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

Normalized counts and metadata from previously published single cell RNA-sequencing data of healthy human airway epithelium15 were downloaded from https://www.genomique.eu/cellbrowser/HCA/. Dimensionality reduction was done using the Seurat Package42 in Rstudio (version 1.2.5019), starting with a principle component analysis. After visual inspection of the principal components using and elbow plot, the first twenty components were used preprint (which was not certified by peer review) is the author/funder.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/10.1101/2021.10.29.466408; this version posted October 29, 2021. The copyright holder for this for graph-based clustering analysis. Clusters of cells were then visualized as diffusion maps (uMAPs). To determine gene expression in ACE2- and TMPRSS2-positive versus negative cells we created two additional metadata slots, in which normalized transcript counts of these genes above 0 were considered positive. Then, cell type assignment and normalized expression of a panel of genes of interest was determined by sub-setting single or double-positive epithelial cells.

Single cell analysis

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators can only draw a single sequence logo for one group of sequences with the same length, and cannot handle sequences with different lengths or other characteristics. It’s essential to align sequence logos in a group level to reveal the similarities and differences on conservation patterns among groups. To solve these problems, we developed MetaLogo, which can draw sequence logos for sequences from multiple groups in one single plot and align multiple logos to highlight the sequence pattern dynamics across groups, and can also provide statistical figures for sequence conservations and group correlations, thus allowing users to investigate functional motifs in a more delicate and dynamic perspective. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized, aesthetic, and publishable sequence logos without any programming experience to present and investigate new knowledges on specific sequence set.

MetaLogo: a generator and aligner for multiple sequence logos

In the past two years, the COVID-19 pandemic has brought obvious impact on human’s health and social activities all over the world. Many laboratories have been seeking clues from blood of COVID-19 patients to help treat the disease, and several potential therapeutic antibodies have been discovered [15]. Among these exploratory studies, Galson et al. deeply sequenced B cell receptors (BCR) from a cohort of COVID-19 patients and discovered convergent immune signatures towards SARS-CoV-2 [16]. In total four hundred sixty-three convergent clonotypes, or CDR3s, were detected, after matching to earlier published studies. These clonotypes were detected in at least four of the COVID-19 patients, but not present in healthy controls or individuals following seasonal influenza vaccination, which means they might response specifically to SARS-CoV-2. Since they did not focus on motifs of these sequences, we downloaded the CDR3s of these clonotypes as well as IGHV gene annotations and tried to use MetaLogo to reveal the conservation patterns among these sequences. Figure 2 shows the results of sequence logos and statistical figures from MetaLogo. Figure 2A shows the multi-group sequence logo of all the 463 clonotypes; MetaLogo automatically divided sequences into different groups according to their lengths. It is obvious that the heads and tails of all the groups show conserved pattern while the middle of them seem more diverse. These logos are aligned by using an adjusted multiple sequence alignment algorithm; similar positions between adjacent groups are connected by light red bands. It is worth noting that in longer groups, like Len20, Len19 and Len18 groups, several Ys in a row are present in sequence logos. In addition, there is an obvious GSY motif in the middle of the sequences in Len16 group. Figure 2B shows sequence counts of each group, which shows that about 30% of sequences have a length of 16. Figure 2C shows entropies of each position of each group. The higher the entropy, the less convergent the position. Figure 2D shows entropy distributions of each group. From the entropy perspective, we can see the middle of the sequences are more diverse than both ends, and longer sequences have more higher entropies, but the Len16 group has an apparent lower median entropy that its adjacent groups, which is interesting. To further explore the characteristics of sequences, we selected sequences from the Len14, Len15 and Len16 groups with annotated V genes IGHV 3-30, IGHV 3-30-3 and IGHV 3-33, perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in bioRxiv preprint doi: https://doi.org/10.1101/2021.08.12.456038; this version posted October 26, 2021. The copyright holder for this which are the most frequently annotated genes in this dataset. From Figure 2E, it reveals that not all sequence with 16 length have the GSY motif. It is indicated that the GSY motif comes from the IGHV 3-30 and IGHV 3-30-3 genes, and more enriched in sequence with 16 length than other lengths. Figure 2F shows the clustering result of groups, groups Len16-V3-30 and Len16-V3-30-3 are clearly clustered together, which shows the specificity of combination of length 16 and annotation IGHV 3-30 (or 3-30-3). According to the above results from MetaLogo, we can conclude that sequences with 16- length and IGHV 3-30 (or 3-30-3) IGHV gene annotation have a clear motif GSY in the middle of the sequences and are distinct from sequences from other groups. We checked the published potential antibodies against SARS-CoV-2 from OAS (Observed Antibody Space) [17] database and found many antibodies contain the motif GSY in their CDR3 sequences, which is consist with our results but reminds the necessity of in-depth study of this phenomenon. The results also indicate the capacity of MetaLogo to discover valuable knowledge among sequences in a convenient way.

