DCcov

Drug repositioning of single and drug combinations using Flux Balance Analysis:

Table of contents

• Publication
• Pipeline
• Abstract
• Results
• Installation
• Analysis

Publication

Kishk, Ali, Maria Pires Pacheco, and Thomas Sauter. ‘DCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Constraint-Based Modeling’. IScience 24, no. 11 (19 November 2021): 103331. https://doi.org/10.1016/j.isci.2021.103331. University of Luxembourg.

Pipeline

Abstract

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).

Results

Repositioned Drugs from Single Gene Deletion, with Their Essential Genes and Pathways

Repositioned Drug Combination from Double Gene Deletion, with Their Essential Gene-Pairs and Pathways

Installation

Matlab:

• COBRA Toolbox V3: https://opencobra.github.io/cobratoolbox/stable/installation.html
• rFASTCORMICS: https://github.com/sysbiolux/rFASTCORMICS

R: apt install r-base r-base-core r-recommended r-base-dev

Most needed R packages can be installed from:

Rscript Install_R_Dependancies.R

• edgeR==3.30.3
• DESeq2==1.28.1
• FactoMineR==2.4
• networkD3==0.4
• ggplot==3.3.3

Python:
apt install python3.7
pip install gseapy==0.9.17 jupyter==4.5.0 numpy pandas seaborn matplotlib

Analysis

Step 1:

Download the raw expression data

Create a new directory to download the read count data in

mkdir data data/time_series_study data/severity_study

Begin downloading the FPKM / RPKM and metadata

The severity study

wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE147nnn/GSE147507/suppl/GSE147507_RawReadCounts_Human.tsv.gz -P data/severity_study/
gunzip data/severity_study/GSE147507_RawReadCounts_Human.tsv.gz

The time-series study

Download manually the FPKM for the polyA of GSE148729: https://filetransfer.mdc-berlin.de/?u=CVXckugR&p=MACT6Xw9

• SupplementaryData2_Calu3_polyA_series1_fpkm.tsv
• SupplementaryData5_H1299_polyA_fpkm.tsv
• SupplementaryData3_Calu3_polyA_series2_fpkm.tsv

PCA analysis on the severity study

mkdir Figs data/severity_study/DEGs
Rscript PCA_analysis.R

DEG analysis on the severity study

Rscript DEG_analysis.R

Step 2:

Calculate the differentially expressed metabolic pathways

Calculate differentially expressed reactions (DER) for the severity study

matlab -nodisplay -nodesktop -r Differentially_Expressed_Reactions_Table.m

Visualize DERs

jupyter nbconvert --to notebook --inplace --execute Visualize_Differentially_Expressed_Pathways.ipynb

Step 3:

Extract the metadata from the FPKM and read count files of the severity study

The time-series study

Extract metadata and\nDividing the lung FPKM according to ecah phenotype to a seperate csv file for model reconstruction

jupyter nbconvert --to notebook --inplace --execute FPKM_Dividing_Timeseries_Study.ipynb

Extract gene lengths for RPKM for the severity study

Rscript Extract_gene_lengths.R

Extract metadata and calculate RPKM for model building for the severity study

jupyter nbconvert --to notebook --inplace --execute Calculate_RPKM.ipynb

Differentially Expressed Metabolic Pathways

Step 4:

Model building, then single and double gene deletion

mkdir models models/timeseries models/severity KO_data KO_data/timeseries KO_data/severity

Model building and single and double gene deletion on the severity study

matlab -nodisplay -nodesktop -r Model_building_severity_Recon3D.m
matlab -nodisplay -nodesktop -r Model_building_severity_Recon2.m

Model building and single and double gene deletion on the time series study

matlab -nodisplay -nodesktop -r Model_building_timeseries_Recon3D.m
matlab -nodisplay -nodesktop -r Model_building_timeseries_Recon2.m

Summerize all KO results from different conditions and models

matlab -nodisplay -nodesktop -r Summarize_KO_Results_severity.m
matlab -nodisplay -nodesktop -r Summarize_KO_Results_timeseries.m

Pathway analysis of the SKO and DKO using KEGG

jupyter nbconvert --to notebook --inplace --execute Enrichment_Analysis_KEGG.ipynb

Metabolic Pathway Analysis of the SKO genes

matlab -nodisplay -nodesktop -r SKO_Metabolic_Pathway_Analysis.m
jupyter nbconvert --to notebook --inplace --execute Visualize_SKO_Pathways.ipynb

Step 5:

Drug Repurpusing for single and drug combinations

mkdir drugbank

Download DrugBank .xml manually to drukbank folder

convert .xml to csv and integrate MedDRA side effect database

jupyter nbconvert --to notebook --inplace --execute DrugBank-MedDRA_Integration.ipynb

Calculate essentiality and safety and apply drug repurusing using DrugBank for single and double gene deletion

jupyter nbconvert --to notebook --inplace --execute Drug_Repurposing_DrugBank.ipynb

Build a tripartite network of all drug-gene-pathways interactions for single and double gene deletion

jupyter nbconvert --to notebook --inplace --execute Drug_Repurposing_Map_Drug_Gene_Pathway.ipynb
Rscript Visualize_Tripartite_Drug_Gene_Pathway.R