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. Author manuscript; available in PMC: 2022 Jan 15.
Published in final edited form as: J Immunol. 2021 Jun 30;207(2):523–533. doi: 10.4049/jimmunol.2100011

HLA-DR marks recently divided antigen-specific effector CD4 T cells in active tuberculosis patients

Rashmi Tippalagama *, Akul Singhania *, Paige Dubelko *, Cecilia S Lindestam Arlehamn *, Austin Crinklaw *, Mikhail Pomaznoy *, Gregory Seumois *, Aruna D deSilva *,†, Sunil Premawansa ‡, Dhammika Vidanagama §, Bandu Gunasena , ND Suraj Goonawardhana †, Dinuka Ariyaratne †, Thomas J Scriba ∥, Robert H Gilman #,**, Mayuko Saito ††, Randy Taplitz ‡‡, Pandurangan Vijayanand *,§§, Alessandro Sette *,§§, Bjoern Peters *,§§, Julie G Burel *
PMCID: PMC8516689  NIHMSID: NIHMS1704979  PMID: 34193602

Abstract

Upon antigen encounter, T cells can rapidly divide and form an effector population which plays an important role in fighting acute infections. In humans, little is known about the molecular markers that distinguish such effector cells from other T cell populations. To address this, we investigated the molecular profile of T cells present in individuals with active tuberculosis (ATB), where we expect antigen encounter and expansion of effector cells to occur at higher frequency in contrast to Mtb-sensitized healthy IGRA+ individuals. We found that the frequency of HLA-DR+ cells was increased in circulating CD4 T cells of ATB patients, and was dominantly expressed in Mtb antigen-specific CD4 T cells. We tested and confirmed that HLA-DR is a marker of recently divided CD4 T cells upon Mtb antigen exposure using an in vitro model examining the response of resting memory T cells from healthy IGRA+ to antigens. Thus, HLA-DR marks a CD4 T cell population that can be directly detected ex vivo in human peripheral blood, whose frequency is increased during ATB disease and contains recently divided antigen-specific effector T cells. These findings will facilitate the monitoring and study of disease-specific effector T cell responses in the context of ATB and other infections.

Introduction

Effector T cells originate from division of antigen-specific T cells upon TCR activation (1). They can be derived from either naïve T cells upon primary exposure with antigen, or from memory T cells upon antigen re-exposure (2). Effector T cells show higher levels of differentiation and activation of effector functions, such as cytotoxicity and production of cytokines/chemokines, compared to memory T cells (3-5). The effector function of CD4 T cells is highly heterogeneous. It is predominantly mediated through the production of various cytokines and chemokines, and the expression of cell surface proteins to act on other immune cells, infected cells, or pathogens (6). CD4 T cells can also display cytotoxic functions and directly eliminate pathogens or infected cells (7, 8). By virtue of their heterogeneity, effector CD4 T cells are critical in controlling a wide range of pathogenic infections including bacteria, viruses, parasites and fungi (6-9).

The ability to identify and isolate effector T cells in peripheral blood in the context of human infection is of high significance for several reasons. First, the effector population is enriched for antigen-specific T cells and is thus an ideal target for the study and isolation of antigen-specific T cells directly ex vivo. Second, examining the frequency and phenotype of effector cells in a given individual provides information on the immune system status and ongoing immune responses. In diagnosed active diseases such as tuberculosis (TB), the effector T cell population could be tracked to monitor the course of infection and response to treatment. In pre-symptomatic individuals, an increase in frequency of effector T cells could be used as a surrogate marker of increased antigen load, suggesting loss of immunological control of the pathogen. Finally, the ability to isolate effector T cells is necessary to shed light on their biology (e.g. generation, heterogeneity and maintenance) which is still poorly understood, especially for human CD4 T cells.

Despite their importance, there is no single phenotypic marker that can identify bona fide effector CD4 T cells in humans. From parallel studies in the mouse model, effector human CD4 T cells have been reported to have a more “differentiated phenotype” with low expression levels of CCR7, CD27, CD28, CD62L, CD127 and high expression levels of CD95, PD1 and KLRG1 (4, 10-13). However, the combination of markers to define effector CD4 T cells tend to vary between studies and pathogens (14), and the resulting phenotype do overlap with other cell subsets such as effector memory T cells (10, 11). Thus, one key challenge to study effector T cells is the ability to readily distinguish, amongst circulating CD4 T cells of a given antigen specificity, those that are effector cells from those that are not.

Here we set out to characterize the phenotype of effector CD4 T cells in active tuberculosis (ATB). ATB is caused by infection with the bacterium Mycobacterium tuberculosis (Mtb) and is the leading cause of death from one single infectious agent worldwide, with approximately 1.4 million deaths annually (15). Most infected individuals contain Mtb after initial infection, becoming latently infected, with no clinical symptoms and no risk of transmission. In some individuals, particularly those who are elderly or immunocompromised, Mtb can develop into active disease, which is associated with clinical symptoms, risk of transmission, and high mortality and morbidity (16, 17). This can occur either early after infection, or after a period of latency (reactivation TB). CD4 T cells are important in the immune control of Mtb (18, 19), and there is evidence for the presence of Mtb-specific memory CD4 T cells in peripheral blood in all stages of Mtb infection (20, 21). However, we expect the prevalence of circulating Mtb-specific effector T cells to be increased in ATB, which is marked by a lack of control of Mtb and thus increased antigen encounter, leading to effector T cell formation and proliferation. Accordingly, we hypothesized that there is a higher frequency of effector CD4 T cells present in human peripheral blood of individuals with microbiologically confirmed ATB compared to Mtb-sensitized, healthy IGRA+ individuals. To test this, we compared the transcriptomic profile of circulating CD4 T cells of individuals with ATB to IGRA+ and IGRA− individuals to give us insight into the molecular profile of human effector CD4 T cells.

Material and methods

Ethics statement

Human study participants were enrolled at the University of California, San Diego Anti-Viral Research Center clinic (United States), the Universidad Peruana Cayetano Heredia (Peru), the National Hospital for Respiratory Diseases, Welisara (Sri Lanka), or The South African Tuberculosis Vaccine Initiative, Western Cape Province (South Africa). Ethical approval to carry out this work was maintained through the La Jolla Institute for Immunology Institutional Review Board (IRB) or the Human research Ethics Committee of the University of Cape Town. The University of Colombo Ethics Review Committee served as the National Institute of Healthy registered IRB for the Kotelawala Defence University. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki, and all participants provided written informed consent prior to participation in the study. All samples were obtained for specific use in this study.

Participants and samples

Mtb sensitization status was confirmed in participants by a positive IFNγ–release assay (QuantiFERON-TB Gold In-Tube; Cellestis or T-SPOT.TB; Oxford Immunotec) and the absence of symptoms consistent with TB or other clinical or radiographic signs of ATB (healthy IGRA+ cohort). ATB was defined as 1) presence of clinical symptoms and/or radiological/histological evidence of pulmonary TB and 2) microbiologically confirmed by Mtb-specific molecular testing on sputum. Patients were not tested the presence of NTM infection. IGRA− uninfected controls had no past medical history of TB, nor exposure to Mtb or evidence of Mtb sensitization as confirmed by a negative IFNγ–release assay. A total of 39 ATB, 43 IGRA+ and 24 IGRA− participants were analyzed in this study. All participants were confirmed negative for HIV. PBMCs were obtained by density gradient centrifugation (Ficoll-Hypaque, Amersham Biosciences) from leukapheresis or whole-blood samples, according to the manufacturer’s instructions. Cells were resuspended at 50–100 million cells per milliliter in FBS (Gemini Bio-Products) containing 10% DMSO (Sigma) and cryopreserved in liquid nitrogen.

PBMC thawing

Cryopreserved PBMC were quickly thawed by incubating each cryovial at 37°C for 2 min, and cells transferred into 9 ml of cold medium (RPMI 1640 with L-Glutamin and 25 mM Hepes (Omega Scientific), supplemented with 5% human AB serum (GemCell), 1% Penicillin Streptomycin (Gibco) and 1% Glutamax (Gibco)) and 20 U/mL Benzonase Nuclease (Millipore). Cells were centrifuged and resuspended in medium to determine cell concentration and viability using Trypan blue and a hematocytometer. Cells were then kept at 4°C until use for flow cytometry, cell sorting or CTV staining. For the measurement of spontaneous cytokine production in HLA-DR+/− CD4 T cells by flow cytometry, prior to the staining, 0.5 x106 cells were incubated with BD Golgi Plug (1:1000 dilution stock, BD Biosciences) for 2-3h at 37°C.

Flow cytometry

Flow cytometry experiments were performed as previously described (22, 23). For surface staining, up to 0.5x106 cells were incubated with 10% FBS in 1X PBS for 10 minutes. Cells were then stained with 100 μl of PBS containing 0.1 μl fixable viability dye eFluor506 (eBioscience, corresponding to 1:1000 dilution of the stock, as per the manufacturer’s recommendation), 2 μl of FcR blocking reagent (Biolegend, corresponding to 1:50 dilution of the stock; we validated internally that this dilution is performing equally to the manufacturer’s recommended dilution of 1:20), and various combinations of the antibodies listed in Table SI for 20 min at room temperature. After two washes in staining buffer (PBS containing 0.5% FBS and 2 mM EDTA (pH 8.0), cells were either used for intracellular staining or resuspended into 100 μl of staining buffer and stored at 4°C protected from light for up to 4 hr until flow cytometry acquisition.

