key: cord-0849776-ee8bzn2l
authors: Yu, Chen; Littleton, Sejiro; Giroux, Nicholas; Mathew, Rose; Ding, Shengli; Kalnitsky, Joan; Petzold, Elizabeth W.; Chung, Hong; Palomino, Grecia Rivera; Rotstein, Tomer; Xi, Rui; Ko, Emily R.; Tsalik, Ephraim L.; Sempowski, Gregory D.; Denny, Thomas N.; Burke, Thomas W.; McClain, Micah T.; Woods, Christopher W.; Shen, Xiling; Saban, Daniel R.
title: Mucosal Associated Invariant T (MAIT) Cell Responses Differ by Sex in COVID-19
date: 2020-12-01
journal: bioRxiv
DOI: 10.1101/2020.12.01.407148
sha: 2908f9ac3cbcf3654fda4690306ddc6af7bd4e88
doc_id: 849776
cord_uid: ee8bzn2l

Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, yet the mechanisms governing this disparity remain incompletely understood. We carried out sex-balanced sampling of peripheral blood mononuclear cells from confirmed COVID-19 inpatients and outpatients, uninfected close contacts, and healthy controls for 36-color flow cytometry and single cell RNA-sequencing. Our results revealed a pronounced reduction of circulating mucosal associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets implicate that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, female MAIT cells possessed an immunologically active gene signature, whereas male counterparts were pro-apoptotic. Collectively, our findings uncover a female-specific protective MAIT profile, potentially shedding light on reduced COVID-19 susceptibility in females.

In the current study, we carried out sex-balanced sampling of peripheral blood mononuclear cells 48 (PBMCs) from COVID-19 patients and control subjects for 36-color flow cytometry and single cell RNA-49 sequencing (scRNA-seq) analyses. A total of 88 samples were analyzed from 45 individuals. Details on 50 subject demographics and sample information are summarized in (Fig. 1A, table 1 and S1). Briefly, we 51 analyzed samples from 28 patients with COVID-19 as confirmed by a positive SARS-CoV-2 PCR 52 and/or IgG seroconversion. These included 9 inpatient subjects (20%), 7 requiring intensive care, 53 henceforth referred to as "hospitalized." An additional 19 subjects were identified in outpatient settings 54 (42.2%), henceforth referred to as "infected". Most of these COVID-19 confirmed cases were sampled longitudinally (a range 1-28 days) including pre-and post-anti-SARS-CoV-2 immunoglobin (IgG) 56 seroconversion. The dates of symptom onset for all confirmed COVID-19 subjects were recorded at 57 enrollment, providing an illness range of 1-40 days. We also recorded symptom severity, obtained via 58 investigator survey on 39 symptoms related to . Additionally, we included 7 59 subjects (15.6%) henceforth referred to as "exposed," who were also sampled at multiple timepoints.

These subjects, despite being close contacts of infected individuals, remained with negligible symptom 61 scores, were negative for SARS-CoV-2 by PCR, and did not demonstrate detectable anti-SARS-CoV-2 62 IgG for at least 2 months after enrollment. Lastly, we included a group of 10 "healthy" subjects (22.2%) 63 who were enrolled prior to the pandemic in 2019 and did not show any symptoms associated with 64 COVID-19 or other respiratory illness (21).

With these flow cytometry data (Table S2) 

CD8 + T cells, as well as CD8 + CD161 hi T cells and other indicated sub-populations (Fig. 1B, and fig. 76 S1, A to E). We noted that a minor population of basophils (Baso) and neutrophils (PMN) primarily from 77 hospitalized patients were detected (Fig. 1B) , despite using a PBMC isolation protocol.

We next set out to examine our flow cytometry dataset for immune populations that exhibited major 79 quantitative changes in COVID-19. Our first strategy was to stratify the data by disease severity (i.e., 80 healthy, exposed, infected, and hospitalized). We noted that samples from hospitalized patients had 81 substantially fewer PBMCs suggestive of lymphopenia (24) . Manual gating of all flow cytometry events 82 was performed for this analysis. Our results showed differences in B cells (naï ve, IgD + non-class 83 switch, and plasmablasts); natural killer (NK) cells (CD56 lo populations); DCs (CD141 + , CD1c + , and 84 pDCs); monocytes (classical, intermediate and nonclassical); CD4 + (EM) and CD8 + (EM)  T cells 85 (Fig. S2) . Interestingly, our data also revealed a high statistical significance (p=0.0006) in CD8 + 86 CD161 hi T cells (Fig. S2 ) prompting us to look closer at these cells. Regarding annotation of this 87 CD161 hi cluster, because the overwhelming majority of events are low to negative for CD56 and for T-88 cell receptor (TCR)-γ (Fig. 1C) , the phenotype is largely consistent with mucosal associated invariant 89 T (MAIT) cells, but not NKT or γ T cells. This designation is congruent with recent work in 90 . We therefore conclude that the frequencies of certain myeloid and lymphocyte 91 populations are affected in COVID-19, including a major effect on CD8 + CD161 hi T cells.

