Nasal anti-CD3 mAb (Foralumab) dampens CD3+ T effector function and decreases NKG7 in COVID-19 through a mechanism involving GIMAP-7 and TGFb1

doi:10.21203/rs.3.rs-2061549/v1

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Nasal anti-CD3 mAb (Foralumab) dampens CD3+ T effector function and decreases NKG7 in COVID-19 through a mechanism involving GIMAP-7 and TGFb1

https://doi.org/10.21203/rs.3.rs-2061549/v1

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T cells are present in early stages of the SARS-CoV-2 infection and play a major role in disease outcome and long-lasting immunity. Nasal administration of a fully humanized anti-CD3 monoclonal antibody (Foralumab) reduced lung inflammation as well as serum IL-6 and C-reactive protein in moderate cases of COVID-19. Using RNA-sequencing and serum proteomics, we investigated the immune changes in patients treated with nasal Foralumab. In a randomized trial, mild to moderate COVID-19 outpatients received nasal Foralumab (100ug/day) given for 10 consecutive days and were compared to patients that did not receive Foralumab. We found that naïve-like T cells were increased in Foralumab treated subjects and NGK7⁺ effector T cells were reduced. CCL5, IL32, CST7, GZMH, GZMB, GZMA, PRF1, and CCL4 gene expression were downregulated in T cells and CASP1 was downregulated in T cells, monocytes and B cells in subjects treated with Foralumab. In addition to the downregulation of effector function, an increase in TGFb1 gene expression in cell types with known effector function was observed in Foralumab treated subjects. We also found increased expression of GTP-binding gene GIMAP7 in subjects treated with Foralumab. Rho/ROCK1, a downstream pathway of GTPases and TGF-b1 signaling, was downregulated in Foralumab treated individuals. TGFb1, GIMAP7 and NKG7 transcriptomic changes observed in Foralumab treated COVID-19 subjects was also observed in healthy volunteers, MS subjects and mice treated with nasal anti-CD3. Our findings demonstrate that nasal Foralumab modulates the inflammatory response in COVID-19 and provides a novel avenue to treat the disease.

Nasal anti-CD3 (Foralumab) modulates the enhanced T cell inflammatory responses in COVID-19 by suppressing effector features in multiple CD3⁺ subsets

The coronavirus disease 2019 (COVID-19) pandemic represents the greatest global public health crises since the pandemic influenza outbreak of 1918 (1). It is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that enters host cells through angiotensin-converting enzyme 2 (ACE2) and neuropilin (NRP1) receptors, resulting in a complex and heterogenous respiratory disease (2). Although, SARS-CoV-2 vaccination prevent hospitalization and mortality (3), concerns about newly emerging coronavirus variants that may be resistant to antibody dependent vaccination strategies have spurred interest in other immune strategies to treat the disease (4, 5).

T cell responses are crucial for SARS-CoV-2 immunity. CD4⁺ and CD8⁺ SARS-CoV-2-specific T cells are present in early stages of the infection and increase over time. They have strong reactivity to the viral proteins and play a major role in lasting immunity (6-8). It has been shown that activated T cells increase in patients with moderate COVID-19 and do not return to normal levels during convalescence contributing to immunopathology which results in respiratory distress and organ damage (9).

We and others have shown the modulation of T cell function by the oral or nasal administration of anti-CD3 monoclonal antibody in animal models of autoimmunity and inflammation including multiple sclerosis (10, 11), diabetes (12-14), arthritis (15), inflammatory bowel diseases (16) and lupus (17). Anti-CD3 induces tolerogenic immune responses by stimulating T regulatory cells (18). In humans, we investigated the effects of nasal anti-CD3 in healthy volunteers and found that nasal administration of anti-CD3 monoclonal antibody (Foralumab) modulated effector CD8⁺ function and induced a regulatory transcriptional program in T cells (under review).

We previously conducted a pilot trial administering nasal Foralumab in subjects with mild to moderate COVID-19 and found a reduction in serum IL-6 and C-reactive protein and more rapid clearance of lung infiltrates in treated individuals (19). Here, we investigated the immune response of these subjects using whole-genome cell transcriptomics and serum proteomics and found that nasal Foralumab dampens CD3⁺ T effector function while increasing TGFb1 expression in COVID-19 through a mechanism involving GTPases of immunity-associated proteins (GIMAPs)

and NKG7. We also show that this effect is not restricted to COVID-19 subjects but is also observed in healthy and MS subjects treated with nasal Foralumab . Taken together, we demonstrate a common mechanism of action of nasal anti-CD3 (Foralumab) that can be used therapeutically for subjects with COVID and other diseases with immune dysfuction.