For intracellular staining, following surface staining cells were fixed for 15 min in PFA 4% at room temperature, and washed with freshly prepared Permeabilization buffer 1X (from 10X stock, eBioscience). Cells were then stained for intracellular antibodies (Table SI for antibody details) in Permeabilization buffer 1X for 30 min at room temperature, washed in staining buffer, resuspended into 100 μl of staining buffer and stored at 4°C protected from light for up to 4 hr until flow cytometry acquisition.

For Eomes staining, cells were stained with surface antibodies as previously described. After two washes in PBS, cells were fixed and permeabilized with the True-Transcription factor buffer Set (BioLegend), according to the manufacturer’s instructions. Subsequently, cells were stained with anti-human Eomes for 30 min at room temperature (Table SI for antibody details). After two washes in PBS, cells were resuspended in 100 μl of staining buffer and stored at 4°C protected until acquisition.

For tetramer staining, cells were incubated for 1h at 37°C with 30 nM of PE-conjugated tetramer, washed twice with PBS, and then stained with surface antibodies as described above. Tetramer is CFP1052–66-DRB5*01:01, an MHC class II tetramer specific for an epitope contained within the CFP10 protein of Mtb (CFP1052-66; QAAVVRFQEAANKQK) (24, 25) and was obtained from ImmunAware (Copenhagen, Denmark).

Acquisition was performed on a BD LSR-II cell analyzer (BD Biosciences) or on a BD FACS Symphony cell sorter (BD Biosciences). Compensation was realized with single-stained beads (UltraComp eBeads, eBioscience) in PBS using the same antibody dilution as for the cell staining. Each antibody was individually titrated for optimum staining. Performance of instruments were checked daily by the flow cytometry core at La Jolla Institute for Immunology with the use of CS and T beads (BD Biosciences), and PMT voltages were manually adjusted for optimum fluorescence detection on each time it was used. A minimum of 150,000 events in the lymphocyte population were recorded for each sample, and data was analyzed using FlowJo software version 10.7.1.

Ex vivo non-naïve CD4 T cell sorting

After PBMC thawing, 10x106 cells were stained with fixable viability dye eFluor 506 (eBioscience) and with anti-human CD19, CD3, CD4, CD8, CD45RA, and CCR7 (Table SI for antibody details), as described in the flow cytometry section above. Cell sorting was performed on a BD FACSAria III cell sorter (Becton Dickinson). A total of 100,000 non-naive CD4 T cells (Figure S1A for gating strategy) was sorted into TRIzol LS reagent (Invitrogen) and used for bulk RNA sequencing. The purity level of the sorted non-naïve CD4 T cells was >99% (Figure S1C).

Memory T cell Proliferation upon Antigen Exposure (MTP-AE) assay

Cell Trace Violet staining

Cryopreserved PBMC from healthy IGRA+ individuals were quickly thawed, and directly used for Cell Trace Violet (CTV) staining. 10μM of working stock was prepared from CellTraceTM Violet Cell Proliferation Kit (Invitrogen) according to the manufacturer’s protocol. Ten million PBMC were transferred to a 1.5ml Eppendorf tube. Cells were washed twice with 1X PBS and centrifuged at 2,500 rpm for 5 min at room temperature. Cells were resuspend in 1 ml of 1X PBS and 2 μl of CTV working solution was added. Cells were gently mixed and vortexed before incubating in the dark for 10-12 min at 37°C (with occasional mixing). Cells were washed twice with 1ml of 20% FBS in 1X PBS for quenching, and resuspended at 10x106 cells/mL in culture media (RPMI 1640 with L-Glutamin and 25 mM Hepes (Omega Scientific), supplemented with 5% human AB serum (GemCell), 1% Penicillin Streptomycin (Gibco) and 1% Glutamax (Gibco)).

Culture

Following CTV staining, cells were plated at 0.5x106 cells/well in a final volume of 250μl of media per well (corresponding to 2x106 cells/mL final cell concentration) in a 96-U bottom well plate. We have previously shown that this cell concentration is suitable for in vitro expansion of antigen-specific T cells (26, 27). Cells were stimulated with Mtb-specific MTB300 peptide pool (28) at 2 μg/mL (final concentration) or an equivalent DMSO concentration (0.3% final). A positive control using plate bound anti-human CD3 antibody (clone OKT3, Invitrogen) and soluble anti-human CD28 antibody (clone CD28.2, BD Biosciences) at 1ug/mL (final concentration) was also included for each experiment. Cells were incubated at 37°C for up to 14 days. From each well, 125μl of the culture supernatant (corresponding to half of the total culture volume) was replaced with fresh media every three to four days and was supplemented with 0.02 U/μL of IL-2 (Prospec) on days 4, 8 and 12.

Flow cytometry staining

Daily, up to 0.5 x 106 cells were stained with anti-human CD19, CD14, CD3, CD4, CD8, CD45RA and HLA-DR (Table SI for antibody details), as described in the flow cytometry section above. Cells were then washed twice with PBS and re-suspended in 100 μl of MACS buffer. 7-AAD viability staining solution (BioLegend) at 1.5 μg/mL was added 30min before acquisition, as per the manufacturer’s recommendation. No washing was performed after this step to maintain a positive extracellular concentration of 7-AAD, which is necessary to retain the dye intracellularly in non-viable cells.

Gating and sorting of CTV stained cells

CD4 T cells were defined as 7AAD−CD14−CD19−CD8−CD3+CD4+ (see gating strategy in Figure S1B). The division gating within CD4 T cells was then defined based on CD4/CTV co-staining plots. Since the dye is expected to equally divide between two daughter cells, an approximate 50% loss in fluorescence compared to Div0 was considered as a first division. The Div0 gate was set based on the CTV fluorescence in the baseline pre-stimulation sample to include all cells; the Div1 lower boundary gate (and Div2+ upper boundary gate) was set at half of the fluorescence of the lower boundary of the Div0 gate (Figure 5B). At day 8 post stimulation, up to 100,000 of Div0 and Div2+ non-naïve CD45RA− CD4 T cells (see gating strategy in Figure S1B) were sorted using a BD Aria II or BD Aria III cell sorter (Becton Dickinson) into TRIzol LS reagent (Invitrogen) and used for bulk RNA sequencing.

Bulk RNA sequencing

RNA sequencing was performed as described previously (22). Briefly, total RNA was purified using an miRNeasy Micro Kit (QIAGEN) and quantified by quantitative PCR, as described previously (29). Purified total RNA (1–5 ng) was amplified following the Smart-Seq2 protocol (16 cycles of cDNA amplification) (30). cDNA was purified using AMPure XP beads (Beckman Coulter). From this step, 1 ng of cDNA was used to prepare a standard Nextera XT sequencing library (Nextera XT DNA sample preparation kit and index kit, Illumina). Whole-transcriptome amplification and sequencing library preparations were performed in a 96-well format to reduce assay-to-assay variability. Quality-control steps were included to determine total RNA quality and quantity, the optimal number of PCR preamplification cycles, and fragment size selection. Samples that failed quality control were eliminated from further downstream steps. Barcoded Illumina sequencing libraries (Nextera; Illumina) were generated using the automated platform (Biomek FXp). Libraries were sequenced on a HiSeq 2500 Illumina platform to obtain 50-bp single-end reads (TruSeq Rapid kit; Illumina).

Bulk RNA sequencing analysis

RNA sequencing analysis was performed as previously described (22). Briefly, the single-end reads that passed Illumina filters were filtered for reads aligning to tRNA, rRNA, adapter sequences, and spike-in controls. The reads were then aligned to UCSC hg19 reference genome using TopHat (v 1.4.1) (31). DUST scores were calculated with PRINSEQ Lite (v 0.20.3) (32), and low-complexity reads (DUST>4) were removed from the BAM files. The alignment results were parsed via SAMtools (33) to generate SAM files. Read counts for each genomic feature were obtained with the htseq-count program (v 0.6.0) (34) using the “union” option. After removing absent features (zero counts in all samples), the raw counts were imported to R/Bioconductor package DESeq2 (35) to identify differentially expressed genes among samples. The sequencing data presented in this study were submitted to the Gene Expression Omnibus under accession numbers GSE161829 and GSE162725 (https://www.ncbi.nlm.nih.gov/geo).

Bioinformatic analysis

Differential expression analysis was done using R Studio version 1.2.5019 and R/Bioconductor package DESeq2 (35). Genes with a TPM count of zero in > 80% samples and genes with a TPM mean value < 1 across all samples were filtered out. For non-naïve CD4 T cells isolated ex vivo from ATB, IGRA+ and IGRA− individuals, differentially expressed genes were selected based on a P-adjusted value < 0.05. For CTV stained CD4 T cells isolated with the MTP-AE assay, differentially expressed genes were selected based on a P-adjusted value < 0.05 and an absolute log2 fold change >1. Heatmaps were created using R or the software Qlucore using raw counts transformed with the vst function in R. Enrichment for Gene Ontology terms associated with biological processes was perform using the online server Enrichr (36, 37). Principal Component Analysis (PCA) was performed with R, using z-score normalized expression values from the total 35,911 genes detected across all four sorted CD4 T cell populations.

