Blood was obtained after receipt of written informed consent from BCG un-vaccinated, HIV-, CoV2– and CoV2+ persons. CoV2- and CoV2+ participants with clinical signs of CoV2 infection (temperature of at least 100.4°F, cough, shortness of breath, chills, sore throat, muscle pain, or new loss of taste or smell) or in close contact with anyone showing these symptoms at the time of sample collection were not enrolled for this study. All studies were conducted in accordance with the Declaration of Helsinki guidelines and approved by the institutional review board of the University of Georgia, Athens.

Peripheral blood mononuclear cells (PBMC) were isolated from freshly obtained blood by Ficoll density centrifugation (GE Healthcare) and cultured in RPMI1640 supplemented with AB human serum. For some experiments, PBMC and MDSC depleted PBMC were cultured with the PepTivator® peptide pools for surface glycoprotein (S), nucleocapsid phosphoprotein (N), or membrane glycoprotein (M) of SARS-CoV2 as per the manufacturer’s instructions (Miltenyi Biotec). Briefly, 1.5 x10⁶ cells were cultured in a flat-bottom 96-well plate in a volume of 150 µl culture media, and peptide pools were added at a final concentration of 0.6 nmol of each peptide. To determine the effect of PD1, PD-L1, galectin, ROS, arginase, and iNOS cells were cultured with the peptide pool of S-protein in the presence or absence of neutralizing (PD1, PD-L1, and galectin) or isotype control antibodies, or chemical inhibitors (ROS, arginase, and iNOS).

Antibodies used for flow cytometry were Alexa Fluor488-anti-CD11b, PE-Dazzel594-anti-HLA DR, PE/Cy7-anti-CD14, APC-anti-CD33, PerCP-Cy5.5-anti-CD66b, BV421-anti-CD19, BV510/PE/Cy7-anti-CD3, APC-eF780/APC-anti-CD4, PE-Fire700/APC-eF780-anti-CD8, AF488-anti-CD45RA, BV421/BV605-anti-CD62L, AF700-anti-CD27, BV421-anti-CD28, PerCP-Cy5.5-anti-PD1, PE-Dazzel594-anti-CD57, PE-anti-CX3CR1, PE-anti-IL-2; LIVE/DEAD fixable aqua stain dye. For neutralization studies, monoclonal antibodies to PD-1, PD-L1, galectin or isotype (10 µg/ml) control were used (all from Biolegend). Chemical inhibitors used were ROS inhibitor catalase (100 U/mL), arginase inhibitor NG-monomethyl-L-arginineacetate (0.5 mM) (both from Sigma Aldrich), and iNOS inhibitor N( ϖ)-hydroxy-nor-L-arginine (0.5 mM; EMD Millipore Corp.).

Freshly isolated PBMC were stained for CD14, HLA DR, and LIVE/Dead Aqua stain; Aqua⁺ dead cells were excluded and CD14⁺HLA DR^-/lo MDSC depleted using flow cytometry were collected separately. MDSC depleted PBMC were cultured for flow cytometry and measurement of cytokines. Total RNA was isolated from MDSC for transcriptomic and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis.

Cells were stained for surface markers using respective antibodies and cell staining buffer. For intracellular IL-2, surface-stained cells were fixed and permeablized using a Fixation/Permeabilization kit and anti-IL2 antibody. Flow cytometry was done on Quanteon and data was analyzed using Flowjo. A minimum of 300,000 Aqua^- LIVE cells were collected for each sample. Controls for each experiment included unstained cells and fluorescence minus one (FMO).

Supernatants collected from PBMC and MDSC depleted PBMC cultures were stored at -80°C. Quantity of IFNγ, TNFα, IL-10, IL-17, IL-12p70 were determined at 24-hours post-stimulation with Peptivator Peptide pools using Bioplex analyte detection kit (Biorad). Plasma levels of IL-6 and IL-8 were determined using IL-6 Quantikine HS (sensitivity 0.09 pg/ml) and IL-8 Quantikine HS (sensitivity 0.4 pg/ml), respectively (both from R&D Systems)

qRT-PCR was done as previously described (54). Total RNA was isolated from sorted CD14⁺HLA DR^-/lo MDSC from CoV- and CoV2+ individuals using TRIzol™ reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol; 250 ng RNA was used for cDNA synthesis using iScript™ cDNA Synthesis Kit (BioRad, USA) according to the manufacturer’s instructions. qRT-PCR was performed using the PowerUp™ SYBR™ Green Master Mix (Thermo Fisher Scientific, USA) and data were acquired with the Applied Biosystems™ StepOne™ Real-Time PCR System (Applied Biosystems/Life Technologies, Germany). The Human acidic ribosomal protein (HuPO) was used as a housekeeping gene. The list of genes and primers used in this study is in Supplementary Table 1 . Data were analyzed to calculate the relative quantification of the genes in comparison to the HuPO gene by comparative Ct method (2-ΔCt) (55).

