key: cord-0272456-vw8ngco2
authors: Schott, Benjamin H.; Wang, Liuyang; Zhu, Xinyu; Harding, Alfred T.; Ko, Emily R.; Bourgeois, Jeffrey S.; Washington, Erica J.; Burke, Thomas W.; Anderson, Jack; Bergstrom, Emma; Gardener, Zoe; Paterson, Suzanna; Brennan, Richard G.; Chiu, Christopher; McClain, Micah T.; Woods, Christopher W.; Gregory, Simon G.; Heaton, Nicholas S.; Ko, Dennis C.
title: Single-cell genome-wide association reveals a nonsynonymous variant in ERAP1 confers increased susceptibility to influenza virus
date: 2022-01-30
journal: bioRxiv
DOI: 10.1101/2022.01.30.478319
sha: ba399adf6e942b4456889dd209cd48a984529907
doc_id: 272456
cord_uid: vw8ngco2

Diversity in the human genome is one factor that confers resistance and susceptibility to infectious diseases. This is observed most dramatically during pandemics, where individuals exhibit large differences in risk and clinical outcomes against a pathogen infecting large portions of the world’s populations. Here, we developed scHi-HOST (single cell High-throughput Human in vitrO Susceptibility Testing), a method for rapidly identifying genetic variants that confer resistance and susceptibility to pathogens. scHi-HOST leverages scRNA-seq (single-cell RNA-sequencing) to simultaneously assign genetic identity to individual cells in mixed infections of cell lines of European, African, and Asian origin, reveal associated genetic variants for viral entry and replication, and identify expression quantitative trait loci (eQTLs). Applying scHi-HOST to influenza A virus (IAV), we identified eQTLs at baseline and in genes that are induced by IAV infection. Integration of scHi-HOST with a human IAV challenge study (Prometheus) revealed that a missense variant in ERAP1 (Endoplasmic reticulum aminopeptidase 1; rs27895) was associated with IAV burden in cells and human volunteers. Functional studies using RNA interference, ERAP1 inhibitor, and overexpression of alternative alleles demonstrated that ERAP1 is exploited by IAV to promote infection. Specifically, the nonsynonymous substitution, which results in a glycine to aspartate substitution at ERAP1 residue 348, would disrupt the substrate binding pocket of ERAP1, likely resulting in a significantly altered preference for substrates, poorer catalytic efficiency, or both. Finally, rs27895 exhibits substantial population differentiation, with the higher frequency of the minor T allele in two African populations likely contributing to the greater permissivity of cells from these populations to IAV infection. scHi-HOST is an important resource for understanding susceptibility to influenza and is a broadly applicable method for decoding human genetics of infectious disease.

Infectious diseases have been a leading cause of mortality throughout human history.

These past exposures to pathogens have conferred strong selective pressures on the human genome, driving adaptation as resistance alleles become more common. When a pathogen remains endemic to a region, resistance alleles continue to provide protection against the pathogen that drove selection, as demonstrated by malaria protection by the sickle cell allele (rs334) (Allison, 1954) . Alternatively, episodic selection by epidemics and pandemics may leave lasting consequences on genomes for future infections, as well as other unexpected aspects of human health impacted by the same cellular pathways (Pittman et al., 2016) . Identification of human genetic differences that regulate resistance and susceptibility to infectious diseases could reveal individuals and populations at risk for more severe infections and genes that could be targeted for novel therapies.

In pursuit of genetic differences that impact resistance and susceptibility, approaches using human populations have focused on identification of either common or rare variants. For influenza A virus (IAV), small, underpowered GWAS using a common variant approach have failed to identify any genome-wide significant loci (Chen et al., 2015; Garcia-Etxebarria et al., 2015; Zhou et al., 2012) . Coupling small candidate association studies with functional evidence has revealed associations of common variants in IFITM3 with severe influenza (Allen et al., 2017; Everitt et al., 2012) . On the other end of the frequency spectrum, ascertainment of individuals with very severe disease and next-generation sequencing has identified rare variants predisposing to severe influenza in IRF7 (Ciancanelli et al., 2015) and TLR3 (Lim et al., 2019) . However, challenges are associated with deconvoluting the mechanisms underlying identified common or rare variants.

Complementary in vitro approaches for identifying human genetic variation can control for differences in exposure, pathogen genetic diversity, comorbidities, and access to medical care and can reveal mechanisms underlying genetic association. Such approaches could be used to broadly probe human genetic diversity for resistance and susceptibility to emerging pathogens and would be a powerful tool, especially as outbreaks develop in single geographic locations.