Inference of HLA-DRB5*5 positivity for each participant in the ATB and IGRA+ cohort was done through a personalized HLA allele specific expression pipeline. Each donor’s specific HLA alleles, including HLA-DRB5, was determined using bulk RNA-Seq data with both Optitype version 1.3.2 and PHLAT version 1.1. Once consensus HLA alleles were obtained from both tools, expression quantification was performed using Salmon version 0.11.2. Briefly, the FASTA sequences for each donor’s corresponding computational HLA typing was downloaded from the IMGT-HLA database (38). These FASTA sequences were then used to create a custom Salmon index used for expression quantification. TPM values for each HLA allele were obtained using the Salmon quant method with the following parameters: --rangeFactorizationBins 4, --validateMappings, --minScoreFraction 1. These parameters were selected to increase quantification accuracy through both alignment validation and restricting alignments to perfect matches. A donor was considered positive for HLA-DRB5*5 expression if expression of the allele was >1 TPM.

Statistical analysis

Statistical analyses were performed using GraphPad Prism Software, version 9. Paired datasets were compared using the nonparametric Wilcoxon test, while unpaired datasets were compared using the nonparametric Mann-Whitney test. P values less than 0.05 were considered significant and 2-tailed analyses were performed. P value of overlap between the two gene signatures in Fig 1A was defined based on the hypergeometric distribution test (considering the 42,852 transcripts detected in ex vivo non-naïve sorted CD4 T cells as the total number of genes).

Figure 1: Transcriptomic profiling of circulating non-naïve CD4 T cells in ATB.

Figure 1:

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Non-naïve CD4 T cells were sorted from ATB (n=24 at diagnosis, and n=22 two months post TB treatment), IGRA+ (n=40) and IGRA− (n=20) individuals as depicted in Fig S1A and their transcriptomic profile defined by bulk RNAseq. (A) Venn diagram showing overlap between differentially expressed genes in ATB vs IGRA+ and ATB vs IGRA− comparisons. (B) Heatmap of the 306 commonly differentially expressed in ATB vs IGRA+ and ATB vs IGRA− comparisons. (C) Top 10 GO biological processes terms enriched in the 178 genes upregulated in ATB vs IGRA+ and ATB vs IGRA− comparisons. HLA-DRB1 gene expression in the ATB cohort at diagnosis compared to (D) IGRA+ and IGRA− cohorts and (E) 2 months post TB treatment. * p < 0.05, ** p < 0.01, *** p < 0.001, non-parametric unpaired Mann-Whitney test (D) and non-parametric paired Wilcoxon test (E).

Results

Increased HLA-DR expression in circulating non-naïve CD4 T cells of active tuberculosis patients compared to healthy IGRA+ or IGRA− individuals

We have previously shown that it is possible to identify transcriptomic signatures in circulating CD4 T cells that can distinguish healthy IGRA+ from IGRA− individuals, reflecting enrichment for Mtb-specific CD4 T cells in the former cohort (22). To define the immune signature of effector CD4 T cells in the context of ATB, we compared the transcriptomic profile of circulating non-naïve CD4 T cells across individuals with ATB (n=24), previous exposure with Mtb but no clinical symptoms of ATB (IGRA+, n=40) and no evidence of Mtb infection (IGRA−, n=20). We specifically excluded classically defined naïve cells that were CCR7+CD45RA+ (see Figure S1A for gating strategy). Effector T cells are expected to have no expression of CCR7 and facultative expression of CD45RA (10-12), and will thus fall within this non-naïve compartment. Circulating non-naïve CD4 T cells showed a distinct transcriptomic profile in ATB patients, with 581 and 778 genes differentially expressed compared to IGRA− and IGRA+ individuals, respectively (adjusted p value < 0.05, Figure 1A). There was a significant overlap between these gene sets, with 306 genes differentially expressed in both (p value of overlap = 2.2x10−16), and we considered this gene list as the expression signature of non-naïve CD4 T cells in ATB (ATB CD4 immune signature (IMS), Figure 1A), which we expect to include genes corresponding to recently proliferated CD4 effector T cells. Of the 306 genes from this ATB CD4 IMS, 178 were upregulated in ATB compared to both healthy cohorts (IGRA+ and IGRA−), while the remaining 128 were downregulated (Figure 1B). We examined the gene sets for shared functional assignments using the Gene Ontology (GO) biological process function from the online platform Enrichr. The upregulated genes were significantly enriched for 66 GO terms (adjusted p value < 0.05). The top 10 included GO terms associated with antigen-specific T cell activation (antigen processing and presentation via MHC Class I, TCR signaling pathway) and inflammation (NIK/NfkB signaling, IL-1 mediated signaling pathway) (Figure 1C). There was no significant enrichment for GO terms in the downregulated genes. HLA-DRB1 was the only previously reported activation marker for T cells amongst the ATB CD4 IMS, with upregulated gene expression in CD4 T cells of ATB individuals compared to both healthy cohorts (IGRA+ and IGRA−) (Figure 1D). Moreover, in a set of 22 matched paired samples of ATB patients collected at diagnosis and 2-3 months after the start of anti-TB therapy, HLA-DRB1 expression in CD4 T cells was significantly reduced upon initiation of treatment (Figure 1E). Thus, RNA expression of HLA-DRB1 on non-naïve CD4 T cells is increased in patients with ATB at diagnosis, before the start of anti-TB therapy.

Increased frequency of HLA-DR+ single-expressers in circulating CD4 T cells of ATB patients compared to healthy IGRA+ or IGRA− individuals

We performed flow cytometry experiments to test if the observed upregulation of HLA-DR expression in CD4 T cells of ATB patients at the RNA level was also reflected in increased protein expression on the surface of CD4 T cells. We found that the frequency of HLA-DR+ cells was indeed higher in circulating CD4 T cells of individuals with ATB, compared to both healthy cohorts (IGRA+ and IGRA−) (Figure 2A and 2B). To determine whether other T cell activation markers might also be upregulated in circulating CD4 T cells of ATB patients, we explored expression of 9 other T cell activation markers, along with HLA-DR, in a smaller set of samples. These markers were selected based on our previous studies aiming at phenotyping antigen-specific T cells in the context of Mtb (22, 39) but also other viral and bacterial infections (40, 41). We found that three of the selected markers (CD25, OX40 and PDL1) showed increased positive frequency amongst CD4 T cells in the ATB cohort compared to IGRA− individuals (Figure 2C and Figure S2A). The majority of HLA-DR+ CD4 T cells did not express any of these three markers, and amongst dual expressers, the highest co-expression was observed for CD25 (Figure 2D and Figure S2B). The frequency of CD4 T cells with positive expression for each of the remaining six markers (CD38, CD69, CD137, CD153, CD154 and PD1) did not show any change across Mtb infected and uninfected cohorts (Figure 2E). Thus, the frequency of HLA-DR+ CD4 T cells is increased in the peripheral blood of ATB patients but not healthy IGRA+ or IGRA− individuals.

Figure 2: Increased frequency of HLA-DR+ single-expressers in circulating CD4 T cells of ATB patients.

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(A) Representative flow cytometry staining plot of HLA-DR expression in CD4 T cells of ATB, IGRA+ and IGRA− individuals. Frequency of (B) HLA-DR+ cells, (C) CD25+, OX40+ and PDL1+ cells, (D) HLA-DR+ cells co-expressing CD25, OX40 or PDL1, (E) CD38+, CD69+, CD137+, CD153+, CD154+ and PD1+ cells in CD4 T cells of ATB, IGRA+ and IGRA− individuals determined by flow cytometry. (A-B) combined data from n=15 ATB, n=11 IGRA+ and n=11 IGRA− individuals. (C-E) combined data from n=4 ATB, n=5 IGRA+ and n=5 IGRA− individuals. CD4 T cells were gated as CD3+CD4+CD8−CD19−CD14− in the live singlet gate of PBMC. * p < 0.05, ** p < 0.01, non-parametric unpaired Mann-Whitney test.

HLA-DR is expressed on antigen-specific CD4 T cells in ATB but not healthy IGRA+ individuals.

Effector T cells originate from antigen-specific T cells that start to divide upon TCR activation. To investigate if the increased population of circulating HLA-DR+ CD4 T cells in ATB recognize Mtb antigens, we stained cells with an MHC class II (DRB5*01:01) tetramer loaded with a CFP10 epitope (24, 25). Tetramer positive (tet+) Mtb-specific CD4 T cells could be identified in 5 out of 6 IGRA+ individuals and 4 out of 5 ATB individuals tested that expressed DRB5*01:01 (Figure 3A, frequency above the limit of detection of 0.01% of total CD4 T cells). Average frequencies were higher in the ATB cohort compared to IGRA+ (Figure 3A, 0.06% and 0.02% tet+ amongst total CD4 T cells, in ATB vs. IGRA+ respectively). In each participant, we looked for co-staining of the tetramer with HLA-DR. We also included the three activation markers that also showed significant upregulation in circulating CD4 T cells in ATB patients compared to IGRA− individuals, namely CD25, OX40 or PDL1 (Figure 2C). In ATB patients, the vast majority (58-88%) of tet+ cells expressed high levels of HLA-DR (Figure 3B and 3C). Less than 20% of tet+ cells expressed CD25 or PDL1, and no tet+ cells expressed OX40 (Figure 3B and 3C). In contrast, when looking at IGRA+ individuals, less than 3% of tet+ cells expressed HLA-DR (Figure 3D and 3E). Thus, HLA-DR marks Mtb-specific CD4 T cells in patients with ATB, but not IGRA+ individuals, consistent with them being recently activated effector cells.

