bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Type I Interferon Transcriptional Network Regulates Expression of Coinhibitory

Receptors in Human T cells

Tomokazu S. Sumida*1, Shai Dulberg2*, Jonas Schupp3*, Helen A. Stillwell1, Pierre-Paul Axisa1,

Michela Comi1, Matthew Lincoln1, Avraham Unterman3, Naftali Kaminski3, Asaf Madi2,4,

Vijay K. Kuchroo4,5,†, David A. Hafler1,5,†

1Departments of Neurology and Immunobiology, Yale School of Medicine, New Haven, CT, USA. 2 Department of Pathology, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 3Section of Pulmonary, Critical Care and Sleep Medicine Section, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA. 4 Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA. 5Broad Institute of MIT and Harvard, Cambridge, MA, USA.

*Equal Contributions

† Correspondence to Drs. Vijay Kuchroo ([email protected]) or David

Hafler ([email protected])

1 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Abstract

While inhibition of T cell co-inhibitory receptors has revolutionized cancer therapy, the

mechanisms governing their expression on human T cells have not been elucidated. Type 1

interferon (IFN-I) modulates T cell immunity in viral infection, autoimmunity, and cancer, and

may facilitate induction of T cell exhaustion in chronic viral infection1,2. Here we show that IFN-I

regulates co-inhibitory receptors expression on human T cells, inducing PD-1/TIM-3/LAG-3

while surprisingly inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I

responses enabled the construction of dynamic transcriptional regulatory networks uncovering

three temporal transcriptional waves. Perturbation of key transcription factors on human primary

T cells revealed both canonical and non-canonical IFN-I transcriptional regulators, and identified

unique regulators that control expression of co-inhibitory receptors. To provide direct in vivo

evidence for the role of IFN-I on co-inhibitory receptors, we then performed single cell RNA-

sequencing in subjects infected with SARS-CoV-2, where viral load was strongly associated

with T cell IFN-I signatures. We found that the dynamic IFN-I response in vitro closely mirrored

T cell features with acute IFN-I linked viral infection, with high LAG3 and decreased TIGIT

expression. Finally, our gene regulatory network identified SP140 as a key regulator for

differential LAG3 and TIGIT expression. The construction of co-inhibitory regulatory networks

induced by IFN-I with identification of unique transcription factors controlling their expression

may provide targets for enhancement of immunotherapy in cancer, infectious diseases, and

autoimmunity.

2 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Main

Immune checkpoint blockade targeting T cell co-inhibitory receptors has revolutionized cancer

treatment. While signals such as IL-27 are associated with T cell expression of co-inhibitory

receptors, in mice3,4, the regulatory mechanisms of co-inhibitory receptors induction in human T

cells are still unknown. Type 1 interferon (IFN-I) are induced during chronic viral infection,

autoimmunity, and cancer5-7, and accumulating evidence suggests that IFN-I may have

immunomodulatory function beyond their conventional role of promoting generation of effector T

cells8-11. Notably, continuous exposure to IFN-I is implicated to promote T cell exhaustion, which

is marked by aberrant expression of co-inhibitory receptors (i.e. PD-1, TIM-3, LAG-3, and TIGIT)

in chronic viral infection and cancer11-16. However, whether IFN-I facilitates induction of immune

checkpoint molecules and T cell function, directly or indirectly, has not been investigated.

The impact of IFN-β on co-inhibitory receptors in human T cells

We have previously identified IL-27 as a crucial cytokine that promotes the induction of a co-

inhibitory receptor module, with T cell exhaustion in a murine tumor models3. Several reports

have suggested that IL-27 functions downstream of IFN-β17, which is a major component of the

IFN-I family. Thus, we hypothesized that IFN-β facilitates the induction of co-inhibitory receptors

in humans. We first assessed the effect of IL-27 and IFN-β on induction of core co-inhibitory

receptors (TIM-3, LAG-3, PD-1, and TIGIT) in vitro using human primary naïve CD4+ and CD8+

T cells (Supplementary Figure 1a). Both IL-27 and IFN-β promoted significantly higher TIM-3

expression compared to control condition without addition of exogenous cytokines. Of note, IFN-

β induced more LAG-3 and TIM-3 expression compared to IL-27 in both CD4+ and CD8+ T cells

(Figure 1a, b, Supplementary Figure 1b). Unexpectedly, both IL-27 and IFN-β suppressed the

expression of TIGIT in CD4+ and CD8+ T cells (Supplementary Figure 1c). We also observed

the increased production of IL-10 by IFN-β; however, IL-10 induction by IL-27 was modest in our

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in vitro culture settings, which may reflect the difference between mouse and human T cell

responses toward IL-27 stimulation (Supplementary Figure 1d). To determine whether these

observations stemmed from the effect of IFN-β on cellular proliferation, we performed a

proliferation assay using cell trace violet dye. We found there was no differences in cellular

division in naïve CD4+ T cells between control and the IFN-β condition. Additionally, there was

even less proliferation of memory CD4+ T cells in the IFN-β condition, indicating that the

induction of co-inhibitory receptors by IFN-β is not driven by a state of higher proliferation in T

cells (Supplementary Figure 2), consistent with previous studies18,19. We further determined the

impact of IFN-β treatment on gene expression kinetics for co-inhibitory receptors by qPCR.

Gene expression dynamics for core co-inhibitory receptors (HAVCR2, LAG3, PDCD1) were

upregulated by IFN-β for most time points. In contrast TIGIT was downregulated, which was

confirmed by protein expression using flow cytometry (Figure 1c, d). We examined the

expression of other co-inhibitory receptors, and found IFN-β induced co-expression of multiple

co-inhibitory receptors (e.g. HAVCR2, PDCD1, LAG3), but inhibited expression of others (TIGIT,

CD160, BTLA) (Figure 1e). Collectively, these data elucidate a role for IFN-β as a cytokine that

can directly control multiple co-inhibitory receptors in both human CD4+ and CD8+ T cells in vitro.

Transcriptomic dynamics of IFN-β response at high temporal-resolution

To uncover the regulatory mechanisms underlying the IFN-β response in human primary T cells,

we generated a transcriptional profile at high temporal resolution. We used bulk mRNA-seq at

ten time points along a 96-hour time course with and without IFN-β treatment (Supplementary

Figure 3a). To avoid inter-individual variation, we selected one healthy subject whose T cells

exhibited a stable response to IFN-β, and repeated the experiment three times at a two-week

interval for each experiment. We identified 1,831 (for CD4+ T cells) and 1,571 (for CD8+ T cells)

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differentially expressed genes (DEGs) across time points with IFN-β treatment, revealing a

temporal shift of gene expression patterns in both CD4+ and CD8+ T cells (Figure 2a,

Supplementary Figure 3b, Methods). The genome-wide transcriptional profiles from three

independent experiments demonstrated highly replicative results across time points (Figure 2b).

Specifically, three transcriptional waves were observed during 96 hours of IFN-β response: early

phase (1-2h), intermediate phase (4-16h), and late phase (48-96h). As expected, we observed

abundant induction of classical IFN stimulated genes (ISGs) (i.e., IFI6, MX1/2, RSAD2,

STAT1/2/3, SP100/110/140), which peaked at the early-intermediate phase. IFN-I induced

cytokines produced by T cells (IFNG, IL10, GZMB, PRF1) were also upregulated at

intermediate-late phase (Figure 2c, Supplementary Figure 3c). Interestingly, OSM, which is

reported to amplify IFN-β response and suppress Th17 differentiation20, was significantly

induced by IFN-β from the early phase, and maintained induction in all time points

(Supplementary Figure 3c). Among DEGs, we identified dynamic expression of 134 TFs for

CD4+ T cells and 100 TFs for CD8+ T cells, which were both up- and down-regulated over the

course of differentiation (Figure 2c).

Lentiviral shRNA based genetic perturbation

To narrow the list of TFs for perturbation, we prioritized TFs that are differentially expressed in

both CD4+ and CD8+ T cells. We then further selected TFs associated with: 1) human tumor

infiltrating T cells (TILs)21-24; 2) HIV specific T cell signature in progressive patients25; and 3) IL-

27 driven co-inhibitory regulators3 (see Methods). We confirmed that these TFs are identified as

interferon stimulated genes (ISGs) in human immune cells by the Interferome dataset26 (Figure

3a). In total, 31 TFs were listed as candidates based on the overlap between ISG, TIL and IL-27

signatures and we chose 19 of them for perturbation. Since TCF-1 (encoded by TCF7) and

Blimp-1 (encoded by PRDM1) are known to express functionally distinct isoforms, we also

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targeted a unique sequence for the long isoforms (TCF7L and PRDM1L, respectively), resulting

in perturbation of 21 different targets in total.

Considering the majority of these regulators are induced at the early (1-2h) and

intermediate (4-16h) phases, it was important to perform gene deletion prior to T cell receptor

activation. For human primary T cells gene knockdown, we adopted lentiviral delivery of

shRNAs with lentiviral gene product X (Vpx) containing virus-like particles (VLPs) system to

efficiently transduce lentivirus into unstimulated primary human naïve T cells27,28 (Figure 3b).

Spinoculation with Vpx-VLPs significantly increased the number of GFP expressing T cells

(~30-60%) compared to normal LV particle transduction without Vpx-VLPs (~1-5%), and

resulted in successful transduction of lentiviral vectors into non-blasting/quiescent cells

(Supplementary Figure 4a). We achieved efficient knockdown of at least 60% gene expression

for 21 target TFs in human naïve CD4+ T cells (Figure 3c).