Knowing that the overwhelming majority of the CD8 + CD161 hi population (henceforth referred to as 93 CD161 hi ) in our and others' datasets (25-27) is likely comprised of MAIT cells, we performed a more 94 focused analysis of this cluster in COVID-19 (Fig. S3A) . First, we analyzed their frequencies by disease 95 severity using the samples taken within 3 days of enrollment, the timepoint most proximal to the initial 96 symptom score recordings. Results were displayed via UMAP contour plots, revealing a reduction in 97 these cells in the SARS-CoV-2 settings (Fig. 1D) . Manual gating from all flow cytometry events 98 revealed a significant reduction when comparing healthy (p=0.0036) or exposed (p=0.0488) subjects 99 versus hospitalized subjects, as well as a negative correlation (p=0.0002) with disease severity (Fig. 1E 100 and Fig. S3B ). We also characterized the frequencies of CD161 hi cells stratified by time post symptom 101 onset, including early (≤ 14 days), middle (15 to 21 days), and late (>21 days) timepoints. In addition, 102 we separated the data by sex given the known sex differences in immune responses in 20) . Results showed that within the CD8 + compartment of healthy subjects, females had greater 104 frequencies of CD161 hi cells relative to males, whereas males had greater frequencies of CD8 + memory 105 T cells (combined EMRA, EM and CM) (Fig. 1F, and G) . No obvious changes of naï ve CD8 + T were 106 found (Fig. S3C) . While the memory cell predominance in males was preserved at all timepoints, the 107 greater abundance of CD161 hi cells in females was lost at early and middle timepoints (albeit not in late 108 disease). The loss of this difference was due to a precipitous drop of CD161 hi cells in females at early 109 and middle timepoints (Fig. 1H) . Lastly, we stratified data from confirmed COVID-19 patients by 110 seroconversion status. This showed CD161 hi cells were higher in females relative to males prior to 111 seroconversion, whereas CD8 + memory cells were higher in males in seroconverted subjects (Fig. 1I ).

Taken together, we identified a female-specific decline in circulating CD161 hi cell frequencies upon 113 exposure/infection of SARS-CoV-2. This sex-specific reduction may be due to extravasation into airway 114 tissues, thereby suggesting a key sex-specific role for these CD161 hi cells in COVID-19.

116 Given the potentially important role for CD161 hi cells in COVID-19, we sought to further characterize 117 this population by scRNA-seq (10x Genomics). We analyzed 48 different PBMC samples from 24 118 subjects across all groups (Table 1 and table S1 ). Data were processed using Seurat 3 package (28) 119 and subsequent transcript-based annotation was carried out ( Fig. 2A, fig. S4, A and B) . Focusing on 120 the T cells in the data ( Fig. 2B and table S3), we were able to identify CD161 hi cells in a single cluster 121 containing high KLRB1 (i.e., CD161) expression, and co-expression of CD3D and CD8A, as well as 122 TRAV1-2 ( Fig. 2C and fig. S4B ), which encodes the Vα7.2 invariant TCR alpha chain on MAIT cells.

Grouping these data by disease severity showed that hospitalized patients had lower frequencies of T 124 cells, including CD161 hi cells (Fig. 2D) , agreeing with our flow cytometry findings and consistant with 125 reported lymphopenia in severe 20, [29] [30] [31] [32] . Also showing the same trend as 126 our flow cytometry data was the high frequency of CD161 hi cells in healthy females (Fig. 2E) , although 127 it did not reach statistical significance due to the variations between healthy females and males. Next, to address the functional role of this CD161 hi cluster in COVID-19, we performed gene enrichment 129 analysis using differentially expressed genes (DEGs). With several top ranked hits consisting of 130 immune pathways and an estrogen-dependent pathway (Fig. 2F) , our results inferred a sex-specific 131 immune response of these CD161 hi cells in COVID-19. To further characterize functional inferences, we 132 applied the CellphoneDB package (33) and analyzed ligand-receptor interactions with monocyte 133 clusters within our data (Fig. S5, A and B) , given the critical link that was previously published between 134 monocyte activation in COVID-19 outcomes (12, 13, 20, 34) . Our results inferred unique interactions 