Nasal Foralumab downregulates the coronavirus pathogenesis pathway in T cells and the expression of CASP1 in T cells, monocytes, and B cells. To investigate the transcriptomic changes in immune cells following nasal Foralumab treatment in SARS-CoV-2 infected patients, we FACS-sorted CD3⁺ (Tcells), CD19⁺ (B cells) and CD14⁺ (monocytes) cells from Foralumab treated and untreated COVID-19 patients at baseline (day − 2) and day 10 as well as in healthy subjects (Figure. 1A; Supplementary Fig. 1A; Supplementary Table 1). To obtain a COVID-19 signature for the subjects in our study we performed bulk RNA sequencing (RNA-seq) in T cells, monocytes and B cells and compared all COVID-19 patients in our study at baseline versus healthy controls. (Supplementary Table 2). Ingenuity Pathway Analysis (IPA) of the top differently expressed genes (DEGs) showed upregulation of coronavirus pathogenesis pathways in all three immune cell populations (Supplementary Fig. 1B-D).

We then compared CD3⁺ cells isolated from COVID-19 infected subjects vs. healthy controls and found increased expression of activation markers including CD69, CD83, ICOS, RGS1 as well as other stress related genes including HIF1A, ATF4, NFKB1 and PR1. Genes related to T cell responsiveness such as IL21R, IL4R, CXCR4 were also upregulated in COVID-19 infected subjects (Supplementary Table 2). Gene set enrichment analysis (GSEA) of DEGs showed upregulation of IL-18 (p < 0.001), TNF (p < 0.0001) and VEGFA-VEGFR2 signaling pathways (p = 0.002) in COVID-19 subjects (Supplementary Table 3). In addition, activation of NLRP3 inflammasome by SARS-CoV-2 (p = 0.003) and host-pathogen interaction of human coronavirus-interferon induction (p < 0.0001) canonical pathways were upregulated in T cells of COVID-19 subjects vs. healthy controls (Supplementary Fig. 1B-D; Supplementary Table 3). Overall, our findings are consistent with the known immune response in subjects infected with SARS-CoV-2 ^20–23.

We next investigated the effect of nasal Foralumab on gene expression in T cells, B cells and monocytes by comparing pre-treatment (day-2) versus follow-up (day 10) in Foralumab treated versus untreated subjects. Unsurprisingly, as they are the target of the Foralumab treatment, the most pronounced changes were found in T cells (Fig. 1B). 306 genes were uniquely expressed in untreated subjects whereas 323 genes were uniquely expressed in Foralumab treated subjects ²⁴ (Data File 1). IPA of the differentially expressed genes (DEGs) in the Foralumab group identified genes that are part of the coronavirus pathogenesis pathway that were downregulated in the Foralumab group but not in untreated controls. Amongst them, eight genes (CASP1, IRF9, IRF7, OAS3, STAT1, BST2, TRIM25, TP53) were downregulated, and 3 genes were upregulated (EEF1A1, FOS1, NUP98) (Fig. 1B; Supplementary Table 4). Of note, Caspase-1 was downregulated not only in T cells, but also in B cells and monocytes in Foralumab versus untreated subjects (Data file 1). Caspase-1 is associated with immune-related COVID pathogenicity and worse COVID-19 outcome ²⁵. In monocytes from Foralumab treated individuals (Fig. 1C) we found that coronavirus pathogenesis pathway was slightly downregulated compared to untreated subjects (Supplementary Table 4).

We thus analysed the effect of nasal Foralumab on gene expression in B cells by comparing pre-treatment versus day 10 in Foralumab treated versus untreated subjects Fig. 1D (Data file 1). Although we found differences in gene expression in B cells, such as downregulation of IL-4 and IL-15 signaling in Foralumab treated subjects, these differences were of smaller statistical significance compared to those observed in T cells (Supplementary Table 4).

Taken together, our results suggest that nasal administration of Foralumab led to the downregulation of genes related to inflammation and coronavirus pathogenesis and that its immunomodulatory effects were mostly pronounced in CD3 + T cells, although we could find some evidence for changes indirectly affecting both monocytes and B cells.

Single cell sequencing identifies T effector subset changes in Foralumab treated subjects.

