WO2022266535A1

WO2022266535A1 - Compositions and methods to regulate viral pathogenesis

Info

Publication number: WO2022266535A1
Application number: PCT/US2022/034180
Authority: WO
Inventors: Le Cong
Original assignee: The Board Of Trustees Of The Leland Stanford Junior University
Priority date: 2021-06-18
Filing date: 2022-06-20
Publication date: 2022-12-22

Abstract

The present disclosure provides compositions and methods for regulating viral pathogenesis and treating diseases and disorders characterized by a viral infection (e.g., COVID-19) with agents that regulate the expression or activity of a viral infection mediating factor (e.g., KCNA6, LGMN, HLA-DPB1, EPHA4, and/or CD7).

Description

COMPOSITIONS AND METHODS TO REGULATE VIRAL PATHOGENESIS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63/212,358, filed June 18, 2021, the content of which is herein incorporated by reference in its entirety.

FIELD

[0002] The present invention relates to compositions and methods for regulating viral pathogenesis and treating diseases and disorders characterized by a viral infection (e.g., CO VID-19) with agents that regulate the expression or activity of a viral infection mediating factor (e.g., KCNA6, LGMN, HLA- DPB1, EPHA4, and/or CD7).

SEQUENCE LISTING STATEMENT

[0003] The text of the computer readable sequence listing filed herewith, titled “39624-

601_SEQUENCE_LISTING_ST25”, created June 20, 2022, having a file size of 1,751 bytes, is hereby incorporated by reference in its entirety.

BACKGROUND

[0004] The emergence of SARS-CoV-2 has led to the CO VID- 19 pandemic with over 150 million reported cases and over three million reported deaths as of May 2021 (WHO). Coronaviruses are a family of enveloped positive-stranded RNA viruses that cause respiratory and intestinal infections in birds and mammals (Cui et al., 2019). Among the currently known human coronaviruses, four (229E, HKU1, NL63, and OC43) are widely circulating and cause mild infections, and three (SARS-CoV-1, Middle Eastern Respiratory Syndrome CoV, MERS-CoV, and SARS-CoV-2) are highly pathogenic (Cui et al., 2019).

[0005] SARS-CoV-2 enters cells in three major steps. The virus Spike protein first binds to its canonical receptor, Angiotensin Converting Enzyme 2 (ACE2). This is followed by proteolytic processing of the Spike protein which can be carried out by several proteases, with TMPRSS2 and Furin being the most well-known. These processes lead to membrane fusion and consequent release of viral RNA into the host cell (Harrison et al., 2020). Recent studies have also implicated the binding of Spike protein to heparan sulfate (Clausen et al., 2020) and cholesterol (Wang et al., 2020; Wei et al., 2020; Zang et al., 2020) as well as soluble-ACE2-mediated host cell attachment (Yeung et al.) as factors in viral entry, suggesting the existence of additional mechanisms that might be responsible for the SARS-CoV-2 tropism and COVID-19 pathology.

[0006] Recently, several research groups have performed CRISPR knockout screens to find factors necessary for SARS-CoV-2 entry and replication (Baggen et al., 2021; Daniloski et al., 2021; Hofimann et al., 2021a; Schneider et al., 2021; Wang et al., 2021b; Wei et al., 2021). While some hits were validated across the different screens, a large number were only found in one or a few but not in others (Bailey and Diamond, 2021). Further, a few experimentally confirmed entry factors (Cantuti-Castelvetri et al., 2020; Daly et al., 2020), most notably neuropilins, were not reported as hits in any of the screens (Bailey and Diamond, 2021). These discrepancies are likely due to the nature of loss of function screens, which can only detect effects of genes that are expressed in the cell lines used.

[0007] To date one of the most efficacious treatments of SARS-CoV-2 infection remains a combination monoclonal antibody treatment that targets the SARS-CoV-2 Spike protein to inhibit ACE2 binding and prevent viral entry (Weinreich et al., 2021). Similarly, current COVID-19 vaccines are highly effective in preventing symptomatic illness and function by triggering an immune response against the Spike protein (Polack 2020, Hass, 2021). However, newer viral strains with mutated Spike proteins have rendered both combination monoclonal antibody therapies and vaccination potentially less effective (Hoffmann et al., 2021b; Wang et al., 2021a; Abu-Raddad et al. NEJM, 2021 ; Wall et al, Lancet 2021). [0008] Taken together, this suggests a critical need to gain further insight into SARS-CoV-2 entry mechanisms across susceptible tissues and cell types, and develop therapeutics targeting viral entry that are resistant to escape mutations.

SUMMARY

[0009] Provided herein are methods of treating or preventing a viral infection in a cell. The methods comprise contacting the cell with an effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors, or a composition thereof, wherein the one or more viral infection mediating factors are selected from the group consisting of KCNA6, LGMN, HLA-DPB1, EPHA4, CD7, and combinations thereof. In some embodiments, the cell is in vivo and the contacting comprises administering the inhibitor to a subject.

[0010] In some embodiments, the viral infection is a coronavirus infection. In some embodiments, the viral infection is a SARS-CoV-2 infection.

[0011] In some embodiments, the one or more viral infection mediating factors comprises KCNA6, LGMN, or a combination thereof. [0012] In some embodiments, the agent that regulates the expression or activity of one or more viral infection mediating factors comprises small molecules, antibodies or antibody fragments, aptamers, proteins, nucleic acids, or a combination thereof.

[0013] In some embodiments, the agent that regulates the expression or activity of one or more viral infection mediating factors comprises dalfampridine, amifampridine, LI-1, or a combination thereof.

[0014] In some embodiments, the methods further comprise contacting the cell with an agent that regulates the expression or activity of one or more additional viral infection mediating factors.

[0015] In some embodiments, the methods further comprise contacting the cell with an antiviral agent.

[0016] Also provided herein are method of treating COVID-19 in a subject. The methods comprise administering to a subject in need thereof a therapeutically effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors, or a composition thereof, wherein the one or more viral infection mediating factors are selected from the group consisting of KCNA6, LGMN, HLA-DPB1, EPHA4, CD7, and combinations thereof. In some embodiments, the one or more viral infection mediating factors comprises KCNA6, LGMN, or a combination thereof.

[0017] In some embodiments, the agent that regulates the expression or activity of one or more viral infection mediating factors comprises small molecules, antibodies or antibody fragments, aptamers, proteins, nucleic acids, or a combination thereof.

[0018] In some embodiments, the agent that regulates the expression or activity of one or more viral infection mediating factors comprises dalfampridine, amifampridine, LI-1, or a combination thereof. [0019] In some embodiments, the methods further comprise administering to the subject an agent that regulates the expression or activity of one or more additional viral infection mediating factors.

[0020] In some embodiments, the methods further comprise administering to the subject an antiviral agent.

[0021] In some embodiments, provided herein are methods and compositions for generating an immune response against an infection mediating factor target (e.g., KCNA6, LGMN, HLA-DPB1, EPHA4, and/or CD7). In some embodiments, the immune response generates or provides an antibody or antibody fragment that prevents the integration of virus via the infection mediating factor target (e.g., blocks interaction of virus with KCNA6).

[0022] Other aspects and embodiments of the disclosure will be apparent in light of the following detailed description and accompanying figures. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] FIGS. 1A-1H show membrane-focused CRISPRa screening identifies potential host factors involved in Spike-dependent SARS-CoV-2 virus entry. FIG. 1 A is schematics showing the design of vector systems used in the CRISPRa screening. FIG. IB is a schematic of a screening pipeline showing different conditions used (ACE2-null, ACE2-positive, at low or high MOI). FIGS. 1C-1F show enrichment scores of CRISPRa screen across different conditions, as indicated, with top hits highlighted and colored by their functional categories. FIGS. 1G-1H show overlap analysis of top 10% hits from SARS-CoV-2 Spike and reference VSVG screens. The unique top hits in SARS-CoV-2 screens identify putative COVID19-specific host factors from pan-lentiviral factors.

[0024] FIGS. 2A-2D show tissue expression and pathway enrichment analysis of top hits from CRISPRa screen. FIG. 2A is a heatmap showing the overall human tissue expression pattern of top screen hits using GTEX data. FIG. 2B is tissue expression body map of top screen hits, showing novel host genes expressed in the neuronal, sensory tissues, airway/lung epithelium, heart, and GI tract. FIG. 2C is gene set overlap analysis using gene ontology (GO) performed on the top 10% hits from each category of screens. The top GO terms of each category of screens were selected for visualization. FIG. 2D is selected functional network clusters involved with the top GO terms. Colored nodes are significant hits identified from the screen and grey nodes are the connecting nodes. The enrichment score of a gene in each category of screens is indicated by color scale within the node. Notable genes within the same family are highlighted.

[0025] FIGS. 3A-3G show validation of top CRISPRa screen hits via pseudoviral and authentic SARS-CoV-2 live virus assays. FIGS. 3A-3B are graphs of the arrayed validation of top hits in cDNA overexpressing cell lines of individual genes, using SARS-CoV-2 Spike-D614G pseudotyped lentiviral assay. The control VS VG-pseudotyped lentivirus results are shown side-by-side. FIGS. 3C-3D are graphs of arrayed validation using time-lapse imaging of replicating SARS-CoV-2 Spike-pseudotyped VSV infection in cDNA overexpression cell lines. FIG. 3E is a schematic of an authentic SARS-CoV-2 virus infection validation workflow. Wild-type SARS-CoV-2 live virus expressed a nanoluciferase in place of ORF7a to allow quantitative measurements of viral entry. FIGS. 3F-3G are graphs of the validation of top hits using SARS-CoV-2 live virus infection. Statistical analyses were performed via two-tailed t-test, *, p<0.05; **, p<0.01; ***, p <0.001; ****, p<0.0001.

[0026] FIGS. 4A-4K show KCNA6 is highly expressed in nasal/olfactory neurons at the sites of COVID19 infection and is a druggable target for inhibiting SARS-CoV-2 viral entry. FIG. 4A shows the expression of ACE2 and KCNA family genes across human tissues. Left-side two columns show the fold change of olfactory/respiratory expression vs control tissue expression. Columns in the middle and right show the expression level in 4 olfactory epithelial samples and 15 control tissues. Data was obtained from Olender et al (BMC Genomics. 2016 Aug 11 ; 17(1): 619). FIG. 4B is visualization of the KCNA6 genome annotations in the GRCh38 (hg38) and GRCh37 (hgl9) references using NCBI Genome Data Viewer. FIG. 4C is a graph of the expression of ACE2 and KCNA6 in the single-cell RNA-seq data of olfactory epithelium obtained from Durante et al. using different versions of genome references. Cell Ranger 6.0 was used for all alignments and the expression was calculated by averaging the ACE2/KCNA6 expression in all cells and normalized to the ACE2 expression from the standard GRCh38 reference genome. FIG. 4D is a graph of the expression of ACE2 and KCNA6 using Salmon - Alevin pipeline, calculated similarly as in FIG. 4C. The standard genome references were used. FIG. 4E is a UMAP depicting the olfactory epithelial cell types from two patients. The clustering method and cell cluster identity were based on Durente et al. FIG. 4F is UMAPs depicting the expression levels of KCNA6 in the individual patients. FIG. 4G is focused UMAPs of the neuronal populations depicting the expression of KCNA6 and OLIG2. Neuronal marker OLIG2 marks SARS-CoV-2 infected cells in the olfactory epithelium from patient autopsy analysis of Cantuti-Castelvetri et al. and Meinhardt et al. FIG. 4H is dot plot expression visualization of OLIG2, KCNA6, NRP1, NRP2, ACE2 in different olfactory epithelial cell types. Sizes of the dots indicate the proportion of cells in the respective cell type having greater-than-zero expression while the color indicates the mean expression. FIGS. 4I-4K show FDA-approved compound 4- Aminopyridine (4-AP, dalfampridine) is a broad-spectrum potassium channel inhibitor (FIG. 41) and inhibitor assays in ACE2-null (FIG. 4J) or ACE2-positive (FIG. 4K) conditions, measuring SARS-CoV-2 spike or VSV-G pseudotyped lentiviruses infection efficiencies in KCNA6 overexpression or control BFP lines treated with different doses of 4-AP, with measured IC50 to the right of each condition.

