GFP expression during the peak of the response, so we focused on early postinfection and memory. Splenocytes were harvested from recipient mice on days 2, 3, 4, and 28 postinfection for analysis. We found Zbtb20 was expressed in approximately half of the OT-I population on day 2 postinfection (Fig. 6A, 6B). By day 3, the proportion of positive cells decreased and was very low by day 4 postinfection. However, by day 28, Zbtb20 reporter was again detectable in a small proportion of cells. To identify T cell populations expressing Zbtb20 during the steady-state in the polyclonal T cell repertoire in a naive mouse, we harvested splenocytes from unmanipulated Zbtb20-GFP mice. We found the phenotype with the highest proportion of Zbtb20-expressing cells was Tcm (defined as CD44+CD62L+) (Fig. 6C–E). Naive CD8 T cells (defined as CD44−CD62L+) also contained a small proportion of Zbtb20-expressing cells. However, CD44+CD62L− and CD44−CD62L− CD8 T cells contained very low proportions of cells expressing Zbtb20.
To determine the Zbtb20 expression profile among polyclonal virus-specific CD8 T cells, we infected Zbtb20-GFP reporter mice on a B6 background intranasally with MHV-68. Their splenocytes were harvested before infection and on day 10, day 14, and day 28 postinfection, then stained with an MHC/peptide tetramer folded with the dominant epitope derived from the ORF61 protein. Reporter expression within the tetramer-positive population was then measured within populations defined by CD44 and CD62L expression (Supplemental Fig. 3A–C). Consistent with data from naive mice, Zbtb20 reporter expression was detected mostly in Tcm (CD44+CD62L+) and naive (CD44−CD62L+) populations (Supplemental Fig. 3D). PCR analysis on FACS-purified GFP+ and GFP− cells showed GFP faithfully represented Zbtb20 mRNA expression in reporter mice (Supplemental Fig. 3E).
Given Zbtb20 expression at the early stages of effector differentiation and in a subset of central memory phenotype cells, we tested how Zbtb20 deficiency affected effector and memory differentiation in vivo.
Zbtb20 deletion enhances cytokine production and favors memory precursor differentiation
We sought to determine how Zbtb20 deletion affected CD8 T cell clonal expansion, accumulation, function, and differentiation. Naive OT-I cells from KO or WT donor mice were adoptively transferred into recipient mice, which were then i.v. infected with LM-actA–OVA. Splenocytes were harvested for analysis on day 7 or day 14 postinfection. We found the number of transferred OT-I cells recovered from the spleens of recipient to be the same at day 7, which measures the peak CD8 T cell response against LM, and day 14, which is during the contraction phase (Fig. 7A, gating strategy Supplemental Fig. 1C). Examining the phenotype of responding OT-I T cells, we found that on both day 7 and day 14 postinfection, Zbtb20 KO OT-I cells were more skewed toward memory precursors (defined as KLRG-1−CD127+) and less toward terminally differentiated effectors (defined as KLRG-1+CD127−) (Fig. 7B). In addition, cytokine production profiles revealed that a higher proportion of Zbtb20 KO OT-I cells could produce IFN-γ or TNF-α as well as both IL-2 and IFN-γ simultaneously (Fig. 7C, 7D). Production of IL-2 is a characteristic of memory cells, consistent with memory precursor skewing, and we detected a higher proportion of KO cells expressing CD27 that is preferentially expressed on Tcm cells (Fig. 7E). We also found a higher proportion of Zbtb20 KO Teff cells expressed CXCR3 during the contraction phase (Fig. 7F), an important chemokine receptor that drives Teff cell to sites of inflammation. Taken together, our data suggested that Zbtb20 KO Teff cells had a phenotype indicative of increased memory potential and enhancements in cytokine production.
