Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

deAndrés-Galiana, Enrique J.; Fernández-Martínez, Juan Luis; Álvarez-Machancoses, Óscar; Bea, Guillermina; Galmarini, Carlos M.; Kloczkowski, Andrzej

doi:10.1016/j.compbiomed.2022.106029

Cited by 2 publications

(3 citation statements)

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“…Pathak et al 54 identified DNAH3 to be one of the proteins responsible for COVID-19 hospitalization. According to ref ( 55 ), SARS-CoV-2 infection causes an inflammatory response, leading to a sudden cytokine storm that results in injury to multiple organs. This may be caused by DNAH17.…”

Section: Resultsmentioning

confidence: 99%

“…For one of the identified proteins, DNAH7, Hu et al 53 have suggested that COVID-19 patients with variations in DNAH7 may have a higher risk of death. Pathak et al 54 identified DNAH3 to be one of the proteins responsible for COVID-19 hospitalization. According to ref 55, SARS-CoV-2 infection causes an inflammatory response, leading to a sudden cytokine storm that results in injury to multiple organs.…”

Section: ■ Resultsmentioning

confidence: 99%

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Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

2023

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COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human–spike protein–protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug–protein interactions can be predicted for human–SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug–protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human–spike PPIs and eventually new drug–protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human–spike PPI and 96.15% for drug–protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

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Section: Resultsmentioning

confidence: 99%

Section: ■ Resultsmentioning

confidence: 99%

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

2023

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“…Computational analyses of transcriptomic data from blood and lung biopsies concluded that drug target searches should be performed separately in the pool of upregulated and downregulated mRNAs [158]. For example, immunosuppressive drugs can be utilized for overexpressed cytokine genes, and under-expressed molecules (MRPL41, COL1A1, PLEKHF1, SPOCK3, FSCN1) can be targeted with drugs for specific pathways associated with the above-mentioned molecules (e.g., dosulepin, mexiletine, bromopride, fenoprofen, tetrahydroalstonine, and cytisine) [158].…”

Section: Cross-ome Bioinformatic Data Mining and Analyticsmentioning

confidence: 99%

COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine

Vlasova-St. Louis,

Fang,

Amer

et al. 2023

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During the COVID-19 pandemic, it became apparent that precision medicine relies heavily on biological multi-omics discoveries. High throughput omics technologies, such as host genomics, transcriptomics, proteomics, epigenomics, metabolomics/lipidomics, and microbiomics, have become an integral part of precision diagnostics. The large number of data generated by omics technologies allows for the identification of vulnerable demographic populations that are susceptible to poor disease outcomes. Additionally, these data help to pinpoint the omics-based biomarkers that are currently driving advancements in precision and preventive medicine, such as early diagnosis and disease prognosis, individualized treatments, and vaccination. This report summarizes COVID-19-omic studies, highlights the results of completed and ongoing omics investigations in individuals who have experienced severe disease outcomes, and examines the impact that repurposed/novel antiviral drugs, targeted immunotherapeutics, and vaccines have had on individual and public health.

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Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

Cited by 2 publications

References 94 publications

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

COVID-19-Omics Report: From Individual Omics Approaches to Precision Medicine

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