Previously, we developed a cellular GWAS platform, Hi-HOST (Hi-throughput Human in vitrO Susceptibility Testing), in which lymphoblastoid cell lines (LCLs; Epstein-Barr virus immortalized B cells) from hundreds of genotyped individuals are exposed to identical doses of a pathogen and assessed for cellular host-pathogen traits including entry (Alvarez et al., 2017; Bourgeois et al., 2021) , replication (Wang et al., 2018) , cell death (Ko et al., 2012; Ko et al., 2009; Salinas et al., 2014) , and cytokine response (Barnes et al., 2022; Schott et al., 2020; Wang et al., 2018) . This approach revealed human genetic differences associated with cellular traits that are also associated with infectious disease risk (Alvarez et al., 2017; Gilchrist et al., 2018; Malaria Genomic Epidemiology, 2019) and outcomes (Ko et al., 2012; Wang et al., 2017) . Thus, while Hi-HOST screening with LCLs captures phenotypes measured from a single cell type, effects of SNPs that can be assayed in LCLs have been validated in other cellular models, animal models, and humans. Others have also carried out studies of cytokine levels (Li et al., 2016; Mikacenic et al., 2013) and of response eQTLs (reQTLs) following stimulation with various pathogens or pathogenrelated molecular patterns (Barreiro et al., 2012; Fairfax et al., 2014; Lee et al., 2014; Nedelec et al., 2016) . However, Hi-HOST and similar studies are labor-and time-intensive and can be skewed due to batch effects in measuring hundreds of individual cellular infections or exposures over a period of months or even years. Additionally, incorporation of new technology for simultaneous identification of eQTLs combined with GWAS of pathogen measurements could help fill the mechanistic gap from genetic variant to cellular infection phenotype.

Here, we present a rapid, high-throughput approach called scHi-HOST (single-cell Hi-HOST) that uses scRNA-seq for simultaneous identification of alleles associated with both gene expression changes and genetic resistance to IAV. This method leverages the genetic diversity of dozens of individuals from multiple populations in a single infection and revealed both phenotypic differences in IAV susceptibility among populations and a common missense variant in ERAP1 (rs27895) associated with IAV burden in LCLs and a human flu challenge study. Coupling scHi-HOST with human challenge studies is a broadly generalizable approach for understanding human genetic susceptibility to infectious disease using a small number of individuals in a rapid timeframe.

We developed scHi-HOST to integrate the identification of common human genetic variants that impact gene expression and cellular infection traits into a single infection assay. scHi-HOST uses scRNA-seq for simultaneous genotypic assignment of thousands of individual cells and dense phenotyping of host and virus. The scHi-HOST pipeline has 4 major steps: 1) infection and scRNA-seq data generation, 2) assignment of individual cells to genotyped individuals, 3) GWAS of pathogen-based burden phenotypes, and 4) eQTL identification of host gene expression using allele-specific expression and total reads across genotypes (Fig. 1A) . In this study, equal number of LCLs from 48 individuals across 3 populations (LWK (Luhya in Webuye, Kenya), GBR (British in England and Scotland), and CHS (Southern Han Chinese) from the 1000 Genomes Project (Genomes Project et al., 2015) ) were mixed and infected for 24 hours with a laboratory adapted human H1N1 IAV strain (A/Puerto Rico/8/34, PR8). This experiment is referred to as 8 scHH-LGC, based on the populations included. Consistent with previous reports of B cells being direct targets for IAV entry (Dougan et al., 2013; Lersritwimanmaen et al., 2015; Tumpey et al., 2000) , LCLs were highly infectable by IAV. A scRNA-seq library was prepared using 10X Genomics Chromium Next GEM Single-Cell 3' technology (Zheng et al., 2016) . Mixed LCLs were frozen into aliquots that can be thawed and screened for future pathogens or other stimuli in <1 week. Fig. 1C ). We achieved a singlet rate of 73% with 99.9% of singlets assigned to a single genotype with > 99% confidence.

LCLs displayed a transcriptional response similar to other IAV infected cell types Zhuravlev et al., 2020) . Differentially expressed genes (DEGs) were dominated by upregulation of interferon (IFN) and interferon-stimulated genes (ISGs) ( Fig. 1D ; Table S1 ). PCA analysis showed convincing separation of uninfected and IAV infected LCLs along PC5, accounting for 4.3% of the variation (Fig. 1E) . Gene set enrichment analysis (GSEA) (Subramanian et al., 2005) confirmed upregulation of ISGs along with multiple other gene sets for viral infection including multiple respiratory virus response gene sets, interferon-induced antiviral modules, and the inflammatory response (Fig. 1F , all FWER p < 0.05; Table S2 ).

At the single-cell level, we found that induction of IFN is widely variable and only detected in a minority of cells (Fig. 1G) . In contrast, ISGs, including OASL, while variably expressed, are more broadly induced throughout the infected culture (Fig. 1H) . Thus, while the antiviral response is robust, it is driven by a small number of cells that highly induce IFN to then induce ISGs more broadly.

To determine if this transcriptional heterogeneity can be explained by cell-to-cell differences in viral burden, we mapped and quantified viral transcripts, given that poly-adenylated IAV RNA was captured with the LCL transcriptome. A UMAP plot revealed that most cells have low but detectable levels of viral reads, but there is a cluster of high-burden cells with a distinct host transcriptional response (Fig. 1I) . This high level of heterogeneity is consistent with previous work using scRNA-seq in IAV-infected A549 lung epithelial cells (Russell et al., 2018) . We observed the most highly burdened cells in the culture expressed the highest levels of IFNs and ISGs in response to virus. Aggregating the IAV reads for each of the 48 LCLs revealed large differences, with the mean influenza reads varying ~20-fold across different LCLs (Fig. 1J) . It was noted that viral burden did not correlate with copy number of EBV (Fig. S1A) , used by the 1000 Genomes Project in immortalizing the LCLs, or with the number of each LCL recovered in the scHi-HOST experiment (Fig. S1B) . Thus, there is wide variation in viral burden across cells at the single-cell level and on aggregate across LCLs and these differences correlate with differences in the expression of important antiviral genes.