Data are expressed as mean values ± standard error mean (SEM). Paired Student t-tests were used to determine the statistical significance of in vitro experiments. Comparisons between CoV2- and CoV2+ subjects was made by non-parametric Mann Whitney U test; comparisons between different parameters were analyzed using Spearman correlation. Statistical analysis was performed using Graphpad Prism 9 (La Jolla, CA); p-values of <0.05 were considered statistically significant.

CD14⁺HLA DR^-/lo cells from freshly isolated PBMC were isolated after excluding dead cells by flow cytometry. RNA was isolated using RNeasy Mini Kit (QIAGEN, Germany) and quality was assessed using Bioanalyzer (Agilent Technologies, USA). All samples were checked for RNA integrity number (RIN). The mean RIN for 10 samples was 6.9 with a standard deviation 2.9. RNA-Seq libraries were prepared in two batches using 10 pg -1 ng total RNA and SMART-Seq HT PLUS kit kit (Takara Bio USA, Inc.). All libraries were sequenced to the depth of average of 34294989 (range from 16694996 to 46416998) using Illumina 500 mid-output PE75 sequencer at the Georgia Genomics andBioinformatics Core (GGBC), University of Georgia, Athens.

Data were available from the GGBC, University of Georgia, Athens Core in the.fastq format. Data pre-processing included concatenating reads for one sample obtained on different lanes of the sequencer into one file. Filtering low quality reads and removal of the 3’ adapter sequences were further performed using the Trim Galore tool, which utilizes the Catadapt program (56). Reads were mapped to the latest version of the human genome hg38 (GRCh38.p13) using HISAT2 (57). Mapped reads were counted against the human GENCODE annotation (v37) (58) using HT-Seq (59). The EdgeR library (60) in the R computing environment was used for quality control of the RNA-Seq data, and ComBat-seq method (61) for correction of batch effects. Differential gene expression analysis was conducted using EdgeR. EdgeR uses empirical Bayes estimation and exact tests based on the negative binomial distribution of the RNA-Seq data, followed by false discovery rate (FDR) correction using the Benjamini-Hochberg method (62). Genes were considered differentially expressed when FDR-corrected p-values were <0.05, or nominal p-value <0.05 in an exploratory approach. Pathways over-represented for DEGs were identified using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (63, 64) and KEGG (65, 66) and Reactome (67) databases. For pathway analysis, all genes with a nominal p-value <0.05 were used as input. DAVID uses a one-tailed Fisher’s Exact test for gene set enrichment analysis. Pathways with nominal p-values <0.1 were considered significantly enriched for DEGs.

All the participants were unvaccinated for COVID-19. CoV2- participants self-reported their COVID-19 status; CoV2+ participants who self-reported positive RT PCR in past were included in the study. Participants were enrolled at a median of 163 days (5.4 months) from the first detection of CoV2 infection ( Table 1 ). Our participant enrollment form included self-report for Type 2 diabetes (T2D), hypertension, tuberculosis, smoking and chewing tobacco, any respiratory complications, or any other ongoing medical condition. Of all the CoV2- and CoV2+ participants, 2 CoV2- individuals and 1 CoV2+ reported T2D and/or hypertension, and 1 CoV2+ participant self-reported suffering from anxiety. None of the participants reported any other co-morbidity.