Figure 3: HLA-DR is expressed on antigen-specific CD4 T cells in ATB but not healthy IGRA+ individuals.

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Mtb-specific CD4 T cells were identified by flow cytometry using CFP1052–66-DRB5*01:01, an MHC class II tetramer specific for an epitope contained within the CFP10 protein of Mtb (CFP1052-66; QAAVVRFQEAANKQK) (24, 25). (A) Frequency of tetramer positive (tet+) cells in ATB (n=4) and IGRA+ (n=5) individuals that were defined as HLA-DRB5*01:01 positive from the cohort analyzed in Figure 1. One ATB and one IGRA+ participants had undetectable levels of tet+ cells (less than 0.01% of total CD4 T cells) and were excluded from the analysis. (B) Representative co-staining plots and (C) frequency of tet+ cells co-expressing CD25, HLA-DR, OX40 or PDL1 in ATB. (D) Representative co-staining plots and (E) frequency of tet+ cells co-expressing HLA-DR in ATB compared to IGRA+ individuals. * p < 0.05, non-parametric unpaired Mann-Whitney test.

HLA-DR+ CD4 T cells are associated with an effector phenotype

To further characterize HLA-DR+ CD4 effector T cells in the ATB cohort, we examined their phenotype using flow cytometry. Effector T cells are expected to downregulate CCR7 (10-12). As expected, we found that the CD45RA−CCR7− phenotype was significantly overrepresented in HLA-DR+ compared to HLA-DR− CD4 T cells. CD45RA−CCR7− cells encompassed 51-74% of HLA-DR+ cells but only 7-21% of HLA-DR− CD4 T cells (Figures 4A and 4B). Key functions of effector T cells are direct killing of infected cells or indirect modulation of the immune response through the expression of cytokines. We compared the expression of cytotoxic molecules, as well as spontaneous cytokine production, in HLA-DR+ vs. HLA-DR− CD4 T cells from ATB patients. We found that HLA-DR+ CD4 T cells were enriched for cells expressing the cytolytic granule Granzyme B (19-72% vs 2-51% GzmB+ cells in HLA-DR+ vs HLA-DR−), the degranulation marker CD107a (1-19% vs. 0.3-6% CD107a+ cells in HLA-DR+ vs HLA-DR−), the activation and exhaustion marker Tim3 (0.5-4.9% vs 0.1-0.8% Tim3+ cells in HLA-DR+ vs. HLA-DR−), and the cytotoxic transcription factor Eomes (13-34% vs 1-7% Eomes+ cells in HLA-DR+ vs. HLA-DR−), in comparison to HLA-DR− CD4 T cells (Figure 4C). HLA-DR+ CD4 T cells also had a higher proportion of cells spontaneously producing TNFα (0.1-2.1% vs 0.1-0.6% TNFα+ cells in HLA-DR+ vs HLA-DR−), but not IFNγ or IL-17 in comparison to HLA-DR− CD4 T cells (Figure 4D). To address whether these phenotypic features were specific to ATB or a general feature of HLA-DR+ CD4 T cells, we compared the differentiation and effector phenotype of HLA-DR+ CD4 T cells in ATB, IGRA+ and IGRA− individuals. The differentiation phenotype (assessed by CD45RA and CCR7 co-expression) was not statistically different between HLA-DR+ CD4 T cells of ATB and IGRA+ individuals, with a predominant CD45RA−CCR7− phenotype (p value > 0.05, Figure S3A). A higher proportion of HLA-DR+ CD4 T cells expressed GzmB and Tim3 in ATB compared to IGRA− but not IGRA+ individuals (Figure S3B). Similar proportion of cells expressing CD107a, Eomes, or any of the three measured cytokines in HLA-DR+ CD4 T cells was observed across all three cohorts (Figure S3B and S3C). Thus, in ATB patients, circulating HLA-DR+ CD4 T cells present characteristics of effector T cells with expression of markers associated with effector phenotype, cytotoxic function and recent activation.

Figure 4: HLA-DR expression marks a subset of CD4 T cells with an effector phenotype.

Figure 4:

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(A) Representative flow cytometry staining plot of CD45RA and CCR7 expression in HLA-DR+ vs HLA-DR− CD4 T cells in ATB. (B) Frequency of CD45RA/CCR7 boolean phenotypes in HLA-DR+ vs HLA-DR− CD4 T cells in ATB patients (n=5) determined by flow cytometry. Frequency of positive cells for (C) cytotoxic and activation markers GzmB, CD107a, Tim3, Eomes and (D) spontaneous production of the cytokines TNFα, IFNγ and IL-17 in HLA-DR+ vs HLA-DR− CD4 T cells of ATB patients (n=5 for Eomes, n=10 for all others), determined by flow cytometry. * p < 0.05, ** p < 0.01, non-parametric paired Wilcoxon test.

Development of a Memory T cell Proliferation upon Antigen Exposure (MTP-AE) assay to examine the phenotype of proliferating T cells upon in vitro stimulation

To further test if HLA-DR+ CD4 T cells in ATB patients are indeed effector T cells, we wanted to examine if this phenotype is consistent with recent proliferation after antigen-specific activation, which is a fundamental hallmark of effector T cells. Identifying recently proliferated cells in humans in vivo is challenging. One commonly used approach is detecting the presence of the protein Ki67, which identifies proliferating T cells in human blood samples, but it can only detect cells that are actively undergoing division, or shortly thereafter, and not the ones that have recently completed cell division (42). Detection of Ki67 also requires nuclear staining and thus cell fixation, which limits downstream analyses that can be performed on positive cells. To address this, we designed an in vitro assay aiming at mimicking Memory T cell Proliferation upon Antigen Exposure (MTP-AE). In the MTP-AE assay, PBMC samples from healthy IGRA+ individuals (which are expected to contain a significant number of Mtb-specific memory CD4 T cells) were stained using the proliferation dye Cell Trace Violet (CTV). CD4 T cells were then stimulated with a pool of Mtb-derived peptide epitopes (28), simulating antigen exposure. IL-2 was given on days 4, 8 and 12 as an additional signal for proliferation. Cells were cultured in vitro for 14 days, and the level of CTV staining was monitored daily to identify divided cells. The MTP-AE assay mimics how memory Mtb-specific CD4 T cells are activated, and start proliferating into differentiated effector cells, similar to what we would expect in vivo in the development of an active infection.

The MTP-AE assay revealed HLA-DR marks recently divided CD4 T cells upon Mtb antigen exposure.

We applied the MTP-AE assay on PBMC from 5 IGRA+ individuals. CD4 T cells were partitioned into those that had not divided (Div0), those that had divided once (Div1 group) and those that had divided at least twice (Div2+ group) based on CTV fluorescence (Figure 5A and 5B). Along with CTV, we also monitored the surface expression of HLA-DR in CD4 T cells using flow cytometry to divide them into HLA-DR+ and HLA-DR− CD4 T cells. A significant number of proliferating cells could be detected from day 4 onwards, with Div1 and Div2+ peaking at day 7 and day 11 post stimulation, respectively (Figure 5C). Bivariate flow cytometry plots of HLA-DR with CTV and a side by side comparison of the frequency of HLA-DR+ cells within undivided (Div0) versus divided (Div1 and Div2+) CD4 T cells showed HLA-DR expression was mostly restricted to divided CD4 T cells (Figure 5D and 5E).

Figure 5: HLA-DR expression marks recently divided CD4 T cells upon Mtb antigen exposure.

Figure 5:

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Using our in vitro model of Mtb-specific T cell proliferation assay, PBMC of healthy IGRA+ individuals were stained with proliferation dye Cell Trace Violet (CTV) and in vitro stimulated with the MTB300 peptide pool (28) for 14 days. Each day post stimulation, CD4 T cells were identified as either undivided (Div0), divided once (Div1) or divided more than once (Div2+) by flow cytometry. (A) Representative co-staining plots of CD4 and CTV expression before (Day 0), 4 days and 10 days post MTB300 stimulation within CD4 T cells. (B) Representative histogram of CTV expression at 4 days and 10 days post MTB300 stimulation. (C) Number of CD4 T cells within each CTV division category in function of days post MTB300 stimulation. (D) Representative co-staining plots of HLA-DR and CTV expression before (Day 0), 4 days and 10 days post MTB300 stimulation within CD4 T cells. (E) Frequency of HLA-DR+ undivided (Div0) and HLA-DR+ divided (Div1/Div2+) CD4 T cells in function of days post MTB300 stimulation. (A-E) Combined data from n=5 IGRA+ individuals. (F) Principal component analysis based on all genes expressed in sorted CD4 T cell populations and (G) heatmap representing the expression of the top 25 genes upregulated in Div2+ vs Div0 non-naïve CD4 T cells upon MTB300 stimulation. Genes highlighted in blue have a flow cytometric antibody available for measurement of their protein expression. Cells were sorted from 5 IGRA+ individuals at day 8 post stimulation with either DMSO or MTB300 (see gating strategy in Figure S1B), and their transcriptomic profile determined by RNAseq. (H) Fold change in positive frequency of selected markers to identify recently divided cells (blue genes in 5G, excluding GZMB) in HLA-DR+ compared to HLA-DR− CD4 T cells in ATB patients (n=10 for CCL3, CTLA4, TNFRSF18, CD82, OX40 and CCL4; n=4 for CD38 and CD25) determined by flow cytometry. Significant markers (p <0.05) are represented in green. (I) Frequency of positive cells for CCL3, CD38 and OX40 in HLA-DR+ CD4 T cells of ATB (similar cohort to (H)), IGRA+ (n=6) and IGRA− (n=6) individuals. * p < 0.05, *** p < 0.001, **** p < 0.0001, non-parametric paired Wilcoxon test.