Comparison of gene expression in uninfected and infected LCLs vs. viral burden revealed that high IFNα expression at baseline in the uninfected state is correlated with low viral burden in infected cells at 24 hours ( Fig. 1K; Fig. S2A ). In contrast, after infection, high induction of IFNα is correlated with high viral burden (Fig. 1L, Fig. S2B ). Thus, high baseline IFNα levels prior to infection appear to be protective; however, during infection, high levels of IAV replication, as evaluated by IAV genome copy number, lead to the highest levels of IFNα induction in individual cells. This is consistent with a previous study using single-cell RNA sequencing of IAV infected cells to link basal expression to susceptibility to infection (O'Neill et al., 2021) .

In summary, in a single infection, we deconvoluted cells from 48 different individuals and observed highly variable flu burden and transcriptional phenotypes. These methods revealed baseline IFN levels contribute to resistance against influenza infection. We next turned to identifying human genetic differences that regulate cellular response to infection. (Fig. S2B) . Correlation coefficient and p-value from Spearman's correlation.

We identified human SNPs associated with variation in gene expression in uninfected and infected cells using RASQUAL (Robust Allele-Specific Quantitation and Quality Control) (Kumasaka et al., 2016) . RASQUAL tests for the association of SNPs vs. gene expression level by measuring both linear regression among genotypes and allele-specific expression in heterozygous individuals while accounting for sex biases and confounding factors. Observedlog(p-values) of the data deviated strongly from theoretical and empirical null distributions, indicating many true positives under both conditions ( Fig. 2A) . We compared the results of RASQUAL analysis on scHH-LGC with a second scHi-HOST experiment of 48 additional LCLs from ESN (Esan from Nigeria), IBS (Iberian from Spain), and KHV (Kinh from Vietnam) (referred to as scHH-EIK) and observed strong correlation between the two datasets (Fig. 2B) . These datasets were combined to provide the greatest power in detecting eQTLs. The most significant eQTLs were detected in both uninfected and IAV-infected conditions (Fig. 2C) .

A 2-step approach was used to identify eGenes (unique genes associated with eQTLs) utilizing Benjamini-Hochberg correction for both the SNPs tested in each window and for the number of genes tested. For uninfected and IAV-infected LCLs at a 2-step false-discovery rate FDR < 0.05, we detected 2265 and 3326 eGenes, respectively ( Table S3) . Many of the eQTL associations replicated in the GTEx dataset (Consortium, 2020) . We observed 3.17-fold enrichment (p=1.26x10 -117 ) for uninfected eGenes in GTEx LCLs (GTEx FDR < 0.01) and 2.65fold enrichment (p = 6.02x10 -140 ) for IAV eGenes, representing 33.01% of the eGenes from the infected sample. Further, enrichment was observed across multiple tissues in GTEx, including whole blood (1.61-fold and 1.54-fold enrichment for IAV and uninfected eGenes respectively, both p<1.9x10 -40 ), spleen (1.66-fold and 1.52-fold enrichment for IAV and uninfected respectively, both p<1.9x10 -40 ), and lung (1.43-fold and 1.33-fold enrichment for IAV and uninfected respectively, both p<1.9x10 -40 ) (Fig. 2D) . Thus, as others have noted (Flutre et al., 2013) , LCLs serve as a valid system for identification of eQTLs that are relevant in many tissues, and we have determined that eQTLs identified in LCLs with scHi-HOST are reproducible across other LCL datasets, related primary tissues, and even other tissues.

The most significant association outside of the HLA region in both uninfected and infected LCLs was the association of rs11080327 with SLFN5 ( Fig. 2E , F, Uninfected 2-step FDR = 5.3 x 10 -140 , IAV 2-step FDR = 1.3 x 10 -249 ). Notably, the expression of this gene increased with IAV infection (1.80-fold; adjusted p = 0.004) (consistent with SLFN5 being an ISG (Katsoulidis et al., 2010) ) and the effect size of the SNP (allelic-fold change (Mohammadi et al., 2017) ) was greater in the infected LCLs ( Fig. 2F ) (Uninfected aFC = 2.6; IAV-infected aFC = 3.6; p = 0.0001).

We observed an enrichment of IAV-induced genes (see Fig. 1D ) in the IAV eGenes (1.4fold enrichment; p = 6.8 x 10 -3 ) but not in the uninfected eGenes (1.19-fold enrichment; p = 0.18), indicating that context-specific eQTLs can be uncovered by the scHi-HOST approach (Fig. 2G) .

In total, we detected 56 DEGs that were also eGenes, many of which were also ISGs (Fig. 2H) .

For example, we discovered an eQTL (rs10495545) for the long-ncRNA NRIR (Negative allelic imbalance for uninfected and infected LCLs (F). As SLFN5 is an ISG, the expression of the gene is higher in IAV-infected LCLs and the association in that context is stronger. (G) IAVinduced DEG (differentially expressed genes; p < 0.05, fold-change > 1.5) are significantly enriched in IAV-infected eGenes. DEGs between uninfected and IAV-infected were identified using a pseudo-bulk approach as described in Method. Fisher's exact tests were used to test the significance of overlap between DEGs and eGenes and calculate p-values. Strong enrichment is observed only with IAV infection (*, p = 6.8x10 

While scHi-HOST is a powerful method for eQTL identification in response to pathogens, it also serves as a method for rapid association testing of functional subsets of SNPs. Given the modest size of our combined scHi-HOST dataset (n = 96), even associations that surpass genomewide significance (traditionally p < 5 x 10 -8 ) must be evaluated carefully for corroborating evidence. For the phenotype of mean IAV burden, a single SNP (rs1923314; p = 1.8 x 10 -8 )

surpassed genome-wide significance ( Fig. S3 ; full summary statistics available at Duke Research Data Repository https://doi.org/10.7924/r4g163n0s.). However, this SNP is in a gene desert and is not a cis-eQTL, making functional follow-up challenging. Therefore, we examined statistical evidence of association for SNPs that have functional consequences: cis-eQTLs as identified above and non-synonymous SNPs based on annotation.