Severe COVID-19 is associated with increased circulating MDSC, which declines upon clinical recovery from the disease (35, 49, 50, 52). Here we sought to investigate the presence of M-MDSC at a median of 163 days (approx. 5 months) from the first detection of CoV2 infection by RT PCR. We found an increased frequency of M-MDSC in peripheral blood of CoV2+ individuals as compared to CoV2- controls (0.2±0.09 vs 0.9±0.2; p=0.03) ( Figures 1A–C ). Of note, the M-MDSC frequency in peripheral blood of one CoV2- participant who self-reported as hypertensive was relatively higher (0.4%) as compared to other CoV2- participants. We have previously shown that M- MDSC expansion during viral infection is dependent on IL-6 (32, 68). Additionally, COVID-19 is associated with elevated levels of IL-6. Therefore, we quantified IL-6 in the plasma of CoV2- and CoV2+ individuals. Despite a long time after recovery from CoV2 infection, we found higher levels of IL-6 in plasma of CoV2+ individuals, compared to CoV2- controls (0.76±0.2 vs 1.3±0.3; p=0.04) ( Figure 1D ). Furthermore, a positive correlation was found between the quantity of plasma IL-6 and the circulating frequency of MDSC (Pearson r= 0.62; p=0.02) ( Figure 1E ). Of note, IL-8 was also elevated in CoV2+ individuals, but its level did not correlate with M-MDSC ( Supplementary Figure 1A ). The plasma IL-6 quantity of the hypertensive CoV2- participant was not higher than the remaining participants.

Effector memory T (T_EM) cells remain long-term after an infection is eliminated. These T cell subsets are critical to eradicating virus by their ability to produce anti-viral cytokines to control viral replication. Apart from the expression of lymph node homing receptors CCR7 and CD62L, CD27 and CD28 along with CD45RA can be used to discriminate naïve and T_EM cells with CD45RA⁺CD27⁺CD28⁺ (naïve T cells), CD45RA^-CD62L^-CD27⁺CD28⁺ (T_EM) (69–72). Memory T cells with senescent and exhaustion phenotype have been reported during severe CoV2 infection, displaying a reduced capacity for antiviral cytokine production (73–75). In this research, we sought to determine if phenotypic T cell abnormalities persist 5 months from the first detection of CoV2 infection. For this, initially, we compared the frequency of various CD45RA^-CD62L^- T_EM subsets present in the CD4⁺ and CD8⁺ compartments of the whole blood of CoV2- and CoV2+ individuals. We found a comparable frequency of CD27^-CD28⁺, CD27⁺CD28⁺, CD27⁺CD28^-, and CD27^-CD28^- T_EM cell subset in the CD4⁺ and CD8⁺ compartments of the two groups ( Figures 2A–C , and Tables 2 , 3 ). The hypertensive CoV2- participant with a higher frequency of MDSC also exhibited elevated frequencies of CD4⁺ and CD8⁺ CD27^-CD28⁺ cells.

Of the four T_EM subsets identified, the frequency of CD27⁺CD28⁺ was highest in CD4⁺ and CD8⁺ T cells. Therefore subsequently, we compared PD1⁺, CD57⁺, and CX3CR1⁺ cells in these T_EM subsets of CD4⁺ and CD8⁺ compartments. As compared to CoV2- individuals, CoV2+ individuals exhibited increased frequency of CD4⁺CD57⁺ T_EM cells (13.8±1.6 vs 30.6±6; p=0.04). Even though the frequency of CD4⁺PD1⁺ and CD4⁺CX3CR1⁺ T_EM cells was also higher in CoV2+ individuals, it was less stringent than CD57⁺ cell frequency ( Figures 2D–F and Table 2 ). In contrast to CD4⁺ T_EM cells, CoV2+ individuals exhibited increased frequency of CD8⁺PD1⁺ and CD8⁺CX3CR1⁺ cells (10.9±4.6 vs 25.9±3; p=0.005 and 12.4±1.8 vs 21.6±2.9; p=0.02, respectively) ( Figures 2G–I and Table 3 ). Of note, the frequency of CD4⁺CD57⁺ T_EM cells, and CD8⁺PD1⁺ or CD8⁺CX3CR1⁺ T_EM cells did not correlate with the circulating MDSC (Data not shown). Collectively these studies suggest persistence of T cells linked to the suppression of their response for long-duration post CoV2 infection.