Zbtb20 deletion affects the memory CD8 T cell phenotype and cytokine production
Using the OT-I transfer and LM-actA–OVA infection model described above, we tracked Zbtb20 KO and WT OT-I cells until later times postinfection, which allowed us to study the role of Zbtb20 in CD8 T cell memory. On day 28 and day 60, we found the number of splenic Zbtb20 KO memory OT-I cells to be the same as WT OT-I cells (Fig. 8A). Consistent with earlier times postinfection, Zbtb20 KO OT-I cells were more skewed toward memory precursors than effector cells on day 28 (Fig. 8B). In addition, a higher proportion of Zbtb20 KO memory OT-I cells could produce IFN-γ or TNF-α (Fig. 8C) as well as both IL-2 and IFN-γ simultaneously (Fig. 8D). Moreover, a larger percentage of Zbtb20 KO memory OT-I cells expressed CXCR3 and CD27 on day 28 (Fig. 8E, 8F). Data summarizing phenotypic marker expression and cytokine production over time postinfection are shown in Supplemental Fig. 1D. Data obtained from memory timepoints therefore showed skewing toward memory CD8 T cells was consistent with earlier times postinfection.
Modulation of key transcription factors in the absence of Zbtb20
A network of transcription factors tightly orchestrates differentiation of effector and memory CD8 T cells. These regulate the expression of crucial cytokine receptors, proapoptotic and antiapoptotic factors, cellular metabolism, and other critical functions. Interrogation of transcription factor expression revealed that Zbtb20 KO Teff cell expressed higher levels of Bcl-6 and lower levels of Blimp-1 on day 7, whereas on day 14, KO CD8 T cell expressed lower Bcl-6 and higher Blimp-1 compared with WT (Fig. 9A, 9B). At the memory timepoint (day 28), both Bcl-6 and Blimp-1 were lower in KO CD8 T cells. We found Zbtb20 KO CD8 T cells had lower expression of Eomes, a transcription factor necessary for memory CD8 T cell differentiation, on day 7 and day 28 postinfection, but not on day 14 (Fig. 9C). T-bet, a transcription factor important for Teff cell differentiation, was expressed at a lower level in Zbtb20 KO CD8 T cells at day 14 and day 28 but did not reach statistical significance at day 7 postinfection (Fig. 9D). Collectively, our data suggested that Zbtb20 affects expression of several transcription factors important for effector and memory CD8 T cell differentiation. Consistent with these data, we also found Zbtb20 KO memory cells expressed lower levels of Bcl-6, EOMES, and T-bet in the MHV-68 infection model (Supplemental Fig. 4).
Single-cell transcriptomic analysis shows enrichment in metabolic and memory pathways in the absence of Zbtb20
Many studies have shown there is substantial heterogeneity in the CD8 T cell response with respect to the potential to differentiate into memory cells. To conduct transcriptomic analyses that could capture this heterogeneity, we performed single-cell RNAseq analysis on OT-I cells during the primary response. Using the OT-I transfer, LM-actA–OVA infection model described, WT and Zbtb20 KO CD8 T cells were purified and CITEseq performed with oligonucleotide-labeled Abs against KLRG-1, CD127, and CD62L to orient gene expression patterns with known effector/memory markers.
UMAP plots showed some overlaps in clusters occupied by WT and KO cells (Fig. 10A–C); however, there were also regions in which there was little overlap. In particular, a higher proportion of WT cells were in clusters 1 and 2, whereas clusters 0 and 3 were more highly represented in conditional KO cells (Fig. 10B–D). Analysis of gene representation in these clusters showed that clusters 1 and 2 were enriched for genes and proteins associated with effector T cells (Zeb2, granzyme A, and KLRG-1 staining) (Fig. 10E–G). In contrast, memory-associated genes and proteins (IL-7R, Cd27, and CD62L staining) were not present in these clusters, and instead seen preferentially in clusters 0, 3, and 5 (Fig. 10H–J), where the majority of KO cells were located. Examination of a wider array of genes expressed in these clusters showed preferential expression of genes associated with effector activity in clusters 1 and 2 (Zeb2, CX3CR1, Klrg1, GZB, and granzyme A) (26–30) (Supplemental Fig. 5A, left panel). Comparison of genes differentially regulated between WT and KO samples showed KO cells expressed higher levels of Pkm and mt-Nd3, necessary for pyruvate synthesis in glycolysis and mitochondrial NADH dehydrogenase, respectively (Supplemental Fig. 5A, right panel). An extended list of metabolism-associated genes that were differentially expressed is shown in Supplemental Fig. 5C.