To analyze our scRNA-seq dataset for potential sex differences in circulating CD161 hi cells, we first 142 sought to examine for phenotypic heterogeneity within this population. To do this, we performed a 143 focused sub-cluster analysis, which generated 3 distinct clusters (Fig. 3A) . However, the added 144 resolution revealed a cluster that expressed TRDC, encoding the constant region of the  chain 145 expressed by γ T cells (Fig. 3B ) and thereby excluded from subsequent analyses. By contrast, the 146 other two clusters had higher KLRB1 expression, as well as TRAV1-2 (Fig. 3B) , therefore referred to 147 here as MAIT and MAIT clusters. Of note, these 2 clusters make up approximately 80% of CD161 hi 148 PBMCs, which is consistent with the previous report of circulating MAIT cell frequencies (25). Our proteins, apoptosis (BAX, STUB1) and the linker histone H1 associated with apoptosis (HIST1H1C, HIST1H1D, HIST1H1E) ( Fig. 3C and table S4 ). Gene enrichment analysis further supported a 154 functional dichotomy for  and  clusters. Whereas MAIT was enriched with several immune process 155 pathways (e.g., IFN-γ, and IL-4 and IL-13 signaling, as well as antigen processing and presenting),

MAIT was enriched in cellular responses to external stimuli, metabolism of RNA, viral infection, and 157 programmed cell death, but not immune processes (Fig. 3, D 

Last for this series of experiments, we sought to determine the dynamics of the two phenotypically 161 distinct clusters by sex over the COVID-19 disease course. By first grouping our data by severity, we 162 found that MAIT was the major phenotype in healthy individuals, while MAIT predominated in 163 exposed and infected groups (Fig. 3, F and G) . There was a noted exception for hospitalized patients 164 (Fig. 3F) , bearing very few cells as seen in our flow cytometry data, consistent with lymphopenia that 165 occurs in severe COVID-19 (24). We then grouped our data by time post symptom onset, as we 166 detailed earlier with our flow cytometry data. Results showed that relative to healthy subjects, MAIT 167 percentages were lower in early, middle, and late timepoints, whereas MAIT demonstrated the 168 converse ( Fig. 3H) . When stratified by sex, we found that MAIT cell frequencies were higher in healthy (Fig. 3I) . However, this difference was lost in exposed/infected setting, where both sexes were 172 comprised mostly of MAIT (Fig. 3J) . Nonetheless, MAIT percentages were statistically greater in 173 females in late disease (Fig. 3J) , which reflects the increased MAIT cells during late infection in 174 females as shown in our flow cytometry findings. Regarding expression of CD69, a T cell activation 175 marker, we did not observe major differences across cluster or sex, but did observe elevated 176 expression in the hospitalized group (Fig. S6, A to D) . This possibly suggests an altered MAIT cell response in hospitalized patients (25) (26) (27) 35) . In short, these results reveal sex specific MAIT cell 178 differences at the quantitative and phenotypic levels in health and COVID-19.

180 To assess potential sex-specific differences in MAIT cells at the tissue level in COVID-19 patient 

In a final experiment, we sought to characterize MAIT cell transcriptomes by sex in the BALF and NPS 200 datasets and determine whether these cells resembled  and  phenotypes we identified in circulating 201 MAIT cells. Cluster analysis was not warranted here given low cell numbers in these datasets. Instead, 202 we leveraged gene modules derived from our respective  and  clusters of circulating MAIT cells.

We found that MAIT cells in BALF and NPS data were largely skewed toward the  module, with 204 minimal sex differences (Fig. S7A) . However, when we directly examined differentially expressed 205 genes (the DEGs between sex, we were able to detect sex differences associated with  and  206 phenotypes. Specifically, in BALF, we found increased IL7R expression in females (Fig. 4J ) and other 207 IL-7 signaling associated genes (CISH and SOCS1) (Fig. 4K) . Given the critical role of this signaling in 208 T cell survival, we explored additional pathway genes, finding that female MAIT cells had upregulated 209 anti-apoptotic genes (BCL2 and FOXP1) and downregulated pro-apoptotic genes (BAX and CASP3) 210 (Fig. S7 , B and C). Also observed in female cells was upregulated anti-proliferative genes (CDKN1B 211 and BTG2) (Fig. S7D) . These patterns matched MAIT gene changes in our PBMC dataset. We were 212 able to find other sex differences, including increased expression of several transcription factors (KLF2, 213 MYC, and CEBPD) (Fig. 4L) . Conversely, male cells had higher expression of CCL2 (Fig. 4 M) , which 214 has been linked to COVID-19 immunopathology (39). In short, our results infer sex differences at the 215 qualitative level in COVID-19, with female MAIT cells possessing a pro-survival and immunologically 216 active phenotype.

