key: cord-0706840-nehfzyxu
authors: Schäfer, Alexandra; Gralinski, Lisa E.; Leist, Sarah R.; Winkler, Emma S.; Hampton, Brea K.; Mooney, Michael A.; Jensen, Kara L.; Graham, Rachel L.; Agnihothram, Sudhakar; Jeng, Sophia; Chamberlin, Steven; Bell, Timothy A.; Scobey, D. Trevor; VanBlargan, Laura A.; Thackray, Larissa B.; Hock, Pablo; Miller, Darla R.; Shaw, Ginger D.; de Villena, Fernando Pardo Manuel; McWeeney, Shannon K.; Montgomery, Stephanie A.; Diamond, Michael S.; Heise, Mark T.; Menachery, Vineet D.; Ferris, Martin T.; Baric, Ralph S.
title: Common Mechanism of SARS-CoV and SARS-CoV-2 Pathogenesis across Species
date: 2021-05-14
journal: bioRxiv
DOI: 10.1101/2021.05.14.444205
sha: ab86aa7178c4df9e998a3fcedabac686f5256786
doc_id: 706840
cord_uid: nehfzyxu

Sarbecovirus (CoV) infections, including Severe Acute Respiratory CoV (SARS-CoV) and SARS-CoV-2, are considerable human threats. Human GWAS studies have recently identified loci associated with variation in SARS-CoV-2 susceptibility. However, genetically tractable models that reproduce human CoV disease outcomes are needed to mechanistically evaluate genetic determinants of CoV susceptibility. We used the Collaborative Cross (CC) and human GWAS datasets to elucidate host susceptibility loci that regulate CoV infections and to identify host quantitative trait loci that modulate severe CoV and pan-CoV disease outcomes including a major disease regulating loci including CCR9. CCR9 ablation resulted in enhanced titer, weight loss, respiratory dysfunction, mortality, and inflammation, providing mechanistic support in mitigating protection from severe SARS-CoV-2 pathogenesis across species. This study represents a comprehensive analysis of susceptibility loci for an entire genus of human pathogens conducted, identifies a large collection of susceptibility loci and candidate genes that regulate multiple aspects type-specific and cross-CoV pathogenesis, and also validates the paradigm of using the CC platform to identify common cross-species susceptibility loci and genes for newly emerging and pre-epidemic viruses.

inapparent infection to lethal outcomes within the first 4 days of infection. We estimated genetic 115 contributions for many of these traits and determined that heritability for these responses was 116 44.4%-80.9%, estimates that agree with previous CC studies 26,28-31 . Importantly, the various CC-117 RIX disease phenotypes measured in response to infection appeared relatively uncorrelated 118 (Figure 2a-c) , suggesting that there are many independent genetic factors driving these 119 responses. We next conducted genetic mapping to identify both genomic regions and specific 120 founder haplotypes driving various aspects of SARS-MA and HKU3-MA disease responses.

Given the likely complex genetic architecture underlying these phenotypes, we identified those 122 loci surpassing community-accepted significance thresholds (p<0.33), with distinct allele effects 123 (Figure 2a-c, Table 1 ) 32 . We identified 11 distinct and high-confidence loci in our RIX population 124 affecting weight loss, mortality, titer, antibody responses or respiratory function after SARS-MA 125 infection or weight loss and mortality following HKU3-MA infection. The effect sizes of these loci 126 varied from 3-23% of the total trait variation (that is, largely moderate effect sizes), the loci were 127 located in different genomic regions with different causal founder alleles, and most loci primarily 128 impacted one or a few traits in this population ( Table 1 ). The number of independent loci and their 129 trait-specific impacts are consistent with our earlier observations of little to no correlation between 130 disease states across the RIX lines (Figure 2a-c) . Together, this analysis highlights the genetic 131 complexity driving CoV disease outcomes and immunity and also the inability of any single animal 132 model of CoV disease to fully address all aspects of the disease response.

To investigate the possibility of pan-sarbecovirus susceptibility loci, we evaluated whether any of 135 our SARS-MA phenotypes also were associated with the haplotypes driving HKU3-MA mortality ( Figure 2d, Table 1 ). For these analyses, we binned CC-RIX lines based on their diplotypes at 137 these HKU3-MA diseases associated loci, then determined whether these diplotype bins provided 138 an improved fit to the relationship between SARS-MA phenotypes and the CC-RIX IDs 139 themselves. For example, was there any genetic signal at quantitative trait loci (QTL) HrS10 or 140 HrS11 that was associated with differential SARS-MA disease when simplifying the underlying 

Relevant for the current pandemic, these parental strains showed similar severe infection 158 phenotypes during SARS-CoV-2 MA10 infection (Figure 3a , right panel, Figure S1 ). We 159 generated 403 F2 mice by intercrossing these strains ( Figure S2 ) and inoculated them 160 intranasally at 9-12 weeks with 1x10 4 PFU of SARS-MA. These F2 mice showed an expanded 161 range of disease responses relative to their parent CC strains, including mortality, weight loss, 162 titer, respiratory function, circulating immune cell and hemorrhage phenotypes ( Figure S3 , Figure   163 3b). We conducted QTL mapping in these F2 mice and identified five significant QTL segregating 164 in this population (HrS24-28, Table 1) , and supply information on other potential loci (p<0.33, 165 Table S1 ). Most of these loci impacted multiple aspects of the SARS-MA disease response during 166 this infection time course (for example analysis of HrS26 on chromosome 9 indicated the locus 167 contributed to mortality at 4 dpi (Odds ratio (OR) of 3.15), as well as lung hemorrhage or 168 congestion (10.2% of population variation), airflow restriction at 2 dpi (7.8% PenH), as well as 169 peripheral neutrophil (11.8%) and lymphocyte (12.4%) levels) at 4 dpi (Figure 3c, Table 1 ).

