Exploring COVID-19 Pandemic Disparities with Transcriptomic Meta-analysis from the Perspective of Personalized Medicine

Abstract

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

Keywords: Bioinformatics; COVID-19; Gender and age disparities; Host-directed drug candidates; Personalized medicine; Transcriptome.

Fig. 1

The parameters considered in the meta-analysis of gene expression datasets. In the analysis, samples were classified according to the collection source (nasopharyngeal and oropharyngeal (NP / OP) swabs or blood) (Parameter 1), gender (Parameter 2), and age (Parameter 3)

Fig. 2

The number of samples used in the meta-analysis of the transcriptome datasets. A The number of samples corresponding to the source of nasopharyngeal and oropharyngeal swab samples. B The number of samples corresponding to the source of blood collection. The pie charts show the number of samples considering gender differences and the dot charts show the gender and age differences between COVID-19 and control phenotypes. The green color corresponds to the COVID-19 patient phenotype, while the yellow corresponds to the control phenotype

Fig. 3

The number of differentially expressed genes (DEGs) in the different cases. A The number of DEGs found for the nasopharyngeal and oropharyngeal swab sources. Venn diagrams represent the common DEG number between cases. Red numbers represent core-DEG numbers accepted in the study. B The number DEGs found for blood sources. Venn diagrams show the common DEG number between cases. The red numbers represent the core-DEG numbers that were accepted in the study. C The results of the comparative analysis of the core-DEGs

Fig. 4

Pathway enrichment analysis results of the core-DEGs. A Core-DEGs belonging to the source of nasopharyngeal and oropharyngeal swab specimens. B Core-DEGs belonging to the source of the blood samples. Pathways highlighted in blue represent common pathways for both sources

Fig. 5

Reconstruction of SARS-CoV-2 and human protein interaction networks. A Protein–protein interaction network between SARS-CoV-2 and humans obtained from database resource. B Integration of viral proteins with core-DEGs resulted from nasopharyngeal and oropharyngeal swab sources. C Integration of viral proteins with core-DEGs resulted from blood sources

Fig. 6

Results of the analysis of drug repurposing. A Top ten drug candidates resulted from nasopharyngeal and oropharyngeal swabs. B The top ten drug candidates resulted from blood sources. The size of the circle varies with statistical significance (p-value), and the most significant drug candidate is represented by the largest circle. Drugs highlighted in blue represent common drug candidates for both sources