To identify the effect of perturbation for each regulator, Principal Component

Analysis (PCA) was applied to changes in RNA expression associated with each transcription

factor knock down (Figure 3d). PC1 divided the impact of perturbation into two modules of

regulators; BATF, MAF, ETS2, HOPX, SP140, BCL3, ID3, and BATF3 constitute ‘IFN-I regulator

module 1’, and IRF1, IRF2, IRF4, STAT1, STAT3, ARID5A, ARID5B, TCF7, PRDM1, PRDM1L,

KLF5, and TCF7L constitute a distinct ‘IFN-I regulator module 2’. To visualize the contribution of

the selected genes to the PCs and the directionality of the contribution, a PCA biplot analysis

was adopted. We found that ISGs are divided into two groups; classical ISGs that are correlated

with ‘IFN-I regulator module 1’ (depicted in green arrows in Figure 3e), and ISGs that are

correlated with ‘IFN-I regulator module 2’ (depicted in orange arrows in Figure 3e), which is

predominantly in PC1. These results suggest that ISGs are bi-directionally regulated by different

modules of TFs (Supplementary Figure 4b). Furthermore, these modules contributed differently

to the regulation of co-inhibitory receptors; TIGIT, CD160, BTLA as one module and LAG3,

HAVCR2, PDCD1 as another module (Figure 3f, Supplementary Figure 4c). Within ‘IFN-I

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regulator module 1’, STAT3 positively regulated HAVCR2 but not LAG3 and PDCD1 expression,

which is predominantly contributed by PC2 in the biplot (Figure 3f). Taken together, our

perturbation with Vpx-VLP system elucidated two distinct TF modules simultaneously regulating

opposing IFN-I target genes, which sheds light on the central roles of non-canonical IFN-I

induced regulators in human T cells.

Dynamic transcriptional regulatory network of IFN-β response

To characterize the impact of differentially expressed transcription factors (DETFs) in response

to IFN-β, we generated transcriptional regulatory networks describing TFs and their target

genes for each of the transcriptional waves identified (Figure 4a, Methods). When comparing

the three regulatory networks, early and late networks had similar numbers of TFs (46 and 42

TFs, respectively), while the intermediate network contained 73 TFs (Figure 4b, top).

Interestingly, the ratio between up and down-regulated TFs differs between the three regulatory

waves. The early and intermediate network contained more up-regulated TFs than down-

regulated TFs; in contrast, the late network had more down-regulated than up-regulated TFs.

Thus, IFN-I induced differentiation involves dominance of up-regulated TFs in the first 16 hours,

replaced by the dominance of down-regulated TFs after 48 hours. We next ranked the TFs

based on the enrichment of their target genes and their centrality in the networks (Methods),

highlighting the significance of each TF to the network (Figure 4b, middle). In the early

regulatory network, MYC and CDKN1B/KDM5B were among the most dominant up and down-

regulated TFs, respectively, in this transcriptional wave. These data indicate that T cell

metabolic activation, cell cycle regulation, and transcriptional activation are promoted by IFN-

β treatment29,30. Interestingly, FLI1, which is novel as a IFN-I downstream TF and was recently

reported to control effector response in T cells31, also dominantly regulated the early

transcriptional wave. In the intermediate regulatory network, MYC, MAF, IRF1, AFF1, ATF3,

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and TBX21 were among the most dominant up-regulated TFs for this transcriptional wave. In

the late network, effector function related regulators are upregulated (PRDM1, RUNX2, MAF,

BCL3); in contrast, the TFs associated with Treg differentiation and maintenance (STAT5A,

FOXP1, MYB) were down-regulated, suggesting the skewed differentiation toward effector-like

signature.

To further study the relationships between the DETFs, we generated hierarchical backbone

networks in order to represent their relationships (Figure 4b, bottom). Interestingly, the top TFs

in all transcriptional time waves were down-regulated in response to IFN-β, while TFs lower in

the network hierarchy were more up-regulated. A few examples from the early transcriptional

wave hierarchical backbone include CDKN1B and MAZ, which appeared at the top of the

hierarchy, whereas KLF5, MYC, and FLI1 were lower in the hierarchy. It was of interest that

these TFs were also highly dominant in the regulatory network. These data suggest that loss of

suppression of TFs at higher hierarchy triggers the activation of downstream effector TFs under

IFN-I response, which was also observed in the intermediate and late regulatory network. The

elucidation of this backbone network enables us to shed light on the regulatory interactions

within each component of the transcriptional network, providing further depth to the extent of

interactions within the network.

While T cell differentiation under IFN-β is characterized by three major transcriptional

waves, we hypothesized that there are key TFs that bridge each wave to the next. To this end,

we specifically identified TFs that participate in more than one of these transcriptional waves,

and termed these ‘Bridging TFs’ (Figure 4c). Examples of dominant ‘Bridging TFs’ between

early and intermediate waves include KLF5 and STAT2. Examples of intermediate to late waves

include MAF, PRDM1, and MYB. Finally, there are TFs that were upregulated throughout the

entire differentiation; such as STAT1, HIF1A, and TBX21. Generally, ‘Bridging TFs’ tend to be

more dominant than other TFs; thus, it is possible that ‘Bridging TFs’ play an important role in

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the transition between different transcriptional waves. Indeed, our perturbation experiment

demonstrated the critical roles of those ‘Bridging TFs’ in the regulation of ISGs and co-inhibitory

receptors (Figure 4c). Our computational analysis revealed the temporal dynamics of complex

regulatory interactions during the IFN-I response and highlighted the usefulness of our approach

in discovering this new aspect of IFN-I induced transcriptional regulation.

In vivo validation of regulatory modules controlling IFN-I/co-inhibitory receptors axis in

human T cells

As it was important to provide direct in vivo evidence for the role of IFN-I on T cell co-inhibitory

receptor expression, we sought to validate our regulatory network in the human setting where

the IFN-I response of T cells is induced acutely. As acute viral infections are strongly associated

with IFN-I responses, we examined a number of clinical models where viral infection is closely

linked to IFN-I T cell response. We found that our analysis of single cell RNA seq (scRNA-seq)

analysis of T cells in COVID-19 patients revealed an extremely high correlation between viral

load and IFN-I score (r=0.8) and time difference between paired samples and the respective

change in IFN-I score (r=0.97)32, providing a unique opportunity to generate a rich dataset to

determine whether the in vitro T cell response to IFN-I can be validated during an acute viral

human infection strongly associated with a IFN-I signal.

By using our scRNA-seq data, we subclustered T cell populations into 13 subpopulations

and identified five CD4+ T cell and five CD8+ T cell subsets (Figure 5a, Supplementary Figure 5a,

b). We first focused on total CD4+ T cells and CD8+ T cells and confirmed that the IFN-I

response signature is higher in progressive patients who required admission to the ICU and

eventually succumbed to the disease (Figure 5b). Expression of co-inhibitory receptors differed

across disease conditions, but the trend was conserved between CD4+ and CD8+ T cells. We

observed a strikingly similar pattern of co-inhibitory receptor expression with IFN-I stimulation in

9 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

vitro and in vivo. Indeed, we observed the upregulation of ‘IFN-I up co-inhibitory receptors’

(LAG3/HAVCR2) and the downregulation of ‘IFN-I down co-inhibitory receptors’

(TIGIT/LAIR1/SLAMF6) in T cells from COVID-19 patients (Figure 5c, d). As expected, ‘IFN-I up

co-inhibitory receptors’ were positively correlated with canonical ISGs expression, but ‘IFN-I

down co-inhibitory receptors’ were not, suggesting that there are different regulatory

mechanisms dictating co-inhibitory receptor expression patterns (Figure 5e). Next, we

investigated which subpopulation of CD4+ and CD8+ T cells is more affected by the IFN-I

response, and computed the IFN-I score across subpopulations for each T cell subtype in

COVID-19 patients (Figure 5f). Within the subpopulations that exhibited higher IFN-I scores,

dividing CD4+/CD8+ T cells and ISG+ CD8+ T cells were uniquely increased in COVID-19

patients, particularly in severe patients32,36. Moreover, these subpopulations and effector T cells

expressed higher level of co-inhibitory receptors and ‘IFN-I regulator module-1’ compared to the

other subpopulations (Figure 5g, Supplementary Figure 5c).

We then examined which subpopulations were more enriched in the three transcriptional

waves of IFN-I response. DEGs specific for each wave were used to compute the scores for the

CD4+ and CD8+ T cell subpopulations in COVID-19 patients (Table 1, Methods). We found that

T cells induced in vitro with IFN-I strongly mirrored the intermediate wave score on dividing

CD4+ and CD8+ T cells, and the late wave score on effector CD4+ T cells and ISG+

CD8+/effector CD8+ T cells (Figure 5h) in COVID-19 patients. Given that expansion of dividing

CD4+/CD8+ T cells are a unique characteristic of COVID-19 patients, we applied the

intermediate phase IFN-I regulatory network with the dividing CD4+ T cell gene expression

signature to examine the relationships between regulators and target genes in this

subpopulation. This analysis highlights the regulators that function in establishing the

characteristics of the dividing CD4+ T cell population under IFN-I response in vivo

(Supplementary Figure 5d). As LAG-3 is the most upregulated co-inhibitory receptor in the

10 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

dividing CD4+ T cell population, we utilized the network analysis to elucidate the specific

regulation of LAG3 and TIGIT that were regulated in an opposite manner under IFN-I response

both in vivo (Figure 5c, d) and in vitro (Figure 1). Analysis of the intermediate wave gene

regulatory network demonstrated that SP140 is a bi-directional regulator for LAG3 and TIGIT

under IFN-I response, which is supported by the observation in COVID-19 patients, where

elevated SP140 and LAG3 but decreased TIGIT expression were demonstrated (Figure 5c, d, i,

j, Supplementary Figure 5c). In addition, the late wave network demonstrated the complex

interaction of regulators for LAG3, HAVCR2, and PDCD1, in which BCL3 and STAT3 are

highlighted as validated positive regulators on LAG3 and HAVCR2 respectively (Figure 5k).

Importantly, both BCL3 and STAT3 were highly elevated in T cells in COVID-19 patients (Figure

5j). This can be highly relevant to effector T cells development under acute viral infection in

which those co-inhibitory receptors play critical role on regulating effector function33 and, of note,

the late wave signature was enriched in effector T cells in COVID-19 (Figure 5h). These findings

strongly suggest that in vitro regulatory network can be utilized as a strong tool to explore

human acute viral response in vivo.