We performed droplet-based 10x CellRanger sequencing in FACS sorted cells from healthy controls, untreated COVID-19 subjects, and Foralumab treated COVID-19 subjects (Fig. 1A). Using graph-based clustering of uniform manifold approximation and projection (UMAP), we captured the transcriptomes of 12 cell types according to the expression of canonical gene markers (Fig. 2A). These included CD4⁺ and CD8⁺ naive T cells (LEF1⁺, LTB⁺), CD4⁺ and CD8⁺ effector memory T cells re-expressing CD45RA (TEMRA) (GZMH⁺, GNLY⁺, CST7⁺), CD8⁺ Central Memory (CM) (COTL1⁺, NELL2⁺) T cells, CD8⁺ Effector Memory (EM) (COTL1⁺, GZMH⁺, GNLY⁺), Non gamma VD2 gamma-delta (TRDV1⁺) T cells, VD2 gamma deltas (TRDV2⁺) T cells, NK-like cells (KLRD1⁺, FCER1G⁺), T regulatory (Treg) (FOXP3⁺), Thelper ( KLRD1⁻, TRDC⁻ GNLY⁻ FOXP3⁻) cells and mucosal-associated invariant T (MAIT) (KLRB1⁺) cells. (Fig. 2A; Supplementary Fig. 2A,B; Data file 2). Prior to treatment (day − 2) (Fig. 2B,C), subjects with COVID-19 displayed a reduction in both naïve CD4⁺ and CD8⁺ T cells compared to healthy controls, which was accompanied by an increase of other cell types including CD4⁺ and CD8⁺ TEMRA, CD8⁺ EM cells, nonVD2 gamma delta, VD2 gamma delta T cells and NK-like cells. No changes were observed in the frequency of Treg, MAIT and T helper cells. (Supplementary Fig. 2C). Following treatment with Foralumab, the COVID-19 associated changes in CD3⁺ T cells resembled the pattern observed in healthy subjects, particularly in naïve CD4⁺ cells, CD4⁺ and CD8⁺ TEMRAs, NonVD2 and VD2 gamma delta T cells and NK-like cells (Fig. 2C). Consistent with these observations, we found a reduction of effector cells in Foralumab treated subjects (Fig. 2D).

We then performed TCR sequencing on CD3⁺ cells (Supplementary Fig. 3A-D). We found that CD3⁺ T cells were distributed in the lower regions of the UMAP and showed the largest clonal expansion (clonal size 20 < X < 100) (Supplementary Fig. 3C), predominantly involving the CD8⁺ EM and TEMRA populations (Supplementary Fig. 3D). There were no differences in clonal expansion following Foralumab treatment suggesting that its effect was not associated with the expansion or contraction of specific selection T cell clones. Our TCR-sequencing data were not sufficiently powered to identify preferred TCR sharing patterns and immunodominance in T cells from Foralumab treated subjects versus control in SARS-CoV-2 infection.

We next compared gene expression of total CD3⁺ T cells in which we compared pre-treatment versus day 10 for both Foralumab treated subjects and untreated COVID-19 patients. We found significant downregulation of effector function genes including NKG7, CCL5, IL32, CST7, GZMH, GZMB, GZMA, PRF1, and CCL4 in the Foralumab treated subjects. (Fig. 3A, B; Data file 3). We further explored this effector gene distribution among the CD3 + subsets (Fig. 3C). Reduction of IL32 was observed in CM and EM CD8⁺ cells as well as in gamma delta T cells in Foralumab treated subjects. Downregulation of GZMH was observed in CD4⁺ effector cells and PRF1 was downregulated in CD8⁺ CM (Fig. 3C). Taken together, our findings demonstrate a reduction of effector features in multiple T cell subsets in Foralumab treated subjects when compared to untreated controls.

We next investigated the cell state score of predefined gene sets associated to T cell exhaustion and activation in COVID-19 ⁹. By aggregating the expression values of co-inhibitors molecules associated with T cell exhaustion, we were able to score and compare baseline (day − 2) versus follow-up (day 10) (Fig. 3D,E). As shown in Fig. 3F, Foralumab treated subjects showed greater reduction of T cell exhaustion scores when compared to untreated controls. Of note, MAF and TGFb1 gene expression was increased in highly exhausted cells when compared to non-exhausted cells while IL2, TNFa and IFNg was unchanged (Supplementary Fig. 4A). The exhaustion score was higher in Tregs, effector memory CD8+, CD8 + TEMRAS and non VD2 gamma delta T cells (Supplementary Fig. 4B).

We further investigated the level of expression of genes that were present in the coronavirus pathogenesis pathway that were found to be decreased in T cell of Foralumab treated subjects in our bulk-RNA-seq data set. CASP1, OAS3, BST2 and IRF7 were found to be highly expressed in the T cells subsets found in our single cell dataset and were modulated by disease (Fig. 3G, H; Supplementary Fig. 5A). In accordance with our bulk-RNA-seq of T cells sorted from Foralumab treated subjects, CASP1 was also significantly downregulated in our single-cell dataset (Fig. 3H). CD4⁺ TEMRA represents the major CD3⁺ subset driving this effect (Fig. 3H). OAS3 and IRF7 genes expression were significantly increased in all CD3⁺ cell subsets at the baseline time point (day-2) when compared to healthy controls and decreased at day 10. We were not able to elect a major CD3 + subset that could alone be associated with the downregulation of these genes expressions in the bulk RNA-seq dataset. Collectively, OAS3 was downregulated in CD8⁺ EM, nonVD and VD2 gamma delta T cells. Finally, nonVD2 gamma delta T cells were found to be the major contributor of BST2 downregulation in Foralumab-treated subjects. (Fig. 3G).

Changes in serum cytokines, growth factors and chemokines in Foralumab treated subjects. We quantified serum cytokines, growth factors and chemokines in COVID-19 subjects following Foralumab treatment as well as in untreated COVID-19 and healthy controls using both OLINK and Multiplex technology.

