Invest Clin 62(4): 316 - 324, 2021 https://doi.org/10.22209/IC.v62n4a03

Corresponding author: Farnoosh Ebrahimzadeh. Department of Internal Medicine, Faculty of Medicine, Mashhad

University of Medical Sciences, Mashhad, Iran. Email: ebrahimzadehf@mums.ac.ir

Drug-disease interactions of differentially

expressed genes in COVID-19 liver samples:

an in-silico analysis

Suzan Omar Rasool1, Ata Mirzaei Nahr2, Sania Eskandari3, Milad Hosseinzadeh4,

Soheila Asoudeh Moghanloo5 and Farnoosh Ebrahimzadeh6

1Department of Clinical Pharmacy, College of Pharmacy, University of Duhok, Kurdistan

Region, Iraq.

2School of Medical Sciences and Health Services, Tabriz University of Medical Sciences,

Tabriz, Iran.

3Department of Genetic, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

4School of Medical Sciences and Health Services, Zabol University of Medical Sciences,

Zabol, Iran.

5Department of Genetic Enginering, Marvdasht Branch, Islamic Azad University,

Marvdasht.

6Department of Internal Medicine, Faculty of Medicine, Mashhad University of Medical

Sciences, Mashhad, Iran.

Key words: COVID-19; liver; cytochrome P450; gene expression.

Abstract. While COVID-19 liver injuries have been reported in various stud-

ies, concerns are raised about disease-drug reactions in COVID-19 patients. In

this study, we examined the hypothesis of gene-disease interactions in an in-silico

model of gene expression to seek changes in cytochrome P450 genes. The Gene

Expression Omnibus dataset of the liver autopsy in deceased COVID-19 patients

(GSE150316) was used in this study. Non-alcoholic fatty liver biopsies were used

as the control (GSE167523). Besides, gene expression analysis was performed us-

ing the DESeq/EdgeR method. The GO databases were used, and the paths were

set at p<0.05. The drug-gene interaction database (DGIdb) was searched for in-

teractions. According to the results, 5,147 genes were downregulated, and 5,122

genes were upregulated in SARS-CoV-2 compared to healthy livers. Compared

to the cytochromes, 34 cytochromes were downregulated, while 4 cytochromes

were upregulated among the detected differentially expressed genes (DEG).

The drug-gene interaction database (DGIdb) provided a list of medications with

potential interactions with COVID-19 as well as metacetamol, phenethyl iso-

cyanate, amodiaquine, spironolactone, amiloride, acenocoumarol, clopidogrel,

Drug disease interaction of genes in COVID-19 liver 317

Vol. 62(4): 316 - 324, 2021

Interacciones fármaco –enfermedad de genes diferencialmente

expresados en muestras de hígado de COVID-19:

un análisis in-silico.

Invest Clin 2021; 62 (4): 316-324

Palabras clave: COVID-19; hígado; citocromo P450; expresión génica.

Resumen. Mientras que se han informado sobre lesiones hepáticas por

COVID-19 en diversos estudios, las preocupaciones se elevan acerca de las re-

acciones enfermedad-fármaco en los pacientes con COVID-19. En este estudio,

investigamos la hipótesis de las interacciones gen-enfermedad en un modelo

in-silico de la expresión génica para buscar los cambios en los genes del citocro-

mo P450. En este estudio se utilizó el conjunto de datos Ómnibus de la Expre-

sión Génica de la autopsia hepática en los pacientes fallecidos por COVID-19

(GSE150316). Las biopsias de hígado graso no alcohólico se utilizaron como

controles (GSE167523). Además, el análisis de la expresión génica se realizó

mediante el método DESeq / EdgeR. Se utilizaron las bases de datos GO y las

rutas fueron ajustadas en p <0,05. La base de datos de la interacción fármaco-

gen (DGIdb) fue investigada para las interacciones. Según los resultados, 5.147

genes se regularon a la baja y 5.122 genes se regularon al alza en el SARS-CoV-2

en comparación con los hígados sanos. En comparación con los citocromos, 34

citocromos se regularon a la baja, mientras que 4 citocromos fueron regulados

al alza entre la expresión de los genes detectados diferencialmente (DEG). La

base de datos de la interacción fármaco-gen (DGIdb) proporcionó una lista

de medicamentos con las interacciones potenciales con COVID-19, así como

con metacetamol, fenetilo isocianato, amodiaquina, espironolactona, amilori-

da, acenocumarol, clopidogrel, fenprocoumon, trimipramina, fenazepam, etc.