Discussion

Here, our systematic, computational and biological approach identifies IFN-I as a major driver of

co-inhibitory receptor regulation in human T cells. While classical ISG induction has been

extensively studied, those investigations have focused primarily on the canonical JAK-STAT

pathway downstream of IFN-I receptor. Given that IFN-I exhibits multiple functions in context-

dependent roles, a more complex understanding of the IFN-I response beyond this canonical

pathway with a more extensive analysis of ISG transcriptional regulation in T cells is critical for

elucidating the mechanism of co-inhibitory receptor regulation.

11 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

In these studies, we build a dynamic gene regulatory network that controls IFN-I response,

and identified key regulatory modules of ISG transcription in T cell responses to IFN-β. Our

approach unveiled two mutually antagonistic modules of ISG regulators, which, when acting

concordantly, may explain how the harmonized IFN-I induced T cell response is achieved.

Within the two modules, we highlighted SP140 as a potential regulator that controls LAG3 and

TIGIT in an opposing manner, and STAT3 as a unique positive regulator for TIM-3. These

findings provide novel insight into the landscape of the ISG transcriptional network, and sheds

light on the large contribution of the noncanonical IFN-I pathway during IFN-I response in T

cells34-36. Although the newly identified regulators (e.g. SP140, BCL3) in this study are not

necessarily directly downstream of the conventional JAK/STAT pathway and may act differently

depending on the context, they are nevertheless attractive targets for manipulation of specific

downstream functional molecules such as co-inhibitory receptors in T cells.

We demonstrate the relevance of our in vitro T cell IFN-I response by integrating scRNA-

seq from COVID-19 patients, where a predominant T cell IFN-I response was observed.

Intriguingly, the expression pattern of co-inhibitory receptors on T cells in vitro are highly

replicated in severe COVID-19 cases, and classical ISGs were well correlated with one module

of co-inhibitory receptors (LAG3/PDCD1/HAVCR2), but not with the other modules

(TIGIT/CD160/BTLA/LAIR1). While dynamics of IFN-I on T cells from COVID-19 patients should

be taken into account with higher temporal profiling, we confirmed that the IFN-I response was

clearly reduced at a later time point; thus, our data based on earlier collection of blood should

reflect the active ISG transcriptomics during human acute viral response. Given the IFN-I

response has been shown to contribute to chronic viral infection and cancer, the novel

regulators we identified can be examined in these diseases. Further investigations of factors

that characterize acute viral infections is likely to differ from chronic viral infections and will be of

interest to explore in the context of long-term human infections not well modeled by COVID-19.

12 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

In conclusion, our systems biology approach identifies the cytokine signals and regulatory

mechanisms that drive expression of co-inhibitory receptors in humans, and provides a pathway

to comprehensively capture the dynamics of their expression in humans. Our results will also

advance the understanding of the host immune response to a variety of viral infections, and

could serve as a resource for mining of existing datasets. Uncovering novel ISG regulators

controlling co-inhibitory receptors will create a foundation for further development of new

therapeutics for a multitude of different malignant and infectious diseases.

13 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Figure legends

Figure 1

IFN-β differently regulates LAG-3, TIM-3, PD-1 and TIGIT in human T cells

Effects of IFN-β on LAG-3, TIM-3, PD-1, and TIGIT expression on human naïve CD4+ and CD8+

T cells cultured with anti-CD3/CD28 for 96h in the absence (Control) or with 500 U/ml IFN-β

(IFN-β). a, Representative histograms of flow cytometry analysis (left), quantitative expression

for LAG-3, TIM-3, and PD-1 expression on naïve CD4+ T cells (n = 6 - 8) (right). b,

Representative contour plots of flow cytometry analysis on surface LAG-3, TIM-3, and PD-1

(left), quantitative analysis for LAG-3, TIM-3, and PD-1 triple-positive cells in naïve CD4+ T cells

(n = 8) (right). c, Gene expression kinetics of LAG3, HAVCR2, PDCD1, and TIGIT quantified by

qPCR with 13 timepoints in naïve CD4+ T cells. Average expression values from two subjects

are plotted. d, IFN-β induces LAG-3 but suppresses TIGIT expression on human naïve CD4+

and CD8+ T cells. Representative contour plots of flow cytometry analysis (left), quantitative

analysis for TIGIT positive cells in naïve CD4+ T cells (n = 8) (right). e, Co-inhibitory receptors

expression pattern under IFN-β treatment in naïve CD4+ T cells by qPCR (n = 4). Red and blue

bars represent higher expression in IFN-β treatment and Control condition, respectively. Data

was represented as mean +/- SD. **p < 0.01, ****p < 0.0001. Paired Student’s t test.

Figure 2

Three waves of dynamic transcriptomic changes by IFN-β in human T cells

a, Gene expression profiles under IFN-β treatment in naïve CD4+ and CD8+ T cells. Differential

expression of gene levels for eight time points with IFN-β stimulation (log2(expression)) are

shown in heatmap. Based on the expression kinetics, the genes are clustered into four

categories: early, intermediate, late, and bimodal (up regulated at early and late phase).

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Representative individual gene expression kinetics from each cluster are shown (mean+/- SD).

b, Correlation matrix of global gene expression representing three transcriptional waves on

CD4+ (left) and CD8+ (right) T cells: early (1-2h), intermediate (4-16h), and late (48-96h). Eight

timepoints with three replicates are shown. c, Temporal transcriptional profiles of differentially

expressed genes for four categories are shown; transcriptional regulators (transcription factors),

ISGs, co-inhibitory receptors, and key T cell associated factors for CD4+ (left) and CD8+ (right) T

cells.

Figure 3

Perturbation of key transcription factors in quiescent human T cells

a, Characterization of candidate TFs for perturbation. Perturbed TFs are listed based on overlap

between differentially expressed TFs of CD4+ T cells and CD8+ T cells. Human ISG score (top;

blue), human TIL co-inhibitory receptors score (green), HIV specific T cell signature genes in

progressive patients (yellow), and IL-27 driven co-inhibitory receptor regulators (orange) are

shown for each TFs. b, Experimental workflow of Vpx-VLP supported lentiviral shRNA

perturbation. Ex vivo isolated naïve CD4 T cells were transduced with Vpx-VLPs, followed by

two times of lentiviral particle transduction before starting T cell activation. T cells were

stimulated with anti-CD3/CD28 in the absence or presence of IFN-β (500 U/ml) for 96h and GFP

positive cells were sorted by FACS. RNAs were extracted from sorted cells and applied for

mRNA-seq. Perturbation for all 21 shRNAs are performed with human CD4+ T cells isolated

from the same individual as in Figure 2. c, Gene knockdown efficiency is shown as relative

expression over scramble shRNA transduced controls. Dotted line represents 60% of gene

knockdown. d-f, PCA plots and biplots based on differentially expressed genes by perturbation.

d, PCA plot demonstrating the two modules of TF regulators on perturbation with 21 TFs.

Characterization of shRNA-based gene knockdown for each TF being plotted. Labels represent

perturbed TF gene names. ‘IFN-I regulator module 1’ is colored in green and ‘IFN-I regulator

15 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

module 2’ is in orange. e, f, PCA biplot showing differential regulation by modules of regulator

TFs; e, for ISGs and f, for co-inhibitory receptors. Orange and green arrows (vectors) are

highlighting two groups of genes effected inversely by the different modules of TFs.

Figure 4

Transcriptional regulatory network under IFN-I response

a, Overview of regulatory network generation. A scheme of the pipeline, in order to generate

preliminary regulatory network is generated from integrating the gene expression kinetics data

coupled with TF-target gene datasets. The key regulators’ perturbation data was further

integrated to refine the preliminary network. b, In depth view of the transcriptional regulation at

each wave. Top row; the representation of regulatory networks highlighting TFs interaction. The

thicker and darker an edge is the more TF-target connections it represents. Target genes are

represented by up and down hexagons, according to their regulatory response to IFN-β. Middle

row; heatmaps representing a ranking of the TFs based on ‘Cent’ stands for centrality and ‘HG’

stands for hypergeometric test. Bottom row; hierarchical backbone networks. Red circles

represent up-regulated TFs, blue circles represent down-regulated TFs. c, Dynamics of TFs

regulation across the transcriptional waves. Each hexagon represents targets from each

transcriptional wave. Green circles represent regulatory TFs which are differentially expressed

only in one transcriptional wave they are connected to, while purple circles represent bridging

TFs, which are DE in all transcriptional waves they are connected to. The thicker and darker an

edge is, the more TF-target connections it represents.

Figure 5

Integration of IFN-I regulatory network with T cells signature in COVID-19

a, UMAP representation of T cells from healthy control samples (n = 13) and COVID-19

samples (n = 18). 13 subcluster were identified. b, IFN-I score for CD4+ and CD8+ T cells across

16 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

the three disease conditions. c, Heatmaps for co-inhibitory receptors expression in CD4+ and

CD8+ T cells across the three disease conditions. d, Expression of key co-inhibitory receptors

between control vs COVID-19 for CD4+ and CD8+ T cells. Average expression per subject for

each gene is shown. *p < 0.05, **p < 0.01, ***p < 0.001. Kruskal-Wallis test. e, Correlation

matrix of ISGs (dark gray) and co-inhibitory receptors (light gray) in CD4+ and CD8+ T cells in

COVID-19 patients. f, IFN-I score for subsets of CD4+ and CD8+ T cells between control vs

COVID-19. g, Heatmap showing co-inhibitory receptors expression for subsets of CD4+ and

CD8+ T cells in COVID-19. h, Computed three transcriptional waves (early, intermediate, and

late) score for the subsets of CD4+ and CD8+ T cells in COVID-19 patients. Scores were

calculated based on upregulated DEGs of CD4+ and CD8+ T cells for each transcriptional wave.

i, Regulatory relationship between regulators in intermediate phase network for LAG3 and TIGIT

are shown. Positive regulation (TF to target) is highlighted in red and negative regulations in

blue. j, Box plots showing expression of key regulators between control vs COVID-19 for CD4+

T cells. Average expression per subject for each gene is shown. *p < 0.05, **p < 0.01, ***p <

0.001. Kruskal-Wallis test. k, Regulatory relationship between regulators in late phase network

for LAG3, HAVCR2, and PDCD1 are shown. Positive regulation (TF to target) is highlighted in

red.