IL-18 is a product of inflammasome activation that plays a role in COVID-19 ²⁶. We found that serum IL-18 was increased in COVID-19 subjects compared to healthy controls (p = 0.003) (Supplementary Fig. 6A,B) and a reduction of serum IL-18 in subjects that received Foralumab (p = 0.05) (Fig. 4A; Supplementary Table 5).

Brain derived neurotrophic factor (BDNF) is a neuronal growth factor produced by immune cells including T cells ²⁷. Increased BDNF secretion has been associated with recovery in COVID-19 ²⁸. We found an increase in serum BDNF in Foralumab treated patients (p = 0.01) that was not observed in untreated COVID-19 subjects or healthy controls (Fig. 4A, Supplementary Table 5). In addition to BDNF, an increased in other growth factors was observed following Foralumab treatment that did not occur in untreated COVID-19 patients including vascular endothelial growth factor A (VEGF-A) (p = 0.003), placental growth factor (PIGF) (p = 0.004), and stem cell factor (SCF) (p = 0.023) (Supplementary Table 5). We observed changes in hepatocyte growth factor (HGF), platelet derived growth factor-BB (PDGF-BB), and serum interferon gamma inducible protein-1 (IP-10) in both Foralumab treated and untreated COVID-19 subjects (Supplementary Table 5). Altogether, these results suggest that Foralumab administration induces the secretion of factors related to tissue repair and re-vascularization, as well as growth factor involved in restoring the damage caused by the inflammatory processes related to COVID-19 infection.

We also used the OLINK to measure 96 inflammatory proteins in the serum. Consistent with the inflammatory nature of COVID-19 infection, we found an elevation in 19 inflammatory proteins including IL-18, IL-8, IL-10, IL-6, IFNg, CSF-1, CXCL11, CXCL10 and CSF-1 in COVID-19 subjects vs. healthy controls (Supplementary Table 6; Supplementary Fig. 6B; Data file 4). We then asked whether any of the proteins measured by the OLINK panel were changed by Foralumab treatment and identified 6 proteins (Fig. 4B). Two proteins were reduced by Foralumab treatment (FLT3L and IL-12B) and 4 proteins were increased following treatment (NT-3, ST1A1, AXIN1 and SIRT2). FLT3L and IL-12B are immune stimulatory cytokines that promote dendritic cell maturation. Neurotrophin-3 (NT-3) is a member of the BDNF family that we also found increased in Foralumab treated subjects. SIRT2 plays a role in T cell metabolism and effector function ²⁹.

Given that Foralumab modulates T cells, we asked which proteomic changes we observed with Foralumab treatment were related to genes expressed in T cell subsets as defined by our scRNA-seq analysis. We found that FLT3LG, SIRT2, SULT1A1 and AXIN 1 were expressed in T cell subsets (Fig. 4C). Of note, even though IL-18, BDNF, and other growth factors changed with Foralumab treatment, they were not selected for this analysis as they were poorly expressed in T cell subsets in our dataset.

We found that FLT3 was decreased in serum samples from Foralumab treated subjects when compared to untreated controls (Fig. 4B; Supplementary Fig. 5B). Several hematopoietic and nonhematopoietic cells can produce FLT3L ³⁰. We could not find any FLT3L gene expression decrease in any CD3⁺ subset when comparing untreated and Foralumab subjects suggesting the involvement of other cell types in the observed effect (Supplementary Fig. 5B). Similarly, we did not find any major changes in SULT1A1 and AXIN1 gene expression among CD3⁺ cells subsets when comparing untreated vs Foralumab treated subjects. In Fig. 4D, we showed that SIRT2 was increased in NK-like cells in Foralumab treated subjects (Fig. 4D).

Changes in T cell expression of GIMAP7, NKG7, and TGFb1 are associated with immunomodulatory effects of nasal Foralumab.

We found that NKG7 was downregulated in CD8⁺ TEMRAs, CD8⁺ EM, nonVD2 gamma deltas and gamma delta T cells when compared to baseline (day − 2) in COVID-19 treated subjects but not untreated controls (Supplementary Fig. 7A). TGFb1 was increased in multiple CD3⁺ subsets including CD4⁺ TEMRA, CD8⁺ TEMRA, CD8⁺ EM, nonVD2 gamma-delta and VD2 gamma-delta T cells following Foralumab treatment when compared to both healthy controls and untreated COVID-19 subjects. The TGFb1 gene expression increase was found in cell types with known effector function that not classical Treg cells (Supplementary Fig. 7B).