Despite the knowledge of sex differences in the immune response as an underlying factor in COVID-19 219 disease outcomes, the sexual dimorphic responses of MAIT cells, an unconventional T cell population 220 deemed important in this disease, remained unknown. We now demonstrate that MAIT cells in females 221 are quantitatively and qualitatively more robust in the SARS-CoV-2 setting, potentially helping 222 understand the immunological reasons for reduced COVID-19 susceptibility in females.

Our finding that MAIT recruitment to airway tissues may be more robust in COVID-19 females was 224 aided first by our observation of higher circulating MAIT cell frequencies in females in the healthy 225 setting. This difference can be explained by the rate of physiological aging-related attrition of MAIT cells 226 that is substantially less pronounced in female blood (40-42). The resultant higher frequencies in 227 circulation enabled us to readily uncover the precipitous percentage drop we saw with MAIT cells 228 relative to exposed/infected females. In trying to elucidate the potential cause of this drop, we 229 considered two possible scenarios: 1) lymphopenia and 2) extravasation, which are not necessarily 230 mutually exclusive. For the former, it is accepted that lymphopenia is associated with severe COVID-19 231 infections (24, 32, 43, 44) , which which agrees with our observations in our hospitalized group (27), though extravasation also likely occurred. In our study, however, we 235 demonstrated that circulating MAIT frequencies drop in our infected outpatient group. As these subjects 236 were not critically ill, our findings point to extravasation as a major reason for the sex-specific drop in 

COVID-19 patients relative to healthy subjects in aggregated data, it is possible that the healthy female 240 frequencies contributed to reaching the statistical difference. Further supporting our conclusion, we 241 were able to show with publicly available scRNA-seq data from COVID-19 BALF samples (36) that 242 females in that study had an increased MAIT cell percentage relative to males, allowing us to conclude 243 that MAIT cell extravasation during COVID-19 may be quantitatively more robust in females.

Our results also suggest that MAIT cells may be qualitatively superior in females, with respect to anti-245 viral immune activity in COVID-19. Leading us to this conclusion, our scRNA-seq analysis of patient 246 PBMCs revealed two distinct clusters of MAIT cells, referred to here as MAIT and MAIT. The  247 cluster was enriched for various immune pathways, such as IFN- signaling, inferring a capacity for 248 anti-viral immune function. In contrast, the  cluster was enriched for cell stress and apoptosis 249 pathways, inferring a frail phenotype roughly similar to a previously described population of double negative MAIT cells (45, 46) . We showed in the healthy setting that MAIT cells in females were skewed 251 toward the  cluster, whereas males comprised the  cluster. Though from these results it could be 252 presumed that the  cluster should be overrepresented in COVID-19 airways of females, this was not 253 the case in the BALF. However, we reasoned that such a finding would be very difficult to make for two 254 main reasons. First, extravasated MAIT cells with an -phenotype would be restricted to the early wave 255 of recruitment, since circulating cells are almost completely skewed to the  module in exposed/infected gives further credence that differences revealed in blood would likewise extend to the tissue.

In summary, we conclude that MAIT cells in females are quantitatively and qualitatively distinct from 262 males and we surmise that this distinction provides a protective advantage in the SARS-CoV-2 setting. Table 1 .

PBMC cells were prepared using Ficoll-Hypaque density gradient method. Briefly, peripheral whole 298 blood was collected in EDTA vacutainer tubes and processed within 8 hours. Blood was diluted 1:2 in 299 PBS then layered onto the Ficoll-Hypaque in 50 ml conical tube and centrifuged at 420g for 25 min. 

Antibody titrations used in this study were previously established by Cytek Biosciences with slight 316 modifications (see Table S2 for flow panel information). All staining procedures were performed at room 

Briefly, BCL files were demultiplexed to generate FASTQ files. FASTQ files were aligned with STAR 343 aligner to the human genome reference GRCh38 from Ensemble database. Feature barcode processing and UMI counting were then performed according to the standard workflow. (QC summary 345 after sequencing). The following criteria were applied as quality control of single cells from all individual 346 samples. Cells that had fewer than 1000 UMI counts or 500 genes, as well as cell that had greater than 347 10% of mitochondrial genes were removed from further analysis. Genes that were expressed by fewer 348 than 10 cells were also excluded. After filtering, a total of 424,080 cells with 18,765 gene features were 349 kept for the downstream analysis. Table S5 .