17 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Figure legends for Supplementary Figures

Supplementary Figure 1

a, FACS gating strategy for isolating naïve CD4+ and naïve CD8+ T cells. FACS isolated cells

were immediately plated on 96 well round bottom plates coated with anti-CD3 (2 μg/ml) and

soluble anti-CD28 (1μg/ml) in the absence or presence of human IL-27 (100 ng/ml) or IFN-β

(500 U/ml). b, Representative histograms of surface expression of TIM-3, LAG-3, and PD-1

assessed by flow cytometry at 72-96 hours after stimulation. Percent single positive cells for

TIM-3, LAG-3, and PD-1 (left) and triple positive cells (right) are shown (n = 6-8). *p < 0.05, **p

< 0.01, ****p < 0.0001. Two-way ANOVA or Repeated-measures one-way ANOVA with Tukey's

multiple comparisons test. c, Representative dot plots of flow cytometry analysis for TIM-3 and

TIGIT expression in naïve CD4+ and CD8+ T cells (left). Cells were treated as a, and analyzed at

72 hours of culture. Percent TIGIT positive cells in naïve CD4+ T are shown (n = 8). *p < 0.05,

**p < 0.01. Repeated-measures one-way ANOVA with Tukey's multiple comparisons test

(middle). qPCR analysis of TIGIT expression over the time course (13 time points from 0 to 96

hours). Each dot represents average expression of two independent individuals’ data (right).

****p < 0.0001. One-way ANOVA with Tukey's multiple comparisons test. d, qPCR analysis of

IL10 and IFNG expression over the time course (13 time points from 0 to 96 hours). Each dot

represents average expression of two independent individuals’ data (left). IL-10 and IFN-γ

production assessed by intracellular staining (right). Cells are treated as in a, and cytokines are

stained intracellularly. Cytokine positive cells are detected by flow cytometry (n = 6). *p < 0.05,

**p < 0.01. Repeated-measures one-way ANOVA with Tukey's multiple comparisons test.

Supplementary Figure 2

Representative plots for T cell proliferation assay using cell trace violet dye. Naive and memory

CD4+ T cells were stimulated with anti-CD3 and anti-CD28 in the absence or presence of IFN-β.

18 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

TIM-3 expression and cellular proliferation were assessed at 24, 48, 72, and 96 hours after

stimulation. Overlayed histogram for control and IFN-β condition were shown at right.

Supplementary Figure 3

a, Schematic experimental setup for high temporal resolution transcriptional profiling. b,

Heatmap showing log fold change of differentially expressed genes expression between IFN-β

and control Th0 condition at each timepoints for naive CD4+ (left) and CD8+ T cells (right).

Genes are clustered based on the three transcriptional wave or bi-modal pattern. c, Line plots

for IFI6, IFNG, LAG3, and OSM expression in naive CD4+ (left) and CD8+ T cells (right).

Supplementary Figure 4

a, Contour plots for total living cells and backgating analysis for GFP positive cells. Primary

naïve CD4+ T cells were transduced with scramble shRNA control LV with or without Vpx-VLPs

pre-transduction. Cells are collected at 96 hours after starting stimulation and analyzed by flow

cytometry. b, c, Heatmaps showing the effect of TFs perturbation under IFN-β stimulation on

ISGs (b) and co-inhibitory receptors (c). Values in the heatmap were normalized by subtractions

of log10 fold change of scramble shRNA control over perturbed expression. The “+” sign

indicates statistically significant effect with adjusted p value < 0.05 (details in Methods).

Supplementary Figure 5

a, b, UMAP representation of T cells from healthy control samples (n = 13) and COVID-19

samples (n = 18) color coded by a, disease conditions and b, each individual. Cells from same

individual were labeled as one subject code, which resulted in 10 individual codes shown in b. c,

Heatmap showing the expression of DETFs for CD4+ and CD8+ T cells in each T cell subset. d,

Bundled regulatory network showing interaction between regulators at intermediate phase and

19 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

transcriptional signature of dividing CD4+ T cells in COVID-19. Regulators at intermediate phase

are marked with circles (red; upregulated TFs, blue; downregulated TFs), and genes that are

differentially expressed in dividing CD4+ T cells in COVID-19 were marked with squares (light

red; upregulated DEGs, light blue; downregulated DEGs).

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Methods

Study subjects

Peripheral blood was drawn from healthy controls who were recruited as part of an Institutional

Review Board (IRB)-approved study at Yale University, and written consent was obtained. All

experiments conformed to the principles set out in the WMA Declaration of Helsinki and the

Department of Health and Human Services Belmont Report.

Human T cell isolation and culture

Peripheral blood mononuclear cells (PBMCs) were prepared from whole blood by Ficoll gradient

centrifugation (Lymphoprep, STEMCELL Technologies) and used directly for total T cell

enrichment by negative magnetic selection using Easysep magnetic separation kits

(STEMCELL Technologies). Cell suspension was stained with anti-CD4 (RPA-T4), anti-CD8

(RPA-T8), anti-CD25 (clone 2A3), anti-CD45RO (UCHL1), anti-CD45RA (HI100) and anti-

CD127 (hIL-7R-M21, all from BD Biosciences) for 30 minutes at 4°C. Naïve CD4+ T cells

(CD4+/CD25neg/CD127+/CD45ROneg/CD45RA+ ) and naïve CD8+ T cells

(CD8++/CD25neg/CD127+/CD45ROneg/CD45RA+) were sorted on a FACSAria (BD Biosciences).

Sorted cells were plated in 96-well round-bottom plates (Corning) and cultured in RPMI 1640

medium supplemented with 5 % Human serum, 2 nM L-glutamine, 5 mM HEPES, and 100 U/ml

penicillin, 100 μg/ml streptomycin, 0.5 mM sodium pyruvate, 0.05 mM nonessential amino acids,

and 5% human AB serum (Gemini Bio-Products). Cells were seeded (30,000-50,000/wells) into

wells pre-coated with anti-human CD3 (2 μg/ml, clone UCHT1, BD Biosciences) along with

soluble anti-human CD28 (1μg/ml, clone 28.2, BD Biosciences) in the presence or absence of

human IFN-β (500 U/ml: Pestka Biomedical Laboratories) or IL-27 (100 ng/ml: BioLegend)

without adding IL-2.

Lentiviral and Vpx-VPLs production

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Lentiviral plasmids encoding shRNA were obtained from Sigma-Aldrich. Each plasmid was

transformed into One Shot Stbl3 chemically competent cells (Invitrogen) and purified by

ZymoPURE plasmid Maxiprep kit (Zymo research). Lentiviral pseudoparticles were obtained

after plasmid transfection of 293FT cells using Lipofectamine 2000 (Invitrogen) or TurboFectin

8.0 Transfection Reagent (Origene). To prepare Vpx-VLPs, 293T cells were co-transfected by

Lipofectamine 2000 or TurboFectin 8.0 Transfection Reagent with the 5 μg pMDL-X, 2.5 μg

pcRSV-Rev, 3.5 μg X4-tropic HIV Env, and 1 μg pcVpx/myc, as described previously with some

modifications37,38. The medium was replaced after 6-12 h with fresh media with 1X Viral boost

(Alstem). The lentivirus or Vpx-VLPs containing media was harvested 72 h after transfection

and concentrated 80 times using Lenti-X concentrator (Takara Clontech) or Lenti Concentrator

(Origene). LV particles were then resuspended in RPMI 1640 media without serum and stored

at -80°C before use. Virus titer was determined by using Jurkat T cells and Lenti-X GoStix Plus

(Takara Clontech).

Lentiviral transduction with Vpx-VPLs

Two step Vpx-VLP and LV transduction was performed as described previously with some

modiciations36. Vpx are pseudotyped with X4-tropic HIV Env to promote efficient entry of Vpx-

VLPs into quiescent human T cells37. FACS-sorted naïve CD4+ T cells were plated at 50,000

cells/well in round bottom 96 well plate and chilled on ice for 15 min. 50 μl of Vpx-VLPs were

added to each well and mixed with cold cells for an additional 15 min, then spinfected with high-

speed centrifugation (1200 g) for 2 hour at 4 °C. Immediately after centrifugation, cells are

cultured overnight at 37 °C. Vpx-transduced cells are spinoculated again with LV particles

containing shRNAs with high-speed centrifugation (1000 g) for 1.5 hours at RT. After 24 hours

of incubation, the second transduction of LV particles with shRNAs were performed as well as

the first time spinoculation. After a second LV transduction, cells were washed and plated into

96-well round-bottom plates pre-coated anti-human CD3 (2 μg/ml) with soluble anti-human

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

CD28 (1 μg/ml), in the presence or absence of human IFN-β (500 U/ml). Cells are collected at

day 4 after anti-CD3/CD28 stimulation and GFP positive cells were sorted by FACSAria.

Real time quantitative PCR

Total RNA was extracted using RNeasy Micro Kit (QIAGEN), or ZR-96 Quick-RNA kit (Zymo

Research), according to the manufacturer’s instructions. RNA was treated with DNase and

reverse transcribed using TaqMan Reverse Transcription Reagents (Applied Biosystems) or

SuperScript IV VILO Master Mix (Invitrogen). cDNAs were amplified with Taqman probes

(Taqman Gene Expression Arrays) and TaqMan Fast Advanced Master Mix on a StepOne

Real-Time PCR System (Applied Biosystems) according to the manufacturer's instructions.

Relative mRNA expression was evaluated after normalization with B2M expression.

