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  1. Article ; Online: Enrichment of SARS-CoV-2 Entry Factors and Interacting Intracellular Genes in Tissue and Circulating Immune Cells.

    Devaprasad, Abhinandan / Pandit, Aridaman

    Viruses

    2021  Volume 13, Issue 9

    Abstract: SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, ... ...

    Abstract SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We found that in general a small fraction of cells express ACE2 in the different tissues, including nasal, bronchi, and lungs. We show that a small fraction of immune cells (including T cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in tissues and circulation) in both healthy and COVID-19-positive patients were significantly enriched for SARS-CoV-2 factors, such as those associated with RHOA and RAB GTPases, mRNA translation proteins, COPI- and COPII-mediated transport, and integrins. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect tissue and circulating immune cells to better understand the virus' mechanism of action.
    MeSH term(s) COVID-19/blood ; COVID-19/etiology ; Dendritic Cells/immunology ; Dendritic Cells/metabolism ; Disease Susceptibility ; Gene Expression Profiling ; Gene Expression Regulation ; High-Throughput Nucleotide Sequencing ; Host-Pathogen Interactions/genetics ; Host-Pathogen Interactions/immunology ; Humans ; Immune System/immunology ; Immune System/metabolism ; Immunity, Innate ; Macrophages/immunology ; Macrophages/metabolism ; SARS-CoV-2/physiology ; Single-Cell Analysis ; Virus Internalization
    Language English
    Publishing date 2021-09-02
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2516098-9
    ISSN 1999-4915 ; 1999-4915
    ISSN (online) 1999-4915
    ISSN 1999-4915
    DOI 10.3390/v13091757
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Enrichment of SARS-CoV-2 Entry Factors and Interacting Intracellular Genes in Tissue and Circulating Immune Cells

    Devaprasad, Abhinandan / Pandit, Aridaman

    Viruses. 2021 Sept. 02, v. 13, no. 9

    2021  

    Abstract: SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, ... ...

    Abstract SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We found that in general a small fraction of cells express ACE2 in the different tissues, including nasal, bronchi, and lungs. We show that a small fraction of immune cells (including T cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in tissues and circulation) in both healthy and COVID-19-positive patients were significantly enriched for SARS-CoV-2 factors, such as those associated with RHOA and RAB GTPases, mRNA translation proteins, COPI- and COPII-mediated transport, and integrins. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect tissue and circulating immune cells to better understand the virus’ mechanism of action.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; data collection ; guanosinetriphosphatase ; integrins ; macrophages ; mechanism of action ; monocytes ; nose ; viruses
    Language English
    Dates of publication 2021-0902
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2516098-9
    ISSN 1999-4915
    ISSN 1999-4915
    DOI 10.3390/v13091757
    Database NAL-Catalogue (AGRICOLA)

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  3. Article ; Online: RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.

    Tao, Weiyang / Radstake, Timothy R D J / Pandit, Aridaman

    Communications biology

    2022  Volume 5, Issue 1, Page(s) 31

    Abstract: Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to ... ...

    Abstract Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.
    MeSH term(s) Algorithms ; Computational Biology ; Gene Expression Profiling ; Gene Expression Regulation/genetics ; Gene Regulatory Networks/genetics ; Humans ; Interferons/genetics ; Interferons/metabolism ; Proto-Oncogene Proteins c-ets/genetics ; Software
    Chemical Substances Proto-Oncogene Proteins c-ets ; Interferons (9008-11-1)
    Language English
    Publishing date 2022-01-11
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-021-02991-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Integration of

    Devaprasad, Abhinandan / Radstake, Timothy R D J / Pandit, Aridaman

    Frontiers in immunology

    2021  Volume 12, Page(s) 669400

    Abstract: Objective: Development and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in ... ...

    Abstract Objective: Development and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in IMIDs has been challenging due to the underlying complex molecular mechanism.
    Methods: Using transcriptome profiles of 40 different immune cells, unsupervised machine learning, and disease-gene networks, we constructed the Disease-gene IMmune cell Expression (DIME) network and identified top DACs and DAGs of 12 phenotypically different IMIDs. We compared the DIME networks of IMIDs to identify common pathways between them. We used the common pathways and publicly available drug-gene network to identify promising drug repurposing targets.
    Results: We found CD4
    Conclusions: Existing methods are inadequate in capturing the intricate involvement of the crucial genes and cell types essential to IMIDs. Our approach identified the key DACs, DAGs, common mechanisms between IMIDs, and proposed potential drug repurposing targets using the DIME network. To extend our method to other diseases, we built the DIME tool (https://bitbucket.org/systemsimmunology/dime/) to help scientists uncover the etiology of complex and rare diseases to further drug development by better-determining drug targets, thereby mitigating the risk of failure in late clinical development.
    MeSH term(s) Computational Biology ; Databases, Genetic ; Drug Repositioning ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Immune System/drug effects ; Immune System/immunology ; Immune System/metabolism ; Immune System Diseases/drug therapy ; Immune System Diseases/genetics ; Immune System Diseases/immunology ; Immune System Diseases/metabolism ; Inflammation/drug therapy ; Inflammation/genetics ; Inflammation/immunology ; Inflammation/metabolism ; Signal Transduction ; Transcriptome ; Unsupervised Machine Learning
    Language English
    Publishing date 2021-05-24
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2021.669400
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Reply.