También, los compuestos dietéticos de isoflavonas, valeriana y cumarina, así

como el metabolismo de la cafeína han mostrado tener posibles interacciones

con la enfermedad COVID-19. Nuestro estudio demostró que los niveles de la

expresión de los genes del citocromo P450 podrían quedar alterados siguiendo

COVID-19. Además, se recomienda utilizar una lista de fármaco-enfermedad

interacción para evaluar en las consideraciones clínicas en otros estudios adi-

cionales.

Received: 15-05-2021 Accepted: 06-07-2021

phenprocoumon, trimipramine, phenazepam, etc. Besides, dietary compounds of

isoflavones, valerian, and coumarin, as well as caffeine metabolism were shown to

have possible interactions with COVID-19 disease. Our study showed that expres-

sion levels of cytochrome P450 genes could get altered following COVID-19. In

addition, a drug-disease interaction list is recommended to be used for evalua-

tions in clinical considerations in further studies.

318 Rasool et al.

Investigación Clínica 62(4): 2021

INTRODUCTION

COVID-19 manifestations in the liver

have been recently reported. According to

available evidence, 2-11% of COVID-19 pa-

tients develop liver disease. In 14-53% of the

cases, abnormal levels of alanine aminotrans-

ferase (ALT) and aspartate aminotransferase

(AST) have been seen during progression of

the disease (1). In mild cases of COVID-19,

liver damage is often transient and can re-

turn to its normal state without any special

treatment. However, patients with severe CO-

VID-19 disease appear to have more severe

liver function disorders (2). Liver damage in

patients with SARS-CoV-2 infection could be

directly due to viral infection of liver cells (3)

and immune-mediated inflammations, such

as cytokine storm. In addition, liver damage

could be due to toxicity of prescribed medi-

cations for COVID-19 (4). In patients with

critical forms of COVID-19, it may lead to

liver failure (5, 6). Many concerns have been

raised about the possibility of significant dis-

ease-drug interactions in COVID-19 patients

through modulations of Cytochrome P450s,

as Ghiaty et al. reported (7). Human cyto-

chrome P450 enzymes are monooxygenases

playing a decisive role in the synthesis of

cholesterol, steroids, and other lipids, as well

as in detoxification and metabolism of drugs

and environmental chemicals. Any changes

in expression of the cytochrome P450 gene

or its post-transcriptional level might dys-

regulate its function (8). The cytochrome

P450 enzyme is responsible for metabolizing

a wide range of internal and external organic

compounds. Besides, many drug compounds

involved in treatment are substrates for this

enzyme. This study aims to examine the hy-

pothesis of gene-disease interactions in an

in-silico model of gene expressions to seek

changes in cytochrome P450 genes.

MATERIALS AND METHODS

We conducted a meta-analysis of Gene

Expression Omnibus (GEO) data on the livers

of COVID-19 patients. Datasets of COVID-19

patients were searched using the keywords

of “COVID-19”, “SARS-CoV-2”, and “new

Coronavirus” through a GEO search. The

GEO databases were chosen from 25 avail-

able sources based on the inclusion criterion

that was available gene expression data of the

livers of COVID-19 individuals. Based on the

similarities to the COVID-19 datasets chosen,

control datasets were quarried. Accordingly,

raw read counts (Illumina platforms) of liver

autopsy in deceased COVID-19 patients of

the GSE150316 GEO dataset were included

with three samples in this study. Besides,

GSE167523 samples of humans’ non-alco-

holic fatty liver disease were used as controls.

Gene expression analysis was performed

using the DESeq/EdgeR method, via iDEPR-

Shiny software (9). Lowly expressed genes

were excluded from the dataset through

data processing. Next, gene IDs were con-

verted to a similar format and data were log

transformed for PCA analysis. Besides, dif-

ferentially expressed genes (DEGs) were de-

tected using the DESeq2 package with the

false detection threshold of (FDR) < 0.1 and

a change equal and higher than 2.