Flow cytometry analysis

Cells were stained with LIVE/DEAD Fixable Near-IR Dead Cell Stain kit (Invitrogen) and surface

antibodies for 30min at 4°C. For intracellular cytokine staining, cells were treated with 50 nM

phorbol-12-myristate-13-acetate (MilliporeSigma) and 250 nM ionomycin (MilliporeSigma) for 4

hours in the presence of Brefeldin A (BD Biosciences) before harvesting. Cells were washed

and fixed with BD Cytofix™ Fixation Buffer (BD Biosciences) for 10 min at RT, then washed with

PBS. Intracellular cytokines were stained in permeabilization buffer (eBioscience) for 30 min at

4°C. The following antibodies were used: anti-LAG-3 (11C3C65, BioLegend), anti-PD-1

(EH12.1, BD Biosciences), anti-TIGIT (MBSA43, eBioscience), anti-Tim-3 (F38-2E2, Biolegend),

anti-IFN-γ (4S.B3, eBioscience), and IL-10 (JES3-9D7, Biolegend). Cells were acquired on a BD

Fortessa flow cytometer and data was analyzed with FlowJo software v10 (Threestar).

RNA-seq library preparation and data analysis

cDNAs were generated from isolated RNAs using SMART-Seq v4 Ultra Low Input RNA Kit for

sequencing (Takara/Clontech). Barcoded libraries were generated by the Nextera XT DNA

23 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Library Preparation kit (Illumina) and sequenced with a 2x100 bp paired-end protocol on the

HiSeq 4000 Sequencing System (Illumina).

After sequencing, adapter sequences and poor-quality bases (quality score < 3) were trimmed

with Trimmomatic. Remaining bases were trimmed if their average quality score in a 4 bp sliding

window fell below 5. FastQC was used to obtain quality control metrics before and after

trimming. Remaining reads were aligned to the GRCh38 human genome with STAR 2.5.239. We

used Picard to remove optical duplicates and to compile alignment summary statistics and RNA-

seq summary statistics. After alignment, reads were quantitated to gene level with RSEM40

using the Ensembl annotation.

Identification of three transcriptional waves

The correlation matrix is created by Pearson correlating41 the IFN-β expression profile of each

time point with all the other time points, creating a symmetric matrix of Pearson correlation

coefficients.

Differential expression calculation

The differential expression (DE)42 of the sequenced genes from every time point was calculated.

The DE was calculated using the DEseq243 R package. An in-house decision algorithm was

built to determine which genes are DE. The algorithm used three separate testing methods

available in DEseq2 for calculating DE genes: Wald44, likelihood ratio test (LRT)45, and time-

course46. For each of the three methods, genes with false discovery rate (FDR) adjusted

P.value bellow 0.05, are regarded as DE. The algorithm defined genes as DE differently for

CD4+ and CD8+ T cells. For CD4+ T cells, if TFs appeared in two out of the three calculating

methods (agree by two), they were regarded as DE. For CD8+ T cells, if TFs appeared in any of

the methods above, they were regarded as DE.

24 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Selection of regulators for perturbation

The list of TFs for perturbation was selected based on following aspects: 1) overlapped

differentially expressed TFs across the time point between CD4+ and CD8+ T cells in our results.

Intersection of DETFs in our in vitro data were chosen; 2) differentially expressed TFs in human

tumor infiltrated T cells21-24 that were significantly correlated with exhausted T cell cluster where

LAG-3/PD-1/TIM-3 were highly upregulated. ‘Human TIL score’ for each gene was calculated by

the number of times it was shared between the four different human cancer TIL datasets21-24; 3)

HIV specific T cell signature in progressive patients compared to stable patients25. 4) TFs that

were included by IL-27 and categorized as IL-27 driven co-inhibitory receptor modules3. ‘Human

ISG score’ for each gene was calculated by the number of times it was shared between the

three different categories (T cells, PBMCs, and all immune cells) of human ISGs identified by

Interferome database. All perturbed TFs were confirmed as IFN-I responsible genes that

showed ‘human ISG score’ more than 1.

Heatmap of perturbed TFs

21 TFs were perturbed using lentiviral shRNA, together with a scramble shRNA control (SCR).

In order to better understand the effect of perturbing said TFs, selected genes of interest (GOIs)

were analyzed. The calculation of the heatmap values in Supplementary Figure 4b and c is as

follows: first the expression values of perturbed GOIs is divided by control values without

perturbation (fold change), this quotient is then logged in base 10. The result of the logarithm of

the GOIs is the subtracted by the logarithm of SCR fold change.

DE analysis was conducted for perturbation against control, genes who yielded n FDR adjusted

P.value lower than 0.05 were regarded as significant and display a white plus on their tile.

PCA and PCA biplot analysis

25 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

The PCA was conducted on a DEGs defined as above as variables, and perturbed genes as

observations. The data was normalized by the scramble shRNA control identically to the

Heatmap of perturbed TFs. Although PDCD1 was not defined as DE genes across the time

course, it was clearly differentially expressed at later time points in mRNA-seq and qPCR, thus

we manually depicted in Figure 5f. Genes of IFN Score B47 (see Table 1) were represented as

ISGs in Figure 3e. The PCA and biplot analysis were calculated and visualized using the R

package FactoMineR48.

Regulatory networks

Following DE analysis in each transcriptional wave, the DE genes were separated to TFs and

their targets (from->to). The targets of all DETFs were determined using ChIP-seq data from the

database GTRD49. TFs and targets defined as DE were added as network nodes, and edges

(connections) were added between them. The network figures were created using the software

Cytoscape 50.

Top Regulatory TFs Heatmaps

We ranked the DE TFs of each transcriptional time wave to identify which are the most

dominant in the overall differentiation process. HG stands for hyper-geometric, the value in the

heatmap is the . of a hypergeometric enrichment test. The targets of each TF

are tested for enrichment of DE targets in the network, relatively to targets that aren’t DE in the

network. The HG calculation was conducted using the python SciPy package51. Cent stands for

centrality, which is a parameter that is given to each node, based on the shortest path from the

node to the other nodes in the network. It represents how central and connected a node is in the

rest of the network52. The centrality calculation was conducted using the python NetworkX53

package. The rank column is an average of both HG and Cent values, after normalization.

Integration of Perturbation Data to Regulatory Networks

26 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

DE analysis was conducted for perturbation against control. Genes that were significantly

affected by a TF perturbation were added as a “validated” edges between the perturbed TF and

the target gene. If a gene was up-regulated by a TF perturbation, the interaction between them

is registered as down-regulation. If a gene was down-regulated by a TF perturbation, the

interaction between them is registered as up-regulation.

Backbone Hierarchical Networks

Using the software Cytoscape, we implemented a hierarchical layout, which takes into account

the directionality of the connections between the TFs. A TF which has only outgoing

connections will be placed at the top of the hierarchy, while a TF which has only incoming

connections will be placed at the bottom.

Bridging TFs Network

DETFs and their targets from the three transcriptional waves were combined to create a

comprehensive network of the dynamic between transcriptional waves. TFs and their targets

were annotated by the transcriptional wave in which they are DE. TFs that appear in more than

one transcriptional wave are regarded as bridging TFs.

Reanalysis of COVID-19 single-cell RNA sequencing data

A PBMC single cell RNA seq data set of 10 COVID-19 patients and 13 matched controls was

reanalyzed which had been previously performed and reported by us32. We have described the

full cohort and detailed methods elsewhere32. From eight of the ten COVID-19 samples, PBMCs

from two different time points had been analyzed. Four of the COVID-19 patients had been

classified as progressive, the other six COVID-19 patients as stable. Informed consent had

been obtained of all subjects and the protocol had been approved by Yale Human Research

Protection Program Institutional Review Boards (FWA00002571, Protocol ID. 2000027690).

Briefly, single cell barcoding of PBMCs and library construction had been performed using the

10x Chromium NextGEM 5prime kit according to manufacturer's instructions. Libraries had been

27 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

sequenced on an Illumina Novaseq 6000 platform. Raw reads had been demultiplexed and

processed using Cell Ranger (v3.1) mapping to the GRCh38 (Ensembl 93) reference genome.

Resulting gene-cell matrices had been analyzed using the package Seurat54 in the software R55

including integration of data, clustering, multiplet identification and cell type annotation. The final

annotated R object was used and re-analyzed in Seurat with default settings - unless otherwise

specified - as follows:

The three cell populations "Dividing T & NK", "Effector T" and "Memory CD4 & MAIT" were each

subsetted and reclustered to obtain a finer cell type granularity as they included a mix of CD4,

CD8, MAIT and gamma delta T cells. Per subset, the top 500 variable genes were determined

by the “FindVariableFeatures” function using the “vst” method. Data was scaled using the

“ScaleData” function regressing out the total number of UMI and the percentage of UMIs arising

from the mitochondrial genome. After Principal Component (PC) Analysis, the first 10 Principal

Components (PCs) were utilized to detect the nearest neighbors using the “FindNeighbors”

function and clustered by Seurat’s Louvain algorithm implementation “FindClusters” using a

resolution of 0.2 for "Dividing T & NK", of 0.3 for "Effector T" and of 0.1 for "Memory CD4 &

MAIT" subsets. Cluster-specific gene expression profiles were established using the

“FindAllMarkers” per cluster and per subset to annotate the clusters. New cell type annotations

were then transferred back to the full dataset.

A new Uniform Approximation and Projection (UMAP) embedding was created by integrating

the datasets on a subject level as follows: A subset containing all T cells was generated, which

was then split by subject. For each subject, the top 2000 variable genes were selected, then

integration anchors determined by “FindIntegrationAnchors” (with k.filter = 150). These anchors

were used to integrate the data using the “IntegrateData” function with top 30 dimensions. The

integrated data was scaled, subjected to a PC analysis and the top 13 PCs used as input for the

“RunUMAP” function on 75 nearest neighbors56.