We found increased MDSC five months post-CoV2 infection ( Figure 1B ). In the next set of experiments, we sought to investigate if these MDSC regulate T cell function in response to CoV2 antigens. For this, we depleted CD14⁺HLA DR^lo/- MDSC from freshly isolated PBMC of CoV2- and CoV2+ individuals, and stimulated whole PBMC and MDSC depleted PBMC with peptide pools of S, N, and M antigens for 16-18 hours. The frequency of CD3⁺CD45RA^-CD62L^- CD4⁺ and CD8⁺ T_EM cells was determined by flow cytometry. We have previously established that depleting CD14⁺HLA DR^lo/- MDSC does not change CD3⁺ T cell percentage or expression of CD69, CD38, and HLA DR T cell activation markers in MDSC depleted PBMC fraction (76). In this research, we found that the net CoV2 specific frequency of CD3⁺CD45RA^-CD62L^-CD4⁺IL-2⁺ was greater in MDSC depleted PBMC cultures when compared to whole PBMC cultures of individuals with past CoV2 infection (p-values 0.007 for S peptide pool, 0.03 for N and M peptide pools) ( Figures 3A, B ). Similarly, the net CoV2 specific frequency of CD3⁺CD45RA^-CD62L^-CD8⁺IL-2⁺ was greater in MDSC depleted PBMC cultures when compared to whole PBMC cultures of individuals with past CoV2 infection (p-values 0.008 for S peptide pool, 0.03 for N and M peptide pools) ( Figures 3C, D ). The cultures of CoV2- individuals did not produce significant IL-2 in response to CoV2 antigens (data not shown).

Subsequently, we quantified T helper cell (T_H) cytokines, cytokines that activate T cells, and immune suppressive cytokine IL-10 in the culture supernatants of whole PBMC and MDSC depleted PBMC stimulated for 48-72 hours with peptide pool of CoV2 antigen. The net IFNγ (p-values 0.004 for S and N peptide pools, 0.008 for M peptide pool), TNFα (p-values 0.004 for S, N, and M peptide pools), IL-17 (p-values 0.03 for S, N, and M peptide pools), and IL-12p70 (p-values 0.04 for S peptide pool, 0.03 N and M peptide pools) produced by MDSC depleted PBMC cultures was greater when compared to whole PBMC cultures ( Figures 4A–D ). In contrast, MDSC depleted PBMC cultures when stimulated with CoV2 antigen peptides produced less IL-10 when compared to whole PBMC cultures stimulated identically (p-values 0.03 for S peptide pool, 0.008 for N and M peptide pools) ( Figure 4E ). These data establish that CD14⁺HLA DR^lo/- MDSC regulates the immunologic response to CoV2 and suppresses T_H1 responses even after 5-6 months from initial exposure to the virus. The cultures of CoV2- individuals did not produce significant IFNγ in response to CoV2 antigens ( Supplementary Figure 1C ).

MDSC utilize multiple mechanisms to confer their suppressor activity. These include biochemical and metabolic mediators ROS, iNOS, and arginase or cell surface inhibitory receptors PD-1, PD-L1, and α-galectin (28, 31, 35, 42, 43, 47). Here we sought to investigate if these mediators regulate T cell response, and delineate which of these mechanisms is utilized by MDSC to control T cell function in CoV2+ individuals. For this, initially, we depleted CD14⁺HLA DR^lo/- MDSC from freshly isolated PBMC of CoV2+ individuals, and cultured whole PBMC and MDSC depleted PBMC with peptide pool of S-antigen in the presence or absence of blocking antibodies for PD-1, PD-L1, or galectin-9 or iNOS, arginase, or ROS inhibitor. The quantity of IFNγ was quantified in the culture supernatants at 48-72 hours by ELISA. Compared to unstimulated controls, PBMC cultured with S-antigen produced more IFNγ (undetectable vs 3.8 ± 1.4 ng/ml; p=0.0001). This was further increased upon blocking PD-1 or PD-L1 (3.8 ± 1.4 vs 4.8 ± 1.5 ng/ml; p=0.01 and 3.8 ± 1.4 vs 4.9 ± 1.6 ng/ml; p=0.02, respectively), or inhibiting iNOS, arginase, or ROS (3.8 ± 1.4 vs 5.6 ± 2.1 ng/ml; p=0.05, 3.8 ± 1.4 vs 5.5 ± 1.9 ng/ml; p=0.05, and 3.8 ± 1.4 vs 6.5 ± 1.4 ng/ml; p=0.04). Of note, blocking galectin-9 did not affect IFNγ production (3.8 ± 1.4 vs 3.5 ± 1.5 ng/ml; p=0.59) ( Figure 5A ).