[0027] FIGS. 5A-5F show network analysis to identify drug candidates from top screen hits, and clinical evidence for top enriched drug categories via retrospective cohort analysis. FIG. 5A is an overview of the drug-target interaction network, showing an induced subgraph of the 50 highest ranked compounds (drugs in blue; screen hits in green; potassium channel genes outlined in red). FIG. 5B shows the top drug classes enriched in hits from the interaction network model by NDC and degree ratio with respect to screen hits. Asterisks indicate drug classes with at least one member targeting a potassium channel. FIG. 5C is the controlled study design for COVID-19 hospitalization from pharmaceutical claims data. FIG. 5D is shows drugs associated with CO VID- 19 hospitalization in the unmatched study rank highly in the drug-target interaction network. FIGS. 5E-5F exhibit real world evidence showing an association between ion-channel-targeting drug classes identified in the screen and increased (FIG. 5E) and decreased (FIG. 5F) risk of COVID-19 hospitalization in propensily-score-matched subjects. Most notably, real-world evidence shows an association between hydrochlorothiazide (alone or in combinations with ACEi) and decreased risk of COVID-19 hospitalization in propensily-score-matched subjects.

[0028] FIGS. 6A-6D show the development of a pseudoviral based platform to screen for novel SARS-CoV-2 entry factors. FIG. 6A is schematics showing the design of vector systems used in the CRISPRa screening. FIG. 6B is ACE2-null and ACE2-positive lines stained with or without RBD-Biotin and Streptavidin-Alexa488. FIG. 6C is 293T-dCas9-VP64 cells stained with CD55-APC five days after transfection with pXPR_502 plasmids encoding gRNAs targeting the promoter of CD55. FIG. 6D is ACE2-null and ACE2-positive cells either mock infected or infected with SARS-CoV-2 Sdl9 pseudotyped lentiviruses.

[0029] FIGS. 7A-7B show comparisons of the screen enrichment scores using low (~ 0.01) or high (~0.1) MOI of SARS-CoV-2614G Spike pseudotyped virus. Scatter plots show the enrichment scores from ACE2-null 293FT cells (HG. 7A) or ACE2-positive 293FT cells (FIG. 7B).

[0030] FIGS. 8A-8B show the identification of the established membrane entry factors for S ARS- CoV-2 in our screens and previous loss-of-function screens. FIG 8A is a bar plot showing the number of established membrane entry factors in the top 10% screen hits. FIG. 8B is a heatmap showing the relative ranking of the established membrane entry factors.

[0031] FIGS. 9A-9E show additional functional network analysis of the top hits in the screen. FIG. 9A is a heatmap showing the gene sets enrichment in different screen conditions. FIGS. 9B-9E show functional network clusters using the top 10% of the hits in different screen conditions.

[0032] FIGS. 10A-10C show focused pooled validation using SARS-CoV-2614G/641D Spike pseudotyped lentivirus. FIG. 10A is a workflow of the focused CRISPRa pooled validation screen. FIGS. 10B-10C are heatmaps showing the enrichment scores of the top hits in ACE2-null 293FT cells (FIG. 10B) or ACE2-positive 293FT cells (FIG. 10C).

[0033] FIGS. 11A and 11B show arrayed CRISPRa validation of screen hits using SARS-Co V-2 Spike pseudotyped lentiviruses. SARS-CoV-2 Spike pseudotyped lentiviruses were used to transduce ACE2-null (FIG. 11 A) or ACE2-positive 293FT/dCas9-VP64 cells (FIG. 11B) stable expressed with different gRNAs targeting the promoters of genes of interest. [0034] FIGS. 12A and 12B are pseudotyped lentiviral assays in ACE2-null 293FT cells. ACE2null lines stably overexpressing cDNAs of putative SARS-CoV-2 entry factors were transduced with lentiviruses pseudotyped with either SARS-CoV-2 Spike D614G protein (FIG. 12A) or VS VG (FIG. 12B).

[0035] FIGS. 13 A and 13B are pseudotyped lentiviral assays in ACE2-positive 293FT cells. ACE2- positive lines stably overexpressing cDNAs of putative SARS-CoV-2 entry factors were transduced with lentiviruses pseudotyped with either SARS-CoV-2 Spike D614G protein (FIG. 13 A) or VSVG (FIG. 13B).

[0036] FIGS. 14A-14C show detection of cDNA expression in the overexpressing cell lines. FIGS. 14A-14B are Western Blots on lysates from ACE2-null and ACE2-positive cells overexpressing either BFP or KCNA6 probed with either ananti-V5 (FIG. 14A) or anti-KCNA6 antibody (FIG. 14B). * denotes the correct band for KCNA6 based on protein size markers. FIG. 14C is a graph of qPCR assay results of the cDNA overexpressing cell lines. LogFC was calculated relative to BFP overexpressing cell lines. [0037] FIGS. 15A and 15B are time-lapse imaging of replicating VSVdG-RABV-G SAD -Bl 9 infection of cDNA overexpression lines. The levels of RABV-G pseudovirus infection tested in ACE2- null (FIG. 15 A) and ACE2-positive (FIG 15B) cDNA overexpression cell lines.

[0038] FIGS. 16A-16D show RNA expression of LGMN and HLA-DPB1 in bronchoalveolar lavage fluids (BALF) of SARS-CoV-2 patients. FIG. 16A is UMAP depicting the major cell types and clusters in the BALF samples obtained from Liao et al. (n=13). FIG. 16B is dot plot visualization of the expression of ACE2, LGMN and HLA-DPB1 in BALF cells. FIGS. 16C and 16D, top left panels are boxplots comparing the average expression levels of LGMN and HLA-DPB1 between SARS-CoV-2 positive and negative cells from severely affected patients. Each dot represents a severely affected patient; top right panels are LGMN and HLA-DPB1 expression of cells obtained from healthy controls, patients with moderate and severe COVID-19 infection; and lower panels are UMAPs depicting the expression of LGMN and HLA-DPB1 in cells obtained from control (n=4) and patients (moderate, n=3; severe, n=6). [0039] FIG. 17 is a graph of the inhibition of viral entry tested in vitro in human cells. Increasing dosages of 3,4-DAP was applied to cells prior to virus infection. Assay separately performed using two variants of SARS-CoV2 and a VSVG negative control.

[0040] FIG. 18 is a graph showing the effect of viral entry when over-expressing drug target gene

(using cDNA) in human cell lines with minimal ACE2 presence (ACE2-null). Three SARS-CoV2 variants were used along with VSVG as negative control virus. [0041] FIG. 19 is a graph showing the effect of viral entry when over-expressing drug target gene (using cDNA) in human cell lines with ACE2 presence (ACE2-positive). Three SARS-CoV2 variants were used along with VSVG as negative control virus.

DETAILED DESCRIPTION OF THE INVENTION

[0042] The present disclosure is directed to methods of treating or preventing a viral infection in a cell and methods for treating CO VID-19 in a subject Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily infects the respiratory tract. However, accumulating evidence from patients indicates that several other organs, such as sensory systems, brain, and heart, as well as immune cell subsets, are susceptible to SARS-CoV-2. To discover host factors and druggable targets that promote Spike-mediated SARS-CoV-2 viral entry across different tissues and cell types, a CRISPR activation (CRISPRa) screen targeting all 6000+ human membrane proteins with and without overexpression of ACE2 using Spike-pseudotyped lentiviruses was performed. Novel putative host factors were identified, as well as validated hits, from recent studies, including CRISPR knockout screens, expressed in a broad spectrum of tissues and organs, with a particular enrichment for previously unknown host factors in neuronal/sensory, respiratory, cardiovascular, and immune systems. Utilizing an authentic SARS-CoV-2 virus assay, new viral-entry promoting genes were validated, and, most notably, it was found that the overexpression of the potassium channel KCNA6 led to a marked increase in infection even in cells with minimal ACE2 expression. scRNA-Seq analysis of nasal tissues from human patients revealed that OLIG2+ cells, previously identified as sites of SARS-CoV-2 infection, have strong KCNA6 expression and low expression of ACE2, suggesting that the presence of KCNA6 may explain sensory/neuronal aspects of COVID19 symptoms. Further, the FDA-approved compound dalfampridine, which broadly inhibits potassium channels, suppressed viral entry in a dosage-dependent manner. Finally, network analysis was used to identify common prescription drugs most likely to modulate the top screen hits. A retrospective analysis of insurance claims for approximately 8 million patients found evidence for a clinical association between enriched drug classes, particularly those targeting potassium channels, and COVID- 19 severity. Taken together, SARS-CoV-2 host factors were identified that expand understanding of potential viral tropism and provide targets for therapeutic intervention against SARS-CoV-2 and other viruses. The studies show a role for novel general and tissue-specific host factors in SARS-CoV-2 entry, providing inhibitors and FDA-approved drugs that find use for treatment or prevention of COVID-19, and demonstrate the utility of CRISPRa screening in delineating the determinants of viral infection. 1. Definitions

[0043] To facilitate an understanding of the present technology, a number of terms and phrases are defined below. Additional definitions are set forth throughout the detailed description.

[0044] The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of’ and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

[0045] For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

[0046] Unless otherwise defined herein, scientific, and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art The meaning and scope of the terms should be clear; in the event, however of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.

[0047] As used herein, “treat,” “treating,” and the like means a slowing, stopping, or reversing of progression of a disease or disorder or reducing the severity or activity thereof when provided a compound or composition described herein to an appropriate control subject. The term also means a reversing of the progression of such a disease or disorder to a point of eliminating or greatly reducing the symptoms. As such, “treating” means an application or administration of the compositions described herein to a subject, where the subject has a disease or a symptom of a disease, where the purpose is to cure, heal, alleviate, relieve, alter, remedy, ameliorate, improve, or affect the disease or symptoms of the disease.

[0048] As used herein, the term “preventing” refers to partially or indefinitely delaying onset of a disease, disorder and/or condition; partially or completely delaying onset of one or more symptoms, features, or manifestations of a particular disease, disorder, and/or condition; partially or completely delaying progression from a particular disease, disorder and/or condition; and/or decreasing the risk of developing pathology associated with the disease, disorder, and/or condition.

[0049] A “subject” or “patient” may be human or non-human and may include, for example, animal strains or species used as “model systems” for research purposes, such a mouse model as described herein. Likewise, patient may include either adults, juveniles (e.g., children), or infants. Moreover, patient may mean any living organism, preferably a mammal (e.g., humans and non-humans) that may benefit from the administration of compositions contemplated herein. Examples of mammals include, but are not limited to, any member of the Mammalian class: humans, non-human primates such as chimpanzees, and other apes and monkey species; farm animals such as cattle, horses, sheep, goats, swine; domestic animals such as rabbits, dogs, and cats; laboratory animals including rodents, such as rats, mice and guinea pigs, and the like. Examples of non-mammals include, but are not limited to, birds, fish, and the like. In one embodiment, the mammal is a human.

[0050] As used herein, the terms “providing,” “administering,” and “introducing,” are used interchangeably herein and refer to the placement of the compositions of the disclosure into a subject by a method or route which results in at least partial localization of the composition to a desired site. The compositions can be administered by any appropriate route which results in delivery to a desired location in the subject.

[0051] Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present disclosure. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

2. Methods of Treating or Preventing Viral Infection or Entry

[0052] The disclosure provides methods of treating or preventing a viral infection or viral entry in a cell. The methods comprise contacting the cell with an effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors, or a composition thereof.

[0053] In some embodiments, the cell is a eukaryotic cell. In some embodiments, the cell is a mammalian cell. In some embodiments, the cell is a human cell. In some embodiments, the cell is in vitro. In some embodiments, the cell is ex vivo. [0054] In some embodiments, the cell is in an organism or host, such that contacting the cell with an effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors comprises administration to a subject

[0055] The viral infection may be a coronavirus infection. The viruses that are members of this very large family are known to be causative agents of the common cold (for example the hCoV and OC43 viruses), bronchiolitis (for example the NL63 virus) or even certain forms of severe pneumonia such as those observed during the SARS epidemic (such as Severe Acute Respiratory Syndrome Coronavirus, SARS-CoV). The present methods are not limited by coronavirus type. In some embodiments, the coronavirus comprises human coronavirus 229E, human coronavirus OC43, SARS-CoV, hCoV NL63, HKU1, MERS-CoV, or SARS-CoV-2. In some embodiments, the coronavirus is SARS-CoV-2.

[0056] The one or more viral infection mediating factors may include any of those disclosed herein. In some embodiments, the one or more viral infection mediating factors are selected from the group consisting of KCNA6, LGMN, HLA-DPB1, EPHA4, CD7, and combinations thereof. In select embodiments, the one or more viral infection mediating factors comprises KCNA6. In select embodiments, the one or more viral infection mediating factors comprises LGMN. In select embodiments, the one or more viral infection mediating factors comprises KCNA6 and LGMN.