To confirm that the phenotype of HLA-DR+ CD4 T cells reflects that of recently divided cells, we investigated the protein expression of several markers for recently divided cells in HLA-DR+ vs HLA-DR− CD4 T cells. To define the optimal markers that can identify recently divided cells, we sorted Div0 and Div2+ non-naïve CD45RA− CD4 T cells at 8 days post stimulation and analyzed their transcriptomic profile by RNA sequencing (Figure S1B for gating strategy). We selected day 8 for sorting as it was the timepoint with the highest number of Div2+ cells before the sharp drop in undivided cells that occurred at day 9 post stimulation, likely due to cell death. Unsupervised analysis showed HLA-DR+ and Div2+ CD4 T cells had a similar gene expression profile in comparison to HLA-DR− and Div0 CD4 T cells (Figure 5F). To translate these transcriptomic similarities at the protein level, we selected the most abundant 25 genes that were upregulated in Div2+ cells compared to Div0 cells (Figure 5G, adjusted p value < 0.05 and logFC > 2, ranked by decreasing transcript per million (TPM) value). Amongst these 25 genes was Granzyme B (GZMB) which was previously found to be upregulated at the protein level in HLA-DR+ compared to HLA-DR− CD4 T cells (Figure 4C). An additional 8 genes for which a fluorochrome-conjugated antibody was commercially available were selected to measure their protein expression in HLA-DR+ and HLA-DR− CD4 T cells of ATB patients by flow cytometry. For each marker, we calculated a fold change expression between the frequency of positive cells within HLA-DR+ and HLA-DR− CD4 T cells. Out of the 8 measured markers, only OX40 and CCL4 did not show positive frequency differences between the HLA-DR+ and HLA-DR− populations (Figure 5H). CCL3, CD38, CTLA4, CD25, TNFRSF18 and CD82 all showed significant higher frequency of positive cells in HLA-DR+ compared to HLA-DR− CD4 T cells (Figure 5H, Figure S3E). The highest fold change between HLA-DR+ and HLA-DR− populations was observed for CCL3, followed by CD38, CTLA4 and CD25 (Figure 5H). These 4 markers also showed very low frequency of positive cells within HLA-DR− cells compared to HLA-DR+, indicating a high specificity for the HLA-DR+ subset (Figure S3E). The frequency of cells expressing CCL3, CD38 and OX40 was also significantly increased in HLA-DR+ cells of ATB patients compared to the IGRA+ cohort (as well as IGRA− individuals for CCL3 and CD38) (Figure 5I). The frequency of positive cells within HLA-DR+ CD4 T cells for the other measured markers was unchanged across all three cohorts (Figure S3D). Taken together, these results indicate that HLA-DR expression is a hallmark for CD4 T cells that have recently divided after exposure to Mtb antigens.

Discussion

In this study, we set out to determine the phenotype of effector CD4 T cells that are generated during ATB disease as a result of antigen encounter. Effector CD4 T cells are expected to form a predominantly short-lived, but effective, arm of the immune response to fight active infections. Through multiple lines of evidence, we found that HLA-DR expression was a marker of such effector cells, and that HLA-DR+ CD4 T cells were increased in individuals with ATB.

This is not the first association between HLA-DR and CD4 T cells in the context of TB. In BCG vaccinated infants and IGRA+ adolescents, frequencies of activated HLA-DR+ CD4 T cells were associated with increased active disease risk (43) and decreased at 27 weeks post-vaccination (44). In terms of antigen-specific CD4 T cells, several reports have shown that HLA-DR expression on cytokine producing CD4 T cells after Mtb-specific in vitro stimulation can distinguish ATB from healthy IGRA+ individuals (45-49) and can also be predictive of progression to ATB (50) and sputum negative conversion upon TB treatment (51, 52). Thus, our data corroborates these findings and establishes that the frequency of HLA-DR+ cells is also increased in Mtb-specific CD4 T cells isolated directly ex vivo in the context of ATB. More importantly, we demonstrated for the first time that HLA-DR marks a population of CD4 T cells with characteristics of effector cells, providing a seminal reason for why clinicians and immunologists should investigate more closely the HLA-DR+ CD4 T cell population in Mtb infected individuals.

We found that HLA-DR+ CD4 T cells presented a cytotoxic phenotype with higher proportion of cells expressing the cytotoxic markers granzyme B, Eomes, Tim3, CD107a. This is not the first association between Mtb-specific CD4 T cells and markers of cytotoxicity. CD4 T cells have been shown to upregulate the expression of granzymes, granulysin and perforin after Mtb-specific in vitro stimulation and the ability to lyse Mtb-infected monocytes (53, 54). More recently, Mtb-specific CD4 T cells isolated from IGRA+ individuals showed increased expression of GzmB and CD107a after antigen-specific in vitro stimulation and expansion (55). Altogether, our and previous observations suggest cytotoxicity is a phenotypic characteristic of Mtb-specific effector T cells, and that this function might be important in driving protective immunity.

In terms of cytokines, we showed that HLA-DR+ CD4 T cells had a higher proportion of cells expressing the cytokine TNFα, but not IFNγ or IL-17. The frequency of TNFα single-positive Mtb-specific CD4 T cells has been positively associated with ATB and suggested as a diagnostic marker to distinguish ATB from healthy IGRA+ individuals (56). TNFα has a critical role in cell death and cytotoxicity (57), corroborating our findings on the association between Mtb-specific effector CD4 T cells and a cytotoxic phenotype. The absence of IFNγ is surprising since it is known to be a major driver of CD4 T cell protective immunity in TB. A previous study has shown that cytokine production (including IFNγ and IL-17) after PMA/Ionomycin stimulation was greater in HLA-DR+ compared to HLA-DR− CD4 T cells from ATB patients (58). In this study, we only assessed spontaneous cytokine production directly ex vivo. Thus, HLA-DR+ CD4 T cells in our ATB cohort might nonetheless have a greater ability to produce IFNγ and other cytokines upon in vitro stimulation in comparison to HLA-DR− CD4 T cells.

We established an in vitro assay using stimulation with an epitope megapool in an appropriate cytokine environment to mimic antigen encounter, combined with the use of a proliferation dye to identify dividing T cells in response to antigen exposure. We named this assay Memory T cell Proliferation upon Antigen Exposure (MTP-AE). Whereas the use of proliferation dyes is not novel, this is to our knowledge the first attempt to leverage them to assess the transcriptomic profile of antigen-specific CD4 T cells that have recently divided. We used a proliferation dye in combination with flow cytometry, and RNA-Seq to determine that the phenotype of HLA-DR+ CD4 T cells significantly overlaps with that of recently divided cells. The MTP-AE assay can be straightforwardly modified using a stimulus of distinct nature (e.g. whole organism, single protein or peptide, etc.) or distinct antigenic specificity, as long as the starting PBMC population contain the corresponding antigen-specific memory T cell population that can be activated. For instance, it could help identifying similarities and differences in the transcriptomic profile of proliferating Mtb-specific CD4 T cells after stimulation with whole organisms such as heat-killed Mtb, in comparison to the Mtb-specific peptide pool that was used in this study. It could also be used with stimuli specific to other pathogens, for instance CMV or Influenza, in samples from Mtb infected and uninfected individuals, to determine if HLA-DR expression in proliferating CD4 T cells can be generalized to other pathogens, or whether it is dependent on the presence of Mtb infection. Additionally, this assay can be easily applied to genomic analyses other than transcriptomics, for instance to decipher the epigenetic profile of HLA-DR+ CD4 T cells, and how it varies across cohorts and over time in Mtb infected individuals. Therefore, this assay is a powerful method to study the molecular profile of recently divided antigen-specific memory T cells, and thus effector T cells. The main caveat of this in vitro expansion assay is the impossibility to distinguish between cells that are activated versus cells that are more prone to survival. An independent validation of the results with phenotyping assays directly ex vivo in patients’ samples can overcome this hurdle. For instance, here using the MTP-AE assay we identified that HLA-DR marks Mtb-specific proliferating CD4 T cells in vitro. But we also found that the frequency of HLA-DR+ CD4 T cells is increased ex vivo in ATB patients, and that this cell population is associated with increased frequency of cells expressing activation and cytotoxic markers, suggesting that in this context, HLA-DR marks activation rather than survival.

Whether HLA-DR+ CD4 T cells reflect circulating CD4 T cells or recirculate after egressing from peripheral tissues remains to be determined. The fact that HLA-DR expression is strongly increased in tissue resident memory T cells present in the BAL and lung of ATB infected patients (59) suggests circulating HLA-DR+ CD4 T cells might originate from Mtb-infected lung tissues or their draining lymph nodes, and then egress into the peripheral circulation. This hypothesis is consistent with the idea that HLA-DR+ CD4 T cells are effector T cells, and are thus expected to be generated at or near the site of infection, where antigen is present.