Examination of stratified QQ plots (Schork et al., 2013) , restricted to eQTLs (FDR < 0.05 in uninfected or IAV-infected lead eGene-eQTL; MAF > 0.1), demonstrated deviation from neutral expectation (Fig. 3A) . rs12103519, associated with expression of TNFSF12 (Tumor Necrosis Factor Superfamily member 12; Uninfected RASQUAL 2-step FDR = 0.001, IAV RASQUAL 2-step FDR = 0.02) had the strongest association with mean IAV reads (p = 1.3 x 10 -expectation ( Fig. 3B) , with a missense SNP (rs27895; G346D) in ERAP1 (endoplasmic reticulum aminopeptidase 1) having the lowest p-value (p = 2.9 x 10 -5 ; Fig. 3C; Fig. S4 ). We tested both TNFSF12 and ERAP1 for a role in regulating IAV infection by reducing expression with siRNA and assessing infection using a IAV mNeon reporter strain (Harding et al., 2017) . While knockdown of TNFSF12 had no effect (Fig. 3D) , ERAP1 siRNA in LCLs led to a consistent decrease in IAV infection, suggesting a proviral role ( Fig. 3E ; p = 0.01 in NA19020, p = 0.01 in NA19399). ERAP1 encodes an aminopeptidase, with the canonical function of trimming Nterminal residues from peptides for MHC class I presentation (Saric et al., 2002; Weimershaus et al., 2013; York et al., 2002) . The derived T allele of rs27895 is associated with higher mean IAV reads and encodes a G346D amino acid change. Notably both ERAP1 and the related ERAP2 have been reported to undergo IAV mediated changes in expression, but the functional consequences of While G346D is predicted by SIFT (Sim et al., 2012) to be "tolerated" and by PolyPhen (Adzhubei et al., 2010) to be "benign," this glycine residue lies within the substrate binding pocket and in the presence of a peptidomimic inhibitor interacts with the first phenylalanine (F6) side chain of this ligand (Giastas et al., 2019) (Fig. 3G, H) . Moreover, residue 346 is proximal to the ERAP1 active site, which is composed of a catalytic zinc that is coordinated by residues H353, H357 and E376, the proton acceptor residue E354, and "sink" residue Tyr438 (Giastas et al., 2019; Kochan et al., 2011; Nguyen et al., 2011) . On the basis of our in silico mutational analysis of the structure of the ERAP1-inhibitor peptide complex (Giastas et al., 2019) , this SNP encoding a glycine to aspartate substitution, would clearly disrupt the substrate binding pocket of ERAP1 ( Fig. 3I) . Indeed, the carboxylate side chain of an aspartate at ERAP1 residue 346 would result in steric clash with the side chain of any substrate other than those which have a glycine at position 6, including alanine, unless a different route to the active site is taken (Fig. 3J, K) . Consequently, there would be a significant impact on catalysis or substrate recognition or both. Hence, a G346D substitution in ERAP1 likely has a dual biochemical effect.

Therefore, we tested the effects of overexpression of alternative alleles of ERAP1 in A549 lung epithelial cells. Strikingly, overexpression specifically of the derived T allele (aspartate; associated with higher IAV burden in scHi-HOST) caused higher IAV burden (p = 0.006), while overexpression of the ancestral C allele (glycine) had no effect (p = 0.7) (Fig. 3L) . This data confirms the proviral role indicated by the RNAi and inhibitor results and further demonstrates the functional effect of the G346D mutation (Fig. 3M) . 

To determine whether rs27895 was also relevant in humans infected with IAV, we turned to a human IAV challenge study. In the Prometheus study (Grzesiak et al., 2021) , volunteers aged 18-55 years were enrolled if baseline antibody titers to the CA09 (influenza A/California/04/09 (H1N1)) strain by hemagglutination inhibition assay were ≤1:10, they were healthy with no comorbidities or risk factors for severe influenza, and they had no evidence of recent respiratory infection or significant smoking history. Thirty-eight volunteers were inoculated intranasally with CA09 IAV on day 0 and underwent daily assessment and sampling during the subsequent 10 day quarantine period. Modified Jackson symptomology scores were calculated using self-reported symptom diaries (Jackson et al., 1958) and nasal lavage was obtained for qPCR quantification to assess viral burden throughout this period and at follow-up visits up to 6 months post-inoculation.