Ligand-receptor interactions between T cells and monocytes were inferred using CellPhoneDB (33).

PBMC scRNA-seq data were randomly downsampled to 50,000 cells and T and monocyte clusters 

Publicly available scRNA-seq data of BALF (36) and of NPS (37) were downloaded and processed 379 using Seurat 3 as previously described (28) 

Data normality and homogeneity of variance were assessed using Kolmogorov-Smirnov test and 391 Bartlett's test, respectively. Due to the distribution and variance of human data, non-parametric 392 statistical tests were favorably used throughout this study unless otherwise specified. Mann Whitney U 393 test was used for two-group comparisons, and Kruskal-Wallis with post hoc Dunn's test was used for 394 comparisons of three groups and more. Spearman's correlation efficiency was used to quantify the 395 correlation of the ranked disease severity (from healthy as 1, to hospitalized as 4). To adjust p-values 396 for multiple hypothesis testing, FDR correction was performed using the Benjamini-Hochberg procedure 397 when appropriate. Two-tailed tests were used unless otherwise specified. A p-value or FDR < 0.05 is 398 consider statistically significant. Graphical data of quantifications presented throughout are expressed 399 as the means ± SEMs and were plotted using Graphpad Prism 8. Other graphs in this study were 400 generated using either the corresponding analytic packages or R package ggplot2.

Data availability

All clinical metadata of participants and samples in this study are included in Table S1 . Data will be 

Wallis test (E). 

Heatmap of interaction counts between major T cell and monocyte subsets. 

An interactive web-based dashboard to track COVID-19 in real 429 time

Considering how biological 431 sex impacts immune responses and COVID-19 outcomes

The pattern of Middle East respiratory syndrome coronavirus in Saudi 434

Arabia: a descriptive epidemiological analysis of data from the Saudi Ministry of Health

Do men have a higher case fatality rate of severe acute 437 respiratory syndrome than women do?

SARS in Singapore--predictors of disease severity

Sex-Based Differences in Susceptibility to Severe Acute Respiratory 441

Syndrome Coronavirus Infection

Sex differences in immune responses

Cutting edge: estrogen drives expansion of the CD4+CD25+ regulatory T 445 cell compartment

Systems analysis of sex differences reveals an immunosuppressive role for 447 testosterone in the response to influenza vaccination

Sex Differences in Plasmacytoid Dendritic Cell Levels of IRF5 Drive Higher 450 IFN-alpha Production in Women

Deep immune profiling of COVID-19 patients reveals distinct immunotypes 452 with therapeutic implications

Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe 454 from Mild COVID-19

Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell 456 Compartment

Longitudinal immune profiling reveals key myeloid signatures associated with 458 COVID-19

Broad and strong memory CD4(+) and CD8(+) T cells induced by SARS-CoV-2

UK convalescent individuals following COVID-19

Robust T Cell Immunity in Convalescent Individuals with Asymptomatic or Mild 462 COVID-19

A single-cell atlas of the peripheral immune response in patients with severe 499 COVID-19

Outcome of SARS-CoV-2 infection linked to MAIT cell activation and 501 cytotoxicity: evidence for an IL-18 dependent mechanism. medRxiv

Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19

COVID-19 severity correlates with airway epithelium-immune cell interactions 505 identified by single-cell analysis

Marked T cell activation, senescence, exhaustion and skewing towards TH17 507 in patients with COVID-19 pneumonia

Pathological inflammation in patients with COVID-19: a key role for 509 monocytes and macrophages

The decrease in number and change in 511 phenotype of mucosal-associated invariant T cells in the elderly and differences in men and 512 women of reproductive age

Circulating mucosal-associated invariant T cell levels and their cytokine levels in 514 healthy adults

Circulating Mucosal-Associated Invariant T Cells in a Large Cohort of Healthy 516

Epidemiological and clinical characteristics of 99 cases of 2019 novel 518 coronavirus pneumonia in Wuhan, China: a descriptive study

A dynamic COVID-19 immune signature includes associations with poor 520 prognosis

Human iNKT and MAIT cells exhibit a PLZF-dependent proapoptotic propensity 522 that is counterbalanced by XIAP

Multiple layers of heterogeneity and subset diversity in 524 human MAIT cell responses to distinct microorganisms and to innate cytokines

Moderated estimation of fold change and dispersion for RNA-527 seq data with DESeq2

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell 529 RNA-seq