In addition to COVID-19 subjects, we have treated healthy volunteers and multiple sclerosis (MS) subjects with nasal Foralumab and performed scRNA-seq analysis on CD3 + subsets in these subjects. This provided the opportunity to ask whether there were common mechanisms by which nasal anti-CD3 modulated T cell function in different human conditions. We found that GIMAP7 and TGF- b1 were upregulated whereas NKG7 was downregulated in all 3 cohorts (Fig. 5A, 5B). Furthermore, we found these same changes in CD3 + cells sorted from the cervical lymph nodes of C57BL/6J mice treated with nasal anti-CD3 (Fig. 5C). Although the immunologic role of GIMAPs is not well defined, they define stages of T-cell development, survival and cell identity ^31,32 and have been reported to be associated with T cell regulatory function ³³.

Lastly, we found that the gene expression of RhoA, ROCK1and CFL1, which are negatively regulated by GTPases was downregulated in Foralumab treated individuals. By aggregating the expression values of these genes, we were able to score the changes in the Rhoa/ROCK1 pathway and found a decrease of this pathway in Foralumab treated subjects (Fig. 5D).

Taken together, we found that nasal anti-CD3 (Foralumab) induces naïve-like cells and restrains effector features in CD3⁺ T cells as exemplified by decreased gene expression of CASP1, CCL4 and NKG7, while TGFb1 gene expression increased. Notably, this effect was not limited to subjects with COVID-19 but was also observed in healthy and MS subjects treated with nasal Foralumab.

T cells play an important major role in subjects with COVID-19 infection as COVID-19 patients develop hyperactive T cell responses that contribute to disease pathogenesis ⁶. Thus, in addition to vaccination and anti-viral strategies to combat COVID-19, dampening of hyperactive T cell responses is a goal of COVID-19 therapeutics ⁴. We previously reported positive effects of nasal anti-CD3 (Foralumab) by reducing inflammatory markers and lung infiltration in subjects with mild to moderate COVID-19 ¹. In the present study we performed detailed immunological analysis to understand how nasal Foralumab modulated T cell function in COVID-19 treated patients.

We found that NKG7, IL32, GZMA, PRF1, CST7, GZMH and CCL5 were among the top DEG downregulated in CD3⁺ upon nasal Foralumab treatment of COVID-19 subjects. In addition to decreased expression of genes related to effector function in T cells, we further found an increase of TGFb1 expression in effector COVID-19 patients treated with nasal Foralumab. TGFb1 expression was found to be increased in effector cells but not naïve or Treg cells suggesting that Foralumab acted to restrain T cell effector function rather than inducing Treg cells in the periphery.

We also found changes in GIMAPs in nasal Foralumab treated subjects. GIMAPs are strictly regulated during T-cell development ³⁴ and mutations in the GIMAP5 gene disrupts signaling pathways and homeostatic survival in naive T cells ³⁵. Of note, we found that naïve T cells are increased in COVID-19 subjects treated with Foralumab. In addition, we found that, GIMAP4, a known proapoptotic gene that is found on the membrane of human CD4⁺ T cells and that tightly regulates Th1 differentiation ³⁶, was downregulated in nasal Foralumab treated subjects. Of note, the CASP1 gene that was decreased following nasal Foralumab and the CD3E gene that is targeted by Foralumab are co-expressed with GIMAP-7, which we found to be increased at the gene expression level.

We propose a mechanism of action whereby nasal Foralumab in humans involves the induction of a quiescence program in T cells through the upregulation of GIMAP7 and TGFb1, concomitantly with the suppression of the NKG7 related genes, including PRF1 and GMZ. The interactions among GIMAPs, NKG7 and TGFb1 to affect immune activation has not been described before, may represent an important pathway to maintain immune homeostasis.

In addition to treatment of COVID-19 subjects with nasal Foralumab, we have treated healthy volunteers and subjects with progressive MS with nasal Foralumab. This provided the opportunity to determine whether there were common changes in human subjects treated with nasal Foralumab treated subjects. Indeed, we found that NKG7 decrease and GIMAP7 and TGFb1 increase in mRNA expression was observed not only in COVID-19 subjects treated with nasal Foralumab but also in healthy volunteers and MS subjects. Of note, these changes were also observed in the cervical lymph nodes of mice treated with nasal anti-CD3.suggesting a common program induced by Foralumab.

We also found that the Rhoa/ROCK1 pathway, known to be negatively regulated by GTPases was downregulated in Foralumab treated subjects. Several important immunomodulatory roles of the Rhoa/ROCK1 signaling pathway and its respective genes in T cell function has been shown including the role of Cofilin in T-cell hyporesponsiveness ³⁷ and increased ROCK activity in dysfunctional T-cells ^38,39. Particularly to the CNS, Rho/ROCK signaling pathway in T cells was shown to inhibit regeneration following CNS damage ⁴⁰. Moreover, reduced EAE severity was shown in mice with a T-cell specific deletion of RhoA gene ⁴¹. Thus, our data suggest an inhibition of the Rho/ROCK signaling pathway as a downstream effect of GIMAP7 upregulation in Foralumab treated subjects that may play a role in Foralumab immunomodulatory effects including its neuroprotective role.