( Fig. 4A ; Table S4 ). Both viral burden (by qPCR) and symptoms increased over time among individuals with one copy of the T allele relative to individuals who were homozygous for the C allele. This was confirmed with association testing using EMMAX, controlling for sex, dose, and relatedness of individuals: the T allele of rs27895 was associated with higher qPCR-measured IAV burden at day 4 (p = 0.01; Fig. 4B ; Table S4 ) and more severe symptoms from days 3-7, with the most significant association at day 6 (p = 6 x 10 -5 ) ( Fig. 4C; Table S4 ). These results demonstrate that the association of the rs27895 T allele with higher IAV burden in scHi-HOST (and validated by expression of alternative alleles in A549 cells) is also observed in nasal lavage following experimental human IAV infection, where the T allele correlates with higher viral burden and more severe clinical disease. (Fig. S5) . These data indicate the SNP is more than 500,000 years old (assuming a generation time of 25 years), well before the earliest dispersals of Homo sapiens out of Africa around 210,000 years ago (Harvati et al., 2019) and consistent with selection at this locus acting on standing variation. Second, the derived T allele of rs27895 is the minor allele throughout the world (MAF = 10% in 1000 Genomes) and is most common in African populations (up to 27% in Mende in Sierra Leonne) ( Fig. 5A ; map generated from (Marcus and Novembre, 2017) ). In contrast, the ancestral C allele, associated with IAV resistance, is most common in Asia and has become nearly fixed in East Asian populations (out of 1008 rs27895

alleles, only one is T in CDX, CHB, CHS, JPT, and KHV 1000 Genomes populations). This is confirmed in gnomAD (Karczewski et al., 2020) where only 15 T alleles were detected out of 19,952 East Asian alleles). Notably, for the 10 known IAV pandemics prior to the 2009 swine flu pandemic, all were likely to have originated in Asia (Potter, 2001) . This history of emergence is likely secondary to the domestication of ducks and pigs in these regions and their close proximity to humans, which facilitates IAV reassortment and spillover to humans (Saunders-Hastings and Krewski, 2016) . Prior to the extensive human migrations that facilitate spread during pandemics, such IAV outbreaks would have been localized to these regions of IAV emergence, acting as a naturally selective force only in these populations to increase the frequency of resistance alleles.

Thus, while we cannot be certain that IAV drove the IAV-resistant rs27895 allele to greater frequency in East Asian populations, the combination of genetic association and functional evidence and the likely origin of historical IAV pandemics supports this model.

If the rs27895 C allele was selected for via pressures from IAV, one would predict greater IAV resistance in regions where the C allele is prevalent. Indeed, we find that LCLs from populations with higher rates of the C allele are overall protected from IAV in vitro. A combined dataset of scHH-LGC and scHH-EIK demonstrated lower mean viral reads in LCLs from European populations (GBR+IBS; 91% C allele) and East Asian populations (CHS+KHV; 98% C allele) compared to African populations (LWK+ESN; 75% C allele) (Fig. 5B) , though the differences within a continental group were much larger than differences between continental groups (Fig.   5C , top, continent effect 5.7% of total variance). Linear regression of the effect of rs27895 and continental group on IAV burden demonstrated that the amount of variation explained by continental group is reduced if rs27895 is incorporated into the model (Fig. 5C, bottom, rs27895 effect 20.3% total variance, continental effect 2.4% of residual variance). Specifically, removing the effect of rs27895 reduces IAV burden in the African continental group (median from 0.11 to -27 0.21), consistent with the higher frequency of the susceptible derived allele in Africa. Additionally, removing the effect of rs27895 increased residual burden in the European continental group (median from -0.55 to -0.33) and East Asian continental group (median from -0.14 to 0.10), consistent with the low frequency of the susceptible derived allele in Europe and even lower frequency in East Asia (Fig. 5D, compare to Fig. 5B ). P-value from ordinary one-way ANOVA with Tukey's multiple comparisons test.

Influenza has been one of the largest causes of human suffering and mortality in modern times. Since 1918, global IAV infections have been a yearly occurrence, with ~30 million symptomatic infections in the United States alone (CDC, 2020). However, there are broad differences in influenza susceptibility and severity, with outcomes from asymptomatic infections (~16% (Leung et al., 2015) ) to death (0.2% in 2014-2015(CDC, 2020)). Here we have developed a rapid scRNA-seq method to identify genetic susceptibility to IAV cellular infection. Integration with human IAV challenge data revealed human genetic differences that confer susceptibility to

The scHi-HOST method has advantages of speed and scalability. Once an infection assay has been optimized for a particular pathogen, scHi-HOST can be carried out and analyzed within a few weeks. However, this requires that LCLs can serve as a relevant model for cellular infection and optimization of timing and dose; utility is limited in cases where the receptor in not well expressed in LCLs. In the case of IAV, LCLs are highly infectable, consistent with B cells being direct targets for IAV entry (Dougan et al., 2013; Lersritwimanmaen et al., 2015; Tumpey et al., 2000) , proliferating in response to IAV (Anders et al., 1984; Poumbourios et al., 1987; Rott and Cash, 1994) and eventually undergoing cell death (Nichols et al., 2001; Tumpey et al., 2000) , likely contributing to lymphopenia during severe infection (Shinde et al., 2011) . Additionally, IAV infection induces global transcriptional changes in B cells, many mediated by IFN (Chang et al., 2007; Priest and Baumgarth, 2013) . As new IAV threats emerge from animal reservoirs, testing these new reassorted strains with scHi-HOST could serve as an early warning of pandemic potential as well as common genetic resistance to these new strains in human populations. This application of scHi-HOST may be particularly relevant with the Centers for Disease Control and Prevention tracking and evaluating potential for emergence and public health impact of IAV strains currently circulating in animals but not humans (Cox et al., 2014) . Testing such strains with scHi-HOST could further evaluate infection potential across diverse humans while also revealing possible resistance alleles against these emerging strains.

scHi-HOST can also be increased in scale for greater power. Our proof-of-concept using 96 LCLs allowed for hundreds of individual cells to be assayed for each LCL but required narrowing the genetic search space to nonsynonymous variants with high minor allele frequencies to reveal the SNP in ERAP1 that was validated experimentally and in human challenge volunteers.

