DOI: 10.5281/zenodo.12561715
title: Analysis of hMSC-derived chondrocytes in response to stimulation with SARS-CoV-2 spike proteins

description: Previous studies have detected the presence of SARS-CoV-2 viral proteins in bronchial cartilage chondrocytes. We hypothesised that the leakage of viral proteins to local joint tissue was due to virus-induced endothelial dysfunction. Studies have also shown upregulation of endothelin-1 (ET-1), the most potent vasoconstrictor, in COVID patients. We are investigating the direct effect of SARS-CoV-2 spike protein (SP) and the host response to chondrocytes.

Human mesenchymal stem cells (hMSCs)-differentiated chondrocytes were treated with either full-length SARS-CoV-2 spike protein (SP) only or a combination of spike protein, neutralising antibody to S1 and endothelin-1 (SAE) to mimic viral insult and host response respectively. RNA sequencing was performed to compared the change in transcriptome in control, SP, and SAE. All samples were processed in the same batch. Default quality control parameters were used.
description: Groupings



Control - human mesenchymal stem cells-differentiated chondrocytes (hMSCs)

⁠SP - hMSCs + 5 ug/mL full-length SARS-CoV-2 spike protein (SP), 24h

⁠SAE - hMSCs + 5 ug/mL SP + 5 ug/mL neutralising antibody to S1 + 100nM ET-1, 24h Three replicates for each group. Samples are collected for bulk RNA seq.


Bioinformatics Analysis

Raw sequencing reads were first filtered for adapter and low-quality sequences, retaining only reads with a length of 40bp or more. Low-quality sequences were identified as reads with over 5% unknown bases (“N”) or reads with more than 50% of bases having a quality value of 11 or lower. Subsequently, the filtered reads were aligned to the GRCh38 reference genome using STAR 2.7.8 (1). RNA expression levels were quantified by RSEM 1.2.31 (2). The raw sequencing data was deposited in the Gene Expression Omnibus (GSE267009).

Two independent researchers performed differential gene expression analysis, henceforth referred to as 'Original Analysis' and 'New Analysis'.



Original Analysis


The default EBSeq 1.18.0 (3) was used to conduct differential expression analysis. Results for comparison between Control and SP, Control and SAE, and SP and SAE are shown in 'C_SP_clean.csv', 'C_SAE_clean.csv', and 'SP_SAE_clean.csv', respectively, and altogether in 'YuanSF_RNASeq_CPOS-230919-SY-19861a_Gene_DiffExpressed.xlsx'.

DEGs with FDR < 0.05 were included in KEGG enrichment analysis using GAGE 2.52.0 (4). Compared with Control, the top 20 upregulated KEGG pathways in the SP group, and all upregulated pathways with p-value less than 0.05 in the SAE group were plotted with ggplot2 3.5.0. Rich ratio is calculated by the number of DEGs in a given pathway divided by the total number of genes in this pathway. Details of KEGG enrichment analysis can be found in 'source codes_kegg.Rmd'. Results of KEGG enrichmnet analysis for each comparison and the generated figures are included in 'Original Analysis Results.zip'.

Genes involved in cellular senescence and cytokine-cytokine interaction were studied. Selected DEGs of interest in these pathways were plotted with gplots 3.1.3.1. Normalized gene expression using TPM was presented in heatmaps, with adjusted p-value. Colours were scaled with respect to each row to emphasize the inter-sample difference. Row Z-Score was produced by subtracting the mean value and then divided by the standard deviation of each row. Details of KEGG enrichment analysis can be found in 'source codes_heatmap.Rmd'. The generated figures are included in 'Original Analysis Results.zip'.



New Analysis


Differential expression analysis was performed using limma 3.58.1 (5) in R. Comparisons were made between the Ctrl and SP groups, Ctrl and SAE groups, and SP and SAE groups. Differentially expressed genes and transcripts were identified based on a threshold of p-value < 0.05. Details can be found in '01_DEGs.R'. Results for each comparison can be found in the corresponding folder in 'New Analysis Results.zip'.

KEGG pathway and GO functional enrichment analyses of the differentially expressed genes (p< 0.05, Fold Change >1) were conducted using ClusterProfiler 4.0.5 (6) in R70. Enrichment significance was assessed using the hypergeometric test, and p-values were adjusted using the Benjamini-Hochberg (BH) method to obtain q-values. Pathways and GO terms with q<0.05 were considered significantly enriched. Further details can be found in '02_PathwayEnrichment.R', '02_PathwayEnrichment_barplot.R' and '02_PathwayEnrichment_dotplot.R'. Results for each comparison and the generated figures can be found in the corresponding folders in 'New Analysis Results.zip'.

Heatmaps depicting the expression levels of genes of interest across samples were generated using ggplot2 3.3.5 in R, with unadjusted p-values. Further details can be found in '03_DEGs_Heatmap.R'. The expression level, p-values, and the generated figures can be found in the corresponding folder in 'New Analysis Results.zip'.
description: Sample Information
SRA Accession No.
GEO Accession No.




Control, replicate 1
SRR28789040
GSM8258185


Control, replicate 2
SRR28789039
GSM8258186


Control, replicate 3
SRR28789038
GSM8258187


Treated with 5 ug/mL full-length SARS-CoV-2 spike protein, replicate 1
SRR28789037
GSM8258188


Treated with 5 ug/mL full-length SARS-CoV-2 spike protein, replicate 2
SRR28789036
GSM8258189


Treated with 5 ug/mL full-length SARS-CoV-2 spike protein, replicate 3
SRR28789035
GSM8258190


Treated with a combination of SARS-CoV-2 spike protein (5 ug/mL), the neutralising antibody to S1 region (5 ug/mL), and 100nM endothelin-1, replicate 1
SRR28789034
GSM8258191


Treated with a combination of SARS-CoV-2 spike protein (5 ug/mL), the neutralising antibody to S1 region (5 ug/mL), and 100nM endothelin-1, replicate 2
SRR28789033
GSM8258192


Treated with a combination of SARS-CoV-2 spike protein (5 ug/mL), the neutralising antibody to S1 region (5 ug/mL), and 100nM endothelin-1, replicate 3
SRR28789032
GSM8258193
description: References




A. Dobin et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).

B. Li, C. N. Dewey, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. Bmc Bioinformatics 12, (2011).

N. Leng et al., EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments (vol 29, pg 1035, 2013). Bioinformatics 29, 2073-2073 (2013).

W. Luo et al., GAGE: generally applicable gene set enrichment for pathway analysis. Bmc Bioinformatics 10, 161 (2009).

M. E. Ritchie et al., limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43, 7 (2015).

G. Yu et al., clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 16, 5, 2012.