    Tao, Weiyang / Radstake, Timothy R D J / Pandit, Aridaman

    Arthritis & rheumatology (Hoboken, N.J.)

    2021  Volume 73, Issue 8, Page(s) 1569–1570

    Language English
    Publishing date 2021-06-17
    Publishing country United States
    Document type Letter ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 2756371-6
    ISSN 2326-5205 ; 2326-5191
    ISSN (online) 2326-5205
    ISSN 2326-5191
    DOI 10.1002/art.41711
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Enrichment of SARS-CoV-2 entry factors and interacting intracellular genes in peripheral immune cells

    Devaprasad, Abhinandan / Pandit, Aridaman

    bioRxiv

    Abstract: SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV associated genes and ...

    Abstract SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV associated genes and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We find that in general a small fraction of cells expresses ACE2 in the different tissues including nasal, bronchi and lungs. We show that a small fraction of immune cells (including T-cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T-cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in circulation and periphery) in both healthy and COVID-19 positive patients were significantly enriched for SARS-CoV-2 factors. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect peripheral immune cells to better understand the virus9 mechanism of action.
    Keywords covid19
    Language English
    Publishing date 2021-03-29
    Publisher Cold Spring Harbor Laboratory
    Document type Article ; Online
    DOI 10.1101/2021.03.29.437515
    Database COVID19

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  7. Article ; Online: Machine learning in rheumatology approaches the clinic.

    Pandit, Aridaman / Radstake, Timothy R D J

    Nature reviews. Rheumatology

    2020  Volume 16, Issue 2, Page(s) 69–70

    MeSH term(s) Arthritis, Rheumatoid/diagnosis ; Arthritis, Rheumatoid/drug therapy ; Big Data ; Clinical Trials as Topic ; High-Throughput Screening Assays ; Machine Learning ; Models, Theoretical ; Precision Medicine
    Language English
    Publishing date 2020-01-06
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2491532-4
    ISSN 1759-4804 ; 1759-4790
    ISSN (online) 1759-4804
    ISSN 1759-4790
    DOI 10.1038/s41584-019-0361-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Finding Gene Regulatory Networks in Psoriasis: Application of a Tree-Based Machine Learning Approach.

    Deng, Jingwen / Schieler, Carlotta / Borghans, José A M / Lu, Chuanjian / Pandit, Aridaman

    Frontiers in immunology

    2022  Volume 13, Page(s) 921408

    Abstract: Psoriasis is a chronic inflammatory skin disorder. Although it has been studied extensively, the molecular mechanisms driving the disease remain unclear. In this study, we utilized a tree-based machine learning approach to explore the gene regulatory ... ...

    Abstract Psoriasis is a chronic inflammatory skin disorder. Although it has been studied extensively, the molecular mechanisms driving the disease remain unclear. In this study, we utilized a tree-based machine learning approach to explore the gene regulatory networks underlying psoriasis. We then validated the regulators and their networks in an independent cohort. We identified some key regulators of psoriasis, which are candidates to serve as potential drug targets and disease severity biomarkers. According to the gene regulatory network that we identified, we suggest that interferon signaling represents a key pathway of psoriatic inflammation.
    MeSH term(s) Biomarkers/metabolism ; Gene Regulatory Networks ; Humans ; Machine Learning ; Psoriasis/genetics ; Psoriasis/metabolism ; Skin/metabolism
    Chemical Substances Biomarkers
    Language English
    Publishing date 2022-07-07
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2022.921408
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Stochastic Inheritance of Division and Death Times Determines the Size and Phenotype of CD8

    Pandit, Aridaman / De Boer, Rob J

    Frontiers in immunology

    2019  Volume 10, Page(s) 436

    Abstract: After antigen stimulation cognate naïve ... ...

    Abstract After antigen stimulation cognate naïve CD8
    MeSH term(s) CD8-Positive T-Lymphocytes/immunology ; Cell Death ; Cell Differentiation ; Cell Proliferation ; Models, Biological ; Phenotype ; Stochastic Processes ; T-Lymphocyte Subsets/immunology
    Language English
    Publishing date 2019-03-14
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2019.00436
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Using Machine Learning to Molecularly Classify Systemic Sclerosis Patients.

    Tao, Weiyang / Radstake, Timothy R D J / Pandit, Aridaman

    Arthritis & rheumatology (Hoboken, N.J.)

    2019  Volume 71, Issue 10, Page(s) 1595–1598

    MeSH term(s) Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Scleroderma, Systemic
    Language English
    Publishing date 2019-09-09
    Publishing country United States
    Document type Editorial ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 2756371-6
    ISSN 2326-5205 ; 2326-5191
    ISSN (online) 2326-5205
    ISSN 2326-5191
    DOI 10.1002/art.40902
    Database MEDical Literature Analysis and Retrieval System OnLINE

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