[0057] Regulators of the factors (e.g. , KCNA6, LGMN, HLA-DPB1, EPHA4, CD7) may be suitable for research, diagnostic, and therapeutic uses. For example, reduction of expression or activity of targets may be used to prevent or reduce viral infection or entry, and consequently, viral pathogenesis. Regulators (e.g., inhibitors) include, but are not limited to, small molecules, antibodies or antibody fragments, aptamers, proteins, nucleic acids (e.g., for gene therapy, antisense, RNAi, etc.), and the like.

[0058] In some embodiments, the agent that regulates the expression or activity of KCNA6 comprises dalfampridine, enflurane, miconazole, promethazine, tetraethylammonium, amifampridine, anti-KCNA6 antibody, an KCNA6 miRNA, or a combination thereof.

[0059] In some embodiments, the agent that regulates the expression or activity of LGMN comprises LI-1, CST6, anti-LGMN antibody, an LGMN miRNA, or agents as disclosed in U.S. Pat Nos. 7,279,550, 9,345,789, and 10,905,728, incorporated herein by reference in their entirety, or a combination thereof.

[0060] In some embodiments, the agent that regulates the expression or activity of HLA-DPB1, comprises anti-HLA-DPBl antibody, an HLA-DPB1 miRNA.

[0061] In some embodiments, the agent that regulates the expression or activity of EPHA4 comprises fostamatinib, ergoloid, cyproheptadine, nilotinib, abiraterone, retapamulin, rhynchophylline, KYL peptide (Murai et al., Mol. Cell Neurosci. 2003 December; 24(4): 1000-1011, incorporated herein by reference in its entirety), the APY peptide, and the VTM peptide, both described in W02004/028551, incorporated herein by reference in its entirety, Cpdl and Cpd2 (isomeric 2,5-dimethylpyrrolyl benzoic acid derivatives described in Noberini et al., J Biol Chem. 2008 Oct 24; 283(43): 29461-29472, incorporated herein by reference in its entirety) anti- EPHA4 antibody (e.g., U.S. Patent Publication No. 20210171645 or U.S.

Pat No. 7,604,799), an EPHA4 miRNA (e.g., U.S. Patent Publication 20070260047), agents as disclosed in U.S. Pat. Nos. 9,629,830 and 10,322,161, U.S. Patent Publication Nos. 20200206220 and 20180127464, incorporated herein by reference in their entirety, or as described in Van Linden et al., Eur. J. Med. Chem. 2012, 47(l):493-500, Parmentier-Batteur et al., J. Neurochem. 2011, 118(6): 1016-1031, and Goldshmit et al., Pios One 6, e24636, 2011, incorporated herein by reference in their entirety.

[0062] In some embodiments, the agent that regulates the expression or activity of CD7 comprises, anti-CD7 antibody (e.g., TXU), an CD7 miRNA.

[0063] Compositions comprising the agents may be formulated, as desired, for the appropriate research or clinic use. Compositions comprising the agents may further comprise excipients or pharmaceutically acceptable carriers. The choice of excipients or pharmaceutically acceptable carriers will depend on factors including, but not limited to, the particular mode of administration, the effect of the excipient on solubility and stability, and the nature of the dosage form.

[0064] Excipients and carriers may include any and all solvents, dispersion media, antibacterial and antifungal agents, isotonic and absorption delaying agents. Some examples of materials which can serve as excipients and/or carriers are sugars including, but not limited to, lactose, glucose and sucrose; starches including, but not limited to, com starch and potato starch; cellulose and its derivatives including, but not limited to, sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatin; talc; excipients including, but not limited to, cocoa butter and suppository waxes; oils including, but not limited to, peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, com oil and soybean oil; glycols; including propylene glycol; esters including, but not limited to, ethyl oleate and ethyl laurate; agar; buffering agents including, but not limited to, magnesium hydroxide and aluminum hydroxide; alginic acid; pyrogen-free water; isotonic saline; Ringer's solution; ethyl alcohol, and phosphate buffer solutions, as well as other non-toxic compatible lubricants including, but not limited to, sodium lauryl sulfate and magnesium stearate, as well as coloring agents, releasing agents, preservatives, and antioxidants. Techniques and formulations may be found, for example, in Remington's Pharmaceutical Sciences, 19th Edition (Mack Publishing Company, 1995). The route or administration and the form of the composition usually dictates the type of carrier to be used.

[0065] The agents or compositions thereof may be formulated for any appropriate manner of administration, and thus administered, including for example, oral, nasal, intraocular, intravenous, intravaginal, epicutaneous, sublingual, intracranial, intradermal, intraperitoneal, subcutaneous, intramuscular administration, or via inhalation. The pharmaceutical compositions can be administered continuously by infusion, although bolus injection is acceptable, using techniques well known in the art, such as pumps (e.g., subcutaneous osmotic pumps) or implantation. Techniques and formulations may generally be found in “Remington's Pharmaceutical Sciences,” (Meade Publishing Co., Easton, Pa.). Therapeutic or pharmaceutical compositions must typically be sterile and stable under the conditions of manufacture and storage.

[0066] The terms “effective amount” or “therapeutically effective amount,” as used herein, refer to a sufficient amount being administered which will relieve to some extent one or more of the symptoms of the disease or condition being treated. The result can be reduction and/or alleviation of the signs, symptoms, or causes of a disease, or any other desired alteration of a biological system.

[0067] The amount required for use in treatment or prevention will vary not only with the particular agent or composition selected but also with the route of administration, the nature and/or symptoms of the disease and the age and condition of the patient and will be ultimately at the discretion of the attendant physician or clinician. The determination of effective dosage levels, that is the dosage levels necessary to achieve the desired result, can be accomplished by one skilled in the art using routine methods, for example, human clinical trials, in vivo studies, and in vitro studies. For example, useful dosages of an agent, or composition thereof, can be determined by comparing their in vitro activity, and in vivo activity in animal models.

[0068] Dosage amount and interval may be adjusted individually to provide plasma levels of the active agent which are sufficient to maintain the modulating effects, or minimal effective concentration (MEC). The MEC will vary for each agent but can be estimated from in vivo and/or in vitro data. Dosages necessary to achieve the MEC will depend on individual characteristics and route of administration. However, bioassays can be used to determine plasma concentrations. Dosage intervals can also be determined using MEC value.

[0069] It should be noted that the attending physician would know how to and when to terminate, interrupt, or adjust administration due to toxicity or organ dysfunctions. Conversely, the attending physician would also know to adjust treatment to higher levels if the clinical response were not adequate, precluding toxicity. Further, the dose, and perhaps dose frequency, will also vary according to the age, body weight, and response of the individual patient. A program comparable to that discussed above may be also used in veterinary medicine for non-human subjects.

[0070] Effective amounts can be achieved by administering single or multiple doses during the course of a treatment regimen (e.g., twice a day, daily, every other day, every 3 days, every 4 days, every 5 days, every 6 days, weekly, and the like)

[0071] An effective amount of the agent may be administered alone or in combination with at least one additional therapeutic agent. In some embodiments, the at least one additional therapeutic agent is administered prior to, concomitantly with, or following administration of the agent that regulates the expression or activity of one or more viral infection mediating factors. The additional therapeutic agent may include, for example, a corticosteroid, an anti-inflammatory agent, an antibiotic, an opioid antagonist, a vitamin or nutritional supplement, or any combination thereof.

[0072] The agent(s) may be co-administered with the at least one additional therapeutic agent As used herein, the terms “co-administration” and “co-administering” refer to the administration of at least two agent(s) or therapies to a subject. In some embodiments, the co-administration of two or more agents or therapies is concurrent. In other embodiments, a first agent/therapy is administered prior to a second agent/therapy. Those of skill in the art understand that the formulations and/or routes of administration of the various agents or therapies used may vary. The appropriate dosage for co-administration can be readily determined by one skilled in the art In some embodiments, when agents or therapies are coadministered, the respective agents or therapies are administered at lower dosages than appropriate for their administration alone. Thus, co-administration is especially desirable in embodiments where the co- administration of the agents or therapies lowers the requisite dosage of a potentially harmful (e.g., toxic) agent(s), and/or when co-administration of two or more agents results in sensitization of a subject to beneficial effects of one of the agents via co-administration of the other agent

[0073] In some embodiments, the methods further comprise contacting the cell with an agent that regulates the expression or activity of one or more additional viral infection mediating factors, as disclosed herein. For example, the method may comprise contacting the cell with an agent that regulates the expression or activity of any or all of: KCNA6, LGMN, HLA-DPB1, EPHA4, and CD7, and an agent that regulates the expression or activity of an additional viral infection mediating factor, e.g., LRRC8D, ADAMI 9, etc. [0074] In some embodiments, the methods further comprise contacting the cell with an antiviral therapeutic. For the purposes of the present invention, the terms “antiviral therapeutic,” “antiviral agent,’ or “antiviral compound” are understood to mean active agents which act on the viral load (also called infectious titer), by inhibiting either directly or indirectly replication and/or transmission of the virus within and between organisms. Antiviral agents may act by inhibiting the replication cycle of the virus or its ability to infect and reproduce in a host Antiviral agents includes both prophylactic and therapeutic agents. Antiviral agents include, for example, darunavir, galidesivir, favilavir/avifavir, favipiravir, interferon beta, lopinavir, ritonavir, remdesivir, nirmatrelvir, bebtelovimab, molnupiravir, and triazavirin, [0075] The additional therapeutic agent may also include glucocorticoids such as dexamethasone and hydrocortisone, convalescent plasma, a recombinant human plasma such as gelsolin (Rhu-p65N), anticoagulants such as heparin and apixaban, IL-6 receptor agonists such as tocilizumab (Actemra) and sarilumab (Kevzara), PIKfyve inhibitors such as apilimod dimesylate, RIPK1 inhibitors such as DNL758, VIP receptor agonists such as PB1046, SGLT2 inhibitors such as dapaglifozin, TYK inhibitors such as abivertinib, kinase inhibitors such as ATR-002, bemcentinib, acalabrutinib, baricitinib and losmapimod, H2 blockers such as famotidine, anthelmintics such as niclosamide, furin inhibitors such as diminazene.

[0076] The agents or pharmaceutical compositions can be additive or synergistic with other agents or pharmaceutical compositions to enable vaccine development. By virtue of their antiviral and immune enhancing properties, the agents can be used to affect a prophylactic or therapeutic vaccination. The agents need not be administered simultaneously or in combination with other vaccine components to be effective. The vaccine applications of the agents are not limited to the treatment of viral infection but can encompass all therapeutic and prophylactic vaccine applications.

[0077] In some embodiments, provided herein are methods and compositions for generating an immune response against an infection mediating factor target (e.g., KCNA6, LGMN, HLA-DPB1, EPHA4, and/or CD7). In some embodiments, the immune response generates or provides an antibody or antibody fragment that prevents the integration of virus via the infection mediating factor target (e.g., blocks interaction of virus with KCNA6).

3. Systems or Kits

[0078] Also disclosed herein is a system or kit comprising the agents or compositions disclosed herein.

[0079] The kits can also comprise other agents and/or products co-packaged, co-formulated, and/or co-delivered with other components. The kits can also comprise instructions for using the components of the kit The instructions are relevant materials or methodologies pertaining to the kit. The materials may include any combination of the following: background information, list of components, brief or detailed protocols for using the compositions, trouble-shooting, references, technical support, and any other related documents. Instructions can be supplied with the kit or as a separate member component, either as a paper form or an electronic form which may be supplied on computer readable memory device or downloaded from an internet website, or as recorded presentation.

[0080] It is understood that the disclosed kits can be employed in connection with the disclosed methods. The kit may further contain containers or devices for use with the methods or compositions disclosed herein.

[0081] In some embodiments, the system or kits include an agent or composition as disclosed herein and a delivery device. The delivery device may include, without limitation, an autoinjector, a pen, an eye/ear dropper, a dropper bottle, a syringe, a pump, or a transdermal patch or implant.

[0082] The kits provided herein are in suitable packaging. Suitable packaging includes, but is not limited to, vials, bottles, jars, flexible packaging, and the like. Individual member components of the kits may be physically packaged together or separately.

[0083] The following examples further illustrate the invention but should not be construed as in any way limiting its scope.