Pathway-level analyses were performed using the novel VAM (23) method that was recently developed to compute cell-level gene-set scores visualized in the UMAP plots. Differentially active pathways were also computed using a rank-sum test. Cluster 2 was associated with gene sets previously shown to be upregulated in effector T cells in addition to gene sets from proinflammatory conditions such as allograft rejection and the IFN-γ response (Fig. 10K–N). Gene sets associated with oxidative phosphorylation and glycolysis were preferentially associated with clusters 0 and 3, where the majority of KO cells were located (Fig. 10O, 10P). A similar pattern of association with clusters 0 and 3 was seen with gene sets previously shown to be downregulated in Teff cells relative to memory or memory precursor cells (Fig. 10Q, 10R). An extended list of pathways differentially expressed in the various clusters is shown in Supplemental Fig. 5B (left panel) and was consistent with effector-associated pathway enrichment in clusters 1 and 2, and memory, glycolysis, and mitochondrial metabolism-associated pathway enrichment in clusters 0 and 3. Comparison of pathways enriched in KO versus WT samples (Supplemental Fig. 5B, right panel) showed glycolysis and mitochondrial metabolism-associated pathways enriched in KO samples. Pathways upregulated in memory cells when compared with either effector or naive cells were also enriched KO compared with WT samples. In contrast, effector-associated pathways were enriched in WT samples.
These data clearly confirm our flow cytometric and Seahorse data, showing in the absence of Zbtb20, the CD8 T cell response skews toward the memory phenotype, with enhancement of both glycolytic and mitochondrial metabolism.
Zbtb20 KO memory CD8 T cells mount a more efficient secondary response and confer better antitumor immunity
As the previous data indicated the absence of Zbtb20-enhanced differentiation toward memory CD8 T cells, we next tested the capacity of Zbtb20 KO and WT memory CD8 T cells to accumulate following secondary antigenic challenge. Using the same OT-I transfer and LM-actA–OVA infection model, we i.v. challenged groups of recipient mice on day 29 or day 81 postinfection with MHV-68–OVA. Fig. 11A shows numbers of OT-I cells both before and after challenge. The secondary infection was insufficient to induce a detectable recall response from WT memory cells; however, Zbtb20 KO memory CD8 T cells expanded robustly upon rechallenge at both timepoints (Fig. 11A, 11B). Both Zbtb20 KO and WT OT-I cells cleared the MHV-68–OVA to undetectable levels within 5 d after rechallenge.
To test whether Zbtb20 KO memory CD8 T cells had enhanced protective capability, we tested their ability to inhibit growth of MC38-OVA tumors. Naive OT-I cells were transferred into recipient mice that were subsequently infected with LM-actA–OVA. At 80 d postinfection, memory OT-I cells were purified then adoptively transferred into recipient mice bearing s.c. MC38-OVA tumors that had been growing for 4 d. In mice that received no CD8 T cells, tumors developed quickly in all mice (Fig. 11C). Tumor growth was significantly slower in recipients of WT memory cells; however, the majority of mice developed tumors (Fig. 11D). In contrast, most mice that received Zbtb20 KO memory OT-I cells did not develop measurable tumors, and those that were measurable grew only to a small size then resolved (Fig. 11C, 11D). Therefore, memory CD8 T cells lacking Zbtb20 conferred superior protection against tumors compared with memory cells in which Zbtb20 was intact.
Taken together, our data show Zbtb20 restrains mitochondrial metabolism and glycolysis together with differentiation toward memory precursor cells. This results in memory CD8 T cells with a higher capacity for cytokine production and a more potent ability to mount secondary and antitumor responses.
Based on phenotypic, functional, and metabolic techniques, in conjunction with transcriptional profiling, it is clear that the absence of Zbtb20 skews CD8 T cell differentiation toward the generation of memory. Interestingly, it seems not all KO memory precursor cells survived, as we did not consistently see a larger memory population in KO mice. Bias away from an effector-type profile was particularly evident in our single-cell RNAseq analyses, which also showed enrichment for genes sets associated with memory. A particularly novel finding is that both glycolytic and mitochondrial metabolism were enhanced, whereas typically perturbations that promote memory differentiation enhance mitochondrial metabolism at the expense of glycolytic metabolism (31–34).