MetaLogo Use case

Users can directly access our public web server (http://metalogo.omicsnet.org, Figure 1F), or install MetaLogo in Python package locally. Two examples of sequence sets are provided with the codes. One set contains sequences of E. coli transcription factor binding sites [13] (Figure 1 A-E; MetaLogo web server, example 1), the other set contains sequences of CDR3s of verified antibodies detected in BCR repertoires of individuals with COVID-19 [14] (See MetaLogo web server, example 2). A detailed tutorial for MetaLogo is provided online (https://github.com/labomics/MetaLogo/wiki). After the installation, users can run MetaLogo directly in the system terminal or import MetaLogo functions into their own scripts or projects. Users can also deploy MetaLogo as a web service on their local area network through Docker, perpetuity.  which is convenient for people with no programming experience. Relevant parameters could be set for MetaLogo web server, including the number limitation of allowed sequences and the size limitation of uploaded files, etc.

MetaLogo Package Install and server deployment

#### MetaLogo supports four different logo layouts, including horizontal, circular, radial, and 3D layouts. As shown in Figure 1 A-E, these diverse layouts are suitable for different scenes specifically. The horizontal layout is the default one, which can deal with most scenarios; the circular layout can more clearly show the conservations across multiple sequence groups; the radial layout is suitable to display sequences with conservative motifs in the middle or at the end of the sequences, rather than at the beginning; the 3D layout makes sequence logos more diverse and aesthetic. MetaLogo allows customization of most of the operable elements in the figure, including figure size, ticks size, label size, labels, title, grids, margins between items, colors of items and so on. Specific range of sequences could also be chosen to display for long sequences. Users can also choose whether to display axis, ticks, labels, group ids, etc. Multiple formats of figures are supported, including PNG, PDF, SVG, PS and EPS.

MetaLogo Layouts

The alignment between sequence logos is based on the Needleman–Wunsch algorithm, which is a classic global sequence alignment algorithm. When using MetaLogo, users can choose two alignment modes, one is pairwise alignments between adjacent sequence groups (Figure 1A), and the other is a global logo alignment among all sequence groups (Figure 1B). For global multi-logo alignment, MetaLogo adopts the method of progressive alignment construction [12]. The closest pair of sequence logos are aligned first, and the next logo closest to the aligned sequence logo set is successively added for alignment. Introduced gaps and inserts of each alignment are retained for subsequent alignments until all logos get aligned. In the alignment process, users need to specify a certain similarity metric we mentioned above, and also the penalty for inserts and gaps. After padding and alignment, MetaLogo can visually highlight the highly similar pairs of positions between groups by connecting them using colorful strips.

MetaLogo Alignment

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

Gene ontology and KEGG pathway analysis was performed using R package stringdb (111). Reactome gene set enrichment analysis was performed using the R package fgsea (112), considering ranked gene lists. The Seurat function AddModuleScore() was used to score individual cells by expression of either a list of genes relating to ISGs or cytokines. This function generates an average module score by calculating the mean expression of each gene in the module corrected for expression of a random set of similarly expressing genes. Gene lists used to define each module are defined in table S15. Heatmaps were generated using ComplexHeatmap, Seurat and pheatmap packages, Violin plots were generated using ggplot2, and dotplots were generated using the Seurat packages in R. A Github repository for all original code used for analysis and visualization will be made public upon publication.

scRNA-seq analyses

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

We identified Neandertal variants in the eQTLGen, GTEx and UK and Japan Biobank cohorts based on a list of previously annotated introgressed Neandertal marker variants, referred to as aSNPs [24]. These aSNPs have been annotated in the 1,000 genomes project dataset (v5a) [24,25] and were defined based on the following conditions: (i) one allele is fixed in the African Yoruba population (ii) the second allele is present in a homozygous state in at least in one of the genomes of the Altai Neandertal, Vindjia Neandertal or Denisovan [2,3,26] and (iii) the allele in (ii) being present in at least one non-African 1,000 Genomes individual. We detected 616,647 autosomal aSNPs in the 1,000 Genomes and 151,388 aSNPs in 15 Papuans from the Simons Genome Diversity Project (SGDP) cohort [27]. In addition, we inferred archaic haplotypes for candidate trans-eQTL aSNPs as genomic regions between the left- and rightmost aSNPs showing linkage disequilibrium of r2 >0.8 with the candidate aSNP in the 1,000 Genomes dataset. We required these inferred haplotypes to overlap with previously annotated haplotypes that have been shown to exceed a length that is not consistent with incomplete lineage sorting (ILS), a genomic phenomenon that can also result in allele sharing properties described in (i-iii).

Annotation of introgressed Neandertal variants

Organization: Bhabha Atomic Research Centre, India.
Work: Isolation of environmental bacteriophages for anti-biofilm activity. Characterization of marine and freshwater bacteriophages against pathogenic bacterial strains for their therapeutic applications and combating multidrug-resistant bacterial strains. Searching the genome of marine and freshwater bacteriophages for the presence of novel metabolic pathways.

Genomics expert with 20+ years experience in R&D, marketing, and business consulting.

My interests are to help improve critical care patient outcomes and responses by understanding infectious lung injury and their risk for secondary bacterial infections that can lead to additional lung injury, systemic responses, and poor patient outcomes. I additionally investigate how glycocalyx, composed of glycosaminoglycans, can influence these host (healthy or injured) pathogen interactions.

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