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Article: Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy.

bioRxiv : the preprint server for biology

2024

Abstract: Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor- ... ...

Abstract	Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. These viruses infect human host cells via the viral spike protein binding to the host cell receptor angiotensin-converting enzyme 2 (ACE2). Using over 1300 IgG sequences derived from convalescent patient B cells that bind with spike's receptor binding domain (RBD), we first established protein structural docking models in assessing the RBD-IgG-ACE2 interaction interfaces and predicting the virus-neutralizing activity of each IgG with a confidence score. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the landscape of IgGs' activity profiles against individual coronaviral variants of concern. With functional analyses and experimental validations, we efficiently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) to prevent the infection of host cells
Language	English
Publishing date	2024-03-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.01.582176
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Article ; Online: ITGA2

Adorno-Cruz, Valery / Hoffmann, Andrew D / Liu, Xia / Dashzeveg, Nurmaa K / Taftaf, Rokana / Wray, Brian / Keri, Ruth A / Liu, Huiping

Genes & diseases

2020 Volume 8, Issue 4, Page(s) 493–508

Abstract: Cancer metastasis is largely incurable and accounts for 90% of breast cancer deaths, especially for the aggressive basal-like or triple negative breast cancer (TNBC). Combining patient database analyses and functional studies, we examined the association ...

Abstract	Cancer metastasis is largely incurable and accounts for 90% of breast cancer deaths, especially for the aggressive basal-like or triple negative breast cancer (TNBC). Combining patient database analyses and functional studies, we examined the association of integrin family members with clinical outcomes as well as their connection with previously identified microRNA regulators of metastasis, such as miR-206 that inhibits stemness and metastasis of TNBC. Here we report that the integrin receptor CD49b-encoding
Language	English
Publishing date	2020-02-01
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2821806-1
ISSN	2352-3042 ; 2352-3042
ISSN (online)	2352-3042
ISSN	2352-3042
DOI	10.1016/j.gendis.2020.01.015
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Article ; Online: EGFR inhibition blocks cancer stem cell clustering and lung metastasis of triple negative breast cancer.

Liu, Xia / Adorno-Cruz, Valery / Chang, Ya-Fang / Jia, Yuzhi / Kawaguchi, Madoka / Dashzeveg, Nurmaa K / Taftaf, Rokana / Ramos, Erika K / Schuster, Emma J / El-Shennawy, Lamiaa / Patel, Dhwani / Zhang, Youbin / Cristofanilli, Massimo / Liu, Huiping

Theranostics

2021 Volume 11, Issue 13, Page(s) 6632–6643

Abstract: Triple-negative breast cancer (TNBC) is one of the most aggressive and metastatic breast cancer subtypes lacking targeted therapy. Our recent work demonstrated that circulating tumor cell (CTC) clusters and polyclonal metastasis of TNBC are driven by ... ...

Abstract	Triple-negative breast cancer (TNBC) is one of the most aggressive and metastatic breast cancer subtypes lacking targeted therapy. Our recent work demonstrated that circulating tumor cell (CTC) clusters and polyclonal metastasis of TNBC are driven by aggregation of CD44
MeSH term(s)	Animals ; Antineoplastic Agents/therapeutic use ; Antineoplastic Agents, Immunological/immunology ; Antineoplastic Agents, Immunological/therapeutic use ; Cell Aggregation/drug effects ; ErbB Receptors/antagonists & inhibitors ; ErbB Receptors/immunology ; ErbB Receptors/physiology ; Erlotinib Hydrochloride/therapeutic use ; Female ; Genes, Reporter ; Humans ; Hyaluronan Receptors/antagonists & inhibitors ; Hyaluronan Receptors/physiology ; Lung Neoplasms/prevention & control ; Lung Neoplasms/secondary ; Mice ; MicroRNAs/genetics ; Molecular Targeted Therapy ; Neoplasm Proteins/antagonists & inhibitors ; Neoplasm Proteins/physiology ; Neoplastic Cells, Circulating/drug effects ; Neoplastic Stem Cells/drug effects ; RNA/genetics ; Triple Negative Breast Neoplasms/drug therapy ; Triple Negative Breast Neoplasms/pathology ; Xenograft Model Antitumor Assays
Chemical Substances	Antineoplastic Agents ; Antineoplastic Agents, Immunological ; CD44 protein, human ; Hyaluronan Receptors ; MIRN30b microRNA, human ; MicroRNAs ; Neoplasm Proteins ; RNA, recombinant ; RNA (63231-63-0) ; Erlotinib Hydrochloride (DA87705X9K) ; EGFR protein, human (EC 2.7.10.1) ; ErbB Receptors (EC 2.7.10.1)
Language	English
Publishing date	2021-04-30
Publishing country	Australia
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2592097-2
ISSN	1838-7640 ; 1838-7640
ISSN (online)	1838-7640
ISSN	1838-7640
DOI	10.7150/thno.57706
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Article ; Online: Unique molecular signatures sustained in circulating monocytes and regulatory T cells in convalescent COVID-19 patients.

