Acute Kidney Injury (AKI) in COVID-19 : In silico Identification of LncRNA-MiRNA-Gene Networks and Key Transcription Factors
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
PURPOSE: Acute kidney injury (AKI) accounts for up to 29% of severe COVID-19 cases and increases mortality among these patients. Viral infections participate in the pathogenesis of diseases by changing the expression profile of normal transcriptome. This study attempts to identify LncRNA-miRNA-gene and TF-gene networks as gene expression regulating networks in the kidney tissues of COVID-19 patients.
METHODS: In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing, Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition, pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05 and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases, including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore, by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks were plotted using Stringdb and Cytoscape v8.
RESULTS: Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients with the healthy kidney tissue samples showed significant changes in the expression of more than 2000 genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these 11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the kidney of patients affected with COVID-19.
CONCLUSION: This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic targets for experimental and preclinical studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Current pharmaceutical design - 29(2023), 24 vom: 16., Seite 1907-1917 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sheikhshabani, Somayeh Hashemi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 21.09.2023 Date Revised 03.10.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/1381612829666230816105221 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360836348 |
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| 100 | 1 | |a Sheikhshabani, Somayeh Hashemi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Acute Kidney Injury (AKI) in COVID-19 |b In silico Identification of LncRNA-MiRNA-Gene Networks and Key Transcription Factors |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Completed 21.09.2023 | ||
| 500 | |a Date Revised 03.10.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a PURPOSE: Acute kidney injury (AKI) accounts for up to 29% of severe COVID-19 cases and increases mortality among these patients. Viral infections participate in the pathogenesis of diseases by changing the expression profile of normal transcriptome. This study attempts to identify LncRNA-miRNA-gene and TF-gene networks as gene expression regulating networks in the kidney tissues of COVID-19 patients | ||
| 520 | |a METHODS: In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing, Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition, pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05 and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases, including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore, by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks were plotted using Stringdb and Cytoscape v8 | ||
| 520 | |a RESULTS: Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients with the healthy kidney tissue samples showed significant changes in the expression of more than 2000 genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these 11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the kidney of patients affected with COVID-19 | ||
| 520 | |a CONCLUSION: This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic targets for experimental and preclinical studies | ||
| 650 | 4 | |a Meta-Analysis | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a gene regulatory network (GRN) | |
| 650 | 4 | |a inflammation | |
| 650 | 4 | |a kidney | |
| 650 | 4 | |a miRNA-Lnc network | |
| 650 | 4 | |a transcriptional regulatory network (TRN) | |
| 650 | 7 | |a MicroRNAs |2 NLM | |
| 650 | 7 | |a RNA, Long Noncoding |2 NLM | |
| 650 | 7 | |a Transcription Factors |2 NLM | |
| 700 | 1 | |a Amini-Farsani, Zeinab |e verfasserin |4 aut | |
| 700 | 1 | |a Kazemifard, Nesa |e verfasserin |4 aut | |
| 700 | 1 | |a Modarres, Parastoo |e verfasserin |4 aut | |
| 700 | 1 | |a Feyzabad, Sharareh Khazaei |e verfasserin |4 aut | |
| 700 | 1 | |a Amini-Farsani, Zahra |e verfasserin |4 aut | |
| 700 | 1 | |a Shaygan, Nasibeh |e verfasserin |4 aut | |
| 700 | 1 | |a Omrani, Mir Davood |e verfasserin |4 aut | |
| 700 | 1 | |a Ghafouri-Fard, Soudeh |e verfasserin |4 aut | |
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