Transcription Factor Driven Gene Regulation in COVID-19 Patients
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Pipeline of the Work
- Step 1: Collecting human proteins interacting with Spike glycoprotein of SARS-CoV-2.
- Step 2: Collecting human Transcription Factors and their PWMs.
- Step 3: Computing those TFs among the ones collected in Step 2 that are significantly associated with the proteins collected in Step 1.
- Step 4: Computing correlations between significant TFs (Step 3) and corresponding target genes and analysing differential regulation in COVID-19 patients and healthy individuals.
- Step 5: Analysing significant TFs identified in Step 3.
- Step 6: Analysing significant genes, their ranking and overlap in different organs.
- Step 7: Analysing key genes.
2.2.1. Identification of Significant TFs
2.2.2. Gene—TF Correlations between COVID-19 Patients and Healthy Individuals
- C(tf,g,org,p) = −1, if g is a target gene of (at least one predicted TFBS associated with occurs in the promoter region of g), the Pearson correlation value between expression data of and g for the organ for condition p (COVID-19 or healthy) is negative and the associated p-value is smaller than .
- C(tf,g,org,p) = +1, if g is a target gene of (at least one predicted TFBS associated with occurs in the promoter region of g), the Pearson correlation value between expression data of and g for the organ for condition p (COVID-19 or healthy) is positive and the associated p-value is smaller than .
- C(tf,g,org,p) = 0, otherwise
- A strictly positive value of for the couple means that the given TF pushes up expression of gene G in COVID-19 patients while it does not do so in healthy individuals, or that the given TF pushes down expression of G in healthy individuals while it does not do so in COVID-19 patients.
- On the contrary, a strictly negative value of for the couple means that the given TF pushes up expression of gene G in healthy individuals while it does not do so in COVID-19 patients, or that the given TF pushes down expression of G in COVID-19 patients while the inverse is not true.
3. Results
3.1. Transcription Factors Significantly Associated with COVID-19 Infection
3.2. Transcription Factors Having a Major Impact on Specific Organs
3.3. Differentially Regulated Genes in Specific Organs
3.4. Drugs
4. Discussion
4.1. Protein–Protein Interaction Network and KEGG Pathway Analysis
4.2. Gene Ontology (GO) Enrichment Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
TF | Transcription Factors |
TFBS | Transcription Factor Binding Site |
C | Correlation |
DC | Differential Correlation |
PWM | Position Weight Matrix |
KEGG | Kyoto Encyclopedia of Genes and Genomes |
GO | Gene Ontology |
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GEOID | Organ | Number of Genes | COVID-19 | Healthy |
---|---|---|---|---|
GSE163151 | Blood | 21,952 | 145 | 113 |
GSE164332 | Brain | 57,996 | 9 | 7 |
GSE164073 | Eye | 25,222 | 9 | 9 |
GSE162736 | Heart | 23,194 | 24 | 24 |
GSE159201 | Intestine | 33,550 | 12 | 12 |
GSE173707 | Kidney | 24,975 | 9 | 9 |
GSE151803 | Liver | 22,316 | 12 | 9 |
GSE147507 | Lung | 20,748 | 21 | 29 |
GSE152075 | Nasopharynx | 19,744 | 430 | 54 |
GSE165890 | Pancreas | 22,057 | 6 | 6 |
GSE156063 | Respiratory tract | 15,811 | 93 | 141 |
GSE153684 | Stomach | 26,501 | 9 | 9 |
GSE171995 | Uterus | 19,715 | 5 | 3 |
TF | Matrix ID–PWM | p-Value | Adjusted p-Value |
---|---|---|---|
KLF2 | MA1515.1 | ||
KLF3 | MA1516.1 | ||
KLF15 | MA1513.1 | ||
KLF6 | MA1517.1 | ||
NRF1 | MA0506.1 | 0.00016 | |
SP9 | MA1564.1 | 0.00027 | |
ZNF460 | MA1596.1 | 0.00061 | |
ELK3 | MA0759.1 | 0.00088 | |