In summary, we found that nasal Foralumab modulates the enhanced T cell inflammatory responses that occurs in SARS-CoV-2 infection by suppressing effector features in multiple CD3⁺ T cell subsets. Furthermore, this effect is not limited to subjects with COVID-19 as it is seen in healthy and MS subjects. The immune effects of nasal Foralumab occurred with minimal side effects making it an attractive therapeutic modality.

Study design and subject groups. Patients with mild to moderate COVID-19 were recruited and treated at the Santa Casa de Misericordia de Santos in Sao Paulo State, Brazil as previously described (3). Subjects were treated with 100ug nasal Foralumab given daily for 10 consecutive days and blood used for immunologic analysis was collected at baseline (day − 2) and day 10 (Fig. 1A). Blood samples were collected under ethical committee approval from the Universidade Metropolitana de Santos UNIMES (CAAE: 38056120.1.0000.5509). Immunologic studies were performed at the Brigham and Women’s Hospital, Boston under IRB approval 2020P000563. All studies performed at the Brigham and Brigham and Women’s Hospital was approved by the Mass General Brigham Human Subjects Research Committee (IRB). Of the 12 Foralumab treated subjects and 16 untreated subjects in the trial, 8 from each group were used for immunologic analysis. For bulk RNAseq, 8 Foralumab treated 8 untreated subjects, and 7 healthy controls were studied. For scRNA-seq, 4 subjects from each group were studied: For OLINK and multiplex analysis, 12 Foralumab treated subjects, 15 untreated subjects and 6 healthy controls were used (Supplementary Table 1). Foralumab (28F11-AE; NI-0401) is a fully human IgG1 anti-CD3 mAb with the Fc portion mutated such that the mAb is non FcR binding in vitro which exhibits only minor cytokine release in vivo while maintaining modulation of the CD3/TCR and T cell depletion ^42,43 (Foralumab was developed by Novimmune and was acquired by Tiziana Life Sciences.

PBMC isolation. PBMC isolation was performed within 4 hours after blood collection according with standard protocol ⁴⁴. Briefly, centrifuged cells were mixed with Ficoll solution. Plasma layer was removed and storage. PBMC pellet was washed with HBSS solution and then resuspended in freezing medium (10% DMSO, 50% FBS, 40% RPMI) and transferred to cryovials. Slow freezing technique using Mr. Frosty Container was used. Cryovials were long-term storage in liquid nitrogen. Aliquots of serum was also obtained and were frozen for later analysis. Samples were shipped in dry ice to The Mass General Brigham, Boston, USA.

Flow cytometry and cell sorting. PBMCs were thawed at 37C into complete RPMI media, washed with PBS and resuspended in FcrBlock human TruStain FcX™ (1:20) and stained for viability (eFluor 506 viability dye, Invitrogen, 1:1000). 3x10⁶ cells from each sample were stained with anti-human CD3 (APC-Cy7, SK7, 1:300, Biolegend), anti-CD19 (APC, 4G7, 1:300, Biolegend), anti-CD14 (FITC, M5E2, 1:200, BD pharmigen) and anti-CD66b (Pe, G10F5, 1:300, BD Pharmigen). Cells were sorted (0.2ml volume). After 4hr, the cells were recombined into a single sample and distributed into 3 replicates for complete staining and into 4 FMO control samples (for cytokines, staining was conducted in 96 well V-bottom plates (Costar). For surface staining, the cells were resuspended in FcR block (30% in MACS buffer for 15’ at 4C), and then incubated (40’ at 4C) with the panel of surface antibodies that included CD19 (LT19, Miltenyi Biotec), antibodies from Biolegend: CD3 (SK7), CD45RA (HI100), CD127 (A019D5), CD56 (NC1M16.2), CD20 (2H7), and LAP (TW4-6H10); antibodies from BD Bioscience: CD4 (SK3), CD8 (SK1), and CD27 (M-T271). After washing with MACs Buffer (0.1%FBS/PBS, 4C), cells were fixed and permeabilized using the eBioscience FoxP3 fixation buffer set, then incubated with permeabilization buffer containing 10% NRS (normal rat serum, 10’ at 4C), followed by incubation (30’ at 4C) with a panel of intracellular antibodies that included antibodies from Biolegend: Ki67 (KI67), FoxP3 (206D), IFNg (4S.B3), IL-17 (BL168), IL-10 (JES3-9D7), and Perf (dG9), and GzmB (GB11, BD Bioscience). The samples were washed with MACS buffer and each entire sample analyzed on a BD FACS Symphony flow cytometer with HTS attachment. For Activation indiced cell marker (AIM) assay, Cryopreserved PBMCs were thawed by diluting the cells in 10 mL complete RPMI 1640 with 5% human AB serum (Gemini Bioproducts) in the presence of benzonase [20 ml/10ml]. Cells were cultured for 20 to 24 hours in the presence of SARS-CoV-2 specific MPs [1 mg/ml], mesopools [1 mg/ml], 15-mers [10 mg/ml], or class I predicted peptides [10 mg/ml] in 96-wells U bottom plates with 1x106 PBMC per well. As a negative control, an equimolar amount of DMSO was used to stimulate the cells as a negative control in triplicate wells, and phytohemagglutinin (PHA, Roche, 1 mg/ml) was included as the positive control. The cells were stained with CD3 AF700 (4:100; Life Technologies Cat# 56-0038-42), CD4 BV605 (4:100; BD Biosciences Cat# 562658), CD8 BV650 (2:100; Biolegend Cat# 301042), and Live/Dead Aqua (1:1000; eBioscience Cat# 65-0866-14). Activation was measured by the following markers: CD137 APC (4:100; Biolegend Cat# 309810), OX40 PE-Cy7 (2:100; Biolegend Cat#350012), and CD69 PE (10:100; BD Biosciences Cat# 555531). All samples were acquired on either BD Symphony cytometer and data was analyzed using FlowJo software (Tree Star).

