Mucins play an essential role in protecting the respiratory tract against microbial infections. The heavily O-glycosylated gel-forming mucins MUC5AC and MUC5B eliminate pathogens by mucociliary clearance while transmembrane mucins MUC1, MUC4, and MUC16 restrict microbial invasion at the apical surface of the epithelium. In this study, we determined the impact of host mucins and mucin glycans on SARS-CoV-2 spike-mediated epithelial entry. Human lung epithelial Calu-3 cells have endogenous expression of the SARS-CoV-2 entry receptor ACE2 and express high levels of glycosylated MUC1 on the surface but not MUC4 and MUC16. Removal of the MUC1 extracellular domain (ED) using the O-glycan-specific mucinase StcE greatly enhanced spike binding and viral infection. By contrast, removal of mucin glycans sialic acid and fucose did not impact viral invasion. This study implicates the glycosylated ED of MUC1 as an important component of the host defense that restricts the severity of SARS-CoV-2 infection.

The glycosylated extracellular domain of MUC1 protects against SARS-CoV-2 infection at the respiratory surface

SARS-CoV-2 (isolate BetaCoV/Munich/BavPat1/2020; European Virus Archive Global #026V-03883; kindly provided by Dr. C. Drosten) was propagated on Calu-3 cells in OptiMEM I (1X) + GlutaMAX (Gibco), supplemented with penicillin (100 IU/mL) and streptomycin (100 IU/mL) at 37°C in a humidified CO2 incubator. Stocks were produced as described previously44. A detailed description of virus production can be found in the supplementary methods.

Production of authentic SARS-CoV-2 virus stock

The ongoing SARS-CoV-2 pandemic has necessitated a dramatic increase in our ability to conduct molecular diagnostic tests, as accurate detection of the virus is critical in preventing its spread. However, SARS-CoV-2 variants continue to emerge, with each new variant potentially affecting widely-used nucleic acid amplification diagnostic tests. RT-LAMP has emerged as a quick, inexpensive diagnostic alternative to RT-qPCR, but has not been studied as thoroughly. Here we interrogate the effect of SARS-CoV-2 sequence mutations on RT-LAMP amplification, creating 572 single point mutation “variants” covering every position of the LAMP primers in 3 SARS-CoV-2 assays and analyzing their effects with over 4,500 RT-LAMP reactions. Remarkably, we observed only minimal effects on amplification speed and no effect on detection sensitivity, highlighting RT-LAMP as an extremely robust technique for viral RNA detection. Additionally, we describe the use of molecular beacons to sensitively identify variant RNA sequences. Together these data add to the growing body of knowledge on the utility of RT-LAMP and increase its potential to further our ability to conduct molecular diagnostic tests outside of the traditional clinical laboratory environment.

Profiling RT-LAMP tolerance of sequence variation for SARS-CoV-2 RNA detection

RT-LAMP reactions were performed using WarmStart® LAMP Kit (DNA & RNA) (E1700) with standard primer concentrations (0.2 μM F3, 0.2 μM B3, 1.6 μM FIP, 1.6 μM BIP, 0.4 μM Loop F, 0.4 μM Loop B) in the presence of 40 mM guanidine hydrochloride (9) in 25 μl on 96-well plates at 65 °C in a Bio-Rad CFX96 instrument. LAMP amplification was measured by including 1X NEB LAMP Dye (B1700) or 1 μM SYTO™-9 (ThermoFisher S34854), 0.5 μM SGFdel or 1.0 μM wt beacon, and fluorescent signal was acquired at 15 second intervals. Synthetic SARS-CoV-2 RNAs were obtained from Twist Bioscience (Control 2 for WT MN908947.3; Control 14 for B.1.1.7; Control 16 for B.1.351; and Control 17 for P.1) and diluted in 10 ng/μl Jurkat total RNA based on the copy number provided by the manufacturer.

RT-LAMP reactions

The COVID-19 pandemic, caused by the viral pathogen SARS-CoV-2, has taken the lives of millions of individuals around the world. Obesity is associated with adverse COVID-19 outcomes, but the underlying mechanism is unknown. In this report, we demonstrate that human adipose tissue from multiple depots is permissive to SARS-CoV-2 infection and that infection elicits an inflammatory response, including the secretion of known inflammatory mediators of severe COVID-19. We identify two cellular targets of SARS-CoV-2 infection in adipose tissue: mature adipocytes and adipose tissue macrophages. Adipose tissue macrophage infection is largely restricted to a highly inflammatory subpopulation of macrophages, present at baseline, that is further activated in response to SARS-CoV-2 infection. Preadipocytes, while not infected, adopt a proinflammatory phenotype. We further demonstrate that SARS-CoV-2 RNA is detectable in adipocytes in COVID-19 autopsy cases and is associated with an inflammatory infiltrate. Collectively, our findings indicate that adipose tissue supports SARS-CoV-2 infection and pathogenic inflammation and may explain the link between obesity and severe COVID-19.