Previous studies have shown a critical role for Zbtb20 in hippocampal development and the correct development of neuronal layers in the cerebral cortex (35–38). Consistent with this, patients with certain mutations in Zbtb20 develop Primrose syndrome (39), which includes intellectual disability, macrocephaly, and increased height and weight (40–45). Detailed study of patients with Primrose syndrome revealed metabolic changes, including reduced glucose tolerance, with prevalence of amino acid and fatty acid catabolism, ketogenesis, and gluconeogenesis (46). This indicates impairment in the normal pathway from glucose to pyruvate and then into the citric acid cycle. Instead, amino acids and fatty acids are converted to glucose and ketone bodies, similar to the processes that occur in diabetes and during prolonged fasting. This indicates Zbtb20 regulates genes associated with glucose and fatty acid metabolism in humans. Consistent with this, data from Zbtb20 KO mice showed disrupted glucose homeostasis, and dysregulation of genes associated with glucose metabolism in the liver (21). These mice had severe growth defects and decreased survival, not living beyond 12 wk of age; however, restoration of Zbtb20 selectively in the liver markedly improved survival. Later work using liver-specific Zbtb20 deletion showed Zbtb20 regulates genes associated with glycolysis and de novo lipogenesis (47), and β cell–specific Zbtb20 deletion lead to aberrant glucose metabolism and altered expression of glycolysis-associated genes (48). To our knowledge, this article is the first to describe a role for Zbtb20 in metabolic control in the immune system. Our single-cell RNAseq data suggest several genes central to glycolysis and mitochondrial metabolism are regulated by Zbtb20, and future studies will confirm whether these genes represent direct or indirect targets of Zbtb20.
It is clear that activated and quiescent T cells have distinct bioenergetic and biosynthetic demands (49). Activation, proliferation, epigenetic, cytotoxic functions, and differentiation of T cells are directed by dynamic changes of their metabolism (50). These changes are evident both in mitochondrial structure and in the choice of predominantly mitochondrial or glycolytic metabolism used by the T cell. Mitochondria have a highly compartmentalized structure, and their morphology can be very dynamic. Mitochondrial morphology is critical for DNA sequestration, reactive oxygen species regulation, oxidative phosphorylation, and calcium homeostasis (51). Interconnected mitochondria are linked to increased demand for ATP or nutrient starvation (13, 14, 52–55), whereas globular and fragmented mitochondria are linked to nutrient excess, lower demand for ATP, or severe cellular stress (56, 57). Mitochondria can adapt their morphology under different cellular activation states in T cells, macrophages, and mast cells (16, 58, 59). Rapidly proliferating Teff cells possess globular mitochondria, whereas memory CD8 T cells contain highly interconnected, tubular mitochondria (16). As memory CD8 T cells rely upon mitochondrial respiration for their energy demands, elongated mitochondria with well-ordered cristae are thought to hold components of the electron transport chain in a more efficient configuration (60). Our data indicate that mitochondria in Zbtb20 KO memory CD8 T cells have a larger volume and surface area compared with WT cells, which is consistent with enhanced oxidative phosphorylation observed in these cells. Interestingly, mitochondrial content was lower in Zbtb20 KO in vitro–derived Teff cells. This is consistent with the observed lower basal and maximal oxidative phosphorylation. Nevertheless, KO effector cells did not exhibit impairments in cytokine production or proliferation, presumably because of the enhanced glycolytic metabolism we observed that provided the necessary ATP and biosynthetic intermediates.