Single Cell 5’mRNA Sequencing and Smart-seq2 RNA-Seq Immune cells from participants that received Foralumab and untreated controls without the use of dexamethasone (Supplementary Table 1) as well as, uninfected patients used as healthy controls were analyzed by scRNA-seq using the 10X Genomics platform. CD3 + T cells were FACS- sorted from on baseline (day-2) and day 10 samples was used for single cell sequencing. Additional, T cell, B cells, and monocytes were FACS sorted from similar FACS sorted gating setting and processed for Smart-seq2-RNAseq. Single cell RNA-seq experiments were performed by the Brigham and Women’s Hospital Single Cell Genomics Core. Sorted cells were stained with a distinct barcoded antibody (Cell-Hashing antibody, TotalSeq-C, Biolegend) as previously described⁴⁵. Next, cells from each condition were pooled together and resuspended in 0.4% BSA in PBS at a concentration of 2,000 cells per µl, then loaded onto a single lane (Chromium chip K, 10X Genomics) followed by encapsulation in a lipid droplet (Single Cell 5′kit V2, 10X Genomics) followed by cDNA and library generation according to the manufacturer’s protocol. 5’ mRNA library was sequenced to an average of 50,000 reads per cell, V(D)J library and HTO (Cell Hashing antibodies) library sequenced to an average of 5,000 reads per cell, all using Illumina Novaseq. scRNA-seq reads were processed with Cell Ranger, which demultiplexed cells from different samples and quantified transcript counts per putative cell. Quantification was performed using the STAR aligner against the GRCh38 transcriptome. Smart-seq2 RNA-seq was performed by the Broad Genomics plataform, Broad Institute of MIT and Harvard. Cells were collected in RNA-free micro tubes containing 5ul of TCL buffer Qiagen, (1031576) and 1% 2-mercaptoethanol (Gibco). After sorting cells lysate was transferred to Eppendorf twin.tec® PCR 96-well plate (Eppendorf, cat. no. 951020401) and sealed with Microseal® ‘F’ Foil (Bio-Rad Laboratories, Inc., MSF-1001). The plate was storage in -80C until transportation to Broad Genomics Platform. Lysate cleanup and reverse transcription of mRNA, transcriptome amplification and PCR cleanup, Nextera XT sequencing-library construction, DNA SPRI bead cleanup and 2x38bp paired sequencing was performed according to Trombetta protocol ⁴⁶. The Sequencing was run on al Illumina NextSeq500. T-cell receptor (TCR) repertoire analysis was conducted primarily through the scRepertoire (v1.3.5) package in R. The filtered contig annotation outputs from CellRanger were preprocessed and mapped to individual cell barcodes using the createHTOContigList() and combineTCR() functions. Resultant demultiplexed TCR data were then merged with the single cell objects created with Seurat (v.4.1.1) prior to analysis and visualization. All plots were generated using tools within scRepertoire, ggplot2 (v3.3.6), and dittoSeq (v1.4.4).

Sequencing analysis Venn diagram visualizations were created using Integrated web-based visualization tool (DiVenn) was used for Bulk-RNA-seq visualization ²⁴. Bulk RNA sequencing Samples were processed according to the Smart-Seq2 protocol and sequenced on Illumina sequencers. Reads in FASTQ were quantified at the transcript level using Salmon (v1.4.0) against the H. Sapiens Ensembl gene catalog (August 2020). Differential expression analysis was conducted using R (v4.0.3) and DESeq2 (v1.30.1). Hypothesis testing was performed by using either DESeq2’s Wald test on appropriate variable contrasts or with a Likelihood Ratio Test (LRT) for models with greater than two variable conditions being tested. For scRNA-seq, the Seurat package was used to process and analyze the data. Harmony was used to batch correction and Celldex was used for cell type annotation. After the unified single-cell analysis pipeline unique transcripts were obtained from 22,000 singlets cells from all samples.All high-quality cells were integrated into an unbatched and comparable dataset and subjected to principal component analysis after correction for read depth and mitochondrial read counts. The AddModuleScore function in Seurat was used to implement the method with default settings found in ⁹. Exhaustion markers were (LAG3, TIGIT, PDCD1, CTLA4, HAVCR2 and TOX). Coronavirus pathway genes that were interfaced in the single cell data is CASP1, IRF9, OAS3, STAT1, BST2, FOS, IRF7, NUP98, STAT2.

