1
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Janusova S, Paprckova D, Michalik J, Uleri V, Drobek A, Salyova E, Chorfi L, Neuwirth A, Andreyeva A, Prochazka J, Sedlacek R, Draber P, Stepanek O. ABIN1 is a negative regulator of effector functions in cytotoxic T cells. EMBO Rep 2024; 25:3456-3485. [PMID: 38877170 PMCID: PMC11315980 DOI: 10.1038/s44319-024-00179-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 05/31/2024] [Accepted: 06/06/2024] [Indexed: 06/16/2024] Open
Abstract
T cells are pivotal in the adaptive immune defense, necessitating a delicate balance between robust response against infections and self-tolerance. Their activation involves intricate cross-talk among signaling pathways triggered by the T-cell antigen receptors (TCR) and co-stimulatory or inhibitory receptors. The molecular regulation of these complex signaling networks is still incompletely understood. Here, we identify the adaptor protein ABIN1 as a component of the signaling complexes of GITR and OX40 co-stimulation receptors. T cells lacking ABIN1 are hyper-responsive ex vivo, exhibit enhanced responses to cognate infections, and superior ability to induce experimental autoimmune diabetes in mice. ABIN1 negatively regulates p38 kinase activation and late NF-κB target genes. P38 is at least partially responsible for the upregulation of the key effector proteins IFNG and GZMB in ABIN1-deficient T cells after TCR stimulation. Our findings reveal the intricate role of ABIN1 in T-cell regulation.
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MESH Headings
- Animals
- Humans
- Mice
- Adaptor Proteins, Signal Transducing/metabolism
- Adaptor Proteins, Signal Transducing/genetics
- Diabetes Mellitus, Type 1/immunology
- Diabetes Mellitus, Type 1/genetics
- Diabetes Mellitus, Type 1/metabolism
- Glucocorticoid-Induced TNFR-Related Protein
- Interferon-gamma/metabolism
- Lymphocyte Activation/immunology
- Lymphocyte Activation/genetics
- Mice, Inbred C57BL
- Mice, Knockout
- NF-kappa B/metabolism
- p38 Mitogen-Activated Protein Kinases/metabolism
- Receptors, Antigen, T-Cell/metabolism
- Receptors, OX40/metabolism
- Receptors, OX40/genetics
- Signal Transduction
- T-Lymphocytes, Cytotoxic/immunology
- T-Lymphocytes, Cytotoxic/metabolism
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Affiliation(s)
- Sarka Janusova
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Science, Charles University in Prague, Prague, Czech Republic
| | - Darina Paprckova
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Juraj Michalik
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Valeria Uleri
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Science, Charles University in Prague, Prague, Czech Republic
| | - Ales Drobek
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Eva Salyova
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Louise Chorfi
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Ales Neuwirth
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Arina Andreyeva
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Prochazka
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Radislav Sedlacek
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Peter Draber
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
- Laboratory of Immunity & Cell Communication, Division BIOCEV, First Faculty of Medicine, Charles University, Vestec, Czech Republic
| | - Ondrej Stepanek
- Laboratory of Adaptive Immunity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic.
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2
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Vaidehi Narayanan H, Xiang MY, Chen Y, Huang H, Roy S, Makkar H, Hoffmann A, Roy K. Direct observation correlates NFκB cRel in B cells with activating and terminating their proliferative program. Proc Natl Acad Sci U S A 2024; 121:e2309686121. [PMID: 39024115 PMCID: PMC11287273 DOI: 10.1073/pnas.2309686121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 05/28/2024] [Indexed: 07/20/2024] Open
Abstract
Antibody responses require the proliferative expansion of B cells controlled by affinity-dependent signals. Yet, proliferative bursts are heterogeneous, varying between 0 and 8 divisions in response to the same stimulus. NFκB cRel is activated in response to immune stimulation in B cells and is genetically required for proliferation. Here, we asked whether proliferative heterogeneity is controlled by natural variations in cRel abundance. We developed a fluorescent reporter mTFP1-cRel for the direct observation of cRel in live proliferating B cells. We found that cRel is heterogeneously distributed among naïve B cells, which are enriched for high expressors in a heavy-tailed distribution. We found that high cRel expressors show faster activation of the proliferative program, but do not sustain it well, with population expansion decaying earlier. With a mathematical model of the molecular network, we showed that cRel heterogeneity arises from balancing positive feedback by autoregulation and negative feedback by its inhibitor IκBε, confirmed by mouse knockouts. Using live-cell fluorescence microscopy, we showed that increased cRel primes B cells for early proliferation via higher basal expression of the cell cycle driver cMyc. However, peak cMyc induction amplitude is constrained by incoherent feedforward regulation, decoding the fold change of cRel activity to terminate the proliferative burst. This results in a complex nonlinear, nonmonotonic relationship between cRel expression and the extent of proliferation. These findings emphasize the importance of direct observational studies to complement gene knockout results and to learn about quantitative relationships between biological processes and their key regulators in the context of natural variations.