Although they were more prevalent in ATB, we also observed HLA-DR+ CD4 T cells in both healthy cohorts (IGRA+ and IGRA−), and most phenotypic features of this cell population were shared across all cohorts. Circulating HLA-DR+ CD4 T cells in IGRA+ individuals may also represent Mtb-specific effector T cells since humans with Mtb sensitization may not control the bacterium completely such that these T cells are exposed to Mtb antigens at a low level or from time to time. They might also represent effector T cells that are specific to other pathogens and that have been recently activated. Alternatively, they could mark a non-effector T cell subset that constitutively express HLA-DR and is ubiquitously present in circulating CD4 T cells in humans. Indeed, even in ATB patients, not all HLA-DR+ CD4 T cells showed positive expression of cytotoxic markers or cytokines. Thus, only a subset of HLA-DR expressing cells might represent effector T cells. Single-cell RNAseq of HLA-DR+ CD4 T cells will shed light on the heterogeneity of this cell population.

The phenotypic features specific of HLA-DR+ CD4 T cells in ATB compared to IGRA+ or IGRA− cohorts included increased frequency of positive cells expressing CCL3 or CD38 (Fig 5I). These two markers also showed the highest differential fold change frequency between HLA-DR+ and HLA-DR− CD4 T cells (Fig 5H). Interestingly, the frequency of CD38+ cells in total CD4 T cells was not different across all three cohorts (Fig 2E), suggesting CD38 is useful in discriminating CD4 T cells in ATB only in conjunction with HLA-DR. CD38 was measured along with HLA-DR in several studies aiming at phenotyping CD4 T cells in the context of ATB and was also found to be upregulated in Mtb-specific CD4 T cells of ATB compared to IGRA+ infected individuals (45, 49, 52). More recently, in patients with acute dengue fever, CCL3 and CD38 were identified as significantly upregulated at the RNA level in antigen-specific CD4 T cells (60), as well as HLA-DR (data not shown). In addition to CCL3 and CD38, we also found increased frequency of positive cells expressing GzmB and Tim3 in HLA-DR+ CD4 T cells of ATB compared to IGRA− (Fig S3B), and increased frequency of positive cells expressing OX40 in HLA-DR+ CD4 T cells of ATB compared to IGRA+ individuals (Fig 5I). Altogether, these additional phenotypic markers might be extremely useful in conjunction with HLA-DR to further delineate effector T cell subsets, and better distinguishing them from effector memory T cells. Since our initial panel was restricted to activation markers that we typically use for our T cell studies (Fig 2 and (22, 39-41)), our co-expression findings could also be extended to a broader range of T cell activation markers, such as CD44, which is a potent marker for antigen experienced T cells (61, 62).

Lastly, the upregulation of HLA-DR in circulating total and antigen-specific T cells has been described in several other human infection models including Epstein-Barr virus (63), HIV (64), dengue (65) and vaccination models of yellow fever (12, 66), smallpox (66) and malaria (67). More recently, the frequency of HLA-DR+ cells was shown to be increased in circulating total (68-71) and SARS-CoV-2-specific (72, 73) CD4 T cells of acute COVID-19 patients and correlated with disease severity (69-71, 73). Thus, HLA-DR might be a useful marker for identifying effector T cells and monitoring immune responses not only in the context of TB, but also many other infection and vaccination models. Overall, the methodological approach and results reported in this study represents a stepping stone to facilitate the investigation of effector T cells in humans.

Supplementary Material

1

Key points.

  • The frequency of HLA-DR+ CD4 T cells is increased in ATB patients

  • HLA-DR+ CD4 T cells in ATB encompass tetramer-positive Mtb-specific cells

  • HLA-DR marks Mtb-specific CD4 T cells that have recently proliferated in vitro

Acknowledgments

We thank the Flow Cytometry Core, the Sequencing Core and the Bioinformatics Core facilities at La Jolla Institute for Immunology for technical assistance, and Shane Crotty and Carolyn Mobermacher for helpful discussion on the manuscript.

Funding:

This work was funded by the National Institute of Allergy and Infectious Diseases of the National Institute of Health (grant number U19 AI118626).

Data availability

The gene expression data included in this study were submitted to the Gene Expression Omnibus under accession numbers GSE161829 (Figure 1) and GSE162725 (Figure 5) (https://www.ncbi.nlm.nih.gov/geo). All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request.