As previously observed ( Figure 4A ) compared to whole PBMC, IFNγ production was more in the culture supernatants of MDSC depleted PBMC cultures (3.8 ± 1.4 vs 11.8 ± 3 ng/ml; p=0.04). To explore the relative contribution of cell surface inhibitory receptors and biochemical mediators on the function of CoV2+ MDSC, blocking antibodies or biochemical inhibitors were added to MDSC depleted PBMC cultures, and the quantity of IFNγ was compared to MDSC depleted PBMC cultured without these inhibitors. We hypothesized that functionally active pathways in MDSC of CoV2+ individuals that may inhibit IFNγ production will be absent in MDSC depleted PBMC cultures. Therefore, the addition of antibodies or chemical inhibitors will have no effect on the IFNγ production in response to CoV2 antigen. However, similar to the whole PBMC culture, blocking PD-1, PD-L1, or inhibiting iNOS also increased IFNγ production in MDSC depleted PBMC cultures (11.8 ± 3 vs 13.4 ± 3.4 ng/ml; p=0.03, 11.8 ± 3 vs 14.5 ± 2.9 ng/ml; p=0.05, and 11.8 ± 3 vs 13.6 ± 3.4 ng/ml; p=0.03), suggesting that none of these pathways contribute to MDSC mediated T cell inhibition. Of note, inhibiting arginase or ROS did not affect IFNγ production (11.8 ± 3 vs 9.8 ± 4.7; p=0.41, and 11.8 ± 3 vs 10.6 ± 3.8; p=0.42, respectively) ( Figure 5A ), suggesting that M-MDSC are primarily responsible for arginase and ROS mediated IFNγ inhibition in PBMC cultures.

Concomitantly, we determined the gene expression of PD-1, PD-L1, NOS2, Arg 1, Arg 2, and p47^{ph ox} subunit of ROS producing enzyme NADPH oxidase in MDSC isolated from CoV2+ and CoV2- individuals. Compared to CoV2- controls, MDSC isolated from CoV2+ individuals exhibited an increased expression of p47^phox and Arg 2 (11.4 ± 3.7 vs 168.7 ± 37.8; p=0.009, and 0.2 ± 0.04 vs 2.5 ± 0.9; p=0.03, respectively) ( Figures 5B, C ). Of note, the expression of PD-1, PD-L1, NOS2, and Arg 1 remained undetectable. These results corroborated with our ELISA results. Specifically, neutralizing PD-1, PD-L1, and iNOS increased, whereas the addition of arginase or ROS inhibitors to MDSC depleted PBMC cultures do not affect IFNγ production. Collectively, these findings suggest that: (1) T cells utilize multiple mechanisms to control T_H1 responses, and (2) MDSC utilizes arginase and ROS pathways to inhibit T cell function in CoV2+ individuals.

To determine whether infection with CoV2 has a long-term effect on gene expression in M-MDSC, we conducted an RNA-Seq study using samples from ten study participants (5 CoV2- and 5 CoV2+), that were profiled in two separate batches. Our quality control procedures identified a strong batch effect in the data ( Supplemental Figure 2 ). Following batch effect correction and normalization, two outlier samples, one in each batch were identified, both of which from the CoV2- group. None of these outliers reported any co-morbidities. These outliers were removed based on their relative log expression distributions, resulting in a dataset of eight samples with a similar distribution pattern ( Supplemental Figure 2 ), which was used for the subsequent differential gene expression analysis. Results from the differential gene expression analysis are shown in Supplementary Table 2 .

First, we were interested in assessing the expression of genes quantified in a group of independent study participants by qRT PCR ( Figures 5 , 6 ). Gene expression results of qRT PCR demonstrate significantly increased expression of p47^phox , Arg2 ( Figures 5B, C ), and TGFβ ( Figure 6F ) in M-MDSC isolated from CoV2+ as compared to CoV2- participants. Additionally, we considered expression levels of transcription factors STAT1, STAT3, STAT5b, HIF-1α, and Nrf2 reported to regulate the expansion and function of MDSC. The median expression of all the genes was greater in M-MDSC isolated from CoV2+ participants, but the differences did not achieve statistical significance ( Figures 6A–E ). Of note, the expression of STAT3 in M-MDSC of CoV2+ when compared to CoV2- participants approached significance (0.4 ± 0.3 vs 15.9 ± 10.6; p=0.07). Collectively, our findings of positive correlation between IL-6 and frequency of M-MDSC ( Figure 1C ) and STAT3 expression ( Figure 6B ) suggest that the IL-6-STAT3 axis drives M-MDSC expansion in CoV2 infection. Contrary to the results obtained using qRT PCR, differences in the expression of genes between CoV2- and CoV2+ samples did not achieve statistical significance in the RNA-seq study. Nevertheless, some trends in expression remained the same, for example, p47^phox , Arg2 and TGFβ were upregulated when quantified by both qRT PCR and RNA-seq ( Figures 6G–I ), and PD-1, PD-L1, NOS2 and Arg1 were undetectable by either method.