28 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Module scores were calculated using the “AddModuleScore” function using a) all genes within

the GO list “RESPONSE TO TYPE I INTERFERON” (GO:0034340)57 and b) all genes

significantly associated with either of the three waves in our in vitro perturbation experiments

(see Table 1). Differential gene expression was established using Seurat’s implementation of

the Wilcoxon Rank Sum test within the “FindMarkers” function with a Bonferroni correction for

multiple testing.

Statistical analysis

Detailed information about statistical analysis, including tests and values used, is provided in the

figure legends. P-values of 0.05 or less were considered significant.

Data and software availability

The sequence data generated in this study will be deposited in the Gene Expression Omnibus

(GEO) and the accession code will be provided prior to publication.

29 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

References

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32 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Acknowledgements

We thank L. Devine and C. Wang for assistance with FACS based cell sorting; G. Wang and C.

Castaldi at Yale Center for Genome Analysis for support with 10x Genomics library preparation

and sequencing; K. Raddassi and L. Zhang for processing scRNA-seq samples; L. Geng and M.

Zhang for preparation of the bulk RNA-seq libraries, and sequencing; M. Taura for assistance

developing Vpx-VLP based lenti viral transduction; N. Chihara and H. Asashima for useful

discussions and input on the manuscript.

Funding

This work was supported by grants to T.S.S. from Race to Erase MS; D.A.H. from the National

Institute of Health (NIH) (U19 AI089992, R25 NS079193, P01 AI073748, U24 AI11867, R01

AI22220, UM 1HG009390, P01 AI039671, P50 CA121974, and R01 CA227473), the National

Multiple Sclerosis Society (NMSS) (CA 1061- A-18 and RG-1802-30153), the Nancy Taylor

Foundation for Chronic Diseases, and Erase MS; V.K.K. from NIH (R01NS045937,

R01NS30843, R01AI144166, P01AI073748, P01AI039671 and P01AI056299) and the Klarman

Cell Observatory; N.K. from NIH (R01HL127349, R01HL141852 and U01HL145567); A.M. from

The Alon fellowship for outstanding young scientists, Israel Council for Higher Education; J.C.S

from DoD (W81XWH-19-1-0131). RNA sequencing service was conducted at Yale Center for

Genome Analysis and Yale Stem Cell Center Genomics Core facility, the latter supported by the

Connecticut Regenerative Medicine Research Fund and the Li Ka Shing Foundation.

Contributions

T.S.S., A.M., V.K.K., and D.A.H. conceptualized the study. T.S.S. performed in vitro

experiments with the help of H.A.S., M.C., and P-P.A.; T.S.S. prepared sequencing libraries and

33 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

M.R.L. processed bulk RNA-seq data alignment; S.D. and A.M. performed computational

analysis to construct gene regulatory network with input from T.S.S.; J.C.S. performed scRNA-

seq analysis on COVID-19 data with the help of T.S.S., A.U., and N.K.; T.S.S., S.D., J.C.S.,

A.M., and D.A.H. wrote the manuscript with input from all authors; T.S.S., A.M., V.K.K. and

D.A.H. supervised the overall study.

Competing interests

D.A.H. has received research funding from Bristol-Myers Squibb, Sanofi, and Genentech. He

has been a consultant for Bristol Myers Squibb, Compass Therapeutics, EMD Serono,

Genentech, and Sanofi Genzyme over the last three years. Further information regarding

funding is available on: https://openpaymentsdata.cms.gov/physician/166753/general-payments.

V.K.K. has an ownership interest and is a member of the SAB for Tizona Therapeutics. V.K.K. is

also a co-founder and has an ownership interest and a member of SAB in Celsius Therapeutics

and Bicara Therapeutics. V.K.K.’s interests were reviewed and managed by the Brigham and

Women’s Hospital and Partners Healthcare in accordance with their conflict of interest policies.

N.K. served as a consultant to Biogen Idec, Boehringer Ingelheim, Third Rock, Pliant, Samumed,

NuMedii, Theravance, LifeMax, Three Lake Partners, Optikira, Astra Zeneca over the last 3

years, reports Equity in Pliant and a grant from Veracyte and non-financial support from

MiRagen and Astra Zeneca. Has IP on novel biomarkers and therapeutics in IPF licensed to

Biotech.

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint Figure (which1 was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made a available under aCC-BY-NC-ND 4.0 International license. CD4+ T cells 100 Control + Control ** ** ** CD4 IFN-β T cells IFN-β 80 60

40

CD8+ 20 Positive cells (%) T cells Normalized to mode 0 TIM-3 LAG-3 PD-1

TIM-3 LAG-3 PD-1

b Control IFN-β Control IFN-β CD4+ T cells 60 3.65 19.1 6.53 29.7 **** Control CD4+ IFN-β T cells 40

20 1.86 18.3 0.84 11.7 CD8+ Positive cells (%) T cells 0 TIM-3+/LAG-3+/PD-1+ TIM-3 TIM-3 (Triple positive cells) LAG-3 PD-1 c HAVCR2 LAG3 PDCD1 TIGIT 50 40 20 4

40 30 15 3 30 20 10 2 20 10 10 5 1 0 0 0 0 (relative to B2M ) mRNA expression 0h 8h 0h 8h 0h 8h 16h 24h 48h 72h 96h 16h 24h 48h 72h 96h 16h 24h 48h 72h 96h 0h 8h 16h 24h 48h 72h 96h Control IFN-β d Control IFN-β e Control IFN-β CD4+ T cells LAG3 3.26 0.69 0.84 0.93 CEACAM1 4 ENTPD1 + ** Control CD4 KLRD1 T cells IFN-β HAVCR2 3 PDCD1 7.45 25.1 HAVCR1 2 PROCR(CD201) CTLA4 25.6 3.62 4.50 9.05 CD274 1 SLAMF6 + CD8 Positive cells (%) C10orf54(VISTA/VSIR) T cells TNFRSF14 0 LAIR1 CD200 TIGIT 0.65 14.8 TIGIT TIGIT LAG-3 BTLA CD160 TNFRSF9 -4 -2 0 2 4 logFC Figure 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint (which was not+ certified by peer review) is the author/funder, who has granted+ bioRxiv a license to display the preprint in perpetuity. It is made a CD4 T cells available under aCC-BY-NC-NDCD8 T cells 4.0 International license. -2 0 2 -2 0 2

IFN-β induction RSAD2 IFN-β induction XBP1 2000 750 1500

1000 500

500 Early 250 0

Early SP140 STAT2

120 30

80 20

40 10

0 Intermediate HAVCR2 HOPX 150 30 Intermediate

100 20 Late 50 10 Late 0 PRDM1 PRDM1 12 4

9 3

6 2 Bimodal 3 1 Bimodal

0 0 0 1 2 4 8 1 2 4 8 0 1 2 4 8 1 2 4 8 16 48 72 96 16 48 72 96 16 48 72 96 16 48 72 96 Time (h) Time (h) Time (h) Time (h) b CD4+ T cells CD8+ T cells 96h_3 96h_3 96h_2 96h_2 96h_1 96h_1 72h_3 72h_3 72h_2 72h_2 72h_1 72h_1 48h_3 48h_3 48h_2 48h_2 48h_1 48h_1 16h_3 16h_3 16h_2 16h_2 16h_1 16h_1 8h_3 8h_3 8h_2 8h_2 8h_1 8h_1 4h_3 4h_3 4h_2 4h_2 4h_1 4h_1 2h_3 2h_3 value value 2h_2 2h_2 0.96 0.95 2h_1 2h_1 0.94 0.93 1h_3 1h_3 0.92 0.91 1h_2 1h_2 0.90 0.89 1h_1 1h_1 1h_1 1h_2 1h_3 2h_1 2h_2 2h_3 4h_1 4h_2 4h_3 8h_1 8h_2 8h_3 1h_1 1h_2 1h_3 2h_1 2h_2 2h_3 4h_1 4h_2 4h_3 8h_1 8h_2 8h_3 16h_1 16h_2 16h_3 48h_1 48h_2 48h_3 72h_1 72h_2 72h_3 96h_1 96h_2 96h_3 16h_1 16h_2 16h_3 48h_1 48h_2 48h_3 72h_1 72h_2 72h_3 96h_1 96h_2 96h_3

Early Intermediate Late Early Intermediate Late

c CD4+ T cells CD8+ T cells BCL3 IFIT1 STAT4 IRF9 CEACAM1 BCL3 STAT1 FLI1 RSAD2 OAS2 IFIT3 IRF4 KLF5 OAS2 IFIT1 ARID5B RSAD2 BHLHE40 OAS1 BCL6 STAT1 OAS1 XBP1 IRF9 IRF1 IFIT3 MYB ISG15 BATF IRF7 IRF1 IFITM1 BHLHE40 ISG15 CXCL10 XAF1 ISGs IRF4 EIF2AK2 ISGs IFI44 XAF1 STAT3 BATF IRF7 HIF1A HERC5 MYC MX1 EIF2AK2 HERC5 IRF9 IFITM1 STAT5A IFI6 AHR MX1 IFI44L STAT1 IFI44L STAT3 IFI44 IFI6 STAT4 ETV6 LAG3 IRF2 LAG3 HIF1A CD274 SLAMF6 NOTCH1 ARID5B CEACAM1 CTLA4 BTLA SP140 BTLA SP140 TNFRSF9 HAVCR2 SLAMF6 STAT2 HAVCR1 LAIR1 LAIR1 ENTPD1

IRF2 CoIRs ETS2 C10orf54 CoIRs HAVCR2 IRF9 C10orf54 RARA STAT1 AREG CD38 STAT2 LTB ETV6 APOBEC3G ELK4 CD69 ETS2 LTB SGK1 CCR6 ICOS CD69 CDKN1B BCL11B CD28 PTGER2 PTGER4 NR4A2 OSM CCR4 ID3 IL6ST DDIT3 IL6ST CD200R1 ZBTB18 KLRG1 RUNX2 IL2RA TLR1 FURIN TET1 SGK1 RARG DPP4 KLRG1 IL2RB SOX4 CD226 POU2F2 IFNG CD200R1 TNF VDR IFNGR1 TCF7 CCL4 TCF7 DPP4 DDIT4 FURIN CDKN1B IL23A POU2F2 ZFP36 TSC22D3 KDM5B ITGB2 Regulators (transcription factors) NR4A2 TGFBR1

