An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Gupta, Shishir; Ponte-Sucre, Alicia; Bencúrová, Elena; Dandekar, Thomas

doi:10.1016/j.csbj.2021.09.017

Cited by 5 publications

(3 citation statements)

References 88 publications

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“…5 , panel a). In our recent study, we have identified that PCNA is also a preferential target of the Ebola virus [22] . ZRANK score for AFUA_1G04950-CDK4 ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Balkenhol

Bencúrová²,

Gupta³

et al. 2022

Computational and Structural Biotechnology Journal

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“…5 , panel a). In our recent study, we have identified that PCNA is also a preferential target of the Ebola virus [22] . ZRANK score for AFUA_1G04950-CDK4 ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Balkenhol

Bencúrová²,

Gupta³

et al. 2022

Computational and Structural Biotechnology Journal

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“…EEF1A affects the prognosis of tumors such as those of the lung and stomach ( Kawamura et al, 2014 ; Li et al, 2017 ). EEF1A1 influences the host–bacterial and viral interactions through the cytoskeleton and its regulation ( Gupta et al, 2021 ). EEF1A can mediate the anti-viral activity of plitidepsin against SARS-CoV-2 and inhibit viral replication in the lungs ( White et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Huang

Liao

et al. 2022

Front. Mol. Biosci.

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Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

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“…EEF1A affects the prognosis of tumors such as those of the lung and stomach (Kawamura et al, 2014;Li et al, 2017). EEF1A1 influences the host-bacterial and viral interactions through the cytoskeleton and its regulation (Gupta et al, 2021). EEF1A can mediate the antiviral activity of plitidepsin against SARS-CoV-2 and inhibit viral replication in the lungs (White et al, 2021).…”

Section: Classification Rules In Monocytesmentioning

confidence: 99%

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An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Abstract: Graphical abstract

Cited by 5 publications

References 88 publications

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Table1.XLSX

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