Back

Human Surfactant Protein A Alleviates SARS-CoV-2 Infectivity in Human Lung Epithelial Cells

Jacob, I. B.; Gemmiti, A.; Xiong, W.; Reynolds, E.; Nicholas, B.; Thangamani, S.; Jia, H.; Wang, G.

2023-04-03 molecular biology
10.1101/2023.04.03.535215 bioRxiv
Show abstract

SARS coronavirus 2 (SARS-CoV-2) infects human angiotensin-converting enzyme 2 (hACE2)-expressing lung epithelial cells through its spike (S) protein. The S protein is highly glycosylated and could be a target for lectins. Surfactant protein A (SP-A) is a collagen-containing C-type lectin, expressed by mucosal epithelial cells and mediates its antiviral activities by binding to viral glycoproteins. This study examined the mechanistic role of human SP-A in SARS-CoV-2 infectivity. The interactions between human SP-A and SARS-CoV-2 S protein and hACE2 receptor, and SP-A level in COVID-19 patients were assessed by ELISA. The effect of SP-A on SARS-CoV-2 infectivity was analyzed by infecting human lung epithelial cells (A549-ACE2) with pseudoviral particles and infectious SARS-CoV-2 (Delta variant) pre-incubated with SP-A. Virus binding, entry, and infectivity were assessed by RT-qPCR, immunoblotting, and plaque assay. The results showed that human SP-A can bind SARS-CoV-2 S protein/RBD and hACE2 in a dose-dependent manner (p<0.01). Human SP-A inhibited virus binding and entry, and reduce viral load in lung epithelial cells, evidenced by the dose-dependent decrease in viral RNA, nucleocapsid protein, and titer (p<0.01). Increased SP-A level was observed in the saliva of COVID-19 patients compared to healthy controls (p<0.05), but severe COVID-19 patients had relatively lower SP-A levels than moderate COVID-19 patients (p<0.05). Therefore, SP-A plays an important role in mucosal innate immunity against SARS-CoV-2 infectivity by directly binding to the S protein and inhibiting its infectivity in host cells. SP-A level in the saliva of COVID-19 patients might serve as a biomarker for COVID-19 severity.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.