HES1 | MA1099.2 | 0.00381 | |
ZBTB14 | MA1650.1 | 0.00418 | |
YY2 | MA0748.2 | 0.00845 | |
ETV3 | MA0763.1 | 0.01290 | |
ELK1 | MA0028.2 | 0.01493 | |
OTX2 | MA0712.2 | 0.01594 | |
ETV5 | MA0765.2 | 0.03179 | |
ETV6 | MA0645.1 | 0.03611 | |
KLF5 | MA0599.1 | 0.03843 | |
SREBF1 | MA0829.2 | 0.03895 | |
ELK4 | MA0076.2 | 0.04098 |
Designation | Organs | Genes |
---|---|---|
4* | Blood, Heart, Nasopharynx, Respiratory tract | HSPBP1 |
3* | Blood, Nasopharynx, Respiratory tract | P4HB |
Blood, Nasopharynx, Respiratory tract | RPS14 | |
Heart, Lung, Nasopharynx | ARCN1 | |
Heart, Nasopharynx, Respiratory tract | PRPF6 | |
Lung, Nasopharynx, Respiratory tract | BZW1 | |
Blood, Nasopharynx, Respiratory tract | RPS25 | |
Blood, Nasopharynx, Respiratory tract | NCL | |
Blood, Nasopharynx, Respiratory tract | HEATR3 | |
Blood, Heart, Nasopharynx | HNRNPH1 | |
Blood, Nasopharynx, Respiratory tract | NOP58 | |
2* | Nasopharynx, Respiratory tract | ARF1 |
Heart, Nasopharynx | POM121 | |
Nasopharynx, Respiratory tract | COQ8A | |
Nasopharynx, Respiratory tract | PCBP1 | |
Nasopharynx, Respiratory tract | DYNLL1 | |
Blood, Nasopharynx | TECR | |
Nasopharynx, Respiratory tract | ACTB | |
Nasopharynx, Respiratory tract | MMS19 | |
Heart, Nasopharynx | PFKP | |
Blood, Nasopharynx | HADHB | |
1* | Nasopharynx | PPP2R1A |
Nasopharynx | RPLP2 | |
Nasopharynx | NUP188 | |
Nasopharynx | VAC14 | |
Nasopharynx | NOM1 | |
Nasopharynx | CYC1 | |
Nasopharynx | SEC61A1 | |
Nasopharynx | PSMD2 | |
Nasopharynx | DNAJC7 | |
Nasopharynx | KARS1 |
Human Genes | Drugs | Adjusted p-Value | Drug Bank ID | Treatment |
---|---|---|---|---|
ARF1, PPP2R1A, HNRNPH1, P4HB, ACTB | Nitrofural | 0.0052 | DB00336 | Topical antibacterial for the prevention and treatment of bacterial infections of the skin |
SEC61A1, ARF1, DNAJC7, PCBP1, PSMD2, TECR, CYC1, P4HB, ACTB, PFKP | Clindamycin | 0.0077 | DB01190 | Antibiotic used to treat serious infections caused by susceptible anaerobic, streptococcal, staphylococcal and pneumococcal bacteria |
HNRNPH1, PSMD2, NCL, ACTB, ARCN1 | Ipratropium Bromide | 0.0101 | DB00332 | Used in the control of symptoms related to bronchospasm in chronic obstructive pulmonary disease (COPD) |
ARF1, PRPF6, PPP2R1A, TECR, RPLP2, ACTB | Ambroxol | 0.0130 | DB06742 | Airway secretion clearance therapy |
ARF1, PPP2R1A, TECR, P4HB, ACTB | Paclitaxel | 0.0133 | DB01229 | Treatment of advanced carcinoma of the ovary, and other various cancers including breast and lung cancer |
ARF1, PPP2R1A, ACTB | Benserazide | 0.0236 | DB12783 | Treat Parkinson’s disease, Parkinsonism, and restless leg syndrome |
ARF1, PPP2R1A, HNRNPH1, ACTB | Amikacin | 0.0351 | DB00479 | An aminoglycoside used to treat infections caused by more resistant strains of Gram-negative bacteria and some Gram-positive bacteria |
ARF1, P4HB, ACTB | Captopril | 0.0420 | DB01197 | An ACE inhibitor used for the management of essential or renovascular hypertension, congestive heart failure, left ventricular dysfunction following myocardial infarction and nephropathy |
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Santoni, D.; Ghosh, N.; Derelitto, C.; Saha, I. Transcription Factor Driven Gene Regulation in COVID-19 Patients. Viruses 2023, 15, 1188. https://doi.org/10.3390/v15051188
Santoni D, Ghosh N, Derelitto C, Saha I. Transcription Factor Driven Gene Regulation in COVID-19 Patients. Viruses. 2023; 15(5):1188. https://doi.org/10.3390/v15051188
Chicago/Turabian StyleSantoni, Daniele, Nimisha Ghosh, Carlo Derelitto, and Indrajit Saha. 2023. "Transcription Factor Driven Gene Regulation in COVID-19 Patients" Viruses 15, no. 5: 1188. https://doi.org/10.3390/v15051188