EXAMPLES

Materials and Methods

[0084] Plasmids and Constructs The following constructs were obtained from Addgene: EFla dCas9- 2A-BlastR (61425, a kind gift from Feng Zhang), pLX304 (25890, a kind gift from David Root), pCMV- VSVG (8454, a kind gift from Bob Weinberg), pTwist EFl Alpha nCoV-2019-Spike-2xStrep (141382, a kind gift from Nevan Krogan), pCI-VSVG (1733, a kind gift from Garry Nolan), pXPR_502 (96923, a kind gift from David Root and John Doench), VSV-eGFP-dG vector (31842, a kind gift from Connie Cepko), and pcDNA3.1 -hACE2 (145033, a kind gift from Fang Li).

[0085] EFla Hygro, EFla ACE22A Hygro, and EFla EGFP 2A ZeoR were Gibson cloned into FUGW using the aforementioned addgene plasmids as PCR templates. Spike variants (Sdl9, Sdl9 D614G, Sdl9 B.1.351 variant) were Gibson cloned into the pCI backbone by replacing the VSVG protein in pCI-VSVG.

[0086] Host factor cDNA containing vectors were ordered from DNASU or Genecopoeia as either lentiviral transfer plasmids or gateway entry vectors. Gateway entry cDNAs were subsequently cloned into the destination vector pLX304 using the Gateway LR clonase kit (Invitrogen 11791019). A list of cDNA vectors is provided in Supplementary Table 2.

[0087] Cell line culture, generation, and validation 293FT cells were maintained in DMEM with high glucose and Glutamax (Gibco 10566016) supplemented with 1% Pen-Strep (Gibco 15-140-122), 1% NEAA (Gibco 11140050) and 10% FBS (BenchMark) at 37C, 5% CO2. 293FTactiv cells were generated by transducing 293FT cells with EFla dCas9-2A-BlastR. Cells were selected with 10 ug/mL blasticidin and kept on the concentration of selection except in cases of double or triple selection, wherein the doses were reduced to 5 ug/mL. ACE2 expressing and control cell lines were generated by transduction of either a pLV EFla hACE2-2A-Hygro plasmid or a pLV EFla Hygro plasmid into either 293FT cells or 293FTactive cells. 293FT or 293FT active cells were selected in 500 ug/mL hygromycin and the doses were reduced to 250 ug/mL in cases of double or triple selection. For cDNA overexpression studies, ACE2null and ACE2positive 293FT cells were transduced with cDNA overexpression vectors and selected with blasticidin (10 ug/mL).

[0088] Pseudoviral assay Every batch of pLV GLuc-2A-EGFP lentivirus was tittered by the addition of 1 x 10^4 cells to wells of a 96 well plate followed by the addition of varying volumes of virus. Media was added to a final volume of 100 uL and polybrene was added to give a final concentration of 8 ug/mL. Cells were spinfected at 1000 x g, 32 C for 45 min before being returned to a 37 C incubator. After 24 hours, supernatant was removed from the wells and replaced with 200 uL of fresh media. 72 hours after the spinfection, cells were washed with PBS and reconstituted in FACS Buffer (IX PBS, 2% FBS), and assayed for percentages of cells that were GFP positive by flow cytometry. All conditions were done in duplicate. For the experimental assay, 1 x 10 ^4 control or modified cells were placed in the wells of the 96 well plate and a volume of virus that gives an MOI of approximately 0.08-0.1 (as determined by the aforementioned titering experiment) was added to each well before the addition of polybrene (8 ug/mL final concentration) and media to a final volume of 100 uL in each well. The assay was then completed as described above for the titering experiment

[0089] Lentiviral, pseudoviral production and transduction Pseudotyped lentiviruses were produced using PEI or JetOptimus transfection reagent according to manufacturer’s protocols with a ratio of 2:2: 1 transfer plasmid:pCMV-dR8.91 :Envelope plasmid. For VSVG pseudotyped viruses, pCMV-VSVG was used as an envelope plasmid. For SARS-CoV-2 Spike protein, pTwist EFl Alpha nCoV-2019-Spike- 2xStrep was used as an envelope plasmid. For spike variants (Sdl9, Sdl9 D614G, Sdl9 S. A. variant) plasmids cloned in house and described in Plasmids and Constructs were used as an envelope plasmid. After 4 hours, media was replaced and ViralBoost (Alstem, VB100) was added to a IX concentration. 48 hours after transfection pseudotyped lentiviruses were collected, passed through a 0.45 uM filter, and frozen down at -80C.

[0090] For large scale production of the membrane protein gRNA library or production of SARS-

CoV-2 Spike protein or Spike variant viruses, 293FT cells in 10 cm dishes were transfected with JetOptimus at the aforementioned ratios, media was replaced 4 hours after transfection, and ViralBoost was added to IX. At 48 hours, viral supernatant was collected and passed through a 0.45 uM filter. For all VSVG pseudotyped viruses, viral supernatant was aliquoted immediately and stored at -80 C. For SARS- CoV-2 Spike and Spike variant pseudoviruses, Lentivirus Precipitation Solution (Alstem, VC100) was added to IX. Viral precipitation was carried out according to the manufacturer's protocol. Following precipitation, lentiviruses were concentrated 10X using either DMEM or Ultraculture media (Lonza, discontinued) and frozen down at -80C.

[0091] Membrane protein CRISPR activation library A list of all known membrane associated proteins was derived from Chong et. al (2018), with the following adjustments: we included representative olfactory receptor genes and removed pseudogenes that may introduce noise during screening. This refined list was used to pull 4 guideRNAs for each gene from the Calabrese human CRISPR activation pooled library. For genes not included in the Calabrese pool, guides were manually designed using the CRISPOR tool (citation, Concordet et al). For all the final guideRNA sequences, we ensured the starting nucleotide was either G or A by adding a starting G when necessary to maintain efficient Pol-IH transcription initiation. The final library design with annotated guideRNA sequences is provided in Supplementary Table 1.

[0092] Oligonucleotide pools were synthesized by TwistBio (CRISPR activation pool) or IDT DNA (additional validation pool) with Esp3I/BsmBI recognition site and PCR amplification sequences appended to the sgRNA sequence. The oligo sequence template was 5’- CATGTTGCCCTGAGGCACAGCGTCTCACACC (SEQ ID NO: 1) [guide sequences, 20 or 21 nt] GTTTCAGTCTTCCGTCACATTGGCGCTCGAGA-3’ (SEQ ID NO: 2). A set of primers (forward: CATGTTGCCCTGAGGCACAG (SEQ ID NO: 3) and reverse: CCGTTAGGTCCCGAAAGGCT (SEQ ID NO: 4)) was used to amplify the oligo pool using the manufacturer's protocol (Twist Bioscience, detailed in Twist oligo pool amplification guidelines). The PCR product was column purified using the Monarch PCR and DNA Cleanup Kit (NEB T1030S) and cloned into the pXPR_502 via Golden Gate cloning using the Golden Gate Assembly kit BsmBI-v2 (NEB 1602L). The product was isopropanol precipitated, electroporated into Stbl4 electrocompetent cells (Invitrogen 11635018) with a Micropulser Electroporator (Bio-Rad) in 0.1 cm cuvettes with conditions as previously described (Sanson et. al.). Cells were allowed to recover in 1 mL of recovery media at 30oC and then amplified in large scale at 25 °C for 17 h in 2-YT Broth. Plasmid DNA was prepped using QIAGEN Plasmid Plus Maxi Kit and sequenced to confirm library coverage and distribution.

[0093] CRISPRa screening in 293FT cells For each screen replicate 100 x 106 CRISPRactiv cells with or without ACE2 overexpression were transduced in total in 6 well plates. In each well, 3 x 10^6 cells were combined with a volume of CRISPRa membrane library viral supernatant to give an MOI of 0.3 before the addition of polybrene (Millipore TR-1003-G) to a working concentration of 8 ug/mL and D10 to a final volume of 2 mL. Plates were spinfected in a tabletop centrifuge at 1000 x g for 45 min at 32 C. Following spinfection, 2 mL of D10 media was added to each well and returned to 37 C incubators. 12 hr post-spinfection, viral supernatant was removed from each well and cells from each well were split into individual 15 cm dishes at a final volume of 25 mL. 48 hours post-spinfection, puromycin (Gibco Al 113803) was added to each plate of transduced and mock-infected cells at a final concentration of 0.5 ug/mL. Medium was replaced with 0.5 ug/mL puromycin-containing medium and cells were selected until mock infected cells were completely killed and transduced cells had recovered to a 90% confluence with minimal cell death in the presence of puromycin selection.

[0094] Guide RNA containing CRISPRactiv cells were maintained at a minimal cell number of 24 x 106 cells to prevent loss of representation. On the day of the spinfection, 24 x 10^6 library cells for each condition (ACE2+ or ACE2null) were harvested for gDNA. For each screen replicate 100 x 106 library cells for each condition were transduced in total in 6 well plates. For each well, cells were combined with viruses pseudotyped with either SARS-CoV-2614G Spike protein or VSVG envelope and carrying the EFla-BleoR-2A-EGFP transfer vector at an approximate MOI or either 0.01 (low) or 0.1 (high). Polybrene (Millipore TR-1003-G) was added to a working concentration of 8 ug/mL and D10 to a final volume of 2 mL before cells were spinfected at 1000 x g for 45 min at 32 C. 2 mL of media was added to each well and the plates returned to a 37C incubator until 12 hr post-spinfection when virus containing media was removed and each well was split into individual 15 cm dishes, Zeocin (Gibco R250-01) was added to transduced and mock-infected cells at a final concentration of 500 ug/mL. Cells were selected until mock infected cells were completely killed and transduced cells had recovered to a 70-80% confluence (approximately 5-7 days post Zeocin addition). Cells for each condition were then pooled and harvested for gDNA extraction. [0095] Genomic DNA isolation, guide RNA amplification and quantification Genomic DNA of screening samples were extracted using Quick-DNA Midiprep Plus Kit (Zymo Research) following the manufacturer’s protocol. Then, 5ug of genomic DNA was added per 50ul PCR reaction mixed staggered primers (synthesized by IDT DNA, Forward Primer: 5’-

AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT (SEQ ID NO: 5) [Stagger, 0-7nt] TTGTGGAAAGGACGAAACACC (SEQ ID NO: 6)-3’

Reverse Primer: 5’-CAAGCAGAAGACGGCATACGAGAT (SEQ ID NO: 7) [8nt-barcode] GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCTACTATTCTTTCCCCTGCACTGT (SEQ ID NO: 8)-3’) to increase the base diversity. At least 4 PCR reactions were used per sample to ensure the coverage. PCR reactions were then pooled and column purified using Monarch PCR and DNA Cleanup Kit (NEB T1030S), visualized on a gel and column purified following gel extraction. The amplified products were quantified, and normalized by concentration, followed by sequencing using Ilumina Miseq Reagent Kit v3 (150 cycles). Saturation analysis was done to confirm sequencing saturation.

[0096] Computational analyses of CRISPR activation screens The sequencing data was deconvoluted using bcl2fastq function (illumina). Reads per each sgRNA were counted and processed using standard Mageck pipeline with an output of RRA and gene ranks. We averaged the positive RRA scores of the biological replicates in each condition and calculated -log (average RRA) scores. Then, enrichment scores were calculated by normalizing -log (average RRA) scores in each condition.

[0097] GTEx v8 tissue specific enrichment was performed using the Multi Gene Query function available on the GTEx website: gtexportal.org/home/multiGeneQueryPage. Gene set overlap analysis was done using top 10% hits in each condition via GSEA-mysigDB (gsea- msigdb.org/gsea/msigdb/annotate.jsp) with Gene Ontology for Molecular Function. Functional interaction networks were constructed using Reactome FTPlugin in Cytoscape (apps. cytoscape. org/apps/reactomefiplugin) .

[0098] Generation of validation targeted activation cell lines Four sgRNAs were designed for every gene of interest and cloned into pXPR_502. sgRNA vectors for a given gene were pooled and these pooled vectors were used to make pooled VSVG pseudotyped lentiviruses using the aforementioned protocol. CRISPR-activated cells were then spinfected at 1000 x g, 32C, for 45 min in an arrayed format with the pooled lentiviruses for a given gene in 48 well plates at a density of 5E4 cells/well. 48 hours after spinfection, cells were split into 6- well plates and selected with 0.5 ug/mL puromycin until they recovered to 70-80% confluence with no indication of continued selection induced cell death. These cells were then subjected to a SARS-CoV-2 S 614G pseudotyped lentiviral assay as described in the aforementioned section.