Clinical immunology (Orlando, Fla.)

2023 Volume 252, Page(s) 109634

Abstract: Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and ... ...

Abstract	Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including convalescent COVID-19 and sero-negative controls. Flow cytometry analyses revealed reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells within the patients who have recovered from severe COVID-19. sc-RNA seq analysis identifies seven heterogeneous clusters of monocytes and nine Treg clusters featuring distinct molecular signatures in association with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocytes and Tregs expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters featuring S100 family genes: one monocyte cluster of S100A8 & A9 coupled with high HLA-I and another cluster of S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, as well as a unique TGF-β high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-lived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (≥ 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.
MeSH term(s)	Humans ; Monocytes ; COVID-19/metabolism ; T-Lymphocytes, Regulatory ; Pandemics ; SARS-CoV-2
Language	English
Publishing date	2023-05-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	1459903-x
ISSN	1521-7035 ; 1521-6616
ISSN (online)	1521-7035
ISSN	1521-6616
DOI	10.1016/j.clim.2023.109634
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Article ; Online: Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

bioRxiv

Abstract	Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of coronavirus variants. Using over 1300 IgG sequences derived from patient B cells bound with the viral spike9s receptor binding domain (RBD), we first established protein structural docking models in assessing the IgG-RBD-ACE2 interaction interfaces and predicting their viral neutralizing activities with a confidence score. The confidence score is calculated as a fraction of IgG-blocking RBD binding sites versus all ACE2-interacting sites. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the IgGs9 activity profiles against viral strains. Using functional analyses and experimental validations, we subsequently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) and preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. To further improve the blockade efficacy for the Delta strain (B.1.617), we rationally redesigned the IgG clones with single amino acid substitutions at the RBD-binding interface. Our work expedites applications of artificial intelligence in low-data regimes when limited data is available, including protein language models (using unlabeled data) and Kriging (using few labeled data) for antibody sequence analysis, activity prediction, and efficacy improvement, which are aided by physics-driven protein docking models for antibody-antigen interface structure analyses.
Keywords	covid19
Language	English
Publishing date	2024-03-04
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2024.03.01.582176
Database	COVID19

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Article: Unique molecular signatures sustained in circulating monocytes and regulatory T cells in Convalescent COVID-19 patients.

bioRxiv : the preprint server for biology

2022

Abstract	Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized multi-omic single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including covenlesent COVID-19 and sero-negative controls. The reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells are significantly associated with the patients recovered from severe COVID-19. Consistently, sc-RNA seq analysis reveals seven heterogeneous clusters of monocytes (M0-M6) and ten Treg clusters (T0-T9) featuring distinct molecular signatures and associated with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocyte and Treg expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters with S100 family genes: S100A8 & A9 with high HLA-I whereas S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, and a unique TGF-β high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-ived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (>= 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.
Language	English
Publishing date	2022-03-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.03.26.485922
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Article ; Online: Dynamic Glycoprotein Hyposialylation Promotes Chemotherapy Evasion and Metastatic Seeding of Quiescent Circulating Tumor Cell Clusters in Breast Cancer.