Notably, the genome-wide significant hit identified by scHi-HOST (rs1923314) does show an association with the COVID19hg A1 phenotype (very severe respiratory confirmed covid vs. not hospitalized covid; p = 0.0004; COVID-19 HGI data freeze 4; (Initiative, 2020) ) with directionality consistent with scHi-HOST (A allele associated with severe COVID and greater mean IAV burden), but further work is necessary to validate the relevance of this SNP to viral infection. As scRNA-seq throughput and cost continue to improve, we anticipate increasing the cells in a single scHi-HOST experiment by an order of magnitude or more, likely revealing high confidence genome-wide significant hits for further validation experimentally and in human challenge datasets. Finally, while the variants identified by scHi-HOST are currently restricted to those with cell autonomous effects (or at least those that require only interactions of a single cell type), future developments may incorporate expansion of the method and computational pipeline to other cell types or even complex organoid models. In this regard, while TNFSF12 did not have a phenotype by RNAi in LCLs, the secreted product of this gene (and others implicated by eQTLs in our analysis) may have important non-cell-autonomous effects in IAV infection that require more complex models to detect.

The simultaneous identification of eQTLs and SNPs associated with more complex cellular phenotypes in scHi-HOST allows for integrated identification of SNPs whose effects on gene expression help dictate the outcome of cellular host-pathogen interactions. Most eQTLs and even response eQTLs did not contribute to control of IAV burden in LCLs. This is a similar challenge to immunologists who confront the fact that ISGs comprise perhaps 10% of the genome, but individually most have little effect against an individual infection (Schneider et al., 2014; Schoggins, 2019) . Regardless, our finding that high expression of IFNs and ISGs across LCLs prior to infection is correlated with lower IAV burden confirms the importance of these genes in aggregate and warrants additional experimental dissection. However, our identification and experimental validation of a nonsynonymous variant in ERAP1 as a major regulator of IAV in cells and humans underscores that despite noncoding variants receiving much attention as accounting for most of the identified GWAS risk alleles of common diseases (Maurano et al., 2012) , coding variation should not be ignored in trying to understand human genetic susceptibility to infectious diseases.

How ERAP1 serves as a proviral factor in IAV infection is unknown. The canonical function of ERAP1 is to trim proteosome-generated peptides from the N-terminus to a preferred 9 residue length for MHC Class I loading. MHC I is then trafficked from the endoplasmic reticulum to the cell surface for surveillance by T-cell receptors on cytotoxic T cells (Saulle et al., 2020) .

This has been reported to be an IAV-regulated process: infection with H1N1 activates p53 to increase ERAP1 expression, resulting in increased MHC I presentation (Wang et al., 2013 (Ombrello et al., 2015) ) has been largely unexplored.

Alternatively to affecting MHC-I presentation, ERAP1 has functions beyond MHC I peptide trimming that may contribute to its role in IAV infection. This includes regulation of immune cell activation and cytokine production through secreted ERAP1 (Aldhamen et al., 2015; Tsujimoto et al., 2020) . However, scHi-HOST is a pooled infection assay, and thus would not be expected to identify genetic differences that impact secreted factors acting through non-cellautonomous effects. ERAP1 also regulates ectodomain shedding of cell surface receptors (Cui et al., 2003a, b Randolph et al., 2021) . What may be critical is the timing of measurement, with these studies using an early 6-hour timepoint compared to our 24-hour timepoint. Indeed, Randolph et al. show that greater European ancestry is associated with higher IFN levels and higher IAV burden at 6 hours, but that by 24 hours, PBMCs with a higher IFN response had substantially lower IAV burden (Randolph et al., 2021) . This is reminiscent of our finding that IFNα levels prior to infection are correlated with lower burden by 24 hours (see Figure 1K ). Differences in cell type may also play a role as Randolph et al. demonstrate many cell-type specific ancestry effects on IAV-induced transcriptional changes, though they also note that the ancestry effect on the IFN response is broadly conserved across all PBMC cell types.

Ultimately, we hope for broad adoption of scHi-HOST as a generalizable tool for rapid assessment of genetic susceptibility and resistance to pathogens that are important for human health or that pose an emerging threat. We have developed scHi-HOST to be a truly accessible resource; scHH-LGC and scHH-GBK aliquots are in liquid nitrogen storage, to be shared with any labs that have developed an infection phenotype in LCLs. In addition to surveillance of emerging IAV strains, scHi-HOST can readily be applied to other pathogens with polyadenylated transcripts for identifying eQTLs in response to pathogen, population differences in resistance and susceptibility, and the genetic differences underlying this variation. Such genetic differences could reveal targets for drug development or FDA-approved drugs for repurposing as important prophylactics or therapeutics for future pandemics. supplemented with 10% fetal bovine serum (FBS), 2 mM glutamine, 100 U/ml penicillin-G, and 100 mg/ml streptomycin.