[0099] Generation of focused validation libraries For ACE2-null and ACE2-positive screens, we separately selected the top hits that showed up in multiple biological replicates and MOI conditions and curated lists of genes for the focused pooled validation: 523 genes for ACE2-null library and 542 genes for ACE2-positive library. We added 2 guides per gene on top of the original library (6 guides per gene total) and 30 non-targeting guide RNAs as control. The oligonucleotide pools were also synthesized by Twist Bioscience. The plasmid library and lentivirus were made as described in the aforementioned section.

[00100] Analysis of RNA-seq and scRNA-seq data The human olfactory epithelium RNA-seq data were directly obtained from Olender et al (BMC Genomics. 2016 Aug 11 ; 17(1 ):619) and visualized using pheatmap in R. For scRNA-seq of the olfactory epithelium, patient 2 and patient 3 data (BAM file) from Durante et al were downloaded from NCBI SRA portal (GSE139522). Because of the overlap of the references, we have built a customized version of GRCh37 reference by deleting the references of RP11- 234B24.4 and GALNT8 in the GRCh37.87. Then the data were aligned using the customized GRCh37 reference via CellRanger 6.0 (10X Genomics). For Alevin single-cell analysis, standard genome references (GRCh38 and GRCh37) built with Salmon (github.com/COMBINE-lab/salmon) were used to process the same datasets as CellRanger. The Salmon/Alevin pipeline was used with default parameters and adjustment of the 1 Ox kit version according to the dataset specifications, namely 10x-v3 for patient2 and 10x-v2 for patients. The result data matrices were processed using Seurat following the methods mentioned in Durante et al. (github.com/satijalab/seurat). Bronchoalveolar lavage fluid (BALF) data from Liao et al was acquired from github.com/zhangzlab/covid_balf and then processed and visualized using Seurat in R

[00101] Flaw cytometry analysis and surface marker staining assays For ACE2 staining, cells were washed with IX PBS and then incubated with biotinylated SARS-CoV-2 Receptor Binding Protein (RBD) (ACROBiosystems SPD-C82E9) at a concentration of 4 ug/mL in FACS Buffer (IX PBS with 2% FBS) for 30 min at room temperature. The cells were washed twice with FACS Buffer and then incubated with streptavidin-Alexa 488 (Thermo Fisher, SI 1223) at a concentration of 2 ug/mL for 30 min at room temperature. Cells were washed twice with FACS Buffer and analysis was carried out on a Cytoflex Flow Cytometer. For CD46 and CD55 staining, cells were washed with IX PBS and then incubated with either APC anti-human CD46 antibody (Biolegend 352405) or APC anti-human CD55 antibody (Biolegend 311311) diluted 1: 100 in FACS Buffer for 30 min at room temperature. Cells were then washed twice with FACS Buffer and analyzed as described above.

[00102] Western Blotting Cells were pelleted, washed with IX PBS, and lysed in RIP A buffer (Cell Signaling Technology 98306). Immunoblotting was performed as described previously (Mahmood et al). The following primary antibodies were used for western blotting: V5 (Thermo R960-25) and KCNA6 (Sigma HPA021516).

[00103] Replicating vesicular stomatitis virus (VSV) pseudovirus generation Recombinant VSV expressing eGFP in the 1st position (VSVdG-GFP-CoV2-S) was generated as previously described (doi.org/10.1016/j.cell.2020.12.004). The plasmid to rescue this virus was generated by inserting a codon optimized SARS-CoV2-S based on the Wuhan-Hu- 1 isolate (Genbank:MN908947.3), which was mutated to remove a putative ER retention domain (K1269A and H1271 A) into a VSV-eGFP-dG vector (Addgene, Plasmid #31842) in Same with the deleted VSV-G. The control virus VSVdG-RABV-G SAD- B19 was also generated by inserting Rabies virus G in the same vector. Both viruses were rescued in 293FT/VeroE6 cell co-culture and amplified in VeroE6 cells and titrated in VeroE6 cells overexpressing TMPRSS2. Sequencing of the amplified virus revealed an early C-terminal Stop signal (1274STOP) and a partial mutation at A372T (-50%) in the ectodomain. Similar adaptive mutations were found in a previous published VSVdG-CoV2-S (doi.org/10.1016/j.chom.2020.06.020).

[00104] Pseudovirus injection assay using Replicating VSV pseudovirus HEK293FT cells were plated in clear 96-well plates at 2x104 cells per well approximately 24 hours prior to infection in lOOul of media containing 10% FBS. Cells were infected with VSVdG-CoV2-S or VSVdG-RABV-G at an MOI of 0.1. Infection was performed by diluting virus in media without FBS and adding 150ul of diluted virus per well. After addition of virus, the plate was spun at 900 ref for 60 minutes at 30°C. Infection was tracked over time using an Incucyte system (Sartorius) in a 37°C and 5% CO2 incubator using 4x magnification and detecting GFP. GFP+ cells were counted using Incuctye Analysis software and data was reported as GFP positive foci per well after normalization to confluence.

[00105] Replication-competent SARS-CoV2 live virus injection assay SARS-CoV-2-nLuc (Dinnon, et al, (2020)) in the form of a passage 1 stock was a kind gift from Jacob Hou and Ralph Baric. The virus was passaged twice in VeroE6 cells and titered by plaque assay on VeroE6 cells. Cells were plated in solid white 96-well plates. Cells were then infected at MOI 0.1 or MOI 0.5, washed, and incubated for 48 hrs before assessment by lytic Nano-Gio assay (Promega) and read on a GloMax plate reader (Promega). Infections and plate reading occurred inside class II biosafety cabinets under biosafety level 3 conditions. [00106] Drug-target network analysis

[00107] Genes were labeled as screen hits by first selecting the top quartile of genes by enrichment score, then removing genes that were also hits in the VSVG screen (>95th percentile by enrichment score). A list of drug-gene interactions was obtained from DrugBank and used to generate a bipartite graph with two node classes, drugs and genes, and edges representing known interactions between protein-coding genes and FDA-approved drugs. From this graph, the “full network,” a subgraph was generated containing only genes that were screen hits and their associated drug interactions (the “screenhits network”). Drugs were labeled according to DrugBank classifications and were ranked by normalized degree centrality as defined below: ndc = n^{screen hits} / m^{screen hits} where n^{screen hits} is the node degree in the screen-hits network and m is its maximum theoretically possible degree in the same network.

[00108] Drug nodes were separately ranked by “degree fraction,” as defined below: degree fraction = n^{screen hits}/ n^{full network} where n^{screen hits} is the node degree in the screen-hits-only network and n^{screen hits} is the node degree in the full network.

[00109] When calculating aggregate rankings for drug classes, we included only classes with greater than 10 members in the full network. Network rankings were compared against experimentally confirmed hits from Yang et al. (2020). Statistical tests are Mann- Whitney U tests and Spearman rank-order correlation as appropriate, unless stated otherwise.

[00110] Claims database The study sample was obtained from de-identified administrative claims for Medicare Advantage Part D (MAPD) members in a research database from a single large US health insurance provider (the UnitedHealth Group Clinical Discovery Portal). The database contains medical (emergency, inpatient, and outpatient) and pharmacy claims for services submitted for third party reimbursement, available as International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), and National Drug Codes (NDC) claims, respectively. These claims are aggregated after completion of care encounters and submission of claims for reimbursement.

[00111] Database-wide drug screen For the initial claims database screen, drugs were defined by their generic names and used by individuals represented in claims data between July 1, 2019, and January 31, 2020. We selected the screening study cohort to be all the CO VID-19 related hospitalized members and 1:10 exactly matched non-hospitalized members based on: age (+/- 1), gender, race, socioeconomic status (SES) index (+/- 0.5), living in counties from New York, New Jersey and Connecticut or counties outside the New York, New Jersey and Connecticut tri-state area, and diagnosis of diabetes without chronic complications, congestive heart failure, chronic pulmonary disease, myocardial infarction, metastatic cancer, liver disease, renal failure, peptic ulcer disease, and hypertension. We included unique drugs with over 2,000 users in the 1 : 10 exact matched cohort in the screening study. For each drug, we considered individuals drug-exposed if they had any reimbursed prescription claims during the study period. We considered individuals non-drug exposed if they had zero reimbursed prescription claims for the analyzed drug during the study period. We calculated the log odds of COVID-19 hospitalization as a binary outcome, comparing drug-exposed to non-drug-exposed individuals, across drug claims meeting our minimum sample size threshold. Odds ratio significance levels were adjusted for multiple hypothesis testing using a Benjamini-Hochberg correction.

[00112] Cohort construction Drug classes were defined by American Hospital Formulary Service (AHFS) codes. For each drug class of interest, we constructed a cohort of individuals with at least 11 months of enrollment in MAPD insurance from January through December 2019 and at least 1 month of enrollment in MAPD in 2020. These individuals had at least one pharmacy prescription claim during their enrollment and lived in counties in New York, New Jersey, and Connecticut (Table A). In our database, COVID-19 hospitalization is more prevalent among individuals insured through MAPD and among residents of the New York, New Jersey, and Connecticut tri-state area. We restricted our analyses to these populations to select for uniform exposure to COVID-19 and a higher prevalence of the COVID-19 hospitalization outcome in our cohort We define our outcome as a claim for a hospitalization with a positive COVID-19 test between January 1 , 2020 and June 26, 2020.

[00113] Prescription drug users were identified by string matching from pharmacy claims for any of the generic names associated with the drug candidate. We considered individuals to be drug-exposed when their total supply days covered >80% of days between their first drug use date after July 1 , 2019 through January 31, 2020. We considered individuals non-drug-exposed if the individual was never prescribed the drug candidates or drugs in the same therapeutic class, between July 1, 2019 and January 31, 2020. We also included one negative control associated with a known COVID-19 confounder, glucose meters, to assess our analysis pipeline’s global confounding control. We considered individuals to be exposed when they have one prescription for a glucose meter between July 1, 2019 and January 31, 2020. [00114] Study covariates For each drug of interest, we extracted the following list of covariates for both drug-exposed individuals and non-drug-exposed individuals:

1. Age

2. Gender

3. Self-reported race and ethnicity

4. Area-specified SES index based on member zip code

5. 2019 diagnoses as selected from the top 200 first three-digit ICD-10-CM code, excluding codes beginning with “Z”

6. Pre-existing conditions defined by diagnosis codes in 2019, including conditions used in the Charlson Comorbidity Index and Elixhauser Comorbidity Index 7. Pre-existing primary treatment-related diagnosis

8. Co-used prescription drug defined as claims between July 1, 2019, and January 31, 2020, for the top 20 therapeutic classes

9. Prior hospitalizations in 2019

10. Count of primary care provider visit in 2019

11. Count of unique drugs prescribed

12. Routine screening adherence in 2019, as indicated by completion of a comprehensive metabolic panel, lipid panel, and complete blood count

13. Flu vaccination in 2019 as a proxy of good health behaviors

14. Special Need Plan: (1) institutional, indicating if a member is from a nursing home; (2) dual plan with Medicaid.

[00115] Controlled study "without propensity score matching We first selected a list of features using a

LASSO model with tuned penalty coefficient based on Bayesian information criteria. The complete list of features includes normalized age, sex, primary treatment-related diagnosis, comorbidity index flags, occurrence flags to first three digits of diagnosis codes, adherence flags to co-used drug therapeutic classes, race, state of residence, and normalized SES index. After feature selection, we added normalized age, normalized SES index, and primary treatment-related diagnosis into the feature list to control for these factors. To ensure model convergence, we excluded features with a prevalence of less than one percent of the cohort. We then fit a Cox proportional hazard model to determine the adjusted hazard ratio of the treatment group, considering time to CO VID-19 hospitalization, controlling for the list of features selected. We allowed baseline time to vary by individual, setting individual baseline time to be time in our database of first CO VID-19 hospitalization for an individual residing in the same state.