Regulators (transcription factors) TGFBR1 FOSL2 KLF3 ITGB2 CD5 S1PR1 NFIL3 TSC22D3 TET1 CD27 SKI CD27 CD5 TNFRSF1A MAF IL23A TOP2B cell related genes T IL27RA TET3 cell related genes T IL27RA TNFRSF1A NFIL3 CXCR3 BCL11B IL10 DDIT4 ZFP36L2 PRDM1 ZFP36L2 ERCC6 CD38 0 1 2 4 8 0 1 2 4 8 0 1 2 4 8 0 1 2 4 8 16 48 72 96 16 48 72 96 16 48 72 96 16 48 72 96

Time (h) Time (h) Time (h) Time (h) bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this preprint Figure 3(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made a available3 2under 1 aCC-BY-NC-ND 4.0 International license. Human ISGs Human IFN-I responsible gene score Human TIL Regulators for co-inhibitory receptors in TILs HIV progressor HIV specific T cell signature in progressive patients IL-27 regulators IL-27 driven regulators for co-inhibitory receptors ID3 MAF IRF2 IRF4 IRF1 IRF9 KLF5 BATF TCF7 BCL3 ETS2 HOPX STAT3 STAT1 SP140 BATF3 PRDM1 ARID5A ARID5B b Only media, no stimulation anti-CD3, anti-CD28 +/- IFN-β

-60h -48h -24h 0h 96h Naive CD4+T cells GFP+ cells are sorted by FACS

Vpx-VLP LV #1 LV #2 TCR stimulation Bulk mRNA-seq Spinfection c d ‘IFN-I regulator module 2’ ● 40 STAT3 1.0 ‘IFN-I regulator module 1’ 60% gene knockdown 0.8 20 ARID5A ●● IRF4 ● STAT1 ARID5B●● ●● 0.6 ●● ●● BATF●●●● ETS2 IRF9 IRF2 ●● 0 0.4 MAF ●● by knock down HOPX PRDM1 ●● PC2 (12.5%) IRF1 ●● SP140 KLF5●● ●● ●● BATF3 TCF7 ●● ●●●● ●●

Relative gene expression 0.2 TCF7L PRDM1L BCL3 −20 0.0 ●● ID3 −20 0 20 40 ID3 PC1 (28.1%) IRF1 IRF2 IRF4 IRF9 BATF BCL3ETS2HOPX KLF5 STAT3 TCF7 BATF3 SP140STAT1 PRDM1 TCF7L ARID5AARID5B PRDM1L e ISGs f Co-inhibitory receptors

● ● 40 STAT3 40 STAT3

SOCS1

TAP1 CD160 PDCD1 TNFRSF9 20 20 IRF4 ●● IRF4 LAIR1 BTLA ● ARID5A ●● ARID5A UNC93B1 ● UBE2L6 ARID5B STAT1 ARID5B STAT1 ● IRF9 ● ● ● BST2 LAG3 ● CEACAM1 ETS2 LAMP3 SLAMF6 ● BATF●●●● SERPING1 ● ● ● ETS2 IRF2 IRF9 IRF2 ● ●● TIGIT ● BATF 0 MAF 0 IFIH1 SOCS3 MAF SP140 CD274 HOPX ●● MX1 ● ●● HOPX PC2 (12.5%) SP140 EIF2AK2 STAT1 ● KLF5 PRDM1 ●● PC2 (12.5%) ●● ●● BATF3 PRDM1IRF1 ●BATF3 ●● KLF5● ● ● ●●TCF7 IRF1 ●● IFI16 ●● ●● ●TCF7 ● BCL3 TCF7L ● ● BCL3 TCF7L PRDM1L PRDM1L HERC5 CASP1 SP100 −20 −20 TRIM38 PHF11 ●● ● ID3 ID3 HAVCR2 −20 0 20 40 −20 0 20 40 PC1 (28.1%) PC1 (28.1%) bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this Rankedversion List posted of October 31, 2020. The copyright holder for this preprint Figure 4 Knockdown Effects (which was not certified by peer review) is the author/funder, who hasCandidates granted bioRxiv (TFs) a license to display the preprint in perpetuity. It is made a -Differential Expression Analysisavailable under aCC-BY-NC-ND 4.0 International license(21. TFs) Gene Expression for perturbation -Expression Clusters Over Time -Presence of regulators Predicted Refined Integrate Integrate Network Model Network Model

Protein-DNA Binding (1,091 TFs) b Early Intermediate Late

TBX21 BATF NOTCH1 BHLHE40 MYC NKX3-1 ZNF614 ELF4 SP140 NFKBIZ BATF3 NFIL3 LMNB1 TRIM22 ARID5B PML IRF2 DRAP1 STAT1 TRIM22 STAT2 NFKBIZ FOSL1 ETV7 ETV7 ETV6 TRIM25 XBP1 STAT4 NCOA3 IRF4 IRF2 INTS12 PRDM1 FOSL2 INTS12 HIF1A PRDM1 TRIM25 ARID5B BATF PML ETV6 ETV7 RUNX2 BCL3 FOSL2 BATF3 STAT1 NBN AFF1 CREM ELF1 NCOA2 TRIM25 ZFP36 PML HIF1A STAT3 STAT2 DRAP1 Up TBX21 KLF5 MAF NFIL3 KLF6 ELF1 Up-regulated STAT1 TRIM22 BCL3 NFIL3 IRF1 KLF5 IRF1 Targets FLI1 MYC TBX21 ATF3 AFF1 Down MAF HIF1A Down-regulated Down Up Targets Down Up Down Up CDKN1B KDM5B HEY1 DDIT3 CDKN1B HMGB2 PBXIP1 IRF1 MYB Up-regulated MYB SMAD3 CDKN1B FOXP1 SIPA1 MAZ ARRB1 TF NFIA EPAS1 ID3 STAT5A

Bundled network CREBBP ID3 RARG VDR ERCC6 AHR ZSCAN22 CHD4 RARG POU2F2 MXD4 Down-regulated GATAD1 TCF7 TF FOXN3 KLF7 BCL11B EHMT2 MEF2C GFI1 ZNF711 ORC1 CBX7 ARNT # of Connection IKZF2 UBTF KLF3 1 RARG NAB2 KLF7 ZXDC CRY1 ZNF217 SOX4 PLAG1 200 CBX7 HNF1B ZBTB18 KLF13 NAB2 POU2F2 REL MORC2 388 MZF1 TET3 SKI TCF7 ID3 ZBTB18 ZNF318 CHD4 NFKBIZ AHRR ZNF589 PHF2 APOBEC3B

Rank Rank Rank Up TFs Cent Up TFs Cent Up TFs Cent HG HG HG ID2 FLI1 PML PML PML MAF MAF NBN IRF2 IRF9 IRF4 IRF1 IRF1 IRF2 MYC MYC ATF3 KLF5 ELF1 ELF1 KLF5 KLF6 BATF ELF4 AFF1 BATF AFF1 BCL3 ETV7 ETV6 ETV6 BCL3 ETV7 ETV7 XBP1 NFIL3 NFIL3 NFIL3 HIF1A S TA T3 S TA T2 S TA T1 HIF1A HIF1A S TA T2 S TA T1 S TA T4 S TA T1 CREM BATF3 BATF3 ZFP36 SP140 TBX21 TBX21 MIER1 TBX21 FOSL1 FOSL2 FOSL2 DRAP1 LMNB1 DRAP1 RUNX2 TRIP13 INTS12 INTS12 NCOA2 NCOA3 PRDM1 PRDM1 NFKBIZ TRIM22 TRIM25 HIVEP1 TRIM25 NFKBIZ TRIM22 TRIM25 TRIM22 ARID5B ZNF614 ARID5B NKX3−1 NOTCH1 BHLHE40 Rank Rank Rank Down TFs Cent Normalized value Down TFs Backbone network Cent Down TFs Cent HG HG HG 1.00 0.75 0.50 0.25 0.00 ID3 ID3 SKI ID3 DBP MAZ REL AHR MYB IRF1 VDR MYB KLF3 GFI1 KLF7 TET3 NFIA TCF7 KLF7 UBTF HEY1 MZF1 NAB2 CBX7 TCF7 SIPA1 CHD4 PHF2 IKZF2 ZXDC SOX4 CBX7 NAB2 CRY1 AHRR ARNT CHD4 DDIT3 RARG RARG KLF13 MXD4 ORC1 RARG FOXP1 EPAS1 FOXN3 HNF1B PLAG1 NR4A2 MEF2C ERCC6 KDM5B ARRB1 EHMT2 S TA T5A NFKBIZ SMAD3 ZNF318 ZBTB18 PBXIP1 HMGB2 BCL11B MORC2 ZNF217 ZNF711 ZNF589 GATAD1 ZBTB18 POU2F2 CDKN1B POU2F2 CDKN1B CREBBP CDKN1B ZSCAN22 APOBEC3B

SOX4 POU2F2 ARNT NFKBIZ

CDKN1B CDKN1B FOXP1

UBTF ETV7 ETV7 PBXIP1 EPAS1

AHR MAZ SMAD3

MAF IRF1 FOSL1 KLF5 FLI1 KDM5B TBX21 BCL11B CBX7 VDR BATF

HEY1 MAF MYB NKX3-1 TRIM22 ELF1 RARG ARID5B BATF3 IRF9 TET3 SKI ZBTB18 SIPA1 KLF3 CHD4 AFF1 IRF1

HNF1B STAT5A INTS12 CRY1 ARRB1 APOBEC3B ARID5B TBX21 STAT4 ZSCAN22 POU2F2 ELF4 GFI1 STAT2 BHLHE40DBP KLF13 MYC TCF7 ID2 BCL3 DDIT3 IRF2 PML XBP1 ID3 HIVEP1NFKBIZ STAT1 HIF1A DRAP1 ETV6 STAT3 TRIM25 CDKN1B NFIA ID3 KLF7 ZNF711 RARG HIF1A ZNF589 SP140 STAT2 PHF2 MIER1 REL PML NFIL3 ERCC6 PML ETV7 Backbone network NFIL3 ATF3 TCF7 NFKBIZ CREM CHD4 ZNF217NR4A2 MXD4 HMGB2 ORC1 ELF1 IRF1 CBX7 CREBBP