Recently, three Genome-wide Association Study (GWAS) in humans identified a locus associated 171 with respiratory failure. This locus (encompassing genes such as SLC6A20, LZTFL1, FYCO1, 172 CXCR6, XCR1, and CCR9) is syntenous with a more proximal region of our chromosome 9 173 locus 33-35 . CCR9 emerged as a strong candidate based on the integration of our data with these 174 studies and the presence of nonsynonomous SNPs in CCR9 as well as synonymous mutations 175 in regulatory flanking sequences.

Identification of CCR9 as a major susceptibility allele during SARS-CoV-2 infection.

To better understand how our contrasting CC strains and this locus regulates SARS-CoV-2 179 disease, we inoculated CC011, CC074, C57BL/6NJ and CCR9 -/mice on a C57BL/6NJ 

Next, we revisited a previous CC-F2 intercross, CC003/UncxCC053/Unc (named CC003 and 219 CC053 from here on) conducted by our group 45 , and utilized our refined analysis pipelines once 220 the original SARS-MA disease loci (HrS5-9) were statistically accounted for. This re-analysis 221 allowed us to identify an additional locus (HrS23 on chr4) impacting both weight loss and 222 hemorrhagic damage to the lungs as determined by gross pathology (Figure 5a, b) , as well as 223 several other suggestive QTLs ( Table S1 ). The chr4 locus also overlapped with the mortality QTL Table 1 ). In addition, the unexplained heritability and suggestive loci (Table   275   S1 ) we have identified, suggests that CoV disease and immunity are complex polygenic traits, 276 with the accumulation of variants across many loci driving final disease susceptibility. Collectively, 277 these studies represent the most comprehensive analysis of susceptibility loci for an entire genus 278 of human pathogens, identify a large collection of susceptibility loci and candidate genes that 279 regulate multiple aspects type-specific and cross-CoV pathogenesis, validate a role for the CCR9- 

Sequences were aligned using free end gaps with the Blosum62 cost matrix, and the tree was 

We would like to thank the Systems Genetics Core Facility (UNC) for maintaining and distributing Mendelian frequencies and showed no apparent defects in development, growth, or fecundity.

Lung tissue from Trim14 D47/D47 were found to lack detectable Trim14 mRNA, likely due to 640 nonsense-mediated decay, as measured by RT-qPCR using a predesigned primer/probe set for 

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For analysis of BAL fluid, mice were sacrificed by ketamine overdose, followed by cannulation of 648 the trachea with a 19-G canula. BAL was performed with three washes of 0.8 ml of sterile PBS

For analysis 650 of lung tissues, mice were perfused with sterile PBS, and the right inferior lung lobes were 651 digested at 37°C with 630 µg/mL collagenase D (Roche) and 75 U/mL of DNase I

Single cell suspensions of BAL fluid and lung digest were preincubated with Fc Block 653 antibody (BD PharMingen) in PBS + 2% heat-inactivated FBS for 10 min at room temperature 654 before staining. Cells were incubated with antibodies against the following markers

BUV395 anti-CD45 (Clone 30-F11

Pacific Blue anti-Ly6C (Clone HK1

Clone M1/69, Biolegend), PE anti-Siglec F (Clone E50-2440

PE-Cy7 anti-CD64 (Clone X54-5/7.1, Biolegend)

M5/114.15.2, Biolegend), BV421 anti-CD3 (Clone 17A2, Biolegend)

GK1.5, Biolegend), APC anti-CD8a (Clone 53-6.7, Biolegend) BV421 anti-B220

6B2, Biolegend) APC-Cy7 anti-CD44 (Clone IM7, Biolegend) BV605 anti-CD62L (Clone MEL-14

All antibodies were used at a dilution of 1:200. Cells were stained for 20 min at 4°C, 664 washed, fixed and permeabilized for intracellular staining with Foxp3/Transcription Factor 665

Staining Buffer Set (eBioscience) according to manufacturer's instructions. Cells were incubated 666 overnight at 4°C with BV421 anti-Foxp3 (Clone MF-14, Biolegend) washed, re-fixed with 4% PFA 667 (EMS) for 20 min and resuspended in permeabilization buffer. Absolute cell counts were 668 determined using Trucount beads (BD)

The small center lung lobe of each mouse was homogenized in 1 ml of PBS and briefly centrifuged 673 to remove debris. Fifty microliters of homogenate were used to measure cytokine and chemokine 674 protein abundance using a Bio-Plex Pro mouse cytokine 23-plex assay

Two separate lung pathology scoring scales, Matute-Bello and Diffuse Alveolar Damage (DAD), 679 were used to quantify acute lung injury