The PR8 (A/Puerto Rico/8/1934) mNeon-HA virus was generated and validated as previously described (Harding et al., 2017) . Both the wild-type and PR8 mNeon-HA viruses were amplified by via injection of the respective virus into 10-day-old embryonated hen eggs (Charles River) for three days at 37C. After incubation, allantoic fluid containing the virus was harvested, briefly spun down to remove debris, and frozen at -80˚C. Viruses were then titered, as previously described, 

Cell samples were counted and checked for viability on a Guava EasyCyte HT system by 7-AAD staining before they were diluted to 1 million cells/mL with an intended capture of 10,000 cells/sample. Each individual sample was used to generate individually barcoded cDNA libraries 

Raw sequencing results were processed using the 10X Genomics CellRanger 4.0. Reads from each sample were mapped to GRCh37. Reads from infected samples were also mapped to the A/Puerto Rico/8/1934 genome. For scHi-HOST-EIK, since the library was single-indexed, we used 10X Genomics Index-hopping-filter to remove index-hopped reads (https://github.com/10XGenomics/index_hopping_filter) before mapping to either human or viral genome.

To assign each barcoded read to an LCL identity, we used Demuxlet (Kang et al., 2017) . Demuxlet takes a bam file with barcoded sample reads and a VCF (obtained from the 1000 Genomes Project, phase 3 release) to computationally determine the LCL contained in each GEM reaction. With barcodes assigned to each of 48 LCLs, we used Subset-Bam (10X Genomics) to subset reads from each sample alignment bam into 48 LCL-specific bam alignment files. From these LCL-specific alignment bams, we used HTSeq-Count (Anders et al., 2019) to produce counts files for differential expression, eQTL discovery, and other transcriptomic analyses.

With the filtered and annotated expression matrix outputs of the CellRanger 4.0 pipeline, we used Seurat V4 (Hao et al., 2021) to perform single-cell analysis of PR8-infected and uninfected LCLs.

Seurat data was annotated with LCL identity and normalized viral reads per droplet for analysis.

Analysis on feature counts data was performed using standard log-normalization. Droplets with > 20% mitochondrial reads were excluded from analysis. UMAP clustering of combined scHi-HOST-LGC and scHi-HOST-EIK datasets was performed with 15 dimensions and resolution of 1.2.

After subsetting sample alignments to LCL-specific alignments, feature counting was performed using HTSeq-Count (Anders et al., 2019) with Gencode v19 feature coordinates. All differential expression testing was performed in R using the DESeq2 (Love et al., 2014) package.

Normalization of the raw counts matrix was achieved using DESeq2's default "Median of Ratios" method. Briefly, DESeq2 computes a pseudo-reference sample using the geometric mean for each gene. It then calculates a ratio of each sample to the computed pseudo-reference for each gene.

The normalization factor ("size factor" in DESeq2) for each sample is computed by taking the median of the ratios of each gene to the pseudo-reference for each sample. Finally dividing the raw counts by the size factor yields normalized counts for further analysis in DESeq2. PCA of uninfected and infected cells was performed using variance-stabilizing transformation (vst) of the counts matrix and principal components were calculated on the 40,000 most variably expressed features.

We used RASQUAL v1.1 (Kumasaka et al. 2016) to identify allele-specific eQTLs (aseQTLs).

RASQUAL uses a probability decomposition approach to estimate aseQTLs by jointly modeling both total allele-specific count and total fragment count. RASQUAL requires two main input files, a genotype file (vcf format) including the phased genotypes and reads counts for reference allele and alternative allele for each variant for each sample, and a gene expression file including read counts of all genes.

We downloaded phased genotype data (vcf format in GRCh37) from the 1000 Genomes Project Phase 3 (available at http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/), and filtered using bcftools v1.9

with parameters of "-q 0.01:minor -m 2 -M 2" to keep only biallelic SNPs and those with minor allele frequency more than 0.01. LCL-specific transcriptome alignments were obtained from Demuxlet and Subset-Bam as above. Given phased genotypes and individual sample alignment, we then quantified allele-specific read counts overlapping with biallelic variants using the ASEReadCounter function from GATK v3.8. The following parameters were used: "-U ALLOW_N_CIGAR_READS -minDepth 1 --allow_potentially_misencoded_quality_scores --minMappingQuality 10 --minBaseQuality 10".

For gene expression input files, featureCounts (Liao et al., 2014) was used to count reads per gene for each individual against GRCh37 reference genome with default parameters. We filtered out low expression and variable genes with < 5 reads in < 5 LCLs. Gene read counts were then corrected using RASQUAL's GC-correction to remove potential sample-specific GC bias. We included sex, top 2 genotypic PCs and transcriptome library size as covariates calculated using Plink v1.9 (Purcell et al., 2007) . For each gene, SNPs within a 1 megabase window from each gene's start and end position were included in our analysis. The final command and parameters

were "tabix filtered.genotype.vcf.gz Chromosome:regionStart-regionEnd | rasqual -y expression_Y.bin -k size_factor.K.bin -n NumberSampleSize -l NumberCisSNPs -m NumberFeatureSNPs -s exon_startposition -e exon_endposition -t -f GeneName".

RASQUAL's "--random-permutation" option was performed to calculate the permuted p values and construct the empirical null distribution. The nominal p values of all variants within each gene were corrected using the Benjamini-Hochberg method, and significant aseQTLs for each gene was defined using a 5% false discovery rate (FDR).