[00116] Controlled study "with propensity score matching For the group of drug-exposed individuals, we applied 1 : 1 propensity score matching (PSM) to construct a matched group of non-drug exposed individuals. The propensity score was built using logistic regression based on age, sex, primary treatment- related diagnosis, comorbidity index flags, occurrence flags to first three digits of diagnosis codes, adherence flags to co-used drug therapeutic classes, race, state of residence, and SES index. We ran 1:1 PSM with a caliper of 0.25 multiplied by the standard deviation of propensity scores. We assessed PSM performance by calculating the standardized mean difference between drug-exposed and non-exposed groups across the primary treatment related diagnosis. PSM is considered adequate when the standardized mean difference between groups is < 0.10 (Zhang et al., 2019). After PSM we report the unadjusted hazard ratio for the drug-exposed group. In addition, we applied the same procedure of feature selection and similarly fit a Cox proportional hazards model for each drug of interest, between baseline (the statespecific time of first COVID-19 hospitalization) to hospitalization or end of follow-up, to investigate the adjusted hazard ratio of the drug-exposed group.

[00117] FIGS. IB and 2B are adapted from templates or created with BioRender.com.

Example 1

Design and validation of a selectable SARS-COV-2 Spike pseudotyped lentiviral system to interrogate Spike-dependent viral entry

[00118] CRISPRa screening works by using dCas9-guideRNA mediate genome binding to recruit transcriptional activation proteins to the promoter region of genes of interest A pooled library of guideRNAs with CRISPRa system then allows systematic overexpression of endogenous target proteins to understand their potential functions in a defined biological process. It is an orthogonal methodology to uncover factors that might be broadly distributed and difficult to assess within a specific cell type or tissue.

[00119] The overall design of our CRISPRa screening integrated a dCas9 synergistic activation mediator (SAM) system, a SARS-CoV-2 Spike-D614G pseudotyped lentivirus, and a guideRNA library targeting all known human membrane proteins, including those that have predicted transmembrane domains despite unknown function (FIG. 1 A). Specifically, to discover novel factors that promote the entry of SARS-CoV-2 into host cells in either an ACE2-dependent or independent manner, we engineered two HEK293FT cell lines for the CRISPRa screening. First, we transduced HEK293FT cells to stably express human ACE2 protein, the major host receptor for SARS-CoV-2 (referred to hereafter as the ACE2-positive line). We fused a Hygromycin resistance marker in-frame via 2A peptide to allow selection of ACE2-expressing cells via Hygromycin treatment In parallel, we generated HEK293FT cells that express only the Hygromycin resistance marker to allow for the discovery of ACE2-independent host factors (the ACE2-null line). We validated these two lines through ACE2 receptor straining via a labeled Spike protein receptor binding domain (RBD) (FIG. 6A). These experiments demonstrated robust expression of ACE2 in the ACE2-positive lines, with clear ACE2-dependent RBD binding.

[00120] Next, we engineered both lines to express the Synergistic Activation Mediator (SAM) system (FIG. 1 A) to allow for activation of gene transcription upon delivery of a guide RNA (gRNA) targeting the promoter region of endogenous genes. To confirm the gene activation efficiencies of the cell lines, we delivered gRNAs targeting two membrane proteins, CD46 and CD55, and observed robust activation efficiencies via antibody staining. We then generated and validated a pseudovirus-based SARS-CoV-2 entry assay as our functional read-out for the CRISPRa screen. To make sure we could titer and select for cells that have been infected by the pseudovirus, we generated SARS-CoV-2 spike-pseudotyped lentivirus carrying a Zeocin resistance marker fused to a GFP reporter via 2A-peptide (FIGS. 1 A-1B). We performed viral titration experiments using the ACE2-positive and ACE2-null lines and confirmed ACE2- dependent pseudoviral infection. Of note, almost all prior CRISPR knock-out screens were performed with the wild-type Spike variant of SARS-CoV-2. Here, we chose to use lentivirus pseudotyped with SARS-CoV-2 Spike protein with D614G mutation for the CRISPRa screen. We had two major considerations: First, the most widespread and concerning strains of virus all bear the D614G Spike mutation; second, the D614G virus strain gave rise to several emerging SARS-CoV-2 variants of concern with different levels of enhanced infectivity or resistance to treatment, necessitating further investigation using this strain to help fight against the ongoing COVID19 pandemic.

Example 2

Screens utilizing CRISPRa library targeting all human membrane proteins identifies putative host factors determining susceptibility to SARS-CoV-2

[00121] To discover potential host factors that may facilitate viral entry using our Spike-D614G pseudovirus, we generated a customized CRISPR gRNA library targeting all known human membrane proteins (-6,213 total). We made sure to include putative membrane proteins as these proteins had poor functional annotations, and could be hidden host factors that are not examined due to their absence in standard genome-wide libraries. We designed 4 guideRNAs targeting each membrane protein in our library, totaling -24,000 (24k) guideRNAs with an extra 221 non-target negative control guideRNAs (-1% of the pool). We then transduced cells with pooled lentivirus bearing this genome-wide membrane protein gRNA library. We used low Multiplicity of infection (MOI) for the guideRNA delivery (<=0.3 MOI in all screens) to ensure that most selected cells had one guideRNA per cell, so that only a single gene of interest will be targeted for high levels of transcriptional activation (FIG. IB). The library cells were then infected with pseudovirus carrying either SARS-CoV-2 Spike protein (CoV-2 group) or VSVG envelope protein (VS VG group, as a reference control). Following selection with Zeocin to enrich infected cells, genomic DNAs were harvested from the surviving cells as well as pre-selection baseline samples. After PCR amplification, we measured the guideRNA frequency distribution via next-generation sequencing (NGS). We then employed the MAGECK pipeline to assess the enrichment of guideRNAs to identify potential host factors (FIG IB).

[00122] We performed the full CRISPRa screen across four different conditions: 1) ACE2-null at low MOI of the Spike-D614G-pseudovirus; 2) ACE2-null at high pseudovirus MOI; 3) ACE2-positive at low pseudovirus MOI; 4) ACE2-positive at high pseudovirus MOI. For each of these conditions, we generated two (ACE2-null) and three (ACE2-positive) separated batches of guideRNA-expressing cells (biological replicates), totaling 10 independent CRISPRa screens. We integrated biological replicates across 4 conditions into a screen enrichment summary (FIGS. 1C-1F). The results demonstrated that our approach could identify factors known to promote Spike-dependent viral entry (e.g., ACE2, TMPRSS2, Neuropilin NRP1/2) as well as new genes/pathways (e.g., ion channels, novel proteases, immune and neuronal receptors), as detailed below.

[00123] Top hits enriched across screen conditions reveal known and novel host factors for SARS- CoV-2 viral entry and pan-lentiviral host genes.

[00124] First, we examined the top screen hits identified in the ACE-null groups beyond the well- characterized viral receptor ACE2 (FIGS. 1C-1D and 7). Several alternative membrane proteins had significant enrichment in this group that implicated the ACE2-independent mechanism of viral entry. Among these genes, some were within known pathways that could promote viral infection, e.g., heparan sulfate synthesis enzyme EXT1 and EXTL2 and vesicle trafficking genes related to endocytosis/exocytosis pathways SYT7 and STX1B. Besides known pathways, several ion channel, protease, and immune genes are consistently ranked as top hits: KCNA6 (Potassium Voltage-Gated Channel Subfamily A Member 6), a potassium channel protein; LRRC8D (Leucine Rich Repeat Containing 8 VRAC Subunit D), volume-regulated anion channel; LGMN (Legumain), a lysosomal protease; ADAMI 9 (ADAM Metallopeptidase Domain 19), a Metalloproteinase; and HLA-DPB1 (Major Histocompatibility Complex, Class II, DP Beta 1), an MHC class II beta chain protein.

[00125] Second, for the ACE2-positive groups, CRISPRa screening identified known host factors including TMPRSS2, NDST enzymes, as well as the recently discovered SARS-CoV-2 entry factor Neuropilin NRP1/2 (FIGS. 1E-1F). We found that NRP2 was strongly enriched, especially under high MOI. NRP1 was also enriched in the ACE2-positive group but with a smaller effect size. Additionally, several neuronal receptor and ion channels (GABA receptors, sodium channels, and potassium channels) were significantly enriched in the ACE2-positive group. Immune genes, e.g., CD7, HLA-DPB1, and proteases that have been implicated through patient tissue profiling, such as TMPRSS1 ID, TMPRSS1 IE, were also among top hits in the presence of ACE2.

Example 3 VSVG control allows identification of SARS-CoV-2 spike-specific and pan-lentiviral effects within the CRISPRa screen

[00126] In addition to SARS-CoV-2 Spike pseudovirus, we also included VSVG lentivirus as a reference virus in all screen conditions. We performed CRISPRa screen in a similar set-up to measure genes that could promote VSVG pseudoviral entry. We identified robust enrichment of low-density lipoprotein receptor (LDLR) family members among the top hits in the VSVG screen, including LDLR, LDLRAD3, and LRP4/8, whereas known SARS-CoV-2-specific host factors, e.g., ACE2, TMPRSS2, and CTSL are all unique to the SARS-CoV-2 screens (FIGS. 1G-1H). This confirmed that our activation screen could identify viral-specific host factors. More importantly, this VSVG reference allowed us to perform overlap analysis on top hits across our screen conditions. This analysis helped to distinguish COVID 19-specific vs. pan-lentiviral host factors (FIGS. 1G-1H).

[00127] Notably, some of the highly enriched genes in SARS-CoV-2 screen are also top VSVG hits, implicating their pan-lentiviral effects. These hits included known non-specific or pan-viral factors, such as apoptosis and growth genes BAXZPKN2, fatty acid biosynthesis genes PIGO/PIGP/FADS6, as well as endocytosis/exocytosis genes GULP1/SCARA5. Other genes were novel pan-lentiviral genes but could also be non-specifically promoting cell survival, e.g., protease ADAM2, Integrin ITGAX, and two genes of the P4-ATPase Flippase Complex (the alpha subunit ATP10D and beta subunit TMEM30A). These overlapped genes were initially labeled as top hits in our screen but hereafter will be discussed as pan- lentiviral factors. Taken together, the use of VSVG reference allowed us to highlight top hits in our CRISPRa screen that promote SARS-CoV-2 viral entry in a COVID19-specific manner, e.g., KCNA6, LGMN, CD7 (FIGS. 1G-1H).

Example 4 CRISPRa screening identifies established SARS-Co V-2 host factors including top validated hits from CRISPR knockout screens

[00128] Next, to understand our screen hits in the context of recently published CRISPR knockout screens, we performed meta-analysis across the different functional genomics studies of SARS-CoV-2 host factors. We downloaded data from 6 published CRISPR Knock-out screens and extracted genes that are present across all the studies. As CRISPR screens are known to be noisy and different studies employed different cell lines or assay designs, we observed very few overlapping hits even amongst the published CRISPR knockout studies. Nonetheless, several studies have consistently been able to identify well-established host factors (e.g., ACE2, CTSL), or have used individual real virus validation to provide additional proof for putative novel host factors (e.g., TMEM41B, TMEM106B, identified by at least two independent studies using CRISPR knockout screening). Thus, to understand the power of the different approaches to identify host factors, we generated a curated 18 gene list including host factors reported by multiple studies (e.g., Neuropilins NRP1/2, Heparan Sulfate pathway genes), and top hits from CRISPR knockout screens (e.g., B4GALT7, TMEM106B) that have been validated using authentic SARS-CoV-2 infection assays. We then cataloged the ability of each CRISPR screen to identify these host factors, defined as if a given SARS-CoV-2 host gene would rank among top 10%, or 90 percentiles (see Methods for details). Our CRISPRa screen was able to identify 12 of these host factors. Further, when we compare the relative ranking of these 18 host factors across different screens, the CRISPRa screen has robust ability to detect known and validated entry factors.

Example 5

Diverse tissue expression profiles of top hits demonstrate that CRISPRa screen can identify putative host factors in neuronal, cardiovascular, and immune systems

[00129] To understand the human body expression of top hits in the CRISPRa screen, we visualized the expression of top-ranked factors using the Genotype-Tissue Expression (GTEx) data (FIG. 2A). Consistent with the functional pathway analysis, these top hits are expressed by diverse tissue/organ types. Many of these hits are expressed in esophagus, mucosa tissues, and lung, including alternative proteases distinctive from the well-established TMPRSS2, e.g., LGMN, TMPRSS11D/11E, TSPAN15. This indicates their potential role to directly facilitate viral infection in the upper airway. [00130] Notably, many host factors enriched in our screen have high expression levels in neuronal, cardiovascular, immune, and gut systems (FIG 2B). These organs are recently shown to be involved in the pathophysiology of CO VID 19 (Puelles et al. 2020). In particular, we noted that many top-ranked hits are ion channels, transporter, or receptors involved in sensory systems (olfactory, taste, eye), such as SCNN1D, EPHA4, and KCNA6. Our analysis confirmed that these genes are expressed in neuronal tissues (FIGS. 2A-2B). Further, recent work has demonstrated that immune cells may also be susceptible to SARS-CoV-2 infection. Our top hits included several membrane proteins that are highly expressed in immune cells within the lung, e.g., CD7 and MHC-II components. These putative host factors may help to facilitate viral entry in immune cell subsets. Intriguingly, we noticed that among all top hits, one gene, KCNA6, is missing from the latest GTEx v8 database. Upon closer examination, we realized that this is due to overlapping lincRNAs at KCNA6 genomic locus, which led to KCNA6 being filtered and dropped from the GTEx genome annotation.