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Affiliation(s)
- Haripriya Vaidehi Narayanan
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Mark Y. Xiang
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Yijia Chen
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Helen Huang
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Sukanya Roy
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
| | - Himani Makkar
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, CA90095
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA90095
| | - Koushik Roy
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT84112
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3
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Gondal MN, Shah SUR, Chinnaiyan AM, Cieslik M. A systematic overview of single-cell transcriptomics databases, their use cases, and limitations. FRONTIERS IN BIOINFORMATICS 2024; 4:1417428. [PMID: 39040140 PMCID: PMC11260681 DOI: 10.3389/fbinf.2024.1417428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Saad Ur Rehman Shah
- Gies College of Business, University of Illinois Business College, Champaign, MI, United States
| | - Arul M. Chinnaiyan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Urology, University of Michigan, Ann Arbor, MI, United States
- Howard Hughes Medical Institute, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
| | - Marcin Cieslik
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
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4
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Lalle G, Lautraite R, Bouherrou K, Plaschka M, Pignata A, Voisin A, Twardowski J, Perrin-Niquet M, Stéphan P, Durget S, Tonon L, Ardin M, Degletagne C, Viari A, Belgarbi Dutron L, Davoust N, Postler TS, Zhao J, Caux C, Caramel J, Dalle S, Cassier PA, Klein U, Schmidt-Supprian M, Liblau R, Ghosh S, Grinberg-Bleyer Y. NF-κB subunits RelA and c-Rel selectively control CD4+ T cell function in multiple sclerosis and cancer. J Exp Med 2024; 221:e20231348. [PMID: 38563819 PMCID: PMC10986815 DOI: 10.1084/jem.20231348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/30/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
The outcome of cancer and autoimmunity is often dictated by the effector functions of CD4+ conventional T cells (Tconv). Although activation of the NF-κB signaling pathway has long been implicated in Tconv biology, the cell-autonomous roles of the separate NF-κB transcription-factor subunits are unknown. Here, we dissected the contributions of the canonical NF-κB subunits RelA and c-Rel to Tconv function. RelA, rather than c-Rel, regulated Tconv activation and cytokine production at steady-state and was required for polarization toward the TH17 lineage in vitro. Accordingly, RelA-deficient mice were fully protected against neuroinflammation in a model of multiple sclerosis due to defective transition to a pathogenic TH17 gene-expression program. Conversely, Tconv-restricted ablation of c-Rel impaired their function in the microenvironment of transplanted tumors, resulting in enhanced cancer burden. Moreover, Tconv required c-Rel for the response to PD-1-blockade therapy. Our data reveal distinct roles for canonical NF-κB subunits in different disease contexts, paving the way for subunit-targeted immunotherapies.
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Affiliation(s)
- Guilhem Lalle
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Raphaëlle Lautraite
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Khaled Bouherrou
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Maud Plaschka
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Aurora Pignata
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), UMR INSERM 1291, CNRS 5051, Université Toulouse III, Toulouse, France
| | - Allison Voisin
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Julie Twardowski
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Marlène Perrin-Niquet
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Pierre Stéphan
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Sarah Durget
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Laurie Tonon
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, Gilles Thomas Bioinformatics Platform, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Maude Ardin
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, Gilles Thomas Bioinformatics Platform, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Cyril Degletagne
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Alain Viari
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, Gilles Thomas Bioinformatics Platform, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | | | - Nathalie Davoust
- Laboratory of Biology and Modelling of the Cell, Ecole Normale Supérieure of Lyon, CNRS UMR 5239, INSERM U1293, Lyon, France
| | - Thomas S. Postler
- Department of Microbiology and Immunology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Jingyao Zhao
- Department of Microbiology and Immunology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Christophe Caux
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Julie Caramel
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Stéphane Dalle
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Philippe A. Cassier
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
| | - Ulf Klein
- Division of Haematology and Immunology, Leeds Institute of Medical Research at St. James’s, University of Leeds, Leeds, UK
| | - Marc Schmidt-Supprian
- Institute of Experimental Hematology, School of Medicine, Technical University of Munich, Munich, Germany
- Center for Translational Cancer Research, School of Medicine, Technical University of Munich, Munich, Germany
- German Cancer Consortium and German Cancer Research Center, Heidelberg, Germany
| | - Roland Liblau
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), UMR INSERM 1291, CNRS 5051, Université Toulouse III, Toulouse, France
| | - Sankar Ghosh
- Department of Microbiology and Immunology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Yenkel Grinberg-Bleyer
- Cancer Research Center of Lyon, Labex DEV2CAN, Institut Convergence Plascan, Centre Léon Bérard, UMR INSERM 1052, CNRS 5286, Université Claude Bernard Lyon 1, Lyon, France