Our Seahorse assays clearly showed Zbtb20 deficiency modulates T cell metabolism; however, there were some subtle differences observed between in vitro– and ex vivo–generated effector and memory cells. Basal and maximal glycolysis and oxidative phosphorylation were uniformly increased in ex vivo Teff and memory CD8 T cells. Although IL-15–generated memory cells also displayed elevated basal and maximal oxidative phosphorylation, glycolytic parameters were similar to WT cells. Teff cells generated with IL-2 had elevated basal, but not maximal glycolysis, but depressed basal and maximal oxidative phosphorylation. Several factors may be responsible for these discrepancies. CD8 T cells responding to an infection in lymph nodes or the spleen are exposed to a variety of proinflammatory mediators, cytokines, and activated APCs that are not faithfully replicated by standard in vitro culture conditions. In addition, concentrations of key nutrients such as glucose and glutamate are in excess in vitro and likely more limiting in vivo (61). A recent study found in vitro–derived effector cells operated at their maximal glycolytic capacity, whereas ex vivo–derived cells had larger spare energetic capacity (61). Ex vivo cells also displayed greater oxidative metabolism and switched more easily between mitochondrial and glycolytic pathways. Therefore, it is possible the increased metabolic flexibility in Zbtb20 KO cells, possibly in addition to exposure to inflammatory factors present uniquely in vivo, results in the metabolic changes in these cells being better revealed in vivo.
Teff cells heavily rely on glycolysis and have high rates of glucose uptake (25), whereas memory CD8 T cells rely on mitochondrial respiration (49). It is clear that the substrate used in the mitochondrial citric acid cycle also influences CD8 T cell function, differentiation, and longevity (50). Glutamine metabolism has been reported to be crucial for survival, proliferation, and effector function of CD4 T cells upon activation (62). Fatty acid oxidation has been linked to superior mitochondrial capacity and longevity of memory CD8 T cells (8, 63). In addition, instead of obtaining fatty acids from their external environment, memory CD8 T cell synthesize their own triacylglycerol using glucose-derived carbon (63, 64). Concomitantly, memory CD8 T cell also upregulate expression of the glycerol channel, aquaporin 9, to facilitate the uptake of glycerol required for triacylglycerol synthesis and storage (64). Subsequent studies showed that medium or short chain fatty acids such as acetate also play important roles as mitochondrial fuels in memory CD8 T cells (65–67). Our studies regarding mitochondrial fuel sources show inhibition of glutaminolysis or glycolysis markedly impair mitochondrial respiratory activity in WT CD8 Tcm cells. However, Zbtb20-deficient memory CD8 T cells tolerated inhibition of either fuel source without significant diminution of mitochondrial respiration, and only when both pathways were inhibited was there a significant reduction. Availability of glucose and glutamate are limiting in many growing tumors, creating an environment not conducive for protective T cell responses. Limited flexibility with respect to mitochondrial fuel sources may restrict the protective capacity of WT CD8 T cells, and increased flexibility on the part of Zbtb20-deficient memory cells may partially explain their increased protective capacity. Future studies will determine the underlying mechanism for this increased fuel flexibility and whether it leads to better accumulation and/or effector activity in tumors after T cell infusion.
SRC is thought to be an important factor contributing to enhanced secondary responses by memory CD8 T cells in response to antigenic rechallenge (8). Therefore, it is likely that the larger SRC we observed in Zbtb20-deficient memory CD8 T cells is at least partly responsible for the greater secondary expansion following virus rechallenge. Improved protective capacity from Zbtb20 KO memory cells was demonstrated by superior ability to protect against MC38-OVA tumors. Although enhanced expansion of memory cells is no doubt important in this protection, a higher proportion of cells expressing effector cytokines such as IFN-γ and TNF-α, and CXCR3, which may promote homing to the tumor site, may also have contributed to antitumor activity.