References

  • 1.Kaech SM, Wherry EJ, and Ahmed R. 2002. Effector and memory T-cell differentiation: implications for vaccine development. Nat Rev Immunol 2: 251–262. [DOI] [PubMed] [Google Scholar]
  • 2.Bradley LM, Duncan DD, Yoshimoto K, and Swain SL. 1993. Memory effectors: a potent, IL-4-secreting helper T cell population that develops in vivo after restimulation with antigen. J Immunol 150: 3119–3130. [PubMed] [Google Scholar]
  • 3.Cui W, and Kaech SM. 2010. Generation of effector CD8+ T cells and their conversion to memory T cells. Immunol Rev 236: 151–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Kaech SM, and Cui W. 2012. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol 12: 749–761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Strutt TM, McKinstry KK, Marshall NB, Vong AM, Dutton RW, and Swain SL. 2013. Multipronged CD4(+) T-cell effector and memory responses cooperate to provide potent immunity against respiratory virus. Immunol Rev 255: 149–164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Sallusto F, and Lanzavecchia A. 2009. Heterogeneity of CD4+ memory T cells: functional modules for tailored immunity. Eur J Immunol 39: 2076–2082. [DOI] [PubMed] [Google Scholar]
  • 7.Cruz-Adalia A, Ramirez-Santiago G, Calabia-Linares C, Torres-Torresano M, Feo L, Galan-Diez M, Fernandez-Ruiz E, Pereiro E, Guttmann P, Chiappi M, Schneider G, Carrascosa JL, Chichon FJ, Martinez Del Hoyo G, Sanchez-Madrid F, and Veiga E. 2014. T cells kill bacteria captured by transinfection from dendritic cells and confer protection in mice. Cell Host Microbe 15: 611–622. [DOI] [PubMed] [Google Scholar]
  • 8.Marshall NB, and Swain SL. 2011. Cytotoxic CD4 T cells in antiviral immunity. J Biomed Biotechnol 2011: 954602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Swain SL, McKinstry KK, and Strutt TM. 2012. Expanding roles for CD4(+) T cells in immunity to viruses. Nat Rev Immunol 12: 136–148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Mahnke YD, Brodie TM, Sallusto F, Roederer M, and Lugli E. 2013. The who's who of T-cell differentiation: human memory T-cell subsets. Eur J Immunol 43: 2797–2809. [DOI] [PubMed] [Google Scholar]
  • 11.Restifo NP, and Gattinoni L. 2013. Lineage relationship of effector and memory T cells. Curr Opin Immunol 25: 556–563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Blom K, Braun M, Ivarsson MA, Gonzalez VD, Falconer K, Moll M, Ljunggren HG, Michaelsson J, and Sandberg JK. 2013. Temporal dynamics of the primary human T cell response to yellow fever virus 17D as it matures from an effector- to a memory-type response. J Immunol 190: 2150–2158. [DOI] [PubMed] [Google Scholar]
  • 13.Okada R, Kondo T, Matsuki F, Takata H, and Takiguchi M. 2008. Phenotypic classification of human CD4+ T cell subsets and their differentiation. Int Immunol 20: 1189–1199. [DOI] [PubMed] [Google Scholar]
  • 14.Appay V, van Lier RA, Sallusto F, and Roederer M. 2008. Phenotype and function of human T lymphocyte subsets: consensus and issues. Cytometry A 73: 975–983. [DOI] [PubMed] [Google Scholar]
  • 15.Organization WH 2019. Global Tuberculosis report 2019. [Google Scholar]
  • 16.Vynnycky E, and Fine PE. 1997. The natural history of tuberculosis: the implications of age-dependent risks of disease and the role of reinfection. Epidemiol Infect 119: 183–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K, Schumacher SG, Ma S, Meermeier E, Lewinsohn DM, and Sherman DR. 2018. Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection. Clin Microbiol Rev 31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Barnes PF, Bloch AB, Davidson PT, and Snider DE Jr. 1991. Tuberculosis in patients with human immunodeficiency virus infection. N Engl J Med 324: 1644–1650. [DOI] [PubMed] [Google Scholar]
  • 19.Flynn JL, and Chan J. 2001. Immunology of tuberculosis. Annu Rev Immunol 19: 93–129. [DOI] [PubMed] [Google Scholar]
  • 20.Lindestam Arlehamn CS, Gerasimova A, Mele F, Henderson R, Swann J, Greenbaum JA, Kim Y, Sidney J, James EA, Taplitz R, McKinney DM, Kwok WW, Grey H, Sallusto F, Peters B, and Sette A. 2013. Memory T cells in latent Mycobacterium tuberculosis infection are directed against three antigenic islands and largely contained in a CXCR3+CCR6+ Th1 subset. PLoS Pathog 9: e1003130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Strickland N, Muller TL, Berkowitz N, Goliath R, Carrington MN, Wilkinson RJ, Burgers WA, and Riou C. 2017. Characterization of Mycobacterium tuberculosis-Specific Cells Using MHC Class II Tetramers Reveals Phenotypic Differences Related to HIV Infection and Tuberculosis Disease. J Immunol. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Burel JG, Lindestam Arlehamn CS, Khan N, Seumois G, Greenbaum JA, Taplitz R, Gilman RH, Saito M, Vijayanand P, Sette A, and Peters B. 2018. Transcriptomic Analysis of CD4(+) T Cells Reveals Novel Immune Signatures of Latent Tuberculosis. J Immunol 200: 3283–3290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Burel JG, Qian Y, Lindestam Arlehamn C, Weiskopf D, Zapardiel-Gonzalo J, Taplitz R, Gilman RH, Saito M, de Silva AD, Vijayanand P, Scheuermann RH, Sette A, and Peters B. 2017. An Integrated Workflow To Assess Technical and Biological Variability of Cell Population Frequencies in Human Peripheral Blood by Flow Cytometry. J Immunol 198: 1748–1758. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Arlehamn CS, Sidney J, Henderson R, Greenbaum JA, James EA, Moutaftsi M, Coler R, McKinney DM, Park D, Taplitz R, Kwok WW, Grey H, Peters B, and Sette A. 2012. Dissecting mechanisms of immunodominance to the common tuberculosis antigens ESAT-6, CFP10, Rv2031c (hspX), Rv2654c (TB7.7), and Rv1038c (EsxJ). J Immunol 188: 5020–5031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Arlehamn CL, Seumois G, Gerasimova A, Huang C, Fu Z, Yue X, Sette A, Vijayanand P, and Peters B. 2014. Transcriptional profile of tuberculosis antigen-specific T cells reveals novel multifunctional features. J Immunol 193: 2931–2940. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Chen M, Sutherland A, Birrueta G, Laubach S, Leonard S, Peters B, and Schulten V. 2019. Analysis of Allergen-Specific T Cell and IgE Reactivity to Different Preparations of Cow's Milk-Containing Food Extracts. Cells 8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Birrueta G, Frazier A, Pomes A, Glesner J, Filep S, Schal C, Jeong KY, McMurtrey C, Vander Schans T, Hildebrand WH, Busse P, Beigelman A, Bacharier LB, Peters B, Sette A, and Schulten V. 2019. Variability in German Cockroach Extract Composition Greatly Impacts T Cell Potency in Cockroach-Allergic Donors. Front Immunol 10: 313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Lindestam Arlehamn CS, McKinney DM, Carpenter C, Paul S, Rozot V, Makgotlho E, Gregg Y, van Rooyen M, Ernst JD, Hatherill M, Hanekom WA, Peters B, Scriba TJ, and Sette A. 2016. A Quantitative Analysis of Complexity of Human Pathogen-Specific CD4 T Cell Responses in Healthy M. tuberculosis Infected South Africans. PLoS Pathog 12: e1005760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Seumois G, Vijayanand P, Eisley CJ, Omran N, Kalinke L, North M, Ganesan AP, Simpson LJ, Hunkapiller N, Moltzahn F, Woodruff PG, Fahy JV, Erle DJ, Djukanovic R, Blelloch R, and Ansel KM. 2012. An integrated nano-scale approach to profile miRNAs in limited clinical samples. Am J Clin Exp Immunol 1: 70–89. [PMC free article] [PubMed] [Google Scholar]
  • 30.Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, and Sandberg R. 2014. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9: 171–181. [DOI] [PubMed] [Google Scholar]
  • 31.Trapnell C, Pachter L, and Salzberg SL. 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25: 1105–1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Schmieder R, and Edwards R. 2011. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27: 863–864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and Genome S Project Data Processing. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Anders S, Pyl PT, and Huber W. 2015. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31: 166–169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Love MI, Huber W, and Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, and Ma'ayan A. 2013. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14: 128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, and Ma'ayan A. 2016. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44: W90–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Robinson J, Barker DJ, Georgiou X, Cooper MA, Flicek P, and Marsh SGE. 2020. IPD-IMGT/HLA Database. Nucleic Acids Res 48: D948–D955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Du Bruyn E, Ruzive S, Lindestam Arlehamn CS, Sette A, Sher A, Barber DL, Wilkinson RJ, and Riou C. 2021. Mycobacterium tuberculosis-specific CD4 T cells expressing CD153 inversely associate with bacterial load and disease severity in human tuberculosis. Mucosal Immunol 14: 491–499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Dan JM, Lindestam Arlehamn CS, Weiskopf D, da Silva Antunes R, Havenar-Daughton C, Reiss SM, Brigger M, Bothwell M, Sette A, and Crotty S. 2016. A Cytokine-Independent Approach To Identify Antigen-Specific Human Germinal Center T Follicular Helper Cells and Rare Antigen-Specific CD4+ T Cells in Blood. J Immunol 197: 983–993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Reiss S, Baxter AE, Cirelli KM, Dan JM, Morou A, Daigneault A, Brassard N, Silvestri G, Routy JP, Havenar-Daughton C, Crotty S, and Kaufmann DE. 2017. Comparative analysis of activation induced marker (AIM) assays for sensitive identification of antigen-specific CD4 T cells. PLoS One 12: e0186998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Scholzen T, and Gerdes J. 2000. The Ki-67 protein: from the known and the unknown. J Cell Physiol 182: 311–322. [DOI] [PubMed] [Google Scholar]