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5
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Gondal MN, Shah SUR, Chinnaiyan AM, Cieslik M. A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations. ARXIV 2024:arXiv:2404.10545v1. [PMID: 38699169 PMCID: PMC11065044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
| | - Saad Ur Rehman Shah
- Gies College of Business, University of Illinois Business College, Champaign, IL USA
| | - Arul M. Chinnaiyan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Urology, University of Michigan, Ann Arbor, MI USA
- Howard Hughes Medical Institute, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
| | - Marcin Cieslik
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI USA
- Department of Pathology, University of Michigan, Ann Arbor, MI USA
- University of Michigan Rogel Cancer Center, Ann Arbor, MI USA
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6
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Guo Q, Jin Y, Chen X, Ye X, Shen X, Lin M, Zeng C, Zhou T, Zhang J. NF-κB in biology and targeted therapy: new insights and translational implications. Signal Transduct Target Ther 2024; 9:53. [PMID: 38433280 PMCID: PMC10910037 DOI: 10.1038/s41392-024-01757-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 03/05/2024] Open
Abstract
NF-κB signaling has been discovered for nearly 40 years. Initially, NF-κB signaling was identified as a pivotal pathway in mediating inflammatory responses. However, with extensive and in-depth investigations, researchers have discovered that its role can be expanded to a variety of signaling mechanisms, biological processes, human diseases, and treatment options. In this review, we first scrutinize the research process of NF-κB signaling, and summarize the composition, activation, and regulatory mechanism of NF-κB signaling. We investigate the interaction of NF-κB signaling with other important pathways, including PI3K/AKT, MAPK, JAK-STAT, TGF-β, Wnt, Notch, Hedgehog, and TLR signaling. The physiological and pathological states of NF-κB signaling, as well as its intricate involvement in inflammation, immune regulation, and tumor microenvironment, are also explicated. Additionally, we illustrate how NF-κB signaling is involved in a variety of human diseases, including cancers, inflammatory and autoimmune diseases, cardiovascular diseases, metabolic diseases, neurological diseases, and COVID-19. Further, we discuss the therapeutic approaches targeting NF-κB signaling, including IKK inhibitors, monoclonal antibodies, proteasome inhibitors, nuclear translocation inhibitors, DNA binding inhibitors, TKIs, non-coding RNAs, immunotherapy, and CAR-T. Finally, we provide an outlook for research in the field of NF-κB signaling. We hope to present a stereoscopic, comprehensive NF-κB signaling that will inform future research and clinical practice.
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Affiliation(s)
- Qing Guo
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yizi Jin
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xinyu Chen
- State Key Laboratory of Oncogenes and Related Genes, Renji-Med-X Stem Cell Research Center, Shanghai Cancer Institute & Department of Urology, Ren Ji Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200127, PR China
| | - Xiaomin Ye
- Department of Cardiology, the First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Xin Shen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mingxi Lin
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cheng Zeng
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Teng Zhou
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian Zhang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, No. 270, Dong'an Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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7
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Roy K, Chakraborty M, Kumar A, Manna AK, Roy NS. The NFκB signaling system in the generation of B-cell subsets: from germinal center B cells to memory B cells and plasma cells. Front Immunol 2023; 14:1185597. [PMID: 38169968 PMCID: PMC10758606 DOI: 10.3389/fimmu.2023.1185597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/09/2023] [Indexed: 01/05/2024] Open
Abstract
Memory B cells and antibody-secreting cells are the two prime effector B cell populations that drive infection- and vaccine-induced long-term antibody-mediated immunity. The antibody-mediated immunity mostly relies on the formation of specialized structures within secondary lymphoid organs, called germinal centers (GCs), that facilitate the interactions between B cells, T cells, and antigen-presenting cells. Antigen-activated B cells may proliferate and differentiate into GC-independent plasmablasts and memory B cells or differentiate into GC B cells. The GC B cells undergo proliferation coupled to somatic hypermutation of their immunoglobulin genes for antibody affinity maturation. Subsequently, affinity mature GC B cells differentiate into GC-dependent plasma cells and memory B cells. Here, we review how the NFκB signaling system controls B cell proliferation and the generation of GC B cells, plasmablasts/plasma cells, and memory B cells. We also identify and discuss some important unanswered questions in this connection.
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Affiliation(s)
- Koushik Roy
- Division of Microbiology and Immunology, Department of Pathology, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Mainak Chakraborty
- Division of Immunology, Indian Council of Medical Research-National Institute of Cholera and Enteric Diseases, Kolkata, India
| | - Ashok Kumar
- Division of Microbiology and Immunology, Department of Pathology, School of Medicine, University of Utah, Salt Lake City, UT, United States
| | - Asit Kumar Manna
- Division of Microbiology and Immunology, Department of Pathology, School of Medicine, University of Utah, Salt Lake City, UT, United States
- Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Neeladri Sekhar Roy
- Department of Biochemistry, School of Medicine, Emory University, Atlanta, GA, United States
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