Our data indicate that Zbtb20 is expressed in the first 2–3 d following CD8 T cell activation and is important in shaping the phenotypic, metabolic, and functional evolution of the antimicrobial response. Expression then declines rapidly, but re-emerges in a small subset of memory CD8 T cells. This may indicate that Zbtb20 exerts its effects during the first few days of the T cell response, then is subsequently active in a defined population of memory cells. Early Zbtb20 activity may exert a sustained effect in part through modulation of the network of other transcription factors critical for T cell differentiation. Blimp-1 suppresses Teff cell proliferation and drives their terminal differentiation, whereas Bcl-6 promotes proliferation, survival, and memory differentiation of CD8 T cells (68). Eomesodermin induces expression of several effector molecules in T cells, such as IFN-γ, perforin, and GZB (69), but also promotes homeostatic self-renewal of memory cells through inducing expression of the IL-15R (70). Reduced expression of Blimp-1 and Eomes at day 7 may contribute to the skewing away from terminally differentiated effector cells and toward memory precursors. Expression of these molecules change during the contraction phase (day 14); however, this could be a reflection of the altered proportions of effector and memory cells during contraction, as effectors die off and the proportion of memory precursors enlarges. We also observed elevated Bcl-6 expression at day 7, which is consistent with promotion of memory precursor development. However, a key function of Bcl-6 is to directly repress genes involved in the glycolysis pathway, including Slc2a1, Slc2a3, Hk2, and Pkm2 (71). As we observed increased glycolytic metabolism in the absence of Zbtb20, the effects of elevated Bcl-6 were likely mitigated by other transcription factors or cofactors.
Although most experiments focused on the CD8 T cell response to Listeria infection, we also tested the extent to which they extended to a different, unrelated infection. Murine gammaherpesvirus infection is a different class of pathogen (virus versus intracellular bacteria), and unlike Listeria, it establishes a persistent infection (72). Although we detected changes in T cell metabolism and altered expression of key transcription factors in both infections, there were important differences. Glycolysis was increased in Zbtb20-deficient CD8 T cells in both infections. Basal and maximal mitochondrial respiratory capacity and SRC were all enhanced in KO memory cells in Listeria infection; however, these changes were of smaller magnitude in MHV-68 infection. The pattern of expression of Bcl-6, Eomes, and T-bet were consistent in memory cells in both infections; however, they differed at the acute timepoints. There are a number of factors that may be responsible for these differences, including Ag persistence, engagement of different pattern recognition receptors, and cellular tropism. Despite these differences, however, it is clear Zbtb20 affects both immunometabolism and the transcriptional network during CD8 T cell differentiation across infection types.
In conclusion, data presented in this study identify Zbtb20 as an important regulator of effector and memory CD8 T cell differentiation and metabolism. Given our data showing improved protection from tumors and the known superiority of memory cells in adoptive T cell therapy, deletion or inhibition of Zbtb20 may provide a promising novel strategy for antitumor immunotherapy.
We thank Dr. Randy Noelle (Dartmouth College) for useful discussions and supplying breeding stocks of Zbtb20-GFP and Zbtb20-fl/fl mice and Drs. Eyal Amiel (University of Vermont) and Juan Cubillos-Ruiz (Weill Cornell Medical School) for discussions regarding analysis of metabolic data. We thank Dr. Mary Jo Turk (Dartmouth College) for kindly providing OT-I breeding stocks.
This work was supported by National Institutes of Health (NIH)/National Institute of Allergy and Infectious Diseases Grant R01 AI122854 (to E.J.U.), NIH/U.S. National Library of Medicine Grant K01 LM012426 (to H.R.F.), and NIH/National Institute of General Medical Sciences Centers of Biomedical Research Excellence Grant P20 GM130454 (to H.R.F.), which supported the Center for Quantitative Biology Single Cell Genomics Core and the Genomics and Molecular Biology Shared Resource at Dartmouth. The Center for Quantitative Biology Single Cell Genomics Core and the Genomics and Molecular Biology Shared Resource at Dartmouth is also supported by NIH/National Cancer Institute Cancer Center Support Grant 5P30CA023108-37. Imaging studies were performed at the Dartmouth Institute for Biomolecular Targeting, supported by NIH Grant P20 GM113132.
The online version of this article contains supplemental material.
Abbreviations used in this article:
broad complex, tramtrack, bric-à-brac, and zinc finger
cellular indexing of transcriptomes and epitopes by sequencing
killer cell lectin-like receptor G1
murine gammaherpesvirus-68 encoding OVA
recombinant human IL-2
recombinant mouse IL-15
spare glycolytic capacity
spare respiratory capacity
central memory CD8 T
effector CD8 T
Uniform Manifold Approximation and Projection
The authors have no financial conflicts of interest.