Expression Level Analysis of ACE2 Receptor Gene in African-American and Non-African-American COVID-19 Patients
Nyamari, M. N.; Omar, K. M.; Fayehun, A. F.; Dachi, O.; Bwana, B. K.; Awe, O. I.
Show abstract
BackgroundThe COVID-19 pandemic caused by SARS-CoV-2 has spread rapidly across the continents. While the incidence of COVID-19 has been reported to be higher among African-American individuals, the rate of mortality has been lower compared to that of non-African-Americans. ACE2 is involved in COVID-19 as SARS-CoV-2 uses the ACE2 enzyme to enter host cells. Although the difference in COVID-19 incidence can be explained by many factors such as low accessibility of health insurance among the African-American community, little is known about ACE2 expression in African-American COVID-19 patients compared to non-African-American COVID-19 patients. The variable expression of genes can contribute to this observed phenomenon. MethodologyIn this study, transcriptomes from African-American and non-African-American COVID-19 patients were retrieved from the sequence read archive and analyzed for ACE2 gene expression. HISAT2 was used to align the reads to the human reference genome, and HTseq-count was used to get raw gene counts. EdgeR was utilized for differential gene expression analysis, and enrichR was employed for gene enrichment analysis. ResultsThe datasets included 14 and 33 transcriptome sequences from COVID-19 patients of African-American and non-African-American descent, respectively. There were 24,092 differentially expressed genes, with 7,718 upregulated (log fold change > 1 and FDR 0.05) and 16,374 downregulated (log fold change -1 and FDR 0.05). The ACE2 mRNA level was found to be considerably downregulated in the African-American cohort (p-value = 0.0242, p-adjusted value = 0.038). ConclusionThe downregulation of ACE2 in the African-American cohort could indicate a correlation to the low COVID-19 severity observed among the African-American community.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A proteomic perspective and involvement of cytokines in SARS-CoV-2 infection 97%
- Laboratory Biomarkers of COVID-19 Disease Severity and Outcome: Findings from a Developing Country 96%
- COVID-19 Disease Severity and Determinants among Ethiopian Patients: A study of the Millennium COVID-19 Care Center 96%
Similar papers in this journal
- Structural variability, expression profile and pharmacogenetics properties of TMPRSS2 gene as a potential target for COVID-19 therapy 98%
- Integrating Bioinformatics and Artificial Intelligence Methods to identify disruptive STAT1 variants impacting Protein Stability and Function 96%
- Epigenetic Evolution of ACE2 and IL-6 Genes as Non-Canonical Interferon-Stimulated Genes Correlate to COVID-19 Susceptibility in Vertebrates 94%
Similar papers in this journal
- Deciphering inhibitory mechanism of coronavirus replication through host miRNAs-RNA-dependent RNA polymerase (RdRp) interactome 94%
- Hypoxia induced sex-difference in zebrafish brain proteome profile reveals the crucial role of H3K9me3 in recovery from acute hypoxia 93%
- Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data 93%
Similar papers in this journal
- A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions 95%
- Analysis of serum trace elements, macro-minerals, antioxidants, malondialdehyde and immunoglobulins in seborrheic dermatitis patients: A case-control investigation 95%
- A small H2O-soluble ingredient of royal jelly lower cholesterol levels in liver cells by suppressing squalene epoxidase 94%
Similar papers in this journal
- Is the infection of the SARS-CoV-2 Delta variant associated with the outcomes of COVID-19 patients? 97%
- Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology 96%
- SARS-CoV-2 testing in the Slovak Republic from March 2020 to September 2022 – summary of the pandemic trends 94%
"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.