To identify important biological pathways

based on common genes affected in the two

datasets, GO databases were used, with the

paths of p <0.05, according to hypergeomet-

ric analysis, having been reported as affected

paths. To identify biological pathways involved

in the studied disease, the drug-gene interac-

tion database (DGIdb) and GO were used. To

interpret the list of the genes based on GO

databases, hypergeometric statistic tests were

used to read the GO pathways and to build a

gene expression regulation network.

Drug disease interaction of genes in COVID-19 liver 319

Vol. 62(4): 316 - 324, 2021

RESULTS

Processing

A total of 26,364 genes were evalu-

ated in 10 samples with the settings of the

counts per million reads mapped (CPM) =

0.5. The difference between replicates in the

SARS-CoV-2 samples compared to healthy

livers showed a significant alteration to the

expression of hundreds of genes in SARS-

CoV-2 infected livers versus the control sam-

ples based on the analysis of the principal

components (PCA) that explained for 67% of

the variance difference between SARS-CoV-2

and control samples (Fig. 1A). Accordingly,

SARS-CoV-2 led to an extensive transcrip-

tional response in the liver (Fig. 1B).

Differentially expressed genes

Finally, 5,147 and 5,122 genes were

detected to have been downregulated and

upregulated, respectively, in SARS-CoV-2

compared to healthy livers (Fig. 2A). Next,

differentially expressed genes that were sig-

nificantly downregulated were subjected to

enrichment analysis based on the GO data-

base. The 10 most significant pathways in

both down- and up-regulated genes are list-

ed in Table I.

Drug-gene interactions

Cytochromes in the livers of COVID-19

patients were examined and quarried. Ac-

cording to the results, 34 cytochromes of CY-

P2E1, CYP3A4, CYP2C9, CYP1A2, CYP2C8,

CYP2A6, CYP4A11, CYP4F2, CYP4A22, CY-

P2C18, CYP8B1, CYP2A7, CYP4F3, CYP7A1,

CYP2C19, CYP2B6, CYP1A1, CYP2D7,

CYP4F11, CYP27A1, CYP3A43, CYP51A1,

CYP4F22, CYP4F12, CYP3A5, CYP26A1,

CYP39A1, CYP11A1, CYP4V2, CYP3A7,

CYP17A1, CYP3A7, CYP3A7-CYP3A51P, and

CYP21A2 were downregulated among the

detected DEGs (Table II). In contrast, 4 cy-

tochromes of CYP4B1, CYP2D6, CYP24A1,

and CYP4F29P were upregulated.

The drug-gene interaction database

(DGIdb) was quarried. As Table II shows,

drugs related to these cytochromes were

extracted. Besides, drug-gene interactions

with the interaction score of higher than 1

were included.

Fig. 1. (A) Principal components (PCA) analysis; (B) Heatmap diagram.

320 Rasool et al.

Investigación Clínica 62(4): 2021

Fig. 2. (A) Volcano plot of differentially expressed genes.

TABLE I

GO DATABASE PATHWAY ENRICHMENT.

Upregulated Downregulated

Number of Genes P-value Number

of Genes

Pathways P-value Number

of Genes

Anatomical structure

morphogenesis

6.3E-71 806 Small molecule

metabolic process

2.7E-155 973

Movement of cells or

subcellular component

1.3E-68 677 Organic acid

metabolic process

7.1E-109 617

Locomotion 1.4E-57 587 Carboxylic acid

metabolic process

7.1E-109 580

Circulatory system

development

2.3E-55 387 Oxoacid metabolic

process

1.6E-107 608

Biological adhesion 6.8E-54 505 Oxidation-reduction process 6.9E-104 552

Cell adhesion 2.1E-53 502 Catabolic process 9.4E-84 1049

Cell motility 2.1E-53 531 Organic substance

catabolic process

9.1E-79 900

Cell migration 3.8E-52 487 Small molecule

biosynthetic process

3.2E-76 386

Regulation of developmental

process

4E-52 754 Lipid metabolic

process

2.3E-75 643

Anatomical structure

formation involved in

morphogenesis

5.1E-51 393 Cellular catabolic

process

3.1E-75 918

Drug disease interaction of genes in COVID-19 liver 321

Vol. 62(4): 316 - 324, 2021

DISCUSSION

This study showed that the function of

the cytochrome P450 genes could get al-

tered following COVID-19. A comparison of

normal liver cells and SARS-CoV-2-infected

hepatic cells showed a decreased expres-

sion level of some of the cytochrome P450

genes. Detoxification is a phenomenon that

eliminates toxins from the body, which oc-

curs in two phases. Accordingly, phase 1 is

catalyzed by cytochrome p450. Products pro-

duced in phase 1 are mainly active oxygen

and compounds causing damage to organs.