[00131] Overall, our expression analysis thus supported the role of novel proteases, ion channels, and immune genes that facilitate viral entry, with several genes, e.g., LGMN, TMPRSS 11D/E, showing strong expression in human esophagus/mucosa tissues in the upper airway, the major site of viral exposure.

Example 6

Functional enrichment and network analysis of top hits uncover known and novel host pathways involved in SARS-CoV-2 viral entry

[00132] We next performed gene ontology and functional network analysis of top hits from the CRISPRa screen. First, via pathway overlap analysis, we identified many known (glycosylation, heparan sulfate and cholesterol, protease) pathways involved in COVID19 pathology. Additionally, several top enriched pathways were connected to clinical observations and potential new biology of viral infection. Most notably, the top pathways included enrichment of ion channels (sodium and potassium), neurotransmitter and neuronal receptors, and immune receptors (FIG. 2C). We then built a detailed functional network (Reactome) using top hits within these most enriched pathways (FIG. 2D). Some of the most prominent gene families present within the functional groups are: voltage-gated potassium channels (Kv channels); Protease and peptidase (including known TMPRSS families and novel proteases such as LGMN), and neuronal receptors (GABA receptors) (FIG 2D).

[00133] These analyses help to provide a holistic view of the top screen hits and the most represented biological pathways. More importantly, the enrichment of potassium channels and new proteases point to specific genes that mediate or facilitate SARS-CoV-2 viral entry. Example 7

Arrayed CRISPRa and cDNA overexpression experiments validate putative new host factors for SARS-CoV-2 viral entry

[00134] To validate top-ranked genes from our screen, we generated focused CRISPRa and cDNA overexpression cell lines in arrayed format. This allowed us to test the effect using a combination of pseudovirus and authentic virus assays.

[00135] First, we generated a set of 30 guideRNAs vectors targeting individual top hits in our screen as well as positive (ACE2, TMPRSS2) and negative controls (non-targeting, NT). We stably transduced ACE2-null and ACE2-positive cell lines with lentiviral guideRNA vectors to activate target genes. We performed pseudoviral infection and measured viral entry via fluorescent reporter expression. In ACE2- null cells, guideRNA targeting ACE2 strongly increased viral entry, whereas activating TMPRSS2 alone had no effects. The top hits from ACE2-null screens could promote pseudoviral infection, with guideRNAs targeting KCNA6, SCNN1D, LGMN, CD7 and MHC-II genes having strong effects. In ACE2-positive lines, TMPRSS2-targeting, but not ACE2-targeting, guideRNAs were able to facilitate viral entry. The effect sizes in ACE2-positive conditions were less prominent due to the presence of ACE2, but LGMN and NRP2 had significant ability to promote pseudoviral entry, comparable to TMPRSS2. In addition, we also employed the focused CRISPRa validation approach to confirm that the top hits had consistent performance with Spike-D614G (used in the full screen) and wildtype (WT) spike pseudovirus. Overall, the majority of top hits maintained their effects when activated individually, consistent with the full screen results.

[00136] Next, to measure if the gain-of-function effects are directly connected to the target protein expression, we generated stable cDNA overexpression cell lines for selected top hits, in both ACE2-null and ACE2-positive conditions (FIGS. 3A-3B). We then performed arrayed cDNA validation using both SARS-CoV-2 Spike-D614G-pseudotyped lentivirus, and the reference VSVG lentivirus. The results using cDNA lines differed from the sgRNA-based CRISPRa validation, and helped to identify bona fide protein expression effects (FIGS. 3 A-3B). First, results from ACE2 and TMPRSS2 positive-control cDNA lines demonstrated the validity of our cDNA-based approach. Second, in ACE2-null conditions, cDNAs of KCNA6 and CD7 had strong ability to promote pseudoviral infection, with KCNA6 over-expression increasing viral entry -4-fold above controls, compared with ~ 15-fold when over-expressing ACE2 (FIG. 3 A). Third, in ACE2-positive conditions, expression of almost all cDNAs enhanced pseudovirus entry, showing better consistency with CRISPRa validation than results from ACE2-null conditions. Notably, CD7, EPHA4, LRCC8D, RDH10, and LGMN showed over ~2-fold increase of viral infection over BFP negative controls, and their effects were specific to SARS-CoV-2 Spike as they did not promote VSVG viral entry (FIG. 3B). Additionally, we used quantitative PCR (qPCR) and Western Blot to confirm the expression of correct cDNAs in the most effective cell lines.

[00137] Further, we employed a replicating VSV pseudovirus assay using time-lapse imaging to quantify the level of Spike-mediated viral infection (FIGS. 3C-3D). The most significant hits validated by the pseudotyped lentiviral (non-replicating) experiments, e.g., KCNA6, LGMN, and HLA-DPB1, all maintained strong ability to promote viral infection. To control for non-specific effects, we performed measurements with Rabies virus (RABV) G protein pseudotyped VSV in the same cDNA lines. The RABV reference results confirmed that all top hits did not promote RABV viral infection. This together with prior VSVG lentivirus results, ensured that our candidate host factors had effects in a SARS-CoV-2 specific manner.

[00138] Finally, we used an authentic SARS-CoV-2 virus assay to measure if the overexpression of cDNA encoding top hits could promote live virus infection (FIG 3E). We engineered SARS-CoV-2 virus carrying a luciferase reporter (Dinnon et al.), and infected cDNA lines in both ACE2-null and ACE2- positive conditions. At 48 hours post infection, we performed infection efficiency measurement along with positive control (ACE2, TMPRSS2) and negative controls (BFP-expressing lines and virus-only without cells to subtract background luminescence). Consistent with our pseudovirus results, KCNA6 and LGMN had strong ability to promote SARS-CoV-2 virus infection compared with control groups. Notably, overexpression of potassium channel KCNA6 leads to ~50-fold increase in live virus infection in ACE2-null condition, compared with ~150-fold for ACE2 cDNA line (FIG 3F). To our best knowledge, this is the strongest ACE2-independent host factor for SARS-CoV-2 that has been validated by authentic virus infection. In addition, LGMN, HLA-DPB1, EPHA4, CD7 also had the ability to promote live virus infection (FIGS. 3F-3G). These results not only validated our findings in a more physiologically relevant setting, but also demonstrated that our pseudoviral system results could translate into effects in authentic virus infection tests.

Example 8

KCNA6 is highly expressed in nasal/olfactory neurons located at sites of SARS-CoV-2 infection and pathology, and may serve as a novel drug target for SARS-CoV-2

[00139] Recently, several groups performed study of COVID 19 host gene expression via RNA-Seq of patient samples. Such studies provided a window to examine host factor expression in relevant cell types and tissues. Thus, we sought to understand if the top-ranked hit KCNA6 may be expressed in human tissues and patient samples, and thus seek evidence for its involvement in viral infection. From our live virus validation, KCNA6 was the strongest hit that mediated SARS-CoV-2 infection in cells with minimal ACE2 presence. Nonetheless, no prior studies implicated this potassium channel’s potential involvement in C0VID19. As noted earlier when we examine the top hits expression pattern, KCNA6 is unusually missing from the latest GTEx database due to genome annotation issues. Hence, we turned to a bulk dataset where KCNA6 expression was detected with the usage of an older reference genome (GRCh37, or hgl9). From this bulk RNA-seq analysis, we noted that KCNA6 has uniquely high expression in the olfactory epithelium and the brain, unlike the well-known SARS-CoV-2 viral receptor ACE2 (FIG 4A). [00140] We then examined if the KCNA6 annotation issue would affect single-cell RNA-seq analysis, a prominent method that has been used in many COVID 19 clinical studies. Given almost all commonly used single-cell RNA-seq pipelines, e.g., lOx genomics CellRanger, STARsolo, Kallisto, will discard multiple mapped reads, we expect that KCNA6 expression can be hardly detected in published single-cell studies when using the latest genome references GRCh38 (or hg38) (FIG 4B). In addition, because the length of KCNA6 annotation is significantly shorter in GRCh38 vs. GRCh37 (including shorter Exon and completely missing introns in the KCNA6 from GRCh38), the GRCh38 annotation would pose a significant challenge for measuring KCNA6 expression even for data pipelines that could handle multiple mapped reads. To test this, we processed a single-cell RNA-seq datasets of the human nasal/olfactory epithelium with biological replicates from two patients. Indeed, due to overlapping annotation, standard CellRanger and the multimapping-aware Salmon - Alevin pipelines cannot detect KCNA6 expression using GRCh38 reference (FIGS. 4C-4D). When we correct genome annotation overlap by removing the lincRNAs, we were able to detect high-expression of KCNA6 in olfactory/nasal tissue samples from human patients using the CellRanger pipeline (FIG 4C). And for the Salmon - Alevin pipeline that takes into account multi-mapped reads, we could readily detect high levels of KCNA6 expression with the standard GRCh37 reference (FIG. 4D).

[00141] Thus, we decided to use updated genome reference for understanding single-cell expression of KCNA6 in tissues that are known to be involved in COVID19 infection and pathology, where ACE2, Neuropilin, or other known factors have failed to fully explain the presence of live virus in human patients. First, human KCNA6 is most significantly expressed in neuronal cells, consistent with the known role of Kvl.6 (encoded by KCNA6) channel (FIG. 4E-4F). Further, multiple prior studies using CO VID 19 patient autopsy samples indicated that the nasal/olfactory epithelium is a site of active SARS- CoV-2 infection, with the virus prominently detected in OLIG2+ neurons. After updating the data processing pipeline, we detected robust high expression of KCNA6 in 0LIG2+ neurons (FIG 4G). This is consistent with clinical reports of viral-infected cells being OLIG2-expressing neurons, where ACE2 receptor and NRP1/2 expression is minimal based on single-cell RNA-Seq (FIG. 4H). The presence of KCNA6 helps to explain sensory and neuronal aspects of C0 VID19 symptoms, including the long-haul CO VID 19 syndrome.

[00142] Finally, as potassium channels are known to be druggable targets, we tested an FDA-approved compounds, 4- Aminopyridine (4-AP, dalfampridine) that target potassium channels broadly (including KCNA-family) for its ability to suppress viral entry (FIG 41). We observed that 4-AP could inhibit viral entry in a dosage dependent manner, and this effect is dependent on KCNA6 expression in ACE2-null condition (FIGS. 4J-K). Specifically, in the ACE2-null group, 4-AP had no effect on VSVG pseudoviral infection, but could block Spike-mediated viral entry when KCNA6 is expressed, with an IC50 of ~490uM (FIG. 4J). In the ACE2-positive group, 4-AP specifically prevented SARS-CoV-2-Spike pseudovirus infection across the control BFP and KCNA6 lines. This pointed to a broad potassium channel effect when ACE2 is present Nonetheless, we observed that KCNA6 expression rescued the inhibitory effects (IC50 of 900uM in BFP control vs. 1 lOOuM in KCNA6 cDNA line) (FIG. 4K). The modest effect size here was likely due to the significantly lower levels of KCNA6 protein in ACE2- positive vs. ACE2-null conditions, thus consistent with the results from ACE2-null lines. Overall, these results demonstrate the druggability of potassium channels, especially KCNA6, as a target for COVID19 that represents a novel mechanism independent from the ACE2-mediated pathways.

[00143] Protease LGMN is a potential host factor for SARS-CoV-2 that demonstrates expression correlation with viral infection, and its specific inhibitor could suppress viral entry.