Dashzeveg, Nurmaa K / Jia, Yuzhi / Zhang, Youbin / Gerratana, Lorenzo / Patel, Priyam / Shajahan, Asif / Dandar, Tsogbadrakh / Ramos, Erika K / Almubarak, Hannah F / Adorno-Cruz, Valery / Taftaf, Rokana / Schuster, Emma J / Scholten, David / Sokolowski, Michael T / Reduzzi, Carolina / El-Shennawy, Lamiaa / Hoffmann, Andrew D / Manai, Maroua / Zhang, Qiang /

D'Amico, Paolo / Azadi, Parastoo / Colley, Karen J / Platanias, Leonidas C / Shah, Ami N / Gradishar, William J / Cristofanilli, Massimo / Muller, William A / Cobb, Brian A / Liu, Huiping

Cancer discovery

2023 Volume 13, Issue 9, Page(s) 2050–2071

Abstract: Most circulating tumor cells (CTC) are detected as single cells, whereas a small proportion of CTCs in multicellular clusters with stemness properties possess 20- to 100-times higher metastatic propensity than the single cells. Here we report that CTC ... ...

Abstract	Most circulating tumor cells (CTC) are detected as single cells, whereas a small proportion of CTCs in multicellular clusters with stemness properties possess 20- to 100-times higher metastatic propensity than the single cells. Here we report that CTC dynamics in both singles and clusters in response to therapies predict overall survival for breast cancer. Chemotherapy-evasive CTC clusters are relatively quiescent with a specific loss of ST6GAL1-catalyzed α2,6-sialylation in glycoproteins. Dynamic hyposialylation in CTCs or deficiency of ST6GAL1 promotes cluster formation for metastatic seeding and enables cellular quiescence to evade paclitaxel treatment in breast cancer. Glycoproteomic analysis reveals newly identified protein substrates of ST6GAL1, such as adhesion or stemness markers PODXL, ICAM1, ECE1, ALCAM1, CD97, and CD44, contributing to CTC clustering (aggregation) and metastatic seeding. As a proof of concept, neutralizing antibodies against one newly identified contributor, PODXL, inhibit CTC cluster formation and lung metastasis associated with paclitaxel treatment for triple-negative breast cancer. Significance: This study discovers that dynamic loss of terminal sialylation in glycoproteins of CTC clusters contributes to the fate of cellular dormancy, advantageous evasion to chemotherapy, and enhanced metastatic seeding. It identifies PODXL as a glycoprotein substrate of ST6GAL1 and a candidate target to counter chemoevasion-associated metastasis of quiescent tumor cells. This article is featured in Selected Articles from This Issue, p. 1949.
MeSH term(s)	Humans ; Female ; Breast Neoplasms/drug therapy ; Neoplastic Cells, Circulating/metabolism ; Paclitaxel/therapeutic use ; Triple Negative Breast Neoplasms ; Glycoproteins ; Biomarkers, Tumor ; Neoplasm Metastasis
Chemical Substances	Paclitaxel (P88XT4IS4D) ; Glycoproteins ; Biomarkers, Tumor
Language	English
Publishing date	2023-05-10
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2625242-9
ISSN	2159-8290 ; 2159-8274
ISSN (online)	2159-8290
ISSN	2159-8274
DOI	10.1158/2159-8290.CD-22-0644
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Article ; Online: ICAM1 initiates CTC cluster formation and trans-endothelial migration in lung metastasis of breast cancer.

Nature communications

2021 Volume 12, Issue 1, Page(s) 4867

Abstract: Circulating tumor cell (CTC) clusters mediate metastasis at a higher efficiency and are associated with lower overall survival in breast cancer compared to single cells. Combining single-cell RNA sequencing and protein analyses, here we report the ... ...