With human and viral reads assigned to each droplet, we calculated viral burden for each droplet as a function of the total reads mapped to that droplet to control for differences in read depth between droplets, resulting in normalized counts of viral reads per droplet. To get burden per LCL phenotypes, we used Demuxlet assignments of droplets to LCLs to create a distribution of viral burden for each LCL. We used the mean of the distributions as phenotype for GWAS.

GWAS analysis on viral burden as calculated above was performed using a linear mixed model implemented in EMMAX with default parameters (Kang et al. 2010) . The input genotypes for each LCL were obtained from the 1000 Genomes Project described as above. EMMAX controls for population stratification and cryptic genetic relatedness by incorporating a kinship matrix. The "emmax-kin" function was used to calculate the pairwise genetic relationships among individuals with Balding-Nichols algorithm. We also incorporated sex to control potential sex influence on phenotypes. In this analysis, we only carried out analysis for SNPs with minor allele frequency > 10%. Furthermore, we excluded all SNPs from analysis significantly deviating from Hardy-Weinberg equilibrium (p < 10 -4 ) in any of the 6 populations to remove variants that may have been affected by genotyping error. After filtering for minor allele frequency and Hardy-Weinberg equilibrium we are left with 5.2 million SNPs for GWAS. P-values reported are corrected for genomic inflation factor (λ = 1.05).

The enrichment of scHi-HOST eGenes in GTEx and enrichment of ISGs in scHi-HOST eGenes were tested using Fisher's exact test. Significance of enrichment was calculated using the following equation:

Where NTotal is the total number of genes, the n1 and n2 are the number of eGenes associated with scHi-HOST and GTEx, and k is the number of eGenes shared between scHi-HOST and GTEx.

The Prometheus human infection challenge study of healthy adult volunteers with Influenza A/California/04/09 (H1N1-2009) was previously described (Grzesiak et al., 2021 weeks of screening. Post-inoculation, subjects were confined to an isolation facility for eight days with daily collection of clinical and physiologic findings, modified Jackson symptomology scores (Jackson et al., 1958) , nasal lavage, and blood samples. Symptom scores were recorded up to day 10 post-inoculation and follow-up interviews and physical examinations were conducted at day 14 and 28.

Quantitative PCR was performed on nasal lavage samples as previously described (Jozwik et al., 2015 (Li et al., 2021) . Imputed variants were filtered using bcftools v1.9 (Danecek et al., 2021) , including minor allele frequency > 0.05, genotype missingness < 0.2, sample missing calls < 0.2.

In the subsequent analysis, only autosomal biallelic variants were included. EMMAX was used to run GWAS on qPCR of IAV load and symptom scores, with details described above.

LCLs (2 x 10 5 cells) were treated for three days in 500 μL of Accell media (Dharmacon) with either non-targeting Accell siRNA #1 or an Accell SmartPool directed against human ERAP1 or TNFSF12 (1 μM total siRNA; Dharmacon) in a 24-well TC-treated plate. After 3-day incubation with siRNA, cells were plated at 30,000 per well in 50μl PBS with 0.35% BSA, 2 mM glutamine, 100 U/ml penicillin-G, and 100 mg/ml streptomycin in 96-well plates. Infections were conducted at MOI 50 with PR8-mNeon-HA for 3 hours before wells were spiked with 75µl RPMI (10% FBS + 1% Pen-Strep). Cells were assayed for Percent mNeon+ cells using a Guava Easycyte flow cytometer at 24 hours post-infection. RNA was collected from 2 x 10 5 pelleted cells using the RNeasy mini kit (Qiagen). qPCR using primers specific for ERAP1 or TNFSF12 were used to validate knockdown relative to 18S rRNA. 

For lentivirus production, HEK-293T cells were transfected with 1 µg pLEX-ERAP1, 0.4 µg pMD2.G and 1 µg pCMV∆8.74. Lentiviral supernatant media was collected after 72 hours. Next, A549 cells in a 24-well plate were transduced with 1 mL lentivirus and selected with 1 µg/mL puromycin after 48 hours. After 3 days selection, surviving cells were expanded.

Cell lines were plated at 25,000 cells per well in 50 µl selection media (DMEM + 5% FBS + 1%

Pen-Strep + 1% Puromycin) in 96-well plates. 1 day after plating, media was removed and replaced with 50 µl PBS with 0.35% BSA, 2 mM glutamine, 100 U/ml penicillin-G, and 100 mg/ml streptomycin and infected at MOI 0.1 with PR8-mNeon-HA for 1hr before infectious media was removed and replaced with selection media. 1 day post-infection, cells were trypsinized and assayed for percent mNeon+ cells using a Guava Easycyte flow cytometer.

In silico mutational analysis of ERAP1 bound to the 10-mer peptide (PDB: 6RQX) was performed using the Pymol mutagenesis function (The PyMol Molecular Graphics System, Version 1.8.6.0

Schrodinger, LLC).

Repository https://doi.org/10.7924/r4g163n0s scRNA-seq data will be available in GEO upon publication.

We thank the investigators and individuals from diverse populations genotyped as part of the 1000 Table S1 . Differentially expressed genes for scHH-LGC comparing uninfected vs. IAVinfected cells. Normalization and differential expression testing performed using DESeq2. Table S2 . GSEA for scHH-LGC comparing uninfected vs. IAV-infected cells using the C2 gene sets. Enrichment for gene sets was calculated using pre-ranked input. Metric for GSEA was sign(Fold-change)*(-log10(adjusted p-value)). 

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