Olink Proteomics and Multiplex. Inflammation panel comprising of 96 protein biomarkers (Olink Proteomics, Uppsala, Sweden) was used on serum samples of patients at baseline (day-2) and day 10. Data from the analyzed protein biomarkers was presented in normalized protein expression (NPX) values, Olink Proteomics’s arbitrary unit on log2 scale. Cytokine/Chemokine/Growth Factor Panel (EPX450-12171-901) in serum samples in patients treated with Foralumab, Foralumab and Dexa, untreated control, and healthy controls. Assay was performed by The Cytokine Core, LLC according with manufacture instructions. Markers were BDNF, EGF, Eotaxin, FGF-2, GM-CSF, GRO-α*, HGF, IFN-α, IFN-γ, IL-1RA, IL-1α, IL-1B, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17A, IL-18, IL-21, IL-22, IL-23, IL-27, IL-31, IP-10, LIF,MCP-1, MIP-1α, MIP-1β, NGF-β, RANTES, PDGF-BB, PIGF-1, SCF, SDF-1α, TNF-α, TNF-β, VEGF-A, VEGF.

Statistical analysis. Results are shown as the mean values (± SEM) or median (± IQR) and considered statistically significant when comparisons between groups, using Student’s t-test, one or two-ANOVA P-values were less than 0.05. * p < 0.05 p < 0.05 **p < 0.01***p < 0.001 ****p < 0.0001. Changes from Baseline was used for Olink and Multiplex analysis. Linear mixed effects model with a random intercept was used for analysis

Acknowledgments

We honor all patients that voluntarily participated in this study. Special thanks to the clinical team involved in sample collection: Joao Neto, Fernanda Santos, Bianca Gobbo, Giovana S. de Paula, Thais M. Santana, Raquel da Mata, Marcelle G. Spinola, Rodrigo Morrone and Gerson D. Keppeke. We also thank Rogerio Dedivitis for supporting this work. We thank Tiziana LifeScience and Brigham and Women’s regulatory team that supported sample importation Jules Jacob, Vaseem Palejwala, William Clementi, Taylor Saraceno, Jessica Hunter, Corey Staiti, Jacob Reynolds and Laura Cox. We thank Kunwar Shailubhai for all his support with this trial. We thank BWH flow cytometry and sequencing core facility members Adam Chicoine, Junning case, Kevin Wei and Zhu zhu for their great assistance. We acknowledge BWH clinical team Tarun Singhal, Jon Zurawski, Fermish Saleh, Belinda J Kaskow, Katherine Hanus, Zhenhua Li, Johnna F. Varghese, Shrishti Saxena that participated in phase I trial. We thank Christian Barro and Danielle Leserve for their work in animal studies that supported the data present in this work and Wesley Brandao for his help with validation assays. Lastly, we thank Francisco Quintana and Vijay Kuchroo for their scientific inputs. T.G.M was supported by Susan Furbacher Conroy Fellowship.

Funding

This work was supported by Ann Romney Center for Neurologic Diseases

Author contribution

T.G.M designed and performed the study as well as wrote the manuscript; C.G and T.L.performed biostatistics analysis, L.M and A.P helped performing PBMC staining, cell sorting and cell preparation for sequencing. A.P was also responsible for sample storage and identification.

K.T.M, D.M, S.I, R.R helped design the study and data discussions. B.C.H performed statistical analysis. V.K., C.B.A and T.C supported the study and provided inputs for data analysis. H.L.W designed the study and wrote the manuscript.

Competing Interest

HW is chair of the Scientific Advisory Board of Tiziana and received consulting fees and stock options from the company. TM and KM have received consulting fees from Tiziana Lifescience to clinically monitor the study in which the samples were used in this present work (1). In addition to providing Foralumab, Tiziana Life Sciences also provided financial assistance to the trial and immunological studies but did not participate in statistical analysis or data interpretation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All the personnel involved in sample collection was acknowledge in this publication and had the opportunity to be listed as co-authors if meeting ICMJE criteria. This Study was conducted based on the Global Code of Conduct for Research in Resource-Poor Settings.

Data and materials availability

RNA-seq and single cell raw data files can be found in NCBI platform: SUB11963368.

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Yes there is potential Competing Interest. HW is chair of the Scientific Advisory Board of Tiziana and received consulting fees and stock options from the company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Nasal anti-CD3 mAb (Foralumab) dampens CD3+ T effector function and decreases NKG7 in COVID-19 through a mechanism involving GIMAP-7 and TGFb1

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