SARS-CoV-2 infects human adipose tissue and elicits an inflammatory response consistent with severe COVID-19

The quality of the raw FASTQ data was examined with FASTQC and then aligned (cell ranger count) to a custom genome including human genome (hg38) and the complete genome sequence of SARS-CoV-2 (2019-nCOV/USA754 WA1/2020) (GenBank: MN985325.1) using the “Cell Ranger” software package v6.0.0 (10x Genomics). R (4.0.4) was used for all downstream analyses. Resulting filtered feature-barcode- (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/2021.10.24.465626; this version posted October 25, 2021. The copyright holder for this preprint 45 matrices were processed using the R package Seurat (v4.0.0). Briefly, count matrices were merged and loaded into Seurat with SARS-CoV-2 counts removed and appended to the metadata. All genes represented in < 10 cells were excluded from downstream analysis. Cells were filtered based on the following criteria: less than 200 distinct genes, less than 100 unique molecular identifiers (UMIs), and greater than 15% UMIs from mitochondrial genes. Each batch was then individually normalized using the “SCTransform” function that included regression for percent mitochondria. Integration features were then calculated using the “SelectIntegrationFeatures” function and passed into “VariableFeatures” of the merged object to maintain the repeatedly variable features across each dataset. Within each batch and condition (tissue and infection status), cells identified as doublets by both “DoubletFinder” (v3) (using pN = 0.25, pK = 0.09, PCs = 1:50, anticipated collision rate = 10%) and scds (top 10% of cells ranked by hybrid scores) were removed (n = 3,165 cells) from the analysis. After applying these filtering steps, the dataset contained 198, high-quality cells. Principal component (PC) analysis (PCA) was performed on the data. The resulting data were corrected for batch effects using the “Harmony” package (29) with the top 50 PCs. Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) coordinate generation and clustering were performed using the “RunUMAP”, “FindNeighbors”, and “FindClusters” (resolution = 0.6) functions in Seurat with PCs 1-50. Manual annotation of each cellular cluster was performed by finding the differentially expressed genes using Seurat's “FindAllMarkers” function with default Wilcoxon rank-sum test and comparing those markers with known cell type-specific genes from previous datasets (30, 31). “FindMarkers” function using the MAST algorithm (latent.vars = ‘participant’) based on a Bonferroni-adjusted P < 0.05 and a log2 fold change > 0.25 was used for targeted differential gene expression analysis.

Alignment and preprocessing of scRNA-seq data

The gel beads-in-emulsion (GEM) single cell 3’ kit v3.1 dual index kit (10X Genomics) was used following manufacturer’s recommendations with slight modifications. Briefly, SVC from SAT and VAT was left untreated or infected with SARS-CoV-2 as described above. After 24hpi, cells were collected, washed, and diluted at a density of 1x103 cells per µl in cold DMEM (Life technologies; 11885-092) media supplemented with 10% FBS (Corning, MT35016CV). 10,000 cells per lane were loaded onto a Chromium Controller in the BSC per manufacturer’s instructions. Following GEM creation, samples were transferred into PCR tube strips prior to transferring into a PTC-200 thermocycler (MJ Research) for RT. The RT parameters were the following: 45 minutes at 53o C, followed by 5 minutes at 85o C, then 15 minutes at 60o C and finally samples were kept at 4o C. Barcoded cDNA was removed to BSL2, and sequencing libraries were prepared per manufacturer's recommendation, with a TapeStation 4200 (Agilent) used for quality control. Libraries were pooled for a final concentration of 10nM and sequenced on a NovaSeq S2 (Ilumina) at the Chan Zuckerberg Biohub (San Francisco).

Single cell RNA sequencing (scRNA-seq)

Autopsy samples were prepared as previously described (110). Briefly, RNA was isolated from formalin-fixed and paraffin embedded tissue with the use of Maxwell RSC RNA FFPE (Promega) according to manufacturer recommendations. RTqPCR was performed using the TaqManTM 2019-nCoV control kit v1 (Thermo Scientific, A47533) to target the three viral genes: ORF1ab, S, and N genes, and the human RPPH1 gene. According to the manufacturer's recommendation, a Ct value below 37 in at least two out of three viral genomic regions was considered positive. A case was considered negative if Ct values were above 40. Values between 37 and 40 were considered undetermined and the assay was repeated. Samples were always run in duplicates. RNA-ISH was used to detect SARS-CoV-2 Spike mRNA in tissue samples (for details see the Supplemental Materials and Material).

SARS-CoV-2 detection in autopsies

5µl of the total RNA was used for 1-step RTqPCR. Genomic N-gene quantification was done with the use of CDC qualified primers and probes amplifying the N1 region, n2019-nCoV (Biosearch technologies, KIT-NCOV-PP1-1000). For subgenomic N-gene quantification, E-gene sgRNA forward primer for SARS-CoV-2 leader sequence was combined with CDC N1 gene reverse primer and probe to detect N-gene sgRNA as previously shown (89, 109). RNA and primers were mixed with the TaqPath 1-step RTqPCR master mix (Applied Biosystems, A15299). A standard curve for Ct values and genome copy numbers was obtained using pET21b+ plasmid with the N-gene inserts. All the samples were analyzed in technical duplicates. The Ct cutoff for positive samples was <38 with amplification observed in both duplicates. The samples were analyzed on a StepOnePlusTM real time PCR machine (Applied Biosystems) using the following parameters: (stage 1) 10 minutes at 50ºC for reverse transcription (RT), followed by (stage 2) 3 minutes at 95ºC for initial denaturation and (stage 3) 40 cycles of 10 seconds at 95ºC, 15 seconds at 56ºC, and 5 seconds at 72ºC.

Genomic and subgenomic absolute gene quantification by RTqPCR

The aim of this study was to determine if human adipose tissue is permissive to SARS-CoV-2 infection. We obtained adipose tissue from consented subjects and exposed these samples to SARS-CoV-2 and measured viral entry, replication, and inflammatory pathways by flow cytometry, RTqPCR, scRNA-seq, and Luminex. All analyses were performed in an unbiased fashion. Formalin-fixed and paraffin embedded lung, kidney, adipose and heart tissue from COVID-19 autopsy samples were evaluated for viral RNA. The n value was not controlled and was dependent on availability of samples.

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