Abstract	Circulating tumor cell (CTC) clusters mediate metastasis at a higher efficiency and are associated with lower overall survival in breast cancer compared to single cells. Combining single-cell RNA sequencing and protein analyses, here we report the profiles of primary tumor cells and lung metastases of triple-negative breast cancer (TNBC). ICAM1 expression increases by 200-fold in the lung metastases of three TNBC patient-derived xenografts (PDXs). Depletion of ICAM1 abrogates lung colonization of TNBC cells by inhibiting homotypic tumor cell-tumor cell cluster formation. Machine learning-based algorithms and mutagenesis analyses identify ICAM1 regions responsible for homophilic ICAM1-ICAM1 interactions, thereby directing homotypic tumor cell clustering, as well as heterotypic tumor-endothelial adhesion for trans-endothelial migration. Moreover, ICAM1 promotes metastasis by activating cellular pathways related to cell cycle and stemness. Finally, blocking ICAM1 interactions significantly inhibits CTC cluster formation, tumor cell transendothelial migration, and lung metastasis. Therefore, ICAM1 can serve as a novel therapeutic target for metastasis initiation of TNBC.
MeSH term(s)	Animals ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/metabolism ; Cell Aggregation ; Cell Cycle ; Cell Transformation, Neoplastic ; Humans ; Intercellular Adhesion Molecule-1/genetics ; Intercellular Adhesion Molecule-1/metabolism ; Lung Neoplasms/metabolism ; Lung Neoplasms/secondary ; Mice ; Neoplastic Cells, Circulating/metabolism ; Neoplastic Cells, Circulating/pathology ; Protein Interaction Domains and Motifs ; Signal Transduction ; Transendothelial and Transepithelial Migration ; Triple Negative Breast Neoplasms/metabolism ; Triple Negative Breast Neoplasms/pathology
Chemical Substances	Biomarkers, Tumor ; ICAM1 protein, human ; Intercellular Adhesion Molecule-1 (126547-89-5)
Language	English
Publishing date	2021-08-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-25189-z
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Article ; Online: Machine learning-assisted elucidation of CD81-CD44 interactions in promoting cancer stemness and extracellular vesicle integrity.

Ramos, Erika K / Tsai, Chia-Feng / Jia, Yuzhi / Cao, Yue / Manu, Megan / Taftaf, Rokana / Hoffmann, Andrew D / El-Shennawy, Lamiaa / Gritsenko, Marina A / Adorno-Cruz, Valery / Schuster, Emma J / Scholten, David / Patel, Dhwani / Liu, Xia / Patel, Priyam / Wray, Brian / Zhang, Youbin / Zhang, Shanshan / Moore, Ronald J /

Mathews, Jeremy V / Schipma, Matthew J / Liu, Tao / Tokars, Valerie L / Cristofanilli, Massimo / Shi, Tujin / Shen, Yang / Dashzeveg, Nurmaa K / Liu, Huiping

eLife

2022 Volume 11

Abstract: Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network ... ...

Abstract	Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.
MeSH term(s)	Mice ; Animals ; Humans ; Triple Negative Breast Neoplasms/metabolism ; Cell Line, Tumor ; Tetraspanins ; Extracellular Vesicles/metabolism ; Machine Learning ; Hyaluronan Receptors/genetics ; Tetraspanin 28
Chemical Substances	Tetraspanins ; CD44 protein, human ; Hyaluronan Receptors ; CD81 protein, human ; Tetraspanin 28
Language	English
Publishing date	2022-10-04
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.82669
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Article ; Online: Unique molecular signatures sustained in circulating monocytes and regulatory T cells in Convalescent COVID-19 patients

bioRxiv

Abstract	Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized multi-omic single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including covenlesent COVID-19 and sero-negative controls. The reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells are significantly associated with the patients recovered from severe COVID-19. Consistently, sc-RNA seq analysis reveals seven heterogeneous clusters of monocytes (M0-M6) and ten Treg clusters (T0-T9) featuring distinct molecular signatures and associated with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocyte and Treg expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters with S100 family genes: S100A8 & A9 with high HLA-I whereas S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, and a unique TGF-b; high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-ived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (>= 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.
Keywords	covid19
Language	English
Publishing date	2022-03-28
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2022.03.26.485922
Database	COVID19

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