Transcriptional Differences for COVID-19 Disease Map Genes between Males and Females Indicate a Different Basal Immunophenotype Relevant to the Disease.
Tianyuan LiuLeandro Balzano-NogueiraAna LleoAna ConesaPublished in: Genes (2020)
Worldwide COVID-19 epidemiology data indicate differences in disease incidence amongst sex and gender demographic groups. Specifically, male patients are at a higher death risk than female patients, and the older population is significantly more affected than young individuals. Whether this difference is a consequence of a pre-existing differential response to the virus, has not been studied in detail. We created DeCovid, an R shiny app that combines gene expression (GE) data of different human tissue from the Genotype-Tissue Expression (GTEx) project along with the COVID-19 Disease Map and COVID-19 related pathways gene collections to explore basal GE differences across healthy demographic groups. We used this app to study differential gene expression of COVID-19 associated genes in different age and sex groups. We identified that healthy women show higher expression-levels of interferon genes. Conversely, healthy men exhibit higher levels of proinflammatory cytokines. Additionally, young people present a stronger complement system and maintain a high level of matrix metalloproteases than older adults. Our data suggest the existence of different basal immunophenotypes amongst different demographic groups, which are relevant to COVID-19 progression and may contribute to explaining sex and age biases in disease severity. The DeCovid app is an effective and easy to use tool for exploring the GE levels relevant to COVID-19 across demographic groups and tissues.
Keyphrases
- coronavirus disease
- sars cov
- gene expression
- genome wide
- end stage renal disease
- ejection fraction
- physical activity
- endothelial cells
- prognostic factors
- electronic health record
- dna methylation
- middle aged
- type diabetes
- big data
- pregnant women
- chronic kidney disease
- risk factors
- mental health
- dendritic cells
- skeletal muscle
- adipose tissue
- quality improvement
- binding protein
- artificial intelligence
- machine learning
- patient reported
- patient reported outcomes
- long non coding rna
- heat stress
- genome wide analysis