These compounds are inactivated by phase

2 enzymes. More than 75% of detoxification

occurs in the liver; however, it occurs in the

intestinal mucosa in some cases.

According to Relats et al. (34), numer-

ous food products react with cytochrome

P450 enzyme-metabolized substances of DG-

Idb. Quarries also showed significant inter-

TABLE II

DRUG-GENE INTERACTIONS.

Symbol log2 Fold

Change

Adjusted

P-value

Drug name (Interaction Score) [reference]

CYP2E1 -16.15 1.70E-50 METACETAMOL (5.55) (10)

VALERIAN (2.77) (11)

PERFLUBRON(5.55)(12)

PHENETHYL ISOCYANATE(5.55)(13)

BRADANICLINE(1.39)(14)

ISOFLAVONE(1.85)(15)

CYP3A4 -13.89 4.04E-40 –

CYP2C9 -13.35 3.14E-35 –

CYP1A2 -12.71 2.62E-28 –

CYP2C8 -12.55 1.67E-45 AMODIAQUINE (2.36) (16-17)

CERIVASTATIN (1.77)(18-19)

CYP2A6 -11.84 1.01E-15 COUMARIN (4.21) (20)

CAFFEINE (4.08) (21)

LETROZOLE (9.57) (22)

3-FORMYLINDOLE (5.32) (23)

CHEMBL1770735 (5.32) (24)

IFOSFAMIDE (2.07) (25)

CYP4A11 -11.62 7.24E-39 SPIRONOLACTONE (1.68)[None found]

AMILORIDE (2.13)[None found]

CYP4F2 -10.97 2.68E-24 ACENOCOUMAROL (23.92) (26)

CLOPIDOGREL (1.2) [ None found]

PAFURAMIDINE MALEATE (7.97)[None found]

PHENPROCOUMON (3.42)(27)

CYP4B1 +10.32 9.33E-12 THALIDOMIDE (1.82) (28-29)

CYP2D6 +6.322 1.77E-03 SPARTEINE(1.84)(30)

CYP24A1 +6.25 2.58E-02 LUNACALCIPOL (12.76) [None found]

CHEMBL255088 (4.25) [None found]

TELAPREVIR (4.25) (31)

DEFERASIROX (3.83) (32)

CALCITRIOL (2.32) (33)

322 Rasool et al.

Investigación Clínica 62(4): 2021

actions with some nutrients, while its clini-

cal significance is ambiguous.

Our search at DGIdb showed possible

effects of COVID-19 on isoflavones, valerian,

coumarin, and caffeine metabolism. Most of

nutrients could be inhibitors of cytochromes.

Accordingly, they reduce metabolic activity

of the cytochrome P450 enzyme.

Medications listed in Table II were found

to have interactions with cytochrome P450 en-

zymes. However, these results require further

clinical research to confirm such interactions.

Our results support the idea of disease-drug in-

teractions in COVID-19 patients.

The most significant interactions were

found to be those of CYP4F2 with acenocou-

marol, with the interaction score of 23.92

followed by letrozole with the interaction

score of 9.57.

On the other hand, with the advent of CO-

VID-19, many people may seek herbal, supple-

mentary, or dietary treatments as there is no

definitive treatment for COVID-19 (11). Our

study found some consistency with the in-vivo

study of the Gurley et al. (11), which showed

interactions of the black cohosh and valerian

with human cytochromes of P450, 1A2, 2D6,

2E1, and 3A4/5 phenotypes.

Limitations of the study

While most similar control datasets

were in non-alcoholic fatty liver disease, the-

re could be some bias towards results of the

study as non-alcoholic fatty liver may have

different forms of gene expression with com-

pletely healthy samples. Besides, lots of con-

founding factors could be effective as results

of various studies show that genetic poly-

morphisms might affect the body’s response

to COVID-19 (35,36).

The present study was a comprehensi-

ve analysis of COVID-19 disease-gene inte-

ractions that could contribute to adverse

effects. The identified differentially expres-

sing genes in this study might contribute to

interactions with COVID-19 and present a

drug-disease-interaction list to be conside-

red for further realistic clinical research.

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