  • 43.Fletcher HA, Snowden MA, Landry B, Rida W, Satti I, Harris SA, Matsumiya M, Tanner R, O'Shea MK, Dheenadhayalan V, Bogardus L, Stockdale L, Marsay L, Chomka A, Harrington-Kandt R, Manjaly-Thomas ZR, Naranbhai V, Stylianou E, Darboe F, Penn-Nicholson A, Nemes E, Hatherill M, Hussey G, Mahomed H, Tameris M, McClain JB, Evans TG, Hanekom WA, Scriba TJ, and McShane H. 2016. T-cell activation is an immune correlate of risk in BCG vaccinated infants. Nat Commun 7: 11290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Soares AP, Kwong Chung CK, Choice T, Hughes EJ, Jacobs G, van Rensburg EJ, Khomba G, de Kock M, Lerumo L, Makhethe L, Maneli MH, Pienaar B, Smit E, Tena-Coki NG, van Wyk L, Boom WH, Kaplan G, Scriba TJ, and Hanekom WA. 2013. Longitudinal changes in CD4(+) T-cell memory responses induced by BCG vaccination of newborns. J Infect Dis 207: 1084–1094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Adekambi T, Ibegbu CC, Cagle S, Kalokhe AS, Wang YF, Hu Y, Day CL, Ray SM, and Rengarajan J. 2015. Biomarkers on patient T cells diagnose active tuberculosis and monitor treatment response. J Clin Invest 125: 1827–1838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Riou C, Berkowitz N, Goliath R, Burgers WA, and Wilkinson RJ. 2017. Analysis of the Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells to Discriminate Latent from Active Tuberculosis in HIV-Uninfected and HIV-Infected Individuals. Front Immunol 8: 968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Riou C, Du Bruyn E, Ruzive S, Goliath RT, Lindestam Arlehamn CS, Sette A, Sher A, Barber DL, and Wilkinson RJ. 2020. Disease extent and anti-tubercular treatment response correlates with Mycobacterium tuberculosis-specific CD4 T-cell phenotype regardless of HIV-1 status. Clin Transl Immunology 9: e1176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Musvosvi M, Duffy D, Filander E, Africa H, Mabwe S, Jaxa L, Bilek N, Llibre A, Rouilly V, Hatherill M, Albert M, Scriba TJ, and Nemes E. 2018. T-cell biomarkers for diagnosis of tuberculosis: candidate evaluation by a simple whole blood assay for clinical translation. Eur Respir J 51. [DOI] [PubMed] [Google Scholar]
  • 49.Silveira-Mattos PS, Barreto-Duarte B, Vasconcelos B, Fukutani KF, Vinhaes CL, Oliveira-De-Souza D, Ibegbu CC, Figueiredo MC, Sterling TR, Rengarajan J, and Andrade BB. 2020. Differential Expression of Activation Markers by Mycobacterium tuberculosis-specific CD4+ T Cell Distinguishes Extrapulmonary From Pulmonary Tuberculosis and Latent Infection. Clin Infect Dis 71: 1905–1911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Mpande CAM, Musvosvi M, Rozot V, Mosito B, Reid TD, Schreuder C, Lloyd T, Bilek N, Huang H, Obermoser G, Davis MM, Ruhwald M, Hatherill M, Scriba TJ, Nemes E, and Team AS. 2020. Mycobacterium tuberculosis-specific T cell activation identifies individuals at high risk of tuberculosis disease. medRxiv. [Google Scholar]
  • 51.Riou C, Gray CM, Lugongolo M, Gwala T, Kiravu A, Deniso P, Stewart-Isherwood L, Omar SV, Grobusch MP, Coetzee G, Conradie F, Ismail N, Kaplan G, and Fallows D. 2014. A subset of circulating blood mycobacteria-specific CD4 T cells can predict the time to Mycobacterium tuberculosis sputum culture conversion. PLoS One 9: e102178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Ahmed MIM, Ntinginya NE, Kibiki G, Mtafya BA, Semvua H, Mpagama S, Mtabho C, Saathoff E, Held K, Loose R, Kroidl I, Chachage M, von Both U, Haule A, Mekota AM, Boeree MJ, Gillespie SH, Hoelscher M, Heinrich N, and Geldmacher C. 2018. Phenotypic Changes on Mycobacterium Tuberculosis-Specific CD4 T Cells as Surrogate Markers for Tuberculosis Treatment Efficacy. Front Immunol 9: 2247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Canaday DH, Wilkinson RJ, Li Q, Harding CV, Silver RF, and Boom WH. 2001. CD4(+) and CD8(+) T cells kill intracellular Mycobacterium tuberculosis by a perforin and Fas/Fas ligand-independent mechanism. J Immunol 167: 2734–2742. [DOI] [PubMed] [Google Scholar]
  • 54.Bastian M, Braun T, Bruns H, Rollinghoff M, and Stenger S. 2008. Mycobacterial lipopeptides elicit CD4+ CTLs in Mycobacterium tuberculosis-infected humans. J Immunol 180: 3436–3446. [DOI] [PubMed] [Google Scholar]
  • 55.Commandeur S, Coppola M, Dijkman K, Friggen AH, van Meijgaarden KE, van den Eeden SJ, Wilson L, van der Ploeg-van Schip JJ, Franken KL, Geluk A, and Ottenhoff TH. 2014. Clonal analysis of the T-cell response to in vivo expressed Mycobacterium tuberculosis protein Rv2034, using a CD154 expression based T-cell cloning method. PLoS One 9: e99203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Harari A, Rozot V, Bellutti Enders F, Perreau M, Stalder JM, Nicod LP, Cavassini M, Calandra T, Blanchet CL, Jaton K, Faouzi M, Day CL, Hanekom WA, Bart PA, and Pantaleo G. 2011. Dominant TNF-alpha+ Mycobacterium tuberculosis-specific CD4+ T cell responses discriminate between latent infection and active disease. Nat Med 17: 372–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Chavez-Galan L, Arenas-Del Angel MC, Zenteno E, Chavez R, and Lascurain R. 2009. Cell death mechanisms induced by cytotoxic lymphocytes. Cell Mol Immunol 6: 15–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Ahmed A, Adiga V, Nayak S, Uday Kumar JAJ, Dhar C, Sahoo PN, Sundararaj BK, Souza GD, and Vyakarnam A. 2018. Circulating HLA-DR+CD4+ effector memory T cells resistant to CCR5 and PD-L1 mediated suppression compromise regulatory T cell function in tuberculosis. PLoS Pathog 14: e1007289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Yang Q, Zhang M, Chen Q, Chen W, Wei C, Qiao K, Ye T, Deng G, Li J, Zhu J, Cai Y, Chen X, and Ma L. 2020. Cutting Edge: Characterization of Human Tissue-Resident Memory T Cells at Different Infection Sites in Patients with Tuberculosis. J Immunol 204: 2331–2336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Tian Y, Seumois G, De-Oliveira-Pinto LM, Mateus J, Herrera-de la Mata S, Kim C, Hinz D, Goonawardhana NDS, de Silva AD, Premawansa S, Premawansa G, Wijewickrama A, Balmaseda A, Grifoni A, Vijayanand P, Harris E, Peters B, Sette A, and Weiskopf D. 2019. Molecular Signatures of Dengue Virus-Specific IL-10/IFN-gamma Co-producing CD4 T Cells and Their Association with Dengue Disease. Cell Rep 29: 4482–4495 e4484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.DeGrendele HC, Kosfiszer M, Estess P, and Siegelman MH. 1997. CD44 activation and associated primary adhesion is inducible via T cell receptor stimulation. J Immunol 159: 2549–2553. [PubMed] [Google Scholar]
  • 62.Baaten BJ, Tinoco R, Chen AT, and Bradley LM. 2012. Regulation of Antigen-Experienced T Cells: Lessons from the Quintessential Memory Marker CD44. Front Immunol 3: 23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Callan MF, Tan L, Annels N, Ogg GS, Wilson JD, O'Callaghan CA, Steven N, McMichael AJ, and Rickinson AB. 1998. Direct visualization of antigen-specific CD8+ T cells during the primary immune response to Epstein-Barr virus In vivo. J Exp Med 187: 1395–1402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Doisne JM, Urrutia A, Lacabaratz-Porret C, Goujard C, Meyer L, Chaix ML, Sinet M, and Venet A. 2004. CD8+ T cells specific for EBV, cytomegalovirus, and influenza virus are activated during primary HIV infection. J Immunol 173: 2410–2418. [DOI] [PubMed] [Google Scholar]
  • 65.Chandele A, Sewatanon J, Gunisetty S, Singla M, Onlamoon N, Akondy RS, Kissick HT, Nayak K, Reddy ES, Kalam H, Kumar D, Verma A, Panda H, Wang S, Angkasekwinai N, Pattanapanyasat K, Chokephaibulkit K, Medigeshi GR, Lodha R, Kabra S, Ahmed R, and Murali-Krishna K. 2016. Characterization of Human CD8 T Cell Responses in Dengue Virus-Infected Patients from India. J Virol 90: 11259–11278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Miller JD, van der Most RG, Akondy RS, Glidewell JT, Albott S, Masopust D, Murali-Krishna K, Mahar PL, Edupuganti S, Lalor S, Germon S, Del Rio C, Mulligan MJ, Staprans SI, Altman JD, Feinberg MB, and Ahmed R. 2008. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28: 710–722. [DOI] [PubMed] [Google Scholar]
  • 67.Schwenk R, Banania G, Epstein J, Kim Y, Peters B, Belmonte M, Ganeshan H, Huang J, Reyes S, Stryhn A, Ockenhouse CF, Buus S, Richie TL, and Sedegah M. 2013. Ex vivo tetramer staining and cell surface phenotyping for early activation markers CD38 and HLA-DR to enumerate and characterize malaria antigen-specific CD8+ T-cells induced in human volunteers immunized with a Plasmodium falciparum adenovirus-vectored malaria vaccine expressing AMA1. Malar J 12: 376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Thevarajan I, Nguyen THO, Koutsakos M, Druce J, Caly L, van de Sandt CE, Jia X, Nicholson S, Catton M, Cowie B, Tong SYC, Lewin SR, and Kedzierska K. 2020. Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19. Nat Med 26: 453–455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Wang F, Hou H, Luo Y, Tang G, Wu S, Huang M, Liu W, Zhu Y, Lin Q, Mao L, Fang M, Zhang H, and Sun Z. 2020. The laboratory tests and host immunity of COVID-19 patients with different severity of illness. JCI Insight 5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Mathew D, Giles JR, Baxter AE, Oldridge DA, Greenplate AR, Wu JE, Alanio C, Kuri-Cervantes L, Pampena MB, D'Andrea K, Manne S, Chen Z, Huang YJ, Reilly JP, Weisman AR, Ittner CAG, Kuthuru O, Dougherty J, Nzingha K, Han N, Kim J, Pattekar A, Goodwin EC, Anderson EM, Weirick ME, Gouma S, Arevalo CP, Bolton MJ, Chen F, Lacey SF, Ramage H, Cherry S, Hensley SE, Apostolidis SA, Huang AC, Vella LA, Unit UPCP, Betts MR, Meyer NJ, and Wherry EJ. 2020. Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Kuri-Cervantes L, Pampena MB, Meng W, Rosenfeld AM, Ittner CAG, Weisman AR, Agyekum RS, Mathew D, Baxter AE, Vella LA, Kuthuru O, Apostolidis SA, Bershaw L, Dougherty J, Greenplate AR, Pattekar A, Kim J, Han N, Gouma S, Weirick ME, Arevalo CP, Bolton MJ, Goodwin EC, Anderson EM, Hensley SE, Jones TK, Mangalmurti NS, Luning Prak ET, Wherry EJ, Meyer NJ, and Betts MR. 2020. Comprehensive mapping of immune perturbations associated with severe COVID-19. Sci Immunol 5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Braun J, Loyal L, Frentsch M, Wendisch D, Georg P, Kurth F, Hippenstiel S, Dingeldey M, Kruse B, Fauchere F, Baysal E, Mangold M, Henze L, Lauster R, Mall MA, Beyer K, Rohmel J, Voigt S, Schmitz J, Miltenyi S, Demuth I, Muller MA, Hocke A, Witzenrath M, Suttorp N, Kern F, Reimer U, Wenschuh H, Drosten C, Corman VM, Giesecke-Thiel C, Sander LE, and Thiel A. 2020. SARS-CoV-2-reactive T cells in healthy donors and patients with COVID-19. Nature. [DOI] [PubMed] [Google Scholar]
  • 73.Meckiff BJ, Ramirez-Suastegui C, Fajardo V, Chee SJ, Kusnadi A, Simon H, Grifoni A, Pelosi E, Weiskopf D, Sette A, Ay F, Seumois G, Ottensmeier C, and Vijayanand P. 2020. Single-Cell Transcriptomic Analysis of SARS-CoV-2 Reactive CD4 (+) T Cells. SSRN: 3641939. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1704979-supplement-1.pdf (1.4MB, pdf)

Data Availability Statement

The gene expression data included in this study were submitted to the Gene Expression Omnibus under accession numbers GSE161829 (Figure 1) and GSE162725 (Figure 5) (https://www.ncbi.nlm.nih.gov/geo). All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request.

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