Prediction of SARS-CoV interaction with host proteins during lung aging reveals a potential role for TRIB3 in COVID-19

Diogo de Moraes; Brunno Vivone Buquete Paiva; Sarah Santiloni Cury; João Pessoa Araújo Junior; Marcelo Alves da Silva Mori; Robson Francisco Carvalho

The RNA-Seq data analysis of lung tissues was performed using 427 samples from males and females available at the GTEx portal (release V7) (https://www.gtexportal.org/) 31 . We first used the BioJupies platform (https://amp.pharm.mssm.edu/biojupies/) 32 to identify the differentially expressed genes (DEG) in lung samples during aging. The expression data of each subject was distributed according to their age range: 30-39; 40-49; 50-59; 60-69 and 70-79 years old (yo), and each age range was compared with the group of young adults (20-29 yo) as a common control (Data S1, Supplementary Table S9 . Genes with Log2 of fold change ≥ |1| and false discovery rate ( FDR) < 0.05 were considered as differentially expressed (DEGs). The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.07.030767 doi: bioRxiv preprint Next, we performed a hierarchical clustering analysis based on Pearson correlation for age ranges using the mean expression (Trimmed Mean of M-values, TMM) of the DEGs found previously in, at least, one age range. This analysis aimed at the identification of clusters with gradients of gene expression across age ranges, showing increased or decreased expression during aging (method adapted from Theunissen et al., 2011 33 ). The clusterization of gene expression profiles on female lung samples generated clusters with less evident gradients compared to the males (Figure 1a and Figure S4 ). Thus, considering the clear clusterization found in males, and the fact that older males seem to have a worse prognosis on COVID-19 6 , we focused on male profiles to identify age-related lung genes.