[00144] In addition to the ACE2-independent host factor KCNA6, the protease LGMN is top-ranked across our screen conditions, and had notable ability to facilitate viral entry in our pseudovirus and authentic virus assays. LGMN, is a human asparaginyl endopeptidase (AEP), also known as legumain, or 5-secretase. It is thought to be a lysosomal/endosomal protease, and intriguing may be activated in agedependent manner. As LGMN is broadly expressed in human tissue, we further examined LGMN levels in human COVID19 patient data through meta-analysis. Our single-cell expression analysis of published Bronchoalveolar lavage fluid (BALF) samples indicated that LGMN expression has strong correlation with virus RNA level and disease status. [00145] Additionally, LGMN is a membrane-bound protease with available inhibitors. Hence, we tested a compound LI-1, a specific LGMN blocker, and validated its ability to prevent viral entry in a dosage dependent manner. Additionally, LGMN cDNA expression could rescue the inhibitory effects as expected. Thus, we provide evidence that host legumain protease is a novel protease host factor during SARS-CoV-2 infection, and that inhibition of LGMN activity finds use to prevent or reduce infection. [00146] Additionally, we tested another potassium channel inhibitor, 3,4-diaminopyridine (3,4-DAP, Amifampridine). 3,4-DAP is a voltage gated potassium channel blocker, currently used to treat Lambert- Eaton myasthenic syndrome that is also approved by FDA. We tested the effect of 3,4-DAP on inhibiting SARS-CoV-2 viral entry using similar set-up, and confirmed that 3,4-DAP has a dosage-dependent inhibitory effects on SARS-CoV-2 viral entry (FIG 17).

[00147] In addition to drug inhibition, the putative targets KCNA6 and LGMN are able to mediate viral entry/infection when using the omicron variant of SARS-CoV2. Moreover, the Omicron variant of SARS-CoV-2 showed less dependency on known pathways like ACE2/TMPRSS2, but maintained strong dependency on the druggable pathway identified, namely potassium channel (KCNA6), and cysteine protease (legumain, LGMN), as shown in FIGS. 18 and 19.

Example 9 Drug-target network analysis using top screen hits

[00148] As our screen targeted all human membrane proteins, the majority of our hits are within the druggable genome. Thus, to translate top hits into actionable clinical insights, we next assessed existing drugs that modulate host factors identified in the CRISPRa screen. We constructed a bipartite graph representing known interactions between 4,929 FDA-approved compounds and 2,325 protein-coding genes, 254 of which we identified as SARS-CoV-2-specific screen hits (FIG 5A, Methods). To evaluate which drugs were likely to modulate viral entry, we first ranked compounds by normalized degree centrality (NDC) with respect to screen hit genes and drug categories by mean NDC. We observed marked enrichment of several drug classes in the ranked list with a propensity for ion-channel targeting, including antidepressant, anticonvulsant, and antipsychotic agents (FIG. 5B). Many of these classes have previous literature support for a role in modulating viral entry (Gordon et al., 2020; Zimniak et al., 2021). To assess the specificity of the highest ranked drugs for SARS-CoV-2 entry relative to their entire interaction profile, we further scored drugs by the proportion of their interactors that were screen hits. This approach identified a similar set of ion-channel-targeting drug categories (FIG. 5B). Example 10 Retrospective validation through health insurance claims

[00149] To provide clinical validation for our drug targets, we performed a retrospective analysis of claims data from a large US health insurance provider, examining associations between common prescriptions and COVID-19 hospitalization rates. We reviewed claims from 7.8 million Medicare Advantage Part D (MAPD) members for compatibility with regional and temporal inclusion criteria (FIG. 5C). The final dataset comprised claims for 234,524 MAPD-insured residents of New York, New Jersey, and Connecticut with at least 11 months of enrollment between January and December 2019 and at least one month of enrollment during 2020, with at least one pharmacy prescription claim from the UnitedHealth Group Clinical Discovery Portal (Table 1). Among these individuals, 2,828 (1.21%) had claims indicating COVID-19 hospitalization during the observation window.

[00150] We first screened the claims database for commonly prescribed drugs associated with reduced odds of COVID-19 hospitalization in a 1:10 matched cohort of hospitalized and non-hospitalized patients. We identified 98 drugs whose odds ratio for hospitalization was significant at a corrected p-value of 0.05. This included drugs in a wide range of mechanistic classes, including broad-spectrum anticonvulsants, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor antagonists, and thiazide diuretics. These drugs were highly enriched in the network ranking (p<le-l 1, Mann-Whitney U test; FIG 5D), suggesting an association between a compound’s ability to modulate viral entry genes and its associated risk of COVID-19 hospitalization.

[00151] As the complete database screen could not account for several potential confounders such as primary diagnosis, we selected four drug classes for follow-up analysis using 1:1 propensity score matching (PSM). The classes, which included loop diuretics, opioid analgesics, selective serotonin reuptake inhibitor (SSRI) antidepressants, and broad-spectrum anticonvulsants, were ranked highly in the drug-target network, had sufficient prescription volume in the claims dataset, and included known potassium-channel-targeting drugs. The hazard ratios for opioid analgesics were not significant either before or after PSM matching. However, we observed a significant risk-associated effect for loop diuretics, SSRI antidepressants, and broad-spectrum anticonvulsants that persisted after PSM. For loop diuretics, the adjusted hazard ratio for COVID-19 hospitalization was 1.256 (p < 0.0001) before PSM and 1.312 (p = 0.00028) after PSM The SSRI class showed an adjusted hazard ratio for COVID-19 hospitalization of 1.221 (p = 0.00019) before PSM and 1.208 (p = 0.0037) after PSM, while the hazard ratios for broad-spectrum anticonvulsants were 1.261 (p < 0.0001) and 1.210 (p = 0.0034) before and after PSM, respectively (FIG. 5E).

[00152] More significantly, as angiotensin pathway genes, including KCNA genes, were the most prominent and validated category of hits in the CRISPRa screen, we evaluated whether common drugs targeting these potassium/sodium channels were associated with decreased risk of hospitalization. We observed that the hydrochlorothiazide alone or in combination with ACEi were consistently associated with a protective effect in the PSM study, even when compared to controls on other first-line antihypertensive agents (FIG. 5F). The approach we used demonstrated that genome-wide druggable protein activation screens could be combined with insurance claims data to provide real world evidence for laboratory-generated hypotheses, and potentially help to identify risk factors or drug-repurposing targets.

[00153] In the work presented here we demonstrate the power of gain of function (GoF) screening to provide novel insight into the tropism of an emerging pathogen. Traditional loss of function approaches limit discovery of novel host factors to the genes expressed within the context of a cell line used for screening. A potential path to overcome these limitations is doing screens in the context of multiple cell lines representing a variety of cell types, but this is laborious and necessitates prior knowledge of viral tropism. GoF screening overcomes these limitations by selecting cell lines with limited or no susceptibility and allowing for the determination of factors that promote viral entry. Using CRISPRa screening, we show here that previously unidentified factors from a diverse set of tissues — such as neuronal (KCNA6), immune (HLAD-DPB1, CD7), and cardiac (EPHA4, LGMN) — are capable of stimulating SARS-CoV-2 viral entry.

[00154] Most strikingly, we show that KCNA6, a voltage-gated potassium channel, is capable of potentiating SARS-CoV-2 entry even in a cellular context where ACE2 expression is undetectable. KCNA6 is a member of the KCNA family of ligand-gated potassium channels and is, as we demonstrate here, expressed highly in OLIG2+ cells, which have been previously shown to be the cells susceptible to SARS-CoV-2 in the nasal cavity (Cantuti-Castelvetri et al., 2020). Olfactory and taste dysfunction are common and persistent symptoms of COVID-19 (Ellul et al., 2020), with a smaller fraction of often hospitalized patients suffering more serious neurological conditions, such as delirium, encephalopathy and stroke (Ellul et al., 2020; Helms et al., 2020). The degree to which these neurological effects are due to infection of neural cells or the side effects of an inflammatory state are still poorly understood (Helms et al., 2020; Song et al., 2021). Experimental studies using brain organoid and neural progenitor cells (NPCs) have shown that these cell types are susceptible to SARS-CoV-2 infection (Bullen et al., 2020; Ramani et al., 2020; Song et al., 2020; Zhang et al., 2020). These infections occur robustly despite the low levels of expression of ACE2 in infected cells in these systems(Ramani et al., 2020; Song et al., 2021) and appear to be uncorrelated with levels of ACE2, TMPRSS2, or NRP1 expression (Song et al., 2021). This suggests that tissue-specific factors working independently or in synergy with Spike protein binding to ACE2 could be promoting viral entry in the context of neuronal tissues.

[00155] Numerous studies have implicated the activity of host ion channels in the infectious activity of viral diseases both in viral entry and in replication (Charlton et al., 2020). In the case of SARS-CoV-2, preliminary studies indicated that pharmacological inhibition of two pore Ca2+ channel 2 (TPC2) lead to the decreased entry of SARS-CoV-2 pseudovirions (Ou et al., 2020). This finding was similar to a study using MERS S pseudovirions that found that knockdown of either TPC1 or TPC2 lead to decreased viral entry — an effect explainable by lowered Furin cleavage activity and impaired endosomal motility (Gunaratne et al., 2018). Here, we show that treatment of KCNA6 overexpressing cells either in the presence or absence of exogenous ACE2 expression with the KCNA (Kvl) family blocker 4- Aminopyridine (4AP) leads to decreased SARS-CoV-2 S pseudoviral entry. This provides a mechanism that extends beyond KCNA6 to the KCNA family or potassium channels more generally, a finding supported by our network analysis and real world clinical data. As our work here indicates that the mechanism of KCNA6 viral entry promotion and 4AP inhibition is dependent on SARS-CoV-2 Spike, it is interesting to note that the presences of K+ ions is a critical requirement for a conformational change in the Bunyavirus spike protein that is required for viral entry (Charlton et al., 2019; Hover et al, 2018; Hover et al., 2016; Punch et al., 2018).

Table 1. Clinical cohort characteristics.

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[00156] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

[00157] Preferred embodiments of this invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

Claims

CLAIMS What is claimed is:

1. A method of treating or preventing a viral infection in a cell comprising: contacting the cell with an effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors, or a composition thereof, wherein the one or more viral infection mediating factors are selected from the group consisting of KCNA6, LGMN, HLA-DPB1, EPHA4, CD7, and combinations thereof.

2. The method of claim 1, wherein the cell is in vivo and the contacting comprises administering the inhibitor to a subject

3. The method of claim 1 or claim 2, wherein the viral infection is a coronavirus infection.

4. The method of any of claims 1-3, wherein the viral infection is a SARS-CoV-2 infection.

5. The method of any of claims 1-4, wherein the one or more viral infection mediating factors comprises KCNA6, LGMN, or a combination thereof.

6. The method of any of claims 1-5, wherein the agent that regulates the expression or activity of one or more viral infection mediating factors comprises small molecules, antibodies or antibody fragments, aptamers, proteins, nucleic acids, or a combination thereof.

7. The method of any of claims 1-6, wherein the agent that regulates the expression or activity of one or more viral infection mediating factors comprises dalfampridine, amifampridine, LI-1, or a combination thereof.

8. The method of any of claims 1-7, further comprising contacting the cell with an agent that regulates the expression or activity of one or more additional viral infection mediating factors.

9. The method of any of claims 1-8, further comprising contacting the cell with an antiviral agent.

10. A method of treating COVID-19 in a subject, the method comprising administering to a subject in need thereof a therapeutically effective amount of an agent that regulates the expression or activity of one or more viral infection mediating factors, or a composition thereof, wherein the one or more viral infection mediating factors are selected from the group consisting of KCNA6, LGMN, HLA-DPB1, EPHA4, CD7, and combinations thereof.

11. The method of claim 10, wherein the one or more viral infection mediating factors comprises KCNA6, LGMN, or a combination thereof.

12. The method of claim 10 or claim 11, wherein the agent that regulates the expression or activity of one or more viral infection mediating factors comprises small molecules, antibodies or antibody fragments, aptamers, proteins, nucleic acids, or a combination thereof.

13. The method of any of claims 10-12, wherein the agent that regulates the expression or activity of one or more viral infection mediating factors comprises dalfampridine, amifampridine, LI-1, or a combination thereof.

14. The method of any of claims 10-13, further comprising administering to the subject an agent that regulates the expression or activity of one or more additional viral infection mediating factors.

15. The method of any of claims 10-14, further comprising administering to the subject an antiviral agent.

16. Use of an agent that regulates the expression or activity of one or more of: KCNA6, LGMN, HLA- DPB1, EPHA4, CD7, or a composition thereof, for treating or preventing a viral infection in a cell.

17. Use of an agent that regulates the expression or activity of one or more of: KCNA6, LGMN, HLA- DPB1, EPHA4, CD7, or a composition thereof, for treating COVID-19 in a subject.