We next took the advantage of the RNA-seq data set to explore additional long-term effects of CoV2 infection on gene expression profiles. This analysis identified 12 new DEGs (FDR <0.05), of which 2 were upregulated and 10 were downregulated ( Figures 7A, B ); and a total of 188 DEGs with nominal p-value cut off of 0.05, of which 63 were up- and 125 downregulated ( Supplementary Table 2 ). Remarkably, despite prolonged time after detected CoV2 (median 163 days/5 months), the absolute fold changes in the expression of most of these genes were quite high, 2- to 378-fold ( Supplementary Table 2 ). Many of these genes were previously implicated in severe CoV2 infection; including some genes that did not reach statistical significance in our study. We show that: 1) some of these genes remained upregulated 5 months after infection (LYZ, PLAC8 [fold increase 1.6; p=0.07)], 2) were upregulated during severe disease and downregulated 5 months after recovery (PFKB3, CXCL8 (fold decrease 1.93; p=0.06), TREML4, THBS1, EREG), or 3) remained downregulated long-term (IL1B, NLRP3) (77–81). Our RNA-seq findings combined with cytokine studies ( Figures 3 , 4 ) suggest that after recovery from COVID-19 disease, M-MDSC may contribute to control cytokine induced hyper inflammation, but retain their capacity to suppress T_H1 cell function, critical for the anti-viral immune mechanism.

We next explored the long-term effects of CoV2 infection on biological processes by conducting pathway enrichment analysis using KEGG and the Reactome databases. In total, this analysis identified 38 pathways significantly enriched for DEGs (nominal p-value < 0.1, Supplementary Table 3 ). Of these, ten pathways are directly related to the regulation of immune function, in particular regulating T cell responses ( Table 4 ). Of note, the genes responsible for upregulated T cell function such as HLA-B, HLA DQB1, and innate immune signaling EREG, IL1B, NLRP3 remained downregulated in M-MDSC of CoV2+ participants ( Figure 7C ). These findings suggest M-MDSC retains the capacity to regulate anti-CoV2 immune function long after CoV2 infection.

The pathological and immunological consequences of M-MDSC after recovery from CoV2 remain unclear. Given the immune regulatory role of M-MDSC in various infections and immunological abnormalities, it is highly likely that M-MDSC in individuals with critical COVID-19 illness contributes to immunosuppression (35, 49–52). While our study design provides robust and reproducible results suggesting an expansion of M-MDSC even after 5-months of the first report of infection, which exhibits the dual function of suppressing T cells responses and downregulated inflammatory responses, the small sample size in our study is a major limitation and reason for some findings unable to reach statistical significance. Thus, other mechanisms driving the response of M-MDSC, such as genetics, comorbidities, age, gender, or initial viral load remain unexplored. Due to the increased rate of vaccination in this community, the identification of non-vaccinated participants with a history of CoV2 infection, and non-vaccinated uninfected controls became a challenge. Thus this study was performed on a small cohort of non-vaccinated CoV2- and CoV2+ individuals. We performed RNA-seq studies in two batches to compare the global gene profile of M-MDSC in CoV2- and CoV2+ individuals; however, we observed a huge batch effect ( Supplemental Figures 2 , 4 ) with less coverage of reads across human transcriptome in Batch 2. This resulted in the identification of limited numbers of DEGs (12 DEGs with FDR <0.05) in M-MDSC from CoV2+ individuals, and the fold change expression of many genes (CSTA, SELL (CD62L), S100A8/A9, FCN1, DUSP6) important for characterization and function of M-MDSC did not reach statistical significance. Additionally, two CoV2- samples were outliers and excluded from the analysis. Despite these limitations, our study clearly demonstrates that M-MDSC retains their capacity to suppress T cell function even after recovery from CoV2 infection. Indeed, future studies performed on large cohorts of individuals receiving the COVID-19 vaccine will be important to establish the effect of M-MDSC on long-term vaccine efficacy.