NAB2 DRAP1INTS12 LMNB1 ETV6 TRIP13 TRIM22GATAD1 NFIL3 MYB

NOTCH1 PLAG1

ZBTB18MORC2EHMT2 STAT1 TRIM25 FOSL2 BCL3 MZF1 MEF2C HIF1A FOXN3 NCOA2 ZNF318 RUNX2 ID3 PRDM1 STAT1 IKZF2 ZFP36 TBX21 ZXDC AHRR

NBN FOSL2 NCOA3 IRF2 MYC PRDM1BATF3 TRIM25 KLF5 KLF6 AFF1 KLF7 RARG NAB2

ZNF589 EHMT2 c ORC1 CREM ARNT SP140 KLF6 REL ZNF711 MORC2 SOX4 EPAS1 ELF4 NCOA3 GFI1 NFIA Early-Intermediate NOTCH1 Intermediate-Late PHF2 APOBEC Inter' Bridging TFs 3B Bridging TFs SMAD3 ZSCAN22 ZNF217 NBN MYC STAT2 CRY1 ATF3 MAF VDR CBX7 ARRB1 PBXIP1 Transcriptional Wave AFF1 HMGB2 KLF5 Targets STAT4 MYB ARID5B BATF ELF1 LMNB1 PRDM1 FOSL2 TCF7 MXD4 NKX3-1 PLAG1 BATF3 ZBTB18 IRF2 INTS12 POU2F2 Non-bridging DRAP1 KLF3 TF NAB2 KLF7 ETV6 IRF4 CHD4 BCL11B MZF1 GATAD1 STAT3 MEF2C Bridging ZNF318 TF KLF13 RARG TRIM25 ETV7 ZNF614 XBP1 AHR ZFP36 FOXP1 STAT5A # of Connection SKI TBX21 ID3 SIPA1 1 Early IKZF2 200 DDIT3 TRIM22 CDKN1B IRF1 HEY1 Late ERCC6 1055 MAZ TET3 STAT1 NFKBIZ FOXN3 RUNX2 BHLHE40 KDM5B FOSL1 PML NFIL3 AHRR HIF1A FLI1 HNF1B ZXDC UBTF Continuous NCOA2 CREBBP Bridging TFs

BCL3 Bimodal BridgingTF bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362947; this version posted October 31, 2020. The copyright holder for this+ preprint a (which was not certified by peer review) isb the author/funder,CD4+ T cells whoc hasCD4 granted+ T bioRxivCD8 a license+ T to displayd the preprint in perpetuity.CD4 It T is cells made Disease Disease * 0.4 *** *** available0.6 under aCC-BY-NC-ND 4.0 International license. 0.5 CD274 PROCR 0.3 0.4 0.06 KLRD1 HAVCR1 0.4 0.4 0.3 TIGIT CD160 0.3 0.3 Dividing CD4+ T TNFRSF14 LAIR1 0.04 0.2 0.2 + 0.2 TIGIT 0.2 LAG3 0.2 Dividing CD8 T BTLA TIGIT LAIR1 SLAMF6 HAVCR2 0.02 Dividing γδ T CD160 TNFRSF14 0.1 0.1 0.1 0.1 Effector CD4+ T 0.0 HAVCR1 SLAMF6 0.00 Effector CD8+ T VSIR VSIR 0.0 γδ T SLAMF6 BTLA + CD8+ T cells IFN-activated CD8+ T CD8 T cells PROCR KLRD1

+ IFN-I score LAIR1 ENTPD1 * ** *** *** *** MAIT CD8 T 3 CD200 CTLA4 0.15 Memory CD4+ T 0.50 0.6 0.6 + TNFRSF9 CEACAM1 Memory CD8 T 1.0 PDCD1 TNFRSF9 2 Naive CD4+ T 0.10 0.25 CTLA4 PDCD1 0.4 0.4 + LAG3

Naive CD8 T LAIR1 HAVCR2 CD274 TIGIT SLAMF6 1 HAVCR2 0.05 0.5 Tregs z-score LAG3 CD200 0.2 0.2 0.00 1 ENTPD1 HAVCR2 0.5 0 0.00 −0.5 UMAP2 CEACAM1 LAG3 −1 Control COVID-19 UMAP1 Control Stable Progressor Control Stable CD4+ T cells Progressor CD8+ T cells + e f CD4 T cells Control COVID 0.6 CXCL10 IFIT1 HERC5 LAG3 RSAD2 IFI6 EIF2AK2 IRF7 XAF1 ISG15 MX1 IFI27 IFI44 IFI44L GBP1 CEACAM1 CTLA4 PDCD1 HAVCR2 ICOS TNFRSF9 CD160 TIGIT SLAMF6 TNFRSF14 LAIR1 BTLA KLRD1 CD226 ICOS CXCL10 HAVCR2 PDCD1 LAG3 IRF7 XAF1 IFIT1 RSAD2 MX1 IFI27 IFI6 HERC5 IFI44L EIF2AK2 ISG15 IFI44 CTLA4 CEACAM1 TNFRSF9 CD160 KLRD1 GBP1 TNFRSF14 TIGIT BTLA CD226 SLAMF6 LAIR1 0.4 1 ICOS 1 CXCL10 CXCL10 IFIT1 0.2 HAVCR2 0.84 HERC5 0.83

PDCD1 LAG3 IFN-I score 0.0 LAG3 RSAD2 IRF7 0.67 IFI6 0.67 XAF1 EIF2AK2 IFIT1 IRF7 Tregs RSAD2 0.51 XAF1 0.5 MX1 ISG15 Naive CD4 T IFI27 MX1 Dividing CD4Effector T CD4Memory T CD4 T IFI6 0.35 IFI27 0.34 HERC5 IFI44 CD8+ T cells IFI44L IFI44L EIF2AK2 0.18 GBP1 0.17 ISG15 CEACAM1 IFI44 CTLA4 Pearson's ρ 0.50 CTLA4 0.02 PDCD1 0 CEACAM1 HAVCR2 TNFRSF9 ICOS 0.25 CD160 −0.15 TNFRSF9 −0.16

KLRD1 CD160 IFN-I score GBP1 TIGIT 0.00 TNFRSF14 −0.31 SLAMF6 −0.33 TIGIT TNFRSF14 + BTLA LAIR1 −0.49 CD226 −0.47 BTLA SLAMF6 KLRD1 MAIT CD8 Naive CD8 T LAIR1 CD226 Effector CD8 T −0.64 −0.66 Dividing CD8 T Memory CD8 T ISGs Co-inhibitory receptors IFN−activated CD8 T CD4+ T cells CD8+ T cells g SP140 CellType CellType Intermediate wave network j TNFRSF14 TNFRSF14 i CD4+ T cells GFI1 0.55 * VSIR PROCR MYC SP140 Naive CD4 T KLF5 CEACAM1 VSIR Dividing CD4 T 0.50 MAF ATF1 BTLA CEACAM1 Effector CD4 T VDR Memory CD4 T STAT1 NOTCH1 MAX 0.45 HAVCR1 HAVCR1 TBX21 ATF3 IRF1 STAT3 PDCD1 TIGIT LAG3 TIGIT 0.40 + ARNT LAIR1 TNFRSF9 CD8 T cells ELF1 FOXP1 ZNF143 TRIM22 CREM 0.35 TNFRSF9 PDCD1 Naive CD8 T FOSL2 CDKN1B Dividing CD8 T STAT4 POU2F2 LAG3 LAG3 STAT5B ETV6

Effector CD8 T Normalized expression 0.30 SLAMF6 BTLA + ISG CD8 T BATF CD274 CD200 Memory CD8 T IRF2 0.25 PROCR SLAMF6 STAT3 KLRD1 HAVCR2 Late wave network CD160 CD274 k Up-regulated ** AHR RUNX2 2.5 CD200 KLRD1 TF Normalized Up-regulating TIGIT CD160 Expression Interaction HEY1 GATAD1 Target gene 1 2.0 ENTPD1 LAIR1 0.8 Down-regulating 0.6 Interaction HAVCR2 CTLA4 0.4 LEF1 HAVCR2 CDKN1B Down-regulated CTLA4 ENTPD1 0.2 TF 0 1.5 PML DRAP1 h Early Intermediate Late

HIF1A Normalized expression CD4+ T cells NFATC1 PRDM1 1.0 0.00 CREBBP in COVID-19 0.04 MYB −0.02 0.05 Naive CD4 T NR3C1 ETV6 IRF1 Dividing CD4 T NFKB1 BCL3 0.02 Effector CD4 T POU2F2 STAT3 −0.04 0.00 TBX21 Memory CD4 T STAT2 STAT1 *** 0.00 RUNX3 2.0 −0.06 EED −0.05 JUNB BCL3 −0.08 −0.02 1.5 STAT5B MAF + 0.02 CD8 T cells 1.0 RUNX1 0.050 in COVID-19 STAT5A 0.10 FOXP1 0.00 MEF2C Naive CD8 T 0.5 0.025 Dividing CD8 T PDCD1 LAG3 −0.02 Normalized expression 0.05 Effector CD8 T 0.0 + ERCC6 0.000 ISG CD8 T IKZF2

In vitro IFN-I transcriptional vawe score −0.04 SP140 Control Memory CD8 T TRIM22 FOSL2

−0.06 0.00 COVID-19