Expression Level Analysis of ACE2 Receptor in African-American and non-African-American COVID-19 patients

Global Malaria belt

source: http://www.malariacampaign.gov.lk/en/travelers-guide

Background

The global 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 the African-American community, the rate of mortality has been lower compared to that of white and native 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 accesibility of health insuarance among Africa-American community, the variable expression of genes has been linked to this observed phenomenon. Little is known about ACE2 expression in African-American COVID-19 patients compared to non-African-American COVID-19 patients.

General Objective

To analyze expression level of ACE2 gene in African-American COVID-19 patients compared to non- African- American COVID-19 patients.

Methods

In this study, transcriptomes from African-American and non-African-American COVID-19 patients were downloaded from the NCBI sequence read archive (SRA) and analyzed for ACE2 gene expression. The reads were aligned to the human reference genome using HISAT2 and the raw counts generated using HTseq-count. EdgeR was used to conduct differential gene expression analysis with statistical analysis being done in R.

Analysis Workflow

Prerequisites for Analysis

The data used for the analysis was retrieved from NCBI. The accession numbers for the data used can be found under accession.

Tools and packages.

Prior to installation of the analysis packages you need to have a unix environment i.e linux or macOS. Moreover you need to install R and RStudio IDE.

Installation of unix packages.

#Fastqc v0.11.9
wget https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.11.9.zip

#Cutadapt v1.15.1
apt-get -y install cutadapt=1.15-1

#Hisat2
apt-get install -y hisat2=2.1.0-1

#Htseq-count
apt-get -y install python-htseq=0.6.1p1-4build1

#Samtools v1.7.1
apt-get install -y samtools=1.7-1

Installation of packages in RStudio

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
    
#edgeR: 3.36.0    
BiocManager::install("edgeR")

Pipeline Summary

A bioinformatics analysis pipeline used for rnaseq analysis. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. The pipeline is written in DSL1; however, we are working on re-writing the pipeline in DSL2.

By default, the pipeline currently performs the following:

Sequencing quality control (FastQC)
Combining quality control reports summaries (MultiQC)
Trimming of adaptors (Cutadapt)
Index the human reference genome (HISAT2-build)
Align reads to reference genome (HISAT2)
Convert sam to bam files (samtools)
Generate gene counts (htseq-count)
Import counts into (R)
Differential gene expression (EdgeR)

Team members.

1. Marion Nyaboke Student, MSc. Bioinformatics, Pwani University, Kenya | Project/Tech Lead.
2. Kauthar M. Omar MSc. Bioinformatics student, Pwani University, Kenya | Writer's Lead.
3. Ayorinde F. Fayehun MPhil candidate, Biotechnology unit, Institute of Child Health, University of Ibadan, Nigeria | writer.
4. Oumaima Dachi MSc. Bioinformatics & Data science, National School of Arts and Crafts, University of Hassan II Casablanca, Morocco.
5. Billiah Bwana MSc. Genetics student, University of Embu, Kenya.
6. Awe Olaitan, University of Ibadan, Oyo State, Nigeria & African Society for Bioinformatics and Computational Biology, South Africa